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ELECTRONICALLY REPRINTED FROM AUGUST/SEPTEMBER 2016 Phase III Trial Failures: Costly, But Preventable Alberto Grignolo, PhD, Sy Pretorius, MD

An examination of recent failures in Phase III studies and innovative approaches to reduce risk.

homas Henry Huxley (1825-1895) once endpoints; Phase III trials that failed due solely to PEER stated, “The great tragedy of science—the safety issues are not included. These 38 failed trials REVIEW slaying of a beautiful hypothesis by an ugly collectively enrolled nearly 150,000 patients. fact.” Indeed, in drug development not all ground-breaking scientific ideas translate Peeling the onion: What are the drivers intoT miracle cures. Biopharmaceutical companies behind these Phase III failures? around the globe strive to develop novel therapies Several groups have sought to analyze data from to fulfill unmet medical needs, yet these efforts all failed clinical trials to uncover the underlying too often die in development. Despite the numer- trends and potential drivers of Phase III failures. ous scientific, technological, and operational ad- Selected trends are discussed below. vances in R&D that would be expected to increase 1. A study by the Tufts Center for the Study of the efficiency and success of drug development, a Drug Development (CSDD)5 evaluating clinical trials significant number of clinical trials still fail to pro- from 2000 to 2009 found that the three most com- duce new, effective, and safe medicines. mon reasons that drugs or trials fail in Phase III of Approximately 70% of Phase II trials are unsuc- development are (see Figure 1 on facing page): cessful.1,2 Although this percentage might seem • Efficacy (or rather lack thereof) — i.e., failure to high, failure of early-phase trials is expected to meet the primary efficacy endpoint some extent, as these trials are “exploratory,” • Safety (or lack thereof) — i.e., unexpected ad- “proof of mechanism,” and “proof of concept” trials verse or serious adverse events in patients.3 What is unexpected, however, is the • Commercial / financial — i.e., failure to demon- percentage of “confirmatory” Phase III trials that strate value compared to existing therapy fail—about 50%.1,2 Theoretically, if early-phase tri- 2. New molecular entities (NME) versus all drugs. A als provide the necessary criteria for moving a drug study by BioMedTracker (BMT) and the Biotechnol- program to Phase III testing, relatively few Phase III ogy Industry Organization (BIO) evaluated R&D trials should fail; but that is not the case. projects involving more than 9,500 different drug In a recent PAREXEL analysis,4 we collected and and biological products from 2004 to 2014.1 In each evaluated data on 38 Phase III failures from mid- phase analyzed, the failure rates of trials involving 2012 through 2015 from a variety of publicly avail- NMEs were higher than trials involving all drugs, able sources (It is important to note that this list and the difference was greatest between Phase III may not be exhaustive and may not include all of development and submission of regulatory ap- the Phase III trials that failed in this time period). plications (See Figure 2 on page 38). Among the As indicated in Table 1 (see facing page), these Phase III trials analyzed, only 61% of NME trials trials failed to meet primary or secondary efficacy succeeded in moving to the application phase TRIAL DESIGN

Phase III Failures: Lack of Efficacy

NUMBER PATIENTS THERAPEUTIC AREA AGENTS/COMBINATIONS TESTED OF TRIALS ENROLLED CARDIOVASCULAR darapladib, evacetrapib, losmapimod, otamixaban, Indications studied: acute coronary syndrome, acute heart 6 58,759 serelaxin failure, atherosclerotic disease, cardiovascular cell damage ENDOCRINE/METABOLIC aclerastide, aleglitazar, basal peglisproa, 4 38,066 Indication studied: diabetes saxagliptin alisertib, , , enzastaurin, ONCOLOGY etirinotecan pegol, ganetespib + docetaxel, iniparib, Indications studied: breast cancer, castrate-resistant + , MAGE-A3, motesanib, 18 19,856 prostate cancer, colorectal cancer, leukemia, non-small cell , , + lung cancer, lymphoma, ovarian cancer, uveal melanoma dacarbazine, trebananib + , True HumanTM antibodiesb, vintafolide, vosaroxin + cytarabine PULMONARY 1 quote_16,485 quote_fluticasone furoatequote_ + vilanterol quote_ Indication studied: chronic obstructive pulmonary disease quote_ quote_ quote_ quote_ quote_ CENTRAL NERVOUS SYSTEM bitopertin, edivoxetine, pomaglumetad methionil, Indications studied: Alzheimer’s disease, depression, 5 9,140 quote_ quote_solaneumab quote_ quote_ quote_ schizophrenia quote_ quote_ quote_ quote_ AUTOIMMUNE Indications studied: ankylosing spondylitis, Crohn’s 4 3,378 apremilast, tabalumab, vercirnon disease, systemic lupus erythematosus TOTALS 38 145,684 34 AGENTS/COMBINATIONS

