Smart Labels in the Clinical Trial Supply Chain

Ignacio Paricio

by Ignacio Paricio

In partial fulfillment of the requirements for the degree of

Master of Science in Engineering and Policy Analysis

at the Delft University of Technology, to be defended publicly on 30 June 2016.

Graduation Committee

First supervisor: Dr. ir. B. Enserink TU Delft

Second supervisor: Dr. J. Rezaei TU Delft

Chair: Prof.dr.ir. A. Verbraeck TU Delft

External supervisor: A. Bachmann Novartis Pharma AG

An electronic version is available at http://repository.tudelft.nl

Preface

This document is the end result of my Master thesis, carried out between February and June 2016 in Basel, Switzerland, which explores the benefits that new technologies can bring to the design and execution of clinical trials.

The thesis is written in such a way that everyone without any prior background in pharmacy can progressively build knowledge to end up understanding complex logistics issues that affect clinical trial supply chains. I myself had little knowledge about the pharmaceutical sector before I joined Novartis in February 2016, and have tried to create a report that captures the learning curve I went through, while at the same time inviting the reader to take part in the same process. This is especially true for the extended introduction provided in the first chapter, which is recommended to all the readers without prior experience in the pharma industry.

Readers with an advanced knowledge or direct experience in the pharmaceutical sector will surely find the conclusions of the third and the sixth chapter the most interesting ones, as they focus on the analysis of a particular type of smart label suitable for the clinical trial supply chain which has not been explored so far. Furthermore, the final conclusions and recommendations presented in the eighth chapter are a shortcut to all the outcomes of this thesis.

Finally, readers with an interest in the discrete-event simulation approach adopted to model a clinical trial supply chain will find plenty of explanations on the model conceptualization in the fifth chapter, as well as in an appendix dedicated to the model specification.

Ignacio Paricio Basel, June 2016

iii

Acknowledgements

I would like to express my deepest gratitude to Amanda Bachmann, my external supervisor at Novartis, for her continuous support and priceless input during our talks and discussions throughout my stay at Novartis.

I would also like to thank all the interviewees from Novartis that dedicated time to support my research. Their input has been crucial to clarify concepts, gather input data and validate the outcomes of this thesis.

Thanks also to the members of my Thesis Committee at TU Delft – Alexander Verbraeck, Bert Enserink and Jafar Rezaei – for their support, thoughtful questions and comments. Their feedback was greatly appreciated and served to improve the quality of this thesis.

Finally, I would like to thank my closest family for being always there throughout my education.

Ignacio Paricio Basel, June 2016

Smart Labels in the Clinical Trial Supply Chain

Acronyms ...... xi List of figures ...... xiii List of tables ...... xvii Executive Summary ...... xix 1 Introduction – From Healthcare Quality to the Clinical Trial Supply Chain ...... 1 1.1 Drug development and its importance for a better healthcare quality – An overview ...... 2 1.2 The drug development process of pharmaceutical companies ...... 2 1.3 Clinical trials: when a candidate drug first meets humans ...... 5 1.4 Supply chain management and the clinical trial supply chain ...... 6 1.5 The material flow of the clinical trial supply chain ...... 7 1.6 The information flow of the clinical trial supply chain ...... 9 1.7 Stakeholders involved in the clinical trial industry ...... 10 1.8 Challenges faced in the clinical trial industry ...... 18 2 Research Definition...... 23 2.1 The potential disruptive role of new technologies in clinical trials ...... 24 2.2 Scope, research objective and research questions ...... 24 2.3 Methodology ...... 25 2.4 Research relevance ...... 27 2.5 Expected outcomes and research plan ...... 27 2.6 Research limitations ...... 28 3 Smart labels – Market Research and Applicability to the Clinical Trial Supply Chain ...... 29 3.1 Deeper insights into the role of labeling in clinical trials ...... 30 3.2 Market research on smart labels ...... 31 3.3 Research on smart labels in the pharmaceutical industry ...... 40 3.4 SWOT of the different applications of smart labels in the clinical trial supply chain ...... 43 3.5 Smart labels in the clinical trial supply chain – Conclusions ...... 48 4 The Regulatory Perspective: Do Smart Labels Fit in the Current Regulatory Framework?...... 51 4.1 Overview of regulatory aspects in clinical trials ...... 52 4.2 Labeling requirements for IMPs in clinical trials ...... 55 4.3 Outcomes of the interviews with experts in clinical trial regulations...... 60 4.4 The regulatory perspective on smart labels – Discussion and conclusions ...... 60

vii 5 A Discrete-Event Simulation Model of the Clinical Trial Supply Chain ...... 65 5.1 Optimization techniques for the clinical trial supply chain – A literature review ...... 66 5.2 The clinical trial supply chain – Model conceptualization ...... 67 5.3 Model boundaries ...... 77 5.4 Assumptions taken and their justification ...... 78 5.5 Key Performance Indicators ...... 80 5.6 Modeling logic in Simio – different objects and model parameters ...... 81 5.7 Model verification and validation ...... 82 6 Case Study: Smart Labels on a Phase III Clinical Trial ...... 85 6.1 Overview of the case ...... 86 6.2 The status quo – analysis and optimization of the safety stock ...... 87 6.3 Implementing smart labels in the clinical trial supply chain ...... 91 6.4 Effects that different parameters have in the usefulness of smart labels ...... 93 6.5 Price of eInk smart labels ...... 99 6.6 Other opportunities and threats not captured in the model ...... 100 6.7 Case Study: Smart Labels on a Phase III Clinical Trial – Conclusions ...... 101 7 Smart Labels in Clinical Trials – The Patient Perspective ...... 103 7.1 Literature review on the motivations and barriers for trial participation and adherence 104 7.2 Smart labels to improve patient adherence ...... 111 7.3 Smart labels to reduce the time burden for patients participating in a clinical trial ...... 116 7.4 Critical assumptions and risks in the implementation of patient-oriented technology ... 117 7.5 The patient perspective of smart labels in clinical trials – Conclusions...... 120 8 Conclusions, Recommendations, Reflection and Further Steps ...... 121 8.1 A glimpse back ...... 122 8.2 Conclusions – Answering the research questions ...... 123 8.3 Recommendations ...... 130 8.4 Reflection on the results and conclusions ...... 132 8.5 Research limitations and suggestions for further research ...... 138 A. Model Specification ...... 141 A.1. Objects in the model and overview of their interactions ...... 141 A.2. Model parameters ...... 143 A.3. Add-on processes ...... 146 B. A Literature Review on New Technologies in Clinical Trials from a Patient Perspective ...... 155 B.1. Generic technology trends in the CTSC ...... 156 B.2. Telemonitoring ...... 156 B.3. Mobile communication devices ...... 157 B.4. Near Field Communication ...... 158

viii B.5. Patient-reported outcomes ...... 158 C. Interview Protocols and Outcomes ...... 159 C.1. Interview protocol ...... 159 C.2. Interview with a Clinical Trial Expert ...... 160 C.3. Interview with a Senior Distribution Process Expert ...... 165 C.4. Interview with a Senior Leader at Clinical Trial Portfolio Level ...... 168 C.5. Interview with a Lead Business Integration Manager ...... 172 C.6. Interview with a Head Clinical Supply Documentation Specialist ...... 175 C.7. Interview with a Senior Consultant in Regulatory Affairs for Clinical Trials ...... 177 References ...... 179

ix x Acronyms

Acronym Definition

API Active Pharmaceutical Ingredient BLA Biologics License Application CDMS Clinical Data Management Systems CMC Chemistry Manufacturing Control CRO Contract Research Organization CT Clinical Trial CTMS Clinical Trial Management Systems CTSC Clinical Trial Supply Chain DED Dynamic Expiry Date DES Discrete Event Simulation DSM Drug Supply Management EDC Electronic Data Capture EHR Electronic Health Records eInk Electronic Ink EM Electronic Monitoring ePaper Electronic Paper ESL Electronic Shelf Labels ESL Electronic Shelf Label EU European Union FDA U.S. Food and Drug Administration FPFV First Patient, First Visit GCP Good Clinical Practice GMP Good Manufacturing Practice GPRS General Packet Radio Service GPS Global Positioning System GSM Global System for Mobile Communications ICH International Conference of Harmonization IMP Investigational Medicinal Product IND Investigational New Drug IOM Institute of Medicine IoT Internet of Things IRT Interactive Response Technology IVR Interactive Voice Response JIT Just in Time KDP Kit Decoupling Point KPI Key Performance Indicator LPFV Last Patient, First Visit LPLV Last Patient, Last Visit MS Member States NDA New Drug Application NFC Near Field Communication NME New Molecular Entity PRO Patient Reported Outcomes

xi QARP Quality Assurance Responsible Person QP Qualified Person RBM Risk-Based Monitoring RFID Radio Frequency IDentification RIS Ready to Initiate Site RPM Remote Patient Monitoring SC Supply Chain SCM Supply Chain Management SIV Site Initiation Visit SOP Standard Operating Procedure SWOT Strengths, Weaknesses, Opportunities and Threats TM Telemonitoring WHO World Health Organization

xii List of figures

FIGURE 1: EVOLUTION OF R&D COSTS PER NME. ADAPTED FROM [11]...... 3

FIGURE 2: THE BIOPHARMACEUTICAL R&D PROCESS FOR A PHARMACEUTICAL COMPANY. ADAPTED FROM [6]. ...4

FIGURE 3: THE CLINICAL TRIAL SUPPLY CHAIN. ADAPTED FROM [21] ...... 8

FIGURE 4: STAKEHOLDER MAP IN THE CONTEXT OF THE DESIGN, PLANNING AND EXECUTION OF CLINICAL TRIALS ...... 18

FIGURE 5: AVERAGE TOTAL COST OF R&D PER APPROVED NEW DRUG, CATEGORIZED IN CLINICAL AND PRECLINICAL COSTS. ADAPTED FROM [10] AND [69] ...... 19

FIGURE 6: ACTUAL AND PLANNED ENROLLMENTS FOR A CLINICAL TRIAL. ADAPTED FROM [21]. ORIGINAL SOURCE: HOFFMANN-LA ROCHE ...... 21

FIGURE 7: RESEARCH FLOW DIAGRAM ...... 28

FIGURE 8: EXAMPLE OF A LABEL OF AN IMP FOR A DOUBLE-BLIND CLINICAL TRIAL ...... 30

FIGURE 9: EXAMPLE OF AN ON-ROLL SELF-ADHESIVE RFID TAG. ADAPTED FROM [105]...... 32

FIGURE 10: OVERVIEW OF A PASSIVE, ADHESIVE SMART LABEL WITH AN EMBEDDED RFID TAG [104] ...... 33

FIGURE 11: OVERVIEW OF DIFFERENT COMMUNICATION TECHNOLOGIES THAT CAN BE APPLIED TO SMART LABELS. LABELS WITH 2D CODES ARE NOT ALWAYS CLASSIFIED AS SMART LABELS...... 34

FIGURE 12: RFID TO TRACK ITEM LEVEL INVENTORY ...... 35

FIGURE 13: DIFFERENCES BETWEEN PASSIVE AND ACTIVE, SENSOR-BASED RFID TAGS ...... 36

FIGURE 14: NFC DEVICES CAN BE USED TO READ ENVIRONMENTAL INFORMATION FROM PRODUCTS CONTAINING AN RFID INLAY. ADAPTED FROM [125] ...... 37

FIGURE 15: APPLYING AN RFID LABEL ON A MEDICAL PRODUCT ENABLES TO TRACK ITS AND ENVIRONMENTAL CONDITIONS. ADAPTED FROM [127] ...... 37

FIGURE 16: EXAMPLE OF ELECTRONIC SHELF LABELS ...... 39

FIGURE 17: SWOT ANALYSIS FOR THE APPLICABILITY OF SMART LABELS AS A MEANS TO ITEM-LEVEL INVENTORY TRACKING ...... 44

FIGURE 18: SWOT ANALYSIS FOR THE APPLICABILITY OF SMART LABELS TO CONTROL ENVIRONMENTAL CONDITIONS ...... 44

FIGURE 19: SWOT ANALYSIS FOR THE APPLICABILITY OF SMART LABELS TO FIGHT COUNTERFEIT DRUGS ...... 45

FIGURE 20: SWOT ANALYSIS FOR THE APPLICABILITY OF SMART LABELS TO CHANGE THE CONTENT OF EXISTING LABELS ...... 45

FIGURE 21: MAP OF ALL STUDIES IN CLINICALTRIAL.GOV AS OF APRIL 2016. EXTRACTED FROM [175] ...... 55

FIGURE 22: PRIMARY AND SECONDARY PACKAGES OF IMPS ...... 57

FIGURE 23: SUMMARY OF LABELING REQUIREMENTS ACCORDING TO THE REGULATIONS IN THE EUROPEAN UNION. ADAPTED FROM [45]...... 58

FIGURE 24: EXAMPLE OF THE SMALLEST TYPES OF LABELS USED IN CLINICAL TRIALS (DISPLAYED AT REAL-SIZE) . 61

xiii FIGURE 25: HIGH RESOLUTION EINK SOLUTIONS ALREADY EXIST IN THE MARKET, MEANING THAT IT IS TECHNICALLY FEASIBLE TO ATTACH EINK LABELS TO CURVED CONTAINERS ...... 62

FIGURE 26: OVERVIEW OF THE KEY DATES DURING THE EXECUTION OF A CLINICAL TRIAL ...... 68

FIGURE 27: DYNAMICS OF THE PATIENT RECRUITMENT AT A STUDY LEVEL, A COUNTRY LEVEL AND A CLINICAL SITE LEVEL FOR A TYPICAL CLINICAL TRIAL ...... 70

FIGURE 28: OVERVIEW OF THE DIFFERENT LOGISTIC STEPS TO TRANSPORT AN IMP FROM THE CENTRAL DEPOT (OR MANUFACTURING SITE) [LEFT] TO THE FINAL CLINICAL SITES [RIGHT] THROUGH INTERMEDIATE REGIONAL DEPOTS [MIDDLE] ...... 72

FIGURE 29: SCREENSHOT OF THE DES MODEL SHOWING A CLINICAL SITE. INSIDE THE CLINICAL SITE THERE ARE THREE ELEMENTS: THE SCREENING (REPRESENTED BY A BED), THE IRT SYSTEM (REPRESENTED BY A COMPUTER) AND THE COMBINER THAT MATCHES PATIENTS AND PATIENT KITS TO REPRESENT THE INTAKE OF DRUGS ...... 75

FIGURE 30: OVERVIEW OF THE KPIS CONSIDERED IN THE DES MODEL ...... 80

FIGURE 31: SCREENSHOT OF THE DIFFERENT MODEL PARAMETERS IN THE SIMIO MODEL, AS WELL AS THEIR VALUES FOR THE CASE STUDY ...... 82

FIGURE 32: VISUAL COMPARISON OF THE NUMBER OF TIMES THAT A PATIENT WAITED FOR MEDICATION IN THE DIFFERENT SCENARIOS. EACH BOXPLOT CONTAINS THE INPUT DATA FROM 30 REPLICATIONS...... 89

FIGURE 33: SCREENSHOT EXTRACTED FROM THE DES MODEL SHOWING THE VALUES FOR THE DIFFERENT CUSTOMIZABLE PARAMETERS. NOTE THAT THE SMART LABELS POLICY (IN YELLOW) CAN BE EASILY ACTIVATED [1] OR DEACTIVATED [0]...... 92

FIGURE 34: EXAMPLE OF A SMALL RFID EINK SMART LABEL PRICED AT 8$/UNIT FOR LARGE ORDERS ...... 100

FIGURE 35: EXAMPLE OF BIG RFID EINK SMART LABEL PRICED BELOW 25€/UNIT FOR LARGE ORDERS ...... 100

FIGURE 36: REASONS FOR PATIENTS TO PARTICIPATE IN CLINICAL TRIALS. EXTRACTED FROM [91]...... 104

FIGURE 37: REAL PICTURE OF BREEZHALER (LEFT) AND SCHEME OF USAGE (RIGHT). ADAPTED FROM [221] AND [222]...... 112

FIGURE 38: THE CLEVERCAP ECOSYSTEM. ADAPTED FROM [223]...... 112

FIGURE 39: MEDSMART, A PILL DISPENSER ORIENTED TOWARDS TELECARE. ADAPTED FROM [224]...... 113

FIGURE 40: ID-CAP (TOP) TRACKS ACTUAL INTAKE OF DRUGS BY PLACING SENSORS IN THE PILLS THAT COMMUNICATE WITH THE READER (BOTTOM). ADAPTED FROM [235]...... 113

FIGURE 41: THE DROPLET ECOSYSTEM - THE BUTTON (LEFT), THE HUB (MIDDLE) AND THE APP (RIGHT). ADAPTED FROM [236]...... 114

FIGURE 42: EXAMPLE OF TAILORED SOLUTIONS FOR MEDICATION TRACKING. ADAPTED FROM [221], [222] [223] AND [224]...... 126

FIGURE 43: EXAMPLE OF A GENERIC SOLUTION FOR MEDICATION TRACKING THAT CAN BE USED REGARDLESS OF THE DOSAGE FORM. ADAPTED FROM [266] ...... 127

FIGURE 44: SCREENSHOT OF THE PATIENT LOOP IN THE SIMIO SIMULATION MODEL ...... 143

FIGURE 45: SCREENSHOT WITH AN OVERVIEW OF THE DIFFERENT ADD-ON PROCESS CATEGORIES IN THE SIMULATION MODEL ...... 147

FIGURE 46: SCREENSHOT IN SIMIO SIMULATION OF AN IRT PROCESS ...... 148

xiv

FIGURE 47: SCREENSHOT IN SIMIO SIMULATION OF AN ORDER TRACKING ADD-ON PROCESS ...... 148

FIGURE 48: ADD-ON PROCESSES FOR THE GENERIC MANAGEMENT OF PATIENT VISITS ...... 148

FIGURE 49: ADD-ON PROCESSES FOR THE CLINICAL SITE-SPECIFIC MANAGEMENT OF PATIENTS ...... 149

FIGURE 50: SCREENSHOT OF THE ADD-ON PROCESSES THAT MODEL THE DEMAND FOR DRUGS OF THE REGIONAL DEPOTS ...... 150

FIGURE 51: SCREENSHOT OF THE DIFFERENT ADD-ON PROCESSES USED TO DISTRIBUTE DRUGS OUTGOING A REGIONAL DEPOT ...... 150

FIGURE 52: SCREENSHOT OF THE SIXTH CATEGORY OF ADD-ON PROCESSES IN SIMIO SIMULATION ...... 151

FIGURE 53: SCREENSHOT OF THE DIFFERENT ADD-ON PROCESSES USED IN THE SIMIO SIMULATION MODEL TO CAPTURE THE LOGIC OF RE-LABELING PROCESSES ...... 152

FIGURE 54: SCREENSHOT IN THE SIMIO SIMULATION MODEL OF THE PROCESSES GROUPED IN THE EIGHTH CATEGORY ...... 152

xvi

List of tables

TABLE 1: DIFFERENT DEFINITIONS OF "SUPPLY CHAIN MANAGEMENT" ACCORDING TO [31]...... 6

TABLE 2: MOTIVATIONS FOR INVESTIGATORS TO PARTICIPATE IN A CLINICAL TRIAL. ADAPTED FROM [30] ...... 13

TABLE 3: OVERVIEW OF METHODOLOGIES TO BE USED ...... 26

TABLE 4: COMMON REASONS FOR THE RE-LABELING OF AN IMP, FROM MORE TO LESS FREQUENT ...... 31

TABLE 5: CHARACTERISTICS OF MODERN EINK DISPLAYS. RAW DATA OBTAINED FROM [67, 135, 136] ...... 39

TABLE 6: BIGGEST PHARMACEUTICAL COMPANIES BY GLOBAL SALES IN 2014. ADAPTED FROM [144]...... 41

TABLE 7: POTENTIAL APPLICATIONS OF SMART LABELS IN THE CLINICAL TRIAL SUPPLY CHAIN ...... 43

TABLE 8: COMPARISON OF CLINICAL TRIAL APPLICATIONS (CTAS) IN SELECTED EMERGING MARKETS. ADAPTED FROM [173] ...... 55

TABLE 9: EXAMPLES OF THE LINKAGE BETWEEN THE CONTENT OF A LABEL AND THE FULFILLMENT OF ITS OBJECTIVES ...... 58

TABLE 10: STANDARD SIZES FOR ESL BEING USED IN THE FOOD INDUSTRY. ADAPTED FROM [178]...... 62

TABLE 11: AVERAGE LEAD TIMES FOR DIFFERENT STEPS IN THE CLINICAL TRIAL SUPPLY CHAIN ...... 73

TABLE 12: LIST AND EXPLANATION OF THE DIFFERENT KPIS IN THE DES MODEL ...... 81

TABLE 13: LIST OF THE DIFFERENT OBJECTS USED IN THE SIMIO SIMULATION MODEL ...... 82

TABLE 14: CLINICAL SITE AND PATIENT INFORMATION OF THE CASE STUDY ...... 86

TABLE 15: PARAMETERS OF THE EXPONENTIAL DISTRIBUTIONS THAT MODEL THE PATIENT ENROLLMENT ...... 86

TABLE 16: ADDITIONAL PARAMETERS FOR THE CASE STUDY ...... 87

TABLE 17: PARAMETERS AND RESPONSES OF SOME SELECTED DIFFERENT SCENARIOS ANALYZED TO DETERMINE THE SAFETY STOCK LEVELS, SORTED BY THE NUMBER OF TIMES A PATIENT WAITED FOR MEDICATION ...... 88

TABLE 18: RELIABILITY FACTORS FOR THE DIFFERENT SCENARIOS ...... 89

TABLE 19: RESULTS OF THE EMPIRICAL VALIDATION TESTS USE TO VALIDATE THE PATIENT ENROLLMENT, SCREENING, DROPOUTS AND SUCCESSFULLY COMPLETION OF THE TRIAL ...... 90

TABLE 20: RESULTS OF THE EXTREME CONDITIONS BEHAVIOR TEST FOR AN INFINITE EXPIRY DATE ...... 91

TABLE 21: RESULTS OF THE EXTREME CONDITIONS BEHAVIOR TEST WHEN THE TIME BETWEEN PATIENTS' VISITS TO THE CLINICAL SITES IS ONE DAY ...... 91

TABLE 22: RESULTS OF THE EXPERIMENTS CARRIED OUT WITH SMART LABELS AND DIFFERENT SAFETY STOCK LEVELS ...... 92

TABLE 23: COMPARISON OF RE-LABELING AND FINANCIAL KPIS IN THE ORIGINAL SCENARIO AND IN ONE WITH SMART LABELS ...... 93

TABLE 24: COMPARISON FOR A BIGGER STUDY OF THE MAIN GENERIC KPIS IN THE ORIGINAL CLINICAL TRIAL SUPPLY CHAIN AND IN A SUPPLY CHAIN WITH EINK SMART LABELS ...... 94

TABLE 25: ADDITIONAL KPI COMPARISONS FOR A BIGGER STUDY ...... 94

TABLE 26: DEFINITION OF DIFFERENT EXPERIMENTS TO ANALYZE HOW THE USEFULNESS OF SMART LABELS VARIES WITH CERTAIN SELECTED PARAMETERS ...... 95

xvii

TABLE 27: RESULTS ON THE IMPACT THAT DIFFERENT PARAMETERS HAVE IN THE USEFULNESS OF SMART LABELS ...... 97

TABLE 28: SUMMARY OF THE INFLUENCE OF KEY PARAMETERS IN THE USEFULNESS OF SMART LABELS FROM A QUALITATIVE PERSPECTIVE ...... 99

TABLE 29: REASONS TO PARTICIPATE IN A CLINICAL TRIAL ON AN AVIAN INFLUENZA VACCINE. ADAPTED FROM [190]...... 105

TABLE 30: BARRIERS TO PATIENT PARTICIPATION ADAPTED FROM ROSS ET AL. [49]. INDIVIDUAL REFERENCES TO EACH OF THE PAPERS CAN BE TRACKED IN THEIR ORIGINAL PUBLICATION...... 105

TABLE 31: RESULTS OF THE SURVEY ON THE BARRIERS FOR PATIENTS TO PARTICIPATE IN CLINICAL TRIALS. ADAPTED FROM [193] ...... 106

TABLE 32: REASONS FOR HEMOPHILIA PATIENTS NOT TO PARTICIPATE IN CLINICAL TRIALS. ADAPTED FROM [201]...... 107

TABLE 33: RESPONSES TO SOME OF THE QUESTIONS TO THE SURVEY OF TABAN ET AL., AS EXTRACTED FROM THEIR PAPER [187] ...... 107

TABLE 34: FACTORS AFFECTING PATIENT ADHERENCE [15, 82, 205-208]...... 108

TABLE 35: PREDICTORS OF POOR PATIENT ADHERENCE, ADAPTED FROM OSTERBERG AND BLASCHKE [80].ONLY THOSE RELEVANT TO CLINICAL TRIALS ARE SHOWN. INDIVIDUAL REFERENCES TO EACH OF THE PAPERS CAN BE TRACKED IN THEIR ORIGINAL PUBLICATION...... 108

TABLE 36: EQUIPMENT NECESSARY TO TELEMONITOR CLINICAL TRIALS OF DIFFERENT DISEASES...... 117

TABLE 37: COMPARISON OF METHODS TO MEASURE PATIENT ADHERENCE TO MEDICATION REGIMES. ADAPTED FROM [80] ...... 119

TABLE 38: RESEARCH QUESTIONS AND SECTIONS WHERE THEY ORIGINALLY WERE ADDRESSED ...... 127

TABLE 39: MAIN OBJECTS THAT FORM THE DES MODEL, AND NUMBER OF DIFFERENT INSTANCES IN THE CASE STUDY ...... 141

TABLE 40: MODEL PARAMETERS GROUPED IN DIFFERENT CATEGORIES ...... 144

TABLE 41: COLOR CODE USED DURING THE MODELING OF THE ADD-ON PROCESSES ...... 147

TABLE 42: TECHNOLOGY TRENDS CHANGING THE CLINICAL TRIAL INDUSTRY ACCORDING TO [43] ...... 156

xviii

Executive Summary

Clinical trials (CTs) are a key step in the research and development process of new drugs, as they serve to adequately assess efficacy and safety questions in regard to the effects of a candidate drug in humans. CTs are also the most critical, time-consuming and expensive step in the biopharmaceutical R&D process, taking on average 6.7 years to complete accounting for 70% of the total R&D costs for a drug and having a clinical success ratio below 30%1.

Pharmaceutical companies face nowadays a series of challenges when it comes to the design, preparation and execution of CTs. These are (i) the increasingly high associated costs, (ii) recurrent delays, (iii) the rising complexity, (iv) low patient adherence and (v) problems with patient recruitment.

C1. Costs have kept rising as the process for researching and developing new drugs increases both in difficulty and length, with clinical supply costs accounting for up to 40% of the total clinical trial spending.

C2. Trials are often carried out under great time pressure, because the financial consequences for every day that the trials are delayed are huge. Re-labeling and over-labeling of Investigational Medicinal Products (IMPs) can be the source for some of these delays. Thus, bringing an efficient labeling solution to the Clinical Trial Supply Chain (CTSC) might help solving this issue.

C3. The increasing complexity is mainly originated because of the rise in the number of studies and the shift towards global trials in the search for patients. There exists a correlation between complexity of clinical trials and compliance problems.

C4. Patients not adhering to the treatment pose a problem both for the sponsor of the CT and for the patients. The former sees how all the resources invested in a patient will no longer yield, while the latter have a worse prognosis originated by poor adherence to medication. On average 48% of CT patients do not adhere properly to the medication regimen, although this figure is treatment- and disease-specific.

C5. CT enrolment rates have declined by 21% from 1999 to 2005 despite of an increase in the number and length of CTs, and up to 80% of the clinical trials fail to meet their patient recruitment deadlines.

The research objective of the present research is to identify, understand and assess the potential disruptive role that smart technology might have in the CTSC chain to help overcoming (totally or partially) these five challenges. For this purpose, the following research question was explored: What are the threats and opportunities in using smart technology to overcome the challenges faced in the supply chain of contemporary pharmaceutical clinical trials? A stakeholder analysis reveals that pharmaceutical companies, patients and regulatory bodies are the main stakeholders to be considered to address this question.

Results of an extensive desk research identify three main potential applications for smart labels to improve the logistics of the CTSC: as a means to track inventory at an item level, as a way to ensure correct environmental conditions during the transportation of drugs and, finally, as an alternative to avoid the costly, time-consuming re-labeling of IMPs. Further research reveals that most of the top

1 This figure is as low as 12%, depending on the source.

xix

10 pharma companies invested resources to test the applicability of passive RFID smart labels in their supply chains to track inventory in the years between 2005 and 2008. A loss of interest, however, followed this initial enthusiasm, at least when it comes to the application of passive RFID tags. In addition, the individual price of the active sensors required to monitor in real-time environmental conditions makes their implementation not feasible at the moment. The implementation of labels with variable content is however unexplored. A combination of electronic ink (eInk) smart labels with communication technologies such as RFID, Bluetooth or NFC can lead to relatively cheap labels that help to address C1, C2 and C3. Because of the combination of potential opportunities and lack of research of this particular type of smart labels, the present thesis centers in eInk smart labels.

SWOT Analysis: Labels with a variable content Strengths Weaknesses 1. More flexibility in the CTSC 1. High setup costs 2. Labels with variable content can save money and delays when re-labeling 2. Resource intensive validation of the system required 3. eInk can communicate via passive tags  low variable costs 3. Require coordination with clinical sites 4. Elimination of booklets

2. A reduced in inventory overage can lead reliable enough or not properly validated to a lower demand for manufacturing 3. Risk of regulatory aversion 3. Might also serve to track inventory 4. Electronic labels might be difficult to adapt to specific label sizes/shapes

An important threat to labeling innovations in the CTSC is regulatory aversion: the pharmaceutical industry being one of the most regulated in the world, it remains to be seen whether such a technology would have a fit in the clinical trials supply chain from a regulatory perspective. Regulations surrounding the clinical trial industry, with a special focus on labeling requirements, are analyzed in an attempt to assess this potential threat.

Regulatory guidelines specify information that needs to be included on clinical labels, but neither the European Directive nor the guidelines provided by the FDA specify explicitly that labels have to be in a physical, paper format, what in principle gives room to electronic labels. Moreover, there is precedent that regulatory agencies have been relatively open to innovation in the past, and that certain degrees of flexibility existed in past pilot RFID projects. In general, as long as technology adds new features while at the same time fulfilling the regulatory requirements, risk of regulatory aversion is low. Some regulatory constraints might however transform into technological problems, especially related to small labels for primary containers or for curved shapes (such as a bottle). Even though curved eInk panels already exist, labeling these containers with this type of smart labels is currently a threat.

Having determined that eInk smart labels are feasible in the CTSC from regulatory perspective, the next step is to quantify the benefits that they can bring. A Discrete-Event Simulation (DES) model is developed for this purpose. The model includes several subsystems, such as the patient recruitment,

patient screening and dropout, patients’ drug intake patterns, drug manufacturing, drug transporting, drug expiration and re-labeling and drug waste, among others.

Once the DES model is calibrated and validated, a relatively small phase III clinical trial is used to assess and compare selected KPIs in the status quo with those obtained when smart labels are implemented.

Parameters Responses (selected KPIs) Patient Safety stock Safety stock Times a patient Avg time kits Inventory Scenario clinical site depot [#] waited [#] waited [h] shipped overage [%] [#] [#] Status quo 800 15 33,2 0,368 11544 44,12 Smart labels 400 5 24,7 0,685 8645 25,05

Patient kits Re-labelings at Costs of Costs of Re-labelings Manufacturing destroyed clinical sites disposing drugs re-labeling [#] costs [€] [#] [#] [€] [€] Status 3192,37 2810,87 0 1,15E+06 23717,5 68796 quo Smart 3762,33 0 1275,83 864420 11775,8 75247 labels

eInk smart labels show promising results, allowing for savings of more than 30€ per patient kit shipped, what justifies their price (below 25€ per unit for the largest sizes) from a variable cost perspective. Most of these savings come from the decrease in inventory overage and waste thanks to the flexibility that these smart labels bring to the CTSC. Besides, the advantages of smart labels increase as the size, the number of treatments, the primary packs per patient kit, the dropout rate, the manufacturing costs and the travel time from depots to clinical site increase. For a medium size phase III trial, savings per patient kit increase to ~50€ per patient kit shipped. Conversely, as the expiry date, the extension of expiry dates and the travel time of drugs from the manufacturing facilities to the depots increase, the usefulness of smart labels decreases.

xxi

Parameter variation Benefits/Usefulness of eInk smart labels number of patients number of treatments ↑ ↑ number of containers per patient kit ↑ ↑ dropout rate ↑ ↑ expiry date ↑ ↑ expiry date extension when re-labeling ↑ ↓ manufacturing costs ↑ ↓ travel time to depots ↑ ↑ travel time from depots to clinical sites ↑ ↓ While↑ eInk smart labels prove to be useful in addressing C1, C2 and C3↑ , their usefulness is compromised when it comes to C4 and C5. However, an extensive literature review allows to conclude that there is room for smart technology to improve patient adherence to medication (C4). Furthermore, technology can also be used to reduce one of the barriers to clinical trial participation: the time burden than trials imply for patients (C5). Smart technology is also found not be able to increase patients motivations to join CTs. Thus, an important conclusion found is that different technological means are required to tackle different challenges faced in the clinical trial industry.

Although promising, the use of technology – remote patient monitoring – to reduce the time burden for patients is left out of scope of the present research because additional research reveals that it heavily calls for disease-specific solutions. Further research is recommended in this area, as still there is room for technological solutions addressing C5.

As for the role of technology to positively influence patient adherence to medication (C4), two separate sets of e-devices were analyzed: devices that automatically track drugs dispensation by measuring what goes out of the primary containers and generic devices (normally generic buttons) that call for patient interaction to monitor the adherence to medication. The latter is recommended because of its simplicity, inexpensiveness (~30-90€ per patient enrolled in a clinical trial) and facility to escalate, all of which makes it suitable for pilot projects in the short term.

xxii

xxiii

xxiv

1.1 – Drug development and its importance for a better healthcare quality – An overview 1

Chapter One ………………………...... 1 Introduction – From Healthcare Quality 1 to the Clinical Trial Supply Chain

Medicine is a science of uncertainty and an art of probability William Osler, physician and founder of Johns Hopkins Hospital

The healthcare industry is an aggregation of different sectors that provide products and services to treat patients with curative, rehabilitative, palliative and preventive care. Assuring a satisfactory healthcare quality is the objective of most healthcare systems across the world. This chapter starts by demarcating the role of drug development in the global healthcare landscape. Clinical trials, a special stage of the drug development process, are then described in detail, focusing on the clinical trial supply chain, stakeholders involved, and the main challenges encountered.

2 1.1 – Drug development and its importance for a better healthcare quality – An overview

1.1 Drug development and its importance for a better healthcare quality – An overview The global healthcare landscape has extraordinarily changed in the last 50 years. Around the world, people are living longer and healthier lives, a trend that has boosted income and prosperity. More children than ever are attending primary school, and maternal mortality has nearly halved over the last two decades [1].

Today, a primary objective of most healthcare systems around the world is to provide the combination of services that optimizes population’s healthcare quality [2], which is defined by the Institute of Medicine (IOM) as “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge” [3].

However, globally, approximately two billion people lack access to medicines [4]. This might be propitiated either by a lack of existing treatments or by a lack of access to existing ones. In many cases, treatment may be unavailable for even some of the most common health concerns, such as cancer, HIV or hypertension.

When no treatments options for a disease are available or known drug discovery comes into play. Modern drugs can be used to prevent or treat diseases that otherwise would lead to morbidity and mortality [5]. For patients, new medicines imply an increase in the quality of life, with fewer hospitalizations, a decrease in the number and impact of negative side effects, an increase in productivity and overall an extended life [6]. The crucial role of drug discovery is apparent in its long history, since its beginnings thousands of years ago with natural remedies to the estimated $825 billion annual global pharmaceutical market that exists today [7].

Drug discovery and development is tightly linked to the finding of New Molecular Entities (NMEs). NMEs are active substances not previously approved by healthcare regulatory authorities, such as the U.S. Food and Drug Administration (FDA) [8]. Pharmaceutical companies are the key global players in the discovery of new NMEs. A study by Robert Kneller, which analyzed the NMEs tied to 252 new drugs approved by the FDA from 1998 to 2007, proved that up to 79% of the NMEs were originated in pharmaceutical companies. The remaining 21% corresponds to developments in biotechnology companies and universities [9]. Results from this study suggest that the drug development process of pharmaceutical companies is of great importance for improving the quality of life of patients suffering from conditions for which no treatments exist. 1.2 The drug development process of pharmaceutical companies The process for researching and developing new drugs is increasing both in difficulty and length. Since the 1950s, the productivity in drug development has declined [10]. This is mostly because of the increase in the R&D costs per NME discovered, which in turn is originated by a lower rate of NMEs per year for any given company [11]. Figure 1 shows this evolution over the last 50 years.

1.2 – The drug development process of pharmaceutical companies 3

Figure 1: Evolution of R&D costs per NME. Adapted from [11].

As a result, nowadays it takes on average 10 years for a drug to reach a commercialization stage, with an average cost between $2 and $5 billion [6, 10-12].

Regardless of the staggering R&D costs, the biopharmaceutical research and development process has remained stable over the last 50 years. Figure 2, adapted from [6], provides an overview of this process, which involves:

1. Basic research: The starting point for drug development is understanding the inner working of human disease at a molecular level. As the knowledge of a disease develops, the potential to discover NMEs that can target this disease increases. This is done by assessing how drug targets (i.e. molecular structures in the body) react to potential drug compounds, resulting in a clinical effect (i.e. potential vaccine or treatment of a disease).

2. Drug discovery: Out of the different potential drug compounds tested, researchers try to select the most promising ones: the molecules that might react to the drug target and potentially become a medicine. These molecules are usually labelled as the lead compounds. This process is carried out in a variety of ways, “including creating a molecule from living or synthetic material, using high-throughput screening techniques to select a few promising possibilities from among thousands of potential candidates, identifying compounds found in nature, and using biotechnology to genetically engineer living systems to produce disease- fighting molecules” [6].

3. Pre-clinical testing: Once promising lead compounds have been identified, it is mandatory to determine how safe a drug is prior to any use in humans. This is done mainly in three different ways:

a. Performing test in living cells b. Using computational models that emulate how the body processes chemical compounds c. Via animal testing

The lead compound(s) are then altered in an attempt to increase their safety and effectiveness, often through several iterations. Besides ethical reasons, this is also to comply

4 1.2 – The drug development process of pharmaceutical companies

with the strict regulatory requirements that agencies that as the FDA set for candidate drugs before they can be studied in humans [13].

During this stage, researches also have to consider the way the drug will be administered to human patients in potential future clinical trials, as well as the feasibility of producing the lead compound at large-scale. Furthermore, storage conditions and (a preliminary) shelf-life are also determined.

Figure 2: The biopharmaceutical R&D process for a pharmaceutical company. Adapted from [6]. 2

4. Clinical trials: Conducting clinical trials that involve human subjects is a crucial means to test the value (or lack of) of a clinical intervention or treatment [14]. This is the most critical, time- consuming and expensive step in the biopharmaceutical R&D process [15]. Clinical trials (CTs) take on average 6.7 years to complete [16], account for 70% of the total R&D costs for a drug [17] and have a clinical success ratio of just 12%3 [6]. This means that approximately just one out of eight drugs being tested in CTs ends up being commercialized.

The main types of CTs are open trials, single-blind trials and double-blind trials. In open trials, both the investigator and the patient know what treatment is assigned to the latter. In single-blind and double-blind CTs, trial subjects are allocated in a randomized way into one of the different treatment groups to avoid bias. While in the former the investigator knows the treatments patients are assigned to, in the latter neither the patients nor the investigator know this; their knowledge is limited to the different treatment options that exist. In these cases, even the physical appearance of the drug has to be the same for both the active drug and the placebo/comparator. In case of an oral route of administration, the most one in clinical trial drugs [19], over encapsulation can be used for this purpose [20].

2 Key: IND: Investigational New Drug Application; NDA: New Drug Application; BLA: Biologics License Application. 3 The figure varies up to 30% depending on the source [18] .

1.3 – Clinical trials: when a candidate drug first meets humans 5

CTs require significant investment, an extensive infrastructure, compliant implementation of regulations, safety measures and the involvement of many different people fulfilling different roles: clinical trial lead investigators, clinical trial managers, doctors, nurses, patients, lab scientists and supply chain managers, among others. They are an essential part of the drug development process and, if run efficiently, can provide the pharmaceutical company with a competitive advantage [21]

The advantages of CTs being properly executed do not however limit to benefits for pharmaceutical companies. The U.S. Food and Drug Administration (FDA) confirmed as part of its “Critical Path Initiative” that streamlining clinical trials is one of its key priorities in leading to improved healthcare quality [22]. Because of the importance of CTs in the biopharmaceutical R&D process, they are studied in detail in the next section.

5. FDA review: If the results of the phase I, II and III CTs are positive (i.e. the drug is proven to be both safe and effective for humans), the pharmaceutical company requests approval from the regulatory agencies to market the drug. This is done by submitting either a New Drug Application (NDA) or a Biologics License Application (BLA). These applications contain an exhaustive description of the results of all the prior phases, from the basic research and drug discovery to the CTs.

6. Post-approval research & monitoring: Also known as phase IV, or surveillance CTs, this phase of the drug development process has the objective to identify and assess long-term effects of the newly developed drug in specific patient groups. Risk evaluation and mitigation strategies are typically demanded by the regulatory agencies.

1.3 Clinical trials: when a candidate drug first meets humans The previous section presented an overview of the drug development process, emphasizing the importance of CTs. This section deepens the analysis by studying their purpose and phases.

Randomized CTs are one of the most powerful tools of clinical research. They enable investigators to evaluate the effectiveness of new candidate drugs while addressing the selection bias and considering the effects of unmeasured confounders [23]. The ultimate objective of a clinical trial is to prove beyond any doubt a causal link between a drug and a biological effect in humans (i.e., drug X causes effect Y) [24]. Put differently, the evidence from the clinical trial is required to adequately assess efficacy and safety questions with regard to a candidate drug [25].

The main parameter used to assess the reliability of clinical trials is the beta error, which statistically is defined as the probability of accepting the null hypothesis (or “failing to reject the null hypothesis”) when it should actually have been rejected. A beta error of 20% is normally accepted in the design of a trial. This implies that 80% of the time that results would be repeatable if the trial was completed again [25].

Traditionally, clinical drug development progresses from “learning” to “confirming” [26]. This is reflected in the four successive phases of CTs.

1. Phase I – Initial safety testing: Phase I of a CT is the time when the candidate drug is first tested in humans. These trials are usually conducted with a relatively small group of healthy volunteers (normally less than 100) [6], and have the goal to test the safety of the drug when used in humans, as well as to determine dosage ranges. Moreover, the side effects in humans of the new candidate drug are observed for the first time [27]. According to the FDA, approximately 70% of new medical treatments pass phase I testing stage [18].

2. Phase II – Safety and efficacy in a reduced group of patients: While phase I aimed mostly at proving a drug safe, phase II builds on this demonstrated safety to focus on the effectiveness 6 1.4 – Supply chain management and the clinical trial supply chain

of the potential new medicine [27]. In phase II CTs, some patients might receive an inactive substance (i.e., a placebo) or another medicine that is usually regarded as the standard treatment for the disease under study (i.e., a comparator). This has the goal to isolate the effects of the drug from the so known placebo effect, an important psychobiological phenomenon whereby treatment cues trigger improvement [28]. The target group is normally expanded to approximately 500 patients suffering the condition or disease under research [6]. If the candidate drug continues to show promising results, preparation for the big-scale phase III clinical trials begin. On average, 43% of the drugs entering phase II pass to phase III (i.e. 30% of all the candidate drugs starting clinical trials pass phase II) [18].

3. Phase III – Statistically significant results: In a phase III trial, subjects must be randomly allocated to the intervention groups. The goal of these trials is to provide a definitive answer on whether a new treatment is better than the control group (that in which patients receive a placebo or a comparator). Results have to be as precise and robust as possible, so that health professionals are convinced to change their practice should the outcome of the CT be positive [29]. The larger the trial, the more statistically significant and reliable the conclusions are. Thus, these trials typically aim at recruiting 1,000 to 5,000 patients across numerous clinical trial sites around the world [6], and can take several years to complete. 80% of drugs that enter phase III will successfully complete this stage (i.e. 24% of all the candidate drugs starting clinical trials pass phase III) [18]. Phase III clinical trials are of the most time-consuming and costliest steps in the overall R&D process [30].

4. Phase IV – Assessing long-term effects: Phase IV trials are characterize in the literature as surveillance studies [27, 29]. Once the candidate drug has been approved by the regulatory entities, commercialized and adopted into clinical practice, pharmaceutical companies continue monitoring the safety and efficacy of the new drug. This phase might be useful to identify uncommon adverse effects not detected in phase III trials [29]. Nevertheless, the study of phase IV CTs are not as common as the rest of the clinical trial phases, especially in the academia or the public sector.

1.4 Supply chain management and the clinical trial supply chain A supply chain can be generically defined as the sequence of processes involved in the production and distribution of a commodity. The definition of Supply Chain Management (SCM), however, is not unambiguous. In their extensively cited paper, Harland identified four main uses of this term by conducting an extensive literature review [31]. These different definitions are aggregated in table 1.

Table 1: Different definitions of "supply chain management" according to [31].

# Type of concept Definition Management of an internal supply chain that comprises business 1 Intra-business functions involved in the flow of materials and information from inbound to outbound ends of the business 2 Inter-business Management of supply relationships with immediate suppliers Management of chain of businesses, ranging from a supplier, a 3 Inter-business supplier’s supplier etc. to the customer, the customer’s customers and so on Management of a network4 of interconnected businesses involved in the 4 Inter-business ultimate provision of product and service packages required by end customers

4 A network can be generically defined as a group or system of interconnected people or things.

1.5 – The material flow of the clinical trial supply chain 7

Note that, although other alternative definitions exist in the literature – Gunasekaran and Ngai define it for example as the 21st century global operations strategy for achieving organizational competitiveness [32, 33] – the level of aggregation provided by Harland is deemed as the most convenient for the present research.

When studying the Clinical Trial Supply Chain (CTSC), an inter-business concept has to be adopted. This is because of the role of patients, external to the intra-business operations of the sponsoring pharmaceutical company. This discards the first definition as suitable for the clinical trial industry.

In addition, patients cannot be simply conceptualized as customers of the clinical trial sponsor, because of two reasons. First, costs incurred during clinical trials are assumed by the pharmaceutical company sponsoring the treatment itself. Second, the behavior (i.e., adherence to the medication regimen established in the study protocol) and evolution (i.e., health status prognosis) of patients are the final factors influencing and determining whether the business case behind a clinical trial is feasible. Thus, business-to-business definitions of supply chain management do not apply either to the CTSC, what leads to discarding concepts 2 and 3.

Because of the determinant role of interconnectivity between patients, investigators and the clinical trial sponsor, a network definition is required to define the clinical trial supply chain. The fourth of the definitions presented in table 1 fits hence the best. Still, some modifications are required. The network present in the CTSC is not limited to businesses, but rather encompasses other important stakeholders that are crucial to the CTSC and have to be defined differently (e.g. regulatory bodies, patients, investigators or clinical sites; see 1.7 – Stakeholders involved in the clinical trial industry for more details). In addition, the concept of end customers presented in the fourth definition requires further specification: in clinical trials, both patients and the sponsor are end customers in a sense – while the former receives medical treatment or medical drugs, the latter receives data that allows to test the safety and efficacy of a drug.

To conclude, an adapted, proper definition for the management of the CTSC could be the management of a network of interconnected stakeholders involved in the ultimate provision of healthcare product and service packages to clinical trial patients, as well as in the collection of patients’ samples for analysis. These stakeholders are defined in 1.7 – Stakeholders involved in the clinical trial industry, after the different stages and unique features of the clinical trial supply chain are presented in the next section. 1.5 The material flow of the clinical trial supply chain To initiate, conduct and complete clinical trials, successful supplies delivery is critical [34]. One of the responsibilities of the CTSC is to deliver the right amount of drugs to the right patients in the right time.

1.5.1 Different stages The material flow of the CTSC extends from the manufacturing of Active Pharmaceutical Ingredients (APIs) to the delivery of Investigational Medicinal Products (IMPs) to the patients. Figure 3, adapted from [21], schematically depicts the different steps involved.

In figure 3, APIs are first essayed at an API pilot plant. Then, drugs are mass produced in the manufacturing facilities. Comparator or placebo drugs are obtained in parallel5. They might be produced internally or provided by an external supplier.

5 Pharmaceutical companies often test their drugs against active comparators (i.e., not placebos) to be able to determine the effects of a new drug in a reliable way. This is especially important for life-threatening diseases like AIDS of cancer, as it is considered unethical to provide placebo to the control group. This practice has

8 1.5 – The material flow of the clinical trial supply chain

API pilot Drug product Packaging Regional Intermediate depots plant manufacturing site B depot 1

Sites/Patients Biotech Drug product Packaging Regional plant manufacturing site B depot 2

Comparator supplier

Figure 3: The clinical trial supply chain. Adapted from [21]

Both active compounds and placebo/comparator drugs are then packaged following the same standards and guidelines. This might happen either at the same facilities where the active drugs are produced (this is the case in Novartis) or at an external location when the activity is outsourced.

Packaged drugs are sent to regional depots, which normally serve multiple countries. Intermediate depots might exist between the regional depots and the clinical sites, where drugs are dispensed to the patients. However, the industry trend is to have a single hub that serves a wide area.

From a supply chain perspective, the three key elements of the CTSC are the manufacturing site (which is normally also used as a central depot), the regional depots and the clinical sites. There are two reasons for this. Firstly, inventory is concentrated in either of these stages. Secondly, resupply triggers are both generated and dealt with at one of these three levels.

1.5.2 Special features of the material flow of clinical trial supply chains The CTSC encompasses much specificity compared to standard supply chains [36]. The material flow of clinical trial supply chains resemble to some extent spare part supply chains because of their network structure, frequency, relatively random demand, and the fact that products (i.e., drugs) are only used at the lowest echelon of the supply chain (i.e. by patients at clinical sites) [27]. Still, special features and particularities of the CTSC are unique and differentiate them from more generic supply chains. The main five unique characteristic features are:

1. Goods are perishable: Similar to the food industry, patient kits contain drugs that cannot be consumed after a certain period of time. The expiry date is a constraint especially at the beginning of the trial. As more information about the stability of the drug is gathered throughout the execution, it is often extended. This triggers re-labeling and over-labeling process, which pose an additional challenge [37].

2. Inability of cross-shipping: Cross-shipping from clinical site to clinical site is discouraged by regulatory authorities, and it must remain the exception [38]. Fleischhacker et al. [27] pointed out that the room for error and delays that cross-shipping entails make this alternative impractical [39] and that some pharmaceutical companies even establish Standard Operating Procedures (SOPs) to prevent cross-shipping. Moreover, cross-shipping from

increased and will continue to increase because of the growing regulatory and economic pressure on drug developers [35].

1.6 – The information flow of the clinical trial supply chain 9

regional depot to regional depot is also discouraged due to custom issues, import licenses and need for re-labeling6.

3. Compliance issues: Clinical trial sponsors have to guarantee that patients always have medication available to take when they need to. In other words, a patient should never have to wait for his/her medication. Deviating from the dosage pattern established in the study protocol originates compliance issues and might deem results from that patient unaccountable for the trial results.7

4. The demand is random, but finite: The demand for drugs in clinical trials can be considered random because the patient recruitment is also stochastic. However, the demand is limited. This is because the sample size required for a clinical trial is part of the study protocol, and specified prior to the commencement of the trial. Once the target number of patients is reached, recruitment is normally closed, and no more patients will be enrolled in the trial [27]. More details about the nature of the demand for drugs in clinical trials are provided in 5.2.2 –Patient enrollment and its stochastic nature.

5. There are several re-supply triggers: At the clinical site level, new demand for drugs might be triggered either via Interactive Response Technologies (IRT) or via safety stock levels. In the first case, computer algorithms of the sponsor calculate the expect demand for drugs during a determined period in the future (the look-ahead period) and then determine the demand of drugs for this timeframe (or often a larger one to reduce the number of shipments). Calculations are performed at a daily basis, and if the required drugs are not enough to meet the future demand, a resupply order is triggered. In the second case, drugs are replenished simply if stock falls under a certain threshold. In practice, a combination of both systems is used: IRT systems to globally adapt to the conditions of the trial and fixed thresholds to guarantee the existence of a safety stock [40].

More detailed insights about the clinical trial supply chain are provided in 5 – A Discrete-Event Simulation Model of the Clinical Trial Supply Chain.

1.6 The information flow of the clinical trial supply chain Although figure 3 is representative of the material flow for the vast majority of CTs, the information flow does not ends with the dispensation of drugs to the patients: CTSCs include information and samples collected from patients for analysis, which flow back to the clinical sites and ultimately to the pharmaceutical companies [34].

1.6.1 The role of labeling in the clinical trial supply chain Labeling is an integrated part of the CTSC, as it belongs to the interface between the material flow and the information flow. The purpose of labeling is to ensure correct traceability, protection of research patients, correct identification of both patients and clinical trial and proper documentation [20]. Overall, even though the labeling process is dealt with by the sponsor as it if was material flow, labels define the core of the information flow flowing from the CT sponsor to the rest of the stakeholders as the IMP moves across the CTSC in its way to the patients.

Labeling also serves as a nexus to connect the CTSC to other networks, such as regulators. If labeling is non-compliant, for instance by not following the EU/FDA Guidelines to Good Manufacturing Practice (GMP), problems during the approval process are likely to occur [41].

6 Source for this information is personal communication with a Lead Business Integration Manager from Novartis. 7 Note that another source of compliance issues is patients not adhering to the medication regimen. This problem is explored in detail in 7 – Smart Labels in Clinical Trials – The Patient Perspective.

10 1.7 – Stakeholders involved in the clinical trial industry

Labeling, and especially re-labeling, are a major concern within the CTSC [37]. Some reasons that might trigger a re-labeling process are the expiration of a product or an eventual reshipment to another depot [21]. In the first case, the underlying reasons are the very conservative expiry dates that are set for research drugs during the initial production stage. As more knowledge of the compound is gathered throughout the trial, it is common that expiry dates of already produced drugs are extended.

1.6.2 Interactive Response Technology systems Once a CT starts, the outcome is likely to differ from the initial forecast. Hence, it is important to modify and optimize clinical trial supplies to ensure that the outcome is as accurate as possible. Interactive IRTs are used to support the management of the CTSC once the trial has started, and are a key element of the upwards information flow, conveying information up to the CT sponsor. These systems analyze the current demand for drugs by establishing real-time connections with all the clinical sites involved in the research, and provide a forecast of the demand during a certain period of time. Modern IRT systems are web-based and can be accessed in real time to manage the drug supply and dispensation, the patient recruitment and randomization and the drug returns in blinded clinical trials, among others [42].

1.6.3 Data collection The clinical trial sponsor needs to receive data on the treatment progress and health condition of the different patients enrolled in a clinical trial. Assessment of this information is normally done by investigators in clinical sites following the guidelines established in the study protocol. This information is then sent back to the pharmaceutical company.

Apart from the information required to assess the safety and efficacy of a drug, it is a growing trend in the industry to gather big data to analyze non-identified, patient-level data from late-stage comparative studies. This has the objective to easily have access to historical clinical trial data to allow for a more efficient future studies, reducing the cost and accelerating the speed of finding meaningful treatments [43].

1.7 Stakeholders involved in the clinical trial industry Understanding the role of different stakeholders of clinical trials is crucial to interpret their dynamics, as well their design and execution.

According to Smith [44], the main stakeholders include the company that conducts or sponsors the research, regulatory entities, organizations or sponsors covering the cost of the research, clinical sites and patients. It is normal, however, that the clinical trial sponsor takes responsibility for the initiation, management and financing [45] [37], assuming the role of two different stakeholders indicated by Smith.

Other authors suggest that, in regard to technology implementation in clinical trials, important stakeholders are the payers [the sponsor of the trial, i.e. the pharmaceutical company itself], patients, consumers, regulatory officials and academic researchers [46]. It is even concluded that engaging these stakeholders adequately would help to ensure that evidence generation on new medical technologies is more consistent and timely [46].

