Rare Disease Clinical Development: Novel Approaches to Overcoming Operational Challenges

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Table of Contents

Unique Challenges in the Rare Disease Space 4

The Landscape for Clinical Research is Changing, and Patients are at the Center 6

New Methodologies and Technologies Suited to Rare Disease Trials 7

Reducing the Patient Burden 10

Rare Disease Clinical Trials in the Era of COVID-19 12

Summary 13

Endnotes 14 WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 3 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

Rare diseases are serious, chronic, progressive conditions that can be disabling and/or life threatening.1 Most remain perplexing to medical science due to their complexity, diversity, and low prevalence. Somewhere between 7,000 and 8,000 different rare diseases have been identified and collectively, represent a significant burden on humankind as they affect an estimated 400 million people, or one out of every 10 people on the planet.2

Eight out of ten rare diseases are genetic in origin, and half of all rare disease patients diagnosed are children. Today, 95 percent of all rare diseases lack a treatment approved by the US Food and Drug Administration (FDA), leaving most patients and their families with devastating consequences and little hope of a cure.3

Consequently, the pharmaceutical industry is increasing its focus on developing novel therapies for many rare diseases, and approvals in the US and Europe are accelerating. In the US 21 of the 48 new approvals (44 percent)4 in 2019 were for rare disease indications and for Europe 21 out of 42 approvals in 20184. (See Figure 1.) There are new technologies and methodologies capable of changing the game in rare disease clinical development, starting with biomarker discovery all the way through to regulatory submission.

Figure 1: Accelerating Rare Disease Approvals5,6

2017 2018 2019

US Food and Drug Administration (FDA) 39% 58% 44%

18 out of 48 NDAs 34 out of 59 NDAs 21 out of 48 NDAs were for rare diseases were for rare diseases were for rare diseases

European Medicines Agency (EMA) 59% 50% 23%

19 out of 32 approvals 21 out of 42 approvals 7 out of 30 approvals were for rare diseases were for rare diseases were for rare diseases

The complexities of clinical trials in rare diseases present obstacles that slow the path to approval and add considerably to drug development costs. Getting the development plan right from the beginning of the trial is critical. The following paper reviews the challenges that biopharmaceutical sponsors face and presents a variety of solutions capable of streamlining the process to speed delivery of rare disease treatments to the market. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 4 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

Unique Challenges in the Rare Disease Space The nature of rare diseases poses several difficulties for sponsors in the clinical development process that in many ways are more intense than those faced in studying the safety and efficacy of treatments for more common diseases. The rarity and severity of the disease and the scarcity of patients to participate in clinical trials impact the study design, approach to patient recruitment, retention strategies, and ultimately the trial cost.

ā Small Population. By definition, a rare/orphan disease is a condition that affects fewer than 200,000 people in the US and in the EU, fewer than 1 in 2,000 people.7,8 The scarcity of patients eligible for clinical trials is exacerbated by the growing number of therapies in development, causing competition for already hard-to-find patients. With so few patients available to participate in these trials, sponsors cannot afford to lose a single enrolled patient.

Due to the very small patient populations, studies in rare disease areas are less likely to have a traditional control population. Often rare disease clinical trials involve complex umbrella, basket, or adaptive designs [see Complex Trial Types definitions below]. As the science associated with the drug development effort evolves and becomes more precise, so does the number of objectives, endpoints, and associated procedures, adding to the overall trial complexity and burden to the patient.9,10

Complex Trial Types11

Rare disease clinical trials often involve complex 1 2 3 umbrella, basket, Umbrella Trial Basket Trial Adaptive Trial or adaptive designs.

A type of clinical trial that A type of clinical trial that A type of clinical trial that tests how well new drugs tests how well a new drug evaluates a medical device or other substances work or other substance works in or treatment by observing in patients who have the patients who have different participant outcomes on a same type of cancer but types of cancer that all prescribed schedule and different gene mutations have the same mutation or modifying parameters of (changes) or biomarkers. biomarker. In basket trials, the trial protocol in accord In umbrella trials, patients patients all receive the with those observations. receive treatment based same treatment that on the specific mutation targets the specific or biomarker found in mutation or biomarker their cancer. found in their cancer. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 5 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

ā Geographic Dispersion. Patients with rare diseases are typically scattered across the world, resulting in the need for sponsors to operate many trial sites, most of which achieve a low patient-to-site ratio. It is common for patients to have to travel to different states in the US and to different countries in the EU to access trial sites. This creates a patient burden that is especially taxing for rare disease patients, who can be significantly ill, and increases costs for sponsors.