aProgram terminated due to focus on other drugs in portfolio and to assess effects on liver fat. bPhase III trial terminated due to insufficient number of per-protocol patients available for primary endpoint analysis and protocol violations

Source: PAREXEL Analysis Table 1. Phase III trials during 2012-2015 that failed to meet primary or secondary efficacy endpoints.

indication.1 Among the Phase III trials evaluated, oncology Reasons for Failures and cardiovascular trials had the highest failure rates (data

60% not shown). Comparing failures for oncology and non-oncol- ogy trials specifically, failure rates for these indications dif- 50% fered more significantly at later stages of development (See 40% Figure 3 on page 38). The oncology trials failed more often than non-oncology trials during the Phase II-to-Phase III tran- 30% sition and, in particular, from Phase III testing to regulatory

Share of Failures 20% submission (48% failure rate for oncology trials vs. 29% for

10% non-oncology trials). The higher failure rate for oncology trials might be due to the inclusion of survival endpoints and the 0% Efcacy Safety Commercial Other need to show efficacy by an improvement in overall survival.6 4. Failure rates differ by type of molecule. A study by the Tufts Source: Tufts Center for the Study of Drug Development (CSDD) CSDD found that the probability of success for clinical trials of small molecules is lower than for trials of large molecules Figure 1. The reasons attributed to Phase III clinical (see Figure 4 on page 39).7 Among the Phase III trials analyzed, trial failures by percentage. only 61% of studies involving small molecules progressed from Phase III testing to the regulatory application phase (39% failure rate). Trials of large molecules, however, were (39% failure rate), whereas 67% of all drug trials moved to the more successful in moving to the application phase, with a application phase (33% failure rate). The high failure rate of success rate of 74% (26% failure rate). The study also found Phase II trials reported in that analysis (62% and 67%, respec- that within the large-molecule subtypes, failure rates for re- tively) is not unexpected for exploratory trials. combinant proteins and monoclonal antibodies were similar 3. Failure rates differ by therapeutic area. The BMT/BIO study overall, but recombinant proteins failed more often than also found that clinical trial failure rates differ by therapeutic monoclonal antibodies in the transition from Phase III testing TRIAL DESIGN

Success Rates by Phase Success Rates by Therapeutic Indications

91% 89% Oncology vs. Non-Oncology Success NME Rates by Phase All 66% 67% Oncology 2013 92% 64% 88% 61% Non-Oncology 2013 71% 66% 67% 38% 33% 52% 41% 15.3% 32% 11.4%