Still, it is recognized in the literature that no available data aggregates the motivators and barriers to various stakeholders to participate or conduct clinical trials [47]. Using the list of stakeholders presented in the above paragraphs as a starting point, this subsection tries to shed some light into the interests, power and importance of different stakeholders in the context of clinical trials.

1.7 – Stakeholders involved in the clinical trial industry 11

1.7.1 Clinical trial sponsor Pharmaceutical companies play a leading role in the drug development process. The sponsor of a clinical trial carries the ultimate responsibility for its initiation, management and financing [5]. The relevance of pharmaceutical companies in setting up and sponsoring clinical trials has resulted in the pharma industry having a strategic position from which it can leverage the impulse and development of new technologies [48].

Clinical trial sponsors are thus the main stakeholder when it comes to influencing the clinical trial, and also one of the most interested in its outcomes. For a pharmaceutical company, success in a clinical trial might imply billions of dollars of revenue during the rest of the years in which its compound is patent-protected, while failure automatically translates into hundreds of millions of dollars and years of research lost [30].

Apart from this general interest, the following are important parameters and Key Performance Indicators (KPIs) for sponsors during the execution of clinical trials, according to a survey to 15 clinical trial sponsors by Pant et al. [47]:

1. Good Clinical Practice (GCP) compliance and protocol adherence (100%) 2. Meeting expected enrolment and managing data (92.5%) 3. Cost-effectiveness (79.1%)

1.7.2 Investigators / Doctors Doctors actively enrolling patients in a clinical trial are referred to as investigators [19]. Years ago, doctors were the main stakeholders that pharmaceutical companies targeted. This is because they were the only intermediary between the pharma companies and the patients, and thus the only means to increase patient awareness of new alternative treatments at a clinical trial stage. As a result, dedicated events such as conferences and symposiums were held to inform doctors of the latest drug developments [37]. Nowadays, however, the responsibilities of an investigator in the context of a clinical trial have been reduced to [47]:

. Ensuring that the trial is conducted following the signed investigator statement, the study design and applicable regulations.

. Protect the welfare, safety and rights of the patients enrolled in the trial that are responsibility of the investigator.

. Control the drugs for the patients under his/her responsibility.

Note that, unlike in public and private healthcare facilities, the role of investigators during the execution of a clinical trial is mostly restricted to following the guidelines provided for the study. If a study is double blinded, for example, the investigator does not know whether one of his patients is following an active treatment or is ingesting placebo [19]. The loss of professional autonomy and the difficulty with the consent procedure are actually barriers to clinician participation in trials, as identified by a comprehensive literature review by Ross et al [49]. These authors found 7 papers indicating the former reason (i.e., loss of professional autonomy) as a clinician de-motivator to take a leadership role in a clinical trial, while 9 papers suggested the same for the latter (i.e., difficulty with the consent procedure) [49].

Note however that, in case of concern about the health status of the patient, the investigator always has the final word when it comes to the treatments that patients follow. As indicated by a Senior Leader at Clinical Trial Portfolio Level from Novartis Pharma AG, it is important to emphasize that the patient is still the patient of the doctor; he is not the patient of the pharma company [19].

12 1.7 – Stakeholders involved in the clinical trial industry

To conclude, an investigator can recommend a patient to discontinue a clinical if his condition is deteriorating or not improving as expected with his participation in the trial but, in case the patient follows the trial, it is usually under the guidelines set up in the study protocol by the clinical trial sponsor.

As for the interests and motivations of doctors to have a leadership role as investigators during a clinical trial, Pant and Joshi found after surveying 27 investigators that [47]:

1. 100% of the respondents mentioned scientific rational of the study as key parameter for investigators to participate in a trial. 2. For 89.7% of the respondents, the potential that patients could obtain from the trial was a motivation to become an investigator. 3. 84.7% also declared that publication of results as an interest in regard to the clinical trial.

These data was validated by conducting interviews at Novartis Pharma AG (see Appendix C – Interview Protocols and Outcomes). When two clinical trial experts were asked about what were, in their opinion, the motivations for doctors to become investigators in a clinical trial, both of them answer that the health improvement that their patients could get from the treatment was definitely a motivation. One of them also mentioned the reputation that a successful trial implies for the investigator (related to the third point above), while the other identified economic incentives as an additional potential interest.

Findings of other authors revealed similar motivations, although with some nuances. Glass et al. [30], for example, also surveyed 5000 investigators in the U.S. (ending up with 762 usable questionnaires) with the aim to determine why they chose to participate in clinical trial research. It concluded that financial remuneration is an important consideration in many physicians’ decision to participate in a trial, but, however, the predominant motive is the prospect of contributing to innovative medical research. Table 2, adapted from the same authors, presents a more detailed view of their findings.

Other authors [50] suggest that innovation is a relatively important factor for investigators to lead a clinical trial: more than 200 European and U.S. clinical investigators indicated that they would even be willing to accept a lower remuneration per patient if they could be involved in a clinical trial of a new, innovative compound. This was confirmed in multivariate analyses [51, 52], which showed that more than 3000 investigators in the U.S. actually accepted a considerably low cost per patient when working with top innovative drug candidates.

As a final reference, a survey by Lamberti et al. with 3156 completed responses by regular clinical trial investigators [53] concluded that the most important factors influencing investigative willingness and participation in a trial are having information at the outset, a sufficient number of eligible patients, adequate payments and recruitment support.

1.7 – Stakeholders involved in the clinical trial industry 13

Table 2: Motivations for investigators to participate in a clinical trial. Adapted from [30]

Very Mean Individual reasons for investigators to participate in a clinical trial important Score (%)

Working with a potentially new therapeutic option for patients who have not responded to available treatment, or for whom there are no approved 87 8,8 treatments

My own site's experience working in the specific indication of the potential 70 8 study The chance to take part in innovative research, whether or not this relates 59 7,4 to my patients The opportunity to share with other physicians what I learned during the 49 7 trial The prospect of additional studies from the sponsoring pharmaceutical 47 6,7 company To supplement the revenues of my practice/institution/department 43 5,8 My experience with the sponsoring company on previous work I have done 43 6,4 with that company The opportunity to interact with other investigators involved in the trial 38 6,3 The amount of money required by my site to start the study until we 30 5,7 receive payment from the organization running the study My level of confidence in other drugs already on the market from that 28 5,8 company A large multinational pharmaceutical company is sponsoring the study 19 5

1.7.3 Patients As mentioned in the last section, doctors used to be the main target of pharmaceutical companies when it comes to stakeholder engagement some decades ago. Today, however, patients are not only the ultimately stakeholder of the drug development process, but also the main stakeholder that pharmaceutical companies seek to satisfy.

There are two reasons for this shift from doctors to patients [37]. First, legal aspects do not allow anymore giving distinctive treatment to doctors seeking for their support. Secondly, new technologies such as the internet or social media make it possible to communicate directly with patients. This is important from the perspective of a pharmaceutical company because readily accessible data and information makes it possible for patients to have open conversations and discussions with their doctors about treatment options [54], what ultimately relates to the products of pharma companies.

Overall the trend is that drug manufacturers can no longer rely simply on proving the safety and efficacy of a drug to guarantee its success. Pharmaceutical companies need to understand consumer dynamics and behavior if they want to meet the increasingly larger patient expectations [5]. In general, patients are becoming more involved when it comes to knowing about the nature and effects of the disease they suffer from, assessing treatment options, knowing about the experiences of other patients, and overall understanding the effects and risks of a disease on family, friends, work life and social life [55].

14 1.7 – Stakeholders involved in the clinical trial industry

These dynamics are especially important when it comes to patient communities. Patient communities and web platforms are playing a big role as personalized medicine standardizes. Both for rare and common diseases, the number of online and social networks where people upload relevant information about their disease symptoms, progress with the treatment and health conditions has increased in the last decade [48].

An example of a patient community is PatientsLikeMe, an online community built to support information exchange between patients, which provides customized disease-specific outcome and visualization tools to help patients understand and share information on their condition [56]. This platform, founded in 2004 by three MIT engineers [57], had more than 200,000 users as of 2013 [58]. Analyses of this initiative found in the literature emphasize how patients might benefit from the disease self-management that this platform enables [56], and how direct patient input and involvement can help identify unmet patient needs and barriers to recruitment in critical trials [59]. Overall, patients becoming more informed consumers is an expected trend in the following years [54].

In regards to the power of patient communities, the outcomes of an interview at Novartis Pharma AG revealed that patient communities have a lot of power nowadays: it is there where decision-making takes place. Because of this, most pharmaceutical companies have a clear patient focus right now [37]. Patient representatives, often acting as delegates of patient communities, even participate in select meetings offer by drug sponsors and investigators, with the aim of obtaining input on unmet medical needs and implications of specific diseases [5].

Following this empowerment of patients and the transformation from a rather passive to an active stakeholder in clinical research, systems that allow patients to also track their treatment are proliferating. Portable medical devices in patient’s homes helps making the treatment more convenient for the patients, and at the same time these telemedicine devices are changing the way that sponsors collect health information of their patients [54].

As for the interests of patients, they normally volunteer for a trial to obtain access to new treatment options that are unavailable other way. This is especially the case for rare diseases or life threatening diseases. Altruistic reasons are however also mentioned in the literature as a motivation for some patients to join clinical trials. Some authors, however, suggest that most study subjects enroll in clinical trials to help advance science for the good of humankind […] and therefore hope that their time, trouble, and risk taking are being used appropriately [60]. An in-depth analysis of the patients’ motivations and barriers to join clinical trials will be provided in 7 – Smart Labels in Clinical Trials – The Patient Perspective.

1.7.4 Regulatory agencies Regulatory agencies are normally country-specific, although many have European coverage for the Member States (MS) of the EU. The most prominent regulators are the FDA in the U.S. and the Directorate General of Health and Food Safety of the European Commission in the European Union.

Both of these agencies provide the guidelines, laws, regulations and administrative provisions to guarantee the implementation of GCP in the conduct of clinical trials on medicinal products for human use [61, 62]

The ultimate objectives and interests of these institutions are similar. For the European Commission, the Clinical Trials Regulation aims to create an environment that is favorable for conducting clinical trials, with the highest standards of patient safety, for all EU MS [62], while the FDA is committed to protecting the participants of clinical trials, as well as providing reliable information to those interested in participating [63].

Overall, the attitude of regulatory agencies towards clinical trials is favorable as they are an integral part of new product discovery and development [63] and are intended to discover or verify the effects

1.7 – Stakeholders involved in the clinical trial industry 15

of one or more investigational medicinal products [62]. However, if the laws, guidelines, regulations or administrative provisions for a clinical trial are not met by the clinical trial sponsor, regulatory bodies have the power to reject a CTA, delay a clinical trial or even stop an ongoing study.

Deeper insights into the role of regulators in the EU, the U.S. and emerging countries are provided in 4 – The Regulatory Perspective: Do Smart Labels Fit in the Current Regulatory Framework?

1.7.5 Clinical sites Clinical sites are the facilities where clinical trials are executed. It is in clinical sites where clinical drugs are ultimately shipped, where patients consume them (unless the trial can partly be executed at home) and where investigators follow up on the health status of patients. Clinical sites range from local clinical facilities with one investigator to large hospitals that serve entire cities [19].

According to Baer et al. [64, 65], seven attributes of exemplary clinical trial sites are the following:

1. Diversification of the clinical trial mix: Offering a variety of clinical trials maximizes the treatment options for patients, what normally results in having the best research options available to them. At the same time, this optimizes the site’s resources and facilities.

2. High accrual activity: For diseases like cancer, a clinical trial site should accrue at least 10% of the patients onto clinical trials. This helps making clinical research part of the culture of the clinical site.

3. Participation in the clinical trial development process: Researchers of a clinical site have to actively take part in the development of trials. For this purpose, they can attend seminars, meetings and conferences organized by sponsors of clinical trials. Ultimately, researches should author journal articles or assume regularly leading roles as investigators on clinical trials. It is important to emphasize that it is actually investigators who enroll patients for a clinical trial, not clinical sites themselves [19]. Thus, from a clinical site perspective, active participation helps guaranteeing a continued engagement of investigators and research staff at the same time research is carried out.

4. Maintenance of high educational standards: Educational certifications of investigators and research staff are of crucial importance for conducting high-quality research. Means to obtain high education quality include communication with clinical trial sponsors and an active participation in scientific publications.

5. Quality assurance: A proper quality assurance program helps to ensure that the site performs at its optimal capacity and that GCP guidelines are met. SOPs can help to achieve this. For example, new SOPs can be developed to address weak points in certain procedures.

6. Multidisciplinary involvement in the clinical trial process: Meaning that ideally both physicians and non-physicians of a clinical site should be aware of the clinical research activities being carried out, as well as of the appropriate conduct, activities and guidelines applicable.

7. Clinical trial awareness programs: The goal of awareness programs is to increase knowledge about clinical sites among doctors and research staff, which in turn can transfer this knowledge to patients.

Although clinical sites might partly define the patient-experience during a clinical trial, it is normally doctors/investigators who serve as a nexus between patients and clinical trials, and not the clinical sites per se. If a clinical site is very active in enrolling patients in asthma trials, for example, the reason is most likely that there are several doctors/investigators in the hospital specialized in asthma and with a motivation towards research. Moreover, clinical sites can by no means force doctors to direct

16 1.7 – Stakeholders involved in the clinical trial industry

patients to a certain clinical trial. In other words, they cannot set guidelines for doctors on which trials to focus on [19].

The interest of hospitals is similar to that of doctors/investigators when it comes to clinical trials, with the exception of reputation, which is often more valued by clinical sites, as they can transform it into business opportunities [19].

When it comes to power, however, doctors/investigators are a much more important stakeholder than clinical sites. Apart from being responsible for the treatment and evolution of the patients: if a doctor changes his practice to another clinical site, patients will follow him [19].

1.7.6 Other stakeholders Other, less relevant stakeholders present in the clinical trial supply chain are the following:

. Packaging hubs/companies: Packaging can either be performed internally by a pharmaceutical company after manufacturing the drugs or be outsourced to third parties. A combination of both modalities can actually exist in a single drug production process. For example, the pharmaceutical company might take care of the initial packaging but outsource re-labeling and over-labeling activities when they are required. A motivation for this is that, once drugs are disseminated across several country depots and regional hubs, it is easier for the pharmaceutical company to pay third parties to perform these activities [66].

Although costs and delays originated by packaging and labeling activities can be substantial [37, 66], packaging hubs/companies are a minor stakeholder in the global context of clinical trials and relative to other stakeholders, because:

1. When these activities are performed internally, they are integrated in the drug supply management division of the pharmaceutical companies. The interface between the packaging activities and the global clinical trial supply chain management vanishes, as their interest (typically measured by KPIs), budget and power converge to the same companies, departments and even individuals.

2. When these activities are outsourced, then the scheme follows a simple hourly/daily contract. The third party follows the indication of the clinical trial sponsor and stakeholder conflicts are less likely to occur than when analyzing other combinations of stakeholders.

. Distribution companies: The physical distribution of IMPs is externalized to logistic companies (agencies like FedEx, for example). Distribution is often regarded as a commodity, with fixed, established tariffs depending on the weight/volume to carry. Logistics is a competitive industry where no player holds enough power to actually modify the dynamics of the clinical trial supply chain. Even when more complex transportation requirements are to be met, for instance in the case of cold supply chains (i.e., those in which certain environmental conditions, such as temperature or humidity have to be ensured), the service is still regarded as a standard commodity. This is partly because the pharmaceutical industry is not the only one relying on such supply chains [67] (e.g. the food industry, beverage industry and animal transporting industry have also similar requirements). Still, proper coordination with the distribution companies is required to ensure an adequate continuity of the clinical trial supply chain.

. Other departments from pharmaceutical companies: The clinical trial division is often just one out of many existing in large pharmaceutical companies. Lab-research activities and production and distribution of commercial drugs, those drugs that have already been

1.7 – Stakeholders involved in the clinical trial industry 17

approved by regulatory bodies, are examples of another major department/division in pharma companies. The reason why these stakeholders are relatively non-important when studying the clinical trial supply chain is twofold:

1. Activities in a pharmaceutical company are typically sequential: Contrary to many other industries, where different departments compete for a limited amount of resources to carry out their different projects, a pharma company always needs to do lab- and animal-research before launching clinical trials, and it always needs to successfully pass clinical trials before being able to commercialize a drug. Thus, internal conflicts over resources are more likely to take place within one of these stages (for example, deciding on dedicating resources to either oncology or immunology lab-research) than in the complementary steps.

2. These different stages do not normally have power over each other: In the end, decision-making on the company portfolio takes place often at a very preliminary stage, when defining the therapeutic areas the pharmaceutical company is actively investing resources in. Moreover, this decision-making is very centralized, as it is a matter of corporate strategy. Although it can be influenced by previous results from drugs being commercialized, the “Commercial Drugs Department” itself does not have power over lab-research or clinical trial execution.

1.7.7 Stakeholders – Conclusions Medical discovery revolves around the needs of the sponsor, the desires of regulatory approval and the increasingly active role of patients [59]. These, along with other stakeholders, define the clinical trial ecosystem. Collaboration between them a can ultimately accelerate drug development [5].

The single most important stakeholder is the clinical trial sponsor, given that this is the key stakeholder when it comes to overall interest, influence and power in the set-up of clinical trials. Moreover, it has the resources and the knowledge to guarantee a successful execution.

Nevertheless, other stakeholders cannot be neglected. Especially important are the regulatory agencies, which have the power to reject, delay and even cancel an ongoing clinical trial if it does not fulfill the specified requirements. Also the patients, who are the ultimate recipient and beneficiary of candidate drugs, have recently gained importance as they become a more active stakeholder in the clinical trial industry and organize into patient communities. Investigators play an important nexus role between the clinical trial sponsors and the patients, as they still are a key stakeholder when it comes to the awareness of clinical trials. Their power in the design of clinical trials is however restricted, as study protocols set up by the sponsor are to be followed, by the exemption of serious concern about patients’ health or safety. Figure 4 presents a stakeholder map of the clinical trial landscape based on the previous sections.

18 1.8 – Challenges faced in the clinical trial industry

Regulatory agencies Clinical trial sponsor

Patients & patient communities

Clinical sites Investigators Outsourcing third parties Additional divisions/departments (e.g. packaging) of the sponsor company Distribution companies

Figure 4: Stakeholder map in the context of the design, planning and execution of clinical trials 1.8 Challenges faced in the clinical trial industry The production and distribution of IMPs involves an extra layer of complexity in comparison to commercial drugs. Some of the factors that pose additional complications in bringing clinical supplies to patients are the lack of fixed routines, the variety of clinical trial setups and designs, the different packaging requirements (which are normally country-specific), the need for randomization and blinding or the incomplete knowledge about the drug product, which might result in re- packaging or content modification [45].

From a general perspective, nowadays pharmaceutical companies face a series of challenges when it comes to the design, preparation and execution of CTs. A literature review on the challenges faced in clinical trials allowed to aggregate them into five different categories, which were validated8. These categories are detailed in the next sub-sections9.

1.8.1 Excessive expenditure The primary driver of the rising costs in drug development are clinical trials [23, 68], which multiplied tenfold from 1991 to 2003 [10]. Figure 5, adapted from Dorsey et al., graphically represents this trend.

8 This validation was performed via personal communication with a Lead Business Integration Manager from Novartis Pharma AG. 9 Note that challenges that are a matter or corporate strategy, such as the resource allocation to different therapeutic areas or the selection of a small-molecule/biologic portfolio are excluded from the present research because no company-specific distinctions are intended to be made.

1.8 – Challenges faced in the clinical trial industry 19

Figure 5: Average total cost of R&D per approved new drug, categorized in clinical and preclinical costs. Adapted from [10] and [69]

Figure 5 confirms that contemporary clinical trials have become prohibitively expensive, as R&D productivity continues to present a major challenge for the pharmaceutical industry [30, 68]. Even if inflation adjusted [70], the total clinical costs per approved drug have almost quadrupled in less than 25 years, what in the ends limits the number of trials that can be undertaken [71]. Reducing costs is critical, as the cheaper trials are, the more opportunities for clinical research, and in turn the higher chances of developing new drugs and treatments. In words of Lauer et al. “What good are trials if […], because of excessive expense, they can be used to answer only a tiny fraction of our important clinical questions?” [23].

Although there are several factors contributing to the overall costs of CTs, it is noted in the literature that clinical supply costs can potentially account for up to 40% of the total clinical trial spending [27]. If this is combined with the fact that CTs themselves account for 70% of the total R&D costs for a drug [17], it can be concluded that up to almost 30% of the costs for drug development can be associated to the CTSC.

1.8.2 Delays Trials are often carried out under great time pressure, because the financial consequences for every day that the trials are delayed are huge [41, 72]. This is not only due to the resources dedicated to the trial itself, but also because one more day dedicated to the trial entails a day less of market exclusivity (i.e. patent-protected) to potentially commercialize the candidate drug [73]. Patents expire 20 years from the date of filing (which is associated to the first steps of the drug development process, when the NME is discovered), and the time dedicated to clinical trials falls within this patent-protected period [74].

Clinical supply management is almost always on the critical path of the drug development process [75]. Among others, packaging, re-labeling and over-labeling can be the source for some of these delays [20, 68, 76]. Thus, bringing an efficient labeling solution to the clinical trial supply chain might help solving this issue [37, 41].

20 1.8 – Challenges faced in the clinical trial industry

1.8.3 Complexity The increase in the number of studies and the shift towards global trials in the search for patients has added a new layer of complexity to the CTSC [21, 27, 34, 72]. Today, 24% of all trials applications in the EU are multinational, and the number of trials being conducted outside Europe is rapidly growing. These figures substantially increase even more when it comes to globalized pharmaceutical companies [77].Bigger, multinational clinical trials bring along a new layer complexity not only because of the challenges faced in managing and optimizing the globalized supply chains [68, 78, 79], but also because pharmaceutical companies must then be able to manage both global and local regulatory requirements [21, 41].

It is mentioned in the literature that there exists a correlation between complexity of clinical trials and compliance problems [23]. It is then when complexity turns into a major issue, as pharma companies require the highest levels of compliance and transparency in their global harmonized CTSC.

1.8.4 Low patient adherence Adherence, or compliance with a medication regimen, is defined as the extent to which patients take medications as prescribed by their health care providers [80]. Over time it has become apparent that many patients enrolled in clinical trials do simply not comply with the treatment [81]. Patients not adhering to the treatment, or eventually dropping out of the trial, pose a problem both for the sponsor of the CT and for the patients. The sponsor sees how all the resources invested in a patient will no longer yield, as only patients that successfully finish the entire treatment can be counted in the CT data set: the rest must be withdrawn from the CT results analysis [36]. Moreover, it can mask efficacy signals during the early phases of CTs [81]. As for the patients, poor adherence to medication regimes can have potential negative health consequences [82-84]. Clinical trial subjects who do not follow the specified treatment have a worse prognosis than those who do [85, 86].

Low adherence to medication regimes is actually a huge problem in CTs: on average up to 48% of the patients of a CT do not adhere to the treatment [81, 87, 88]. A major reason for the low rate of participation and dropout of patients during clinical trials is the need for frequent in-person visits to the clinical sites, which result in a time burden and travel costs [49]. This is specially a problem for rare diseases, which are increasingly becoming a major target of CTs, and which typical trials involve patients air travelling to clinical sites [10]. Other important reasons are intolerable side effects or lack of efficacy. In addition, dealing with non-compliant patients in these cases is harder, as there is a dilemma on whether results from these patients should not be considered (“noncompliant”) or are actually part of the outcomes of the trial [81]. The problem of patient adherence is treated in detail in 7 – Smart Labels in Clinical Trials – The Patient Perspective.

1.8.5 Patient recruitment While the length and complexity of clinical trials keeps increasing, clinical trial enrolment rates have declined by 21% from 1999 to 2005, according to the Tufts Center for the Study of Drug Development [89]. Recruiting patients is a time burden that often originates delays in clinical trials [10, 23, 68, 90, 91]. Some authors indicate that up to 80% of the clinical trials fail to meet their patient recruitment deadlines [92]. Figure 6, adapted from [21], displays a generic evolution of planned and actual enrollment rates for CTs. Given that patient recruitment still takes place once a trial has started, this poses extra challenges for the CTSC.

1.8 – Challenges faced in the clinical trial industry 21

Figure 6: Actual and planned enrollments for a clinical trial. Adapted from [21]. Original source: Hoffmann-La Roche

Even if requirements in terms of the total number of patients is met, the challenge of demand uncertainty for clinical supplies remains [19, 40, 72, 93]. When a trial begins, a variety of external factors inevitable make the study deviate from initial forecasts [21, 68]. This inherent demand uncertainty is originated by a range of reasons, such as random enrollments at sites, the outcomes of the screening to the patients and the randomization process [27, 34]. These issues will be analyzed in detail in 5.2 – The clinical trial supply chain – Model conceptualization, and relates back to another of the challenges identified: complexity to manage clinical trials.

1.8.6 Smart labels – A brief introduction: can they help to overcome the challenges of the clinical trial supply chain? Smart labels (or e-labels , which henceforth will be used indistinctly) are, generally speaking, item identification slips that contains more advanced technologies than conventional bar code data [94]. Smart labels typically use RFID or NFC technology to expand these conventional bar code functionalities, although they are not limited to these technologies.

Smart labels can be used, for example, to automatically track item-level inventory as it moves throughout the clinical trial supply chain by placing RFID tags on the drugs and placing RFID readers in strategic points, potentially helping to reduce supply costs and allowing to deal with complexity in an easier way.

They can also be used to monitor the environmental conditions to which the IMPs are subjected to, enabling to increase compliance and potentially save cost and delays originated by, for example, temperature excursions.

The role of smart labels is however not limited to inventory tracking. E-labels can also be used to communicate more efficiently with the patients, for instance by embedding NFC technology into the label, enabling patients to use their smartphones to access more information provided by the sponsor, like instructing videos on how to take the medication or reminders on medication dosages to be taken.

Moreover, there have been recent developments that combine e-labels with e-devices (i.e. electronic devices specifically designed for clinical trials), to enable, for example, telemonitoring or telecare.

22 1.8 – Challenges faced in the clinical trial industry

The possibility for clinical trial sponsors to directly communicate with enrolled subjects, or to monitor patients participating in a clinical trial remotely, may also have a potential impact to deal with those challenges that are patient-related.

The technologies behind smart labels, as well as their applicability to the clinical trial supply chain, are analyzed in 3 – Smart labels – Market Research and Applicability to the Clinical Trial Supply Chain.

1.8 – Challenges faced in the clinical trial industry 23

Chapter Two ………………………...... 22 Research Definition

Research is creating new knowledge Neil Armstrong, astronaut and aerospace engineer

The first chapter has provided background on the role of clinical trials in the global healthcare landscape. The purpose, different phases and supply chain that govern clinical trials were described in detail. Additionally, a stakeholder analysis of the clinical trial industry was performed, and its most prominent challenges were delineated. As a conclusion, the potential role of smart labels to overcome some of these challenges was introduced. This chapter delineates the research definition in order to assess the opportunities and challenges of such a technology in the context of clinical trials. For that purpose, a set of research questions are posed. Then, methodologies to be used to answer these research questions are described, and the expected outcomes and research plan to be followed are presented.

24 2.1 – The potential disruptive role of new technologies in clinical trials

2.1 The potential disruptive role of new technologies in clinical trials Medical progress depends on the evaluation of new diagnostic and therapeutic interventions within clinical trials [95], which represent the most critical, time-consuming and expensive step in the biopharmaceutical R&D process [6, 16, 17].

With increasing pressure to speed up drug development while minimizing the associated costs, it has become crucial for pharmaceutical companies to optimize the Clinical Trial Supply Chain (CTSC) [36]. The CTSC accounts for up to 40% of the clinical research expenditure of pharmaceutical companies [27] and ranges from the manufacturing of APIs to the delivery of IMPs to the sites/participants, while subjected high to compliance standards. The CTSC is very costly and a well- recognized bottle neck both in the design and the management of CTs [96]. Significant supply issues need to be addressed: reducing inventory overages, for example, is a logical response to budgetary pressure [27].

More generally, five main challenges for clinical trials associated to the CTSC have been identified in the introduction. These challenges are high associated costs, delays, complexity, patient adherence to the treatment and patient recruitment.

Despite these challenges, the CTSC structure has not changed for decades, even having operational performance levels well below other similar process industries [97]. The current predominantly batch and centralized manufacturing model has resulted in product supply chains, which typically are between 1 and 2 years in length and have a huge associated cost of inventory [27, 97]. This inventory- heavy operating model is progressively being regarded as unsustainable and inflexible. New trends in clinical studies will require that batches can be re-supplied more frequently or even immediately for a site request or individual patient need [21]. In essence, the literature suggests that more pull- driven, demand based supply chains could help in reducing the delays and costs of CTs [21, 97].

New technologies are quickly reshaping health care. However, the effect these technologies have had on drug development so far has generally been limited [10]. In the context of clinical trials, the last major technological development was moving from paper-based processes to capturing data electronically. This implies that the CT industry is long overdue for the implementation of technological innovations, what can be considered as an opportunity in dealing with the above mentioned challenges [43].

Health authority guidelines increasingly refer to opportunities to use electronic means. New, reliable packaging and labeling technologies are available on the market, and they can improve quality control and costs of re-labeling or over-labeling [34]. Smart labels and e-labels, for example, might allow for re-purposing and re-labeling later in the distribution chain, driving down operational costs and preventing clinical trial delays thanks to a higher flexibility of delivery [37]. Moreover, the use of information technologies to increase the transparency of the supply chain can reduce product waste [34].

In essence, the introduction of new technologies in the CTSC has the potential to enhance patient safety, supply flexibility and delivery compliance.

2.2 Scope, research objective and research questions Clinical trials are complex and dynamic systems that depend on biological, pharmacologic, social and logistic-related components [10]. The scope of the present research is limited to the last of two of these components.

The research objective is to identify, understand and assess the potential disruptive role that smart technology might have in the clinical trial supply chain, from the first packaging of IMPs to the use by end patients. In particular, the research is intended to evaluate how smart label technology can help

2.3 – Methodology 25

in overcoming (totally or partially) the five challenges that CTs face nowadays from a social and logistics perspective: high costs, delays, complexity, low patient adherence and patient recruitment.

The main stakeholders to be considered within the research objective are pharmaceutical companies, regulatory agencies and patients, which were identified as the most powerful stakeholders in the context of clinical trials in 1.7 – Stakeholders involved in the clinical trial industry.

With the objective of clearly addressing the research objective, the following research question is posed:

What are the threats and opportunities in using smart technology to overcome the challenges faced in the supply chain of contemporary pharmaceutical clinical trials?

The following associated sub-questions are also to be answered:

1. What are the main challenges faced in contemporary clinical trials?

2. What different types of smart labels applicable to the clinical trial industry exist?

3. What is the fit of smart labels in the highly regulated pharma industry? What are the regulator’s views on smart labels?

4. What type of smart label can be implemented in the clinical supply chain, so as to improve the logistics from the perspective of a pharmaceutical company? How can this smart label help to deal with the increasing complexity and to reduce expenditure and delays?

5. What are the barriers and motivations for patients to join clinical trials? Can smart technology help in solving the problem of patient recruitment?

6. What determines patient adherence during a clinical trial? How can smart technology increase adherence?

Note that the first research sub question has already been answered in 1.8 – Challenges faced in the clinical trial industry as part of the initial research carried out. The following section describes the methodology to be used to answer the research questions.

2.3 Methodology A set of different methods will be used to answer the different research sub-questions, and eventually the research question. As a starting point, a systematic literature review will be carried out to get acquainted with the state of the art in the implementation of new and smart technologies in clinical trials. By evaluating the current state of the art, it is expected to demarcate the most promising technology (i.e., the one that has the bigger potential among the stakeholders considered).

A market research will be performed to identify different types of smart labels applicable to the clinical trial supply chain. Within the scope of the market research will be different smart label technologies, current application of smart labels in other industries with a supply chain similar to that of clinical trials, and an analysis of past attempts to implement smart labels by the pharmaceutical industry. Additionally, interviews will be conducted with individuals of the Network of Key Opinion Leaders at Novartis Pharma AG. The interviews are expected to serve to capture the views of different stakeholders and to either support or challenge the findings in the literature. This combination of methodologies will answer the second research sub-question, and will serve as a starting point for the remaining ones.

A set of interviews and desk research will be conducted to answer the third research sub-question. In particular, an analysis of the current regulations in the context of clinical trials in Europe and the US

26 2.3 – Methodology

will be performed, with special emphasis on the laws and guidelines with regard to labeling. The interviews are expected to address directly representatives of different pharmaceutical regulatory agencies

Discrete Event Simulation (DES), and specifically the software Simio Simulation, will be used to answer the fourth research sub-question. Although clinical trials historically have not relied on analytic model-based approaches for optimization purposes [10], lately modeling and simulation have come into play as a means to increase the efficiency of drug development. For example, it is usually advocated that inventory control policies in the CTSC are selected using simulation [36, 98]. Simulation is preferred over traditional analytical methods because of two reasons. First, simulation is better equipped at handling shelf-life concerns and the perishability of drugs in the clinical trial process [99]. Second, simulation has the advantage of considering uncertainty and stochastic factors, both typical in the CTSC, in a way that can be captured neither by analytical models nor by programming solutions [100]. To answer the fourth research sub-question, a model of the status quo will be first conceptualized and designed. Knowledge from Novartis will be used both to calibrate the model and to validate it during a case study. Then, the effects of the implementation of smart labels will be explored by taking this model as the starting point.

A set of interviews and desk research will be conducted to answer the fourth and fifth research sub- questions. Despite the interest, collaboration and involvement of Novartis Pharma AG in the current research, it is unlikely that direct interviews with patients can be conducted due to confidentiality issues and resource constraints. Instead, it is expected to obtain input from the Key Opinion Leaders at Novartis, some of whom have direct links to patients. Although these methodologies have the disadvantage of not relying on primary sources, they also have advantages over direct interviews with patients. For example, even if interviews with patients were possible, it is unlikely that a big enough set of interviews could be conducted, making it difficult to validate them and to extrapolate the outcomes. Moreover, issues like patient adherence or patient recruitment are likely to be disease- specific, and a small sample might result in bias on the factors motivating barriers to participation or low adherence to medication regimens.

Table 3 presents an overview of the methodologies to be used in answering the different research sub- questions.

Table 3: Overview of methodologies to be used

Research sub-question Methodology 1 Literature review; interview validation Market research 2 Literature review Interviews with Key Opinion Leaders at Novartis Pharma AG

3 Discrete Event Simulation (using Simio Simulation as software tool) Input data to calibrate and validate the model from Novartis Pharma AG Desk research 4 Interviews with representatives of pharmaceutical regulatory agencies Literature review 5 Interviews with Key Opinion Leaders at Novartis Pharma AG Literature review 6 Interviews with Key Opinion Leaders at Novartis Pharma AG

2.4 – Research relevance 27

2.4 Research relevance The scientific relevance of the present research can be summarized in two different points. Firstly, it is expected to develop knowledge on simulating clinical trial supply chains via DES. An especially relevant contribution is the calibration and the validation of the final model with real data from a leading pharmaceutical company. Secondly, the role of smart labels in streamlining future clinical trials will be assessed. To the best of my knowledge, there exists no literature on this topic to date, even though pharmaceutical companies are currently investing resources to explore the potential of such technology.

Furthermore, the social relevance of the problem is threefold: to begin with, more efficient, faster and cheaper clinical trials would allow to answer a bigger fraction of world’s clinical questions, what in turn has the potential to enhance the drug development process, leading to better global healthcare quality. On top of that, the consequences of relying of new technologies in the interface between clinical research and patients are within the scope of the research: motivations and barriers for patients to participate in clinical trials, as well as to adhere to medication, will be analyzed in order to identify unmet needs that might be solved leveraging on technology. Finally, the evaluation of policies set by public institutions in regard to clinical trial regulations are also part of the current of the research.

2.5 Expected outcomes and research plan Expected outcomes of the present research are:

1. Understanding what factors determine the patient adherence to medication during a clinical trial

2. Understanding the barriers and motivations of patients to participate in clinical trials

3. Understanding the regulatory requirements of CT… a. ... when it comes to the implementation of smart technology in the CTSC b. …when it comes to the implementation of smart technology in the interface clinical site/patients 4. An analysis of the different types of smart labels relevant in the clinical trial context

5. Understanding what type smart technology has potential to be implemented in the context of CTs… a. … to maximize the logistic-efficiency from the perspective of a pharmaceutical company b. … to increase patient adherence to the treatments c. … to minimize patients’ barriers to clinical trial participation, as well as to increase their motivations

6. A validated DES (Simio) model of the current CTSC at Novartis

7. Implementation of smart technology in the validated DES model, carrying out experiments and obtaining statistically relevant results to assess the effectiveness of the policy.

8. Discussion on the potential role of smart technology in ameliorating the challenges faced by contemporary clinical trials

A research flow diagram of the workflow and means to achieve these outcomes is presented in figure 7.

28 2.6 – Research limitations

Desk research / Literature review

Ch. 1 Ch. 7 (partially) Ch. 3 Ch. 4 (partially) Overview of the drug Desk research on patient’s Market research on smart Desk research on development & CTs perspective in CTs label technologies labeling regulations

Ch. 4 DES model Preparation of interview

conceptualization protocol Interviews at Novartis

Ch. 5 Interview analysis DES Model implementation and validation

Ch. 5, 6 Gathering data for the model

Ch. 6 Ch. 7 Ch. 4 Case study using DES: Smart labels to increase patient The regulatory perspective Smart labels vs status quo recruitment and adherence on smart labels

Ch. 8 Challenges and opportunities of smart labels in clinical trials

Conclusions / Deliverables Figure 7: Research flow diagram

2.6 Research limitations Although the present research expects to analyze the CTSC in great detail, it is not without limitations. The following are ex-ante, known limitations of this work:

1. Biologic and pharmacologic aspects of CTs are not considered in the present research (e.g. alteration of chemical compounds in the different phases, in-depth analysis of side effects or any other biological/chemical analysis).

2. Direct interviews with patients or trial-eligible candidates are unlikely to happen, although alternatives will be presented.

3. The opportunity cost of carrying out CTs is not analyzed. This means that the role of CTs in the drug development is regarded as non-replaceable.

4. Real pilot projects in which smart labels are implemented in the CTSC are not feasible within the scope of this Thesis Research.

5. Real data from a single pharmaceutical company will be used.

6. This research is not intended to be disease-specific, but rather explore the fit of smart labels into a generic clinical trial supply chain.

2.6 – Research limitations 29

Chapter Three ………………………...... 3 Smart labels – Market Research and 3 Applicability to the Clinical Trial Supply Chain

People are too complicated to have simple labels Philip Pullman, British author of several best-selling books

The research definition has established that the main objective of the present research is to understand and assess the role that smart technology might play in clinical trials. This chapter elaborates on the concept of smart labels and concludes with a preferred potential application of smart labels in the context of clinical trials. For this purpose, a more detailed introduction of the role of labeling in clinical trials is first given. Then, in an attempt to give additional background to the reader, an overview of different smart label technologies is presented. Later, the different applications of smart labels that are relevant in a pharma context from a logistics perspective are presented. A desk research on the press releases by the biggest pharmaceutical companies is then carried out, with the objective to assess the current implementation of smart labels by pharmaceutical companies. Finally, a SWOT analysis of the most promising applications is carried out and conclusions are drawn.

30 3.1 – Deeper insights into the role of labeling in clinical trials

3.1 Deeper insights into the role of labeling in clinical trials Before addressing the advantages and opportunities that smart labels might bring to clinical trials, it is necessary to understand the status quo: the current role and challenges faced in the labeling process of IMPs. This introductory section provides additional background into these areas.

3.1.1 The role of labeling in clinical trials The correct labeling of an investigational medicinal product is an important and integral part of the conduct of a clinical trial. Labeling has implications in the safety of the study subjects, the adequate use, traceability and identification of the IMPs, and overall in an adequate documentation of the trial [20].

Because of these implications, it is mandatory that the labeling complies with the relevant regulatory requirements (see also 4.2 – Labeling requirements for IMPs in clinical trials). Non-compliance results in additional requests by the competent authority, which end up delaying the clinical trial and causing costs.

The decision process on the labeling of IMPs for a clinical trial is a complex process. This is mainly because of a dual reason. First, it needs to consider many stakeholders involved in the clinical trial supply chain [20], as information to very different parties needs to be conveyed using a single means. Second, a best-balanced choice from the perspective of the clinical trial sponsor in regard to timing optimization, regulatory compliance, cost considerations and flexibility to the clinical trial supply chain is to be met.

Figure 8 shows an example of a real label – with some specific information, such as the expiry date, still not populated – for a double-blinded study.

Figure 8: Example of a label of an IMP for a double-blind clinical trial

3.1.2 Main challenges faced in the labeling process The labeling process has to be considered early in the planning of a clinical trial because many aspects of this process can substantially impact the quality and efficiency of the trial logistics [72]. Given that the early start (and finish) of a clinical trial is tightly link to the time to market the future potential drug, a crucial criterion is that labels are available on time to avoid any delays [20].

When it comes to costs, normally the work involved in the labeling process is much more expensive than the label itself [20], and thus it is important to avoid unnecessary duplication of steps. Still, the

3.2 – Market research on smart labels 31

re-labeling and over-labeling of IMPs is a task that is normally performed in the execution of clinical trials.

Re-labeling poses a dual problem, as it requires times and money, what may translate into delays and cost overruns: drugs need to be shipped back to a certified site by the sponsor, a new label is put on, and then they are re-sent back out along their journey. In addition, label compliance challenges are also reported to be exceptionally high during the re-labeling process: up to 10% of the labels placed at a re-labeling stage need to be replaced once more due to design failures, printing issues or simple human error [101].

Re-labeling might happen in several different scenarios. From most to less common, a non- exhaustive list is presented in table 4. The reader should note that expiry dates revisions are surely the most common reason to re-label an IMP.

Table 4: Common reasons for the re-labeling of an IMP, from more to less frequent

# (1 – most common; Reason for re/over-labeling Source(s) 5 – least common) 1 Extension of the expiry date as more information on [19, 20, 37, 40, 66] drug stability is learned 2 Regulatory non-compliance [20, 101] 3 New countries joining the trial [40] 4 Retrospective change in country specific [19, 37] requirements 5 Reshipping from one depot to another to match [40, 66] supply and demand

The process of re-labeling implies so much effort that normally re-labeling is mostly done only if the drugs are already in certified facilities, such as a regional depot. If drugs expire at a clinical site, where re-labeling cannot normally take place, drugs are simply destroyed (which is another costly waste) [37, 45] instead of shipped back to the certified facilities authorized to re-label.

Dealing with complexity in logistics, other of the main challenges faced in clinical trials (1.8.3 – Complexity) can also be partially attributed to the labeling process. On the one hand, an early labeling of IMPs is associated to a loss of flexibility in the supply chain, because products are already attached with country-specific information (e.g. language of the information or country specific requirements). On the other hand, in CTs with several countries, and especially in double-blind studies, the packaging and labeling process of IMPs is a critical step, as errors are difficult to detect and might transform into compliance issues once it is too late to react, even making the whole trial useless [20].

3.2 Market research on smart labels This section starts by provided an expanded definition of smart labels and analyzing the different technologies on which they can leverage. Different fields of application of smart labels in the clinical trial industry are then demarcated.

3.2.1 What is a smart label? A smart label is, generally speaking, an item identification slip that contains more advanced technologies than conventional bar code data [94]. Smart labels typically use RFID or NFC technology to expand these conventional bar code functionalities.

The first – and still most used – smart labels leveraged on RFID technology. Radio Frequency IDentification (RFID) refers to small electronic devices that consist of a chip, typically being able to

32 3.2 – Market research on smart labels

carry up to 2000 bytes of data, and an antenna [102]. RFID tags are created incorporating an ultra- thin RFID-tag to a traditional label. This is done by sandwiching the chip and the antenna between a paper substrate and the adhesive, so that the final device can be applied like a regular, adhesive label [103] (see figure 9 for an example). By combining human-readable information and embedded bar code RFID technology, smart labels expand beyond a simple tagging-functionality [104], and allow for identification and communication between the RFID-tags and the RFID-readers.

Laminate face stock

Laminate liner

Web of finished inlays containing antenna and IC chip

Figure 9: Example of an on-roll self-adhesive RFID tag. Adapted from [105].

RFID tags can be either active or passive. Active labels contain their own battery, making it possible to broadcast with a range of up to 100 meters. Conversely, passive RFID tags, the most used ones [106], do not require a battery, as power to the chip is obtained wirelessly from currents induced in the antenna when the label is within the range of a RFID reader [103, 107]. Note that this also implies that the device is inactive while not within this range. Figure 10 shows an example of how a real, passive RFID smart label looks like.

Although e-labels have been traditionally linked to RFID and to a context of tracking material flow in supply chains, new developments are expanding both the technology on which smart labels leverage and their range of applicability.

NFC, for example, is nowadays widely used when it is the final user who has to have access to the information contained in the smart label. Near Field Communication (NFC) is a subset within the family of RFID technology. More specifically, NFC operates at a frequency of 13.56MHz, and was originally designed to exchange data in a secure way. The main advantage of NFC over RFID is twofold: first, NFC devices can act both as tags and readers, allowing them to communicate peer-to- peer [102]. Second, NFC technology can be embedded in a smartphone [108], This is the reason why it is used when end-customers are involved, as adoption of RFID readers is minimal when compared to the adoption of smartphones.

3.2 – Market research on smart labels 33

Figure 10: Overview of a passive, adhesive smart label with an embedded RFID tag [104]

However, RFID has also some advantages over NFC. First, RFID has a wider spectrum of uses [108] than NFC. Second, while the range of use of NFC is measured in a few centimeters, RFID’s range of use can escalate up to hundreds of meters with active tags.

Other communication standards are Global Positioning System (GPS), Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Bluetooth or simple 2D printed data matrix codes. Figure 11 compares the main characteristics technologies with the ones already presented for RFID and NFC.

34 3.2 – Market research on smart labels

GSM / RFID NFC Bluetooth GPS 2D Code GPRS

Applies Position, Position & Position & Position & Position Data to env. data (need a data (need a data (need a conditions reader), env. reader), env. reader) , data) conditions (if conditions (if active) active)

Data Send & Send & Send & Send & Receive None Transfer receive receive receive receive

Tracking Real time Non-real Non-real Non-real Real time Non-real time time time time Special No Yes, RFID Yes, readers Yes, No No reading reader or enabled Bluetooth devices smartphones activated required devices Reading Unlimited Tens/hundre Centimeters Meters/tens Unlimited Visual range range ds of meters of meters range (with contact GSM) Other - - Smartphones - Requires Proximity as readers combinatio required n with GSM/GPRS Cost Very high Very low Very low Very low (if Very high Inexpensive (passive) to passive) (combined high (active) with GSM)

Figure 11: Overview of different communication technologies that can be applied to smart labels. Labels with 2D codes are not always classified as smart labels.

3.2.2 Smart labels to improve item level inventory tracking Although the roots of RFID can be traced back to the use of radars to alert of incoming planes during World War II (see [109] for a comprehensive evolution of RFID since then), the commercial expansion of RFID technology took place in Europe and the U.S. in the 1980s, when it was applied to transportation systems, animal tracking and other business applications [110].

The use of RFID smart labels has expanded since then and is now being used in the aerospace, food, beverage, logistics, manufacturing, healthcare, pharmaceutical and retail industries, among others [111]. In all these industries, the main application of RFID smart labels is item level inventory tracking. This is because, when it comes to the individual identification, tracking and even communication with individual inventory items, smart labels pose several advantages over traditional, barcode labels. Some of these advantages are [112, 113]:

1. While barcode readers call for a direct line of sight to a label, RFID readers do not require this (regardless of an active or a passive RFID tag being scanned).

3.2 – Market research on smart labels 35

2. A RFID reader can identify and read information from a tag from up to 100 meters, while traditional readers are typically limited to 5.

3. Barcode readers require on average 0.5 seconds to scan a tag, while RFID readers can scan up to 40 tags per second.

4. Traditional barcodes have no write/read functionality, while information can be written to and read from RFID tags in an active way.

5. RFID labels allow to bring inventory level items into the Internet of Things (IoT). IoT aims at connecting information systems with real-world objects [107]. RFID readers can server as the interface to couple real-world objects (smart labels) with business processes (for example, SAP).

Figure 12 presents an overview of a packaging facility embedded in a RFID ecosystem.

Figure 12: RFID to track item level inventory

All these advantages bring obvious benefits to the management of inventory and supply chains at an ever-decreasing price. In fact, the cost of RFID tags has been falling steadily over the past decade, and it is expected that it will further decline as the adoption of RFID ramps up [114].

Although prices are very dependent on volume, the memory of the tag and whether it is active or passive, in general, as of today (April 2016), active tags can be purchased from $25, while passive tags with a 96-bit memory – which can store a signed integer up to ~10 – range from $0.07 to $0.15 [106, 115-117]. 28

In the context of RFID smart labels, industrywide there several projects and enterprises trying to produce ultra-low-cost smart labels. The European FlexSmell project, for instance, is an initiative to provide wireless read-out compatible smart labels at a reduced price [118]. Its main potential application lies in the development of smart packaging and tracking solutions for perishable goods, what could fit the pharma industry. Alternatives like this result in silicon-based, passive tags that can

36 3.2 – Market research on smart labels

store a unique serial number that can be purchased for $5 cents, a price level at which the technology becomes truly competitive [106]. It is even expected that these type of smart labels will cost as low as $1 cent in the upcoming years [119].

Although the variable costs (or costs per tag) for RFID passive labels are relatively low, there are other elements to consider when it comes to enable RFID technology in a supply chain. RFID readers, for example, are also required in combination with RFID tags in order to track the inventory at an item level. RFID readers can be assumed to cost between $500 and $2000 [120]. Still, the cost of a fully functional RFID ecosystem is much higher than the fixed cost of RFID readers plus the variable cost of RFID tags, as companies implementing this technology are likely to hire system integrators, purchase new enterprise applications to read, filter and manipulate the data and invest in new warehouse management systems [121].

3.2.3 Smart labels to guarantee correct environmental conditions Several industries are very interested in monitoring the environmental conditions to which several types of perishable, dangerous or delicate products are subjected to as they move across the supply chains. Recently, heavy-logistic industries have explored the possibility of semi-active RFID tags to provide records of the environmental conditions of goods as they move throughout the supply chain.

Today, it is already possible to have smart labels that monitor temperature of humidity [118]. The main drawback of these type of smart labels is that they require the integration of components, such as sensors that adds dramatically to the costs, as the RFID label automatically becomes active due to the batteries needed to power the sensors [107] (see figure 13).

US$ >10

US$ 0.07 - 0.015

Passive Active RFID tag RFID tag incorporating sensors

Includes: ID, memory Includes: ID, memory, sensors Passive, no battery required Active, battery required

Figure 13: Differences between passive and active, sensor-based RFID tags

Examples of application of smart labels to guarantee correct environmental conditions can be found in the food and beverage industry. In these industries, quality assurance is a prerequisite that cannot be questioned, and there has been research to improve the food safety and quality by the application of smart labels in measuring shelf life indices such as temperature, humidity, pH etc. [122]. This, in combination with the fact that sensory monitoring is specially suitable for perishable food products [107], makes the food and beverage industries suitable for these type of smart labels.

When it is the final user who has to have access to retrieve the historic environmental conditions from the, usually NFC technology is embedded into the tag. As explained in 3.2.1 – What is a smart label?, NFC is more user friendly because it allows to use a smartphone as a tag reader instead of dedicated equipment, such as RFID readers (see figure 14). NFC tags be purchased for as low as $1 cent per tag for high sales volumes [123]. Despite being inexpensive and user-friendly, the main disadvantage of NFC tags is that once the information has been written into the tag, it cannot be altered anymore [124].

3.2 – Market research on smart labels 37

Another important design question is whether the smart label communicates directly with centralized tracking facilities or they use interfaces for this purpose. For example, they can include GSM, the same technology mobile phones use to access the network, to autonomously transmit data. The main inconvenient is that this is very likely to drive prices up further. A second alternative is that these labels communicate with an RFID reader or similar middle-point device that in turn handles communication with the tracking facilities. Although cheaper, this second alternative does not allow for real time information, losing any chance to actually react before environmental conditions deviate enough for a product to be disposed.

Figure 14: NFC devices can be used to read environmental information from products containing an RFID inlay. Adapted from [125]

In the pharma industry the application of smart labels to monitor and guarantee correct environmental conditions has the potential to improve the control of transportation, increasing compliance and product quality and ultimately reducing waste. Cold chains (a temperature- controlled supply chain), for instance, are common in the pharmaceutical industry, sometimes because of product requirements and sometimes to extend the shelf life of IMPs [126]. Figure 15 shows concept of application of smart labels to track environmental conditions.

Figure 15: Applying an RFID label on a medical product enables to track its and environmental conditions. Adapted from [127]

38 3.2 – Market research on smart labels

In addition, there have been announcements of smart labels with added measuring capabilities, such as acceleration or pressure. However, how mature these developments are and whether their commercial production is feasible remains often unclear [118].

A second, more primitive and inexpensive potential application of labels that control temperature conditions is using check point or color mark labels. These labels are typically passive in nature, and consist on phase change materials embedded in vacuum isolated panels [72]. This allows, for example, to have a visual indicator on a label – thanks to the phase change of the embedded material – when temperature rises above a certain threshold without a need for any integrated circuit.

3.2.4 Self-writing smart labels? Knoll first reported the concept of a self-writing smart label in 2008 [103]. This self-writing technology is based on doping front migration: an electrochemical effect occurring in structures composed of intrinsically conductive polymers, and allows for ultra-low cost devices that do not need any kind of active battery.

From the perspective of the CTSC, however, two main limitations arise: the first and main limitation is that to date, this self-writing label only has a real application as a humidity sensor [128, 129]. While this might be interesting from the perspective of monitoring and guaranteeing correct environmental conditions in the CTSC without needing more expensive active tags, some other attractive features of a potential self-writing label, such as modifying the content of the label, cannot be achieved via doping front migration.

A second limitation that builds on the first one is that labels that rely on doping front migration are normally operated at elevated humidity. Nevertheless, this limitation seems to have been overcome, as the literature reports that new chemical techniques allow for operation under relative humidity as low as 11%. To give an idea of what this means, there is no city in the U.S. with a monthly average relative humidity below 21 according to national statistics [130]. Still, it is not yet clear whether this technology would fit in the pharma environment, where compliance is of major importance.

Because of these limitations, passive self-writing smart labels are excluded as a viable commercial alternative. Active, electronic ink labels, also known as ePaper, will be considered instead.

3.2.5 Electronic paper and electronic ink labels Electronic paper (ePaper), also known as electronic ink (eInk), is a technology that mimics the appearance of ordinary ink on paper [131]. This technology, widely used in eBook readers since the 1990s [132], is nowadays being implemented by retailers, allowing them to modify tag information in a wirelessly and centralized way (see figure 16).

The implementation of Electronic Shelf Labels (ESL) by common retailers started in 2009, when two U.S. grocery-store chains implemented RFID-based LCD displays that were attached to store shelves and served as product identification and price tag [133]. With a typical chain retailer making up to 12,000 price tags changes per week [133], the motivation was to save costs in printing paper labels and in the labor force involved in performing these modifications. In order to modify the tag information remotely, Wi-Fi was discarded because a single Wi-Fi node cannot support a sufficiently large number of tags. Similarly, wiring was also discarded because shelves fixtures are often moved. The company Altierre [134] decided then to use active RFID tags in combination with LCD displays and RFID readers in the ceilings of the stores to remotely modify e-labels.

3.2 – Market research on smart labels 39

Figure 16: Example of electronic shelf labels

Although innovative, this implementation had a dual problem: it was very expensive and it relied on active batteries that had to be changed. ePaper was later found to be the solution to both problems. An eInk display is cheaper than an LCD, and its battery consumption is minimum. In fact, eInk displays require zero power to display a static image [135]. This also allows to rely on the power that can be obtained via induction from the RFID reader using passive RFID tags instead of active ones, what translates into a second iteration of cost reductions. The main disadvantage of eInk displays when compared to LCD is their refresh ratio, which is orders of magnitude lower. However, given that eInk tags are generally updated at most once per day, this limitation does not pose a challenge. Some other technical characteristics of modern ePaper displays are presented in table 5.