ā Lack of Disease Knowledge/Difficulty in Diagnosis. In many rare diseases, it is challenging to obtain a deep understanding of the disease process (i.e., etiology, genetics, pathophysiology, and natural history) given the lack of clinical and scientific data collected due to the rarity of cases.12 When a condition is rare, finding healthcare providers who are familiar with a specific disease and have seen other patients with it may be especially difficult. Once connected with the appropriate clinician, patients and their families often have to go through a diagnostic odyssey given the clinical and genetic heterogeneity of rare diseases. Diagnostic criteria may be either unclear or not fully established, especially for non-genetic rare diseases. And in genetic diseases, mutations within the same gene can be associated with different diagnoses.13

Also, there is limited familiarity with rare diseases among both primary care physicians and specialists, with only a small number of experts in each disease. As a result, misdiagnoses are common. It takes an average of 4.8 years for a patient with a rare disease to be correctly diagnosed after consultation with an average of 7.3 doctors. In many cases, patients and caretakers develop deep expertise in their condition.14

Within pharmaceutical companies, knowledge of rare diseases is often dispersed across the clinical, operational, and real-world evidence domains among various stakeholders. Pharma companies are also using specialized global advisory boards of academic and community based specialists to assist them in better understanding the rare disease community, its challenges and needs.

ā Pediatric Population. Roughly 50 percent of rare diseases affect children, 30 percent of whom don’t survive past their 5th birthday.15,16 Having a predominately pediatric population leads to more ethical and legal considerations when recruiting and enrolling patients in clinical trials, leading to slower recruitment timelines.17 Enrollment of human subjects, in general, requires informed and voluntary consent of the research participants to understand the risks and benefits of the research. Children are considered a vulnerable research population as they are minors and have a lack of autonomy and decision making capacity to ethically and legally consent to research participation. To apply the general ethical principle of respect for persons, research in children requires both parental permission and child assent.18 Protections for children involved in research are unquestionably critical, but add another layer of consideration when a team is designing a treatment trial.

ā Trial Design: For a patient or the parent of a pediatric patient, the chance of being in a control arm of a randomized control trial may be a disincentive, especially when an investigational drug may be able to offer a more efficacious treatment option compared to the standard of care. Additionally, if patients find out they are in a control arm, they may drop out of the trial. These factors complicate the decision process for patients, and they represent risks to a trial’s retention, completion, and valid statistical conclusions.

ā High Disease Burden. Rare diseases are often severely debilitating and/or life threatening. The physical and logistical difficulties that patients and their caregivers face can make trial participation onerous and patient retention challenging. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 6 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

ā Genetic Origins. 80 percent of rare diseases are caused by abnormalities in a person’s genetic makeup. Thus, identifying the right diagnosticians (i.e., geneticists and genetic counselors) is critical to determining the appropriate genetic testing strategies and gaining access to this type of testing.

ā Data Volume and Complexity. Because multiple sources of data are collected, standard methods of importing, cleaning, harmonizing, and reconciling data are no longer workable. Integrating omic data (genomic, proteomic, epigenomic, transcriptomic, etc.) and other data sources with traditional Electronic Medical Records (EMR) data, standardizing it across trials, and applying analytical methods that are robust, reproducible, and high throughput requires processes that most organizations may not be equipped to handle.

ā Increasing Financial Burden to Patients and Payers: The average annual cost per patient to treat a rare disease is about $150k compared to $33k for a non-rare disease, posing a high financial burden for both patients and payers19. Many rare disease treatments are costly — sometimes over $100,000 annually. Covering 40 percent of cost is simply unmanageable for most patients to pay out-of-pocket and payers to cover.20

Rare disease clinical trials call for specialized expertise as well as methodologies and technologies designed to overcome the difficulties in finding patients and investigators, accommodating patients’ limitations, and managing the complexities of data capture and analysis. Fortunately, progress is being made on both the scientific and operational front to improve trial success.