PI to PII PII to PIII PIII to NDA/BLA NDA/BLA to Overall Approval

Note: Each phase success rate represents the rate at which products successfully completed that PI to PII PIII to NDA/BLA to phase and moved to the next phase. PII to PIII NDA/BLA Approval Source: BMT/BIO Source: BMT/BIO Figure 2. The difference in rate success of trials involving NMEs was greatest between Phase III devel- Figure 3. Failure rates for oncology and non-oncolo- opment and submission of regulatory applications. gy trials differed more significantly at later stages of development. to regulatory review (34% failure rate for recombinant proteins Not surprisingly, failures of large Phase III trials also result vs. 13% for monoclonal antibodies; data not shown). in a material financial burden to biopharmaceutical compa- The European Center for Pharmaceutical Medicine (ECPM) nies and are a contributor to poor capital productivity. Con- organized a seminar in September 2014 titled “Why Clini- sidering the substantial financial investment in R&D, few new cal Trials Fail?” Based on presentations delivered by various drugs are being approved by regulators. One study found that experts at the seminar, we have grouped the drivers behind for every billion U.S. dollars spent on R&D, the number of new Phase III failures identified by these experts into six catego- drugs approved has decreased by approximately 50% every ries: basic science, clinical study design, dose selection, data nine years since 1950 (inflation-adjusted).8 And, the financial collection and analysis, operational execution, and other. consequences of failed drug development often go beyond Table 2 (see facing page) provides examples of specific events R&D. An examination of the most impactful Phase III failures in each category that could lead to Phase III failure. of 2014 found that these failures resulted in the termination of drug programs and employees as well as financial losses Impact of Phase III failures: Human and financial for investors, as shown in Table 3 (see page 40; note that only The effects of Phase III failures are significant, with the most some of these failures are also listed in Table 1). With slower poignant being the impact on human lives and financial cost. growth in R&D spending,9 it is important that biopharmaceuti- First and foremost is the negative impact on the thousands of cal companies use the limited funds and resources efficiently patients who enroll in clinical studies hoping to find a viable and effectively to get drugs to market and provide new medi- treatment option. As mentioned previously, PAREXEL’s analy- cines that meet unmet medical needs. sis revealed that in a relatively narrow time frame (2012-2015), nearly 150,000 patients had enrolled in Phase III trials that Selected strategies to reduce the risk of eventually failed. Most of these patients had cardiopulmonary late-stage failure diseases, diabetes mellitus, or cancer.4 Patients who partici- Although no simple solution exists yet for preventing Phase pate in clinical trials often have difficult-to-treat or later-stage III failures to confirm the efficacy of a new drug or biologic, diseases with few, if any, standard treatment options. For several approaches have been and are currently being de- many of these patients, time may be running out, and a clini- veloped to reduce the risk of such failure. Given our unique cal trial is the last option for a potential cure or prolongation perspective in working with hundreds of companies across of life. Thus, the impact of failed drug trials can be widespread thousands of clinical trials and compounds, we and numerous and may worsen patient prognosis, quality of life, and emo- colleagues at PAREXEL are exposed to these approaches on a tional well-being. daily basis and briefly highlight some of these in the next sec- TRIAL DESIGN

Failure Triggers

DRIVERS OF FAILURE EXAMPLES • Beneficial effects in animal models not reproduced in humans Inadequate basic science • Poor understanding of target disease biology • Patient population definition changed from Phase II to Phase III Flawed study design • Phase II surrogate endpoint not confirmed by Phase III clinical outcomes • Insufficient sample size • Inadequate dose finding in Phase II Suboptimal dose selection • Poor therapeutic indices • Phase II “false positive” effects were not replicated in Phase III • Overoptimistic assumptions on variability and treatment difference Flawed data collection and analysis • Missing data; attrition bias; rater bias • Wrong statistical tests; other statistical issues • Data integrity issues; GCP violations Problems with study operations • Recruitment, dropouts, noncompliance with protocol • Missing data; unintentional unblinding Other • Insufficient landscape assessment of current standard of care and precedents

Source: European Center for Pharmaceutical Medicine; PAREXEL Analysis Table 2. Specific events in key areas of clinical trial conduct that can result in Phase III failure.