Table 5: Characteristics of modern eInk displays. Raw data obtained from [67, 135, 136]

Parameter Description Power Zero while displaying a static image. Approximately 0.5 micro amps hour per consumption cm2 while switching the display. Visibility ePaper displays are not self-illuminated. They require exterior light to be read. The angle of vision is 180º. Design eInk displays can be manufactured in any 2D shape, including circles, triangles or any other abstract shape Thickness The thickness of a eInk display is between 380 and 560 microns Environmental eInk displays can withhold temperatures from -25º to +60º and can stay in conditions humid environments, like freezers. This is also valid for the communication technologies they use. Material and eInk displays are non-glass made and can be produced shatterproof, dustproof durability and waterproof for enhanced durability

One of the first pilot implementations of eInk labels was a reusable RFID-enabled tag used to help airlines and airports to track the location of luggage and to send real-time updates to passenger’s cell phones on the status of their suitcases [137]. This reusable tag relied on RFID to transmit an ID code that could be linked to a bag’s flight information, while at the same time displaying the passenger and flight data on an eInk display, depending only on the energizing power transmitted from an RFID reader.

40 3.3 – Research on smart labels in the pharmaceutical industry

Today, commercial developments that provide store retailers the option to have battery-free ESL that can be updated remotely already exist [138, 139]. Latest developments allow to power and update the content of these smart labels using NFC instead of RFID. This has the advantage of not requiring expensive RFID readers, but the downside of having to interact with the labels from a much closer distance [140, 141].

The market for ESLs is rapidly growing, and it is estimated that it could reach USS2 billion by 2019 [142], what might bring more competitive products and improved technological solutions.

Applied to the clinical trial supply chain, electronic ink labels would have the potential to display variable content, which is especially useful to:

. Allow for changes in the expiry date of IMPs automatically [37, 40, 66].

. Eliminate booklets. Booklets are multi-lingual labels attached together to the packaging of an IMP [41]. They have the purpose to provide more flexibility to the clinical trial supply chain, for instance allowing reshipping IMPs to a different country in the last-minute, but come at a cost. Still, the use of booklets has increased in clinical trials during the last 15 years [143].

. Display different information to different stakeholders in the clinical trial supply chain. For example, detailed information about storage conditions might be especially important to distribution agencies, whereas route of administration or dosage quantity could be emphasized for patients and even become patient-specific.

. Reduce waste at a clinical site level [37].

. Bring more flexibility to the supply chain by attaching label with variable contents instead of fixed, country-specific contents to IMPs at earlier stages.

3.3 Research on smart labels in the pharmaceutical industry So far, different technologies behind smart labels have been presented, and potential applications have been described. A question that remains open is whether the pharmaceutical industry has actually invested resources to assess the potential of smart labels already, as well as knowing the reasons for the adoption/rejection of such a technology. This section aims at clarifying this question by a systematic desk research on the developments published by the top 10 pharmaceutical companies.

3.3.1 Scope and methodology To extrapolate trends in the implementation of smart label technologies in the pharmaceutical context, a review on the developments from the top 10 biggest pharmaceutical companies (see table 6) was carried out.

The main source of information were the press releases sections of the different companies’ websites, along with a web search engine desk research to also cover potential secondary sources. Keywords used in both cases were the name of the company and “smart labels”, “e-labels” or RFID”, among other similar ones. Note that this research is not limited to applications in clinical trials, but covers the whole range of operations of the above pharmaceutical companies.

3.3 – Research on smart labels in the pharmaceutical industry 41

Table 6: Biggest pharmaceutical companies by global sales in 2014. Adapted from [144].

Global sales 2014 Global sales 2013 Growth # Company Growth [%] [$m] [$m] [$m]

1 Novartis 47101 47468 -367 -1

2 Pfizer 45708 47878 -2170 -5

3 Roche 39120 39163 -43 0

4 Sanofi 36437 37124 -687 -2

5 Merck & Co. 36042 37437 -1395 -4 Johnson & 6 32313 28125 4188 15

Johnson

7 GlaxoSmithKline 29580 33330 -3750 -11

8 AstraZeneca 26095 25711 384 1

9 Gilead Sciences 24474 10804 13670 127

10 Takeda 20446 19158 1288 7

3.3.2 Results The desk research found the following results, categorized by pharmaceutical company:

1. Novartis: In 2006, an article entitled Novartis Trial Shows RFID Can Boost Patient Compliance shows promising results on an ongoing development of an RFID-based system to measure therapy compliance objectively [145]. However, that same year other press releases indicate that Cost Emerges as Major Issue in Novartis RFID Trial [146]. From that point on, no other press releases were found.

2. Pfizer: In 2005, Pfizer stated that they would start using smart labels in high value drug bottles [105]. One year later, in 2006, Pfizer formally began a pilot project attaching a high-frequency RFID tag to each bottle of Viagra in the U.S., with the objective to fight against counterfeit drugs [147]. However, continuation of this project was not found during the present desk research. The next pilot project of Pfizer in implementing smart labels dates from 2014, when the company announced in the Mobile World Congress of Barcelona that they were developing a mobile platform for brand adherence and label comprehension. The approach of this project was quite different from that seen in 2005, as this one was patient-oriented and based on NFC technology [148].

3. Roche: A patent called Verfahren zur Herstellung eines Smart Labels mit laserbeschriftbarem Klebeetikett [Process of production of Smart Labels with a Laser Marking Adhesive Label] was filled by F. Hoffman-la Roche AG in 2007 [149]. However, no indication of application of this patent or other forms of smart label was found.

4. Sanofi: In 2011, Sanofi converted some of its logistics processes to RFID (Radio Frequency Identification) technology […] to automate and optimize the company's production of insulin [150]. The next indication of use of smart label technology by Sanofi takes place 4 years later, in 2015, when the company leaded a project on e-labeling to suppress booklets and increasing compliance [151]. Other pharmaceutical companies participating in this project are Johnson & Johnson and Roche.

5. Merck & Co.: In 2006, the German chemical and pharmaceutical company starting developing printable RFID tags in collaboration with the Technical University of Darmstadt [152]. However, no further application of smart labels by Merck & Co. was found.

42 3.3 – Research on smart labels in the pharmaceutical industry

6. Johnson & Johnson: There is evidence that in 2008 the health-care product manufacturer Johnson & Johnson tried to deploy RFID devices to track orthopedic components and monitor promotional products and displays [153].

7. GlaxoSmithKline: In a pilot project involving the HIV medication Trizivir in 2006, GSK tested the applicability of RFID tags in verifying drugs’ source and authenticity [154, 155]. Although this project was discontinued due to technical difficulties, one year later, in 2007, GSK said that it will continue conducting pilots to demonstrate how RFID can be used to secure the pharma supply chain, contradicting a recent news report [156]. From that point on, however, no press release has been found announcing further projects or breakthroughs by GSK when it comes to the implementation of smart labels.

8. AstraZeneca: The drug producer AstraZeneca has conducted one of the biggest pilot projects on the implementation of RFID tags in the clinical trial supply chain. In 2007, the company was a pioneer in implementing RFID for error prevention and recording procedures, by delivering over 30 million RFID enabled syringes of the anesthetic Diprivan [157, 158]. No recent developments have been found however since then.

9. Gilead Sciences: The company implemented RFID in their offices in 2009 to improve quality systems and regulatory compliance [159]. The items to track where however not restricted to drugs. In fact, it extended to F&D Admin regulated documents, archived records and biologicals such as tissue samples. For that purpose, RFID readers and antennas were placed above the ceiling tiles in each attorney’s office. Because of this approach, it is doubtful whether this can be consider an example of application of smart labels in the clinical trial supply chain. Furthermore, no other developments were found.

10. Takeda: No smart label developments by Takeda were found.

3.3.3 Smart labels in the pharmaceutical industry – conclusions, discussion and limitations From the above results, the following conclusions can be drawn:

1. In the last decade, the main role of smart labels in the pharma industry was thought to be inventory tracking and fighting counterfeit drugs.

2. With very few exceptions, most pharmaceutical companies invested resources in exploring the potential of RFID smart labels for inventory tracking and validation in between the years 2005 and 2008. From that date on, it looks like the interest vanished and most of the projects were discontinued. This might indicate that overall the outcomes obtained were not up to the expectations. In some cases, it is explicitly mentioned that costs were an issue (case of Novartis), while in some others the reasons were never made public.

3. Although the initial interest seems to be gone, new, more recent developments (2014- present) are exploring other alternatives of implementation of smart labels. These new approaches do not rely exclusively on RFID technology and are more patient-oriented. Examples of this are the developments by Sanofi in implementing e-labels to eliminate booklets and enhance patient compliance or the research of Pfizer in smartphone applications that communicate with the medication via NFC to increase brand adherence and patient comprehension.

4. No example of smart labels with a variable content, similar to the approach presented in 3.2.5 – Electronic paper and electronic ink labels.

Note however that, in interpreting these results, the following limitations are also to be considered:

3.4 – SWOT of the different applications of smart labels in the clinical trial supply chain 43

. These results are only based on information that was made public by the companies, and might represent just a fraction of smart label developments if put into perspective with internal, confidential projects.

. Because of resource limitations, the desk research was limited to the biggest 10 pharmaceutical companies in terms of sales. Apart from not being exhaustive, smaller startup companies are usually more innovative that biggest companies, where organizational hurdles and company inertia can hinder innovation. Nevertheless, the pharmaceutical sector is characterized by continuous M&As in which, normally, bigger companies acquire smaller ones to buy and implement their ideas, so innovative approaches often flow to these bigger companies. Moreover, it is undeniable that the analysis of these companies allows examining some trends in the sector.

3.4 SWOT of the different applications of smart labels in the clinical trial supply chain The market research on smart labels (3.2 – Market research on smart labels) and the research on previous applications of smart label technologies by pharmaceutical companies (3.3 – Research on smart labels in the pharmaceutical industry) have unveiled different potential applications of smart label technology in the clinical trial supply chain. Table 7 summarizes the four different potential applications from a logistics perspective, as well as the unmet need that they would help addressing.

Table 7: Potential applications of smart labels in the clinical trial supply chain10

Using smart labels in the CTSC Unmet need Technology it is based on for…

Enhancing compliance, dealing with logistics Item-level inventory tracking Passive RFID tags complexity in an automated way

Controlling environmental Assuring compliance in the Active RFID tags that conditions clinical trial supply chain include sensors

Dealing with fake drugs; Passive RFID tags Fighting counterfeit drugs enhancing compliance NFC tags Avoiding delays and costs Creating labels with variable eInk caused by information of content Passive RFID tags finished IMPs changing

Each of these potential applications has some advantages and disadvantages. Figures 17, 18, 19, and 20 present a SWOT analysis for each of the potential applications based on an application to the clinical trial supply chain of the different approaches presented in the last version. The different elements for each of the figures were face-validated by a Business Manager from Novartis Pharma AG.

10 Note that this table is based on the research done in the present chapter, which assumes the perspective of a pharmaceutical company. The reader should refer to 7 – Smart Labels in Clinical Trials – The Patient Perspective for an analysis of applicability from a patient perspective.

44 3.4 – SWOT of the different applications of smart labels in the clinical trial supply chain

SWOT Analysis: Inventory tracking

Strengths Weaknesses 1. Previous experience in pilot projects 1. Desk research suggests that this was by most pharma companies already rejected by pharma 2. Deal with complexity  compliance companies 2. The volume of drugs in the CTSC 3. Technology established in other is much lower than in the industries: in improved commercial supply chain, knowledge and lower prices lowering the benefits 3. High setup costs 4. Low variable costs

Opportunities Threats 1. Overall variable costs (costs 1. Handling big data per tag are projected to go down) 2. New disruptive technologies such as 2. Risks in the business implementation organic labels can lead to breakthroughs and validation of the system 3. Easy to implement as a second use if 3. Reliability problems might lead to other type of e-label is implemented compliance issues 4. Real option for future commercial SC

Figure 17: SWOT analysis for the applicability of smart labels as a means to item-level inventory tracking

SWOT Analysis: Environmental conditions

Strengths Weaknesses 1. Cold chains are relatively common in 1. Include sensors that rise the costs two the pharma industry orders of magnitude when compared to passive RFID tags 2. Potential to improve compliance

3. The technology has already been 2. Individual tracking is not that tested in the food & interesting when it comes to environmental conditions beverages industries

Opportunities Threats 1. A pallet level implementation might increasingly become 1. Depending on the technology used, interesting as costs go down information might not be received real time along the distribution 2. New technologies (doping front migration) can already measure chain humidity without an active RFID tag 2. Even if real time, reacting to data 3. Can serve to track inventory as well obtained is very complex because of the many parties involved

Figure 18: SWOT analysis for the applicability of smart labels to control environmental

3.4 – SWOT of the different applications of smart labels in the clinical trial supply chain 45

SWOT Analysis: Fighting counterfeit drugs

Strengths Weaknesses 1. From a global perspective, this might 1. This is not a real problem in the allow to fight the counterfeit drugs CTSC, but rather in the commercial business, estimated at US$ 32 billion a drug supply chain year by the WHO

Opportunities Threats 1. A cyphered code can be included very easily in other types of smart labels, 1. There are many other ways to fight serving as a mechanism against counterfeit drugs, from new counterfeit drugs regulations to law enforcement 2. Although not interesting for the CTSC, 2. Too much reliance on a single this is a real issue in the commercial counterfeit system could actually be drug supply chain, where it has a fit counterproductive

Figure 19: SWOT analysis for the applicability of smart labels to fight counterfeit drugs

SWOT Analysis: Labels with a variable content

Strengths Weaknesses 1. More flexibility in the CTSC 1. High setup costs 2. Labels with variable content can save money and delays when re-labeling 2. Resource intensive validation of the system required 3. eInk can communicate via passive tags  low variable costs 3. Require coordination with clinical sites 4. Elimination of booklets

Opportunities Threats 1. Opportunity for re-labeling at 1. No previous experience in using this clinical sites, which is currently not technology in the pharma industry allowed, resulting in a reduction of waste 2. Compliance risk if the system is not 2. A reduced in inventory overage can lead reliable enough or not properly validated to a lower demand for manufacturing 3. Risk of regulatory aversion 3. Might also serve to track inventory 4. Electronic labels might be difficult to adapt to specific label sizes/shapes

Figure 20: SWOT analysis for the applicability of smart labels to change the content of existing labels 46 3.4 – SWOT of the different applications of smart labels in the clinical trial supply chain

3.4.1 Application #1: Item level inventory tracking In figure 17, one of the main strengths of using smart labels as a means to track inventory is dealing with the complexity in logistics in a more automatized way, what has the potential to increase compliance. Additionally, this is a widespread application of smart labels in other industries, implying that a competitive environment exists, what is an advantage from the perspective of a potential user. Besides, this also translates into more know-how, especially if considered that most of the pharmaceutical companies themselves gathered first-hand experience during the design and execution of some pilot projects.

Although the variable costs of the passive RFID tags required to track inventory is relatively low (and are expected to keep decreasing), the up-front set up costs are high. The order of magnitude of the investment required to have a running, validated system is hundreds of thousands of dollars for big corporations [160]. Other crucial weaknesses are the fact that the throughput in clinical trials is much lower than in the commercial drug supply chain, lowering the advantages of tracking inventory at an item level. Moreover, a final reason that discourages further research is the fact that most of the top pharmaceutical companies already invested resources and discarded this technology, as explained in 3.3 – Research on smart labels in the pharmaceutical industry.

However, the opportunity still exists to leverage on other type of smart labels to track inventory: if another type of e-label with communication technologies was implemented in the clinical trial supply chain, it should be relatively easy to expand its capabilities to also track inventory. Handling the big data associated with tracking inventory, among others, are the main threats of this application.

3.4.2 Application #2: Controlling environmental conditions A second potential application of e-labels in the CTSC is the control of environmental conditions (see figure 18). Strengths of this approach are, firstly, that cold chains are relatively common in the clinical trial industry, what translates into a relatively high number of scenarios where these labels would be applicable. Additionally, there is also know-how developed in this sector thanks to the food and beverages industries, although previous accumulated experience is significantly less than in the case of inventory tracking labels.

The main problem of labels that are able to actively track environmental conditions is their price, which rises dramatically due to the need for active sensors. The cost is even higher if data is required real-time, given that GPRS or similar technologies are then required (this was detailed in 3.2.1 – What is a smart label?). Still, a threat even in this case is that preventing an excursion from the recommended storage conditions is difficult. For example, if it is detected in real-time that some drugs are dangerously approaching the upper temperature limit while being transported in a truck, preventing them from being spoiled would imply contacting the outsourcing transporting company, who would then have to contact the driver, who would have to stop the truck and find a solution. The whole process should take place in a relatively short period of time, deeming its feasibility questionable.

However, some additional opportunities also exist: for instance, a potential solution to deal with costs would be to implement active sensors just in secondary recipients (or even batches), because environmental conditions are typically common to all packages being transported together. Another potential opportunity would be to track inventory leveraging on the active sensors that these labels already possess. Note however that both opportunities cannot co-exist.

In conclusion, the high variable costs of these type of labels, together with the fact that their role in dealing with the main challenges faced in the logistics of clinical trials is unclear, makes these alternative relatively unattractive.

3.4 – SWOT of the different applications of smart labels in the clinical trial supply chain 47

3.4.3 Application #3: Fighting counterfeit drugs Counterfeit drugs (see figure 19) are a major concern for the pharmaceutical industry, as demonstrated by the past pilot projects that tried to leverage on technology to mitigate the problem. Counterfeit drugs may be harmful for patients, because they could be contaminated or contain a wrong dose [161]. Moreover, they originate compliance issues and financial loss for pharmaceutical companies. Overall, the market for counterfeit drugs is globally estimated at US$ 32 billion a year by the WHO [162].

Although the implementation of technology, and in particular smart labels, to deal with this issue has a promising future, the reality is that this is not a problem in the context of clinical trials, because of three reasons [19]. First, .in clinical trials it is difficult to identify and imitate due to limited information, which in any case is concentrated in the sponsor pharmaceutical company. Second, shipment sizes in CTs are fairly small when compared to shipments for commercial drugs, making stealing less attractive. Finally, re-selling – or selling fake drugs – would not be possible because of the limited information about the drug and the fact that the market for the candidate drug is not even established.

Because the scope of the present research is limited to the clinical trial industry, applicability of smart labels to fight against counterfeit drugs is discarded.

3.4.4 Application #4: Labels with variable content Potential strengths of labels with a variable content in the clinical trial supply chain are numerous (see figure 20). First of all, they bring flexibility to the clinical trial supply chain, what in turn help dealing with the increasing complexity of global trials. This is achieved via two ways: first, by eliminating booklets and making labels non-country specific, because the language and country- specific content on the label can be modified. Second, by allowing to re-label at any time throughout the supply chain.

As explained in 3.1.2 – Main challenges faced in the labeling process, re-labeling is one of the main challenges faced in the labeling process, which often results in delays and cost overruns in the logistics of clinical trials. A combination of eInk smart labels with passive RFID technologies would allow to control the content of the labels from a centralized location. Furthermore, as explained before, the variable cost of passive tags is relatively low. Total variable costs would however rise because of the additional display and electronics required in the label11.

Besides, this approach has the additional opportunity of allowing re-labeling at the clinical sites, creating a double source for savings: not paying for the disposal of drugs and save money from the manufacturing and packaging of new replacement drugs. Another opportunity is that, if embedded with passive RFID tags, this would give the opportunity to easily track inventory.

The potential implementation of this type of smart labels is not without challenges. A first weakness, similarly to that explained in 3.4.1 – Application #1: Item level inventory tracking, is that the set up costs of an RFID ecosystem for a big pharmaceutical company is in the order hundreds of thousands of dollars at best. In this case, in order to allow for a centralized management of the labels, additional coordination efforts with the clinical sites would also be required. Clinical sites different widely in terms of size, technological means, staff and training. Thus, a high degree of heterogeneity should be expected when coordinating with clinical sites. While some might already have the equipment required to deal with smart labels, others might be so small that the justification for the investment could be challenged.

11 Figures on the current prices for eInk smart labels will be provided later. See 6.5 – Price of eInk smart labels.

48 3.5 – Smart labels in the clinical trial supply chain – Conclusions

Technological limitations might also constraint the potential of this approach. Reduced label sizes and cylindrical shapes in primary containers, for example, could pose a challenge for eInk labels. This becomes even more relevant if combined with the fact that, to the best of my knowledge, the pharmaceutical industry has no experience in what the implementation of such a technology would imply. This translates into validation and reliability issues that probably would end up rising the initial investment required to set the system in motion.

A final risk is regulatory aversion. The pharmaceutical industry in general and the clinical trial landscape in particular are highly regulated environments. It is still to be determined whether replacing the traditional, widely standardized paper labels with eInk labels is feasible.

Despite these challenges, the potential of smart labels with a variable to mitigate some of the main challenges faced in the clinical trial supply chain, as well as the fact that no previous research in this area exists, make this application an attractive area of research. In the next chapters, the regulatory fit of smart labels will be analyzed. Then, the flexibility and potential savings that this technology could bring to the clinical supply chain will be quantified by means of discrete-event simulation.

3.5 Smart labels in the clinical trial supply chain – Conclusions A literature review on the applicability of smart labels in logistics resulted in three potential applications for smart labels in the clinical trial supply chain: as a means to track inventory at an item level, as a way to ensure correct environmental conditions during the transportation of drugs and, finally, as an alternative to avoid the costly, time-consuming re-labeling of IMPs.

In parallel, an analysis of the evolution of smart labels in the pharmaceutical industry based on a systematic keyword desk research revealed another potential use of smart labels: fighting counterfeit drugs. This analysis also served to find out that the top ten pharmaceutical companies invested resources to test the applicability of passive RFID smart labels in their supply chains in the years between 2005 and 2008. The motivation was in most of the cases an automatization of inventory control. A loss of interest, however, followed this initial enthusiasm, at least when it comes to the application of passive RFID tags.

SWOT analyses were performed for each of the potential applications of smart labels. The different applications were discussed in four separate interviews with individuals with different roles at Novartis Pharma AG (see Appendix C – Interview Protocols and Outcomes), and unanimous feedback was received on the potential of combining RFID passive tags with electronic paper, so that variable information of smart labels can be changed remotely. Note also that this is the field of application of smart labels less explored in the pharmaceutical industry, with no papers published to the best of my knowledge. A combination of eInk and passive RFID tags has the following advantages:

. It makes possible to re- or over-label IMPs automatically, potentially saving costs and delays, and thus addressing two of the challenges faced by clinical trials mentioned in 1.8 – Challenges faced in the clinical trial industry.

. Remote control of the content of the labels might allow for re/over-labeling at the clinical site. A centralized, cloud based modification of the label content might solve the issue that only certified personnel can perform re-label activities. This would reduce number of replacement of IMPs needed at clinical sites, reducing the inventory overage. Moreover, waste would also be reduced, creating a new source for savings, as the clinical trial sponsor is responsible for the destruction of unused and/or returned investigational medicinal products [45].

. If the digital display of this type of e-labels is big enough to cover all the content of a regular label, booklets could be eliminated.

3.5 – Smart labels in the clinical trial supply chain – Conclusions 49

. Because of the low power consumption of eInk, passive tags can be used. These tags are relatively inexpensive, especially when compared to active tags or labels which sensors embedded. Developing an ecosystem that could benefit from this technology would however imply an up-front cost that cannot be neglected.

. eInk labels could bring more flexibility to the supply chain by attaching label with variable contents instead of fixed, country-specific contents, allowing for batches to be more easily re-supplied and overall a more pull-driven supply chain

. Given that a passive RFID tag is necessary in order to update a label remotely (via connection with the RFID reader), this alternatively can easily be expanded to track inventory.

. Information could be presented differently to the various stakeholders of the clinical trial supply chain, allowing for more effective communication.

. From a technical perspective, the physical parameters in terms of power consumptions, visibility, thickness, withholding of environmental conditions and durability are up to the standards of labels currently being used in clinical trials.

50 3.5 – Smart labels in the clinical trial supply chain – Conclusions

3.5 – Smart labels in the clinical trial supply chain – Conclusions 51

Chapter Four ………………………...... 4 The Regulatory Perspective: Do Smart 4 Labels Fit in the Current Regulatory Framework?

In the pharma industry, changes in the regulations are industry driven, partly because regulatory bodies are not always aware of upcoming technologies in the market A Senior Leader at Clinical Trial Portfolio Level from Novartis Pharma AG

The last chapter identified electronic ink as an innovative technology that could add value to the clinical trial supply chain. One of threats identified for this technology was regulatory aversion: the pharmaceutical industry being one of the most regulated in the world [163], it remains to be seen whether such a technology would have a fit in the clinical trials supply chain from a regulatory perspective. This is the objective of this chapter, which is structured as follows: in the first section, an overview of regulatory aspects in clinical trials is given. This focuses on supranational regulations, on the regulatory framework in the EU and the U.S., the biggest markets when it comes to clinical trials, and on the trends being followed by emerging markets. Then, specific labeling regulations in the EU and the U.S. are analyzed in-depth, and the outcomes of interviews with experts in labeling regulations are presented. Finally, conclusions on the barriers and opportunities that regulations pose for smart labels based on ePaper are drawn.

52 4.1 – Overview of regulatory aspects in clinical trials

4.1 Overview of regulatory aspects in clinical trials Governments have long had conflicting objectives regarding the regulation of the pharmaceutical industry. On the one hand, they want to encourage innovation, investment, exports and the development of new drugs. On the other hand, incentivizing innovation by means of patents result in monopolies, which require further regulation [164] and might lead to asymmetric situations of regulatory capture, in which regulation is acquired by the industry and is designed and operated primarily for its benefit [165].

In the context of clinical trials, however, this dilemma is largely reduced. While the global healthcare landscape is characterized by ethical concerns and discussions regarding the impact that patent- protected monopolies have on end patients, in clinical trials pharmaceutical companies sponsor the treatments themselves. From this perspective, the position of pharmaceutical regulators is therefore easier to define when it comes to establishing guidelines, laws and requirements for the design and execution of clinical trials.

From a regulatory perspective, it is important that newer and more innovative products go through robust clinical studies, normally involving randomized clinical trials [25], in order to prove the safety and efficacy of new drugs, as well as to provide support and rationale for new treatments. Still, in clinical trials there might be additional risk to patients compared to those treated with marketed products [45]. Thus, the conduct of clinical trials takes place in a highly regulated and structured environment [20], where meeting the regulatory requirements for conducting drug studies is an essential part of the clinical research [166].

The ultimate impact that regulators have in the drug development process is debated. Some authors suggest that the general perception is that regulatory agencies contribute to failures in the drug development process because they are averse to risk [5]. This is because these entities might, for example, rely on excess on regulatory precedent. Some other authors, however, have opposite impressions, concluding that that flexible regulatory mechanisms do not actually pose barriers for innovative applications [167].

In what follows, a brief and general overview of supranational clinical trial regulations is provided. Then, specific regulations in the European Union (EU), the United States and in emerging markets are given, with the purpose to provide the reader with some background information.

4.1.1 Supranational regulations The Declaration of Helsinki, adopted in 1964 by the World Medical Association General Assembly, constitutes the first set of ethical principles for medical research involving human subjects. Although primarily directed to physicians, it became a world-wide agreed set of guidelines of conduct of clinical trials [20].

Referring to the Declaration of Helsinki, the International Conference on Harmonization (ICH) adopted in 1996 the Guideline for Good Clinical Practice (GCP). The GCP was defined as an international standard to guarantee the quality of clinical trials in terms of patient protection, ethical implications and scientific aspects. Complying with the GCP standards ensures the safety and protection of rights of the patients and the reliability of the data generated during the execution of a clinical trial. Moreover, GCPs were also intended to facilitate the harmonization of data acceptance by different regulators worldwide [20].

The ICH guideline is currently integrated into national regulations in the U.S., Japan and countries belonging to the EU.

4.1 – Overview of regulatory aspects in clinical trials 53

4.1.2 The EU Clinical Trial Regulation The Clinical Trial Directive 2001/20/EC (the Directive) was introduced in May 2004 with the aim to harmonize how critical trials are regulated across the EU. It introduced a legal basis for GCP and a standardized procedure for the application and approval of clinical trial authorizations, the Clinical Trial Application (CTA) [168]. Formalizing a CTA includes different steps, such as registering the trial in the European database, preparing documents such as the trial protocol, IMP-related information or consent forms, and submitting the papers to the Competent Authorities [169].Despite the efforts to unify regulations across the EU, the Directive remained much criticized and it is perceived to have failed to meet its expectations.

Partly motivated by this criticism, the European regulatory framework concerning clinical trials on IMPs for human use is going to change in an attempt to overcome the lack of harmonization that currently characterizes procedures in different Member States (MS) of the EU. The European Commission published in July 2012 a proposal for a Regulation on clinical trials, which overall should make the EU a more attractive place to conduct clinical trials by providing a modern, consistent regulatory framework, taking account of appropriate adaptations for risk and addressing the global dimensions of CTs while ensuring compliance with GCP [168]. The most important changes that this new Regulation brings are, according to Ward [168]:

. The fact that it is a Regulation and not a Directive makes it directly applicable to all MS without the need to transpose into national legislation [168]. A much higher degree of harmonization is thus expected.

. An EU portal and database will be introduced. This will allow a single submission by sponsors of clinical trials to all MS concerned in their trial, regardless of the number of MS involved: MS are to work together coordinating their assessment on the trial.

. Transparency-wise, there is a significant policy change regarding public access to data and information generated during clinical trials and contained in the EU database. By default, all data has to be publicly accessible. Sponsors can apply for an exemption if certain confidentiality criteria are met.

. Reporting requirements are largely simplified as sponsors will use a single EU portal to interact with regulatory stakeholders.

The application of the Regulation, now formally Regulation (EU) No 536/2014, is expected by October 2018 the latest [170], with the exact date depending on the state of development of the EU portal and database.

The reader interested in general regulatory guidelines on CTs in the EU can find further details in [62, 168-170].

4.1.3 The U.S. Food and Drug Administration In the U.S., the ICH guidelines described in 4.1.1 – Supranational regulations also apply, as well as the GMP and GCP standards. However, due to geopolitical considerations, the regulations involving the conduct of CTs in the U.S. are more unified than those of the EU.

The U.S. Food and Drug Administration (FDA), an agency belonging to the U.S. Department of Health and Human Services, is the competent authority charged with the regulation of most drugs, what includes clinical trials and clinical trial applications. However, contrary to the regulatory framework in Europe, the FDA does not officially approve or give authorization for clinical trials to be executed. Instead, the FDA has 30 days after an Investigational New Drug (IND) application is submitted to review it and, if additional information is required or solid reasons are found not to proceed with the study, the clinical trial is placed on a clinical hold [166].

54 4.1 – Overview of regulatory aspects in clinical trials

The IND application must contain information about the three following areas [171]:

1. Animal Pharmacology and Toxicology Studies: Mainly preclinical data that allows to determine the safety of the drug on humans, although it can also use clinical data (often clinical trials in foreign countries).

2. Manufacturing Information: Relates to the composition, stability, manufacturer and controls used in the production process of the drug, and is assessed to ensure that the company can adequately produce and supply consistent batches of the drug [171].

3. Clinical Protocols and Investigator Information: Includes detailed protocols for the clinical study, information on the qualification of the investigators and informed consent from the research subjects.

Apart from serving as an application for a new clinical trial, an IND also serves the company sponsor to obtain an exemption from the FDA to a legal requirement by the Federal Laws that establishes that a drug has to be the subject of an approved marketing application before it is transported or distributed across state lines [171]. This effectively allows the clinical trial sponsor to ship the investigational drug to clinical investigators in many states without limitations.

4.1.4 Emerging markets A country can be classified as developing or emerging according to several indexes: GDP per capita, GDP growth, Human Development Index, Gini Coefficient or percentage of population under the national poverty line are some examples of the figures that are typically considered in tagging a country as emerging [172].

When it comes to a clinical trial regulatory perspective, however, the definition of an emerging or developing country is much more straightforward. Primary markets – the most important one being the European Union and the U.S. – are those in which regulatory agencies perform exhaustive assessments of the safety, quality and efficacy of new drugs being tested in human subjects, while secondary markets are the countries that depend on the approval of the primary countries [173]. Figure 21 shows that most of the clinical trials are still conducted in primary markets, especially in the European Union and in the United States.

With the objective of adapting clinical trials to their own local laws and regulations, key emerging markets such as China, Russia or India nowadays are increasingly establishing new regulatory frameworks as part of the clinical trial application, as noted by Singh and Wang [173]. Table 8, adapted from these authors, shows a comparison between CTA requirements in selected countries.

These new regulations might originate delays in the planning and execution of clinical trials by global pharmaceutical companies, as the number of regulatory layers increases. Moreover, others authors suggest that the regulatory framework in developing countries is of concern because there is no assurance that the ethical considerations and local needs are adequately addressed [174].

Although the number of studies in emerging markets is relatively low at present, the expected trend is that the pharmaceutical market growth in the U.S. and the EU will be flat, while the emerging markets will grow by 14-17% and eventually account for 25% of the global pharmaceutical market [173]. Thus, country specific regulations might increase become more relevant in the future and add a new layer of complexity to the planning, design and execution of global clinical trials.

4.2 – Labeling requirements for IMPs in clinical trials 55

Figure 21: Map of all studies in ClinicalTrial.gov as of April 2016. Extracted from [175]

Table 8: Comparison of Clinical Trial Applications (CTAs) in selected emerging markets. Adapted from [173]

Regulatory Required approval time Country patients per Special requirements for CTA study [months] China 100 – chemical Extensive CMC & quality testing upfront CTA 16-22 300 – biologics EC approval after agency approval

Extensive CMC regulations India 100-200 9-12 EC approval in parallel with agency approval

Straightforward CTA approval process Taiwan 0-50 4-6 EC approval in parallel with agency approval

Russia No guidance 2-4 EC application is the same as CTA application

Straightforward CTA approval process Brazil No guidance 6-8 Sequential review process by 3 agencies

Straightforward CTA approval process Mexico No guidance 3-4 Sequential review process by 2agencies

4.2 Labeling requirements for IMPs in clinical trials After providing an overview of regulatory aspects in clinical trials in the last section, this section focuses on requirements specifically applicable to labeling. Understanding these requirements is crucial to determine the fit of smart labels into the clinical trial supply chain: from the initial packaging by the drug manufacturing company to the patients. This section often refers to the thesis

56 4.2 – Labeling requirements for IMPs in clinical trials

research done by Dr. Astrid Weyermann to obtain a Master of Drug Regulatory Affairs in the University of Bonn [20].

4.2.1 Overview of labeling requirements in clinical trials Labeling is an integral part of the approval process of an IMP. The IMP should be labelled in a way that it includes at least the mandatory information specified by the regulatory authorities. Non- compliance with labeling requirements might result in problems and delays in the approval process [20].

Overall, the purpose of regulatory requirements and guidelines on the labeling of IMPs to be used in clinical trials is to provide added value in the following areas [20]:

. Protection of the patients / study subjects . Identification of the IMP . Traceability of the IMP . Proper use of the IMP . Identification of the study . Proper documentation of the trial

According to the GCP guidelines, some pieces of information that typically have to be conveyed along with the IMP are its storage conditions, the expiry date of the drug products or relevant instructions for the consumption of the IMP [20]. This has to be done in a way that the involved stakeholders (patients, investigators, distributors etc.) can understand and react to this information. Although no specific regulation dictates the material, size, font or order in which the information has to appear on a label, physical, paper-labels are nowadays standardized in the clinical trial industry [41].

4.2.2 The Rules Governing Medicinal Products in the European Union Labeling for IMPs to be used in European CTs should comply with the requirements of Directive 2003/94/ECAs. This requirements are specified in the Annex 13 of the Rules Governing Medicinal Products in the European Union [45], which indicate that the following information should be included on labels unless its absence can be justified:

1. Name, address and telephone number of the sponsor, CRO or investigator

2. Dosage form, route of administration, quantity of dosage units and, in case of open trials, name/identifier and strength of the drug

3. Batch or control packaging number

4. Trial reference code

5. Trial subject identification number

6. Name of the investigator (if not included previously)

7. Directions for use

8. Caution statement like For Clinical Trial Use Only, or similar wording

9. Storage conditions

10. Period of use (expiry date, use-by etc.)

11. Caution statement Keep Out Of Reach of Children except when the product is not to be taken at home

4.2 – Labeling requirements for IMPs in clinical trials 57

Some information is not required if certain conditions are met. For example, if a contact card has been given to the patients, the address and telephone number of the main contact can be neglected. Similarly, should a central randomization system be used, additional information can be not included on the label [20].

When the primary package and the outer pack (see figure 22, adapted from [176]) are intended to remain together, and there is reduced space in the primary package, the information to be included can be reduced to the following [45]:

1. Name of the sponsor CRO or investigator (can be replaced by a symbol or logo)

2. Route of administration and in case of open trials, name/identifier and strength of the drug

3. Batch or control packaging number

4. Trial reference code

Additionally, when it is necessary to change the expiry date of an IMP, an additional label with the same batch number but new expiry date might be superimposed on the old one. This operation can only be performed after adequate authorization from the manufacturing site. Re-labeling must be performed in accordance with the GMP principles, and following all the applicable SOPs. For instance, in a clinical site, only a clinical trial site pharmacist or other healthcare professionals certified can perform the re-labeling of an IMP, which furthermore has to be in accordance with national regulations [45].

Regulations also establish that, when drug products are blinded, measures need to be taken to guarantee the blind is achieved and that unintentional unblinding does not occur due to changes in appearance between different batches of packaging materials. At the same time, however, identification of blinded products needs to be allowed when necessary, even within a short time span in case of emergency [45].

Primary package: Direct contact with the product

Secondary package or outer pack: Contains several primary packages

Figure 22: Primary and secondary packages of IMPs

Figure 23 provides a summary of the labeling requirements according to the European regulation, and table 9 links these requirements to the objectives of labeling specified in 4.2.1 – Overview of labeling requirements in clinical trials.

58 4.2 – Labeling requirements for IMPs in clinical trials

# Label content General case: Both for 1 Name, address and telephone number of the sponsor, primary and secondary CRO or investigator packaging

2 Dosage form, route of administration, quantity of dosage 1-11 units and, in case of open trials, name/identifier and strength of the drug Primary package: 3 Batch or control packaging number When it remains 4 Trial reference code together to outer pack 5 Trial subject identification number 1-5 6 Name of the investigator (if not included previously) 7 Directions for use Primary package: 8 For Clinical Trial Use Only Limited space 9 Storage conditions 10 Period of use (expiry date, use-by etc.) 1, 3, 4, 5 2 might be included 11 Keep Out Of Reach of Children

Figure 23: Summary of labeling requirements according to the regulations in the European Union. Adapted from [45].

Table 9: Examples of the linkage between the content of a label and the fulfillment of its objectives

Labeling Objective Example of contents in the label that fulfill the objective

Protection of the patients Dosages, route of administration, expiration date, caution statements (e.g. For Clinical Trial Use Only), patient number

Identification of the IMP Medication number, investigator, patient number

Traceability of the IMP Label ID, core packaging control number, bar code

Caution statements (e.g. For Clinical Trial Use Only), storage Proper use of the IMP conditions, additional consumption indications

Identification of the study Study code, investigator, sponsor name and address

Proper documentation of All of the above the trial

4.2 – Labeling requirements for IMPs in clinical trials 59

Note that, although minor deviations from the guidelines specified in this section may exist, in general the MS of the EU are aligned with the labeling requirements presented12. For example, according to the Institute of Clinical Research of the UK [177], the following label contents should appear in a clinical IMP:

1. Contents and dosage form 2. Treatment/drug name or code 3. Other pharmacologically active ingredients 4. Strength 5. Patient number 6. Visit reference 7. Method and route of administration 8. Dosage instructions 9. Warnings / Caution statements 10. Storage conditions 11. If necessary, special destruction instructions 12. Batch number 13. Expiry date 14. Sponsors name and address 15. Name of investigator

Note that this is almost fully aligned with the requirements established in the Directive 2003/94/ECAs. The only pieces new pieces of information (those underlined in the list above) relate to special cases in which additional information shall also be included according to the Directive, such as comparator drugs (which are another active ingredients) or radioactive drug products that require special destruction instructions.

4.2.3 Labeling requirements according to the FDA In the United States, the labeling guidelines of IMPs for clinical trials are similar to the ones for commercialized drugs. Requirements are similar to those specified for the European Union. The reason for this lies on both regions having adopted the GMP and GCP guidelines mentioned in 4.1.1 – Supranational regulations. Nevertheless, the following exceptions are to be taken into account:

1. In the U.S., it is not necessary to include the expiry date if appropriate specifications are met […] and the IND does not need reconstitution before use [20].

2. The statement Caution: New Drug Limited by Federal law to investigational use is compulsory.

4.2.4 Other general requirements Some additional requirements not related to the content of the labels, but rather to other general requirements are the following:

1. The longevity of both the material where the information is contained and the “ink” needs to be guaranteed for at least the maximum potential period of use of the drug products under the determined storage conditions [20]

2. The blinding (in single-blind or double-blind studies) should be done in a way that prevents unauthorized breaking. [20]

12 This is also confirmed by analyzing the outcomes of the interviews to clinical trial labeling experts, which can be found in Appendix C – Interview Protocols and Outcomes.

60 4.3 – Outcomes of the interviews with experts in clinical trial regulations

4.3 Outcomes of the interviews with experts in clinical trial regulations The previous sections have demonstrated that current regulations do not contemplate the inclusion of smart labels, neither with variable content nor more standardized RFID or NFC labels, in the clinical trial supply chain.

With the goal to clarify what would be the potential fit of eInk smart labels in the clinical trial context, two dedicated interviews were conducted with a Senior Consultant in Regulatory Affairs for Clinical Trials and a Head Clinical Supply Documentation Specialist. The outcome of both interviews can be found in Appendix C – Interview Protocols and Outcomes. The following is a summary of the main conclusions extracted from these interviews:

1. Both interviewees confirmed that eInk smart labels should be easily accepted by regulatory authorities as long as they fulfill all the established requirements. A typical problem to the introduction of smart labels in the clinical trial supply chain is that in general information present on a label should be read with your eyes, without any additional tool, because patients always have to be able to read the information. This is not an issue for eInk smart labels because they do present the information in a visual way.

2. In general, clinical trial authorities are open and flexible to innovation as long as they are beneficial for the study and for the patients. As confirmed also in a third interview with a Senior Leader at Clinical Trial Portfolio Level (also available in Appendix C): the FDA and other regulatory agencies are open to these technologies. In fact, it is actually companies who innovate looking for a competitive advantage, and then regulations evolve over time if the innovation proves to be beneficial to patients and for compliance. Adaptive trials allowed by regulators are also pointed in the literature [68].

3. General Regulations and Directives established by the European Commission and the U.S. FDA are conservative when it comes to content updates. A representative time scale between changes is 10 years. This might pose a bottleneck: if you have an innovative product or service that is not compliant with the law, you will have to wait until the law has been amended or revised to actually implement it.

4. Countries build their Country Specific Requirements (CSR) on top of the general guidelines provided by the main authorities (European Commission / FDA). This implies that acceptance of technology in the clinical trial supply chain from a regulatory perspective can be expected to be relatively homogeneous once new developments are approved by the main authorities.

The same logic explained in the first point applies also to other smart labels or e-devices. For example, as long as the requirements for the IMP labeling are fulfilled, sponsors are relatively free to use devices to track medication, provide reminders on the next drug intake to the patients or facilitate data sharing. However, in the last case, data protection issues would have to be dealt with: especially when you move in the direction of social connection [in the context of clinical trials], you always need to be careful about confidentiality issues13.

4.4 The regulatory perspective on smart labels – Discussion and conclusions This chapter has introduced the regulatory framework in which clinical trials take place. Special attention has been paid to the labeling requirements, as they are of major concern to determine the feasibility of implementation of eInk smart labels in the context of clinical trials. In this section, the

13 Note that although the implications of this are not obvious now, they will gain relevance in 7 – Smart Labels in Clinical Trials – The Patient Perspective.

4.4 – The regulatory perspective on smart labels – Discussion and conclusions 61

main conclusions obtained are summarized, and an additional discussion on what some requirements imply is elaborated.

4.4.1 Regulatory fit of smart labels The knowledge gathered in this chapter allows to draw the following conclusions:

1. Overall the guidelines specify information that needs to be included on clinical labels, but neither the European Directive nor the guidelines provided by the FDA specify explicitly that labels have to be in a physical, paper format, what in principle gives room to electronic labels.

2. There is precedent that regulatory agencies have been relatively open to innovation in the past. For example, by the time pilot projects were conducted with RFID labels in the clinical trial supply chain (see 3.3 – Research on smart labels in the pharmaceutical industry), no regulations were in place to handle antennas and sensors embedded into regular labels. Regulatory mechanisms did not pose a barrier for this innovative application in the past, because label requirements in terms of content, longevity and readability were not altered.

3. Existing guidelines are proven to be flexible in the past, what might allow to modify the requirement described in the last point requirements in the future. For example, the study code mentioned above can be useful for traceability purposes when a single IMP is used in different clinical trials in parallel. However, if information regarding the study code could be embedded in the memory of the label, regulatory agencies might agree to use this as an alternative.

4. There might be a future risk due to diverging regulations, especially as emerging markets such as Russia, India or China try to establish their own regulatory guidelines in an attempt to become primary markets.

4.4.2 Label sizes and shapes Labeling regulations establish requirements on the contents of the label, which technically can be achieved by means of e-labels. This is relatively straightforward in outer packs, which are usually attached to flat boxes and have enough room to fit a conventional smart label. For primary packs, this can be more challenging. Some information, such as the study code or the batch number, is data required on the primary package, regardless of it being very small (e.g. a pill bottle) or it staying together with the outer package. This regulatory constraint might transform into a technological limitation in the context of eInk smart labels.

In a pharmaceutical company like Novartis, labels for clinical trials range from 32x18 mm (smallest) to 119x52mm largest. To give an example, figure 24 presents (in real size) some of the smallest labels used for clinical trials in the pharmaceutical industry.

Figure 24: Example of the smallest types of labels used in clinical trials (displayed at real-size)

62 4.4 – The regulatory perspective on smart labels – Discussion and conclusions

This regulatory constraint might transform into a technological limitation in the context of eInk smart labels, although ESL of reduced sizes already exist. The current standardized sizes for ESL used in the food industry, adapted from [178], are presented in table 10.

Table 10: Standard sizes for ESL being used in the food industry. Adapted from [178].

Size Pixel Dimensions Resolution (dpi) Active Area (mm) Outline Dimension (mm) 1.44" 128 x 96 111 dpi 29.312 x 21.984 40.512 x 28.80 x 1.00 1.54" 200 x 200 184 dpi 27.6 x 27.6 37.32 x 31.8 x 1.18 2.04" 172 x 72 95 dpi 48.16 x 20.16 59.20 x 29.20 x 1.18 2.0" 200 x 96 111 dpi 45.8 x 21.984 57 x 28.80 x 1.00 2.0" 230 x 94 124 dpi 47.04 x 19.22 59.5 x 29.2 x 1.18 2.7" 264 x 176 117 dpi 57.288 x 38.192 70.42 x 45.80 x 1.00 2.9" 296 x 128 112 dpi 66.9 x 29.1 80.4 x 38.1 x 1.18 4.3" 800 x 480 217 dpi 93.6 x 56.16 108.8 x 64.8 x 1.17 4.41" 400 x 300 113 dpi 89.6 x 67.2 98.50 x 80.42 x 1.00 6.0" 1024 x 758 212 dpi 122.368 x 90.581 138.40 x 101.8 x 1.18 7.4" 800 x 480 126 dpi 161.6 x 96.96 169 x 111 x 1.20 10.2" 1280 x 1024 160 dpi 203.2 x 162.56 218.30 x 171.76 x 1.20

The problem becomes bigger when electronic displays have to be adapted to curve shapes, such as a bottle. Even though technological solutions already exist (see figure 25), additional challenges (e.g. technical or financial) might be associated with these labels.

Figure 25: High resolution eInk solutions already exist in the market, meaning that it is technically feasible to attach eInk labels to curved containers

4.4.3 Label durability The requirement in regard to the longevity of the label material and the information it contains should not pose a problem for e-labels, as eInk displays require zero power while displaying a static image [135]. Storage conditions are not likely to be a problem, as there exist eInk-based devices which

4.4 – The regulatory perspective on smart labels – Discussion and conclusions 63

temperature usage range goes from -25º to +60º and can stay in humid environments, like freezers [136].

4.4.4 Access to the label The requirement that labeling should prevent unauthorized breaking of the blinding and unauthorized changes of information can also be achieved via a combination of e-Ink labels and RFID devices that update the information, as this can be cyphered the same way it was done when pilot projects.

Regulations establish that the information contained in the label should be accessible by several stakeholders apart from the clinical trial sponsor (e.g. patients, investigators etc.). This has a twofold impact on the potential implication of e-labels: first, they have to be reliable enough not to fail to meet this purpose (e.g. because of technical failures in a non-admissible percentage). Second, if information is stored in the memory of the label (e.g. the study code), this is to be accessible by all the stakeholders. This might pose a challenge, especially after analyzing the expert input received during the interviews.

64 4.4 – The regulatory perspective on smart labels – Discussion and conclusions

4.4 – The regulatory perspective on smart labels – Discussion and conclusions 65

Chapter Five ………………………...... 5 A Discrete-Event Simulation Model of 5 the Clinical Trial Supply Chain

The beauty of the clinical supply business is that every study is different A Clinical Trial Expert from Novartis Pharma AG

A prominent type of smart label was demarcated in 3 – Smart labels – Market Research and Applicability to the Clinical Trial Supply Chain. That chapter concluded that an eInk smart label can help to deal with the complexity of clinical trials by bringing flexibility into the clinical trial supply chain. Additionally, a label with a variable content might help to reduce delays originated by the often required re-labeling processes, as well as the associated costs of externalizing these processes. A second iteration from cost savings comes from the waste that currently is generated by the general absence of re-labeling in clinical sites, where drugs which label needs to be updated are generally discarded. If it was possible to re-label these drugs at the clinical site, they may not need to be disposed anymore.

The regulatory fit of such a technology in the clinical trial supply chain was analyzed in 4 – The Regulatory Perspective: Do Smart Labels Fit in the Current Regulatory Framework?, concluding that although some regulatory requirements might transform in technical challenges, there is in general room for eInk labels in the context of clinical trials.

The objective of this chapter is to build a Discrete-Event Simulation (DES) model – using Simio Simulation Software – that allows to further understand the dynamics of the clinical trial supply chain, as well as to eventually quantify the impact in terms of enhanced flexibility and savings that the implementation of smart labels would imply. For that purpose, the model conceptualization of different stages in the clinical trial supply chain is first presented. The model boundaries and assumptions taken during the model development are then delineated. Later, key performance indicators used in the model, as well as some basics on the model logic, are described. Finally, an overview into the steps followed to verify and validate the model is given.

66 5.1 – Optimization techniques for the clinical trial supply chain – A literature review

5.1 Optimization techniques for the clinical trial supply chain – A literature review There have been some attempts in the literature to optimize the Clinical Trial Supply Chain (CTSC) via quantitative, computer-assisted methodologies. This section presents the results of a non- exhaustive literature review based on different combinations of the terms “clinical trial supply chain”, “optimization”, “simulation”, “model” and “inventory”. The different approaches analyzed served to form a better perspective of a model conceptualization of a clinical trial supply chain. Some limitations were however identified. It is hoped to cope with them in the present thesis.

Abdelkafi et al. applied Monte Carl simulation techniques to optimize the CTSC. Moreover, a Bayesian principle was used to reevaluate supply strategies over time. However, in this paper the delivery system was isolated from production [36]. It is questionable whether this is a reasonable assumption, as it is clearly the interface between the patterns of batch manufacturing and delivering what determines the performance of the system.

Fleischhacker et al. developed a mathematical model to deal with the inventory overage in CTs. [27]. Their model conceptualization includes a new class of multi-echelon inventory models to deal with unique features of the clinical trial supply chain (e.g. the fixed patient horizon or the inability to transfer drugs across different clinical sites). The methodology and conceptualization face however three issues. First, the model relies on assumptions, such as a complete up-front manufacturing of drugs (i.e. prior to the clinical trial) or fixed dosage levels of IMPs, which are not common practices in the industry [19, 37]. Second, dropout of patients was not considered as part of the model conceptualization. Finally, the result of their model is a complex nonlinear integer programming problem. Even though the authors manage to develop a solution by transforming the original formulation into a linear integer equivalent, the process has the disadvantage to be relatively opaque in comparison with other alternatives. The current research aims at more transparency and to relax the assumptions above thanks to the advantages of using DES as a research methodology.

Chen et al.’s work [15] is closely related to the approach followed in the present research, and at some points served as inspiration for the modelling of the CTSC. They presented a simulation-optimization concept that includes patient demand simulation and forecast of demand based on scenarios. Three basic subsystems were considered: manufacturing of APIs, conversion of APIs into IMPs and packaging and labeling. Moreover, the authors considered the scenario in which multiple campaigns for different drugs are tested using a single supply chain. However, some assumptions, like the absence of re-labeling when products expire, have to be relaxed if the potential of smart labels is to be explored. In addition, what they considered to be satisfactory customer service levels (>90%), is still deemed insufficient according to the input received from experts at Novartis.

Other authors focus on specific parts of the CTSC in a higher level of detail. Anisimov developed a statistical approach to model drug supply demand [96]. This paper provided great insights into many subsystems of the CTSC that will be later on integrated in the model conceptualization, such as the initial safety stock or triggers of uncertainty. Because of the limited scope, the conceptualization lacks however some of the subsystems that bring additional stochasticity to the forecasting of the demand for drugs, such as the transport time of drugs across the supply chain, the screening process of patients or the dropout of patients. All these subsystems, along with the ones implemented by this author, are expectations for the development of the model in the present research.

The use of risk pooling strategies to deal with the CTSC from a tactical perspective is also reflected in the literature [179]. The authors of this paper paid special attention to demand forecasting and to the integration of the production subsystem with the supply chain subsystem, with the final goal to develop a simulation-optimization computational framework to determine appropriate safety stock levels. Some ideas (e.g. accounting for the cost of disposing drugs or for stochastic batch

5.2 – The clinical trial supply chain – Model conceptualization 67

manufacturing times) were extracted from this paper and implemented later in the model conceptualization of a DES model for the CTSC described in the next section. However, a couple of questionable assumptions taken in this paper were also identified. First, screening processes of patients signing up for clinical trials, are not considered, missing once more part of the stochasticity that characterized the CTSC. Second, re-labeling of drugs is also not considered: it is assumed that when drugs expire, they are automatically disposed. This is actually not a common practice in the industry [37], and actually a potential advantage of smart and e-labels lies in an easier re-labeling process.

5.2 The clinical trial supply chain – Model conceptualization The design and management of global clinical studies consists on several simultaneous stages. From the patients’ side, this includes the patient recruitment planning and forecast, patient screening, randomization and patients’ visits to the clinic sites. From an inventory perspective, this involves the manufacturing and packaging of drugs, the shipment to the regional depots, the demand forecast by IRT systems and the shipment from the regional depots to the clinical sites.

This section addresses the conceptualization of the clinical trial supply chain. It should be noted that the conceptual models described in this section do not contain an exact description of the CTSC, but rather general guidelines on how the system is captured in the model.

5.2.1 Stochastic nature of the clinical trial supply chain – An overview A main issue in the CTSC is the balance of future supplies with anticipated demands [180]. The issue is complex because the demand is intrinsically stochastic. Uncertainties in the demand stem from five sources: patient enrollment, patient screening, the randomization process, patient dropouts and intrinsic logistic uncertainties (such as shipping time). These five components induce a stochastic nature into the CTSC, and are described in subsequent sections.

The uncertainty in demand leads to risks for the CTSC to fulfill its objective: supplying the right amount of drugs to the right patient at the right time. Various techniques are used in the clinical trial industry in order to mitigate these risks [36]:

1. Increased overage: Inventory overage is the excess inventory produced on top of the total net demand during a clinical trial, and has the objective to secure the supply chain and guarantee that patients receive medication at the right time [40].

2. Increased shipment frequency: An increased number of shipments can be used to push supply as fast as possible to the clinical sites, where demand is originated.