The Landscape for Clinical Research is Changing, and Patients are at the Center

For nearly forty years, the US Orphan Drug Act of 1983 has provided incentives for US pharmaceutical companies to develop treatments for rare diseases. This approach has been instrumental in spurring research in areas for which there is no reasonable expectation that sales of the treatment could compensate for development costs. Since the program was enacted, the FDA has approved over 600 drugs and biologics for rare diseases.21

Despite this, industry has generally favored investments in treatments for large patient populations. Blockbuster drugs that generate revenue of more than USD 1billion have enabled companies to fund large research and development budgets, which have paved the way to new and innovative technologies and methodologies. The era of “one-size-fits- millions” treatments is fading as regulators and patients demand more specific and predictable treatments and the era of precision medicine has come. Scientific breakthroughs in genomic research and biomarkers have made it possible to personalize treatments, putting patients again at the center of the equation — where they should be22.

Advances in research technologies and methods have enabled companies to invest in more specific treatments, which are well-suited to the smaller populations and challenges that come with engaging patients with rare diseases. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 7 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

New Methodologies and Technologies Suited to Rare Disease Trials

In recent years, new technologies and novel methodologies have emerged that can help sponsors streamline the development process and mitigate many of the issues that stand in the way of accelerating therapies to a waiting market. Cell and gene therapies represent new ways of activating the human body’s immune system in the treatment of diseases. Coupled with new technologies like block chain, IOT connected devices, and natural language processing, the time and costs to develop new treatments is coming down.

In particular, rare disease trials produce voluminous data, and a unified data environment is essential to coping with it effectively. Advanced methods and technologies can be used to save time, energy, and money needed to integrate and make sense of various data sources. The essential components include:

ā Predicting potential outcomes through the use of biomarkers: The use of biomarkers can significantly improve the probability of success of a treatment23. Until recently, there were limited biomarkers available because discovering them depended on large volumes of data and advanced analytical methods to identify them. Access to more data and the development of new algorithms has led to the discovery of more of them. With rare disease biomarkers, we can gain early, precise indicators of response to therapy, which is critical for timely treatment administration, particularly in diseases with sudden and severe onset.

ā Data classification tools and methodologies: Understanding and classifying data is a true challenge. Whether the data are based on a standard data nomenclature, a proprietary one, or one that is emerging, applications must have the ability to recognize and classify a data term efficiently. Artificial Intelligence (AI) and Machine Learning (ML) can accomplish this, but the data models must first be developed. This will require high volumes of data to “train” the algorithms and develop models that can then be used to assess the quality of and use data in registries or other Real-World Evidence (RWE) sources such as Electronic Health Records (EHRs) and electronic Clinical Outcomes Assessments (eCOA) instruments24.

These technologies support better patient identification, country and site selection, as well as patient enrollment and retention. With the addition of advancements in data processing and analysis, clinical trials will be able to move potential treatments to patients at a much greater scale and speed than ever before.

BIOMARKERS AS A METHOD FOR PATIENT IDENTIFICATION The use of biomarkers is valuable in all stages of rare disease research and development and, as seen in Figure 2, greatly improves the probability of success. The use of biomarkers more than triples success rates over the course of the development life cycle.20 As biomarkers are used to further characterize a disease and can be used as indicators of response to therapy, they identify which patients are most likely to benefit from a treatment. They’re thus fundamental to practicing precision medicine, delivering medical care for optimal efficiency or therapeutic benefit for particular groups of patients, and streamlining the development process. The FDA encourages their integration into product development and approval, as well as clinical practice. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 8 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

Figure 2: Success Rates with and without Biomarkers, by Phase25

100% 94.5% With Selection Biomarkers Without Biomarkers 83.9% 80% 76.7% 76.5%

63.0% 60% 55.0% 46.7% 40%

28.8% 25.9% 20%

8.4% 0% Phase I to II Phase II to III Phase III to NDA/BLA NDA/BLA to Approval Phase I to Approval

Omic data are crucial to understanding disease risk, prognosis, and treatment efficacy in rare diseases. Until recently, omic data could not easily be integrated into a patients’ clinical dataset, and omic analysis was an ad hoc process hindered by traditional barriers between computational and scientific staff. Now, sophisticated analytical platforms using machine learning can merge omics data and clinical data (derived from EMR systems and CRFs) in a standardized way for a more complete picture of the patient’s health.