of the organization and increase the chance of success of its Success Rates by Molecule Type Phase III trials. By evaluating its small-molecule drug projects over a five-year period (2005–2010), AstraZeneca identified the factors associated with project success and developed a 96% 91% framework that now drives its development process. The 5R 84% framework guides R&D teams in identifying the right target, 74% the right tissue, the right safety, the right patients, and the 63% 61% right commercial potential (See Figure 5 on page 41).Having 53% the right culture—i.e., a culture where the facts and data are 38% ansition probability 32% confronted with brutal honesty, and courageous kill-early de- Tr cisions are encouraged where appropriate—is an important, 13% additional dimension to this framework.10 Applying more rigor and discipline to the development process holds potential Phase I-II Phase II-III Phase III- NDA/BLA Sub- Phase I- NDA/BLA Sub NDA/BLA App NDA/BLA App for weeding out likely failures earlier in the process, thereby reducing Phase III failure rates. Small molecule Large molecule

Source: Tufts Center for the Study of Drug Development (CSDD) Adequate Phase II testing Many Phase III trials fail because of a fundamental lack of Figure 4. According to a study, the probability of understanding of the mechanisms of action of NMEs. To ad- success for clinical trials of small molecules is lower dress this issue and improve the chance of NMEs being suc- than for trials of large molecules. cessful during Phase II testing, Pfizer performed an analysis of 44 Phase II programs during a four-year period (2005-2009) tion. These strategies can be implemented during the entire to identify factors associated with success.11 The result of development process, in specific phases of development, and/ that analysis led to the “Three Pillars of Survival” framework or during clinical trial design. that helps the R&D teams determine three key elements that increase the likelihood of an NME surviving Phase II testing Applying more rigor to the overall development process and moving on to Phase III testing: Pillar 1 involves exposure Over the past few years, most drug development companies of the drug to the target site of action; Pillar 2 involves bind- have established and adopted more disciplined protocol, ing of the drug to the target; and Pillar 3 involves expression progress and portfolio review frameworks. In 2011, AstraZen- of pharmacology (see Figure 6 on page 42). Rushing to get to eca aimed to overhaul its R&D process to improve the health Phase III without adequate understanding of these three criti- TRIAL DESIGN

cal components is risky and more often leads to unpleasant • Modeling and simulation have contributed to regula- surprises in Phase III. Not only does this proposed approach tory approval and labeling decisions in recent years and in Phase II make intuitive sense, but we also believe that it are currently being employed as a potential solution for holds potential for reducing late stage failures. mitigating late-stage failure risk.12 In a review of 198 ap- plications submitted to the FDA between 2000 and 2008, Optimized Phase III Clinical Trial Design modeling and simulation was found to provide pivotal Flaws in clinical trial design are a major driver of Phase III fail- or supportive insights into effectiveness and safety that ures. Several strategies have been developed for optimizing contributed to the approval of 126 applications.13 Addition- trial design. The next section aims to highlight a few of them: ally, it provided information on dosage, administration, • More rigorous protocol review and optimization is an and safety that supported 133 labeling decisions. The FDA approach that many companies are employing in varied encourages sponsors to incorporate quantitative modeling degrees ranging from stage gate reviews and sign-offs by and trial simulation in the drug development plan and to various internal committees to live, in-practice simulations seek regulatory guidance on these strategies through par- of protocols to identify and resolve potential glitches proac- ticipation in an end-of-phase 2A (EOP2A) meeting.14 The tively. Typically, a number of trade-off decisions need to be EOP2A meeting provides the sponsor with an opportunity made in the compilation of most Phase III protocols—e.g., to discuss modeling and simulation plans and receive will the protocol include a specific secondary objective that feedback from the FDA on how to optimally implement has commercial value but potentially prolongs the dura- these strategies to quantify the exposure-response rela- tion of the study? Identifying and quantifying the impact tionships and select appropriate doses for Phase III trials. of these trade-offs is helpful in designing better protocols. Modelling and simulation is currently being used more Likewise, in the right capable hands, the vast amount of broadly than just for the selection of an optimal dose. data available in public sources (e.g., Clinicaltrials.gov) Modeling and simulation of various clinical trial designs is can often be harnessed to determine what worked well and one such example that is being used in an effort to design what did not. optimal Phase III studies.