3. Dynamic supply: Dynamic supply rules imply, for example, shipping drugs to a clinical site before a patient successfully passes the screening phase.

4. Real-time inventory tracking: A real-time time tracking of the inventory of drugs, in combination with the real-time demand forecast provided by IRT systems, allows for a higher flexibility in the supply chain, what in turn helps overcoming the challenges derived from its stochastic nature.

How these different techniques are implemented into conceptual model is described in 5.2.12 – Securing the clinical trial supply chain, after necessary background on other basic aspects of the model conceptualization have been provided.

5.2.2 Patient enrollment and its stochastic nature An adequate patient enrollment is crucial for a successful design and execution of a clinical trial. This starts with the sample size, i.e., the total number of patients to be enrolled from a study perspective. Many clinical trials do not result in positive results not because the hypothesis was incorrect, but

68 5.2 – The clinical trial supply chain – Model conceptualization

because the clinical sponsor or the investigators failed to include a large enough number of patients in the study [25].

The execution of a clinical trial starts when the first patient successfully completes his first visit to the clinical site. This is known as First Patient, First Visit (FPFV). From that point on, patients keep enrolling until the number of patients required by the study design guidelines are met [40]. Patients can be assumed to enroll at random times at the clinical site [15, 36, 40]: these stochastic patient recruitment patterns result in an uncertain demand for drugs during a CT.

Although not much literature on the stochastic demand forecasting for CTs is available, some methods currently being used are [15, 36]:

1. Using non-stationary normal distributions 2. Using historical data 3. Modelling the enrollment of patients as a non-stationary Poisson processes

A combination of the last two alternatives is used in the present research. The enrollment of patients takes place between two key dates: the First Patient, First Visit (FPFV) and the Last Patient, First Visit (LPFV). These dates can be easily obtained when looking at historical data of clinical trials. Note that a third key date during the execution of clinical trial is the Last Patient, Last Visit date (LPLV), which marks the end of the execution of the CT. Figure 26 provides a visual overview of the implications of these dates in a CT.

Data collection and analysis

Execution of Enrollment in Execution of the CT begins the CT ends the CT ends

Figure 26: Overview of the key dates during the execution of a clinical trial

It is also important to emphasize that, although the demand (i.e., the patient enrollment for a trial) is intrinsically stochastic in nature, the actual number of patients enrolling for a trial is always within less than a ±1% range from that expected [40]. This is because, if necessary, CT sponsors influence the trial design on the go, for instance asking countries to advertise the trial or adding new countries to the trial if the necessary number of patients is not being met. From a design perspective, it is crucial to be very accurate in the number of individuals participating in the trial, as it greatly determines the validity of the statistical results obtained.

The total number patients in a given country is something that is specified beforehand by the clinical trial managers during the design of the trial, although again this can be changed later if there are problems in getting close to the total patients required for the study. Factors determining the number of patients expected or required from a country are the size of the country population-wise, the number and quality of the clinics and the relevance of the disease under consideration in specific ethnicities of the country, among others.

How the number of patients per country is distributed across the different clinical sites is however not decided ex-ante. The reason is that the enrollment of patients in the different sites within a country is considered random in time. Location-wise, it is essentially dependent on proximity and geographical considerations. To give an example of the variability of patient enrollment among

5.2 – The clinical trial supply chain – Model conceptualization 69

different clinical sites, a study by Butler et al. [181] concluded that, in a massive trial for tolvaptan, a drug to fight heart failure, 18% out of 660 clinical sites failed to enroll a single patient. 51% of them enrolled less than patients, and among the remaining sites, only 30 enrolled more than 30 subjects.

Overall, it can be concluded that stochastic patient enrollment does not normally affect the recruitment of the total number of patients required for the entire study, although the price to pay is bringing clinical-site- and country-level uncertainties to the clinical trial supply chain. Figure 27 summarizes the dynamics of the patient recruitment at a study level, a country level and a clinical site level.

From a modelling perspective, assuming that provides the total number of days during which patients enroll and knowing the total number of patients enrolled per country (both obtained 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 − 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 from historical data), it is possible to obtain the parameters required to define a non-stationary Poisson process that is statistically equivalent (but not the same) to the historical data. This methodology has the advantage to rely on theoretical statistical distributions rather on fixed historical data, what allows to generalize results by conducting experiments with several replications.

To conclude, in the discrete event simulation model, the total number of patients and how these patients are distributed among the different countries taking part in the trial is determined ex-ante. However, the time at which patients enroll and the clinical site they go is fully randomized. On the one hand, the enrollment distribution is assumed to follow a Poisson distribution which parameter is calculated from historical data. On the other hand, if there are clinical sites in a given country, the 𝜆𝜆 probability that a patient goes to a specific site is 1/ . 𝑘𝑘 𝑘𝑘

70 5.2 – The clinical trial supply chain – Model conceptualization

Study

The number of patients required for the study is decided ex-ante by the clinical trial managers and the

statistical analysis team. This

number does not change during the execution is normally reached very accurately.

Country level

There are guidelines for the number of patients per country, but this can be

modified and even more

countries can be added if necessary,

Clinical site level

Number of patients per clinical site is random. Variations of up to 100% of the expected patients per site are normal. Sites can be added or removed (in case there are not enrollments at all).

Figure 27: Dynamics of the patient recruitment at a study level, a country level and a clinical site level for a typical clinical trial

5.2.3 Patient screening – a new layer of stochasticity Patients interested in joining a clinical are not automatically prescribed the treatment: a screening phase is required in order to ensure that the patient fulfills the necessary criteria to benefit from the candidate drug.

In this phase, which typically lasts from one to four weeks, the current health status of the patient is assessed by the leading investigator of the clinical site where the patient enrolls. Moreover, should other chemical compounds still exist in the blood stream of the patient (e.g. from the previous treatment that he was taking), they need to be eliminated prior to the commencement of the trial.

Generally speaking, about 75% of the patients pass the screening phase14, although this is very dependent on the disease under consideration and on the study design.

Patient screening brings a new layer of stochasticity and complexity into the clinical trial supply chain: a patient that is in the screening phase might actually require a patient kit in a relatively short period of time, what might suggest that drugs need to be shipped immediately. At the same time, however, if the candidate drug is determined not to be suitable for the patient, the patient kits sent

14 This number if the result of informal communications with experts at Novartis. It is illustrative for a generic case in the clinical trial industry rather than real data representative for Novartis.

5.2 – The clinical trial supply chain – Model conceptualization 71

might be sent too soon to be used by other patients before they expire, or they might not be demanded at all.

In order to deal with this dilemma, pharmaceutical companies rely on inventory overage or dynamic supply rules, as introduced in 5.2.1 – Stochastic nature of the clinical trial supply chain – An overview. When inventory overage is used, normally a safety stock level is determined at each clinical site. This safety stock is shipped before the trial starts, allowing for patients successfully finishing the screening phase to start the treatment immediately [96]. Once consumed, the safety stock is resupplied in the next shipment to the clinical site. Although this overage serves as a buffer, it comes at a cost: sponsors know that these excess medication might be returned or destroyed due to several reasons (e.g. changes in country specific requirements or expiry date change).

A dynamic supply rule, on the other hand, would imply shipping additional drugs to the clinical site where the patient is being screened before knowing whether the patient will be actively enrolled in the trial. Advantages and disadvantages of this strategy are similar to those of inventory overage.

5.2.4 Randomization schemes in clinical trials In single-blind (i.e., only the investigator knows what treatment the patient is following) or double- blind (i.e. neither the investigator nor the patient know to what treatment the patient is assigned) clinical trials, patients are randomized into one of the existing treatment groups once they successfully finish the screening phase. Different treatments might be a variety of dosages of the candidate drug, a comparator treatment or a placebo drug. The randomization schemes used by pharmaceutical companies can be classified into two different groups [96]:

. Unstratified randomization: Patients are randomized into the different treatment groups in a centralized manner, without regard to the clinical site. This means that regardless of the clinical center they sign up at, a central location assigns them to a fixed group. For example, in a clinical trial in which there is only an active [A] and a placebo [P] treatments, a centralized location might establish that patients will be randomized in the sequence AAPP. A global list of enrolled patients is then used, and new enrollments are assigned either to the active or to the placebo treatment depending on their arrival in this global list. This has the advantage to minimize the total variance of the study, but the price to pay are more complex logistics and higher variance in individual clinical sites.

. Clinical site-stratified randomization: In a clinical site-stratified randomization model, patients are randomized separately in each clinical site according to independent permuted blocks. Following the example above, each clinic will have its own AAPP sequence to follow. This largely simplifies logistics because it is easier to anticipate the stochastic demand originated by the random patient recruitment. However, the total variance of the distribution of treatment across the study is higher.

The benefits of unstratified randomization most often outweigh those of clinical-site-stratified randomization, and thus the first system is generally used to randomize patients [40].

5.2.5 Dropout of patients Once patients have been successfully enrolled into the clinical trial, they might drop out of the study. As anticipated in 1.8 – Challenges faced in the clinical trial industry, this poses a major challenge for the sponsor, as trial results for the patient can no longer be accounted. In addition, this also adds uncertainty to the clinical trial supply chain, because patient kits might remain unused or expire before another patient can use them.

72 5.2 – The clinical trial supply chain – Model conceptualization

Dropout can be assumed to follow a uniform distribution [15]: patients are assumed a constant likelihood of dropout at any point during the treatment. If a patient discontinues the treatment, the associate patient kits remain in the clinical site and will be used by another patient, if possible.

5.2.6 Intrinsic logistic uncertainties in the clinical trial supply chain As explained in 1.5 – The material flow of the clinical trial supply chain, the CTSC consists of mainly three different layers: the manufacturing site (or central depot), the regional depots and the clinical sites.

The logistics behind this supply chain, however, are not as straightforward as it might seem at first sight. The reason for this is the strict regulatory guidelines that pharmaceutical companies are subjected to when executing clinical trials. Figure 28 provides an overview of the different steps required for an IMP to reach the clinical sites in a generic case.

Receipt at depots: good arrival check, duty of care check

Acknowledgement of RIS checklist sign-off Technical release FDA approval (if in the U.S.) receipt (IRT) Shipment clearance

Transport to regional depot Transport to clinical site

QARP checks IND/CMC Planning for site Doc reviews & QP release (if EU) initiation visit

Figure 28: Overview of the different logistic steps to transport an IMP from the central depot (or manufacturing site) [left] to the final clinical sites [right] through intermediate regional depots [middle]

A brief explanation of the process is the following: first, drugs, which are assumed to have been produced already, are verified and then issued a Certificate of Conformance. Then, they are made available for transportation, what is referred to as the technical release. Drugs are then transported either by truck or by plane to the regional depot. Before being accepted in the regional depot, however, it is necessary to obtain approval from the FDA in case the depot is in the U.S. If not in the U.S., a compulsory check of the goods received, as well as a duty of care check is still established in the GMP guidelines followed by pharmaceutical companies.

After the IMPs are accepted, the Quality Assurance Responsible Person (QARP) has to verify that the Investigational New Drug (IND)/IMP, as well as the Chemistry Manufacturing Control (CMC) follow the expected quality guidelines. Moreover, (European) GMP establishes that a Qualified Person (GP) is ultimately in charge of the batch release: the QP who certifies a finished product batch before release may do so based on his personal knowledge of all the facilities and procedures employed, the expertise of the persons concerned and of the quality system within which they operate. Alternatively he may rely on the confirmation by one or more other QPs of the compliance of intermediate stages of manufacture within a quality system which he has accepted [182].

At this point, the Ready to Initiate State (RIS) is reached. RIS is a site-specific checkpoint which guarantees that all regulatory and business items required to allow IMP release to a given site and to proceed with the initiation of that site are in place. RIS is associated with a universal checklist in the clinical trial process. This implies that drugs can be dispatched to the clinical site when this is required.

After arriving to the clinical site, clinical sites use Interactive Response Technology (IRT) to acknowledge receipt of the IMPs. As explained in 1.6.2 – Interactive Response Technology systems,

5.2 – The clinical trial supply chain – Model conceptualization 73

IRT enables activities such as randomization into clinical trial and tracking the site’s demand for medication by creating a solid interface between the clinical site and the pharmaceutical company.

As a last step, in case the shipment is the first one being received for the clinical study by a specific clinical site, the Site Initiation Visit (SIV) is planned. SIV has a triple goal: to provide training to the staff in the clinical site that will participate in the trial, to crosscheck the data reported versus the source data; and to ensure that patients’ well-being is enforced according to both ethical and regulatory considerations [183].

Note that although the first thought might be that the physical transport time of drugs is orders of magnitude bigger than that required to fulfill the regulatory and administrative requirements, this is actually not the case. Table 11 shows an average of number of working days required for each of the steps displayed in figure 28, considering both average values for a regional depot/clinical sites in the U.S. and in the EU. Input for this table was obtained by Novartis distribution experts.

Table 11: Average lead times for different steps in the clinical trial supply chain

# Average lead time Working days U.S. Working days EU 1 Technical release 1 1 Transport time to regional 2 10 7 depot 3 FDA approval 5 - Receipt at depots: good arrival 4 6 6 check, duty of care check 5 QARP checks IND/CMC 6 9 6 Reviews & QP release - 6 7 RIS checklist sign-off 1 1 Shipment clearance to the 8 2 2 clinical site 9 Shipment arrival to the site 1 to 2 3 to 4 10 Acknowledgement of receipt 1 1 11 Planning for SIV 1 1 34 38

Note also that, on average, the shipment of an IMP from the manufacturing plant to the clinical site can easily take 6 weeks. Out of these 6 weeks, almost five correspond to the shipping and quality and regulatory checks of the drugs to the regional depot; an average period of time of just above a week is required to transfer these IMPs to the clinical site from there.

In the DES model, steps 1-7 are considered in an aggregated way, with a standard deviation of 3 days. Then, steps 8-10 will also be aggregated, considering a mean value of 4.5 working days for the U.S. and 6.5 for EU. A standard deviation of 1 day will be considered in both cases. Finally, planning for SIV will be considered in case a clinical site is receiving IMPs for a new clinical study for the first time.

5.2.7 Manufacturing of drugs and shipment to the regional depots A limitation found in the literature when it comes to the modeling and optimization of clinical trial supply chains is that pharmaceutical companies are assumed to produce drugs for the entire trial prior to its commencement [27]. This is actually not the case according to the input received from industry experts during the present research [37, 40].

In fact, although it is true that the initial production is usually the biggest single drug manufacturing process during the trial, follow-up drug production is demand-based. However, because

74 5.2 – The clinical trial supply chain – Model conceptualization

manufacturing facilities of a pharmaceutical company are used to produce different types of drugs, this follow-up production takes the form of batches, i.e., it is not real-time.

The input received from drug supply management experts is that normally a new batch of drugs is produced once every 3 to 4 months. Production of other APIs and the time it takes to clean the machines between the manufacturing of different compounds are the reasons for this low frequency. Although these figures are study- and drug-dependent, it is assumed that a new batch is generated following a normal distribution with average 3.5 months and standard deviation 0.3 months, although with an absolute minimum time of 3 months, which represents the outcome obtained from experts’ input at Novartis for a generic inter-batch production time15. Furthermore, following this input, it is also assumed that enough drugs can be produced in each batch to satisfy the demand of the depots16.

After drugs are produced, they are packaged in the same facilities (note that in a generic case this could also be outsourced). For the present research, the assumption is taken that after the inter-batch time drugs are immediately ready to be shipped. In addition, all the drugs manufactured are immediately pushed to the regional depots, from where they can be relatively rapidly shipped to the clinical sites to satisfy the patient demand. The time this shipment takes was indicated in the previous section (see 5.2.6 – Intrinsic logistic uncertainties in the clinical trial supply chain).

As for the costs, it is assumed that the cost of the aggregated process of manufacturing, packaging and labeling the drugs costs on average 100€ per patient kit. Note however that once more this is a very study-specific figure. The model has the advantage of being very easily customizable when it comes to manufacturing times/costs, so the figure can easily be adapted to particular scenarios.

5.2.8 Shipping from the regional depots to the clinical sites Shipping from the regional depots to the associated clinical sites takes place under two premises. The first one is when the IRT systems require that a new resupply is required. IRT systems are clinical site- specific, and analyze on a daily basis the combined demand for drugs of all the patients enrolled in the site for a given period in the future (this will henceforth be referred to as the look-ahead period). If the current inventory is below the drugs required for this period of time, then a resupply order to the regional depot is triggered. The amount of resupply normally covers a period longer than the look- ahead period in order to reduce the cost by lowering the frequent shipments.

A resupply order can also be triggered if the drugs in the clinical site fall below a pre-defined threshold, which represents a minimum safety stock.

The time it takes for patients kits to be shipped from regional depots to the clinical sites are those indicated in 5.2.6 – Intrinsic logistic uncertainties in the clinical trial supply chain.

5.2.9 Patients visits to the clinical site and consumption of patient kits Once the first patient of a clinical site is actively enrolled, he/she ideally has drugs immediately available thanks to the safety stock of that clinical site, which was sent at the SIV stage (i.e., before the FPFV). The IRT system is then likely to immediate order more patient kits, as expected demand has suddenly gone up because of the medication required for this new patient.

The patient keeps visiting the clinical site according to the study protocol. Normally, tens of visits of a patient to the clinical site are required before the trial is completed.

15 In the DES model, these figures are implemented as a parameter, so that they can be easily changed. 16 This is because the size of the batches of commercial drugs are orders of magnitude larger than those for clinical trials, while both use the same manufacturing facilities.

5.2 – The clinical trial supply chain – Model conceptualization 75

Assuming that the patient does not drop out, a new patient kit will be consumed in each visit of the patient, and both the new expected demand and the remaining inventory of the clinical site will be tracked by the IRT systems.

In the DES model, the matching process is represented using a combiner, a Simio object that matches parent and member entities (see figure 29). Patients are the parent object, which need a member object (the patient kits) to successfully complete a visit to the clinical site. If patients are not to wait for medication to arrive, a member object needs to be waiting in the combiner for the patient object, and not vice versa. More information about the model specification can be found in Appendix A – Model Specification.

Figure 29: Screenshot of the DES model showing a clinical site. Inside the clinical site there are three elements: the screening (represented by a bed), the IRT system (represented by a computer) and the combiner that matches patients and patient kits to represent the intake of drugs

5.2.10 Expiration of drugs and re-labeling Expiration of drugs is part of the model conceptualization, as this is a key aspect to model to understand the potential role of eInk smart labels.

It is assumed that drugs can expire anywhere in their way through the supply chain, although, with normal, generic values for the different model parameters17, this will happen most frequently while they are in the regional depot, in the clinical site or being shipped from the depot to the site.

In the first case, a re-labeling process is triggered. Re-labeling at regional depots is assumed to take place even if the drug has not expired yet, but will expire in a short period of time (another parameter easily customizable by the user of the model). This is to minimize the number of drugs that expire either in the clinical site or while being shipped to it.

If the drugs expire in the clinical site, then they are disposed, as generally no re-labeling is possible at the sites (as explained in 3.1.2 – Main challenges faced in the labeling process). If drugs expire on their way to the clinical site, then they will be disposed after their arrival.

17 An overview of the different model parameters will be presented later in this chapter. A detailed list is also available in Appendix A – Model Specification. 76 5.2 – The clinical trial supply chain – Model conceptualization

The redesign work of labels is assumed to be 200€ every 10 patient kits, while the hourly price of the activities carried out by the operators who actually perform the re-label activities is 150€/hour. Moreover, operators can re/over-label up to 100 patient kits per hour18.

5.2.11 Inventory waste The conceptual model regards two scenarios in which drugs are discarded. First, because of the fixed time horizon of clinical trials, when the trial is terminated any additional inventory is discarded, as regulations establish that it cannot be reused [27, 36].

A second scenario in which drugs are discarded is when they expire at a clinical site. Re-labeling at a clinical site is not possible because it must be certified personal of the sponsor pharmaceutical company who perform this task. Although, as explained in the previous section, some special measures to avoid this scenario are part of the model conceptualization, the case in which drugs do expire at a clinical site is still contemplated.

The CTSC includes also the management of the return and destruction process of drug packages that are left over [72]. These drugs have to be disposed according to guidelines established by the competent authority, which represents an important cost that builds on top of the extra drug production [179].

Pharmaceuticals are ideally disposed of by high temperature (i.e. above 1,200ºC) incineration, and the cost can be assumed to be 4.1€/kg [184], plus additional transportation costs from the clinical site to the certified facilities (80€ per clinical site in the EU and 50€ per clinical site in the U.S.). The simplifying assumption that each patient kit weights one kilogram (including packaging plus all the content) will be taken.

5.2.12 Securing the clinical trial supply chain Four techniques were identified in 5.2.1 – Stochastic nature of the clinical trial supply chain – An overview to secure the clinical supply chain and mitigate the risk of patients not having the patient- kits when they need them:

1. Increased overage 2. Increased shipment frequency 3. Dynamic supply rules 4. Real-time inventory tracking

The implementation of these methods into the model is required given that, without these strategies, the likelihood of patients not receiving their medication at the right time dramatically increase. This is to be avoided because, as described in 1.5.2 – Special features of the material flow of clinical trial supply chain, clinical trial sponsors have to guarantee that patients always have medication available to take when they need to in order not to compromise the data and results obtained from the study.

The first method is an integral part of the model, as safety stocks are considered both for the regional depots and for the clinical sites. The level of safety inventory for either of them is a parameter in the model easily configurable: higher levels of safety stock in either location automatically lead to a higher increased overage.

The shipment frequency is not used as a means to secure the supply chain per se, but rather follows the dynamics of the model endogenously thanks to the developments and outcomes of the rest of the model conceptualization: more shipments will be triggered if the IRT systems of the clinical sites

18 This numbers are the result of informal communications with employees at Novartis. They are illustrative for a generic case in the clinical trial industry rather than real data representative for Novartis, and are not based on hard data for any particular study.

5.3 – Model boundaries 77

demand more drugs. Still, the shipment frequency can be modified indirectly via the parameter Days_Resupply_Period. Note that the strategy of automatically pushing the entire inventory from the regional depots to the clinical sites is intuitively not appropriate, because the demand for drugs at the clinical sites is stochastic and having (at least some) stock at the regional depot allows for a more efficient use of patient kits.

The third method, dynamic supply rules, is not part of the model conceptualization. The reason is that the outcome of the discussion with experts at Novartis indicated that this is not a common practice.

Finally, real-time inventory tracking is implemented at a clinical-site level via the algorithms of the IRT systems themselves. These algorithms, which are developed independently for each individual clinical site, calculate the forecasted demand and assume that information about the number of drugs both at the clinical site and in transit from the central depot to the clinical site is known.

5.3 Model boundaries The model boundaries determine the elements, processes and subsystems that are in and out of the model development. Understanding the boundaries is important to discern what aspects of the logistics involved in clinical trials can be analyzed and assessed with model from those that cannot. After reviewing the model conceptualization in the last chapter, it is important to discern what aspects fall beyond scope of the model. The following is a list of elements that are not included within the model boundaries:

1. All the activities involved in the drug development process prior to the clinical trial stage (see 1.2 – The drug development process of pharmaceutical companies for an overview) are not considered.

2. Administrative requirements prior to the clinical trial: For example, contracting manufacturers, preparation of the CTA or documentation to comply with GMP procedures, pre-applications for export/import licenses etc. are not part of the model.

3. Patient adherence to the treatments is not considered: even though the model conceptualization includes the possibility of patients dropping out, patients not correctly adhering to the medication (i.e., taking less patient kits than established by the study protocol) but also not discontinuing the treatment is something not considered19.

4. Labeling regulations: the role of labeling regulations is not part of the model scope. This implies, for example, that changes in regulatory guidelines or cases in which there are compliance problems with already existing regulations cannot trigger re-labeling processes. Patient kits are considered aggregated entities that already fulfill all the legal requirements needed for it to move across the supply chain and to be consumed20.

5. Scenarios in which several indications21 are simultaneously tested for a single drug fall beyond the model boundaries. The model can however be expanded to model complex logistics of multi-indication clinical trials if it is assumed that the same parameters (e.g., the number of visits per patient, the time between visits, the dropout rate, the screening

19 Note however that a specific section of the present research, 7 – Smart Labels in Clinical Trials – The Patient Perspective, is dedicated to understanding patient adherence to the treatment. Nevertheless, the methodology used is different (i.e., not DES). 20 Note that the last chapter (see 4 – The Regulatory Perspective: Do Smart Labels Fit in the Current Regulatory Framework?) already studied in detail the regulatory requirements for clinical trial labeling. This chapter builds on the conclusions made before, allowing to neglect the regulatory perspective and to focus only on the logistics of the clinical trial supply chain. 21 In medical terminology, an "indication" for a drug refers to the use of that drug for treating a particular disease.

78 5.4 – Assumptions taken and their justification

acceptance ratio etc.) apply to the trials of all indications. Note however that this can be argued to be a restrictive assumption.

6. Also, scenarios in which additional countries are added to the clinical trial once the trial has started are not captured within the model conceptualization.

7. The stochasticity of several different treatments (e.g. placebos, comparators and active treatments) is captured by an expansion in the safety stock in the clinical sites.

8. Transportation costs are not considered: even though smart labels might affect the total number of shipments required for drugs (e.g., in case inventory overage can be reduced thanks to smart labels), the transportation costs of these shipments are not part of the model conceptualization. The reason for this is that shipments are not often exclusive for the clinical trial supply chain. Global pharmaceutical companies like Novartis often merge to some extent their supply chains for commercial drugs and for clinical trials. Because of this, it would be very difficult to estimate the real impact that fewer shipments would have (e.g., there might be empty space in a truck that ships drugs to the European depot that is already paid for). Note however than transportation costs are considered in the case drugs disposals (when drugs flow back from clinical sites to incinerating facilities), because this limitation does no longer apply.

9. Packaging and manufacturing activities are not processes explicitly modeled: Instead, their consideration is reduced to the time it takes to fully manufacture and pack new batches and the associated aggregated costs are considered. This means that when patient kits appear in the model, it is assumed that they have been already manufactured and packaged.

10. Accidents (such as loss of patient kits while transporting) and human error (such as incorrect packaging of products) are not part of the model conceptualization.

5.4 Assumptions taken and their justification From a rational perspective, assumptions are statements that one makes to make the development of models easier. As a general rule, the more restrictive the assumptions are, the lower the modeling efforts required. In general, however, it does not matter whether assumptions are true as much as whether they are useful to achieve the goals intended for the model.

A list of assumptions taken during the present development of a model for the clinical trial supply chain is presented below:

A1. The production capacity is not limited.

A2. Patients within a country randomly enroll in the different available clinical sites for this country.

A3. Chances that a subject is eligible for participation in the clinical trial are the same across the world.

A4. Dropout of patients happens with the same likelihood in all the clinics of the different countries participating in the clinical trial.

A5. Only patients that have already been randomized can drop out.

A6. Patients cannot dropout before the first drug intake.

A7. Patients’ dropouts can only become effective right before a visit to the clinic.

5.4 – Assumptions taken and their justification 79

A8. There are no day and night cycles, and weekends and holiday periods are treated as normal days.

A9. At the beginning of the clinical trial, there is enough time to fill the safety stocks as per study protocol. Moreover, all the safety stocks are sent to the clinics at the same time.

A10. There is no inventory limitation at the clinical site or the depot.

A11. It takes the same time to ship to any clinical site within the domain of a regional depot.

Confirming that the underlying assumptions are valid helps ensure that the methods of analysis were appropriate and that the results will be consistent with the goals. The following list provides a discussion or justification for the different assumptions presented:

A1. Clinical trials rarely suffer from a lack of production capacity, since the volume of drugs used for a CT is small compared to the demand for commercial drugs [15] and the same manufacturing facilities are used. Additionally, an unlimited production capacity does not mean that drugs are available whenever requested, as an inter-batch production time is still considered. It does imply however that when a batch is produced, it can be as big as required.

A2. This is a simplifying assumption because in reality clinical sites might differ in terms of reputation, size of quality of the services they offer, what translates into an uneven patient distribution across clinical sites. The relevance of this assumption is however mitigated if a) the KPIs are analyzed22 and b) the role of the IRT systems is taken into account. Firstly, because all KPIs treat aggregated or average values (e.g., total patient kits shipped, inventory overage, average time patients waited for drugs etc.), the importance of differences in clinical sites would anyway be faded out. Secondly, IRT systems forecast the expected demand in real time, and thus the overall dynamics of the system in terms of patient kits shipped are not really much different (except for the safety stocks) in the real and the simplified cases. Note that this assumption also implies that all clinical sites have the same safety stock levels.

A3. This is another simplifying assumption that allows to work with average values. Because no country-specific analyses are made (i.e., results and responses only cover full trial outcomes), the assumption can be justified.

A4. Justification is the same as that for A3.

A5. The most common reasons for patients to dropout a study are adverse side effects, obtaining no benefit for the treatment or randomization aversion (for a detailed analysis, see 7 – Smart Labels in Clinical Trials – The Patient Perspective). None of these happen during the screening phase.

A6. While A5 implied that patients could not dropout during the screening period, this one expands this assumption to consider that patients cannot dropout before their first drug intake either. Justification for this follows the same logic as in A5.

A7. In reality patients can dropout at any point during the treatment. Because of simplification considerations, the assumption that they can only dropout right before a new visit to the clinic is taken. Justification for this assumption is that, from a logistics perspective, this case is still realistic, as the problem that dropouts entail for the supply chain are still captured: drugs that were shipped will not be used by the originally assigned patient, leading either to another patient taking it instead or to a potential disposal if it expires before this happens.

22 The different KPIs to be considered in the model are presented in the next section.

80 5.5 – Key Performance Indicators

A8. Again, the reasoning is similar to that of A3 and A4. In this case, however, it could be argued that holiday periods and weekends might bring additional stochasticity into the model in case some of the events could take place during the weekends (e.g., patients visits) but other could not (e.g. shipments).

A9. The first part of the assumption is actually very realistic, as the RIS is something required before the commencement of the trial. The second part (i.e., all safety stocks sent to the sites at the same time) does not alter the dynamics because the important implication from a supply chain perspective remains intact: all sites have safety stock before FPFV.

A10. The assumption is realistic for regional depots, which normally store drugs for several different clinical trials running at once. As for the clinical sites, this assumption makes sense if combined with A2. In reality, clinical sites might estimate beforehand a range of expected enrollment and verify that they have inventory (for this and other trials) in accordance. If A2 is accepted, then this assumption translates into clinical sites accurately verifying their inventory capacity (because it is taken for granted that they all have the same demand expectations), which is most often the case.

A11. This assumption results logically from A2, A3, A9 and A10. 5.5 Key Performance Indicators A set of Key Performance Indicators (KPIs) are required to evaluate factors that are crucial to the dynamics and outcomes of the clinical trial supply chain. These KPIs have to capture the change in dynamics that the implementation of eInk smart labels would imply. Given that the potential benefits of these smart labels have already been identified from a qualitative perspective, KPIs need to consider at least those business metrics that are affected. Figure 30 shows a screenshot of how the set of KPIs are displayed in the Simio simulation model. An explanation of the KPIs is provided in table 12.

Figure 30: Overview of the KPIs considered in the DES model

5.6 – Modeling logic in Simio – different objects and model parameters 81

Table 12: List and explanation of the different KPIs in the DES model

# Name Units Description 1 Maximum time patients wait for hours The global maximum time that a patient drugs needed to wait for drugs during the simulation period 2 Average time patients wait for hours Average of the waiting times for patients to drugs take their medication kits at the clinical sites, considering all visits that a single patient make 3 Times a patient had to wait for # Number of times that a patient has to wait for medication a patient kit in one of the visits to the clinical site. 4 Total patients kits kit shipped # Total number of patient kits shipped from the central depot to all clinical sites, for all patients, for all their visits to the site 5 Inventory overage % Ratio of total patient kits consumed by patients to total patient kits produced. Meaningful to assess after LPLV. 6 Number of re-labelings # Total number of patient kits re-labeled 7 Time spent in re-labeling hours Total number of man-hours that the re- labeling activities entail 8 Cost of re-labeling € Total cost of the re-labeling activities 9 Kits destroyed at clinical sites # Total number of kits that had to be destroyed at the clinical sites because a re-labeling was necessary but not possible

5.6 Modeling logic in Simio – different objects and model parameters There are always several modeling approaches to capture the same real system. In Appendix A – Model Specification, a detailed description of how the model conceptualization was specified into a DES model using Simio Simulation is given.

In an attempt to make the model understandable for potential external users, table 13 (adapted from the above mentioned appendix) summarizes the modeling logic used in the Simio model regarding the main objects that conform the simulation model. For an exhaustive list, the reader should refer to the appendix.

Furthermore, a set of model parameters are defined in the model. They allow to perform easy modifications that allow to adapt the simulation dynamics to different clinical trial studies and setups. Figure 31 gathers the different parameters of the model, with some values that will later be explained in the next chapter (see 6 – Case Study: Smart Labels on a Phase III Clinical Trial). A description of these parameters and overall more insights into the DES model created using Simio Simulation can be found in Appendix A – Model Specification.

82 5.7 – Model verification and validation

Table 13: List of the different objects used in the Simio Simulation model

Simio object Object name23 Object description category Patient_Kit Standard entity that represents the drugs Patient Subjects that enroll in the clinical trial Entity Pieces of information sent from the clinical sites to IRT_Order the regional depots to request resupplies after forecasting the demand Manufacturing _Facilities Facilities that produce batches of Patient_Kits Source IRT_ClinicalSite_X Electronic systems that generate IRT_Orders Enrollment _Country_X Country specific source that generates Patients Merges IRT_Orders (member) with Patient_Kits Depot_X (parent) in the depot to satisfy the demand for Combiner patient kits of the different clinical sites Merges Patient_Kits (member) with Patients (parent) ClinicalSite_X to model the intake of drugs Successful Gathers the Patients that have finished the trial _Participation_X Sink Gathers the Patients that were not eligible for the Non_Eligible_X trial Dropout_X Gathers the Patients that dropped out from the trial

Figure 31: Screenshot of the different model parameters in the Simio model, as well as their values for the case study

5.7 Model verification and validation Verification and validation of computer simulation models is conducted during their development with the ultimate goal of producing an accurate and credible model [185].

23 Note that an X in the name indicates a reference to a specific clinical site/regional depot.

5.7 – Model verification and validation 83

5.7.1 Model verification Model verification activities correspond to a large extent to model debugging activities [186]. Simio’s built in debugger was one of the main tools used to debug the model. Additionally, the following modelling techniques were applied in order to guarantee an accurate representation of the conceptual model:

. Internal labeling in each of the modeling blocks.

. Logic color code to easily follow though model trace.

. Several output labels were used while developing the model, to make sure that key internal variables had not been changed inadvertently.

5.7.2 Model validation The development of the model was both iterative and incremental: small models were created and at first to test each of the features described in the model conceptualization. Developments in small models allowed for an easy validation of each of the sub components of the final model, because:

. Simio’s trace feature, which logs all the discrete events occurring in the system, were easily interpretable, as the number of events was limited.

. Simio’s step function allowed for an easy track of the entities, especially the shipments.

. Basic animation features were focused on the critical spots of each sub-model.

. Attention was focused on sequential aspects.

The first milestone was to have a single clinical site from once country working (initially France). Although relatively simple, this implied the model specification of all the conceptualization steps described in 5.2 – The clinical trial supply chain – Model conceptualization. The dynamics of this preliminary model were face validated through a series of iterations with a clinical trial expert at Novartis. After the first iteration, for example, it was revealed that the production patterns were not realistic enough to capture the supply push in the CTSC. This and other subsequent changes were implemented through a total of three rounds of iterations, until the one-clinical-site model passed successfully the face validation.

Once working and validated, the structure of this one-clinical-site model was duplicated to create all the other clinical sites belonging to the same regional depot (in this case, because France was the original country used, to the European depot). Simio’s search function was used as an additional verification and validation tool: given that a systematic nomenclature was used throughout the simulation (e.g. ClinicalSite_France_1 or Depot_EU_to_CS_France_1), it was possible to use the number of hits for a search to verify that the whole structure of the one-clinical-site model had been successfully extrapolated to other clinical sites. In total, the model contains 37 processes/objects that are clinical site-dependent. For example, if after searching for Germany_2 only 35 hits are found, it means that the duplication of the original structure was not completed. This allows to rapidly change the structure of the model via copy-pasting (an enhanced feature in Simio) and to easily verify that the new structure is implemented as it should

The final validation was performed once the data for the case study was treated and entered into the model. This allowed for a comprehensive face-validation and to intuitively assess the results of other validation tests, such as an empirical-based validation and an extreme conditions behavior tests. These final validation steps are described in 6.2.4 – Additional validation of the model.

84 5.7 – Model verification and validation

5.7 – Model verification and validation 85

Chapter Six ………………………...... 6 Case Study: Smart Labels on a Phase III 6 Clinical Trial

Learning by doing, peer-to-peer teaching, and computer simulation are all part of the same equation Nicholas Negroponte, founder and Chairman Emeritus of MIT's Media Lab

This chapter adapts the model conceptualization presented in chapter five to a specific case study – a phase III clinical trial – in an attempt to quantify the benefits of bringing smart labels to the clinical trial supply chain. For that purpose, an overview of the case study is first provided. Then, the status quo is defined, and its performance is assessed by means of the different KPIs presented in the last chapter, which are obtained via experiments conducted with the simulation model. Smart labels are later implemented, allowing for a comparison of these KPIs in both scenarios. Additional considerations, such as the price of smart labels or the influence that the size of the study and other parameters have in the cost and benefits of smart labels are also studied.

86 6.1 – Overview of the case

6.1 Overview of the case A phase III clinical trial is selected for the case study because of two reasons: first, these trials are bigger, capturing better the logistic complexities. Second, phase III trials are more patient-oriented than phase I or phase II trials, what allows to generalize results more easily [37].

The phase III clinical trial selected was carried out in five different countries: France, Germany, Estonia, the U.S. and Canada. Relevant data on the number of clinical sites and recruitment targets can be found in table 14.

Table 14: Clinical site and patient information of the case study

Country Served by Number of clinical sites Expected number of patients France European depot 4 19 Germany European depot 5 32 Estonia European depot 2 30 Canada North American depot 4 15 U.S. North American depot 17 81 32 177 As explained in the model conceptualization (see 5.2.2 – Patient enrollment and its stochastic nature), the enrollment of the patients is modeled as a Poisson distribution. Note however that, according to the historical data used as input, the arrival distribution is heavily country-dependent. Because of this, different Poisson distributions depending on each of the countries participating in the trial were defined. The parameter of the associated exponential distributions (interarrival times) for each country24 are gathered in table 15. 𝜆𝜆 Table 15: Parameters of the exponential distributions that model the patient enrollment

Country Rate parameter [days] Expected patient arrivals per day [1/ ] France 4.87 0.21 𝝀𝝀 𝝀𝝀 Germany 9.73 0.10 Estonia 4.56 0.22 Canada 7.68 0.13 U.S. 1.80 0.55

Finally, some other generic information relevant to define the study setup is presented in table 16. Note that some generic data was defined during the model conceptualization (see 5.2 – The clinical trial supply chain – Model conceptualization), and hence it is not study specific. Examples of this are the travel times from the manufacturing facilities to the regional depots in the EU and the U.S., the traveling time from these regional depots to the different clinical sites, the costs of re-labeling, manufacturing and disposal of drugs and the inter-batch production time.

24 Note that the parameters for the different exponential distributions were obtained after treating raw data obtained from the real study.

6.2 – The status quo – analysis and optimization of the safety stock 87

Table 16: Additional parameters for the case study

Parameter Value Units FPFV May 8, 2012 - LPFV October 1, 2012 - Number of patient visits until completion 38 # Average time between patient visits 9.6 days days Screening acceptance ratio 82% % Dropout ratio between each visit25 0.366% % Number of treatments 1 # Initial expiry date 6 months Assumed expiry date extensions 6 months Safety time before expiration for patient 30 days kits to be relabeled at the regional depot IRT systems demand forecasting period 30 days IRT systems resupply period 60 days Containers per patient kit 1 primary packs

6.2 The status quo – analysis and optimization of the safety stock The preliminary model definition presented in the last section lacks the optimization of two decision parameters for the clinical trial supply chain: the safety stock in the clinical sites and the safety stock in the depots.

The reason why these parameters are not inputs for the model lies on the assumptions taken in the model development. Specifically, during the model conceptualization it was assumed that all the clinical sites are equivalent, while in reality there might be differences in terms of the size of the medical facilities and the average number of patients enrolled (see 5.4 – Assumptions taken and their justification). Thus, real data in terms of safety stocks for the clinical sites cannot be used as an exogenous input, as the model requires a generic safety stock to be used for all clinical sites. The safety stock for the clinical sites in turn pull the dynamics of the supplies from the depot. Hence, an optimization of the safety stocks for both clinical sites and depots is required as a first step.

With the objective of calibrating these two parameters for the model, a set of experiments was conducted. A minimum reliability factor of 99,5% was set as a requirement for the clinical supply chain to fulfill its objective: supply the right amount of drugs to the right patient at the right time26. This reliability factor can be defined as:

𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘 𝑡𝑡ℎ𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 6.2.1 Model treatment 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑡𝑡𝑡𝑡 𝑡𝑡ℎ𝑒𝑒 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 The model treatment consists on the set of conditions under which the experiments were carried out. The model of the clinical trials supply chain can be considered a finite system, because there are single events that determine its start (i.e., the initial shipment from the manufacturing facilities to supply the safety stocks) and its end (i.e., LPLV). Important determinants of the model treatment are the run length, the warm-up period and the number of replications.

25 Note that this was extrapolated statistically from the total average dropout ratio in order to incorporate it realistically in the model. 26 The choice for this minimum requirement for the reliability factor is the result of informal communications held with clinical trial experts at Novartis. However, the number does not represent any strategic consideration or threshold for Novartis itself.

88 6.2 – The status quo – analysis and optimization of the safety stock

. Run length: The run length used is 3 years. This accounts for the initial supply period when patients have not arrived (~2 months), the patient enrollment period (~ half a year), the time during which patients stay in the treatment (~one year) plus a security factor of one year (~ 50%) to guarantee that bias is not introduced by cutting down replications in which the trial extends because of the stochastic dynamics (e.g. because of stochastic enrollment LPFV actually takes place one year after FPFV).

. Warm-up period: A warm-up period is not required for this model. The reason is that the model endogenously contains a pseudo warm-up period in which the safety stocks are supplied. Thus, by the times FPFV occurs, the status of the system is always the same for each replication.

. Number of replications: 30 replications per scenario were used to compare different combinations of safety stock allowing for statistical comparison.

If not specified otherwise, the model treatment specified above applied to all subsequent experiments carried out with the simulation model.

6.2.2 Running the experiments and results obtained Running large sets of experiments with the final model is a relatively computationally intensive task if a personal computer is used. Using a PC with an Intel i7-4700MQ CPU and 8GB of RAM, the average time for a replication to be completed is around 10 minutes. However, because of the way that Simio Simulation is optimized for multicore systems, once replication per CPU core is run in parallel. As a result, using the processor above – 8 cores – a replication can be assumed to take between one and two minutes. If 30 replications per scenario are used and 20 scenarios are analyzed, the required computation time is hence roughly between 10 and 20 hours.

Given these time scales and to avoid using brute force to optimize the system, increments of 500 were first used for the depots and increments of 5 for the clinical sites. This allowed to form a first impression on the variation of the reliability factor when safety stocks were modified. A second iteration used a finer optimization strategy to achieve more granularity in the results. Increments of 100 were used for the safety stocks in the depots and increments of 2 for the clinical sites.

Table 17 displays the results for 14 different, selected scenarios. The table indicates the safety stock levels used for each of them and the response for certain KPIs, such as the number of times that patients waited for medication, the average time those patients waited or the inventory overage. In addition, table 18 presents the reliability factors of the different scenarios.

Table 17: Parameters and responses of some selected different scenarios analyzed to determine the safety stock levels, sorted by the number of times a patient waited for medication

Parameters Responses (selected KPIs) Safety Safety stock Times a patient Avg time Total patient Inventory Scenario stock depot clinical site waited [#] waited [h] kits shipped [#] overage [%] [#] [#] 19 1000 20 30,8 0,347 12854,2 49,80 18 800 20 32,5 0,394 12256,2 47,83 4 800 15 33,2 0,368 11544 44,12 17 1000 15 36,8 0,432 12068 46,55 13 800 8 45,4 0,526 10522,8 38,93 12 500 15 48,3 0,733 10735 39,51 3 500 8 57,4 0,964 9887,83 35,10 15 200 8 82,1 1,61 9949,1 34,59 22 300 10 93,0 2,86 9451,3 31,98

6.2 – The status quo – analysis and optimization of the safety stock 89

Table 18: Reliability factors for the different scenarios

Scenario 19 18 4 17 13 12 3 15 22 Reliability 99,760 99,735 99,712 99,695 99,569 99,549 99,419 99,175 99,016 factor % % % % % % % % %

After the second iteration of experiments, the granularity obtained was deemed sufficient because (i) changes in the responses of the model have an appropriate size to compare scenarios without their values overlapping (see for example scenarios 18 and 19 or scenarios 4 and 18), (ii) given the model boundaries and assumptions taken – and explained in 5.3 – Model boundaries and 5.4 – Assumptions taken and their justification – a finer optimization up to the units of safety stock is deemed unrealistic and (iii) the optimization of a reliability factor it not a business process, but rather an approach taken in this research to overcome the assumption that clinical sites are equal to each other. The supply chain for clinical trials is always on the safe side, so reliability factors slightly higher than the threshold set are preferred [19].

As an additional visual comparison, figure 32 presents boxplots comparing the times patients waited for medication in the different scenarios presented in table 17 and table 18.

Figure 32: Visual comparison of the number of times that a patient waited for medication in the different scenarios. Each boxplot contains the input data from 30 replications.

6.2.3 Selection of safety stocks Scenarios 3, 15 and 22 were added to table 17 and table 18 to illustrate how some scenarios were automatically discarded because they did not fulfill the minimum objective for the reliability factor, set at 99,5%. These scenarios are thus deemed non-suitable, a process that was repeated with other different scenarios not shown in the tables presented in the last section for the sake of readability.

Out of the remaining scenarios, 12 and 13 can be discarded because of being too close to the lower threshold, as in both of them 8 out of the 30 replications did not fulfill the objective.

90 6.2 – The status quo – analysis and optimization of the safety stock

Finally, out of the remaining scenarios, scenario 4 was chosen. Motivation for this is the fact that scenario 4 statistically outperforms scenario 7 in terms of times a patient waited, average time waited and inventory overage ( = 5% applicable to all the comparisons). It is statistically equal to 18 in terms of the number of times that patients waited, while it is better in terms of overage. Finally, even 𝛼𝛼 though scenario 19 outperforms scenario 4 in terms of the number of times that patients waited for medication, the inventory overage rises substantially.

To conclude, scenario 4 is chosen, what implies that the levels of safety stock to be used to model the status quo are 800 patient kits in the depots and 15 in the clinical sites.

6.2.4 Additional validation of the model As indicated in 5.7.2 – Model validation, the final validation of the model was performed after the data for the case study was treated and entered into the model.

Once the safety stocks were calibrated, a comprehensive face-validation of the model dynamics was possible. Additionally, an empirical-based validation and an extreme conditions behavior test were carried out.

. Face validation: The model was presented to a Clinical Trial Expert from Novartis, and the dynamics of each of the subsystems (i.e., patient enrollment, expiration of drugs, IRT systems, drug supplies etc.) were explained first separately, and then altogether. Results for the test were positive, with the exception of a couple of remarks made which were actually two of the assumptions already explained in 5.4 – Assumptions taken and their justification (the initial inventory shipment taking place at the same time for all clinical sites [A9] and the uniform distribution of patient arrivals [A8]).

. Empirical-based validation: An empirical-based validation test on the enrollment of patients was carried out. The model was run during a period of three years in 30 replications. Table 19 shows the results of Student T-tests for a hypothesized mean (i.e. the theoretical expected value) on the distribution of number of patients finishing the clinical trial, patients not eligible for the study and dropouts. In all cases, the null hypothesis H0 was that the average of the distribution was equal to the expected average, and a significance level of = 0.05 was chosen. Results of the test indicated that H0 had to be accepted for all three variables. 𝛼𝛼 Table 19: Results of the empirical validation tests use to validate the patient enrollment, screening, dropouts and successfully completion of the trial

Variable Expected value p-value Result Total number of patients 157.44 0.0813 Accept H0 that finished the trial Total number of patients 39.42 0.2744 Accept H0 not eligible for the trial Total number of patient 22.14 0.1999 Accept H0 dropouts

. Extreme conditions behavior test: The model was run under several extreme conditions in order to assess its proper functioning. The tests carried out were and the corresponding results are:

o Dropout per visit is 50%: All patients ended up either not being eligible for the trial or dropping out. The number of patient kits shipped dropped dramatically to 2724 throughout the trial, verifying that the IRT systems worked as expected under these conditions.

6.3 – Implementing smart labels in the clinical trial supply chain 91

o Infinite expiry date: When an infinity expiry date is set into the model of the status quo, the KPIs are expected to dramatically improve. Table 20 confirms that these expectations are met in the model. Note that the same safety stocks (i.e., 800 for the depots and 15 for the clinical sites) were used in both cases.

Table 20: Results of the extreme conditions behavior test for an infinite expiry date

Times a patient Avg time Total patient Inventory Number of re- waited [#] waited [h] kits shipped [#] overage [%] labelings [#]

Status quo 33,2 0,368 11544 44,12 3192,37 Infinite expiry 0 0 9676 32,19 0 date

o Time between visits is just one day: If the extreme case that patients visit the clinic once per day (but maintaining that the total number of visits is 38), it is expected that the number of times that patients have to wait, as well as the average time they wait, substantially increase. This reason for this is that, even if the IRT systems work properly, the replenishment of stock both to the central depot and especially to the clinical sites might not happen in time because of the transport times. The number of re-labelings should conversely be lower, because drugs should not expire that often. Table 21 confirms that the behavior of the model is that expected.

Table 21: Results of the extreme conditions behavior test when the time between patients' visits to the clinical sites is one day

Times a patient Avg time Total patient Inventory Number of re- waited [#] waited [h] kits shipped [#] overage [%] labelings [#]

Status quo 33,2 0,368 11544 44,12 3192,37 Daily visits 57 2,685 13645 58,52 2264

6.3 Implementing smart labels in the clinical trial supply chain The model conceptualization was extended to include smart labels into the model. The implementation of a policy of eInk smart labels is assumed to bring to the clinical trial supply chain the following modifications, extracted from 3.5 – Smart labels in the clinical trial supply chain – Conclusions:

. Re-labeling takes place immediately when required.

. Re-labeling can take place at the clinical sites.

. Cost of re-labeling does not require the application of physical labeling anymore. Instead, total costs are reduced to the re-design of the labels (200€ every 10 patient kits).

During the model specification stage, smart labels were implemented into the model in a way that they can be easily activated/deactivated by modifying a simple parameter (see figure 33). This was done to streamline the conduct of experiments, as well as in an attempt to make the DES model user- friendly. More information about how this was done can be found in Appendix A – Model Specification.

92 6.3 – Implementing smart labels in the clinical trial supply chain

Figure 33: Screenshot extracted from the DES model showing the values for the different customizable parameters. Note that the smart labels policy (in yellow) can be easily activated [1] or deactivated [0].

Once smart labels were implemented in the model, a new set of experiments was conducted to analyze their behavior under different safety stock levels. The model treatment during the optimization process was similar to the one presented in the last section. Furthermore, the optimization logic in terms of the increments used for the safety stock is similar to that presented in the last section: rather than setting an automatic optimization process, a set of optimization iterations were used, each of them with a higher granularity. In this case, however, it was quickly discovered that scenarios had to focus on relatively low safety stocks when compared to those presented in the last section. Table 22 compares the KPIs of the status quo with different selected scenarios that make use of eInk smart label technologies. Note that all the scenarios presented in this table already fulfill the requirement in terms of reliability factor > 99.5%.

Table 22: Results of the experiments carried out with smart labels and different safety stock levels

Parameters Responses (selected KPIs) Safety Safety stock Times a patient Avg time Total patient Inventory Scenario stock depot clinical site waited [#] waited [h] kits shipped [#] overage [%] [#] [#] Status 800 15 33,2 0,368 11544 44,12 quo Smart 800 15 0 0 9667 33,79 labels 1 Smart 500 8 3 0,0651 8839 27,56 labels 2 Smart 400 8 14,8 0,414 8640 25,78 labels 3 Smart 300 8 42,6 1,427 8480 24,28 labels 4 Smart 400 5 24,7 0,685 8645 25,05 labels 5 Smart 300 5 58,6 1,748 8440 23,22 labels 6

6.4 – Effects that different parameters have in the usefulness of smart labels 93

In table 22, it can be observed that, if the same safety stock levels used for the status quo are maintained (scenario smart labels 1), smart labels substantially improves all KPIs: no patients wait for medication, inventory overage is reduced by 25% and patient kits manufactured are decreased by more than 16%.

In fact, the safety stock levels can be reduced to 5 for the clinical sites (67% lower) and to 400 in the regional depots (50% lower) – see scenario smart labels 5 – and still the behavior of the model with smart labels outperforms the status quo. This scenario is chosen to compare with the status quo because (i) the reliability factor is higher than that of the status quo, so that it cannot be argued that part of the improvement is due to a laxer reliability factor (discarding scenarios like 4 or 6) and (ii) it does not go to the other end and drastically increase the reliability factor at the expense of a higher overage (like scenarios 1 or 2). Scenario 4 would also be another valid candidate, but was discarded because of a significantly higher average time that patients waited for medication.

Using scenario smart labels 5 as a reference, other KPIs explained in 5.5 – Key Performance Indicators also show a substantial improvement when smart labels are integrated in the clinical trial supply chain. A comparison between these KPIs in scenario smart labels 5 and the status quo can be found in table 23.

Table 23: Comparison of re-labeling and financial KPIs in the original scenario and in one with smart labels

Patient kits Re-labelings Costs of Manufacturing Costs of re- Re-labelings [#] destroyed at clinical disposing costs [€] labeling [€] [#] sites [#] drugs [€] Status 3192,37 2810,87 0 1,15E+06 23717,5 68796 quo Smart 3762,33 0 1275,83 864420 11775,8 75247 labels 5

Assuming that the transportation costs are not affected by smart labels (see 5.3 – Model boundaries for the motivation behind this), the amount of savings that on average smart labels generate per patient kit can be obtained by subtracting the sum of costs of the scenario smart labels 5 to the original scenario, and then dividing by the number of kits shipped. For this particular study, results indicate savings of 33.7€ per patient kit.

6.4 Effects that different parameters have in the usefulness of smart labels Results obtained in the last section show that smart labels are promising, and might result in savings of more than 30€ per patient kit for a total of ~300,000€ for the entire study. This figure is however study dependent, because the inventory overage and percentage of re-labeling processes, among others, also depend on the complexity of the study. In this section, more insights into the role of smart labels are studied by modifying some of the parameters defined in the DES model.

6.4.1 Increase the size of the study With the objective to understand how the impact of smart labels affects bigger studies, a new, fictional study setup was tested. Most of the parameters presented in table 16 for the original study were maintained. The only parameters changes were:

. The number of treatments was increased to 3, what affects the level of safety stock that clinical sites require

. The study magnitude multiplier was set to 4, implying that four times more patients than in the original study enroll.

94 6.4 – Effects that different parameters have in the usefulness of smart labels

. The study time multiplier was set to 2, meaning that the total enrollment time of patients (i.e., LPFV-FPFV) is (on average) twice as long as that of the original study.

. The number of containers per patient kit was increased to 25.

Note that an important assumption for this fictional study is that, even though the number and enrollment distribution of patients changes, the countries and clinical sites involved are the same.

After performing a similar safety stock analysis to that presented in the last section27, the KPIs for two scenarios (with and without smart labels) that fulfill the criterion for the reliability factor and are similar in terms on performance are presented in table 24.

Table 24: Comparison for a bigger study of the main generic KPIs in the original clinical trial supply chain and in a supply chain with eInk smart labels

Parameters Responses (selected KPIs) Safety Safety stock Times a patient Avg time Total patient Inventory Scenario stock depot clinical site28 waited [#] waited [h] kits shipped [#] overage [%] [#] [#] Original 1200 15 103,9 0,263 42824 42,67 Smart 500 5 50 0,250 28342 13,46 labels

Table 24 indicates that, in this case, with stock levels that are almost two thirds lower than that required in the original scenario, smart labels still improve all KPIs significantly. Especially the inventory overage, which is reduced by almost 70%, is a potential source of savings in terms of manufacturing costs. Table 25 compares this and other financial KPIs.