Such analytical platforms are able to accelerate biomarker discovery, which in turn makes it easier to identify patients who will best respond to a therapeutic agent or those who are among the most likely to suffer Adverse Events (AEs). This is especially important when little is known about the disease.

The right technology platform will help prevent costly mistakes by identifying duplicate, mismatched, and outlier samples and will allow for the possibility of re-collecting or re-assaying problematic samples. For example, in a rare, lymphoproliferative disorder (idiopathic Multicentric Castleman Disease) that is difficult to diagnose and life threatening, advanced analytics were used to identify biomarkers in a subset of patients with increased response to the disease’s only FDA-approved therapy. Such early, precise indicators of response to therapy are critical for timely treatment administration, particularly in diseases with sudden and severe onset.

INTEGRATING DATA FOR COUNTRY AND SITE SELECTION An analytics platform built on a foundation of data from across thousands of clinical trials can be used to select and prioritize top-performing countries and sites — those that perform better than industry benchmarks. If, for example, compared to the industry, a country is doing well in recruiting patients or a site in delivering data points, rare disease sponsors can prioritize those areas to help expedite the trial. Data from proprietary and public sources on sites’ past performance, patient populations, competitive trial congestion, and Key Opinion Leaders (KOLs) can be synthesized to identify the best sites — an especially critical step in planning trials for rare diseases in which patients are geographically dispersed. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 9 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

CLASSIFYING AND MEASURING THE QUALITY OF DATA FROM VARIOUS SOURCES As data continues to be collected from different sources, it conforms to different standards, making it difficult for AI algorithms to use it. Although much data is based on International Classification of Diseases (ICD) 10 codes, Observational Medical Outcomes Partnership (OMOP) codes, or other standard or proprietary data vocabulary, it must be classified and given a common nomenclature for the platform to recognize and use a term efficiently. Large volumes of data are essential for developing algorithms that detect patterns and grouping them into clusters that can be reviewed and potentially classified.

Centralized Statistical Analysis (CSA) uses ML algorithms and high-dimensional analytics to highlight trends and relationships and to reveal anomalies in the data that then can be used to identify data patterns and develop classifications.

In combination with query management (between sites and the Sponsor), CSA is also a very powerful tool for detecting potential issues at sites. It is this combination of machine learning and human intervention that is the secret to increased patient safety and data validity. To function fully, the tool should be part of an integrated platform that cleans and manages data and is used across all monitoring functions to identify, document, and manage Key Risk Indicators (KRIs).

An integrated platform that is designed with these essential components will enable researchers to accept data from multiple sources from different countries, sites, and even patient homes. These various data sources include Interactive Response Technology (IRT), imaging, Electronic Data Capture (EDC), traditional Case Report Forms (CRF), electronic Clinical Outcomes Assessments (eCOA), Electronic Health Records (EHR), Electronic Medical Records (EMR), and Real- World Evidence (RWE). The data must flow into the platform, without delay, overcoming integration challenges, negating the need for reconciliation, and ultimately eliminating human error.

To ensure updates are reflected globally, it is essential that applications on the platform are interoperable so information flows freely from one tool to the next. For instance, potential AEs that appear on CRFs in the EDC system can be automatically pushed to the pharmacovigilance system of choice.

With the benefit of an integrated data management and analytics environment designed to accommodate large volumes of clinical and omic data in complex and non-standard trial designs, sponsors can streamline trial operations. They can reduce their risk, save money in not having to manage multiple separate systems, and most critically, speed their rare disease therapy to market.

ELECTRONIC INFORMED CONSENT Patients involved in rare disease clinical trials want and deserve to understand what is happening to them and how their disease is progressing. One specific area where technology can play a role is in securing the patient’s informed consent. The industry is moving away from using paper-based, extensively written, informed consent documents to multimedia content that explains trial concepts clearly and respectfully. Today, patients, guardians, or caregivers can learn about the trial and give their consent/assent via a simple, electronic tool that is accessible anywhere that’s convenient for the patient. The format is patient friendly, multimodal, and interactive so patients can ask questions about the trial that they want to discuss with the research team. Experience has shown that eConsent increases patient understanding from 58 percent (paper) to 75 percent (eConsent)26, as well as supports compliance, since all e-signatures and related documents are stored in one place. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 10 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

PATIENT REGISTRIES For patients, guardians, and caregivers, one of the most important benefits to trial participation is information. While a desire for feedback is not unique to rare disease trials, the general lack of knowledge about rare disease makes the hunger for information all the more acute in studies of orphan drugs.