Financial Consequences of Phase III Failures

SPONSOR DRUG INDICATION CONSEQUENCES OF FAILURE • 150 jobs (65% of workforce) terminated • $6-$7M in termination costs (95% charged in 3Q 2014; 5% in 1Q 2015) metastatic castration-resistant Exelixis cabozantinib • Reversed $2.1M of previously recorded stock-based prostate cancer compensation expenses • Cancelled 692,896 stock options due to unrealizable performance objectives OncoGenex Pharmaceuticals/ metastatic castration-resistant • Share price fell 60% custirsen Teva Pharmaceutical prostate cancer • Collaboration with Teva terminated Industries • Share price fell almost 2% GlaxoSmithKline MAGE-A3 Non-small cell lung cancer • Development halted Nymox • Share price fell nearly 82% NX-1207 benign prostatic hyperplasia Pharmaceutical • Development halted pegnivacogin • Share price fell 60% coronary artery disease undergoing Regado Biosciences + • 20 jobs (60% of workforce) terminated percutaneous coronary intervention anivamersen • $2M in termination costs charged in 4Q 2014 • Development halted Eli Lilly tabalumab systemic lupus erythematosus • $63M in termination costs charged in 3Q 2014 • Share price fell 62% Merck & Co./ vintafolide platinum-resistant ovarian cancer • Withdrawal of marketing applications for vintafolide & Endocyte companion imaging products

Source: Modified and reprinted with permission from GENETIC ENGINEERING & BIOTECHNOLOGY NEWS, February 2015, published by GEN Publishing, Inc., New Rochelle, NY. Table 3. The financial and other company-related losses resulting from a selection of Phase III trial failures in 2014. TRIAL DESIGN

these might have on clinical trials. At the same time, many THE ‘5R’ Framework industry insiders hold a somewhat skeptical view—especially Right target about the accuracy of data in these systems. • Strong link between target and disease • Differentiated ef cacy • Ongoing surveillance of the quality of data being collected • Available and predictive biomarkers during a Phase III trial and tying this to a properly planned Right tissue risk-based monitoring protocol allows companies to set • Adequate bioavailability and tissue exposure • De nition of PD biomarkers certain quality triggers that would result in increased moni- • Clear understanding of preclinical and clinical PK/PD • Understanding of drug-drug interactions toring when and where needed. This is helpful in reducing Right safety execution risk, as errors at the site level are often discov- • Differentiated and clear safety margins • Understanding of secondary pharmacology risk ered too late in the process—i.e., once the study is clini- • Understanding of reactive metabolites, genotoxicity, drug-drug interactions • Understanding of target liability cally completed and the database is locked. Often, at this

Right patients time, very little can be done to salvage the situation and • Identi cation of the most responsive patient population improve the quality and potential for success in the trial. • De nition of risk-bene t for given population