Table 25: Additional KPI comparisons for a bigger study

Total re- Re-labelings at Manufacturing Costs of disposing Costs of re-

labelings [#] clinical sites [#] costs [€] drugs [€] labeling [€] Status 4197,1 0 4,28E+06 77769,4 246580 quo Smart 10929,3 4176,67 2,83E+06 18458,7 218586 labels

Performing a similar cost analysis to that carried out in the last section, it can be concluded that smart labels can bring savings of 54.24€ per patient kit. This suggests that the implementation of smart labels with variable content has a more beneficial effect the bigger and complex the clinical trial is.

6.4.2 Variation of other parameters Last section proved that as the size of the study grows, the impact that smart labels have in the CTSC is greater – quantified by the savings they bring per patient kit. To try to generalize further the study of the impact of electronic paper labels into a clinical supply chain, a set of different experiments was conducted. In each of these experiments, single parameters of the model were changed with the goal to evaluate what to expect from smart labels in different studies with different setups.

27 Note that both the model treatment and the optimization strategy used were the same as those described in 6.2.1 – Model treatment, 6.2.2 – Running the experiments and results obtained and 6.3 – Implementing smart labels in the clinical trial supply chain. 28 In this case, these safety stock is referred per active treatment. Because there are three treatments for this study and it is assumed that they are equally distributed, the total safety stock for a clinical site is 45 items.

6.4 – Effects that different parameters have in the usefulness of smart labels 95

Especial attention was given to those parameters that were not tested in the fictional bigger study analyzed in the last section (e.g. dropout rate), to those that are heavily study-specific (e.g. the expiry date, the manufacturing costs, the containers per patient kit) and to those logistics parameters that are either uncertain (e.g. travel times) or strategic ones (e.g. resupply period/frequency). Table 26 summarizes the main changes in parameters that were tested.

Table 26: Definition of different experiments to analyze how the usefulness of smart labels varies with certain selected parameters

Experiment # Parameter Variation Final value 1 Dropout ratio between each visit +100% 0.0006913 2 Initial expiry date +50% 36 weeks 3 Expiry date extensions +100% 48 weeks 4 Manufacturing costs +50% 150€/patient kit 100 primary 5 Containers per patient kit +99 kits/patient kit 6 IRT systems resupply period +50% 90 days 7 Average travel time to the depots29 +50% 45 days Average travel time from depots to 9.75 days [EU] 8 +50% clinical sites 6.75 days [NA]

The model treatment for these experiments was analogous to the one presented earlier in this chapter: 30 replications were run, no warm-up period was used and the simulation time was 3 years.

However, a difference with previous optimization exercises is that this time – for each experiment shown in table 26 – both the original CTSC (i.e., without smart labels) and the smart label-enabled CTSC were run. This was necessary to reassess the benefits of smart labels in each of the experiments. Then, these benefits were compared to those observed for the original case study (presented in 6.3 – Implementing smart labels in the clinical trial supply chain). In the end, this approach allowed to evaluate whether the benefits of smart labels in each of the new experiments are higher or lower than in the original case study, serving thus as a pseudo sensitivity analysis to draw conclusions on how variation of different parameters impact the usefulness of electronic paper smart labels in the CTSC. Note also that because it was intended to isolate parameters for analysis, the same safety stock determined for the original case study (both with and without smart labels) were used. The main results are presented in table 27.

Even though the full set of KPIs were obtained after running the experiments summarized in table 26, table 27 gathers only four of them: the reliability factor, the total patient kits shipped, the inventory overage and the total costs. This was done (a) because of readability reasons and (b) to simplify the analysis of the results. Motivation to choose these KPIs is that they aggregate the main indicators of a CTSC:

. The reliability factor – now a KPI because it was not used as a threshold to optimize safety stocks - gives a quick overview on the improvement in terms of supply chain securement created by smart labels. This is because, per definition, the reliability factor also aggregates the times that patients waited for medication. Note that because safety stocks were not changed (again, to isolate the variation of parameters), reliability factors cannot directly be compared across different experiments. The improvement in the reliability factor should be compared instead.

29 As explained in 5.2.6 – Intrinsic logistic uncertainties in the clinical trial supply chain, the travel time to the European and the North American depots are slightly different (30 and 29 days, respectively). For this experiment both were brought to a value of 45 days.

96 6.4 – Effects that different parameters have in the usefulness of smart labels

. The total patient kits shipped provide a quick summary of the simplification in supply chain processes achieved. . The inventory overage is an important KPI in CTSCs. It aggregates kits produced, destroyed, and remaining at the end of the study. . Finally, the total costs presented in table 27 are the sum of manufacturing costs, disposal costs and costs of re-labeling

6.4 – Effects that different parameters have in the usefulness of smart labels 97

Table 27: Results on the impact that different parameters have in the usefulness of smart labels

Reliability Total Patient Inventory Total Experiment Scenario Factor [%] Kits Shipped [#] Overage [%] costs [€] Original 99,61 11544 44,12 1242514 Original case Smart labels 99,71 8645 25,05 901712,4 study Improvement with smart 0,10 2899 19,07 340801 labels (abs. difference) Original 99,59 11270 46,26 1,22E+06 Smart labels 99,73 8099 25,2356 899683 Dropout rate Improvement with smart +100% 0,14 3171 21,02 317067 labels (abs. difference) KPI improvement over 36,49% 9,37% 10,25% -6,96% original case study Original 99,91 10110 36,4883 1051280 Smart labels 99,72 8657,14 25,0268 902354 Expiry date Improvement with smart -0,19 1453 11,46 148926 +50% labels (abs. difference) KPI improvement over -281,25% -49,89% -39,90% -56,30% original case study Original 99,73 11297,9 43,383 1228160 Extension Smart labels 99,71 8653,64 25,0187 946949 Improvement with smart expiry date -0,02 2644 18,36 281211 +100% labels (abs. difference) KPI improvement over -121,55% -8,79% -3,70% -17,49% original case study Original 99,61 11552,9 44,6393 1827250 Smart labels 99,71 8653,64 25,0187 1,38E+06 Manufacturing Improvement with smart 0,10 2899 19,62 444280 costs +50% labels (abs. difference) KPI improvement over -2,98% 0,01% 2,89% 30,36% original case study Original 99,61 11518,6 44,0082 1721900 Primary packs Smart labels 99,71 8653,64 25,0187 950096 Improvement with smart per container 0,10 2865 18,99 771804 = 100 labels (abs. difference) KPI improvement over -9.3 -1,17% -0,42% 126,47% original case study Original 98,93 12511,1 48,5232 1336850 Smart labels 99,59 8826,04 27,2822 979002 Resupply Improvement with smart 0,66 3685 21,24 357848 period +50% labels (abs. difference) KPI improvement over 526,49% 27,11% 11,38% 5,00% original case study Original 99,45 11809,1 45,7478 1286300 Smart labels 99,10 8590,79 25,0753 949518 Travel time to Improvement with smart -0,34 3218 20,67 336782 depots +50% labels (abs. difference) KPI improvement over -427,82% 11,01% 8,40% -1,18% original case study Original 99,36 11586,6 44,6533 1249180 Travel time Smart labels 99,64 8617,14 24,932 946350 Improvement with smart from depots to 0,29 2969 19,72 302830 sites +50% labels (abs. difference) KPI improvement over 176,04% 2,43% 3,42% -11,14% original case study

98 6.4 – Effects that different parameters have in the usefulness of smart labels

The following points discuss the results gathered in table 27:

. Experiment dropout rate +100%: It can be concluded that a higher dropout rate leads to a better impact of smart labels. Especially in terms of patient kits shipped and inventory overage there is an improvement over the original effect of smart labels in the unchanged case study. Note that the relatively high improvement in the reliability factor has to be put into perspective: the improvement with smart labels when dropout is higher is 36% better than that without smart labels (absolute differences of 0.10 vs 0.136), but this implies only a difference of approx. 0.04% in absolute terms. This consideration applies to all the subsequent points. Finally, even though the improvement in terms of costs is lower in this experiment, this is also because fewer patient kits are shipped in total: higher dropout implies that less patients continue with the treatment and the IRT systems of the model trigger less replenishments. This drives down manufacturing costs and in turn total costs.

. Experiment expiry date +50%: All the KPIs variations after the implementation of smart labels are much worse than those presented in the original case study. This is logical if it is understood that the main advantage of smart labels captured in the model is that they allow for variable content – and thus variable expiry date – on the labels. However, the worsening of the reliability factor does not imply that the original scenario is better than the one with smart labels, because the safety stocks were kept constant and equal to those of the initial case study. Note that other benefits of smart labels not captured in the model (flexibility in terms of meeting regulatory concerns, reshipments etc.) would partly offset these figures. Still, it can be concluded that as the expiry date increases, the benefits of smart labels decrease.

. Experiment extension expiry date +100%: The same discussion made in the point above applies to this case, although everything is mitigated because the first expiry date is not affected, but rather subsequent ones.

. Experiment manufacturing costs +50%: The improvement with/without smart labels for this experiment should be equal for all KPIs to the original case study, with the exception of the total costs. A visual comparison of the KPIs confirms that the expectations are met. Again, the reader should be careful when comparing the row KPI improvement over original case study because very small variations can be reflected in relatively high percentages. The total costs are higher because manufacturing costs are higher. The lower waste that smart labels bring to the CTSC helps ameliorating this costs, hence improving their benefit when manufacturing costs are higher.

. Experiment primary packs per container = 100: In terms of costs, smart labels are more useful as the number of primary packs per container increases. This is because relatively less manual work is required the higher the primary packs contained in an outer pack.

. Experiment resupply period +50%: Overall smart label KPI improvements are higher in this experiment. The logic behind this is that a higher resupply period implies a smaller shipment frequency from the depots to the clinical sites. A lower shipment frequency results in an accumulation of stock in the central depots and higher volume of the relatively less frequent shipments. This results in (a) higher chances that drugs expire in clinical sites, where re- labeling is not possible (without smart labels) and (b) relatively higher re-labeling efforts at the depots, although this is damped by the existence of the parameter safety days to re-label in the depot before drugs expire. Note that the changes in the reliability factor are considerable in this case, mainly because of consideration (a).

. Experiment travel time to depots +50%: This case posed a challenge to interpret, because for the first time the reliability factor with smart labels was worse than without smart labels.

6.5 – Price of eInk smart labels 99

Further analysis revealed that this is due to the unchanged safety stock during the experiment (as explained before, the safety stock of the initial case study with and without smart labels were maintained to isolate parameters). Because of the much longer shipments from the manufacturing facilities to the depots, the main logistic problem of the supply chain is now its length. A period of almost two months takes place before drugs arrive to sites from the manufacturing facilities. This calls for additional safety stock in the depots, both with and without smart labels. Because the safety stock was comparatively bigger without smart labels, the behavior turned out to be better. It can thus be concluded that the longer the travel time to the depots, the less useful smart labels are.

. Experiment travel time from depots to sites +50%: Contrary to the previous case, this leads to a higher usefulness of smart labels. The logic behind this is that chances that a drug expires in a clinical site are higher the longer the transportation time to the clinical site is. Smart labels allow to re-label at sites, contributing further if this is required.

Table 28 aggregates in a qualitative way what has been learned about parameter sensitivity of electronic paper smart labels in this and last section.

Table 28: Summary of the influence of key parameters in the usefulness of smart labels from a qualitative perspective

↑ ↑ 6.5 Price of eInk smart labels In order to assess the benefits of eInk smart labels from an economic perspective, it is necessary to compare the savings per patient kit introduced in the last two sections with the actual price of the labels.

Currently eInk/ePaper smart labels adapted for clinical use do not exist. The price analysis carried out relies of existing products for the retail sector that have similar characteristics to those expected in a clinical supply chain.

Four different vendors were approached and the order of magnitude of the individual price of smart labels for large order quantities (>1000) were asked. Two of them accepted to provide input for the present research. A third one did not answer and a fourth one was not interested in disclosing prices for a research project.

The price of a relatively small eInk smart labels with embedded passive RFID communication technologies (see figure 34) was indicated to be 8$/unit for orders of more than 1000 units. The characteristics of this particular label might be attractive for relatively small labels, although the thick frames and the lack of flexibility make it less feasible for rounded primary containers, such as a bottle. As explained in 4.4.2 – Label sizes and shapes, technology already allows for this, but the level of maturity is below that of more conventional eInk displays.

100 6.6 – Other opportunities and threats not captured in the model

Figure 34: Example of a small RFID eInk smart label priced at 8$/unit for large orders

The price for a second set of much larger RFID eInk smart labels was also obtained from commercial suppliers (see figure 35). In this case, the unitary priced was set below 25€/unit. These labels are big enough to be used in outer packs for clinical kits.

Figure 35: Example of big RFID eInk smart label priced below 25€/unit for large orders

In both the original case study (presented in 6.3 – Implementing smart labels in the clinical trial supply chain) and in the larger study (presented in 6.4.1 – Increase the size of the study), the implementation of smart labels would make sense from a variable cost perspective, given that the price of a smart label itself is lower that the benefits it bring to the clinical trial supply chain.

Note that results from this analysis neglect additional savings from not using booklets, potential transportation costs, and additional re-labelings that might take place for reasons other than expiry date modification.

6.6 Other opportunities and threats not captured in the model The simulation model presented in the last chapter and its application to a real case study has allowed to understand better the dynamics of the clinical trial supply chain when smart labels are implemented. However, because some aspects of the real CTSC lie beyond the model boundaries, additional opportunities and threats of smart label technology in the context of clinical trials have been not captured in the simulation.

The main additional opportunities that smart labels entail are the following:

1. More flexibility to the supply chain: Smart labels with variable contents would allow, for example, for easier reshipments from one clinical site to another or between different regional depots. Quantifying the value flexibility is complicated, but this cannot be neglected in a supply chain with the characteristics of the CTSC; the finite horizon and the expiration of products make flexibility highly valuable.

6.7 – Case Study: Smart Labels on a Phase III Clinical Trial – Conclusions 101

2. The potential elimination of booklets would bring a new iteration of cost savings by implementing smart labels, what would improve further the variable cost analysis presented in the last section.

3. Out of the different re-labeling reasons considered in 3.1.2 – Main challenges faced in the labeling process, only re-labeling due to expiry date modification was considered in the model. Thus, the total number of re-labelings is likely to be higher, making eInk smart labels even more interesting.

4. Transport costs might decrease as well due to the fewer shipments from the manufacturing facilities when smart labels are considered. This was left out of the model boundaries because it is normal to share transport vehicles between the CTSC and the commercial drug supply chain, but the huge difference in the number of patient kits shipped between the original scenario and the one with smart labels suggests that additional savings should be possible.

5. Customized messages could be displayed for different stakeholders across the clinical trial supply chain. For example, identification statements could be shown while drugs are in the manufacturing/packaging facilities, caution statements for transportation could be displayed for the transportation companies and the final content established in the regulatory guidelines (specified in 4.2 – Labeling requirements for IMPs in clinical trials) could be adapted once patient kits are about to be given to the clinical sites.

6. eInk smart labels could also be used for item level inventory tracking.

7. Smart labels could be recycled by picking them up from consumed patient kits, allowing for an extended lifespan and a reduction of costs given that existing labels could be used in other clinical trials.

There are also a couple of threats to the implementation of smart labels that have not been captured in the model. These are:

1. Even though the implementation of smart labels makes sense from a variable cost perspective, the upfront investment required to setup such a system is huge, and can be estimated in the order of hundreds of thousands of euros for a big pharmaceutical company [160].

2. Very high compliance standards are required in the context of clinical trials, what calls for a thorough validation of the system that would be both costly and time-consuming.

3. As explained in 4.4.2 – Label sizes and shapes, the application of smart labels to small or cylindrical containers might require more sophisticated technology than the labels presented in the last section, what could worsen the variable cost analysis.

6.7 Case Study: Smart Labels on a Phase III Clinical Trial – Conclusions The assessment of the different KPIs after implementing smart labels in the clinical trial supply chain allows to draw the following conclusions:

1. From a variable cost perspective, the benefits of smart labels outweigh their costs.

2. The biggest improvements when smart labels are implemented relate to the inventory overage required to secure the supply chain and to the patient kits being disposed after expiration.

102 6.7 – Case Study: Smart Labels on a Phase III Clinical Trial – Conclusions

3. The main sources of savings from smart labels in the clinical trial supply chain are the reduction in manufacturing costs (due to this reduction in inventory overage) and in disposal costs (due to the fewer number of patient kits being disposed).

4. As the size, the number of treatments, the primary packs per patient kit, the dropout rate, the manufacturing costs and the travel time from depots to clinical site increase, the advantages of smart labels increase too. Conversely, as the expiry date, the extension of expiry dates and the travel time of drugs from the manufacturing facilities to the depots increase, the usefulness of smart labels decreases.

5. Threats to the implementation of smart labels are the high upfront costs required to setup and validate a smart label ecosystem across the huge supply chain. These costs can be estimated in hundreds of thousands of dollars at best.

6. Other benefits and opportunities of smart labels are the increase of flexibility in the supply chain, extra savings from fewer shipments and the elimination of booklets, tracking inventory, reusing a single eInk smart label for several studies by picking it up at the end of the trial or displaying stakeholder-specific information.

As a final remark, even though the simulation model has been used mainly to analyze the impact of smart labels in the clinical trial supply chain, it does allow to carry out some other studies, such as:

. The optimization of the days ahead to look for demand and the resupply period for a specific clinical trial setup to minimize patient waiting time.

. The trade-off between resupply frequency and transportation costs.

. The analysis of the trade-off between inventory overage (via safety stocks) and patients waiting for medication.

. The optimization of the number of days before expiration that patient kits should be relabeled in the depot so that they do not expire at clinical sites.

6.7 – Case Study: Smart Labels on a Phase III Clinical Trial – Conclusions 103

Chapter Seven ………………………...... 7 Smart Labels in Clinical Trials – The 7 Patient Perspective

Drugs don't work in patients who don't take them Charles Everett Koop, former US Surgeon General

Previous chapters (3 – Smart labels – Market Research and Applicability to the Clinical Trial Supply Chain) have focused on a desk research on smart labels from a logistics perspective: the application of smart labels was focused on improving the efficiency of the clinical trial supply chain. In other words, the material flow of the clinical trial supply chain was the center of attention and, as such, pharmaceutical companies were the main stakeholder considered.

Then, the fit of a particular kind of smart label especially suited for logistics was analyzed from a regulatory perspective in 4 – The Regulatory Perspective: Do Smart Labels Fit in the Current Regulatory Framework?, and it was determined that both regulatory precedents and current specific laws and guidelines show promising potential to bring eInk labels to the clinical trial supply chain.

From a quantitative perspective, the impact of such labels was explored in 6 – Case Study: Smart Labels on a Phase III Clinical Trial via Discrete Event Simulation, after describing the conceptualization of the model in 5 – A Discrete-Event Simulation Model of the Clinical Trial Supply Chain.

Still, the patient perspective remains to be explored. What are the (de)motivators of patients during clinical trials? What factors influence patient adherence to the treatment? How can smart technology address unmet needs from a patient perspective? Does the eInk, RFID-based smart label described in previous chapter add value from a patient perspective? Is there any potential on smart labels to ameliorate the problems derived from patient recruitment and patient dropouts during a clinical trial? This chapter addresses these and other similar questions, and is structured as follows: first, a literature review on the motivations and barriers for patients to join a clinical trial and to adhere to medication is carried out. After developing insights into the behavior of patients, the role of smart labels to improve patient adherence is then analyzed. Later, a similar exercise is repeated focusing on the barriers to clinical trial participation. Finally, conclusions on the potential of smart technology from a patient’s perspective are drawn.

104 7.1 – Literature review on the motivations and barriers for trial participation and adherence

7.1 Literature review on the motivations and barriers for trial participation and adherence In order to assess whether smart label technology has potential to solve patient-related issues in the execution of clinical trials, such as the challenging patient recruitment or the relatively low adherence to medication regimens, it is first necessary to determine what are the motivations and barriers for patients to join a clinical trial and to comply with the study protocol.

This section presents the outcome of a keyword-based desk research carried out with this objective. Keywords used were different combinations of “clinical trial”, “patients”, “motivators”, “motivations”, “barriers”, ”awareness”, “adherence” and “dropout”. A total of 23 relevant articles were found using this procedure, and 5 more where then tracked down from their referenced facts. In what follows, this section aggregates the results of this literature review.

7.1.1 Motivations for patients to participate in clinical trials Although motivations for patients to join a clinical trial might be disease-specific, the common sense that an improvement in health status is major motivator is well-reflected in the literature.

Figure 36, for example, shows the results that Chong et al. obtained when asking 190 schizophrenia patients participating in a trial about their reasons that led to their enrollment [91]. Other authors also suggest that having no treatment choice is a main reason for a person to participate in a clinical trial [187, 188].

Figure 36: Reasons for patients to participate in clinical trials. Extracted from [91].

More altruistic reasons are however suggested when patients are not chronically affected by a disease, or when their lives are not in danger. For example, for a chronic disease such as pulmonary arterial hypertension, Carroll et al. [189] concluded that the potentials for personal benefit and risk are at least as important as altruistic motives for patient to participate in clinical trials, whereas when analyzing the reasons for 364 subjects to participate in a clinical trial on an avian influenza vaccine, Costas et al. [190] found out that altruistic reasons were more prominent. Table 29 presents detailed findings of the latter study.

In a similar line of argumentation, Adresen et al. also found that the most prominent motivator for patients to participate in a randomized clinical trial for depression was the desire to help others and/or to collaborate with the progress of science [191]. Findings by Burgess et al. also support this view [188].

7.1 – Literature review on the motivations and barriers for trial participation and adherence 105

Table 29: Reasons to participate in a clinical trial on an avian influenza vaccine. Adapted from [190].

Reasons to participate in a clinical trial on an avian Number [#] Percentage [%] influenza vaccine (note that only one option could be chosen)

Collaboration with science 155 42.6

Influence of other people 137 37.6

Protection against pandemic 57 15.7

Coverage of expenses 15 4.1

Other authors emphasize the importance of socio-demographics such as age, ethnicity, belief in Fate/God, income or education on the motivations for participation in clinical trials. For example, after surveying 180 adults participating in a dental trial and obtaining a 98% response rate, Friesen & Williams [192] concluded that individuals in the age group 45-59 were significantly less likely to consider financial reimbursement as a motivation to participate in research and that overall motivations for participation were dependent on the income group patients belonged to. Moreover, African Americans and individual with less education rated Fate/God as important determinants to their research participation.

Finally, the literature also suggests that the dedicated attention and regular follow ups that patient receive in clinical trials are an important motivation for the majority of the patients (up to 90.5% according to a study by Burgess et al. [188]).

7.1.2 Barriers to clinical trial participation As of 2016, the most comprehensive and aggregated research on patient barriers to participation in randomized clinical trials is the one carried out by Ross et al in 1999 [49]. These authors performed a systematic literature review of three bibliographic databases from 1986 to 1996 and identified 78 papers in this regard. Table 30 presents an overview of their conclusions that aggregates the barriers into different categories.

Table 30: Barriers to patient participation adapted from Ross et al. [49]. Individual references to each of the papers can be tracked in their original publication.

Barrier to participation in clinical trials Number of identified papers [#]

Patient concerns about information and consent 27

Patient preferences for a particular treatment 15

Time burden because of additional procedures and 13 appointments

Worry about uncertainty of treatment 9

Travel problems and costs 8

Clinician influencing patient decision not to join 6

106 7.1 – Literature review on the motivations and barriers for trial participation and adherence

Although exhaustive, table 30 has the limitation to be based on relatively old data. In what follows, findings by other authors that rely on more recent research are also discussed.

According to Weckstein et al. [193], less than 5% of patients with cancer participate in clinical trials. In their research, they surveyed 1,755 cancer patients to determine the most important barriers to trial participation, obtaining a 71% response ratio from trial-eligible patients. Table 31 summarizes their results.

Table 31: Results of the survey on the barriers for patients to participate in clinical trials. Adapted from [193]

Reasons for nonparticipation in clinical trials for Number [#] Percentage [%] patients who were offered a clinical trial

Possible side effects 155 42.6

Concern about random assignment 137 37.6

Cost/no insurance 57 15.7

Overwhelmed 32 12

Time 155 42.6

Physician recommended not to participate 137 37.6 Other (surgical biopsy, moving, uncertainty of 57 15.7 study treatment effect)

The authors also note that 44% of non-participating patients did not remember to have heard about clinical trial opportunities, what would imply that physicians and doctors might also pose a barrier to clinical trial participation [193]. This view that is shared by other authors [194, 195].

Concerns about side effects and random assignment are also reported to be major barriers to participation in other studies. After 86 patients and 130 oncologists responded their survey on cancer clinical trials, Meropol et al. [196] found that fear of random assignment to the placebo and fear of side effects were the two main barriers to patients. It is also interesting to emphasize that, although the former was also perceived to be a patient de-motivator by oncologists, the latter was ranked as a much less important barrier than the actual patient perception. Other also reached similar conclusions about the weight of side effects and randomization in a patient’s decision to join a CT [197-199].

The importance of costs as a de-motivator can play a major role in some demographic groups, as concluded by Markman et al. after analyzing the responses of 1,767 U.S.-based patients to a survey [200]. Note however that, as confirmed via interviews with experts at Novartis Pharma AG, this financial distress does not come from the cost of the treatment itself in the case of clinical trials, but rather from the associated expenses of traveling to the clinic, not being able to work etc. [37].

As table 31 suggests, the time burden that clinical trials pose to patients is another potential obstacle for participation. Henrard et al. [201] reached a similar conclusion after analyzing the results of a questionnaire filled out by 62 adults with hemophilia that intended to evaluate what motivates patients suffering from this disease to participate in clinical research. Results from their study are summarized in table 32. The extra time needed for the trial was also the second main barrier to

7.1 – Literature review on the motivations and barriers for trial participation and adherence 107

patients according to Melisko et al. [199], although these authors emphasized the necessity of a demographic breakdown.

Table 32: Reasons for hemophilia patients not to participate in clinical trials. Adapted from [201].

Reasons for hemophilia patients not to participate in Number [#] Percentage [%] clinical trials (note that only one option could be chosen)

Clinical trials imply too many visits to the hospital 23 45.1

I care about the risks (side-effects) of a new drug. I do not 20 39.2 want to take these risks. I do not want to change my coagulation factor 4 7.8 concentrate I do not want to change my actual treatment scheme 3 5.9

These authors also identified factors that could influence the willingness of patients to participate in a trial, finding that age, patient’s interest in having more information about clinical trials, patient’s current knowledge about clinical trials and profession were the most prominent factors, with an overall power as discriminant of 100, 36, 26.3 and 14.1, respectively [201].

Lack of clinical trial awareness is also a barrier to clinical trial enrollment. This obstacle is largely country- and demographic-dependent. For example, while Leiter et al. [202] concluded that clinical trial awareness in the United States had increased from 68% to 74% from 2008 to 2012, a study by Taban et al. revealed that awareness in Turkey in 2011 was just above 50% [187]. Table 33 summarizes this and some other findings from Taban et al.’s research paper, based on a sample size of 504 Turkish patients. The authors discussed that the negative impact of the news in the written and visual media had an influence on Turkey not leaning much towards clinical trials.

Table 33: Responses to some of the questions to the survey of Taban et al., as extracted from their paper [187]

Respondents from Turkish patients to survey questions Yes [#] ([%]) No [#] ([%]) (missing entries did not know / did not want to answer)

Do you think that CTs are performed in Turkey? 263 (52.2) 171 (33.9)

Did you ever participate in a CT? 7 (1.4) 497 (98.6)

Would you participate in a trial if your doctor asked you 170 (33.7) 294 (58.3) to?

Should new drugs be developed? 493 (97.8) 9 (1.8)

Should new drugs be tested on human beings in the 360 (71.4) 120 (23.8) development period?

Note that although the data is presented in an aggregated way, both Taban et al. and Leiter et al. also concluded that demographic factors within the country affect awareness of individuals. Both reported education as an important factor affecting the attitude towards clinical trials [187, 202], and

108 7.1 – Literature review on the motivations and barriers for trial participation and adherence

Leiter et al. also found other important factors such as income and internet use [202]. Awareness is also driven down in case of preventive clinical trials [197].

7.1.3 Factors affecting patient adherence to the medication regimen The WHO defines adherence to a treatment as “the extent to which a person’s behavior (taking medication, following a diet and/or executing lifestyle changes) corresponds with agreed recommendations from a health care provider [203]. In their extensively cited paper, Dimatteo et al. concluded that a correct adherence to the treatment reduces the risk of poor medical outcomes by 26% while multiplying but three the likelihood of good treatment outcomes [204]. Still, the WHO estimates an average adherence rates in the first world of only about 50%[203].

Several factors have been identified to influence long-term medication adherence. A non-exhaustive list of some of the factors found in the literature are presented in table 34, based on a review by Zschoke et al. [205] but also on other sources [15, 82, 206-208].

Table 34: Factors affecting patient adherence [15, 82, 205-208].

# Factor affecting patient adherence 1 Complexity of the treatment 2 Duration of the treatment 3 Condition characteristics (severity and chronicity of the disease, complicating factors etc.) 4 Impact of beneficial or adverse effects 5 Changes in patients’ lifestyles 6 Communication between the patient and the physician 7 Socio-economic variables (i.e. health literacy or substance use disorders) 8 Patients previous treatment experiences 9 Patients satisfaction with the current treatment 10 Patient loss of interest 11 Number of daily doses

Previous research on the predictors for patient adherence has also been carried out by several other authors. After conducting a literature review, Osterberg and Blaschke [80] concluded that the major predictors of poor adherence to medication regimes are those shown in table 35.

Table 35: Predictors of poor patient adherence, adapted from Osterberg and Blaschke [80].Only those relevant to clinical trials are shown. Individual references to each of the papers can be tracked in their original publication.

Predictor of poor patient adherence Number of identified papers [#]

Presence of psychological problems (especially depression) 3

Presence of cognitive impairment 2 Inadequate follow-up 2 Patient’s lack of belief in benefit of treatment 2 Poor provider – patient relationship 2 Side effects of medication 1 Complexity of the treatment 1

7.1 – Literature review on the motivations and barriers for trial participation and adherence 109

Some of the recommendations that these authors provide in order to improve adherence to a medication regimen are [80]:

1. Emphasize to the patients the value of the treatment and the effect of the adherence.

2. Account for patient’s availability to adhere to the medication regimen and, if necessary, design supports to promote adherence.

3. Provide simple and clear medication regimen instructions; simplify the regimen as much as possible.

4. Encourage the use of a medication-taking system.

5. Obtain help from family, friends and community services when needed.

Osterberg and Blaschke also concluded that new technologies such as smart pill containers, reminders via electronic devices and personal digital assistants can help patients adhering better to the treatment [80].

Pugatsch et al. [209] carried out another research on the factors affecting patient adherence. After reviewing all clinical trials carried out from 2008 to 2013 at the Hadassah CF Center they concluded that the study length was inversely correlated with adherence, and that adherence decreased significantly if the drugs were not provided as “ready to be used”. Moreover, they noted a substantial decrease in adherence to the medication treatment during holiday periods. Finally, the authors found no correlation between the number of visits (as defined per study protocol) and patient adherence.

Patient adherence to the medication regimen is also disease specific. Adherence rates are normally higher in patients suffering from an acute condition (i.e., one in which symptoms appear and change or worsen rapidly, as in a heart attack) than in those suffering a chronic disease (i.e., a condition that develops or worsens over an extended period of time, as in atherosclerosis) [80]. In the latter case, the literature suggests that there is a dramatic drop of patient adherence especially after the first 6 months of therapy [210, 211]. Most deviations in patient adherence are caused by omissions of doses (and not by additions) and delays in the between-dose time [212].

Less frequent dosage patterns or fixed-dose combination drugs reduce the pill burden on patients can help improving patient adherence according to Matsui [213]. The relationship between daily doses and rate of compliance is well-supported in the literature [214, 215]. Moreover, Matsui notes that the use of special containers and packaging can further help in enhancing adherence, even suggesting that new smart technologies that communicate with the patient have a promising potential.

7.1.4 Motivators and barriers for clinical trial participation and medication adherence – Conclusions and discussion When it comes to the motivators of clinical research participation, the following conclusions can be drawn from the results of the literature review presented above:

1. The possibility to obtain health benefits from the clinical trial and access to new treatments not reachable otherwise are the most common motivators for a patient to join a clinical trial in case of life-threatening diseases.

2. In case of less severe diseases, some other motivations gain importance. These are altruistic reasons, coverage of the expenses of the treatment, collaboration with science and dedicated medical attention.

3. In any case, from a general perspective, there is no potential for smart labels or smart devices to contribute to a higher motivation of patients to join clinical trials. Smart labels cannot

110 7.1 – Literature review on the motivations and barriers for trial participation and adherence

enhance the health benefits of a chemical drug, do not provide access to new treatments per se, do not further contribute to the coverage of the treatment expenses (which are already fully covered during a clinical trial) and cannot improve the perception of patients of collaborating with other patients/science during a clinical trial.

As for the barriers to participate in a clinical trial, the following points summarize the main conclusions obtained:

1. A first set of barriers to patients when it comes to clinical trial participation has to do with their own perception on costs and benefits: potential side effects, the chances to be randomized to the placebo treatment group and especially the time burden that clinical trials imply are the main de-motivators.

2. A second set of barriers to participation come externally to patients. These are external recommendations (e.g. by doctors or physicians) on whether or not to participate in a trial, demographic considerations and awareness of clinical trials. Awareness of clinical trials itself is also influenced by demographic considerations, as regular internet users were found to have a significantly higher degree of awareness, likely motivated by the existence of patient communities.

3. Even though smart labels cannot contribute to expand the motivations of patients to join clinical trials, there is room for new technologies to mitigate the barriers to participation. Specifically, mobile health technologies may help to overcome the time burden that clinical trials imply for patients by reducing the patient visits to the clinic via telemonitoring [71]. Section 7.3 – Smart labels to reduce the time burden for patients participating in a clinical trial analyzes in detail this possibility.

4. Awareness of clinical trials might be enhanced via social online platforms. An example of this is the online treatment collaboration site CureTogether.com, which has crowd-sourced information on the treatment effectiveness for over 630 diseases [216] or PatientsLikeMe.com, an online platform where patients (currently more than 200,000) share their experiences with treatments [57]. Although awareness is just one of the many barriers faced in the patient enrollment to clinical trials and the effectivity of these solutions are very likely to heavily depend on demographic factors, further research is suggested in this area.

5. Other barriers, such as the randomization process, potential side effects, physicians’ attitudes towards CTs or demographic considerations are less likely to be influenced by mobile or social technology (i.e., smart labels, e-devices or internet communities).

Finally, the following conclusions relate to the potential impact of smart technologies on patient adherence to medication:

1. Similarly to the barriers and motivations for patients to join clinical trials, patient adherence to the treatment is largely determined by condition characteristics (chronicity, cognitive impairment, psychological disorders), effectivity of the medication, side effects and demographic considerations, which cannot be directly influenced by the inclusion of new technologies in the clinical trial supply chain.

2. Some other factors influencing patient adherence to medication regimes might be however potentially positively affected by smart labels and e-devices: the perceived complexity of the treatment, the number of daily doses and the presence of holiday periods are inversely correlated with patient adherence. If new technologic means could be used to either reduce this perceived complexity or to establish a reliable system of reminders for patients to take

7.2 – Smart labels to improve patient adherence 111

their doses, patient adherence could be enhanced. The next section deepens into this analysis.

7.1.5 Limitations of the literature review The literature review carried out in this section is not without limitations. The most important ones are the following:

1. General conclusions regarding motivations and barriers were intended to be drawn from the beginning. I acknowledge that both motivations and barriers for clinical trial participation are most likely disease-specific (which is actually already suggested in the first two points of these conclusions) and also heavily depends on demographic factors.

2. Conclusions about barriers and motivations for patients to join clinical research are based on the analysis of the outcomes of 28 papers. Although this was enough to identify trends and gather general conclusions, the research is still not exhaustive, even more considering that patient motivations/barriers to join a CT have a disease-specific component. However, the fact that some of these papers integrated knowledge from previous research on their conclusions alleviates this limitation. As noted in 7.1.3 – Factors affecting patient adherence to the medication regimen, some authors already consider that smart containers, packaging and communication system with patients can help patients adhering better to the treatment.

7.2 Smart labels to improve patient adherence Because higher patient adherence to the medication regimen is correlated with better treatment outcomes, the possibility of collecting adherence data from patients is increasingly being explored in the context of clinical trials (see Appendix B – A Literature Review on New Technologies in Clinical Trials from a Patient Perspective). Patient adherence rates are typically measures as the percentage of the prescribed doses actually taken by the patient during a certain period of time, although there is a trend to expand this definition to include data on dose taking (e.g. the prescribed number of pills a day are actually consumed) and timing of doses (e.g. the period between two dosages is the one prescribed) [80].

The literature review carried out on factors affecting patient adherence concluded that, although no single intervention can improve the adherence of all patients, overall new technologies such as smart pill containers, reminders via electronic devices and personal digital assistants can help patients adhering better to the treatment [80, 213, 217] For example, for those patients who are unable to maintain a complicated medication regimen, e-labels could set reminders and communicate with patients e-devices (either dedicated for the treatment or common ones, such as smartphones) to help them following the medication patterns.

7.2.1 Market research on current solutions to improve patient adherence There are currently several pilot, in-development and established projects and products targeted at improving patient adherence, while at the same time providing some telemonitoring opportunities. Breezhaler [218], CleverCap [219] or MedSmart [220] are examples of this. It is important to emphasize that, in all cases, these approaches are suitable to those trials in which patients consume their medication at home rather than in a clinical site.

Breezhaler (see figure 37, adapted from [221] and [222]) is an inhaler that contains a small, disposable module that can detect and report usage. Media releases by Novartis Pharma AG indicate that a new version that can also communicate wirelessly with a patient’s smartphone is being developed [218]. It is expected that this device will then be able to send data to the cloud, allowing patients to access their own data and track their treatment, while at the same time makes it possible for healthcare providers to monitor the patient’s evolution with the treatment remotely.

112 7.2 – Smart labels to improve patient adherence

Figure 37: Real picture of Breezhaler (left) and scheme of usage (right). Adapted from [221] and [222].

CleverCap (see figure 38, adapted from [223]) is a product from Qualcomm Life that follows a similar design, but applied in this case to an oral rather than a respiratory route of administration. CleverCap is defined as a device-enabled medication adherence platform to enhance adherence to prescribed regimens, and substitutes the caps of standard sizes containers with an e-device that reports data on pill consumption via the patients’ smartphones, allowing physicians and patient themselves to track their progress. Additionally, it features audio and visual alerts to remind patients when it is time to take a dose.

Figure 38: The CleverCap ecosystem. Adapted from [223].

MedSmart, a product by AlertUtah [224], is also based on the tracking of pills, but is even more oriented towards telecare than the previous alternatives, not only informing the physician when a patient misses a dose, but also reporting remotely and autonomously that pills need to be refilled. MedSmart also differs from the previous ones in that it does not need to connect to an external e- device to call, email or text investigators or care providers, as the device possess its own communication technology (see figure 39). As a downside, it is relatively big device and more expensive than the previous portable alternatives.

7.2 – Smart labels to improve patient adherence 113

Figure 39: MedSmart, a pill dispenser oriented towards telecare. Adapted from [224].

Other similar e-devices developed by other companies are GlowCap [225], iRemember [226], eCap [227], MedSignals Pill Case [228], Philips Medication Dispensing [229], MedMinder [230], PivoTell’s dispenser [231], MemoBox [232], Lumma [233], Adheretech [234].

Other developments, such as ID-CAP by eTech, measure patient adherence by tracking the actual ingest of pills [235]. This is done by embedding ingestible wireless sensors to patients pills (see figure 40). The capsule has the same size and dissolves like a normal capsule, does not interfere with the medication, leaves no trace in the patient’s organism and is self-powered. Once ingested, information is sent to the reader for tracking purposes.

Figure 40: ID-Cap (top) tracks actual intake of drugs by placing sensors in the pills that communicate with the reader (bottom). Adapted from [235].

A final set of devices analyzed as part of the market research are generic, non-medication specific developments that could be applied to track adherence. Droplet, for example, is a Bluetooth adhesive button designed to stick onto virtually any object and deliver configurable functionality such as

114 7.2 – Smart labels to improve patient adherence

reminders or custom messages [236]. Three elements define the Droplet ecosystem: the button, a smartphone app and the hub (see figure 41).

Figure 41: The Droplet ecosystem - the button (left), the hub (middle) and the app (right). Adapted from [236].

The Droplet button can communicate directly with the smartphone app, but if the user moves out of range or the smartphone runs out of battery, the hub (which plugs directly into a socket) can communicate with both the button (via Bluetooth Low Energy) and the Droplet servers (via Wi-Fi) within a range of around 30 meters, ensuring that no button press goes unnoticed. The battery of the button lasts for a year and the button plus the hub have a retail price of US$39 [237].

Droplet is not the unique development when it comes to customizable buttons. Flic is a similar alternative [238], and has the advantage that the battery can be easily replaced by the user and its retail price is reduced to US$27 [238]. However, there is no associated hub, meaning that communication between the button and the patient’s smartphone could be lost if, for instance, the patient is out of range or his smartphone is turned off.

Bttn is a third similar alternative [239], but in this case the user’s smartphone is not an interface between the button and the servers. Instead, Bttn can connect directly to a centralized server via GPRS or Wi-Fi, deeming an associated hub unnecessary. This development is however substantially bigger than the previous buttons, and its cost is higher, with a retail price of US$94 [239].

7.2.2 Does an eInk smart label help to address the patient adherence problem? In the last section, several different applications of new technologies to increase patient were presented. These developments differ significantly from the eInk label presented in 3.5 – Smart labels in the clinical trial supply chain – Conclusions as a means to streamline the clinical trial supply chain.

While smart labels have been determined to be effective in improving the logistics for clinical trials, e-devices (rather than e-labels) seem to be the current trend when it comes to bringing new technologies to clinical trials from a patient perspective. Moreover, it is these e-devices which have the potential to deal with the challenges of patient recruitment and patient adherence to the treatment in the context of clinical trials.

Would it be possible to combine both approaches? With most of the developments presented in the last sections, currently this seems difficult because of three reasons. First, smart labels are a generic product that can be brought to the clinical trial supply chain regardless of the characteristics of the

7.2 – Smart labels to improve patient adherence 115

drug being tested. E-devices, however, are very dependent on the route of administration, which is dependent on the route of administration. For example, Breezhaler and CleverCap, two different e- devices presented in the last section, are not interchangeable, and none of them can be said to be a standard e-device to track adherence. Secondly, from a market perspective, e-devices are produced by different companies, whereas a standard production of eInk smart labels is more feasible. Combining the features of both approaches into a single device would imply huge coordination efforts that in practice are not assumable. Finally, while labeling is subjected to relatively strict regulation requirements and has to be present both on primary and secondary packaging, e-devices provide an auxiliary function that normally takes the form of a secondary packaging alone. Because of this, as confirmed during the interviews with regulatory experts (see Appendix C – Interview Protocols and Outcomes), these e-devices are likely to face much less regulatory pressure.

The last devices presented in 7.2.1 – Market research on current solutions to improve patient adherence, such as Droplet, can however overcome this limitations. Droplet, Bttn or Flinc30 are standard buttons that can be adapted to any container, eliminating the route of administration- specific approach of other developments. Moreover, these products can adapt to any dosage patterns via the customizable app. Finally, implementation of these buttons could be done either at a primary package or at a secondary package level, depending on the nuances of the medication.

An ideal, tailored-made solution might be having a button embedded directly into the eInk smart label, so that there is just a single point of reference for patients, simplifying the use, and also both the e-device and the e-label can use the same communication systems (NFC, Bluetooth, RFID etc.), saving costs.

7.2.3 Smart labels to improve patient adherence – Conclusions A market research has revealed that there already exist several e-devices that have the goal of improve and track patient adherence to the medication regimen. In parallel, some other, more generic button- based developments have also been found to be able to track medication intakes. The advantage of the former, being specifically design to automatically track the patients’ progression with the treatment, is also a limitation in a way, given that they do not allow for standardization either across different studies or with smart labels. Generic devices might serve the same purpose at a lower cost, allowing besides for standardization in the clinical trial supply chain, what again has advantages both for patients and for clinical trial sponsors.

Given that the purpose of tracking adherence is enhancing compliance, a key feature of a device to be used in following patients’ adherence to medication is reliability. Flinc could be discarded because it does not offer any guarantee that information will reach the users’ smartphones or the server in case the button is out of range or the smartphone turned off/out of battery. Droplet features a hub that is in charge of solving this problem by communicating with the button should the patients’ smartphone not be in range/available. Bttn can communicate directly with the servers using GPRS, avoiding the risk of interruption in the information flow.

In any case, these three devices are only market examples of a wider concept: generic buttons to track events or activities. From the perspective of the clinical trial supply chain, this concept has a better fit in parallel with eInk smart labels than several different tracking devices depending on the study design, the diseases or the route of administration. Also, they are in comparison relatively inexpensive and their application can be escalated easily across multiple studies.

These applications have the disadvantage over previous devices that they do not automatically track the intake of drugs, but the patient has to manually activate them. However, they are likely much more efficient than simple patient diaries or apps because custom notifications can only be dismissed

30 This list is not exhaustive. Some other, more suitable commercial products may exist.

116 7.3 – Smart labels to reduce the time burden for patients participating in a clinical trial

by going to the physical location and pushing the button itself, what in theory makes it much more likely that the patient actually completes the task rather just dismissing a notification [236]. The scenario in which a patient pushes the button but does not consume the drug is rather equivalent to having an automatic-tracking device, for example CleverCap, and discarding the pills instead of taking them.

To conclude, a combination of eInk smart labels and generic tracking devices would allow to have labels with variable content, helping to deal with the complexity of clinical trials and potentially reducing delays, while at the same time enhancing patient adherence and allowing for big-data collection. Even if implemented alone, alternatives like Droplet are recommended to start gathering experience in big-data analysis, as well as to determine in pilot projects whether the patterns captured by the device are actually representative for the adherence patterns of patients.

7.3 Smart labels to reduce the time burden for patients participating in a clinical trial Historically, the delivery of interventions in clinical trials has been conducted using conventional “face-to-face” approaches [240]. As part of the conclusions of the literature review carried out in 7.1 – Literature review on the motivations and barriers for trial participation and adherence, it was determined that the time burden for patients that clinical trial participation entails is an important barrier to clinical trial participation. Mobile health technologies may help to overcome the time burden that these face-to-face visits trials imply for clinical trial patients by reducing the patient visits to the clinic via telemonitoring [71].

Telemonitoring (TM), telehealth, telemedicine, electronic monitoring or remote patient monitoring (RPM) are defined as the use of electronic communication and information technologies to allow interaction between providers and patients in different locations (e.g., wound consultation by a physician at an offsite location using audiovisual equipment, monitoring blood pressure, etc.) [241]. RPM is fundamentally sustained on two pillars:

1. Tracking the medication intake of the patients.

2. Obtaining information about the patients’ health conditions remotely.

The first point was largely discussed in the last section. It was emphasized that there are several alternatives to track the adherence to the medication regimen, although they are largely dependent on the route of administration. Still, it was concluded that standardized alternatives might be used to implement a generic smart label that can track patient adherence.

The second point, obtaining information about the patients’ health conditions, is more challenging. This is because the analyses of disease-specific conditions call for different input information, which in turn require different devices to measure different vitals. To illustrate this, a non-exhaustive literature review based on the keywords telemonitoring clinical trial was performed, and 12 random articles were selected31. The articles corresponded to 7 different diseases. Information about the devices required to successfully telemonitor the evolution of the patient was analyzed. Table 36 gathers the results.

31 One of the articles was later discarded because it presented a generic theoretical approach.

7.4 – Critical assumptions and risks in the implementation of patient-oriented technology 117

Table 36: Equipment necessary to telemonitor clinical trials of different diseases

Disease / health condition Equipment for telemonitoring Source Chronic heart failure Blood pressure measurement [242, 243] Electrocardiogram signals + Pulse oximetry [244] + Respiratory rate meter + Scale Chronic obstructive pulmonary disease Spirometer [245, 246] Pulse oximeter Bathroom scale Diabetes Blood glucose meter [247] Blood pressure reader Scale Hypertension Blood pressure meter [248, 249]

Essential tremor Wireless motion sensors [250]

Epilepsy Electronic diaries32 [251]

Multiple sclerosis Accelerometers [252]33

The conclusion that can be extracted from table 36 is that RPM calls for different means and equipment depending on patient’s health conditions, with little convergence across diseases. Although the literature reflects that telemonitoring is plausible, (current) technology cannot deal with the requirements of telemonitoring in a standardized way: developments have to be disease-specific. Disease-specific policies fall beyond the scope of the present research, and thus further research is recommended in exploring means that allow to reduce the time burden that clinical trials might pose to patients. Mitigation this barrier to participation can lead to a higher patient satisfaction and to an increased, easier enrollment of patients in clinical trials.

7.4 Critical assumptions and risks in the implementation of patient-oriented technology The research done in 7.2 – Smart labels to improve patient adherence and 7.3 – Smart labels to reduce the time burden for patients participating in a clinical trial demarcates and assesses some of the most promising patient-oriented technology trends and applications of e-devices, e-labels and electronic monitoring in the context of clinical trials. However, results from most of these studies are based on controlled pilot projects. For a large-scale implementation of such technologies in clinical trials, the following critical assumptions are to be reviewed34:

1. Every patient has access to electronic means to communicate with e-labels/e-devices: If e- labels are the only source of information of an IMP, or if e-devices are used in a clinical trial, then it has to be guaranteed that every patient (and doctor) can communicate with them. Taking this assumption might be based on two different grounds: first, the fact that (almost) everyone has access to a personal smartphone/table/e-device35. Second, that clinical trial sponsors provide the patients with all necessary equipment.

32 These diaries are fulfilled directly by patients. 33 This paper presents the results of a 7-day experiment rather than a full clinical trial. 34 Note that some of these assumptions also affect the smart labels presented in 3 – Smart labels – Market Research and Applicability to the Clinical Trial Supply Chain. 35 The validity of this argument would be restricted to the developed countries.

118 7.4 – Critical assumptions and risks in the implementation of patient-oriented technology

2. Patients are ready to integrate new technologies into their treatments: Even if patients have access to e-devices and can actively communicate with smart labels/devices on their medication packages, it is still necessary to assume that patients are willing to take advantage of technology to, for instance track their progression, set reminders or proactively obtain information from the label. To give an example, a pharma company conducted a pilot project recently in which they developed an app for 224 HIV patients to improve their medication adherence [253, 254]. Although it was found that the experience was overall positive for patients and that adherence was enhanced, some patients were not prepared to use such a system, especially older ones. This assumption is even more important when it comes to measurement devices (e.g. blood pressure, insulin etc.), where additional skills to operate equipment might be required [255].

3. The technology to be implemented is reliable enough not to cause compliance issues: For example, correct functioning of the electronic monitoring equipment is one of the main assumptions that ensure internal validity of unbiased EM measurement. A failure rate below 0.5% has been reported in past studies [256]

4. E-labels can replace paper labels from a regulatory perspective: If focusing on labels, many conceptual e-labels are based on code-scanning or even direct communication with auxiliary devices in order to transfer information about the IMP. If e-labels were to be implemented on a large scale during the execution of clinical trials, then health regulators must allow for e- labels substituting traditional paper-labels.

5. Overall benefits of telecare and EM are (or will be) higher than its cost: A basic assumption for the development of patient-oriented technology in CTs is that the sponsor (most often the pharmaceutical company) will only implement new medical technology if there is evidence that the returns it brings outweigh the required investment. Although it can generally be assumed that the experience curve will keep driving cost of technology down [257], other studies suggest that the clinical effectiveness of the use of telecare and EM that leverages on e-devices is doubtful [258].

6. Pill count can reliably be used to determine compliance: This assumption is important when it comes to devices that assess compliance by controlling the content of a medication dispenser (vials, syringes, etc.), and is not required for e-devices that actually measure patients’ vitals. Not all methods to measure patient adherence can be assumed to be equivalent. Table 37, adapted from Osterberg and Blaschke [80], shows a comparison between the most prominent ones.

Some authors suggest that there are significant disparities between compliance measurements determined from pill count and the measurement of drug presence in biological fluids [81]. A study by Denhaerynck et al. concluded that up to 62% of the patients (n=155) involved in a pilot EM clinical trial self-reported mismatches between bottle opening and actual drug intake [256]. For example, if patients ingest correctly medications that were previously removed from the bottle due to either privacy reasons or the fact that the EM bottle is unpractical or embarrassing [259, 260], adherence to the treatment would be underestimated, resulting in a mismatch with what would be obtained by measuring their vitals.

This issue can be mitigated to some extent by giving patients the opportunity to notify discrepancies [260]. The problem with this method is that it creates bias itself and limits the benefits of an automatic adherence tracking [261]. Moreover, some authors report that capsule counts is a better identifier itself than self-reports [262].

7.4 – Critical assumptions and risks in the implementation of patient-oriented technology 119

Table 37: Comparison of methods to measure patient adherence to medication regimes. Adapted from [80]

Direct methods Advantages Disadvantages Directly observed therapy Most accurate Impractical for routine use

Measurement of the Objective Expensive; variations in level of medicine in blood mentalism might lead to false impressions Measurement of the Objective; can also be Very expensive biologic marker in blood used to measure placebo Indirect methods Pill counts Objective; quantifiable; Data is easily altered by the easy to perform patient

Patient questionnaires Simple; inexpensive Easily distorted by the patient; & self-reports susceptible to error

Electronic medication Precise; tracks also Expensive; requires data monitors intake patterns; transfer quantifiable Patient diaries Correction for poor Easily altered by the patient recall

7. There is an absence of EM-induced adverse effects: EM – or automatic medication tracking – can increase the burden of patients participating in a CT, which might lead to lower enrollment rates to CTs using this methodology. Furthermore, it might be the case that EM results in an increased but waning patient-adherence [256]. There exist reported outcomes on how being monitored alters patient habits [263-265], although there is no unanimity on whether this is beneficial or not in the literature.

Even if all these assumptions are deemed reasonable, there are still some challenges, technical limitations and risks associated with the use of smart labels and e-devices in the context of clinical trials. These risks are:

1. Compliance: The initial enthusiasm with the use of new technologies might decrease after a few weeks in the trial. In combination with a lack of personal doctor-patient contact, this might lead to a decreased adherence to the treatment. [71]

2. Data privacy: There is always a risk of third parties (such as insurance companies) gaining access to personal, clinical data, whether intentionally or by accident. Legal protection in these cases is minimal when compared to the one provided in regular clinical trials [71].

3. Technical limitations: A device running out of battery while gathering some crucial data might originate compliance issues. Moreover, these devices might need to be recalibrated, or might break or be lost. In any case, non-compliance could endanger the results of a CT that is leveraging on smart technology.

4. Training: If patients are to measure some of their health data themselves, proper training is required. This might pose a burden both on the patients and on those conducting the CT.

120 7.5 – The patient perspective of smart labels in clinical trials – Conclusions

7.5 The patient perspective of smart labels in clinical trials – Conclusions The patient perspective has been analyzed to deepen further into two of the challenges currently faced in clinical trials identified in 1.8 – Challenges faced in the clinical trial industry: patient recruitment and patient adherence.

A literature review has revealed that smart labels and e-devices can help to improve patient adherence by reducing the perceived complexity of the treatment or by establish a reliable system of reminders for patients to take their doses. Moreover, one of the barriers to patient participation in clinical trials, the time burden for patients, can potentially be mitigated by telemonitoring and mobile technologies. It was also concluded, however, that there is no potential on such technologies to increase the patients’ motivation to join clinical research.