One resource that can be invaluable to trial participants is a designated patient portal through which patients can “interact” with the trial and gain disease-related information. Ideally, such a portal would provide:

ā Updates on the research being done in the study, to include enrollment statistics, study progress, alerts about the treatment

ā Details about treatments and success rates and side effects

ā Reference materials

ā A longitudinal view of the patient’s trial experience

It might also offer a messaging mechanism so that patients can at least communicate with the Principle Investigator (PI) or the study medical team, but also potentially to other patients (a channel that would have to be monitored to prevent bias or the spread of misinformation).

A disease-specific portal might also be helpful in connecting people and/or their doctors to research that could help in shortening the time to diagnosis.

Reducing the Patient Burden Typically, patient burden in trials is assessed qualitatively by interpreting the patient experience inferred from the procedures in the protocol’s schedule of events. This process, however, does not allow those responsible for protocol development to evaluate tradeoffs. Is the knowledge gained from a particular clinical activity worth it, given the burden it would impose on patients? The reality of a patient’s overall experience and the burden it represents cannot be evaluated systematically.

UNDERSTANDING THE PATIENT BURDEN Tools are now available to compare a protocol (pre-recruitment) to past protocols over the last 20 years in the same (or similar) therapeutic area and the same development phase to assess a protocol’s complexity and the accompanying patient burden. Algorithms consider factors such as anxiety, pain, invasiveness, harmful exposure, hospitalization, time required, number of questions on surveys, and the type of survey. Each component is assessed and placed on a scale of severity and further developed and validated. Subjective measures of pain and anxiety have been validated through surveys with clinical site staff and patients. Through this systematic, metric-based approach, it is possible to arrive at indices for protocol complexity and patient burden. Figure 3 illustrates some of the factors used to assess patient burden in two protocols. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 11 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

Figure 3: Assessment of Patient Burden27

EXAMPLE:

CPT Code 36415 96416 (Venipuncture) (Chemotherapy Administration > 8 hours)

Is It Invasive? Minimally Invasive Minimally Invasive

Level of Pain Minimal Moderate to Severe

Travel Required Yes Yes

Is There Harmful Exposure? No Yes

Time < 5 minutes > 8 hours

Patient Burden Low High

This evaluation step allows sponsors to optimize their protocols, balancing scientific needs and operational efficiency with an understanding of the patient perspective which is especially important in a rare disease clinical trial. This in turn accelerates enrollment; balances the three competing goals of speed, cost, and quality; and improves study planning by providing insights to the downstream impact of protocol decisions.

TRIAL VIRTUALIZATION One way that sponsors can reduce the patient burden for rare diseases and simultaneously strengthen patient retention is to take advantage of the exponential progress made in mobile technology to move data capture outside of the clinical trial site. Already, a large amount of trial data is electronic (one Sponsor estimated that amount to be 80 percent), and the next step is for it to be collected remotely.

Trials can be designed to fall anywhere along a continuum from being completely site based on the one end to completely virtual on the other. Ideally, sponsors can customize the design to reflect the best mix of onsite/virtual touchpoints for a seamless experience for the particular patient population. This flexibility is especially valuable in rare diseases trials where there are usually many data collection points with patients who can be dispersed geographically. A partially virtualized trial might, for example, be structured around remote electronic consent, an initial site visit, ongoing patient- reported data via mobile tools, and site visits reserved for a physical exam, lab tests, or treatment.

Some degree of virtualization reduces the burden on sites and patients, which improves patient retention and lowers development costs since sponsors can reduce the number of clinical sites or eliminate them all together. In one orphan drug study, the dropout rate for patients required to make site-based visits was 67 percent, compared to 3 percent for those who were given in-home visits.28 It can also allow patients to participate who might not otherwise be able to because of geographic and/or physical restrictions. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 12 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

DESIGN WITH A SYNTHETIC CONTROL ARM (SCA)™ In trials of treatments for rare diseases, a randomized control arm may be difficult or impossible for multiple reasons. There may be ethical concerns about assigning one group of patients to receive the standard of care (SOC), which in most rare disease cases, may only be palliative care. This is especially true for pediatric populations. To avoid this, sponsors have used historical control groups drawn from literature on one or a few previous clinical trials, but this approach introduces biases due to differences in baseline covariates, sites, and other factors.