Right commercial potential

• Differentiated value proposition versus standard of care Completeness and clarity of submissions and interaction with • Focus on market accesaccesss,, payer and provider • Personalized healthcarehealth-care strategy strategy, ,including including diagnostic diagnostic and and biomarkers biomarkers regulatory agencies Many drugs fail to get approved because the information Source: Cook et al. submitted to the regulatory agency is insufficient to allow for a determination on safety and efficacy, not because the Figure 5. AstraZeneca’s framework to help guide R&D teams, focusing on the five “R’s” of the develop- drug is actually unsafe or ineffective. A review of marketing ment process. applications for NMEs submitted to FDA from 2000 to 2012 revealed that about 50% of those reviewed failed to obtain approval during the first-cycle.2 However, nearly 50% of these • Adaptive designs are gaining momentum as a way to re- failed applications were eventually approved on resubmis- duce failure risk. This is, in large part, due to the fact that sion after applicants addressed the FDA’s concerns related to these adaptive designs provide an opportunity to assess safety, manufacturing, and labeling. Delayed approvals and interim data and to sense-check some of the initial uncer- non-approvals may be reduced or prevented by seeking advice tainties or assumptions that were made at the outset of the from regulators during the drug development process. The trial.15,16 More importantly, these provide an opportunity to FDA offers sponsors and applicants the opportunity to sched- change course during a trial and to correct these incorrect ule formal meetings at critical points in the development and assumptions in a prospectively planned manner that does regulatory process to discuss the plan and identify areas of not jeopardize statistical validity and the operational integ- concern that may need adjustment.21 rity of the trial.17 • Biomarkers are being used increasingly to assess efficacy Other bold/promising/theoretical/philosophical strategies that could in a rapid and more objective manner.18 A particular chal- reduce Phase III failures lenge associated with the use of biomarkers in clinical trials Other areas that are currently being explored and could hold is the requirement to validate these biomarkers as relevant potential in reducing Phase III failure risk include: disease-modifying endpoints and correlate changes in • Replacement of the current gold standard, the randomized these endpoints to clinically significant changes in disease controlled trial, with real-world evidence progression.19 Likewise, enrichment strategies often in- • Wearable devices that collect real-time data22 volving genotyping are being used increasingly to optimize • Adaptive licensing the proposed study population to those individuals most • Next-generation sequencing and improved understanding likely to respond to the treatment under investigation.20 of the genetic basis of disease • Basket/master protocols De-risking study execution Although Phase III failures cannot be eliminated, the risk of A number of approaches are currently being used to mitigate these failures can be reduced. The strategies discussed in this study execution risks. These include: article may provide sponsors with some of the tools aimed at • Leveraging data from a variety of sources (e.g., electronic minimizing the risk of failure in their drug development plans. health records [EHR], prior site performance, central labs, etc.) to ensure that the selection of patients, sites, and coun- Conclusion tries are optimal. The impact of EHRs on clinical trials is We believe that the current failure rate in Phase III studies is carefully tracked by numerous interested parties, and it is not unacceptably high, and that industry is keen on reducing this difficult to imagine the potentially transformative impact that failure risk, although some in industry may believe that fail- TRIAL DESIGN

6. Khozin S, Liu K, Jarow JP, Pazdur R. Regulatory watch: Why do oncol- The ‘Three Pillars of Survival’ ogy drugs fail to gain US regulatory approval? Nat Rev Drug Discov. 2015;14(7):450-1.