While analyzing the current market for devices that track patient adherence, it was discovered that many of them are study specific. This is not necessarily a drawback, as dedicated devices allow for tailored solutions in medication tracking. However, this might also be the reason why the use of technology as an aid to adherence has a low penetration rate in clinical trials. Other generic solutions might serve the same purpose as these dedicated devices at a lower cost, and also allowing for integration with the eInk smart labels described in previous chapters. In a nutshell, these combination would allow for next-generation labels with variable content and medication tracking capabilities, easy to standardize and potentially ameliorating four of the challenges for clinical trials: costs , delays (originates by re-labeling or lack of flexibility in the CTSC), complexity (by allowing variable content and providing more room to deal with uncertainty) and patient adherence. Additionally, this standardized adherence tracking solution reinforces the information flow that flows back from patients to the clinical sponsors, allowing to gather big data to analyze patient-level data. Given that pharmaceutical companies do not have much experience in the analysis of big data flowing back throughout the supply chain from the patients, this might be an inexpensive way to test the usefulness of patient-level data in the context of clinical trials, as well as to determine in pilot projects whether the patterns captured by the device are actually representative for the adherence patterns of patients.

As for the patient recruitment, a desk research revealed that in order to successfully telemonitor clinical trials – reducing thereby the time burden for patients – tracking medication adherence is just one of the two fundamental pillars. The second pillar, assessing the patient health status remotely, is much more difficult to tackle, because of its heavily disease-specific nature. While there is room for improvement in telemonitoring and it can potentially make trials more friendly for patients, this has been left out of the scope of the present research because a) this should be analyzed for every particular disease and b) it does not only depend on the clinical trial supply chain anymore, but also on biological considerations.

7.5 – The patient perspective of smart labels in clinical trials – Conclusions 121

Chapter Eight ………………………...... 8 Conclusions, Recommendations, 8 Reflection and Further Steps

In literature and in life we ultimately pursue, not conclusions, but beginnings Sam Tanenhaus, American historian, biographer, and journalist

This chapter summarizes the main conclusions obtained throughout the present thesis research. Based on a discussion of these conclusions, a set of recommendations are provided, focusing also on next steps to implement the technologies presented in this thesis in the clinical trial supply chain. Later, a reflection on the conclusions from several perspectives is given. Finally, the limitations of the research are outlined and suggestions for further research are presented.

122 8.1 – A glimpse back

8.1 A glimpse back This thesis began with an extended introduction on the role that clinical trials play in the drug development process. Special focus was given to the clinical trial supply chain and to the different stakeholders that form it. Moreover, five key challenges faced nowadays in clinical trials were identified: the excessive expenditure, the relatively regular delays, the increasing complexity, the low patient adherence and the problems associated with the recruitment of patients.

In the second chapter, the research proposal was defined, and a set of research (sub-) questions were posed in order to assess the potential disruptive role that smart technology can play in the context of clinical trials.

Chapter three adopted the perspective of a pharmaceutical company sponsoring clinical trials to focus on an analysis of different smart label technologies that could be used to improve the logistics of the clinical trial supply chain. An extensive desk research on different smart label applications in other similar supply chains and on previous pilot projects of pharmaceutical companies with e-labels ended up with four potential applications: item level inventory tracking, controlling of environmental conditions, fighting counterfeit drugs and labels with variable content via eInk or ePaper. After performing SWOT analyses and discussing the different applications through a set of interviews, the last potential application was selected for further research.

Because of the strict regulations to which the pharmaceutical industry is subjected, in chapter four the feasibility of such smart label from a regulatory perspective was analyzed. An intensive literature review and desk research on existing regulations was carried out, and interviews with regulatory experts were set up. That allowed to conclude that there is a regulatory fit of eInk smart labels in the clinical trial supply chain, although some of the regulations might transform into additional technical constraints. The requirements for the labeling of small or cylindrical medical containers are an example of this.

Having concluded that eInk smart labels are feasible from a regulatory perspective, the next step was to quantify the benefits that they can bring to the clinical trial supply chain. For that purpose, a discrete-event simulation model was developed. In chapter five, the conceptualization of this model was presented, along with the model boundaries, assumptions and KPIs to be considered. In chapter six, the model was used to explore the impact of smart labels in a case study of a relatively small phase III clinical trial, concluding that the benefits that eInk labels outweigh their individual price. Moreover, it was concluded that the benefit of smart labels increase as the size, the number of treatments, the primary packs per patient kit, the dropout rate, the manufacturing costs and the travel time from depots to clinical site increase, the advantages of smart labels increase too. Conversely, as the expiry date, the extension of expiry dates and the travel time of drugs from the manufacturing facilities to the depots increase, the usefulness of smart labels decreases.

In the seventh chapter, the research perspective changed to adopt that of patients, what allowed to deepen further in the challenges of patient recruitment and patient adherence to the medication. A systematic literature review revealed that there is room for technology to improve patient adherence to medication regimens: if technologic means are used to either reduce the perceived complexity of the treatment dosage patterns or to establish a reliable system of reminders for patients to take their doses, patient adherence can be enhanced. It was firstly determined that the eInk smart labels presented before cannot help to fulfill this objective by themselves, and that e-devices seem better fitted to do so. Out of the several alternatives considered, it was secondly concluded that standard e- devices such as generic buttons that can be used to track medication are recommended over disease- or route of administration-specific approaches because of their simplicity, low cost and potential to escalate rapidly. These three features fit better within the current status and experience that pharma companies have in analyzing patient level big data.

8.2 – Conclusions – Answering the research questions 123

It was also concluded that smart technology by itself cannot help increasing the motivators for clinical trial participation. Conversely, it can (partially) reduce the barriers for patients to enroll in clinical trials. This is because the time burden that trials imply for patients is an important (but not the only) de-motivator to take part in clinical research, and there is room for telemonitoring to reduce this time burden. Telemonitoring relies on medication tracking and on measuring the patient health status remotely. While the former issue can be tackled, the latter is heavily disease-specific and does not allow for standard technological solutions.

8.2 Conclusions – Answering the research questions In chapter two (see 2 – Research Definition), a research question and a set of research sub-questions were posed with the objective to guide and center the present research. In this section, each of the research sub-questions is first explicitly addressed. Then, an answer to the main research question is given.

RQ 1. What are the main challenges faced in contemporary clinical trials?

The main challenges faced in contemporary clinical trials are excessive expenditure, the relatively regular delays, the increasing complexity, the low patient adherence and the problems associated with the recruitment of patients.

RQ 2. What different types of smart labels applicable to the clinical trial industry exist?

The three main different types of smart labels applicable for the clinical trial industry are those that track inventory at an item-level, the ones that control environmental conditions and e-labels that allow variable content36.

Each of these applications can leverage on one or several different communication and interaction technologies. The main ones are GSM/GPRS, RFID, NFC, Bluetooth and GPS.

GSM/GPRS and GPS are less suitable because of their high associated costs, which are hardly justified by the advantages they bring. An exception for this might be when the application is tracking environmental conditions, as real-time information is crucial to take reactive measures that prevent drugs from spoiling.

RFID is the most common communication technology used in smart labels in other industries, but pilot projects by pharma companies in the last decade discarded its suitability for the clinical trial supply chain, at least when it comes to inventory tracking. Combined with other applications, such as variable content on the labels, RFID might become an interesting alternative.

Bluetooth and NFC are the preferred communication technologies to be used in clinical smart labels. This is because these technologies allow for an easy interaction with patients via their smartphones, without a need for especial equipment.

Smart labels with variable content can leverage on LCD or eInk/ePaper technology. LCD is relatively more expensive and relies on active batteries that had to be changed. An eInk display is cheaper than an LCD, and its battery consumption is minimum. In fact, eInk displays require zero power to display a static image. This also allows to rely on the power that can be obtained via induction from the RFID reader using passive RFID tags instead of active ones, what translates into a second iteration of cost reductions. The main disadvantage of eInk displays when compared to LCD is their refresh ratio, which is orders of magnitude lower. However, given that eInk tags are generally

36 Note that a fourth category identified in this thesis, smart labels to fight counterfeit drugs, is not presented here because results indicated a much lower suitability for clinical trials.

124 8.2 – Conclusions – Answering the research questions

updated at most once per day, this limitation does not pose a challenge, and eInk is recommended over LCD.

RQ 3. What is the fit of smart labels in the highly regulated pharma industry? What are the regulator’s views on smart labels?

No regulatory aversion exists when smart labels have the purpose to track inventory, control environmental conditions or fight counterfeit drugs. There exist pilot projects being carried out (or carried out in the past) by pharmaceutical companies with these technologies, and regulatory barriers have not been a concern in any case. The reason for this is that they add additional features to the packaging of drugs rather than neglecting or addressing differently the established regulatory guidelines.

Conversely, eInk labels with a variable content do replace the traditional labels, and hence a more thorough analysis is required. The outcomes of the present research indicate that neither European Directive nor the guidelines provided by the FDA specify explicitly that labels have to be in a physical, paper format, what in principle gives room to electronic labels. This view was supported during two interviews with experts in labeling requirements. Additionally, there is precedent that regulatory agencies have been relatively open to innovation in the past.

Moreover, eInk smart labels fulfill the regulatory requirements in regards to durability, visibility, ease of access, thickness and withholding of environmental conditions. Regulatory constraints that transforms into a technological threat are the requirements to the labeling of small, cylindrical primary containers. Even though technically it is already possible to create eInk smart labels that can be applied to this reduced, curved surfaces, the technology is not yet mature.

RQ 4. What type of smart label can be implemented in the clinical supply chain, so as to improve the logistics from the perspective of a pharmaceutical company? How can this smart label help to deal with the increasing complexity and to reduce expenditure and delays?

Interviews with several different clinical trial experts from Novartis indicated that an eInk electronic label has the highest potential to deal with the logistic challenges in the clinical trial supply chain. A discrete-event simulation model was developed in order to quantify the improvement that these labels can bring to the clinical trial supply chain.

Results indicate that eInk labels help to reduce expenditure and delays by allowing for an automatic re-labeling of IMPs across the supply chain. Re-labeling can occur because of several reasons, but the most common is an extension of the expiry dates of the drugs. The logic behind this is that very conservative expiry dates that are set for research drugs during the initial production stage. As more knowledge of the compound is gathered throughout the trial, it is common that expiry dates of already produced drugs are extended.

Sources from costs savings when eInk e-labels are implemented are a lower inventory overage (and hence a reduction in the manufacturing of drugs), lower disposal costs and a streamlined re- labeling process. The reduction in the delays arise from an optimal utilization of patient kits even when they call for re-labeling (mainly due to expiry date modification) at a clinical site level.

Additionally, the flexibility and traceability that these labels bring to the clinical supply chain also help to deal with the increasing complexity of clinical trials. This is especially important for global clinical trials, where eInk smart labels could allow for non-country dependent labels and easier reshipments across different parts of the supply chain.

The advantages of smart labels increase as the size, the number of treatments, the primary packs per patient kit, the dropout rate, the manufacturing costs and the travel time from depots to clinical site increase, the advantages of smart labels increase too. Conversely, as the expiry date, the

8.2 – Conclusions – Answering the research questions 125

extension of expiry dates and the travel time of drugs from the manufacturing facilities to the depots increase, the usefulness of smart labels decreases. For a small study (~200 patients), eInk smart labels can bring benefits of ~30€ per patient kit, while this figure increases to ~50€ per patient kit for larger, more complex trials (~800 patients).

RQ 5. What are the barriers and motivations for patients to join clinical trials? Can smart technology help in solving the problem of patient recruitment?

The most common motivator for patients to join clinical trials is the possibility to obtain health benefits and access to new treatments not reachable otherwise. In case of less severe diseases, some other motivations gain importance. Examples of these are altruistic reasons, coverage of the expenses of the treatment, collaboration with science and dedicated medical attention. In any case, from a general perspective, there is no potential for smart labels or smart devices to contribute to a higher motivation of patients to join clinical trials. Smart labels cannot enhance the health benefits of a chemical drug, do not provide access to new treatments per se, do not further contribute to the coverage of the treatment expenses (which are already fully covered during a clinical trial) and cannot improve the perception of patients of collaborating with other patients/science during a clinical trial.

As for the barriers to participate in a clinical trial, a first set has to do with patients’ own perception on costs and benefits from a clinical trial: potential side effects, the chances to be randomized to the placebo treatment group and the time burden that clinical trials imply are the main de- motivators.

A second set of barriers to participation are external to the patients. These are recommendations by doctors or physicians on whether or not the patient should participate in a trial, demographic considerations and awareness of clinical trials.

There is room for new technologies to mitigate some of the barriers from the first set: specifically, mobile health technologies may help to overcome the time burden that clinical trials imply for patients by reducing the patient visits to the clinic via telemonitoring. Note however that some other barriers, such as the randomization process, potential side effects, physicians’ attitudes towards CTs or demographic considerations are less likely to be influenced by mobile or social technology.

Focusing on reducing the time burden, remote patient monitoring is fundamentally sustained on two pillars:

1. Tracking the medication intake of the patients.

2. Obtaining information about the patients’ health conditions remotely.

While the first point, discussed in the next RQ, is feasible, the second point is more challenging, because telemonitoring calls for different means and equipment depending on patient’s health conditions, with little convergence across diseases. Although the literature reflects that telemonitoring is plausible, (current) technology cannot deal with the requirements of telemonitoring in a standardized way: developments have to be disease-specific. Disease-specific policies fall beyond the scope of the present research, and thus additional research is recommended in exploring means that allow to reduce the time burden that clinical trials might pose to patients. Mitigating this barrier to participation can lead to a higher patient satisfaction and to solving the problem of patient recruitment.

126 8.2 – Conclusions – Answering the research questions

RQ 6. What determines patient adherence during a clinical trial? How can smart labels increase adherence?

Similarly to the barriers and motivations for patients to join clinical trials, patient adherence to the treatment is largely determined by condition characteristics (chronicity, cognitive impairment, psychological disorders), effectivity of the medication, side effects and demographic considerations, which cannot be directly influenced by the inclusion of new technologies in the clinical trial supply chain.

Some other factors affecting patient adherence to medication regimes might however be positively influenced by smart labels and e-devices. This conclusion is drawn from the fact that the perceived complexity of the treatment, the number of daily doses and the presence of holiday periods are inversely correlated with patient adherence. If new technologic means could be used to either reduce this perceived complexity or to establish a reliable system of reminders for patients to take their doses, patient adherence could be enhanced.

Two different approaches were studied: devices that automatically track drugs dispensation by the primary containers and generic devices that call for patient interaction to monitor the adherence to medication.

The first approach allows for tailored solutions to track medication, normally in an automatic way. For example, special containers that automatically count the number of pills going out of a bottle can be used (see figure 42). The information they gather can then be shared with the clinical trial sponsor. However, this advantage might also be the reason why the use of technology as an aid to adherence has a low penetration rate in clinical trials: the call for specific devices that are dependent on the route of administration, or more generally on diseases or particular clinical studies makes this alternative expensive and very difficult to generalize and escalate.

Figure 42: Example of tailored solutions for medication tracking. Adapted from [221], [222] [223] and [224].

Other generic solutions might serve the same purpose as these dedicated devices at a lower cost, generating information that flows back from patients to the clinical sponsors, allowing to gather and analyze patient-level big data (see figure 43). Given that pharmaceutical companies do not have much experience in the analysis of big data flowing back throughout the supply chain from the patients, this might be an inexpensive way to test the usefulness of patient-level data in the context of clinical trials, as well as to determine in pilot projects whether the patterns captured by the device are actually representative for the adherence patterns of patients. In this regard, an initial assumption would be that patients actually adhering to medication are highly correlated with patients tracking their adherence correctly. In addition, these generic solutions have the advantage that they can be easily integrated in patient kits and be combined with other smart technology solutions, such as eInk e-labels.

8.2 – Conclusions – Answering the research questions 127

Figure 43: Example of a generic solution for medication tracking that can be used regardless of the dosage form. Adapted from [266]

The reader interested in knowing more details of a particular research sub-question should follow the links indicated in table 38.

Table 38: Research questions and sections where they originally were addressed

Research sub-question Original research and additional details

1 1.8 – Challenges faced in the clinical trial industry

3 – Smart labels – Market Research and Applicability to the Clinical 2 Trial Supply Chain

4 – The Regulatory Perspective: Do Smart Labels Fit in the Current 3 Regulatory Framework? 5 – A Discrete-Event Simulation Model of the Clinical Trial Supply 4 Chain 6 – Case Study: Smart Labels on a Phase III Clinical Trial 7.1 – Literature review on the motivations and barriers for trial participation and adherence 5 7.3 – Smart labels to reduce the time burden for patients participating in a clinical trial

7.1 – Literature review on the motivations and barriers for trial 6 participation and adherence 7.2 – Smart labels to improve patient adherence

Answering the different research sub-questions allows to address the main research question: what are the threats and opportunities in using smart technology to overcome the challenges faced in the supply chain of contemporary pharmaceutical clinical trials?

To begin with, it is interesting to mention that no single technology was found to be able to help with all the challenges faced in the clinical trial industry. eInk smart labels were to be a promising

128 8.2 – Conclusions – Answering the research questions

alternative to deal with the complexity, delays and increasing costs faced in the logistics of clinical trial supply chains, while generic tracking devices were found to be a potential solution to ameliorate the problem of low patient adherence to medication during clinical trials. In addition, no standardizable technological means were found to help with the problem of low patient recruitment.

Furthermore, it is also remarkable that the distinction between these two different approaches also finds a counterpart in the two pillars of clinical trial supply chains: the material flow and the information flow. Medication tracking devices reinforce the information that flows back from patients to pharmaceutical companies.

Smart labels have an impact too in the information flow, both because they can be used to stakeholder-specific information and because, if combined with RFID, they can also reinforce the visibility that sponsors have on their supply chain. This is however a potential opportunity not directly address in the present research37, where the role of smart labels in the information flow is mainly in the direction sponsor clinical sites.

In addition, smart labels also affect→ the two-ways of the material flow. Being the labels of the IMPs, smart labels are actually part of the material flow themselves on their way from the manufacturing facilities to the patients. Besides, from a higher-level perspective, they have an impact in the logistics of the material flow by reducing the inventory overage, adding flexibility and reducing the time needed for re-labeling processes. Finally, because smart labels largely reduced the number of patient kits to be destroyed, the material flow in the direction clinical sites sponsor is also affected.

In what follows, the different opportunities, advantages, threats and→ disadvantages of either of these approaches – smart labels and medication tracking devices – are summarized:

. Opportunities and advantages of using eInk smart labels

1. A better variable cost structure once smart labels are implemented, resulting in lower supply chain expenditure.

2. They allow for variability of content (such as the expiry date) once the patient kits are at the clinical site level, decreasing potential delays due to expiration of drugs and disposal costs.

3. These labels bring additional flexibility to the clinical trial supply chain, what helps to deal with complexity of large trials. This is a benefit per se, because carrying out large clinical trials creates new market opportunities, resulting for example in the treatment of new indications.

4. Faster re-labeling at the regional depots, what is especially important in the case of large trials with several primary containers per patient kit.

5. Elimination of booklets and easier reshipments across countries, depots and even clinical sites.

6. This approach has been demonstrated to fit in the current regulatory framework surrounding clinical trials.

7. The variable content can also be used to display stakeholder specific information as patient kits move through the supply chain, or to adapt the labels for patient that require slight modifications on their dosage patterns.

37 This is because the main methodology used to explore policies in the clinical trial supply chain – discrete-event simulation – is not well suited for this analysis, as full traceability of the different entities is always ensured.

8.2 – Conclusions – Answering the research questions 129

8. Enhanced inventory compliance standards could be achieved by using eInk smart labels to track inventory too.

9. Gathering experience and assuring a real option38 for the future. By investing in smart label technology in a controlled environment like clinical trials, even it not profitable, pharmaceutical companies will have the experience, technology and system readiness to rapidly implement new generation of smart labels in the future, or to implement smart labels in the commercial drug supply chain.

. Threats and disadvantages in using eInk smart labels

1. The upfront investment requirement is in the order of hundreds of thousands of euros, at best, for a large pharmaceutical corporation.

2. A system validation is required to ensure the high compliance standards required in the context of clinical trials, what may cause additional costs and delays.

3. In implementing such a system, coordination with the clinical sites would be required in order to setup the necessary equipment and to train the staff. A high degree of heterogeneity should be expected given that clinical sites are very different in terms of size, technological means, training and staff.

4. There is a potential technical threat in labeling small, cylindrical containers. Alternatives already exist, but the technology might still not be mature.

. Opportunities and advantages in using generic medication tracking e-devices

1. Potential increase in patient adherence to medication regimens by reducing the perceived complexity of the treatments, what in turn improves the prognosis of clinical trial patients.

2. These devices reinforce the dual character of the clinical trial supply chain: not only transferring products down to the patient level, but also retrieving information back from patients to the trial sponsor.

3. This alternative is relatively inexpensive and easy to test in pilot projects.

4. Standard approach, easy to escalate both within and across clinical trial studies.

5. From a business perspective, this is a differentiator in the execution of clinical trials and might allow marketing considerations.

6. These devices also pose a real option, allowing pharmaceutical companies to gain experience in big data analysis and to leverage on the study of patient’s patterns to improve the efficiency of future clinical trials.

. Threats and disadvantages in using generic medication tracking e-devices

1. The assumption that pill count can reliably be used to determine compliance has to be taken.

2. Another critical assumption to be taken is that every patient has access to technological means (e.g. smartphones) to communicate with the e-devices or e-

38 A real option is the right — but not the obligation — to undertake certain business initiatives, such as deferring, abandoning, expanding, staging, or contracting a capital investment project.

130 8.3 – Recommendations

labels attached to the medication. This might be a limitation depending on the country under consideration.

3. There is a risk that some patients are not ready to integrate new technologies into their treatments, for example those not accustomed to technology.

4. Informed consent and confidentiality considerations have to be taken into account, especially when it comes to dealing with ethics committees.

5. This technology has to be proven reliable enough not to cause compliance issues.

6. These devices serve to overcome a limited spectrum of barriers to proper medication adherence. Some other biologic (such as side effects, condition characteristics), pharmacological (such as the randomization process, the effectivity or the medication) and demographic factors cannot be directly influenced by the inclusion of new technologies in the clinical trial supply chain.

8.3 Recommendations Based on the conclusions presented in the last section, recommendations for clinical trial sponsors in regards to the implementation of technology in their clinical trial supply chains are presented below. Note that although the recommendations are mostly targeted to pharmaceutical companies because of their relatively much higher influence on the setup of clinical trial, they take into consideration other stakeholders analyzed throughout the research, such as patients and regulatory entities.

8.3.1 eInk smart labels – assessment and implementation eInk smart labels have potential to save costs and reduce delays in the clinical trial supply chain. Furthermore, they bring flexibility to the clinical trial supply chain and help managing the complexity of global, large clinical trials: the current trend as pharmaceutical companies fight against the problem of low patient recruitment. This flexibility is brought not only by an easier handling of supplies, but also by allowing to rapidly adapt to different country specific requirements and to re- label IMPs.

However, the development of a smart-label ecosystem is likely to be costly and relatively slow due to the need to coordinate with other stakeholders. In addition, the validation required to ensure high compliance standards might take from several months to some years.

Because of the resource intensive character of such an implementation, it would be advisable to wait for a formal assessment of a pilot project until:

1. The new European Clinical Trial Regulation is released. This Regulation will come into effect by October 2018 at the latest. This new Regulation might bring some new opportunities for smart labels, but also threats for established systems. An example of a new opportunity is the fact that the Regulation establishes that investigational and auxiliary medicinal products should be appropriately labelled […] to allow for the distribution of those products to clinical trial sites throughout the Union [267].

2. eInk technology on small, curved shapes to develop further, lowering the costs and improving its reliability. In this thesis it has been demonstrated that the required technology already exists, but it is expected that the price will substantially go down as it is implemented in other sectors, mostly targeted at reading solutions (newspapers, e-readers etc.).

Put into perspective with the pace at which technological and digital opportunities are implemented in the context of clinical trials, very slow when compared to other industries because of the long

8.3 – Recommendations 131

duration of both the preparation and execution of trials, the above events will occur in a relatively short period of time: several months to a maximum of two years (when the new Regulation is at the latest released).

When it comes to implementation of smart labels, conducting pilot tests is highly recommended. Pilot tests of these technology are however more difficult to set up than with other IT solutions. The reason for this is that they call for implementation at the latest stage of the clinical trial supply chain (i.e., the clinical sites), and thus the impact of conducting a pilot project for a full study is similar to that of a full implementation. In other words, a small scale prototype for a specific clinical trial would involve a lot of resources, because it would imply the implementation and validation of systems and procedures to communicate with the labels at several different final clinical sites.

A second approach for setting up a pilot project would to make it clinical-site specific instead of study-specific. This has the advantage to be much less resource intensive, because the whole dynamics of smart eInk smart labels can be tested by coordinating with a single clinical site. The inconvenience is that, if a single clinical site is to receive smart labels, then the patient kits to be sent to the site have to be differentiated form the others from the first echelon of the supply chain (i.e., after being produced and before arriving at a regional depot). With this approach however the system could be validated, what was identified as one of the main threats in the last section. Another alternative within this second approach would be to select large, well-equipped clinical sites for the pilot testing. Regardless of the communication technology being used – let it be RFID, NFC or WiFi, for example – most big hospital are already equipped to handle it. This could lead to savings in installing the required equipment at clinical sites, and also to a streamlined validation process if the staff is used to working with these technologies.

8.3.2 Generic adherence tracking devices – assessment and implementation Generic tracking devices are one of the two alternatives presented in 7.2 – Smart labels to improve patient adherence. The advantage of this approach over tailor-made devices to track patient adherence is that the former are relatively inexpensive, easy to use and simple to escalate.

Because of these advantages, testing of such a system in a pilot project is recommended in the short term. In addition, there are several reasons why the planning and execution of a pilot project with a generic medication adherence tracker is relatively easy to handle: (i) it can be applied to any study, regardless of the drug being tested; (ii) even within a study, these devices can be implemented following a staggered approach, supplying them to some patient first and then escalating (iii) at a cost of only 30-90€ per patient – and for the entire trial – a pilot project in a relatively small clinical trial with ~200 patients like the one presented in 6 – Case Study: Smart Labels on a Phase III Clinical Trial, costs would be 6,000 – 18,000€, what is a low amount in comparison to other sources from costs in the clinical trial supply chain (iv) these devices are not substitutes for any other aspect in the execution of clinical trials, but offer additional features on top of the regular patient kits. Hence, reliability or non-compliant issues would not be critical.

Such a pilot project would allow pharmaceutical companies to generate insights into the usefulness of such a system, while at the same time developing know-how for a potential future escalation. This would also help to strengthen the dual character of the supply chain (products flow down and information flows up) and might serve also as a marketing strategy, as dosage-reminders would bring added value for patients and might set the basis for a differentiation strategy.

8.3.3 Other recommendations Some additional recommendations can be given based on what has been learned throughout this thesis:

132 8.4 – Reflection on the results and conclusions

1. The underlying reasons behind low patient adherence and low patient recruitment are not exclusively linked to the clinical trial context. The time burden for patients, for example, also affects millions of people worldwide suffering chronic diseases. The clinical trial industry should pay attention to developments in the global healthcare sector, as initiatives in telemonitoring or electronic monitoring triggered by hospitals can actually be applicable to clinical trials. A heavy disease-specific character is expected, so this might have to be tackled by the different therapeutic areas within a pharmaceutical company.

2. The presence, influence and power of patient communities are growing, mostly leveraging on the growth of interconnectivity via the internet. This is a great opportunity to tackle another of the barriers for clinical trial participation identified in this thesis: the lack of clinical trial awareness. Clinical trial sponsors should invest resources in making sure that information about new treatment alternatives that they develop get to the right patients.

8.4 Reflection on the results and conclusions In 8.2 – Conclusions – Answering the research questions, explicit answers to the research questions posed in 2 – Research Definition have been given. This section reflects on the conclusions drawn by analyzing and comparing them with the state of the art found in the literature for different applicable fields: the conceptualization and results of CTSC models, the current trends in clinical trials, a generic SCM perspective and the implementation of electronic paper labels in other industries. The analysis of the literature does not intend to be exhaustive, but rather serve to either validate the conclusions of this thesis or to open new discussions and lines of further research.

8.4.1 Reflection on the model conceptualization of a clinical trial supply chain and the results obtained Some of the results of the simulation model can be checked against the literature with relative ease. To begin with, after an extensive optimization exercise for a CTSC, Fleischhacker et al. indicate in their conclusion chapter that the heuristic of pushing inventory to sites becomes increasingly inefficient as trials become more global, […] while at the same time the heuristic to hold all the inventory centrally also performs quite poorly [27]. This was indeed the case for the original case study and for the scenario with smart labels. In both cases, the optimal strategy was found to fall between these two policies, by combining safety stock both centrally and in the clinical sites.

One of the most complex part of the simulation model developed is the inclusion of IRT systems, which determine the resupply dynamics of the CTSC. This approach was taken after interviews with clinical trial experts at Novartis, and a reflection based on the literature reinforces this decision. After analyzing several different alternatives (e.g. upfront inventory stacking or fixed time period resupplies), Peterson et al. [98] concluded that the use of computer-controlled resupply triggers result in efficiency savings in terms of reduced overage requirements. This view is also shared by other studies [268] and specialized pharma articles [269, 270].

When it comes to the main parameters affecting the optimization process of a CTSC, Abdelfaki et al. concluded that the stocking levels, the resupply quantities and the shipment frequency determine the process of optimizing a site supply strategy [36]. In the model developed as part of the present thesis, stocking levels of both clinical sites and regional depots are also one of the key parameters used (see 6.2 – The status quo – analysis and optimization of the safety stock and 6.3 – Implementing smart labels in the clinical trial supply chain). Moreover, resupply quantities are also an important part of the model conceptualization, subjected to real-time optimization in an endogenous via the modeling of the IRT systems, as discussed in paragraph above. Frequency of reshipments is another parameter of the model. During the case study, it was maintained constant following input received from clinical trial experts during the model conceptualization. However, it was varied during the parameter sensitivity analysis carried out in 6.4 – Effects that different parameters have in the

8.4 – Reflection on the results and conclusions 133

usefulness of smart labels to test the usefulness of eInk smart labels. Thus, the model developed encompasses the parameters used in the literature, and in addition features more than 20 additional ones that allow to adapt it to different clinical trial studies or even different CTSCs.

Comparison with other CTSC optimization papers also allow for a self-critique. For example, after an optimization research carried out by Chen et al. [179], part of the conclusions were that production (i.e., drug manufacturing) constraints might lead to lower customer service levels. In 5.2.7 – Manufacturing of drugs and shipment to the regional depots, it was explained however that during the present research it has been assumed that production is not constrained, even though the stochasticity of production is accounted for. This means that new batches are not immediately available when required, but when a new batch is created, it can always be big enough to meet the demand. This assumption was the result of expert input from Novartis, following the logic that the same manufacturing facilities are used for the production of commercial drugs and clinical trial drugs. Because the size of the batches of commercial drugs are orders of magnitude larger than those for clinical trials, the production capacity can be consider more than sufficient to meet trial demands. However, this apparent contradiction with Chen et al. might indicate that results might not be generalizable to other, smaller pharmaceutical firms with more limited production capabilities, or to research facilities dedicated to clinical trials only.

8.4.2 Reflection of the conclusions against the literature of clinical trial supply chains Last section reflected on the development of the model and the direct conclusions obtained from it. In this section, a bigger-picture perspective is taken to discuss about the recommendations given from a more generic point of view, that of the clinical trial supply chain – regardless of optimization techniques being used.

Bielmeier and Crauwels conclude in their paper Managing the extended R&D supply chain that pharma companies can obtain a competitive advantage by developing innovative patient oriented supply chains [21]. The three pillars on which they base this concept are:

1. Better drug product identification that enables to fulfill the compliance of regulatory requirements more easily.

2. Zero-stocks, by monitoring in real time patient enrollment data.

3. Site and packaging control systems that allow to link orders directly to patients.

The ePaper smart label opportunity presented shares a wide range of similarities with the concept presented by these authors. The third point presented above is fully aligned because the variable content of these smart labels allows for stakeholder specific – and thus patient specific – content. For example, if for some reason a patient has to take a dosage different from the standard one, these labels would allow to customize the information for him. Also, as presented in the SWOT analysis in 3.4.4 – Application #4: Labels with variable content, an opportunity exists to use the communication technologies embedded in these labels (e.g. RFID) to allow for a better product identification and item level inventory tracking, what relates back to the first point presented by Bielmeier and Crauwels.

Conversely, the second point presented by Bielmeier and Crauwels has been proven not to be possible, at least with the current technology and for global trials. Even though real time patient enrollment data has been assumed to be possible via IRT systems, neither the status quo nor the scenario with smart labels would lead to satisfactory outcomes – especially in terms of reliability factor or customer service levels – with zero-stock policies. It is true that it has been proven that eInk smart labels would largely reduce the inventory overage, but the lead times in transporting drugs during the execution of clinical trials make it impossible to reduce inventory to zero – at least with the

134 8.4 – Reflection on the results and conclusions

current state of technology and for global trials – without affecting reliability of supply. Hence, results of the current research disagree with the second point presented above.

Another potential source of competitive advantage for pharma companies when it comes to supply chain optimization is the movement towards more pull-driven and demand based supply chains. An example of this is the work of Srai et al. [97], also shared by Santa Maria [34]. In the context of clinical trials, and within this pull-driven approach, these authors pointed out the necessity to rapidly escalate the delivery of drugs if required. Current problems to escalate are summarized in one of the challenges identified during the present research: the complexity of managing global supply chains and meeting different regulatory requirements. It has been emphasized that the smart label-based solution proposed helps to deal with this complexity. On the one hand, it allows for a centralized control (even if activities are decentralized), coping with the complexity of escalation more easily. On the other hand, variable content allows to deal with country specific regulatory requirements almost in an automated way, allowing for more flexibility and thus faster escalations.

In a similar line of argumentation, Gram et al. [93] also regard labeling as a key element to bring flexibility to the clinical supply chain. These authors define the concept of Kit Decoupling Point (KDP) and Just in Time (JIT) labeling. KDP is the point when medication becomes country specific, while the concept of JIT labeling applies to the labeling of outer packs per “ship-to-depot-order” instead of per batches or packaging order. The authors conclude that delaying the KDP as much as possible and applying a JIT labeling approach can largely reduce the challenges of the supply chain – mainly the complexity originated from different country regulatory requirements and the delays originated by re-labeling. For this purpose, they suggest the use of NFC or RFID tags to convey part of the label information. A limitation for this approach identifies in this thesis – and also recognized by the original authors – is that current regulations do not allow the replacement of visual, directly readable information with information contained in e-labels that can be only accessed using special equipment39. Thus, it can be concluded that the outcomes of the present thesis align to a large extent with the one of these authors, but an innovative alternative means that does not face regulatory aversion is presented instead: eInk smart labels that can be controlled from a central location.

When it comes to the implementation of new technology into the clinical trial supply chain, the threats and disadvantages considered seem to cover those found in the literature. Marla, for example, emphasizes the importance of the ability to integrate technology across different stakeholders of the CTSC [75], a view supported by other authors [72]. This potential threat was identified in the context of eInk smart labels, especially when it comes to enabling a proper ecosystem for a centralized control once they are at the clinical sites. The requirement for elevated upfront investments and validation of the system itself are other challenges identified by this author that are also pinpoint in the present research.

Despite these limitations, the literature still suggests that the clinical trial industry should move in this direction [72]. The argumentation why these challenges are worth taking is that by connecting services, clinical trial sponsors can reduce risks and non-compliance issues while enabling responsiveness and flexibility to adapt to real-time events during the study [72].

8.4.3 Reflection on the results from a global supply chain management perspective This thesis contributes to supply chain management theory by quantifying the value of flexibility in clinical trial supply chains. The means to gain this flexibility is through the implementation of IT solutions – smart labels – in the CTSC. After a literature review on 34 publications of application of

39 In addition, another limitation found in the research of Gram et al. [93] is that they seem to mix different smart label concepts. While at first they present simple NFC/RFID tags to convey information, then they also present temperature monitoring control as a regular feature. In this thesis, it has been demonstrated that the active sensors required for temperature monitoring result in a completely different approach (and costs of orders of magnitude higher) than simple, passive tags (see 3.2 – Market research on smart labels).

8.4 – Reflection on the results and conclusions 135

IT in Supply Chain Management (SCM), Gunasekaran and Ngai concluded in their acclaimed paper that the six major components of IT-enabled SCM are [32]: (i) strategic planning, (ii) virtual enterprise, (iii) e-commerce, (iv) infrastructure, (v) knowledge and IT management and (vi) implementation. The authors also emphasize that the foundations of a well-developed IT-enabled supply chain management reside in the strategic planning and the infrastructure. The research carried out in this thesis was mostly oriented towards the strategic planning of smart labels into the clinical trial supply chain.

A self-critique is that the infrastructure required – which in the case of smart labels in the CTSC can be hardly separated from the implementation and validation it requires – has not been considered in such a great level of detail. Throughout the research, the order of magnitude of the investment to implement a smart label-enabled ecosystem has been indicated, and the validation and implementation of such an IT landscape has been regarded as a potential threat since smart labels applications were first presented in 3.2 – Market research on smart labels. The analysis however stopped there. The rationale behind this is that such an IT development is largely dependent on the company under consideration. Previous IT experiences with smart labels, current technologies being used in the supply chain, the level of globalization of the trials conducted, the supply chain structure in terms of number of central/regional depots and the company internal structure themselves are factors largely affecting the analysis of how to implement and validate a suitable infrastructure for a smart label enabled ecosystem. In the literature, the implementation of RFID ecosystems is assumed to be a function of the benefits of the firm corrected for economies of scale [271]. This thesis never intended to be company specific, but rather to unveil the potential of a new technology – which had never been considered before to be applicable to the CTSC – from a strategic perspective. Furthermore, even with this approach results obtained in the literature are accurate in the order of magnitude, at best.

To conclude the analysis of IT implementation, further steps would imply (a) the planning and development of such an IT enabled landscape and (b) the integration and coordination with other stakeholders that would be part of it (e.g., the clinical sites). The key role of integrating business processes is also emphasize by other authors [272], who suggest using cross-functional teams to cope with it. This is very likely to pose additional challenges both for researches and for pharmaceutical companies themselves, but might be the way for competitive advantage. This is also shared in one of the last conclusions that Gunasekaran and Ngai highlight in their paper: successful strategic information systems are not easy to implement in SCM. They require major changes in how a business operates internally and with external partner [32].

Leaving aside IT implementation, one of the main characteristics of a CTSC is its length – it normally takes months from the manufacturing of a drug until its consumption – with a relatively short product life. However, a unique feature is that this short product life can be extended when more information about the stability of the compound is obtained. General literature on supply chain management indicates that these type of supply chains inhibit a firm's ability to respond quickly to consumer requirements [79], and electronic means that provide a direct relation between these consumers and the manufacturers are proposed. From a general perspective, the research and results of this thesis support that this view applies to the CTSC. In the CTSC, responding to customer requirements can apply either to adapting drugs to the patients or to meeting regulatory guidelines. In both cases, this is a challenge that calls for re-labeling, and a new electronic means – electronic paper smart label – is proposed to bind drug packages to the clinical trial sponsor more easily, regardless of their stage in the CTSC, allowing thus for more flexibility in response.

Discrete Event Simulation (DES) is one of the most frequently used modeling approaches – above system dynamics – used in the literature to optimize supply chains [273]. The main motivations to use DES are (i) the easiness to include endogenous dynamics and (ii) the relative simplicity of

136 8.4 – Reflection on the results and conclusions

modeling [274]. DES also captures uncertainty and complexity in a way that is convenient for the analysis of supply chains [275]. These reasons were part of the motivation to use DES as the main methodology to explore the CTSC in this thesis. However, stochastic models are not without limitations. Some authors suggest that too much attention is being paid to these types of models, partially neglecting the assumptions regarding demand distribution and stationarity that they require [79]. These limitations were mitigated in the present research by country-specific demand distributions, which were handled by an endogenous demand-response system, via the modeling of IRT. Still, I acknowledge that some other assumptions concerning stationarity were taken: the lack of transport accidents, the constant dropout rate over time and the lack of holiday periods are examples for this. However, I believe that these are not the critical assumptions taken in the model. In understanding the dynamics in a CTSC with smart labels, the uncertainty stemming from study- specific parameters (e.g. price of drugs, number of indications being tested, primary containers per outer pack etc.) are substantially more relevant than demand distribution and stationarity assumptions used.

From a general supply chain management perspective, an apparent limitation of the present research is that transportation costs, which according to some authors might be a large component of total logistics costs [79], are not considered as part of the model conceptualization. The rational for this was that the clinical trial supply chain and the commercial supply chain for a given company often share means of transport. Hence, even if fewer shipments for a clinical trial are required, the logistics effort might be similar – or even the same – if a similar pattern of commercial drugs is already being shipped to the countries involved in the trial. This is company and study specific (i.e., depends on the countries involved in a clinical trial, which in turn depend on the clinical trial study protocol, and thus complicates the exercise of quantifying the savings that fewer shipments in the clinical trial supply chain actually bring to a company. In any case, considering this cost would lead to an even better scenario for the smart labels presented in this thesis, because the associated decrease in inventory overage would imply fewer shipments.

8.4.4 Reflection on the impact of eInk labels using analogies in other industries Putting into perspective the findings regarding smart labels with variable content in the clinical trial supply chain is complex, mainly because of the peculiarities of (a) the supply chain itself and (b) the technology – electronic paper smart labels – explored in this thesis, which is relatively uncommon as of 2016.

Particularities of the CTSC are the finite patient time horizon, the short life cycle of products, the possibility for re-labeling of expiry dates, the relatively low throughout, and the fact that products are only used when they reach the last echelon of the supply chain (i.e., the patients). Because of these distinctive features, the CTSC shares some features – but not all of them – with perishable supply chains and with supply chains for spare parts. No valid analogies were found in spare parts supply chains. However, some can be found by focusing on perishable supply chains.

One of the best examples is probably food supply chains, where it is possible to find previous research on the potential of RFID tags. Note however that the core of this thesis is not the RFID technology itself – which is just a means to communicate and update the content of the labels, as it could be NFC, Bluetooth, Wi-Fi or GSM – but the re-labeling of products (in essence, the variability of label content), mostly due to expiration.

To give an example, Kelepouris et al. noted that clear benefits to the food supply chain from implementing RFID tags are continuous traceability, reduced spoilage and swift identification [276], a view widely shared in the literature [67, 277]. These advantages are in line with the advantages for RFID tags in the CTSC identified in 3.4.1 – Application #1: Item level inventory tracking, but do not help in solving the issues of the perishability of the products, with which a better analogy can be established.

8.4 – Reflection on the results and conclusions 137

Grunow and Piramuthu take one step further to actually use smart labels to have deeper insights into the perishability of food products, arguing that the expiry date may or may not reflect the true state of the perishable food item [278]. The approach they take is however similar to that presented in 3.4.2 – Application #2: Controlling environmental conditions, as it is limited to tracking environmental conditions for a better assessment of the state of food products. This approach is also taken by other authors [277, 279].

These authors also mention in their paper Norway’s Keep-it Technologies [280], a firm that presented a pseudo-smart label40 that is actually comparable – to some extent – to the smart labels presented in this research. The method they use provides the product's remaining shelf life based on elapsed time and its exposed temperature range beginning with its life at the production line [278]. This serves as a substitute for the traditional printed expiry date that can precisely indicate the freshness of food products. The firm claims that this product might have potential to reduce the disposal of food in good status, which is estimated at NOK 12 billion (~ EUR 1.3 billion) in Norway only [280]. This has the similarity with the outcomes of this thesis that using technology to have dynamic expiry dates in perishable supply chains can actually lead to reduced waste. However, the second main advantage of the smart labels presented in the current research – the flexibility that they bring into the CTSC and the associated reduction in overage – does not have any analogy in this product. This is easily understandable if the differences in the natures of drug expiration (typically after several months) and food expiration (in the range of days) are accounted for.

A second analogy comes from the work of Tromp et al. [281]. The starting point of these authors is that, in the majority of perishable food items, the expiry date that products have attached is normally on the conservative side. This is because of the uncertainty in (a) the initial number of microbes in the food and (b) the storage and transport temperature conditions [281]. A Dynamic Expiry Date (DED) is then suggested. The DED is based on a model that calculates the number of microbes (and thus the expiry date) as a function of the number of microbes and the temperature, allowing for adjustments of the DED. The authors claim that this approach would allow to decrease opportunity losses by 80%. A major limitation of this paper is that the authors do not deal with the physical update of expiry dates on the labels, which would be the most interesting part to establish a proper analogy with this thesis. Instead, they conclude that a weakness to overcome […] is that shelves of retail outlets may be filled with a lot of different expiry dates, while nowadays mostly only one or two expiry dates are available of one article. This may introduce a rather chaotic presentation to the consumer [281]. Thus, also this analogy strengthens once again the argumentation that dynamic expiry dates reduce waste, the technological means to actually achieve this DED cannot be discussed based on this article. The smart labels presented in this thesis are suggested as an approach if expiry date extensions are desired.

A different approach to put into perspective the findings of this thesis in regards with eInk smart labels is to analyze the retail sector, where Electronic Shelf Labels (ESL) based on eInk and communication technologies are increasingly being used. Although this ensures that the technologies under analysis are comparable, the main drawback is that no comparison or analogies between supply chains41 can be established.

The main advantages of ESL in retail stores are a reduction of operational costs, the elimination of pricing errors and a better margin optimization via dynamic prices [282]. The first benefit finds a direct relation to the application of eInk smart labels in clinical trials via the reduction in re-labeling costs. Still, results of the case study conducted in chapter six indicated that the biggest savings do not

40 The alternative by Keep-it Technologies is defined as a pseudo-smart label because it does not actually embed communication technologies, which are a common feature in smart labels. Still, it possess all the other typical characteristics. 41 Note that the use of ESL in the retail sector does not even follow a supply chain logic.

138 8.5 – Research limitations and suggestions for further research

originate in a reduction of re-labeling costs, but in reduced overage and waste. The second benefit can hardly be related to the CTSC because most errors are and would still be originated by label designers, and the third one does not find an analogy in the packaging of IMPs whatsoever.

When it comes to key technology adoption questions, more similarities between ESLs and e-labels for the CTSC can be found. In the retail sector, some of these are the possibility to capitalize on technology already existing in the store, ensuring price integrity via acknowledgements of price changes, the need for different label sizes, and the possibility for both individual stores and the head office to control prices [283].

These implementation issues find direct analogies with the adoption of eInk smart labels in the CTSC. The possibility to capitalize on existing technology can be related to the opportunity to leverage on existing communication equipment (e.g. RFID readers) in regional depots and (some) clinical sites to lower down costs. Moreover, as discussed in the last section, this is an opportunity to conduct pilot projects at relative ease. Ensuring price integrity transforms into guaranteeing that compliance standards with regard to labeling are met during the execution of clinical trials. This is a threat discussed in this thesis that calls for additional layers of validation of the system. The need for difference label sizes – and shapes in the case of clinical trials – has also been widely discuss. Finally determining who is responsible for price changes in the context of the CTSC is another potential discussion, although during this thesis it has been assumed that this would be limited to the clinical trial sponsor because (i) this allows for a centralized management, which typically allows for higher compliance standards and (ii) the underlying reasons why eInk smart labels allow for re-labeling is that qualified and certified personnel (i.e., that from the sponsor) can perform these activities remotely.

Overall, the comparison with the retail sector allows to validate that the main questions regarding technology implementation have been accounted for in this thesis. The comparison is however far from being one-to-one. On the one hand, ESLs do not operate in a supply chain context. On the other hand, some other characteristics important for ESLs, such as the ability to accommodate large volumes of price changes, are not relevant in the context of the CTSC.

8.5 Research limitations and suggestions for further research Many of the limitations of the present research were known ex-ante, and were presented in 2.6 – Research limitations.

In addition to those, when it comes to eInk smart labels, the exclusion of studies with several indications during the model conceptualization, the huge variability range of the costs to manufacture and package different types of chemical compounds and the highly study-dependent impact of variable content smart labels in the clinical trial supply call for additional, study specific research. Before considering a pilot project with this type of e-labels in a new clinical trial, it is advised to thoroughly include in the model specific study parameters and also to consider the setup costs, which are company-specific.

Overall, however, the main research limitation of this thesis is that it was not possible – because of resource constraints – to conduct a pilot project for either of the technological solutions found to mitigate the challenges faced in the clinical trial industry. This would be the logical next step in gathering more insights and assessing first-hand the suitability of both approaches.

Moreover, further research is also advised in exploring means that allow to reduce the time burden that clinical trials might pose to patients. Telemonitoring has been identified as a potential (partial) solution to the barriers to clinical trial participation. However, telemonitoring calls for disease specific research, as the suitability of at-home equipment to measure a patient’s health status depends on his health condition.

8.5 – Research limitations and suggestions for further research 139

Finally, another barrier to clinical trial participation that was identified but not tackled during the present thesis is the lack of clinical trial awareness. Social online platforms are becoming more and more important as the power and influence of patient communities increase, and alternatives such as PatientsLikeMe or CureTogether are rapidly growing. Pharmaceutical companies should try to explore means to reach out to patients directly, so that they can become aware of different trials for the diseases they suffer. Information should be presented transparently rather than in a marketed way, allowing for patients (together with their doctors) to determine that is the most appropriate treatment for their conditions. Ideally, this should not be a company specific development, but should try to be comprehensive in the different clinical trials offer across the world.

Appendix A ………………………...... AA. Model Specification

In 5 – A Discrete-Event Simulation Model of the Clinical Trial Supply Chain, the conceptualization of a Discrete-Event Simulation (DES) model for a Clinical Trial Supply Chain (CTSC) has been provided. This appendix describes how the real simulation model was specified in terms of the concepts explained in that chapter, and is referred to the construction and description of the conceptual model in the DES software Simio Simulation.

A.1. Objects in the model and overview of their interactions Simio Simulation is an object oriented simulation package, meaning different states of the system are defined by the interactions, changes and statuses of the different objects that form it.

Thus, as a first step to understand the model specification, it is required to describe the different objects that form the DES model. Table 39 summarizes them.

Table 39: Main objects that form the DES model, and number of different instances in the case study

Simio Number of different object Object name42 Object description instances43 category Standard entity that represents the Patient_Kit 1 drugs Patient 1 Subjects that enroll in the clinical trial Entity Pieces of information sent from the clinical sites to the regional depots to IRT_Order 1 request resupplies after forecasting the demand Manufacturing Facilities that produce batches of 1 _Facilities Patient_Kits Electronic systems that generate Source IRT_ClinicalSite_X 32 IRT_Orders Enrollment Country specific source that 32 _Country_X generates Patients Merges IRT_Orders (member) with Patient_Kits (parent) in the depot to Depot_X 2 satisfy the demand for patient kits of Combiner the different clinical sites Merges Patient_Kits (member) with ClinicalSite_X 32 Patients (parent) to model the intake of drugs

42 Note that an X in the name indicates a reference to a specific clinical site/regional depot. 43 This is valid for the case study presented in 6 – Case Study: Smart Labels on a Phase III Clinical Trial. 142 A.1 – Objects in the model and overview of their interactions

ReLabeling_Depot Represents the manual re-labeling of 2 _X patient kits on the depots Server Stage prior to successful enrollment Screening_X 32 in a trial where patients are screened Successful Gathers the Patients that have 32 _Participation_X finished the trial Gathers the Patients that were not Sink Non_Eligible_X 32 eligible for the trial Gathers the Patients that dropped out Dropout_X 32 from the trial

Combiners are a key element of the model, because they are present whenever two of the entities described in table 39 – patient kits, patients or IRT orders – interact. In Simio, combiners have two different inputs: the member input and the parent input. Entities going through the parent input will remain when they are matched by a member.

Two types of combiners exist in the model: those that combine IRT orders (member) with patient kits (parent) to symbolize that a new order has been processed, and those that combine patient kits (member) with patients (parent) to represent the intake of drugs. Note that although there are two types of combiners, the number of instances depends on the clinical supply chain being modelled, as the former depends on the number of regional depots and the latter on the number of clinical sites of a study.

A.1.1. The supply dynamics Once the behavior of the combiners is understood, it is possible to evaluate the full supply dynamics of the model with an example. If a clinical site – for example, U.S. 4 – has just successfully enrolled a patient, then its associated IRT system will calculate the expected demand of drugs for that patient for a particular period of time in the future. Then, it will compare this demand with the drugs available in the inventory of the clinical site. Assuming that there are not enough drugs, the IRT systems will generate a number of IRT_Orders, which will be sent to the associated regional depot. The number of IRT_Orders to be sent equals the number of additional Patient_Kits required. IRT_Orders travel instantaneously to represent the flow of informatics information.

Once IRT_Orders arrive to the regional depot, it tries to match them with Patient_Kits. If this is possible, then this is done, and the Patient_Kits are shipped to the clinical site. If this is not possible because there are not enough patient kits, then the regional depot will in turn generate an order for the manufacturing facilities.

The manufacturing facilities are not linked however via IRT systems to the depots, because they cannot respond instantly to demand. Instead, they produce batches of drugs intermittently for this and other studies under consideration. Once the time to produce the batch of the drug under study arrives, then the quantity of this batch is equal to the sum of all the demand of all the regional depots (i.e., as explained in 5.4 – Assumptions taken and their justification, the production quantity is not constrained).

Coming back to the clinical site, and assuming that there were Patient_Kits in the depot to be shipped, drugs that arrive are sent to a second combiner that matches them (members) with the patients waiting for them (parents).

Note that in order to avoid that patients have to wait for drugs – which is a problem in clinical trials because of the causality concerns that this implies into the safety and efficacy of a drug – safety stocks are used to secure the supply chain. A.2 – Model parameters 143

A.1.2. The patient dynamics A second set of dynamics that can be explored after understanding the supply dynamics is the logic that patients follow in the model. Patients appear in the model in one of the different sources – Enrollment_Country_X – for each country. Patients are then directly related to a random clinical site, where they go through the screening phase. If results from screening are not successful, patients leave the model using the sink Non_Eligible_X.

Conversely, if patients pass the screening phase, they start actively participating in the trial. A patient visits a clinical site several times during the trial. This is represented by patient loops in the clinical sites (see figure 44) that subjects go through.

Figure 44: Screenshot of the patient loop in the Simio Simulation model

There are two scenarios in which patients leave this loop. First, if a patient drops out before he finishes the required number of visits to the clinic, he leaves the model using the sink Dropout_Country_X. Second, if the patient successfully complete the number of visits required for the study, it is directed to the sink Successful_Participation_Country_X.

A.2. Model parameters As introduced in figure 31 (in the main body of this thesis), a set of parameters was defined to control decision- and study specific-variables of the model. All the parameters of the model are gathered in table 40 144 A.2 – Model parameters

Table 40: Model parameters grouped in different categories

Category # Parameter Units 1 Clinic_Visits_Per_Patients # 2 Time_Between_Visits days 3 Dropout_Per_Visit % Clinical trial 4 Number_of_Different_Treatments # setup 5 Screening_Acceptance_Ratio % 6 Study_Magnitude_Multiplier - 7 Manufacturing_Cost_Per_Patient_Kit €/kit 8 Safety_Stock_Depots # 9 Safety_Stock_Clinical_Site_per_Treatment # 10 Days_Look_Ahead_Demand days Supply chain 11 Days_Resupply_Period days dynamics 12 Factor_Inventory_Overage_In_Depot - 13 Avg_Time_Between_Manufacturing_Batches weeks 14 StDev_Between_Manufacturing_Batches weeks 15 Min_Time_Between_Manufacturing_Batches weeks 16 Batch_Expiry_Date weeks 17 Expiry_Date_Extension_Relabel weeks Re-labeling 18 Safety_Days_to_Relabel_in_Depot days dynamics Primary packs 19 Containers_Per_Patient_Kit patient kit 20 Avg_TravelTime_Manufacturing_to_Depot_EU days 21 Avg_TravelTime_Manufacturing_to_Depot_NA days 22 StDev_Manufacturing_to_Depot days Travel times 23 Avg_TravelTime_Depot_to_Site_EU days 24 Avg_TravelTime_Depot_to_Site_NA days 25 StDev_Depot_to_Site days Policy activator 26 Smart_Labels_Policy_OnOff -

A brief description of each of the different parameters presented in table 40 is the following:

1. Clinic_Visits_Per_Patients: Number of times that a patient has to visit a clinical site to consider that participation in the study is successfully finished.

2. Time_Between_Visits: Time interval between patient visits to a clinical site.

3. Dropout_Per_Visit: Global likelihood that a patient drops out the clinical trial between two consecutive visits to a clinical site.