These problems can be minimized by constructing a synthetic control arm (SCA) from an archive of trial data that allows for anonymized, aggregated analyses, or from real-world evidence (RWE). As an example, there are registries and biobanks of real-world data that could contain data that would help identify disease patterns. One such example is the Finnish government’s database of all citizen data from the last 50+ years that includes genetic sequencing, medications, birth and death records, and all doctor visits. The need for aggregated trial data in order to construct a viable SCA for the rarest of diseases is critical and the lack of data on some diseases may reduce the ability to successfully implement. However, the growth in these types of registries and biobanks is starting to fill in the data gap.

Several approaches can be used to select patients for the SCA so that they match patients in the trial against any of 100+ possible variables. Machine learning algorithms automatically detect errors in the data, and results are presented visually in a way that is familiar to biostatisticians and is designed to facilitate exploratory analyses.

The SCA provides a superior alternative to using a single arm or historical controls from literature, where covariates cannot be matched. And, the FDA looks favorably upon the use of SCAs, and interestingly, patient advocacy groups in the rare disease space are more than usually informed and enthusiastic about their use as well.

This approach is helpful in trial design as sponsors can identify where and how many patients there may be for the disease, given specific inclusion/exclusion criteria. Most importantly, it means 1) fewer patients are needed to complete a trial, a big factor where there are so few patients in rare diseases from which to draw and 2) no patients are assigned to a control arm receiving only the SOC.

Rare Disease Clinical Trials in the Era of COVID-19 The rise of COVID-19 has led to a profound impact on clinical trials and rare disease trials are especially impacted. Real-time and detailed reporting and analytics are critical for sponsors and CROs to assess the day-to-day impact of the pandemic on a trial at the patient, site and country level so they can quickly implement changes to mitigate the risk of trial failure. Rapid and safe implementation of protocol amendments is vital to address both site closures and the fact that trial participants no longer receive or have access to the investigational product. Inaccessible sites mean that alternative, remote approaches to drug supply, monitoring study conduct, compliance, patient safety and data quality are needed. The more trials can be safely “virtualized,” to varying degrees e.g., hybrid trials, the more likely they will be able to successfully proceed.29 The challenges and solutions outlined in this whitepaper become even more critical for rare disease trials in the face of the additional COVID-19 restrictions. WHITE PAPER RARE DISEASE CLINICAL DEVELOPMENT: 13 NOVEL APPROACHES TO OVERCOMING OPERATIONAL CHALLENGES

Summary As the age of the blockbuster drug gives way to precision medicine and more targeted patient populations, industry is applying innovative methods and technologies that reduce the cost, complexity, and risk of researching and developing treatments for rare diseases. Solutions range from analytical platforms that can identify patients...to tools and trial designs that reduce the burden on patients... to methods for increasing patient engagement and sophisticated approaches for managing and analyzing data, Building a strategy that applies all of these these solutions to rare disease clinical drug development enables underserved populations to benefit from these investments and to find the cures they need sooner.

Rare Disease Clinical Trial Challenges and Solutions

CHALLENGE Biomarker Discovery Country/ Selection Site eConsent Patient- Centered Design ePRO/eCOA Trial Virtualization Patient Portal Protocol/ Burden Indices Centralized Statistical Analysis Integrated Platform Integrated Platform Patient scarcity X X X X X X X

Difficulty in using X control arms Geographically X X X X X dispersed population Pediatric population X X X X X

High disease burden X X X X X X X

Difficult diagnosis; lack X X of disease understanding Genetic origins X X

Data volume and X X complexity Financial risk X X X X X X X X X X

About Medidata Medidata is leading the digital transformation of life sciences, creating hope for millions of patients Medidata helps generate the evidence and insights to help pharmaceutical, biotech, medical device and diagnostics companies, and academic researchers accelerate value, minimize risk, and optimize outcomes. More than one million registered users across 1,400 customers and partners access the world’s most-used platform for clinical development, commercial, and real-world data. Medidata, a Dassault Systèmes company (Euronext Paris: #13065, DSY.PA), is headquartered in New York City and has offices around the world to meet the needs of its customers. Discover more at www.medidata.com and follow us @medidata, The Operating System for Life Sciences™. Medidata, Medidata Rave and Acorn AI are registered trademarks of Medidata Solutions, Inc., a wholly owned subsidiary of Dassault Systèmes. [email protected] | +1 866 515 6044

