Future of risk based monitoring in clinical trials
Drug development is a complex and resource intensive endeavor. The average cost of developing a new drug, has been estimated to be $2 to $3 billion. However, the success rate of clinical trials is very low around and is estimated to be between 3-5%. The common reasons for failure of clinical trials include failure to demonstrate efficacy or safety, budgeting and financing, failure of subjects meeting protocol eligibility criteria, poor investigator site selection, patient withdrawals and dropouts. Considering the growing demands to get better and affordable treatment options, there needs to be fundamental shift required in drug development and specifically the clinical trials oversight processes to mitigate risks and reduce failures. The International Council for Harmonisation in the E6 R2 addendumhas now provided guidelines for adaptation of risk based approach to trial conduct and monitoring to implement mitigation strategies for potential risks which might derail the conduct of the trail. The industry is steadily gearing up to put together the required processes, systems and teams to align to the new ways of working. However with the changing landscape of drug development which includes novel therapies like gene therapy, remote/decentralized trials, growing use of wearable technologies, esource, electronic health record/electronic medical records interoperability, implementation of artificial intelligence and machine learning algorithms, the future of risk based approach towards managing clinical trials is going to be very different from what we see now. This paper explores the impact of these new developments on the future of risk based monitoring in clinical trials.
Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20:273-86.
Hwang TJ, Carpenter D, Lauﬀenburger JC, Wang B, Franklin JM, Kesselheim AS. Failure of investigational drugs in late-stage clinical development and publication of trial results. JAMA Intern Med. 2016;176:1826-33.
Getz KA, Zuckerman R, Cropp AB, Hindle AL, Krauss R, Kaitlin KI. Measuring the incidence, causes, and repercussions of protocol amendments, Drug Inf J. 2011;45:265-75.
Hirschhorn WM. Understanding the protocol as a foundation for developing a viable patient recruitment campaign. Available at: http://www. temple.edu. Accessed on 26 March 2010.
National Academy of Sciences. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, D.C.: National Academies Press; 2010.
Sertkaya A, Wong H-H, Jessup A, Beleche T. Key cost drivers of pharmaceutical clinical trials in the United States. Clin Trials. 2016;13(2):117-26.
Andersen JR, Byrjalsen I, Bihlet A, Kalakou F, Hoeck HC, Hansen G, et al. Impact of source data verification on data quality in clinical trials: an empirical post hoc analysis of three phase 3 randomized clinical trials. Br J Clin Pharmacol. 2015;79(4):660-8.
Guidance for Industry Oversight of Clinical Investigations- A Risk-Based Approach to Monitoring. 2013. Available at: https://www.fda. gov/media/116754/download. Accessed on 3 March 2020.
E6(R2) Good Clinical Practice: Integrated Addendum to ICH E6(R1), 2018. Available at: https://www.fda.gov/media/93884/download. Accessed on 3 March 2020.
Procedure for Failure Mode, Effects and Criticality Analysis (FMECA). National Aeronautics and Space Administration. 1966.
Measuring the impact of Risk Based Monitoring, The past present and future of RBM, Trancelerate Biopharma Inc. Available at: http://transcelerate biopharmainc.com/wp-content/uploads/2019/12/ RBM-Metrics-Report_December-2019.pdf. Accessed on 2 March 2020.
CFR - Code of Federal Regulations Title 2: 21 CFR 11.3(b)(6). Available at: https://www.accessdata. fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=11.3. Accessed on 3 March 2020.
Use of Electronic Health Record Data in Clinical Investigations Guidance for Industry July, 2018. Available at: https://www.fda.gov/media/97567/ download. Accessed on 2 March 2020.
Kush R, Alschuler L, Ruggeri R, Cassells S, Gupta N, Bain L, et al. Implementing Single Source: The STARBRITE Proof-of-Concept Study. J Am Med Inform Assoc. 2007;14(5):662-73.
Shwarz-Boeger U, Kiechle M, Paepke S, Harzendorf N, Zahlmann G, Harbeck N, et al. EHR and EDC Integration in Reality. Applied Clinical Trials, 2009.
Wearables in clinical trials. Available at: https:// www.pharmavoice.com/article/2019-03-wearables/. Accessed on 2 March 2020.
Vanaken H. Digitally Enhanced: Janssen drives effort to bring suite of "smart" clinical trials into practice in 2017, Applied Clinical Trials 2016. Available at: http://www.appliedclinicaltrialsonline .com/digitally-enhanced-smart-trial-platform. Accessed on 2 March 2020.
WIPO Technology Trends 2019 - Artificial Intelligence. Available at: https://www.wipo.int/ edocs/pubdocs/en/wipo_pub_1055.pdf. Accessed on 2 March 2020.
Nyce, Charles (2007), Predictive Analytics White Paper (PDF), American Institute for Chartered Property Casualty Underwriters/Insurance Institute of America. Available at: https://www.the-digital-insurer.com/wp-content/uploads/2013/12/78-Predictive-Modeling-White-Paper.pdf. Accessed on 3 March 2020.
Model Approach for Risk Based Monitoring, Module 3: Risk Assessment Trainer Guide, Transcelerate Biopharma Inc, Available at: https://transceleratebiopharmainc.com/wp-content/uploads/2013/10/TransCelerate-RBM-Train-the-Trainer-Module-3.pdf. Accessed on 2 March 2020.