4. Number_of_Different_Treatments: Number of treatments conducted in parallel in the trial.

5. Screening_Acceptance_Ratio: Likelihood that a patient interested in participating in the trial is suitable to do so,

6. Study_Magnitude_Multiplier: Parameter than can be used to easily multiple the number of patients expected for the clinical trial. The total length of enrollment (i.e., LPLV-FPFV) is also extended by the same factor.

A.2 – Model parameters 145

7. Manufacturing_Cost_Per_Patient_Kit: Cost of manufacturing a single patient kit.

8. Safety_Stock_Depots: Minimum level of inventory that depots require to have before requesting the production of more drugs.

9. Safety_Stock_Clinical_Site_per_Treatment: Minimum level of inventory per different treatment options in the trial that clinical sites require to have before sending an IRT order to its associated regional depot to ask for a replenishment.

10. Days_Look_Ahead_Demand: Period on time in advance considered by the IRT systems to determine if triggering resupply orders is required. For example, if this parameter is set to 30 days, then the IRT systems will compare the combined demand of all the patients enrolled in the associated clinical site in the next 30 days with its current inventory. If the inventory is found to be lower than the expected demand, new IRT orders are triggered.

11. Days_Resupply_Period: Period on time in advance considered by the IRT systems to calculate the total number of IRT orders to be created when a resupply orders are triggered. Building on the previous example, if the IRT systems find that the inventory is not enough to cover the expected demand, then the number of IRT orders triggered will be equal to the total demand during the Days_Resupply_Period. Ultimately this parameter – in combination with the Days_Look_Ahead_Demand – control the frequency of the resupplies to clinical sites.

12. Factor_Inventory_Overage_In_Depot: Multiplier used to represent the inventory overage used by the depots to secure the clinical trial supply chain when they request the production of more drugs.

13. Avg_Time_Between_Manufacturing_Batches: Average period of time that it takes to the production plant to produce batches for the drug under study.

14. StDev_Between_Manufacturing_Batches: Standard deviation of the time that it takes to the production plant to produce batches for the drug under study.

15. Min_Time_Between_Manufacturing_Batches: Minimum period of time that it takes to the production plant to produce batches for the drug under study. In the model, this parameter is used in combination with the two previous ones in the following formula, used to determine the inter-batch manufacturing time:

_ = . ( . ( _ _ _ _ , 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 _ _ _ ), _ _ _ _ ) 𝑚𝑚𝑚𝑚𝑚𝑚ℎ 𝑚𝑚𝑚𝑚𝑚𝑚 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵ℎ𝑒𝑒𝑒𝑒 16. Batch_Expiry_Date𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝑀𝑀: 𝑀𝑀Time𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 that it takes𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 a ℎnewly𝑒𝑒𝑒𝑒 𝑀𝑀 created𝑀𝑀𝑀𝑀 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 patient𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 kit 𝑀𝑀to𝑀𝑀 expire.𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵ℎ𝑒𝑒𝑒𝑒

17. Expiry_Date_Extension_Relabel: Time by which the expiry date is updated when re-labeling.

18. Safety_Days_to_Relabel_in_Depot: Safety margin – in terms of days – taken to re-label a patient kit that expires soon in a regional depot. For example, if this parameter is set to 7 days, then drugs will be re-labelled at the regional depots even 7 days before they expire. This has the goal to mitigate the risk of drugs expiring at a clinical site, where re-labeling is typically not possible, and thus patient kits that have expired are disposed.

19. Containers_Per_Patient_Kit: Total number of containers per patient kit.

20. Avg_TravelTime_Manufacturing_to_Depot_EU: Average period of time that it takes patient kits to travel from the manufacturing facilities to the European depot. 146 A.3 – Add-on processes

21. Avg_TravelTime_Manufacturing_to_Depot_NA: Average period of time that it takes patient kits to travel from the manufacturing facilities to the North American depot.

22. StDev_Manufacturing_to_Depot: Standard deviation of the period of time that it takes patient kits to travel from the manufacturing facilities to the regional depots.

23. Avg_TravelTime_Depot_to_Site_EU: Average period of time that it takes patient kits to travel from the European regional depot to a European clinical site.

24. Avg_TravelTime_Depot_to_Site_NA: Average period of time that it takes patient kits to travel from the North American regional depot to a North American clinical site.

25. StDev_Depot_to_Site: Standard deviation of the period of time that it takes patient kits to travel from the region al depots to the clinical sites.

26. Smart_Labels_Policy_OnOff: On/off toggle for the smart label policy.

Note that even though 26 parameters are defined in the model, not all of them are suitable for conducting experiments. In particular those belonging to the category clinical trial setup are to adapt the model to different clinical trial studies, an approach shared by the parameters in the category re- labeling dynamics. An exception in the latter group could be the Safety_Days_to_Relabel_in_Depot, which can be used as a decision variable. The parameters in the category travel times were set to make the model easily modifiable in case it is needed, but they are rather fixed over time, are not study dependent and cannot be used as decision variables. Finally, the on/off toggle for the smart label policy can be used to easily set up experiments in Simio Simulation.

A final remark to be made is that, in order to fully adapt the model to different clinical trial setups, additional modifications to that of the parameters are required. In particular, the distribution of patient enrollments and the number of clinical sites and have to be adapted. In section 5.7.2 – Model validation, a shortcut to ensure that new clinical sites are correctly implemented in the model – via copy pasting from the existing ones – in a relatively effortless manner was presented.

A.3. Add-on processes In Simio, add-on processes are a combination of programming building blocks that allow user to customize the dynamics that different events trigger in the model. The purpose of this section is to describe the intern logic of the model by means of briefly presenting and describing the existing add- on processes.

For an easy representation and in an attempt to make the model user-friendly, the add-on processes were aggregated in categories according to different subsystems described in the model conceptualization: 1 – IRT systems; 2 – Order trackers; 3 – Management of patient visits to the clinical sites; 4 – Demand for drugs of the regional depots; 5 – Distributor of the regional depots to the clinical sites; 6 – Manufacturing of drugs; 7 – Re-labeling and 8 – Timers and other events. Figure 45 provides an overview of these categories.

A.3 – Add-on processes 147

Figure 45: Screenshot with an overview of the different add-on process categories in the simulation model

In the next subsections, the processes that form each of the categories are described. Note that for verification and traceability purposes, a color code was assigned to the blocks that form this processes. The logic followed is presented in table 41.

Table 41: Color code used during the modeling of the add-on processes

The block is clinical site-specific The block is regional depot specific, and belongs to the European depot The block is regional depot specific, and belongs to the North American depot The block serves as a switch to turn on/off the smart label policy

A.3.1. Add-on processes category 1 – IRT systems The modeling of the IRT systems is one of the most complex and sophisticated parts of the model. In a nutshell, the IRT systems analyze the levels of safety stock required and the patients enrolled in the a given clinical site to forecast the demand for drugs of that particular clinical site for a given time period – specified in the parameter Days_Look_Ahead_Demand – and then, if the current drugs in the inventory are not enough to cover the demand for that period, trigger resupply orders to the associated regional depot. Note that, because of this considerations, IRT systems are clinical site specific.

The number of drugs demanded in this case is normally higher that those that would result from the analysis of Days_Look_Ahead_Demand, because the parameter Days_Resupply_Period is used instead. According to input received during the model validation phase, the latter is typically higher than the former to reduce the number of reshipments (and thus the associated costs) to a clinical site.

Figure 46 presents a screenshot of an IRT process in Simio. For a better analysis, the reader is referred to the simulation model itself. 148 A.3 – Add-on processes

Figure 46: Screenshot in Simio Simulation of an IRT process

A.3.2. Add-on processes category 2 – Order trackers The order trackers are one of the simplest add-on processes used in the model (see figure 47). Because all the IRT_Orders are represented by the same entity, unique attributes are required in order not to lose information about the clinical site they belong too. Order tracking processes assign the final destination of future orders to the IRT_Orders, information that they will transmit to the Patient_Kits once they matched in the combiner of the associated regional depot.

Figure 47: Screenshot in Simio Simulation of an order tracking add-on process

A.3.3. Add-on processes category 3 – Management of patient visits There are two sub-categories within the management of patient visits: (i) processes that are generic and (ii) processes that are specific for each clinical site.

The generic processes are depicted in figure 48. The first and the third process displayed aim at tallying the time that patients might have to wait for medication – ideally this should be an exceptionally rare case – by measuring when a patient enters the system and when he leaves.

Figure 48: Add-on processes for the generic management of patient visits

The second process of figure 48 updates the number of visits that a patient has made to a clinic.

A.3 – Add-on processes 149

The second set of processes are those that depend on the clinical site under consideration. Figure 49 shows them.

Figure 49: Add-on processes for the clinical site-specific management of patients

The first of the processes in figure 49 is related to the patients, but also serves as a link to the supply dynamics of the model. It assigns to patients an attribute indicating the associated depot to which their clinical site is associated. This serves as support for other processes that will be presented later that analyze the demand of the regional depots.

The second process – equivalent to the third – decides whether a patient has to continue in the loop presented in figure 44, in case it still has remaining visits to the clinical site, or whether it has finished the treatment and should be directed to the associated Successful_Participation_X sink. Note that the dropout of patients is not modelled in the DES model as an add-on process, as simpler methods exist (selection weight on outgoing links of a node).

A.3.4. Add-on processes category 4 – Demand for drugs of the regional depots The add-on processes’ that govern the demand for drugs of the regional depots – requested to the manufacturing facilities – are similar both in terms of complexity and logic to those of the IRT systems described in A.3.1 – Add-on processes category 1 – IRT systems, and are also a core pillar of the dynamics of the supply for drugs. A screenshot of the processes in this category is presented in figure 50.

In figure 50, note that there are two set of processes corresponding to the two regional depots used in 6 – Case Study: Smart Labels on a Phase III Clinical Trial. Additionally, the first set of blocks – the add- on process for the European depot – has two additional blocks because both processes are run at equally spaced time intervals, but sequentially instead of in parallel because they use some common variables.

The process works a massive IRT systems that tracks all the patients enrolled in clinical sites associated to the depot and forecasts their combined demand for a given time period (the average period between batches plus a safety margin). However, unlike in the IRT systems, no entities representing orders are generated here. The reason for that is that this process is only run when a new batch is produced, and thus it directly triggers the creation of drugs in the central manufacturing facilities. 150 A.3 – Add-on processes

Figure 50: Screenshot of the add-on processes that model the demand for drugs of the regional depots

A.3.5. Add-on processes category 5 – Distributor of the regional depots to the clinical sites The fifth category of add-on processes builds on that presented in A.3.2 – Add-on processes category 2 – Order trackers. While in that processes an attribute to IRT_Orders was set to identify their associated clinical site, processes of this category – see figure 51 – retrieve that information once an IRT_Order is matched with a Patient_Kit, so that the kit is sent to the appropriate clinical site44.

Figure 51: Screenshot of the different add-on processes used to distribute drugs outgoing a regional depot

In figure 51, both the first and the third process are aimed at retrieving at a model-level the clinical site that an IRT_Order has associated. The reason why there are two processes for the same task is that there are also two different situations under which obtaining this attribute from the IRT_Orders is required: (i) when the inventory is >0 and thus the order can be instantly attended and (ii) when there is no inventory in the depot and the order has to wait. In the former case, the first process is used (note that there is no queue of IRT_Orders. In the latter case, the third process is used, because the replenishment is not triggered by a new IRT_Order arriving to the depot but by one that was already waiting.

44 Note that the logic how IRT_Orders are matched with Patient_Kits follows a first come first served pattern, representing that older orders are given priority.

A.3 – Add-on processes 151

Finally, the second process in figure 51 uses the attribute retrieved from the IRT_Order to determine the clinical site where the Patient_Kit that is attending that order has to go (note that because the Patient_Kit is the parent object going through the combiner, only it survives).

An equivalent set of processes to that presented in figure 51 is also used to serve the North American depot.

A.3.6. Add-on processes category 6 – Manufacturing of drugs The manufacturing of drugs is specified as a single, generic add-on process in (see figure 52). The process is relatively simple because most of the algorithms used to determine the number of drugs that have to be produced are the input received from the depot demand estimations. Thus, the process actually builds on the manufacturing patterns and quantities established in A.3.4 – Add-on processes category 4 – Demand for drugs of the regional depots.

Figure 52: Screenshot of the sixth category of add-on processes in Simio Simulation

In particular, the first block of figure 52 sets the expiry date – as indicated in the model parameters – to each Patient_Kit produced. The expiry date simply calculated as the sum of the current time of the system and the lifetime expected for the batch.

The second block simply counts the Patient_Kits exiting the manufacturing facilities, while the computes their associated costs.

Finally, the last block indicates to the Patient_Kits to what depot they should go. This information is once again retrieved from the processes detailed in A.3.4 – Add-on processes category 4 – Demand for drugs of the regional depots.

A.3.7. Add-on processes category 7 – Re-labeling There are in total 7 add-on processes to model the re-labeling of drugs. One of them serves for all clinical sites, three for the European depot and three for the North American depot. Because the last two are equivalent, figure 53 presents only the first four of these seven processes.

All the processes captured in figure 53 are run once per day through the simulation time. The first of them, dedicated to the drugs expiring at clinical sites, performs a search in the inventory of all clinical sites in the model (something that is study-dependent) and then, if smart labels are not activated, process to destroying the Patient_Kits. Conversely, if smart labels are activated, the Patient_Kit is relabeled with a new expiry date and the associated costs are calculated. The first block of this process is a filter not to destroy Patient_Kits if there are no more patients for that particular clinical site. This represents just a workaround to stop the shipments to clinical sites for which the trial has ended – i.e. to avoid that safety stock keeps being sent as drugs expire, because there are no more patients.

The second process depicted in figure 53 follows a similar logic, but applied to the regional depots. The main difference is that in the regional depots drugs expiring – or about to expire, because of the parameter Safety_Days_To_Relabel_In_Depot used to minimize the number of drugs expiring at the clinical sites – are sent for manual re-labeling instead of destroyed. Also, the color of the Patient_Kits that have expired is changed for an easy traceability. The first block is, once more, to prevent that drugs are sent for re-labeling if the trial has finished. 152 A.3 – Add-on processes

Figure 53: Screenshot of the different add-on processes used in the Simio Simulation model to capture the logic of re-labeling processes

The fourth process is just an auxiliary variable to tally the time spent in re-labeling, which is in the third process to compute the re-labeling costs. In this process – third in figure 53 – a new expiry date is set for the Patient_Kit that has been re-labeled, the costs are calculated, the number of total re- labelings is tracked and the color of the Patient_Kit is reverted to the original.

A.3.8. Add-on processes category 8 – Timers and other events This category contains processes – see figure 54 – that do not fit in any of the others, but still are required for the proper functioning of the model.

Figure 54: Screenshot in the Simio Simulation model of the processes grouped in the eighth category

A.3 – Add-on processes 153

The first process in turn groups the update of many KPIs and variables of the model, with a frequency of one hour between updates. First, it regulates the stochastic variable time to transport drugs from the manufacturing facilities to the different depots. Note that this time is updates based on a time basis rather than on a Patient_Kit basis because, when multiple Patient_Kits are sent together, the same transport time is expected (in reality, they would likely share the same means of transport, for instance the same plane). The rest of the blocks update the number of Patients non-eligible, enrolled, finished and dropouts for a visual indication in the model. Finally the total number of re-labelings and its associated costs are also computed based in the sum of all their components (i.e., at clinical sites and at the different depots).

Finally, the second add-on process presented in figure 54 is only executed once: when the simulation is about to end. It is at this point when the number of remaining kits are counted, the inventory overage for the study is computed, and the total disposal costs – including those of the Patient_Kits remaining at the end of the study – and the reliability factor are calculated. 154 A.3 – Add-on processes

A.3 – Add-on processes 155 Appendix B ………………………...... BB. A Literature Review on New Technologies in Clinical Trials from a Patient Perspective

Smart technology, such as electronic media, mobile devices, communication systems and computer databases, have the potential to facilitate clinical trials both for investigators and patients by making them less costly and more efficient [71].

A literature review was performed to get acquainted with the state of the art with regard to the implementation of such technologies (e.g. e-devices, e-labels, smart labels, RFID technology etc.) in the context of Clinical Trials (CTs) and the Clinical Trial Supply Chain (CTSC), assuming a patient perspective.

The relevant literature was found by doing a systematic literature research and was based mostly on journal papers. Initially, a keyword-based search was performed. Preferred terms were a combination of tech-based terms (e.g. technology, RFID, smart labels, e-devices, NFC etc.) and CT-related terms (e.g. clinical trials, clinical research, clinical supply chain, clinical study etc.).

In what follows, the main outcomes of the literature review are categorized into 5 main groups: generic technology trends in the CTSC, telemonitoring, NFC, patient-reported outcomes and mobile communication devices. 156 B.1 – Generic technology trends in the CTSC

B.1. Generic technology trends in the CTSC In their ambitious paper Technology Trends that will Transform Clinical Trials, Wiggington [43] presents a framework consisting of four different technology trends that they argue are changing entire CT-related ecosystems to adapt to a new paradigm. Table 42 presents an adapted version of this framework:

Table 42: Technology trends changing the clinical trial industry according to [43]

Technology trend Description Implementation via Technology basis Mobile Expansion of mobile Wearable technology GPS, NFC phone capability

Social Self-measurement and Internet Dedicated social tracking of personal networks45 habits Cloud computing Accessing software or Internet Dedicated software data on demand (CDMS, CTMS) across the network Big data Leveraging on Cooperative online Big data analytics (un)structured, partnerships software (CDMS, available large CTMS, EDC) volumes of data

Although the framework presented by these authors can be used to assess the fitness of technology in current industry trends, the paper has the limitation that it does not present any concrete course of action for pharmaceutical companies to leverage on these technologies in order to increase the efficacy of clinical trials.

In a similarly-oriented paper, Aron Shapiro discusses the importance of computer technology in CTs, and tries to anticipate the changes that this will bring in the way sponsors (i.e. pharma companies) manage their studies [284]. As a starting point, the author argues that the use of technology in clinical trials gained momentum about 10 years ago with the implementation of electronic, rather paper- based, clinical data management system, a view that is widely shared in the literature [10, 43, 240, 285]. Then, he provides some recommendations on how to expand the use of systems such as Clinical Trial Management Systems (CTMS), Risk-Based Monitoring (RBM) in the context of CTs. While these are some specific measures that can be taken in the pharmaceutical industry, they are rather a form of incremental innovation that has been implemented and improved regularly over the past decade industrywide.

B.2. Telemonitoring Telemonitoring might play an important role for CTs in the future. In a systematic review, Inglis et al. suggest that telemonitoring and virtual visits to the clinical sites are beneficial for CTs because of a dual reason. First, they lead to a higher patient retention rate and improved care [10, 244]. Second, they increase the reliability and validity of the trial itself, because the effect of the drug is tested while the patient continues having a normal life [244].

Passive remote monitoring will also allow to increase detection of events that are currently undetectable [10]. For instance, remote, wearable sensors can now monitor tremor for as long as 10 hours daily [250], enabling the use of accelerometers to assess seizures in epilepsy [251] and gait in

45 Examples mentioned by the authors are start-ups like Teatro or online treatment collaboration sites like CureTogether.com.

B.3 – Mobile communication devices 157 multiple sclerosis [252]. Moreover, other authors like Louis et al. conclude that telemonitoring might define future strategies for an effective management of patients with chronic diseases, such as chronic heart failure [286].

A study of Alzheimer disease concluded that, in order to achieve a greater participation from patients into existing clinical trials, home visits were the most decisive factor [287]. Furthermore, a survey to over 2000 adults by the Harris Poll in winter 2014 concluded that almost two-thirds of the participants would be willing to accept videoconferences as “doctor’s visits”[288].

Perhaps the most surprising telemonitoring-based study so far is the one of glucosamine described by McAlindon et al. [289]. 205 patients participated in an Internet-based clinical trial, signing consent forms and clinical records remotely and receiving the drugs by regular mail. Results were promising: overall, the patient dropout rate was just 20% and the feedback from the remaining patients was that “they would be happy to participate in another such trial in the future”. The estimated cost of the trial was half that expected from a clinical-site-based approach [289].

A final advantage of telemonitoring and virtual visits is that they allow for a centralized assessment of disease states, hence increasing standardization during the trial and reducing variability [10].

To conclude, although telemonitoring and virtual site visits are unlikely to completely replace in- person visits to clinical sites, the literature suggests that there is a lot of potential to facilitate patient recruitment, enhance adherence to the treatments and lower the patient dropout rate thanks to these modern approaches.

B.3. Mobile communication devices The exceptional growth of the market for mobile communication devices through the Internet, together with the increase in human connections via social media, provide for clinical research patients to take active roles during clinical trials [71]. 64% of American adults owned a smartphone in 2015, up from 35% in the spring of 2011, a figure that is expected to reach 80% by 2017 [76].

Mobile communication devices, in combination with health technologies, might help to overcome certain challenges of traditional clinical trials, such as poor adherence, compliance issues, insufficient participation and limited PROs [71].

The most promising application concerns the use of wearables that monitor in real-time the patient’s health data (e.g. cardiac rhythms, lung status, glucose levels or blood pressure) and transfer it via the mobile communication devices to a central research center. The advantages of this approach are threefold. First, it enhances compliance and adherence to the treatment. Second, it reduces the time and cost burden for patients, as they have to travel to the clinical sites less often, what in turns reduces the chances of dropout [290]. Third, it allows for standardization of results.

Despite this potential, the literature suggests contradictive findings when it comes to the implementation of health and communication devices in CTs. A study by Ryan et al. on the effectiveness of using self- and telemonitoring in a randomized clinical trial showed no improvement in asthma control or efficacy [291]. However, in a clinical trial for a candidate drug to treat Parkinson disease, a single clinical site was successfully used to conduct assessments of 50 individuals in 23 states via telemonitoring. Videoconferences were used to verify the diagnoses that the patients reported themselves and to perform some secondary assessments in a standardized way [10]. Such a study would typically call for several clinical sites and a higher time burden that was avoided via telemonitoring. 158 B.4 – Near Field Communication

B.4. Near Field Communication Near Field Communication (NFC) is a technology that allows for a short range (<10cm) wireless communication. In a clinical environment, NFC is useful for patients because it allows them to acquire data from clinical e-devices and launch clinical applications by simply bringing their smartphones close, without need for manual interaction. Because of this, Morak et al. [292] strongly suggest in their paper that NFC, in combination with regular smartphones, is likely to become the preferred technology in future telemonitoring and telecaring scenarios.

A couple of studies have tested the usefulness of NFC in improving patient outcomes during pilot clinical trials. The controlled, randomized MOBITEL study [293] trained patients, who were randomized to the telemonitoring group, to connect their phones to a telemonitoring service, and then measure and send their blood pressure, body weight data and dosage of medication taken. A second study by Morak. Et al [242] used a sensing device for the concurrent acquisition of blood pressure and electrocardiogram signals, which were used by 21 patients over a period of seven days. Global adherence rate was over 82%, way above the average of clinical trials (43 to 78%) [80].

It can be concluded that the potential of NFC lies in increasing patient adherence to treatments via telemonitoring. However, telemonitoring is not without challenges. As concluded by Sanders et al. [255], concerns about privacy and dislike of technology in the context of CTs deter the uptake of telemonitoring.

B.5. Patient-reported outcomes Patients of clinical trials have traditionally had a passive role, being treated as uninvolved subjects on whom medical procedures were conducted [10]. Because of this, research participants did not have a voice in the design of a study, and, despite bearing with the health risks of the trial, they were not even informed of the outcomes of the research, including those reporting newly discovered harmful side effects [294, 295].

Nowadays, clinical research patients are increasingly taking active roles, and the FDA encourages clinical sponsors to pay close attention to Patient Reported Outcomes (PRO). PRO are inputs received directly from the patients covering purely symptomatic (disease activity) status, functional (physical and psychological) status, satisfaction with the treatment and adherence to the therapy [296].

Enabling technologies to facilitate patients rising PRO will ultimately allow clinical sponsors to have a greater access to knowledge and people. While patients taking these expanded roles are a reality in studies concerning rare diseases, there is an opportunity for enhanced PRO in CTs for common disorders, such as autism, a disease for which very organized patients communities exist [10].

Online platforms, such as PatientsLikeMe [57], are an example of global, non-disease specific patient communities that portray the willingness of patients to take an active role in the management of their health conditions.

Appendix C ………………………...... CC. Interview Protocols and Outcomes

C.1. Interview protocol Face interviews were conducted at Novartis Pharma AG as part of the research methodologies. The following is the generic interview protocol used for these interviews:

General introduction

Good morning. Thank you for your willingness to join this meeting today and for taking the time to help me with my research. As you probably know, I am researching the potential for smart labels in the clinical trial supply chain as part of my Master Thesis here at Novartis. As a [interviewee’s role], your input is much appreciated in order to fulfill this goal.

Before we begin, I would like to ask you if I can record our conversation. This would allow me to capture all the details while at the same time making the conversation more interactive. This recording would not be shared with anyone and would have the sole purpose of allowing me to transcribe some of the outcomes. Afterwards it would be deleted. You will have access to the final, transcribed report of the interview, and of course you will also take the final decision on whether I can partially or totally include this interview in the final deliverables of my research.

In case you allow me to publish (some of) the outcomes of this interview, you will also have the possibility to decide whether you want your identity to remain confidential (and be quoted, for instance, as a Senior Clinical Trial Expert at Novartis) or you allow for full disclosure.

Interview questions

Different depending on the interviewee. More details can be found in the following sections.

Closing

I would like to thank you again for your time and participation. As promised at the beginning, this interview will partially remain confidential if you wish so. The next step for me is to transcribe the most relevant outcomes of the interview. Once this is done, I will share them with you (and only with you) so that you can give your approval on the parts you allow to be published, either quoting you or without revealing your identity. 160 C.2 – Interview with a Clinical Trial Expert

C.2. Interview with a Clinical Trial Expert The following are the questions and outcomes of an interview with a Clinical Trial Expert from Novartis Pharma AG, conducted on 01.04.2016 in Basel, Switzerland.

1. Could you briefly comment on the main challenges faced in the design, planning and execution of clinical trials?

Forecasting is a major challenge. Global clinical trials can be of smaller or larger size, and the design can also be simple or relatively complex. Therefore, also the drug forecast, packaging design and supply strategy of the clinical supplies for global clinical trials can be of very challenging complexity. It is difficult to correctly plan every individual activity in every depot/clinical site, what leads to some uncertainties.

At the beginning, the initial forecast is built out of the net demand, which is fixed, and the overage, which is variable. During the execution of the trial, the overage can be optimized as more data is available. As a result, in the end demand forecast is most of the time different from the reality.

2. How do you define overage? Could you provide some insights on how the inventory overage is planned/determined/optimized?

It is important to distinguish between the net demand (number of drugs required for the patients enrolled in the trial) and the overage used to secure the supply chain. Securing the supply chain is required because both the shipment from the central depot to the regional hub and from the regional hub to the clinical site take time. Inventory overage is defined as the number of drugs produced minus the net demand.

Contributing factors for this overage are the number of patients, the number of sites, the number of countries, the number of pack types, the enrollment period, the expiry date of the drugs and overall the study design complexity: the more complex, the higher the overage.

3. How are drugs produced during a clinical trial? Are they all produced upfront?

Drugs are not produced at the beginning for the entire study. Typically, periods of 6 months are considered, but this is flexible. For instance, if a study is designed to last 3 years, we start by producing drugs for the first 6 months and then, if what we have produced turns out to be not sufficient, we start another round of production after 4 months, and then the next resupply might be shifted back. Similarly, if some of the last resupplies are not required, then they are cancelled.

4. In general, how is inventory distributed over the supply chain? How much remains in the manufacturing facilities / country depots / clinical sites? What determines this?

The inventory kept at clinical sites is influenced by their characteristics. For instance, their capacity, commitment or number of enrolled patients affect how much inventory they store.

Moreover, cost management also affects it. Having a weekly shipment is likely to be more expensive than a monthly shipment, and deciding between one or the other also affects how inventory is distributed.

It also depends on the study design. For instance, if patients are coming to the site once per week it is not the same as if the patient has to come to the site once per six months. In the latter case, you might expect more drugs accumulating in the depots because fewer shipments to the sites are done. C.2 – Interview with a Clinical Trial Expert 161

Overall when we are doing the setup, it is very important to ensure that every site has the drugs it needs for every patient for the next month at least: the site at any time should have enough drugs for a reasonable number of days as required by the study protocol.

Going one step back in the supply chain, what you ship to the intermediate depots is in general what you get from the production plant, what in turn is demand driven according to the needs of the patients. However, some safety stock is always also stored in the packaging facilities.

5. Can drugs be shipped from the clinical site back to a depot? No, this does not happen.

6. And from one country-depot to another? For instance if we suddenly find some extra demand in one country and have spare inventory in another?

That may happen.

7. Is the secondary packaging [outer packaging of drugs] country-specific? No, in global trials, the ones that we conduct here in Basel, drugs can be shipped to any country. Booklet labels are used for this. Booklets are not country specific, as they contain labels for all the different countries participating in the study, both in terms of languages and country specific labeling requirements

8. Do patients enroll once the clinical trial has started? Yes, this is absolutely normal during clinical trials. The trial execution starts with the first patient, first visit, and the enrollment of other patients follow.

9. Do you [the clinical trial sponsor company] limit the number of patients that can enroll for a trial?

This is referred to as capping. Every patient is associated with costs, and in the pharmaceutical industry it is also very important to be financially compliant. During the study design, a country might commit to having 10 patients for a specific clinical trial. If we assume, for example, an average cost of EUR 50 thousand per patient, then the sponsor allocates half a million euros for that country for this trial. In the end, if the country randomized 20 patients instead of 10, then the sponsor would have to double its budget, what might not be possible. In the end, there is not only a drug forecast but also a financial forecast: this is the reason why capping exists.

That said, both clinical trial sponsors and clinical sites do not often rely on capping because of ethical considerations. Only a capping at a study level is often used, not normally at a country/clinical site level (from perspective of sponsor), although clinical sites might establish some caps themselves.

10. Imagine we have 6 patients enrolled at a clinical site now, and we expect 20 one month from now. Would we ship drugs for the current patients or for the total number of expected patients?

We do not ship to the site when there are no patients. Commitment to 20 patients does not mean that we ship for 20 patients. IRT systems verify every day what is the demand for all the patients of a clinical site for a certain number of days in the future. The moment the inventory

162 C.2 – Interview with a Clinical Trial Expert

of the site is not big enough, a resupply shipment is triggered. Usually this is an automated task, but, if required, the system can be overridden to trigger manual resupplies that guarantee that an unexpected high number of patients can be randomized in a short period.

11. Isn’t this [not having the drugs in the clinical site in advance] a potential source for delays when patients enroll for the trial?

Assuming this was communicated in advance, this method does not normally cause delays because there is a screening phase between the time a patient arrives to the clinic and the time he/she starts the treatment. Patients successfully passing the screening phase are randomized. Between the beginning of the screening and the randomization stages there is usually a period of 3-4 weeks for a majority of the trials. A key design question is whether the shipment takes more or less time that this screening period. In any case, there is always some safety stock at each site to ensure that a patient does have medication on time. In general, the system is designed in a way that the site will not run out of drugs, as minimum drug levels are managed by the IRT system.

However, if this was not communicated and there are many sites that simultaneously want to randomize a large number of patients, this can consume the available kits before the resupply arrives.

12. Aren’t there problems with labeling in these cases? To my understanding some of the drug kits are patient-specific. How does this fit with the safety stock?

Kits are treatment-group specific and are uniquely identified with kit numbers. In a double- blinded active/placebo trial, it is a normal clinical practice to send patient kits for both treatments to the clinical site for each patient. This helps removing some statistical biases. Because of this, if a site is randomizing 3 or more patients you can be pretty sure that the site already has some safety stock for both drugs [active drug / placebo] that can be used if needed by another patient.

13. What triggers the replenishment of a country depot / clinical site? Is it a certain threshold (e.g. reorder points when inventory is below quantity X)?

This is an automated task with IRT systems. The system looks ahead, for a certain period (example 30 days), for the demand of all the patients in a clinical site. As soon as the system realizes that there are not enough drugs for 30 days, the system triggers a resupply.

Another way is using minimum trigger resupply levels. For example a clinical site should never fall below X kits. If it falls below, a replenishment is triggered.

It is a study management decision for the clinical team to decide what replenishment strategy is used.

14. According to your experience, what is the best practice? With automated forecast systems the first approach is the best, because you ship exactly what you need.

However, most of the times a combination of both systems is used. A safety stock is kept using fixed thresholds and the regular demand for patients is ensure via IRT systems that (re)supply according to the forecasted demand for a certain period of time.

C.2 – Interview with a Clinical Trial Expert 163

15. What kind of randomization schemes are used at Novartis? [Unstratified randomization vs center-stratified]

It is difficult to generalize how patients are randomized. For us [perspective of drug supply management] the best is that randomization takes place at a clinical site level [center- stratified]. This means that if a site has 2 patients [and assuming the proportion active:placebo is 1:1], then one of them is in the active treatment and the other in the placebo. Then we always have a balance. If you randomize patients centrally, it can be the case that one country has 20 patients in one group and 2 in the other. From a logistics perspective this is more difficult and might increase overage.

However, in global clinical trials most of the times we use central randomization [unstratified randomization] although, again, this cannot be generalized.

16. I guess that this also has some advantages: for instance, with a central randomization [unstratified randomization] you might have more variance at single clinical sites/countries, but the overall variance across the study is minimized, right?

Exactly, in the end if you have central randomization you can be pretty sure that for large number of patients the proportion active:placebo will be very close to 50% [assuming again than the desired the proportion active:placebo is 1:1].

Blocks are typically used in the randomization process of patients. For example, let’s imagine that size-4 blocks are used. This means that the randomization pattern can be: . Active/Placebo/Active/Placebo . Active/Placebo/Placebo/Active . Placebo/Active/Placebo/Active . Placebo/Active/Active/Placebo . Placebo/Placebo/Active/Active . Active/Active/Placebo/Placebo

Imagine randomization is conducted at a clinical site level. If 3 clinics enroll just one patient, then there would be 3 individuals belonging to the same treatment. The likelihood of having imbalance is much higher than in central randomization: that is the reason why central randomization is preferred.

17. As a Clinical Trial Expert, you are probably aware with the issues that low patient adherence and patient dropouts imply for clinical trials. What are, in your opinion, the factors affecting low patient adherence and eventually patient dropout from a clinical trial?

My guess is that there can be different factors affecting this. First, we cannot forget that we are talking about people suffering from diseases who usually want to benefit from drugs; it might be the investigator’s decision to determine whether a patient is benefiting or not from the treatment.

Second, it could also be related to competition and alternative treatments: if another treatment comes to the market and a patient regards it as more promising, he might decide to discontinue his participation in the clinical trial to switch to the new treatment. Also, the dropout of patients is likely to be indication specific. For some indications the drug might be quite effective, while for others other alternatives might be comparatively better positioned.

It is also common that, for long trials, after some time the patient readiness to stay in the trial goes down. In the first year you typically see small percentages of dropouts. This increases during the second and the third years. I guess this is a normal reaction, because patients also move around to look for their best treatment alternatives.

164 C.2 – Interview with a Clinical Trial Expert

We always should be both compliant and ethical: if one patient is not benefiting from out drug, we cannot try to keep this patient taking drugs that are not improving his condition.

18. From a clinical trial supply chain perspective, do you consider dropouts in a proactive way? For example, if you know that in average there is an X% dropout rate, is the production of drugs is scaled down from the beginning?

As I said before, we consider that every randomized patient is on treatment. We start by considering the worst scenario, and then, if there are dropouts, subsequent manufacturing of drugs is scaled down.

19. Are you familiar with the concept of smart labels? I had a role related to labeling when RFID labels started to be commercialized.

20. What is then, in your opinion, the potential that smart labels might have for the clinical trial supply chain?

The first concern that comes to my mind and this is (or at least was by the time I worked with it) expensive technology, what might be a barrier for implementation. You would have to update all the systems in all the locations to support smart labels: at the central facilities, at the depots and even at the clinical sites. This might be the future, but I don’t know if the potential of smart labels is enough to justify this cost.

21. Assuming that the upfront investment was not a problem and imagining that this could be easily implemented, do you think that smart labels could improve the logistics of clinical trials?

I am not sure whether this would bring significant improvements from a logistics perspective.

22. What about re-labeling of IMPs, for instance to update the expiry date? If that could be automatized with smart labels that contain variable content instead of fixed one, then that would be an advantage.

C.3 – Interview with a Senior Distribution Process Expert 165

C.3. Interview with a Senior Distribution Process Expert The following are the outcomes of an interview with a Senior Distribution Process Expert from Novartis Pharma AG, conducted on 08.04.2016 in Basel, Switzerland.

1. How critical is the labeling process within the CTSC? Can labeling issues cause a delay in a clinical trial?

It might, yes, although this does not happen on a regular basis.

2. How important are the costs and delays caused by re/over-labeling? You normally re/over-label if you have limited, expensive supplies which expiry date can be extended. The costs of this can be however significant. You witness situations where, for example, you have 100 kits in 3 countries. That means that you need to generate 100 labels, send them to these 3 countries and employ people to apply them. Besides, sometimes part of the kits to be relabeled can be on a European hub and other on an American hub.

3. What triggers re/over-labeling of an IMP? Normally it is the extension of the expiry date what triggers re/over-labeling of an IMP. Occasionally, it could be also because of additional countries included in the trial: if you add a country into an existing trial and you want to use already existing kits, you might have to add additional labels to the kits.

4. Could relocation of drugs across multiple regional depots a normal reason for re-labeling or over-labeling?

Hardly at all. That would be an exceptional situation. We do transfers from one hub to another, but this is usually with supplies that can be used in both regions. A situation in which you move something from one hub to the other and then you have to initiate labeling activities is a speculative scenario.

5. And from one clinical site to another? No, that is not even allowed.

6. Are these relabeling activities outsourced? At the point where you want to re-label kits, they are already disseminated in the different countries participating in the trial, maybe even at the sites. Because of this, in the majority of the cases, these activities are outsourced.

7. If we simplify the clinical trial supply into three levels: the [centralized] packaging facilities [in Basel], regional depots and clinical sites, where would you say that relabeling happens the most?

This depends again very much on the study, but I guess that in the majority of the cases it would happen in the regional depots.

8. Can you re/over-label in the clinical sites at all? We tend not to do that, but it still can happen. If in a particular study you are short of supplies then you try to save every single kit. In these cases you will try to send the labels with the updated expiry date to the clinical site so that they can perform the re-labeling there.

166 C.3 – Interview with a Senior Distribution Process Expert

9. What do you understand by a smart label? What CTSC issues could they help to solve? A first example that comes to my mind is electronic labels in which the expiry date can be changed electronically, so that you don’t need over-labeling.

Another potential application of smart labels is in combination with the smartphones of patients, so that they can control the content of the kit.

Finally, I am aware of chips that can be integrated in the labels, so that you can control the pick and pack activities just by placing the items close to a reader device, without the need of optically scanning a bar code.

10. We have discussed that revising the expiry date is probably the most common reason for labeling, and that electronic smart labels [those on which you can update the information of the label automatically] might help dealing with this issue more efficiently. Do you think it is possible to quantify the costs of current relabeling (when it comes to preparing the site, employing people, preparing the printers etc.) and compare it with the price and the price evolution of smart labels?

The price of the labels themselves is high for the time being, when compared to standard labels. Moreover, a smart label could be useful to contain the variable information of the label, but booklets would still be required to contain label information in multiple languages.

11. If you have an electronic label which content you can modify, why would you need booklets at all? Wouldn’t it be technologically feasible to change the language of a label?

The problem in this case is the size of the smart label. On the one hand, if the label is too small or is curved, it might be difficult to find an electronic label that fits the shape. On the other hand, if the electronic panel gets big, its cost will rise.

Another issue is the source of energy. If you have a smart label that only contains the variable information then the use of energy is minimal. You can even get the energy from the [reader] device. My concern is what you would do if you want to store much more information in this label.

Another point: all these warehouses are outsourced. If you want to change information, even on an electronic label, there is human activity involved: someone needs to take each kit, gets close to the updating device, press the button to trigger the change on the label and put it back again.

12. What if you had all the labels in a warehouse connected to an RFID reader, and you were able to trigger all these changes remotely, using the cloud?

The concept sounds perfect, but remember that we are still talking about clinical trials: someone needs to verify the information. If the reliability of this procedure could be verified without human intervention, then the idea would be brilliant, but right now this sounds a bit like science fiction, especially in the world of current clinical trials, where everything needs to be checked three times.

Overall, as an idea it is absolutely brilliant, but there are many practicalities to consider. Even putting aside initial investments (the label itself is pretty expensive), it would still be tricky to adapt the current network to achieve decent efficiency. We need to check what technology offers, and I don’t think that the technology is that far yet.

C.3 – Interview with a Senior Distribution Process Expert 167

13. I agree that right now everything looks quite expensive, but if we think about the timeline of clinical trials and the inertia of the pharma industry, maybe an idea like this could be implemented at best after 6-7 years. Isn’t there a lot of potential in this timespan for prices to drop, like it happens with other technologies, like smartphones?

Of course the price of this technology can go down. The thing with smartphones and similar devices is that they are driven by a mass business, where in here [context of clinical trials] we are in a much more controlled environment, because there are people lives at stake. Because of the scale, it is much easier to technologically develop smartphones than smart labels.

14. Agree, but still the pharmaceutical industry is not alone, right? For instance the food industry is also quite interesting in this, and the more players push in one direction, the more competitive it gets.

Food industry: big volumes, all the same; even the commercial pharma industry is better- suited because all the commercial medication is the same, every single kit, at least for a specific country, looks the same. In clinical trials however the problem is that every single kit is individual.

15. Could you share some insights about how inventory is distributed across the clinical trial supply chain?

I can give you some other contacts that might be better-suited to answer this question. In general, just to give you an idea, the majority of the inventory should be in the middle, in the regional hubs, but once again this is study dependent. You shouldn’t keep much in the central depot, where you manufacture and pack the kits. You want to push it closer to the patients. In general, you might leave 10-20% behind [in the central depot] in order to deal with demand fluctuations.

168 C.4 – Interview with a Senior Leader at Clinical Trial Portfolio Level

C.4. Interview with a Senior Leader at Clinical Trial Portfolio Level The following are the outcomes of an interview with a Senior Leader at Clinical Trial Portfolio Level from Novartis Pharma AG, conducted on 18.04.2016 in Basel, Switzerland.

1. Could you briefly comment on the main challenges faced in the execution of clinical trials from the perspective of the clinical trial supply chain?

A main challenge is the uncertainty faced in clinical forecasting. It is not easy to predict patient enrollment because of two reasons. First, it is difficult to know where potential patients are. Second, you always have to consider the competitive angle: especially in some therapeutic areas like oncology, several clinical trials for the same disease can be running at the same time. In the end, patients and their doctors often have several, potentially confusing criteria to assess what treatment may be is better suited for them, what in turn creates uncertainty, making it difficult to predict patient enrollment.

2. Is patient adherence to the treatment a problem during the execution of clinical trials? Yes, in general this is a problem. The magnitude of the problem is however disease specific. When dealing with diseases like cancer, the treatment can be life-saving, and in additions clear self-interest there may be additional supporting mechanisms from the patients’ family, friends etc. to encourage him/her to follow the treatment meticulously. Both of these considerations ameliorate the problem in cancer patients. For non-life-threatening diseases, both the motivation of the patient to adhere to the treatment and the supporting mechanisms are lower, and then the adherence problem might get bigger.

3. How is patient (non-)adherence related to dropout? My guess is that dropout might be more related to the investigator advising the patient to discontinue the treatment because the benefits that the patient is getting aren’t up to the expectations. Is this correct?

In my opinion, non-adherence could lead to dropout, but I don’t know if this happens a lot. This is again very disease-specific. In general, dropout has to do with the benefits that the patient is obtaining from the treatment. If a patient feels better with the treatment it is unlikely that he discontinues the treatment.

There are however some interesting exceptions. In cancer treatments, for example, patients might be feeling worse while taking the treatment, but after running blood analytics the investigator may actually inform the patient that the treatment is working.

4. What are the trends of trials being carried out today? For example, are they more focused on life-threatening diseases? Or on diseases that affect the 1st world? Is the industry shifting towards rare diseases?

The trends are mixed when it comes to clinical trials, as this is related to different company strategies. In general, a main role of pharmaceutical companies is to support their patients, but at the same time the commercial viability angle cannot be neglected. Generally, clinical trials are conducted in those areas where an overlap between a patient need and a business opportunity exist.

This implies that, for example, industry may appear to assign relatively more resources to HPV [Human papillomavirus] vaccines or such other so-called “western” disease condition, than to say, to malaria, although it can be argued that the need for the latter is bigger from a purely patient perspective.

C.4 – Interview with a Senior Leader at Clinical Trial Portfolio Level 169

Defying this logic, many resources are invested today in cancer treatments, as there is a major patient need worldwide and medical progress in this area is urgently needed and also commercially viable.

5. Are you familiar with the development of RFID/NFC/Bluetooth/etc. smart labels? What is your view on them?

I am aware that, in general, label technology has evolved a lot, and I think that smart labels could be used and that they could bring a benefit to the pharma industry. However, the pharma is always a little bit behind when it comes to implementing the newest technologies.

6. Why is that? There is a triple reason for this: first, the pharma environment is highly regulated, so bringing new technologies is always resource-intensive and complex. Second, compliance of smart labels with current standards would have to be validated. Finally, there are large associated costs, in part because of the previous considerations.

7. Do you think, then, that it is feasible to introduce smart labels oriented to the clinical trial supply chain/to the patients from a regulatory perspective?

I think so, yes, although it would take effort, the FDA and other regulatory agencies are open to these technologies. In fact, it is actually companies who innovate looking for a competitive advantage, and then regulations evolve over time if the innovation proves to be beneficial to patients and for compliance. Put differently, changes in the regulations are industry driven, partly because regulatory bodies are not always aware of upcoming technologies in the market.

8. Do you think that a smart label could help in increasing patient adherence? There is a lot of potential in using technology to improve patient adherence.

9. What are the critical assumptions to consider when it comes to bringing e-labels or e- devices to the patients?

If evaluating patient adherence via e-devices, an important assumption has to do with surrogated and real markers. A surrogate marker is, for example, counting the pills that are popped out of a bottle (assuming they are actually taken by the patient), while a real marker would be a measurement of drug levels in a patient’s blood. The assumption would be that surrogate markers are equivalent or can replace real markers.

Another important point is that, although the industry might be ready to implement some complex technology, patients might be not. Labels are there to bring information to the patients and, in the end, it is the patient who has to understand it.

10. What triggers re/over-labeling of an IMP? Re-labeling and over-labeling is a routine task in the clinical trial supply chain. Re/over- labeling is automatically triggered to reflect new information when something in the label changes, typically the expiration date. Country specific requirements [for clinical labeling] could be another reason, although this happens less often.

170 C.4 – Interview with a Senior Leader at Clinical Trial Portfolio Level

11. Are trials conducted at large hospitals? I have seen from historical data that 5-10 patients enroll on average into a clinical site. Is this low number because they are rather local clinical sites or because they are rare diseases?

Trials are conducted both in large hospitals and in local clinical sites. The important thing is that a clinical site has always a focus in certain areas; it has an inherent knowledge expertise for certain diseases.

For example, imagine a hospital with a couple of doctors specialized in asthma. If this doctors have a good reputation, word of mouth makes it more likely that people go to this site in case they suffer from this disease. In the end, likelihood of enrollment for an asthma treatment would be significantly higher in this site.

12. What is the role of doctors as a stakeholder during clinical trials? What are their interests and their power during the execution of the trial?

As a first clarification, doctors that formally enroll patients in a trial are called investigators.

A doctor will become an investigator if he believes that some of the patients will benefit from the trial. A secondary source of motivation is the boost in reputation that might follow if the treatment actually works.

The investigator always has the final word when it comes to the treatments that patients follow. The patient is still the patient of the doctor; he is not the patient of the pharma company. If a doctor sees that the dosage is too high then he can modify the dosage. Whereas increasing the dosage is not very easy because of safety aspects, reducing the dosage is relatively easy. Moreover, the investigator can easily stop the treatment of a patient if he is not benefiting from it or he is getting worse.

13. In a blinded study, does the investigator know the treatment [active/placebo] that a patient is following?

There are many types of blinded studies: single blind, double blind, pharmacist blind, investigator blind etc.

Even in double blind trials, there is always a code break card that can be used to find out about the treatment that a patient is following in case of a health related emergency for a given patient.

14. From a stakeholder perspective, are investigators the same stakeholder as the clinical site? The interest of hospitals is similar to that of doctors/investigators when it comes to clinical trials. Maybe reputation is more important for them, as they can transform it into business opportunities.

However, doctors/investigators are a much more important stakeholder than clinical sites. Apart from being responsible for the treatment of the patients, if a doctor changes his practice to another clinical site, patients will likely follow him/her.

15. Can a clinical site commit to a trial as such and then inform their doctors about a preferred treatment?

You cannot force doctors to become investigators for a treatment.

C.4 – Interview with a Senior Leader at Clinical Trial Portfolio Level 171

16. In order to assess different options for patients in a CT when it comes to technological aid, it would be interesting to know about the typical route of administration (pills/syringes etc.) in clinical trials. Could you provide some insights this?

Normally pills are used during clinical trials because people prefer them, as they are easier to use.

172 C.5 – Interview with a Lead Business Integration Manager

C.5. Interview with a Lead Business Integration Manager The following are the outcomes of an interview with a Lead Business Integration Manager from Novartis Pharma AG, conducted on 26.04.2016 in Basel, Switzerland.

1. What are the main issues in the labeling of IMPs? From a global perspective, the main issues have to do with the modification of labels, what is commonly referred to as re-labeling or over-labeling, and more specifically with the delays that this might originate.

2. When is re-labeling or over-labeling required? There are mainly two situations when re/over-labeling is required. The most important one has to do with modifications of the expiry date of clinical products. This is relatively common during the execution of clinical trials: as more knowledge of the chemical compound is gathered, the initial, conservative expiry dates can often be extended.

The second reason has to do with changing regulatory requirements. Each country has specific requirements when it comes to the information presented in clinical labels. If retroactive changes are demanded, re-labeling of clinical drugs is necessary to continue with the trial.

In addition, in the context of smart labels, it might be interesting to explore the possibility of re-labeling clinical products once at the clinical site. Currently this is not an option because clinical sites are not allowed to modify labels of IMPs. This means that clinical drugs that need re/over-labeling have to be discarded if they are at the clinical site. If smart labels allowed somehow for the clinical trial sponsor to directly modify labels even though they are physically at the clinical site, this could lead to a waste decrease. Additionally there’s a potential to decrease re-labeling costs.

3. How can smart labels potentially contribute to the clinical trial supply chain? There are three main ways how smart labels could contribute to the CTSC: a. Providing a better tracing, tracking and identification of IMPs as they move throughout the supply chain. In the end, this transforms into higher compliance, a key aspect in the execution of clinical trials.

b. Allowing to guarantee that the environmental conditions of the drugs are adequate, from the manufacturing site to the patients. Some drugs are temperature- or humidity-sensitive, and smart labels have the potential to guarantee that appropriate conditions have been maintained during the storage and shipment of the clinical drugs. This translates into higher product quality and, again, higher compliance.

c. Reduced costs and delays: Labeling is often in the critical path of a clinical trial’s execution. This means that whenever a delay is originated in the labeling process, the entire trial is likely to suffer from this delay. This is a major concern for pharmaceutical companies, because the patent protected time of the candidate drug being tested keeps running during the trial. A single day of market-exclusivity can translate into hundreds of thousands of dollars per day, and therefore delays of CTs are often associated to huge costs as well.

If smart labels allowed for an easier re-labeling of IMPs, delays and costs could potentially be reduced, leading to more efficient clinical trials.

C.5 – Interview with a Lead Business Integration Manager 173

4. Are counterfeit drugs an issue in the context of clinical trials? Counterfeiting drugs are not really a problem when it comes to clinical trial drugs because of the following reasons: . Difficult identification of drug due to limited information (e.g. drug name, strength) on the label (blinding). Information about the candidate drug is still limited and normally concentrated in the sponsor pharmaceutical company

. In clinical trials, the shipment size is fairly small when compared to shipments for commercial drugs which makes stealing less attractive

. Complicated re-selling process due to limited information on the drug and its efficacy. Sometimes the market is not even established.

5. Is a phase III clinical trial representative to understand the logistics and dynamics of clinical trials? Why?

Finding a representative study is actually quite difficult in the clinical trial industry, as studies are very heterogeneous. However, from a global perspective I think that a phase III clinical trial makes sense because of a couple of reasons: first, these trials are bigger, capturing better the logistic complexities. Second, phase III trials are more patient-oriented than phase I or phase II trials, which are sometimes conducted on healthy subjects (this has exceptions, like in the field of oncology).

6. When it comes to the manufacturing of drugs during clinical trials, is it common to produce all up-front, before the trial starts?

That is not a common practice. It might be that the initial production of drugs is larger than subsequent ones, but then drugs are normally produced on demand, according to the input received by IRT systems, among others.

7. Is it a common clinical practice to send both an active drug patient kit and placebo patient kit to a clinical site when there is a single patient enrolled in either of the treatments?

There is always safety stock in the clinical sites for both the active and the placebo drug. However, if just one patient was enrolled in a clinical site of a blinded study with an active treatment and a placebo, then only the treatment the patient is following will be resupplied. This will hold true as long as there is safety stock for both treatments.

8. What are the stakeholders you would consider in assessing the implementation of smart labels in clinical trials?

Firstly I would consider the pharmaceutical company itself: the manufacturing side, packaging and supply. The later you move into the supply chain, the more players involved, especially external. For example, distributors and third parties can also be included. Then, drugs arrive to clinical sites, and investigators and patients are also involved. Especially patient groups and communities gain more and more power. Finally, regulators are also an important stakeholder throughout the clinical trial process.

9. What is the interest of the doctors when it comes to clinical trials? My guess is that their main interests are the well-being that new treatment alternatives can bring to the patients and the economic incentive that becoming and investigators implies.

174 C.5 – Interview with a Lead Business Integration Manager

10. How important are doctors as a stakeholder when compared to patients? Years ago, doctors were the main stakeholders that pharmaceutical companies targeted. This is because they were the only intermediary between the pharma companies and the patients, and thus the only means to increase patient awareness of new alternative treatments at a clinical trial stage. As a result, dedicated events such as conferences and symposiums were held to inform doctors of the latest drug developments.

This has changed a lot in the last years, and nowadays patients are the main stakeholder that pharmaceutical companies seek to satisfy. There are two reasons for this shift. First, legal aspects do not allow anymore to give distinctive treatment to doctors seeking for their support. Secondly, new technologies such as the internet or social media make it possible to communicate directly with patients and, more importantly, with patient communities. Patient communities have a lot of power nowadays: it is there where decision-making takes place. Because of this, most pharmaceutical companies have a clear patient focus right now.

11. In the literature it is mention that costs can be a de-motivator for patients to participate on clinical trials. However, my understanding is that the clinical trial sponsor actually pays for the treatment, at least throughout the clinical trial itself. Could you please comment on this?

Costs of the treatments during a clinical trial are assumed by the sponsoring pharmaceutical company. It would make no sense that patients had to pay for a trial drug. These probably refer to the costs that being enrolled in a trial pose to patients from other angles. For instance, if they have to travel regularly to the clinical site, there might be a cost or time burden into their lives.

C.6 – Interview with a Head Clinical Supply Documentation Specialist 175

C.6. Interview with a Head Clinical Supply Documentation Specialist The following are the outcomes of an interview with a Head Clinical Supply Documentation Specialist from Novartis Pharma AG, conducted on 10.05.2016 in Basel, Switzerland.

1. How dynamic are the country specific requirements concerning labeling? More and more countries coming with stringent requirements. E.g. Spain being the first country in EU to demand having the expiry date on primary packs for IMPs ahead of the EU directive which is scheduled to be effective in 2018.

2. Why do countries have specific labeling requirements at all? As per my information, the countries need to obey the area regulations; e.g. All EU countries need to follow the EU directive regulations but cannot be limited to. All the countries typically have HA/ MoH [Health Authorities / Ministries of Health], which have their own requirements in addition to the region (i.e. EU circle), based on some incidences/ accidents; or this may be required as per the need of patients.