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Endnotes

1. de Vrueh, R. Ph.D., Baekelandt, E.R. F., and de Haan, J.N.H., “Priority Medicines for Europe and the World, A Public Health Approach to Innovation,” March 12, 2013. 2. https://globalgeners.org/rare0facts/ 3. https://globalgeners.org/rare0facts/ 4. https://www.ema.europa.eu/en/documents/report/human-medicines-highlights-2018_en.pdf 5. https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=reportsSearch.process&rptName=2&reportSelectMonth=3&reportS electYear=2020&nav 6. https://www.ema.europa.eu/en/medicines 7. https://rarediseases.info.nih.gov/diseases/pages/31/faqs-about-rare-diseases 8. https://www.eurordis.org/content/what-rare-disease 9. https://csdd.tufts.edu/csddnews/2019/7/10/press-release-tufts-csdd-impact-report-julyaugust-2019-volume-21-number-4 10. https://csdd.tufts.edu/s/julaug-summary.pdf 11. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/797057 12. de Vrueh, R. Ph.D., Baekelandt, E.R. F., and de Haan, J.N.H., “Priority Medicines for Europe and the World, A Public Health Approach to Innovation,” March 12, 2013. 13. Scotton, Chiara and Ferlini Alessandra, “Biomarkers in Rare Genetic Diseases,” http://dx.doi.org/10.5772/633545 14. Engel PA., Bagal S., Broback M., Coice N. Physician and Patient Perceptions Regarding Physician Training in Rare Diseases: The Need for Stronger Educational Initiatives for Physicians. J Rare Dis. 2013 1-15 15. Scotton, Chiara and Ferlini Alessandra, “Biomarkers in Rare Genetic Diseases,” http://dx.doi.org/10.5772/633545 16. “The Global Challenge of Rare Disease Diagnosis: The benefits of an improved diagnosis journey for patients,” Shire, accessed at: https:// www.shire.com/-/media/shire/shireglobal/shirecom/pdffiles/patient/shire-diagnosis-initiative-pag-leaflet.pdf 17. Khaleel, S. L. (n.d.). Rare Disease Patient Recruitment And Retention. Retrieved from https://www.clinicalleader.com/doc/rare-disease- patient-recruitment-and-retention-0001. 18. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767214/ 19. https://www.statista.com/statistics/373353/average-cost-for-orphan-drugs-per-patient-per-year/ 20. https://rarediseases.org/advocate/policy-priorities/issues/ 21. https://www.fda.gov/industry/developing-products-rare-diseases-conditions 22. De Vries, Glen, Blachman, Jeremy. The Patient Equation: The Data Driven Future of Precision Medicine and the Business of Healthcare. Hoboken, New Jersey: Wiley 2020. 23. Chi Heem Wong, Kien Wei Siah, Andrew W Lo. “Estimation of clinical trial success rates and related parameters” Biostatistics, Volume 20, Issue 2, April 2019, https://doi.org/10.1093/biostatistics/kxx069 24. Grant, R., Low, G., Rothmeier G., “Medidata’s Industry Leadership with EHR and eSource” whitepaper published by Medidata Solutions 2018 25. https://www.bio.org/sites/default/files/Clinical%20Development%20Success%20Rates%202006-2015%20-%20BIO,%20 Biomedtracker,%20Amplion%202016.pdf 26. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3590180/ 27. Eardley A, Cribb A, Pendleton L. Ethical issues in psychosocial research among patients with cancer. European Journal of Cancer 1991;27:166-169; Emanuel EJ, Fairclough DL, Wolfe P, et al. Talking with terminally ill patients and their caregivers about death, dying, and bereavement: Is it stressful? Is it helpful? Archives of Internal Medicine 2004;164:1999-2004. 28. Khaleel, Samiya Luthfia, “Rare Disease Patient Recruitment and Retention,” White Paper, accessed at: https://www.clinicalleader.com/doc/ rare-disease-patient-recruitment-and-retention-0001 29. https://www.medidata.com/wp-content/uploads/2020/05/COVID19-Response4.0_Clinical-Trials_2020508_v3.3.pdf