• Pillar 1: 7. Tufts CSDD. Comparative success rates for self-originated/licensed- Drug exposure at the target site in and small/large molecule drugs: a 2010 analysis. PAREXEL’s Bio/ of action is necessary to elicit a pharmacological effect over a Pharmaceutical R&D Statistical Sourcebook 2010/2011. desired time period. 8. Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing get Site arget r • Pillar 2: T Ta Target occupancy is a prerequisite the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov. for expression of pharmacology 2012;11(3):191-200. Pharmacolog y and target modulation. Expression of Binding to 9. EvaluatePharma. World preview 2018: embracing the patent cliff. • Pillar 3: Exposure at Functional modulation http://www.evaluategroup.com/public/EvaluatePharma-World-Pre- of the target is a prerequisite for expression of pharmacological view-2018-Embracing-the-Patent-Cliff.aspx. activity to test the mechanism. 10. Cook D, Brown D, Alexander R, et al. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Source: Morgan et al. Drug Discov. 2014;13(6):419-31. 11. Morgan P, Van Der Graaf PH, Arrowsmith J, et al. Can the flow of Figure 6. Pfizer’s framework to help determine three medicines be improved? Fundamental pharmacokinetic and phar- key elements that raise the likelihood of an NME sur- macological principles toward improving Phase II survival. Drug Discov viving Phase II testing and moving on to Phase III. Today. 2012;17(9-10):419-24. 12. Stone JA, Banfield C, Pfister M, et al. Model-based drug development ure is the price to be paid for innovation. As a first step, it is survey finds pharmacometrics impacting decision making in the phar- important to understand the reasons and root causes driving maceutical industry. J Clin Pharmacol. 2010;50(9 Suppl):20S-30S. these failures. Our research identified recently failed Phase 13. Lee JY, Garnett CE, Gobburu JV, et al. Impact of pharmacometric analyses on new drug approval and labelling decisions: a review III studies that have enrolled nearly 150,000 patients. Based of 198 submissions between 2000 and 2008. Clin Pharmacokinet. on data from our analysis and others, we have listed the main 2011;50(10):627-35. reasons why Phase III trials fail. In addition, and given our 14. FDA. Guidance for Industry End-of-Phase 2A Meetings. 2009. Avail- unique perspective in working with hundreds of sponsors able at: http://www.fda.gov/downloads/DrugsGuidanceCompliance- across thousands of trials, we have highlighted some of the RegulatoryInformation/Guidances/ucm079690.pdf. approaches that pharmaceutical companies are implement- 15. Krams M, Dragalin V. Considerations and optimization of adaptive ing in an effort to reduce these costly late-stage failures. trial design in clinical development programs in adaptive trials in: Along with our colleagues in the pharmaceutical industry, we He W, Pinheiro J, Kuznetsova OM (Eds.). Practical Considerations are optimistic about the potential of some or all of these ap- for Adaptive Trial Design and Implementation, Statistics for Biology and proaches to improve the Phase III success rate. Health. Springer Science+Business Media. New York, New York. 2014. 16. Gallo P, DeMets D, LaVange L. Considerations for interim analyses in adaptive trials, and perspectives on the use of DMCs in: He W, Acknowledgements Pinheiro J, Kuznetsova OM (Eds.). Practical Considerations for Adap- Dawn Giles from PAREXEL International and Jennifer Swan- tive Trial Design and Implementation, Statistics for Biology and Health. son from JS Medical Communications, LLC, contributed to Springer Science+Business Media. New York, New York. 2014. the development of this article by providing assistance with 17. Dragalin, V. Adaptive designs: terminology and classification. Drug research, writing, and editing. Information Journal. 2016;40:425-435. 18. Marrer E, Dieterle F. Promises of biomarkers in drug development--a References reality check. Chem Biol Drug Des. 2007;69(6):381-94. 1. Stopke E, Burns J. New drug and biologic R&D success rates, 2004- 19. Kelloff GJ, Bast RC Jr, Coffey DS, et al. Biomarkers, surrogate end points, 2014. PAREXEL’s Bio/Pharmaceutical R&D Statistical Sourcebook and the acceleration of drug development for cancer prevention and 2015/2016. treatment: an update prologue. Clin Cancer Res. 2004;10(11):3881-4. 2. Sacks LV, Shamsuddin HH, Yasinskaya YI, et al. Scientific and regula- 20. Kelloff GJ, Sigman CC. Cancer biomarkers: selecting the right drug for tory reasons for delay and denial of FDA approval of initial applica- the right patient. Nat Rev Drug Discov. 2012;11(3):201-14. tions for new drugs, 2000-2012. JAMA. 2014;311(4):378-84. 21. FDA. Formal Meetings Between the FDA and Sponsors or Applicants 3. ICH Expert Working Group. General Considerations for Clinical Tri- of PDUFA Products Guidance for Industry. 2015. http://www.fda.gov/ als E8. 1997. Available at: http://www.ich.org/fileadmin/Public_Web_ downloads/Drugs/GuidanceCompliance%20RegulatoryInformation/ Site/ICH_Products/Guidelines/Efficacy/E8/Step4/E8_Guideline.pdf. Guidances/UCM437431 Accessed October 23, 2015. 22. Kumar S, Nilsen WJ, Abernethy A, et al. Mobile health technol- 4. PAREXEL. Independent analysis of Phase III trial failures 2012–2015. ogy evaluation: the mHealth evidence workshop. Am J Prev Med. Unpublished data. 2013;45(2):228-36. 5. Tufts CSDD. Causes of clinical failures vary widely by therapeutic class, phase of study. Tufts CSDD study assessed compounds enter- Alberto Grignolo, PhD, is Corporate VP, Global Strategy, PAREXEL; ing clinical testing in 2000-09. Tufts CSDD Impact Report. 2013;15(5). Sy Pretorius, MD, is Chief Scientific Officer, PAREXEL

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