3. Do countries build their specific requirements on the general guidelines (e.g. GCP/GMP/ European Directive etc.) or do they rather take completely different approaches?

In my opinion, the countries do follow the general guidelines (e.g. FDA CFR, Annex 13, etc.) and build their own guidelines based on these. I have seldom come across completely different guidelines.

4. Years ago, some pilot clinical trials with RFID labels were conducted. To my understanding, no specific labeling regulations concerning smart labels (e.g. RFID or NFC communication technologies) existed. Do you know what the attitude of regulators was regarding these projects? Were they open to these kind of innovation being pushed by the pharma industry?

I have very limited information for this type of innovation, since I have not seen this in the practical world. Though there are several smart label concepts coming (e.g. RFID), I do not see them widely accepted by sponsors, due to several reasons. There are no separate regulatory guidelines (as per my information).

5. Another example is the introduction of centralized randomization systems. Regulators reacted to this technology push by the pharma industry and today no specific labeling information concerning randomization (e.g. the randomization number) is required on the label. Is this in your opinion normal? Are regulators flexible/open to innovation brought up by the industry?

Frankly speaking I am not aware of this technology. But in my opinion, regulators are not opposed to the innovations and the new technologies; it is just that they need to be sure of the validation/ qualification (proven) of such new concepts. At the end, they need to be sure that it is safe for patients- consistently.

6. Do you know other examples of changes in clinical trial regulation following industry driven innovation?

As current small example is the need for video while the informed consent is taken from the patient. What started as a simple paper form, has highly evolved, thanks to the new technology.

Besides there are several mobile apps (government based as well) which are [present in clinical trials] due the new technology.

176 C.6 – Interview with a Head Clinical Supply Documentation Specialist

As another example, we can see that the IRT systems are advanced than they were earlier, and this is well acknowledged by the HA authorities.

7. Do you think that an eInk based smart label [information would be presented in a digital display rather than in physical paper] would fit into the current regulatory framework?

In my opinion, yes this should fit in today’s scenario. Why I say this: there are constant updates in the regulations and the most recent EU directive needs to have the Exp date [expiry date] printed on the primary packs, which will make the Revised expiry date labeling impossible because without tampering the seals it would be difficult to do this activity. I am sure this would pose up as a great challenge to the industry.

Thankfully due to this digital display change, we would be able to update the Exp date on the labels of the primary packs without even opening the packs. Of course this would incur costs but we do have an opportunity.

8. Smart labels and devices that apart from fulfilling the regulatory requirements also feature some other functionalities, such as medication tracking, automatic reminders or social connection are emerging (see figure below). Are these, in your opinion, likely to face regulatory pressure in the context of clinical trials?

Though these additional features like alarms, reminders and social connections are ‘good’ to have, I do not see them coming as a mandate from regulatory world, unless we have a system in place to provide the information to the patients/ investigators.

C.7 – Interview with a Senior Consultant in Regulatory Affairs for Clinical Trials 177

C.7. Interview with a Senior Consultant in Regulatory Affairs for Clinical Trials The following are the outcomes of an interview with a Senior Consultant in Regulatory Affairs for Clinical Trials, conducted on 26.05.2016 in Basel, Switzerland.

1. How dynamic are, in your opinion, the regulations set by the Competent Authorities (e.g. de FDA of the European Commission) with regard to general labeling requirements for IMPs? Do changes occur in a monthly/yearly basis?

Regulations set by the FDA or the European Commission are quite conservative and do not change regularly. To give you an idea about a representative timescale, you would see new regulatory developments once every ten years, or even less often.

An example of a new Clinical Trial legislation is the new EU regulation 536/2014, which will introduce some updates in the labeling process. Even though this new legislation was officially adopted in 2014, it will take some years until it goes live and until all the EU Member States implement it into their local laws.

2. And when it comes to country specific requirements? The timescale is similar. Modifications in the regulations are not because they imply changes in the law of the country, which is a time-consuming process.

3. Why do countries have specific labeling requirements at all? I don’t have a clear answer for this, but this might have to do with requirements that were present in the local laws in the past and then were tried to be preserved.

4. Do countries build their specific requirements on the general guidelines (e.g. GCP/GMP/ European Directive etc.) or do they rather take completely different approaches?

Country specific requirements are most often built on general guidelines (GCP/GMP) and regulations, such as the European Directive. In the future they will have to build it on the upcoming European Regulation.

5. Years ago, some pilot clinical trials with RFID labels were conducted. To my understanding, no specific labeling regulations concerning smart labels (e.g. RFID or NFC communication technologies) existed. Do you know what the attitude of regulators was regarding these projects? Were they open to these kind of innovation being pushed by the pharma industry?

I do not have information about this specific example, but I would assume that, in regards to labeling, you can always use new technology if you fulfill the regulations established for the general labeling process. In the end you always have to be compliant with the local law.

6. Another example is the introduction of centralized randomization systems. Regulators reacted to this technology push by the pharma industry and today no specific labeling information concerning randomization (e.g. the randomization number) is required on the label. Is this in your opinion normal? Are regulators flexible/open to innovation brought up by the industry?

With the current systems you can track back the randomization number of a specific patient either via the medication number or the patient number, which are part of the content of the labels. In general information present on a label should be read with your eyes, without any additional tool, because patients always have to be able to read the information.

178 C.7 – Interview with a Senior Consultant in Regulatory Affairs for Clinical Trials

Generally speaking, I think that regulatory entities are flexible and open to innovation, as long as you are compliant with the current law. However, this might be a bottleneck: if you have an innovative product or service that is not compliant with the law, you will have to wait until the law has been amended or revised to actually implement it. As discussed at the beginning, this may be challenging because laws do not change that often.

7. Do you think that an eInk based smart label [information would be presented in a digital display rather than in physical paper] would fit into the current regulatory framework?

Yes, I think this would work, as long as all the required information is visible to the patients.

The logic is similar to above: for these devices, as long as you fulfill the requirements for the IMP labeling then you are relatively free to use this.

I know about some studies using, for example, automatic SMS reminders [for patients to take their medication] and, in general, this originated no issues.

9. Similarly, do you think that eInk smart labels could be used to convey additional information (e.g. reminder on next dose intake), as long as the requirements established in the labeling regulations are met?

Yes, I think so. However, all of these items that we have discussed in the last few questions would probably have to be assessed by ethics committees too. This is because some of them might raise questions or concerns about data protection. Especially when you move in the direction of social connection, you always need to be careful about confidentiality issues that might arise.

References

1. Hôpital Albert Schweitzer. The Importance of Healthcare. 2015 [cited 2016 March 19]; Available from: https://hashaiti.org/vision/the-importance-of-healthcare/. 2. Teleki, S.S., C. Damberg, and R.T. Reville, Quality of Health Care: What Is It, Why Is It Important, and How Can It Be Improved in California’s Workers’ Compensation Programs? Support RAND, 2006: p. 1. 3. Institute of Medicine, Crossing the Quality Chasm: A New Health System for the 21st Century, N.A. Press, Editor. 2001, Institute of Medicine: Washington, D.C. 4. Grover, A., et al., Pharmaceutical Companies and Global Lack of Access to Medicines: Strengthening Accountability under the Right to Health. Journal of Law, Medicine and Ethics, 2012. 40(2): p. 234-250. 5. Reynolds, K.S., Acceleration of drug development: A collaboration of many stakeholders. Clinical Pharmacology and Therapeutics, 2013. 93(6): p. 455-459. 6. PhRMA, The Process Behind New Medicines, in Biopharmaceutical Research & Development, PhRMA, Editor. 2015. 7. Johns, M.A., A. Russ, and H. Fu, Current drug targets and the druggable genome, in Chemical Genomics. 2012. p. 320-333. 8. Newhouse, J.P., Why is there a quality chasm? Health affairs, 2002. 21(4): p. 13-25. 9. Kneller, R., The importance of new companies for drug discovery: Origins of a decade of new drugs. Nature Reviews Drug Discovery, 2010. 9(11): p. 867-882. 10. Dorsey, E.R., et al., Novel methods and technologies for 21st-century clinical trials: a review. JAMA neurology, 2015. 72(5): p. 582-588. 11. Munos, B., Lessons from 60 years of pharmaceutical innovation. Nature Reviews Drug Discovery, 2009. 8(12): p. 959-968. 12. Dimasi, J.A. and H.G. Grabowski, The cost of biopharmaceutical R&D: Is biotech different? Managerial and Decision Economics, 2007. 28(4-5): p. 469-479. 13. FDA, Guidance for Industry - Oversight of Clinical Investigations, in Guidance for Industry, U.S.D.o.H.a.H. Services, Editor. 2013, Food and Drug Administration. 14. Collier, R., Clinical trial registries becoming a reality, but long-term effects remain uncertain. CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne, 2009. 180(10): p. 1007-1008. 15. Chen, Y., et al., Simulation-optimization approach to clinical trial supply chain management with demand scenario forecast. Computers and Chemical Engineering, 2012. 40: p. 82-96. 16. Hanscott, R., The future of wireless technologies in clinical drug trials. EPC - European Pharmaceutical Contractor, 2002. Autumn: p. 30-32. 17. Berndt, E.R. and I.M. Cockburn, Price indexes for clinical trial research: a feasibility study, U.S.B.o.L. Statistics, Editor. 2014, U.S. Bureau of Labor Statistics. 18. Getz, K. and D. Borfitz, Informed consent: a guide to the risks and benefits of volunteering for clinical trials. 2002: Centerwatch Incorporated. 19. Paricio, I., Interview with a Senior Leader at Clinical Trial Portfolio Level from Novartis Pharma AG. 2016, Attached in the present report. 20. Weyermann, A., Labelling Requirements for Investigational Medicinal Products in Multinational Clinical Trials: Bureaucratic Cost Driver or Added Value? , in Mathematisch- Naturwissenschaftlichen Fakultät 2006, Rheinischen Friedrich-Wilhelms-Universität: Bonn. p. 71. 21. Bielmeier, P. and G. Crauwels, Managing the extended R&D supply chain. Pharmaceutical Engineering, 2012. 32(4): p. 10-20. 22. FDA. Key FDA Critical Path Activities. 2008; Available from: http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPa thOpportunitiesReports/UCM110357.pdf. 23. Lauer, M.S. and R.B. D'Agostino Sr, The randomized registry trial - The next disruptive technology in clinical research? New England Journal of Medicine, 2013. 369(17): p. 1579- 1581.

179

24. Kovesdy, C.P. and K. Kalantar-Zadeh, Observational Studies Versus Randomized Controlled Trials: Avenues to Causal Inference in Nephrology. Advances in Chronic Kidney Disease, 2012. 19(1): p. 11-18. 25. Droms, C.M., M. Ferguson, and K. Giuliano, The Stakeholder Approach: A New Perspective on Developing and Marketing Clinical Trials. Health Marketing Quarterly, 2014. 31(1): p. 1-12. 26. Sheiner, L.B., Learning versus confirming in clinical drug development. Clinical Pharmacology and Therapeutics, 1997. 61(3): p. 275-291. 27. Fleischhacker, A., A. Ninh, and Y. Zhao, Positioning inventory in clinical trial supply chains. Production and Operations Management, 2015. 24(6): p. 991-1011. 28. Colagiuri, B., et al., The placebo effect: From concepts to genes. Neuroscience, 2015. 307: p. 171-190. 29. Hackshaw, A., A Concise Guide to Clinical Trials. A Concise Guide to Clinical Trials. 2009. 1- 215. 30. Glass, H.E., The importance of medical innovation in an investigator's decision to take part in clinical trials. Drug Information Journal, 2008. 42(6): p. 537-543. 31. Harland, C.M., Supply chain management: Relationships, chains and networks. British Journal of Management, 1996. 7(SPEC. ISS.): p. S63-S80. 32. Gunasekaran, A. and E.W.T. Ngai, Information systems in supply chain integration and management. European Journal of Operational Research, 2004. 159(2 SPEC. ISS.): p. 269- 295. 33. Gunasekaran, A., C. Patel, and E. Tirtiroglu, Performance measures and metrics in a supply chain environment. International Journal of Operations and Production Management, 2001. 21(1-2): p. 71-87. 34. Santa Maria, V., Securing the clinical supply chain by increasing information flow: Successful pharmaceutical production starts with trials that are well supported in the lab and the supply chain. Pharmaceutical Processing, 2015. 30(8): p. 14-15. 35. Milne, C.P., K.I. Kaitin, and J.A. DiMasi, Mandatory comparator trials for therapeutically similar drugs: An assessment of the facts. American Journal of Therapeutics, 2007. 14(3): p. 231-234. 36. Abdelkafi, C., et al., Balancing risk and costs to optimize the clinical supply chain - A step beyond simulation. Journal of Pharmaceutical Innovation, 2009. 4(3): p. 96-106. 37. Paricio, I., Interview with a Lead Business Integration Manager from Novartis Pharma AG. 2016, Attached in the present report. 38. European Comission, Comission Directive 2005/28/EC of 8 April 2005 laying down principles and detailed guidelines for good clinical practice as regards investigational medicinal products for human use, as well as the requirements for authorisation of the manufacturing or importation of such products. 2005, European Comission: Brussels. 39. Andrews, C., Strategic value of clinical supplies. Pharmaceutical Engineering, 2004. 24(5 SUPPL.): p. 12-17. 40. Paricio, I., Interview with a Clinical Trial Expert from Novartis Pharma AG. 2016, Attached in the present report. 41. Smith, P., Labels in clinical trials. Pharmazeutische Industrie, 2015. 77(1): p. 57-62. 42. Clinical Leader. IRT (IWRS/IVRS). 2014 [cited 2016 March 25]; Available from: http://www.clinicalleader.com/solution/irt-iwrs-ivrs. 43. Wiggington, C. Technology Trends that WIll Transform Clinical Trials. 2014. 1-7. 44. Smith, M.C., Pharmaceutical Marketing: Strategy and Cases. 1991: Taylor & Francis. 45. European Comission, The Rules Governing Medicinal Products in the European Union - Annex 13, in EU Guidelines to Good Manufacturing Practice, C.G.-. Pharmaceuticals, Editor. 2010, European Comission: Brussels. 46. Tunis, S. and D. Whicher, The need to engage stakeholders in defining, designing, and implementing clinical trials. Oncology (Williston Park, N.Y.), 2010. 24(11): p. 1057-1058. 47. Pant, R. and Y. Joshi, A study of practical parameters and their relative importance as perceived by various stakeholders in clinical trials. Journal of Young Pharmacists, 2011. 3(1): p. 60-64. 48. van Cauwenberghe, C.E., Stakeholders portrait in the pharmaceutical industry. Drug Development and Delivery, 2012. 12(1): p. 38-44.

180

49. Ross, S., et al., Barriers to participation in randomised controlled trials: A systematic review. Journal of Clinical Epidemiology, 1999. 52(12): p. 1143-1156. 50. Glass, H.E. and R. Kane, Why Investigators Take Part in Clinical Trials. Applied Clinical Trials, 2000. 9(6): p. 46-54. 51. Glass, H.E., Do Clinical Grant Payment Practices in Phase 3 Clinical Trials Influence Subsequent Clinical Investigator Prescribing Behavior? Disease Management, 2004. 7(1): p. 77-87. 52. Glass, H.E., The Marketplace for Clinical Grants. Monitor, 2005. 19(4): p. 69-74. 53. Lamberti, M.J., et al., Factors influencing investigative site willingness and ability to participate in clinical trials. Drug Information Journal, 2011. 45(3): p. 377-390. 54. Santilli, J. and F. Randy Vogenberg, Key strategic trends that impact healthcare decision- making and stakeholder roles in the new marketplace. American Health and Drug Benefits, 2015. 8(1): p. 15-20. 55. Raitoharju, R. and E. Aamio, A theoretical approach to information needs across different healthcare stakeholders, in IFIP Advances in Information and Communication Technology. 2007. p. 497-504. 56. Frost, J.H. and M.P. Massagli, Social uses of personal health information within PatientsLikeMe, an online patient community: What can happen when patients have access to one another's data. Journal of Medical Internet Research, 2008. 10(3). 57. PatientsLikeMe. 2016 [cited 2016 April 29]; Available from: https://www.patientslikeme.com/about. 58. Comstock, J., PatientsLikeMe has 200K users, calls for new lexicon. 2013, MobiHealthNews. 59. Wicks, P., Could digital patient communities be the launch pad for patient-centric trial design? Trials, 2014. 15(1). 60. Walter, S.D., et al., Clinical experience may affect clinician compliance with assigned treatment in randomized trials. Journal of Clinical Epidemiology, 2013. 66(7): p. 768-774. 61. FDA. Clinical Trials and Human Subject Protection. 2015 [cited 2016 April 24]; Available from: http://www.fda.gov/ScienceResearch/SpecialTopics/RunningClinicalTrials/. 62. European Comission. Transition between Directive 2001/20/EC and Regulation EU No 536/2014 Clinical trials - General information 2016 [cited 2016 April 24]; Available from: http://ec.europa.eu/health/human-use/clinical-trials/information/index_en.htm#ct2. 63. FDA. Conducting Clinical Trials. 2014 [cited 2016 April 24]; Available from: http://www.fda.gov/drugs/developmentapprovalprocess/conductingclinicaltrials/default.htm. 64. Baer, A.R., et al., Implementing clinical trials: A review of the attributes of exemplary clinical trial sites. Journal of Oncology Practice, 2010. 6(6): p. 328-330. 65. Zon, B.R., et al., Part 2: Implementing clinical trials: A review of the attributes of exemplary clinical trial sites. Journal of Oncology Practice, 2011. 7(1): p. 61-64. 66. Paricio, I., Interview with a Senior Expert in Distribution Process Expert from Novartis Pharma AG. 2016, Attached in the present report. 67. Kärkkäinen, M., Increasing efficiency in the supply chain for short shelf life goods using RFID tagging. International Journal of Retail & Distribution Management, 2003. 31(10): p. 529-536. 68. Miller, J., Complex clinical trials are posing new challenges across the clinical supply chain. BioPharm International, 2010. 23(4): p. 18-20. 69. DiMasi, J.A., R.W. Hansen, and H.G. Grabowski, The price of innovation: New estimates of drug development costs. Journal of Health Economics, 2003. 22(2): p. 151-185. 70. CoinNews. US Inflation Calculator. 2016 [cited 2016 March 27]; Available from: http://www.usinflationcalculator.com/. 71. Geller, N.L., D.Y. Kim, and X. Tian, Smart technology in lung disease clinical trials. Chest, 2016. 149(1): p. 22-26. 72. Parexel, Optimizing the Clinical Trial Supply Chain, in Pharma Voice,. 2015. p. 1-10. 73. Fernandez, D.S. and J.T. Huie, Strategic balancing of patent and FDA approval processes to maximize market exclusivity. 74. FDA. Frequently Asked Questions on Patents and Exclusivity. 2014 [cited 2016 March 30]; Available from: http://www.fda.gov/Drugs/DevelopmentApprovalProcess/ucm079031.htm. 75. Marla, V., Optimizing the extended clinical supply chain: Strategic advantages for clinical trials. Pharmaceutical Engineering, 2005. 25(2): p. 58-64.

181

76. Smith, A., U.S. Smartphone Use in 2015, PewResearchCenter, Editor. 2015, PewResearchCenter. 77. EMA, Clinical trials submitted in marketing-authorisation applications to the European Medicines Agency. 2013, European Medicines Agency. 78. Rowland, C., Clinical trials seen shifting overseas. International journal of health services : planning, administration, evaluation, 2004. 34(3): p. 555-556. 79. Thomas, D.J. and P.M. Griffin, Coordinated supply chain management. European Journal of Operational Research, 1996. 94(1): p. 1-15. 80. Osterberg, L. and T. Blaschke, Adherence to medication. New England Journal of Medicine, 2005. 353(5): p. 487-497. 81. Czobor, P. and P. Skolnick, The secrets of a successful clinical trial: Compliance, compliance, and compliance. Molecular Interventions, 2011. 11(2): p. 107-110. 82. Brown, M.T. and J.K. Bussell, Medication adherence: WHO cares? Mayo Clinic Proceedings, 2011. 86(4): p. 304-314. 83. Butler, R.J., et al., Effects of nonadherence with prescription drugs among older adults. American Journal of Managed Care, 2011. 17(2): p. 153-160. 84. Martin, L.R., et al., The challenge of patient adherence. Therapeutics and Clinical Risk Management, 2005. 1(3): p. 189-199. 85. LaRosa, J.C., Poor compliance: the hidden risk factor. Current atherosclerosis reports, 2000. 2(1): p. 1-4. 86. Horwitz, R.I. and S.M. Horwitz, Adherence to Treatment and Health Outcomes. Archives of Internal Medicine, 1993. 153(16): p. 1863-1868. 87. Kemmler, G., et al., Dropout rates in placebo-controlled and active-control clinical trials of antipsychotic drugs: A meta-analysis. Archives of General Psychiatry, 2005. 62(12): p. 1305- 1312. 88. Cramer, J.A. and R. Rosenheck, Compliance with medication regimens for mental and physical disorders. Psychiatric Services, 1998. 49(2): p. 196-201. 89. Tufts Center for the Study of Drug Development, Growing Protocol Design Complexity Stresses Investigators, Volunteers. 2008. 90. Gaul, C., et al., Motivation of patients to participate in clinical trials. An explorative survey. Medizinische Klinik, 2010. 105(2): p. 73-79. 91. Chong, S.A., et al., An assessment of the understanding and motivations of patients with schizophrenia about participating in a clinical trial. Contemporary Clinical Trials, 2009. 30(5): p. 446-450. 92. Getz, K.A. and A. De Bruin, Breaking the development speed barrier: Assessing successful practices of the fastest drug development companies. Drug Information Journal, 2000. 34(3): p. 725-736. 93. Gram, K., et al., Patient centric innovations in the clinical supply chain. Pharmaceutical Engineering, 2014. 34(3): p. 20-24. 94. Rouse, M. Smart Label. 2012 Nobember 2012 [cited 2016 April 11]; Available from: http://internetofthingsagenda.techtarget.com/definition/smart-label. 95. Köpcke, F. and H.U. Prokosch, Employing computers for the recruitment into clinical trials: A comprehensive systematic review. Journal of Medical Internet Research, 2014. 16(7). 96. Anisimov, V.V., Drug supply modelling in clinical trials (Statistical Methodology). American Pharmaceutical Outsourcing, 2010. 11(3): p. 50-55. 97. Srai, J.S., et al., Future supply chains enabled by continuous processing-opportunities and challenges May 20-21, 2014 continuous manufacturing symposium. Journal of Pharmaceutical Sciences, 2015. 104(3): p. 840-849. 98. Peterson, M., et al., Optimizing clinical trial supply requirements: simulation of computer- controlled supply chain management. Clinical trials (London, England), 2004. 1(4): p. 399-412. 99. Ravichandran, N., Stochastic analysis of a continuous review perishable inventory system with positive lead time and Poisson demand. European Journal of Operational Research, 1995. 84(2): p. 444-457. 100. Ottjes, J.A., et al., Simulation of a multiterminal system for container handling. OR Spectrum, 2006. 28(4): p. 447-468. 101. WhatIf, Presentation: Opportunities for an e-label to add significant value to the MyDose initiative. 2016.

182

102. Thrasher, J. RFID vs NFC. 2013 [cited 2016 April 11]; Available from: http://blog.atlasrfidstore.com/rfid-vs-nfc. 103. Knoll, M., A self-writing smart label based on doping front migration. Electrochimica Acta, 2008. 54(2): p. 216-219. 104. RFIDbg. What is a "smart label"? 2015 February 9 2015 [cited 2016 April 11]; Available from: http://www.rfid.bg/en/what-is-a-smart-label. 105. Fairley, M., The RFID smart label challenge. Labels and Labelling, 2005(2): p. 86-89. 106. Binder, C.R., et al., Smart labels for waste and resource management: An integrated assessment. Journal of Industrial Ecology, 2008. 12(2): p. 207-228. 107. Chaves, L.W.F. and C. Decker. A survey on organic smart labels for the Internet-of-Things. in INSS 2010 - 7th International Conference on Networked Sensing Systems. 2010. 108. Create-QR-Codes.org. NFC versus RFID. 2016 [cited 2016 April 11]; Available from: http://www.create-qr-codes.org/nfc/versus.html. 109. Roberti, M. The History of RFID Technology. 20215 [cited 2016 April 14]; Available from: http://www.rfidjournal.com/articles/view?1338. 110. Arizona, U.o. A Brief History of RFID. 2009 [cited 2016 April 11]; Available from: http://www.u.arizona.edu/~obaca/rfid/history.html. 111. marketsandmarkets.com. Smart Label Market by Type (EAS, RFID, Sensing, & ESL), Tracking Application, Industry (Aerospace, Data Centre & Library, Food & Beverage, Healthcare & Pharmaceutical, Logistics, Manufacturing, & Retail) & Geography - Analysis & Forecast to 2014 - 2020. 2015 [cited 2016 April 11]; Available from: http://www.marketsandmarkets.com/Market-Reports/smart-label-market-192651809.html. 112. Modrak, V., P. Knuth, and J. Novak-Marcinein, Advantages and risks of RFID in business applications. International Business Management, 2010. 4(1): p. 28-34. 113. Pleşea, D.A., RFID technology and its advantages for labelling food products. Amfiteatru Economic, 2008. 10(SUPPL. 2): p. 40-43. 114. RFIDJournal.com. Can I buy a 5-cent RFID tag? 2015 [cited 2016 April 10]; Available from: https://www.rfidjournal.com/faq/show?84. 115. RFIDJournal.com. How much does an RFID tag cost today? 2015 [cited 2016 April 10]; Available from: https://www.rfidjournal.com/faq/show?85. 116. Barcoding.com. How much does an RFID tag cost? 2016 [cited 2016 April]; 12]. Available from: http://www.barcoding.com/faq/rfid-tag-cost.shtml. 117. Roberts, C.M., Radio frequency identification (RFID). Computers and Security, 2006. 25(1): p. 18-26. 118. Briand, D., et al. Printed sensors on smart RFID labels for logistics. in 2012 IEEE 10th International New Circuits and Systems Conference, NEWCAS 2012. 2012. 119. Säilä, J. The magic 5 cents per tag. 2012 [cited 2016 April 12]; Available from: http://www.rfidarena.com/2012/4/5/the-magic-5-cents-per-tag.aspx. 120. RFIDJournal.com. How much do RFID readers cost today? 2015 [cited 2016 April 11]; Available from: https://www.rfidjournal.com/faq/show?86. 121. RFIDJournal.com. How much does a fully functional RFID system cost? 2015 [cited 2016 April 11]; Available from: https://www.rfidjournal.com/faq/show?87. 122. Perretti, G., et al. Study of an RFId smart label for food quality and safety. in IMETI 2011 - 4th International Multi-Conference on Engineering and Technological Innovation, Proceedings. 2011. 123. GoToTags. [cited 2016 May 1]; Available from: https://gototags.com/products/nfc-encoder- software/pricing/. 124. KimTag. NFC Tags Explained. 2013 [cited 2016 May 1]; Available from: http://kimtag.com/s/nfc_tags. 125. Hrinya, G. Smart technology redefines the label industry. 2016 [cited 2016 April 11]; Available from: http://www.labelandnarrowweb.com/issues/3-2015/view_features/smart- technology-redefines-the-label-industry. 126. Gyeszly, S.W. Total system approach to predict shelf life of packaged food products. in ASTM Special Technical Publication. 1991. 127. Aegis Tec. Medical Technologies. 2016 [cited 2016 April 25]; Available from: http://www.aegismedtec.com/rfid-solutions/.

183

128. Schoo, C. and M. Knoll, Self-writing smart labels employing ambient water harvesting. Sensors and Actuators, A: Physical, 2013. 195: p. 79-82. 129. Schoo, C. and M. Knoll, Self-writing smart labels for humidity monitoring. Sensors and Actuators, A: Physical, 2014. 205: p. 1-5. 130. CityRating.com. Relative Humidity. 2016 [cited 2016 April 6]; Available from: http://www.cityrating.com/relativehumidity.asp. 131. Primozic, U. Electronic paper explained: what is it and how does it work? 2015 [cited 2016 April 11]; Available from: https://www.visionect.com/blog/electronic-paper-explained-what-is- it-and-how-does-it-work. 132. Manley, L. and R.P. Holley, History of the Ebook: The Changing Face of Books. Technical Services Quarterly, 2012. 29(4): p. 292-311. 133. Swedberg, C. Two Food Chains Trial RFID-based Electronic Shelf Labels. 2009 [cited 2016 May 1]; Available from: http://www.rfidjournal.com/articles/view?4737. 134. Altierre. [cited 2016 May 1]; Available from: http://www.altierre.com/wordpress/. 135. eInk. Disruptive benefits of eInk. 2013 [cited 2016 April 30]; Available from: http://www.eink.com/display_products_surf.html. 136. eInk. eInk Aurora™: Active Matrix ESL. 2013; Available from: http://www.eink.com/display_products_aurora.html. 137. Swedberg, C. Reusable Electronic Baggage Tag Powered by RFID. 2010 [cited 2016 May 1]; Available from: http://www.rfidjournal.com/articles/view?7928. 138. Huayuan. [cited 2016 May 1]; Available from: http://www.huayuansh.com/esl-nfc-shelf-label/. 139. PowerShelf. [cited 2016 May 1]; Available from: http://powershelf.net/wordpress/myportfolio/electronic-shelf-labels-esls/. 140. Swedberg, C. Retailers Adopt NFC-enabled Electronic Shelf Labels. 2013 [cited 2016 May 1]; Available from: http://www.rfidjournal.com/articles/view?10741. 141. Hoffelder, N. Panasonic Demos NFC-Powered E-ink Shelf Labels (video). 2016 [cited 2016 May 17]; Available from: http://the-digital-reader.com/2016/05/14/panasonic-demos/. 142. ABI Reserach. Electronic Shelf Labels Revenues to Reach US$2 Billion by 2019. 2014 [cited 2016 May 13]; Available from: https://www.abiresearch.com/press/electronic-shelf-labels- revenues-to-reach-us2-bill/. 143. ISPE, ISPE Good Practice Guide: Booklet Labels. 2013, ISPE. 144. GlobalData. Top 25 pharma companies by global sales. 2015 [cited 2016 April]; 17]. Available from: http://www.pmlive.com/top_pharma_list/global_revenues. 145. Collins, J. Novartis Trial Shows RFID Can Boost Patient Compliance. 2006 [cited 2016 April 17]; Available from: http://www.rfidjournal.com/articles/view?2438. 146. Roumeliotis, G. Cost Emerges as Major Issue in Novartis RFID Trial 2006 [cited 2016 April 17]. 147. O'Connor, M.C. Pfizer Using RFID to Fight Fake Viagra. 2006 [cited 2016 April 17]; Available from: http://www.rfidjournal.com/articles/view?2075. 148. World Mobile Congress, Health & Welness. 2014, B2Match: Barcelona. 149. Sacherer, K.D., Verfahren zur Herstellung eines Smart Labels mit laserbeschriftbarem Klebeetikett. 2009, Google Patents. 150. Manufacturing Chemist. RFID project at sanofi-aventis. 2011 [cited 2016 February 17]; Available from: http://www.manufacturingchemist.com/technical/article_page/RFID_project_at_sanofiaventis/ 36455. 151. Sanofi, e-labeling : innovation for patient or regulation nightmare, Sanofi, Editor. 2015. 152. Wessel, R. Merck KGaA Funds Printable RFID. 2006 [cited 2016 February 17]; Available from: http://www.rfidjournal.com/articles/view?2111. 153. O'Connor, M.C. Johnson & Johnson Finds Value in Multiple RFID Apps. 2008 [cited 2016 February 17]; Available from: http://www.rfidjournal.com/articles/view?4046. 154. Roumeliotis, G. GSK turns to RFID to protect its AIDS drug. 2016 [cited 2016 February 17]; Available from: http://www.in-pharmatechnologist.com/Drug-Delivery/GSK-turns-to-RFID-to- protect-its-AIDS-drug. 155. O'Connor, M.C. GlaxoSmithKline Tests RFID on HIV Drug. 2006 [cited 2016 February 17]; Available from: http://www.rfidjournal.com/articles/view?2219.

184

156. Bacheldor, B. GlaxoSmithKline Remains Committed to RFID. 2007 [cited 2016 February 17]; Available from: http://www.rfidjournal.com/articles/view?3584. 157. Harrop, P. Opportunities in Pharma RFID and Smart Packaging. 2006 [cited 2016 April 17]; Available from: http://www.idtechex.com/research/articles/opportunities_in_pharma_rfid_and_smart_packagi ng_00000487.asp. 158. Packaging-Gateway. Smart packaging moves pharma forward. 2005 [cited 2016 April 17]; Available from: www.packaging-gateway.com/features/feature11/. 159. Motorola, Motorola RFID. 2009. 160. Maurno, D.A. Going for (Not So) Broke: The True Cost of RFID. 2005 [cited 2016 May 15]; Available from: http://www.inboundlogistics.com/cms/article/going-for-not-so-broke-the-true- cost-of-rfid/. 161. FDA. Counterfeit Medicine. 2015 [cited 2016 May 15]; Available from: http://www.fda.gov/Drugs/ResourcesForYou/Consumers/BuyingUsingMedicineSafely/Counte rfeitMedicine/. 162. Akunyili, D., Lessons from Nigeria: the fight against counterfeit drugs in Africa. Diabetes Voice. September, 2006. 163. Sherouse, P.M.O., The McLaughlin-Sherouse List: The 10 Most-Regulated Industries of 2014, M. Center, Editor. 2016, George Madison University. 164. Maynard, A. and K. Bloor, Regulation of the pharmaceutical industry: promoting health or protecting wealth? Journal of the Royal Society of Medicine, 2015. 108(6): p. 220-222. 165. Stigler, G.J., The theory of economic regulation. The Bell journal of economics and management science, 1971: p. 3-21. 166. Holbein, M.E.B., Understanding FDA regulatory requirements for investigational new drug applications for sponsor-investigators. Journal of Investigative Medicine, 2009. 57(6): p. 688- 694. 167. Zineh, I. and J. Woodcock, Clinical pharmacology and the catalysis of regulatory science: Opportunities for the advancement of drug development and evaluation. Clinical Pharmacology and Therapeutics, 2013. 93(6): p. 515-525. 168. Ward, M., The EU clinical trial regulation – A regulator’s perspective. Regulatory Rapporteur, 2015. 12(10): p. 5-9. 169. Giannuzzi, V., et al., Clinical Trial Application in Europe: What Will Change with the New Regulation? Science and Engineering Ethics, 2016. 22(2): p. 451-466. 170. Abou-El-Enein, M. and C.K. Schneider, Deciphering the EU clinical trials regulation. Nature Biotechnology, 2016. 34(3): p. 231-233. 171. FDA. Investigational New Drug (IND) Application. 2008 [cited 2016 April 29]; Available from: http://www.fda.gov/drugs/developmentapprovalprocess/howdrugsaredevelopedandapproved/ approvalapplications/investigationalnewdrugindapplication/default.htm. 172. Alkire, S. and M.E. Santos, Acute multidimensional poverty: A new index for developing countries. United Nations development programme human development report office background paper, 2010(2010/11). 173. Singh, R. and O. Wang, Clinical trials in "emerging markets": Regulatory considerations and other factors. Contemporary Clinical Trials, 2013. 36(2): p. 711-718. 174. Andrade, C. and T. Appaiah, Clinical Trials in Developing Countries: Challenges in Design, Execution, and Regulation, in Clinical Trial Design Challenges in Mood Disorders. 2015. p. 167-173. 175. ClinicalTrials.gov. Map of All Studies on ClinicalTrials.gov. 2016 [cited 2016 April 24]. 176. Outsourced Pharma. Pharmaceutical Contract Packaging: Primary And Secondary Packaging Services. 2016 [cited 2016 April 30]; Available from: http://www.outsourcedpharma.com/doc/pharmaceutical-contract-packaging-overview-0002. 177. The Institute of Clinical Research. What are the labelling requirements of a clinical trial? 2007 [cited 2016 April 29]; Available from: http://www.icr-global.org/community/special-interest- groups/pharmacy-special-interest-group/frequently-asked-questions/. 178. eInk.com. Standard Electronic Shelf Labels Tags. 2016 [cited 2016 May 13]; Available from: http://www.eink.com/esl_tags.html.

185

179. Chen, Y., J.F. Pekny, and G.V. Reklaitis, Integrated planning and optimization of clinical trial supply chain system with risk pooling. Industrial and Engineering Chemistry Research, 2013. 52(1): p. 152-165. 180. Shah, N., Pharmaceutical supply chains: Key issues and strategies for optimisation. Computers and Chemical Engineering, 2004. 28(6-7): p. 929-941. 181. Butler, J., et al., Relationship between clinical trial site enrollment with participant characteristics, protocol completion, and outcomes: Insights from the Everest (efficacy of vasopressin antagonism in heart failure: Outcome study with Tolvaptan) trial. Journal of the American College of Cardiology, 2013. 61(5): p. 571-579. 182. European Comission, Working Party on Control of Medicines and Inspections in EU Guide to Good Manufacturing Practice, r.e. Single market, industries under vertical legislation: Pharmaceuticals and cosmetics, Editor. 2001, European Comission: Brussels. 183. Liénard, J.L., et al., Impact of on-site initiation visits on patient recruitment and data quality in a randomized trial of adjuvant chemotherapy for breast cancer. Clinical Trials, 2006. 3(5): p. 486-492. 184. World Health Organization. Guidelines for Safe Disposal of Unwanted Pharmaceuticals in and after Emergencies. 1999 [cited 2016 May 18]; Available from: http://apps.who.int/medicinedocs/en/d/Jwhozip51e/2.3.html. 185. Banks, J., et al., Discrete-Event System Simulation, Fifth Edition, ed. Upper Saddle River. 2010: Pearson Education, Inc. 186. Pruyt, E., Small System dynamics models for big issues: Triple jump towards real-world complexity. 2013: TU Delft Library. 187. Taban, H., et al., Patients' motivation about clinical trials: A local perspective from Turkey. Marmara Medical Journal, 2011. 24(3): p. 181-186. 188. Burgess, L.J., et al., Patients' motivations for participating in cardiovascular clinical trials: A local perspective. Cardiovascular Journal of Africa, 2009. 20(4): p. 220-223. 189. Carroll, R., et al., Motivations of patients with pulmonary arterial hypertension to participate in randomized clinical trials. Clinical Trials, 2012. 9(3): p. 348-357. 190. Costas, L., et al., Motivations for participating in a clinical trial on an avian influenza vaccine. Trials, 2012. 13. 191. Andresen, E.L., et al., Patient motivation for participating in clinical trials for depression: Validation of the motivation for clinical trials inventory-depression. International Clinical Psychopharmacology, 2010. 25(1): p. 7-16. 192. Friesen, L.R. and K.B. Williams, Attitudes and motivations regarding willingness to participate in dental clinical trials. Contemporary Clinical Trials Communications, 2016. 2: p. 85-90. 193. Weckstein, D.J., et al., Assessment of perceived cost to the patient and other barriers to clinical trial participation. Journal of Oncology Practice, 2011. 7(5): p. 330-333. 194. Moffitt, K., et al., Statewide cancer clinical trial navigation service. Journal of Oncology Practice, 2010. 6(3): p. 127-132. 195. Kemeny, M.M., et al., Barriers to clinical trial participation by older women with breast cancer. Journal of Clinical Oncology, 2003. 21(12): p. 2268-2275. 196. Meropol, N.J., et al., Barriers to clinical trial participation as perceived by oncologists and patients. JNCCN Journal of the National Comprehensive Cancer Network, 2007. 5(8): p. 655- 664. 197. Hall, M.J., et al., Barriers to participation in cancer prevention clinical trials. Acta Oncologica, 2010. 49(6): p. 757-766. 198. Mills, E.J., et al., Barriers to participation in clinical trials of cancer: A meta-analysis and systematic review of patient-reported factors. Lancet Oncology, 2006. 7(2): p. 141-148. 199. Melisko, M.E., et al., Patient and physician attitudes toward breast cancer clinical trials: Developing interventions based on understanding barriers. Clinical Breast Cancer, 2005. 6(1): p. 45-54. 200. Markman, M. and R. Luce, Impact of the cost of cancer treatment: An internet-based survey. Journal of Oncology Practice, 2010. 6(2): p. 69-73. 201. Henrard, S., N. Speybroeck, and C. Hermans, Participation of people with haemophilia in clinical trials of new treatments: An investigation of patients' motivations and existing barriers. Blood Transfusion, 2015. 13(2): p. 302-309.

186

202. Leiter, A., et al., Clinical trial awareness: Changes over time and sociodemographic disparities. Clinical Trials, 2015. 12(3): p. 215-223. 203. Sabaté, E., Adherence to long-term therapies - evidence for action. 2003, World Health Organization. 204. Robin Dimatteo, M., et al., Patient adherence and medical treatment outcomes A meta- analysis. Medical Care, 2002. 40(9): p. 794-811. 205. Zschocke, I., et al., Assessing adherence factors in patients under topical treatment: Development of the Topical Therapy Adherence Questionnaire (TTAQ). Archives of Dermatological Research, 2014. 306(3): p. 287-297. 206. Brawley, L.R. and S.N. Culos-Reed, Studying adherence to therapeutic regimens overview, theories, recommendations. Controlled Clinical Trials, 2000. 21(5 SUPPL. 1): p. 156S-163S. 207. Hovstadius, B. and G. Petersson, Non-adherence to drug therapy and drug acquisition costs in a national population - A patient-based register study. BMC Health Services Research, 2011. 11. 208. Ingersoll, K.S. and J. Cohen, The impact of medication regimen factors on adherence to chronic treatment: A review of literature. Journal of Behavioral Medicine, 2008. 31(3): p. 213- 224. 209. Pugatsch, T., et al., Adherence pattern to study drugs in clinical trials by patients with cystic fibrosis. Pediatric Pulmonology, 2016. 51(2): p. 143-146. 210. Cramer, J.A., et al., Medication compliance feedback and monitoring in a clinical trial: Predictors and outcomes. Value in Health, 2003. 6(5): p. 566-573. 211. Haynes, R.B., H.P. McDonald, and A.X. Garg, Helping patients follow prescribed treatment: Clinical applications. Journal of the American Medical Association, 2002. 288(22): p. 2880- 2883. 212. Burnier, M., Long-term compliance with antihypertensive therapy: Another facet of chronotherapeutics in hypertension. Blood Pressure Monitoring, 2000. 5(SUPPL. 1): p. S31- S34. 213. Matsui, D., Strategies to measure and improve patient adherence in clinical trials. Pharmaceutical Medicine, 2009. 23(5-6): p. 289-297. 214. Claxton, A.J., J. Cramer, and C. Pierce, A systematic review of the associations between dose regimens and medication compliance. Clinical Therapeutics, 2001. 23(8): p. 1296-1310. 215. Coleman, C.I., et al., Dosing frequency and medication adherence in chronic disease. Journal of Managed Care Pharmacy, 2012. 18(7): p. 527-539. 216. CureTogether. [cited 2016 May 5]; Available from: http://curetogether.com/. 217. Paes, A.H.P., A. Barker, and C.J. Soe-Agnie, Impact of dosage frequency on patient compliance. Diabetes Care, 1997. 20(10): p. 1512-1517. 218. Novartis Pharma AG. Novartis Pharmaceuticals collaborates with Qualcomm in digital innovation with the Breezhaler(TM) inhaler device to treat COPD. 2016; Available from: https://www.novartis.com/news/media-releases/novartis-pharmaceuticals-collaborates- qualcomm-digital-innovation-breezhalertm. 219. QualcommLife. CleverCap. 2016; Available from: http://www.qualcommlife.com/ecosystem/ecosystem/fitness-medical-devices/87-clevercap. 220. ePill. MedSmartPlus. 2016; Available from: http://www.epill.com/medsmartplus.html. 221. Mims. Ultilbro Breezhaler Inhalation Powd Cap. 2016 [cited 2016 May 8]. 222. Novartis Pharmaceuticals Canada Inc. Product Monograph - Seebri Breezhaler. 2013 [cited 2016 May 8]; Available from: https://www.ask.novartispharma.ca/download.htm?res=seebri%20breezhaler_scrip_e.pdf&re sTitleId=797. 223. ClinOps. How to Improve Medication Adherence in Clinical Trials. 2015 [cited 2016 May 8 ]; Available from: http://clinopstoolkit.com/2015/08/how-to-improve-medication-adherence-in- clinical-trials.html. 224. Alert Utah. MedSmart. 2016 [cited 2016 May 8 ]; Available from: http://alertutah.com/product/medsmart/. 225. Vitality. GlowCap. 2016; Available from: http://www.vitality.net/. 226. Kee, E., iRemember is the world’s first smart pill organizer. 2014, CoolestGadgets. 227. Information Mediary Corp. eCap. 2016; Available from: http://www.informationmediary.com/ecap.

187

228. MedSignals. Pill Case. 2016; Available from: http://www.medsignals.com/medsignals-pill- case. 229. Philips. Medication Dispensing Service. 2016; Available from: https://www.lifeline.philips.com/health-solutions/health-mdp.html. 230. MedMinder. 2016 [cited 2016 May 5]; Available from: https://www.medminder.com/. 231. PivoTell Ltd. PivoTell Dispenser. 2016 [cited 2016 May 5]; Available from: http://www.pivotell.co.uk/index.html. 232. Lomas, N., Memo Box Predicts When You’ve Forgotten Your Pills. 2016, TechCrunch. 233. Lumma. 2016 [cited 2016 May 5]; Available from: http://www.getlumma.com/. 234. Adheretech. 2016 [cited 2016 May 5]; Available from: https://adheretech.com/. 235. eTectBio. ID-Cap. 2016 [cited 2016 May 5]; Available from: http://etectbio.com/. 236. Droplet. 2016 [cited 2016 May 6]; Available from: http://www.dropletlife.com/. 237. Robarts, S., Droplet pushes your buttons for reminders and activity tracking. 2015, Gizmag 238. Flic. 2016 [cited 2016 May 6]; Available from: https://flic.io/. 239. Bttn. 2016 [cited 2016 May 6]; Available from: https://bt.tn/. 240. Rosa, C., et al., Using e-technologies in clinical trials. Contemporary Clinical Trials, 2015. 45: p. 41-54. 241. LeadingAge, Telehealth and Remote Patient Monitoring for Long-term and Post-acute Care: A Primer and Provider Selection Guide, L.C.f.A.S. Technologies, Editor. 2013, LeadingAge,. 242. Morak, J., et al., Near field communication-based telemonitoring with integrated ECG recordings. Applied Clinical Informatics, 2011. 2(4): p. 481-498. 243. Florea, V.G. and I.S. Anand, Clinical trial report: Reevaluating telemonitoring in heart failure. Current Heart Failure Reports, 2011. 8(2): p. 84-86. 244. Inglis, S.C., et al., Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane database of systematic reviews (Online), 2010. 8. 245. Ringbæk, T., et al., Effect of tele health care on exacerbations and hospital admissions in patients with chronic obstructive pulmonary disease: A randomized clinical trial. International Journal of COPD, 2015. 10(1): p. 1801-1808. 246. Pinnock, H., et al., Effectiveness of telemonitoring integrated into existing clinical services on hospital admission for exacerbation of chronic obstructive pulmonary disease: Researcher blind, multicentre, randomised controlled trial. BMJ (Online), 2013. 347. 247. Pressman, A.R., et al., A novel telemonitoring device for improving diabetes Control: Protocol and results from a randomized Clinical Trial. Telemedicine and e-Health, 2014. 20(2): p. 109- 114. 248. Margolis, K.L., et al., Effect of home blood pressure telemonitoring and pharmacist management on blood pressure control a cluster randomized clinical trial. JAMA - Journal of the American Medical Association, 2013. 310(1): p. 46-56. 249. Kerby, T.J., et al., Adherence to Blood Pressure Telemonitoring in a Cluster-Randomized Clinical Trial. Journal of Clinical Hypertension, 2012. 14(10): p. 668-674. 250. Pulliam, C.L., et al., Continuous in-home monitoring of essential tremor. Parkinsonism and Related Disorders, 2014. 20(1): p. 37-40. 251. Fisher, R.S., et al., Seizure diaries for clinical research and practice: limitations and future prospects. Epilepsy & behavior : E&B, 2012. 24(3): p. 304-310. 252. Weikert, M., et al., Accelerometry is associated with walking mobility, not physical activity, in persons with multiple sclerosis. Medical Engineering and Physics, 2012. 34(5): p. 590-597. 253. Dolan, B. Medication adherence app reminds pharmacy's HIV patients to take meds. 2015 [cited 2016 May 5]; Available from: http://mobihealthnews.com/39996/medication-adherence- app-reminds-pharmacys-hiv-patients-to-take-meds. 254. Motti, J. Study: Mobile pharma app increases med adherence. 2015 [cited 2016 May 5]; Available from: http://www.fiercemobilehealthcare.com/story/study-mobile-pharma-app- increases-med-adherence/2015-02-01. 255. Sanders, C., et al., Exploring barriers to participation and adoption of telehealth and telecare within the Whole System Demonstrator trial: A qualitative study. BMC Health Services Research, 2012. 12(1). 256. Denhaerynck, K., et al., Examining assumptions regarding valid electronic monitoring of medication therapy: Development of a validation framework and its application on a European sample of kidney transplant patients. BMC Medical Research Methodology, 2008. 8.

188

257. Brown, A., B.J. Meenan, and T.P. Young. Price trend analysis and its implications for the development of new medical technologies. in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. 2007. 258. Wootton, R., Twenty years of telemedicine in chronic disease management-an evidence synthesis. Journal of Telemedicine and Telecare, 2012. 18(4): p. 211-220. 259. Dunbar-Jacob, J., et al., Electronic methods in assessing adherence to medical regimens. Technology and methods in behavioral medicine. Mahwah, NJ: Lawrence Erlbaum Associates, 1998: p. 95-113. 260. Deschamps, A.E., et al., Prevalence and correlates of nonadherence to antiretroviral therapy in a population of HIV patients using Medication Event Monitoring System®. AIDS Patient Care and STDs, 2004. 18(11): p. 644-657. 261. Fennie, K.P., C.A. Bova, and A.B. Williams, Adjusting and censoring electronic monitoring device data: Implications for study outcomes. Journal of Acquired Immune Deficiency Syndromes, 2006. 43(SUPPL. 1): p. S88-S95. 262. Warren, S.R., et al., Medication adherence assessment in a clinical trial with centralized follow-up and direct-to-patient drug shipments. Clinical Trials, 2013. 10(3): p. 441-448. 263. Wendel, C.S., et al., Barriers to use of electronic adherence monitoring in an HIV clinic. Annals of Pharmacotherapy, 2001. 35(9): p. 1010-1015. 264. Wagner, G.J. and B. Ghosh-Dastidar, Electronic monitoring: Adherence assessment or intervention? HIV Clinical Trials, 2002. 3(1): p. 45-51. 265. Fulmer, T.T., et al., An intervention study to enhance medication compliance in community- dwelling elderly individuals. Journal of gerontological nursing, 1999. 25(8): p. 6-14. 266. TrustedReviews. Droplet is a physical button that makes sure you don't forget. 2015 [cited 2016 May 8 ]; Available from: http://www.trustedreviews.com/news/droplet-is-a-physical- button-that-makes-sure-you-don-t-forget. 267. European Comission, Regulation (EU) No 536/2014 of the European Parliament and of the Council on clinical trials on medicinal products for human use, and repealing Directive 2001/20/EC 2014. 268. Dowlman, N., M. Lang, and D. McEntegart, Optimizing the clinical supply chain through trial simulation. Applied Clinical Trials, 2004. 13: p. 40-46. 269. Do, M. Best Practices in IRT Implementation: A Checklist for Asia-Pac. 2014 [cited 2016 June 3]; Available from: http://www.appliedclinicaltrialsonline.com/best-practices-irt- implementation-checklist-asia-pac. 270. World Pharma. Virtually there: the power of IRT. 2013 [cited 2016 June 3]; Available from: http://www.worldpharmaceuticals.net/features/featurevirtually-there-the-power-of-interactive- response-technologies-irt-4157927/. 271. Pramatari, K., A. Karagiannaki, and C. Bardaki, Radio frequency identification (RFID) as a catalyst for improvements in food supply chain operations, in Delivering Performance in Food Supply Chains. 2010. p. 432-455. 272. Lambert, D.M. and M.C. Cooper, Issues in supply chain management. Industrial Marketing Management, 2000. 29(1): p. 65-83. 273. Tako, A.A. and S. Robinson, The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 2012. 52(4): p. 802-815. 274. Persson, F. and M. Araldi, The development of a dynamic supply chain analysis tool- Integration of SCOR and discrete event simulation. International Journal of Production Economics, 2009. 121(2): p. 574-583. 275. Jain, S., et al. Development of a high-level supply chain simulation model. in Winter Simulation Conference Proceedings. 2001. 276. Kelepouris, T., K. Pramatari, and G. Doukidis, RFID-enabled traceability in the food supply chain. Industrial Management and Data Systems, 2007. 107(2): p. 183-200. 277. Kumar, P., et al., Overview of RFID technology and its applications in the food industry. Journal of Food Science, 2009. 74(8): p. R101-R106. 278. Grunow, M. and S. Piramuthu, RFID in highly perishable food supply chains - Remaining shelf life to supplant expiry date? International Journal of Production Economics, 2013. 146(2): p. 717-727.

189

279. Wang, Z., Y. Cui, and J. Shi, A Framework of Discrete-Event Simulation Modeling for Prognostics and Health Management (PHM) in Airline Industry. IEEE Systems Journal, 2015. 280. Keep-it Technologies. The Shelf Life Indicator. 2016 [cited June 2 2016]; Available from: http://keep-it.com/. 281. Tromp, S.O., et al., Retail benefits of dynamic expiry dates - Simulating opportunity losses due to product loss, discount policy and out of stock. International Journal of Production Economics, 2012. 139(1): p. 14-21. 282. Store Electronic Systems. ROI of electronic shelf labeling: 0.5% to 1% of sales. 2015 [cited June 4 2016]; Available from: http://www.store-electronic-systems.com/en/content/roi- electronic-shelf-labeling. 283. Mitchell, S., 10 Electronic Shelf Edge Label (ESL) Questions Retailers Should Ask, in Business to Community. 2013. 284. Shapiro, A., Clinical trials at our fingertips: A salute to technology. Retina Today, 2015. 2015(November-December): p. 22-24. 285. Pietanza, M.C., et al., Harnessing technology to improve clinical trials: study of real-time informatics to collect data, toxicities, image response assessments, and patient-reported outcomes in a phase II clinical trial. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2013. 31(16): p. 2004-2009. 286. Louis, A.A., et al., A systematic review of telemonitoring for the management of heart failure. European Journal of Heart Failure, 2003. 5(5): p. 583-590. 287. Karlawish, J., et al., How redesigning AD clinical trials might increase study partners willingness to participate. Neurology, 2008. 71(23): p. 1883-1888. 288. Hyatt, A., Infographic – Telehealth Index: 2015 Consumer Survey. 2015, American Well. 289. McAlindon, T., et al., Conducting clinical trials over the internet: Feasibility study. British Medical Journal, 2003. 327(7413): p. 484-487. 290. Gelfand, E.W., Asthma Clinical Trials vs Real World Effectiveness: Comparing Apples to Oranges - Introduction. Journal of Allergy and Clinical Immunology, 2003. 112(5 SUPPL.). 291. Ryan, D., et al., Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: Multicentre randomised controlled trial. BMJ (Online), 2012. 344(7854). 292. Morak, J., A. Kollmann, and G. Schreier. Feasibility and usability of a home monitoring concept based on mobile phones and near field communication (NFC) technology. in Studies in Health Technology and Informatics. 2007. 293. Scherr, D., et al., Effect of home-based telemonitoring using mobile phone technology on the outcome of heart failure patients after an episode of acute decompensation: randomized controlled trial. Journal of medical Internet research, 2009. 11(3): p. e34. 294. Partridge, A.H. and E.P. Winer, Informing clinical trial participants about study results. Journal of the American Medical Association, 2002. 288(3): p. 363-365. 295. Dorsey, E.R., et al., Communicating clinical trial results to research participants. Archives of Neurology, 2008. 65(12): p. 1590-1595. 296. FDA, Guidance for industry: Patient-reported outcome measures: Use in medical product development to support labeling claims: Draft guidance. Health and Quality of Life Outcomes, 2006. 4.

190