Skip to main content

Advertisement

SpringerLink
Log in

Aiding the Adoption of Master Protocols by Optimizing Patient Engagement

  • Analytical Report
  • Published:
Therapeutic Innovation & Regulatory Science Aims and scope Submit manuscript

Abstract

Master protocols (MPs) are an important addition to the clinical trial repertoire. As defined by the U.S. Food and Drug Administration (FDA), this term means “a protocol designed with multiple sub-studies, which may have different objectives (goals) and involve coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure.” This means we now have a unique, scientifically based MP that describes how a clinical trial will be conducted using one or more potential candidate therapies to treat patients in one or more diseases. Patient engagement (PE) is also a critical factor that has been recognized by FDA through its Patient-Focused Drug Development (PFDD) initiative, and by the European Medicines Agency (EMA), which states on its website that it has been actively interacting with patients since the creation of the Agency in 1995. We propose that utilizing these PE principles in MPs can make them more successful for sponsors, providers, and patients. Potential benefits of MPs for patients awaiting treatment can include treatments that better fit a patient’s needs; availability of more treatments; and faster access to treatments. These make it possible to develop innovative therapies (especially for rare diseases and/or unique subpopulations, e.g., pediatrics), to minimize untoward side effects through careful dose escalation practices and, by sharing a control arm, to lower the probability of being assigned to a placebo arm for clinical trial participants. This paper is authored by select members of the American Statistical Association (ASA)/DahShu Master Protocol Working Group (MPWG) People and Patient Engagement (PE) Subteam. DahShu is a 501(c)(3) non-profit organization, founded to promote research and education in data science. This manuscript does not include direct feedback from US or non-US regulators, though multiple regulatory-related references are cited to confirm our observation that improving patient engagement is supported by regulators. This manuscript represents the authors’ independent perspective on the Master Protocol; it does not represent the official policy or viewpoint of FDA or any other regulatory organization or the views of the authors’ employers. The objective of this manuscript is to provide drug developers, contract research organizations (CROs), third party capital investors, patient advocacy groups (PAGs), and biopharmaceutical executives with a better understanding of how including the patient voice throughout MP development and conduct creates more efficient clinical trials. The PE Subteam also plans to publish a Plain Language Summary (PLS) of this publication for clinical trial participants, patients, caregivers, and the public as they seek to understand the risks and benefits of MP clinical trial participation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Bhatt A. Evolution of clinical research: a history before and beyond James Lind. Perspect Clin Res. 2010;1(1):6–10.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Beckman R, Natanegara F, Singh P, et al. Advancing innovative clinical trials to efficiently deliver medicines to patients. Nat Rev Drug Discov. 2023;21(8):543–4.

    Article  Google Scholar 

  3. FDA Website. Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry, March, 2022.

  4. FDA Website. Complex Innovative Trial Design Meeting Program, 2023.

  5. Antonijevic Z, Beckman R. Platform Trial Designs in Drug Development: Umbrella Trials and Basket Trials; 1st Edition. Taylor & Francis Group, 2018.

  6. DeSalvo, A. CTTI Case Study Exchange: Building Better Clinical Trials: Be the Match Helps Patients More Easily Find & Join relevant Trials. Retrieved from https://connects.ctti-clinicaltrials.org/case_study_exchange?utf8=%E2%9C%93&filter%5Bsearch_text%5D=&filter%5Bcategory%5D%5B%5D=&filter%5Borganization_type%5D%5B%5D= on July 11, 2023.

  7. Antonijevic Z, Beckman R, Huml J, et al. Patient Benefits from Innovative Designs in Rare Diseases (Chapter 10). In Huml R (Editor), Rare Disease Drug Development: Clinical, Scientific, Patient and Caregiver Perspectives. Springer Publishing, Cham 2021.

  8. Tang R, Beckman R, Liu Y, et al. Novel Approaches to Clinical Trials in Rare Diseases (Chapter 9). In: Huml R, editor., et al., Rare Disease Drug Development: Clinical, Scientific Patient and Caregiver Perspectives. Cham: Spinger Publishing; 2021.

    Google Scholar 

  9. Kim E, Herbst R, Wistuba J, et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 2011;1(1):44–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. National Institutes of Health, N. C. Lung-MAP: Master Protocol for Lung Cancer. Retrieved from https://www.cancer.gov/types/lung/research/lung-map on July 11, 2023.

  11. The Cancer Letter, Guest Editorial. Lung-MAP: A five-year recap on the first master protocol trial in cancer research. Retrieved from https://cancerletter.com/guest-editorial/20200221_1/ on July 11, 2023; dated February 21, 2020.

  12. Chen C, Li N, Yuan S, et al. Statistical design and considerations of a phase 3 basket trial for simultaneous investigation of multiple tumor types in one study. Stat Biopharm Res. 2016;8:248–57.

    Article  Google Scholar 

  13. Beckman R, Antonijevic Z, Kalamegham R, Cong C. Adaptive design for a confirmatory basket trial in multiple tumor types based on a putative predictive biomarker. Clin Pharmacol Ther. 2016;100:617–25.

    Article  CAS  PubMed  Google Scholar 

  14. He L, Ren Y, Chen H, et al. Efficiency of a randomized confirmatory basket trial design constrained to control the family wise error rate by indication. Stat Methods Med Res. 2022;31:1207–23.

    Article  PubMed  Google Scholar 

  15. Park JJH, Siden E, Zoratti M, et al. Systematic review of basket trials, umbrella trials, and platform trials: a landscape analysis of master protocols. Trials. 2019;20:572. https://doi.org/10.1186/s13063-019-3664-1.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Clinicaltrials.gov. A Trial to Evaluate Multiple Regimens in Newly Diagnosed and Recurrent Glioblastoma (GBM AGILE). Retrieved from7https://classic.clinicaltrials.gov/ct2/show/NCT039770447 on July 11, 2023.

  17. Clinicaltrials.gov. I-SPY TRIAL: Neoadjuvant and Personalized Adaptive Novel Agents to Treat Breast Cancer (I-SPY). Retrieved from https://clinicaltrials.gov/ct2/show/NCT01042379 on July 11, 2023.

  18. ClinicalTrials.gov. Lung-MAP: A Master Screening Protocol for Previously-Treated Non-Small Cell Lung Cancer. Retrieved from https://clinicaltrials.gov/ct2/show/NCT03851445 on July 11, 2023.

  19. Meyer E, Mesenbrink P, Dunger-Baldauf C. The evolution of master protocol clinical trial designs: a systematic review of the literature review. Clin Therapeutic. 2020;42(7):1330–60.

    Article  Google Scholar 

  20. FDA Website. FDA Patient Engagement Partnerships (CTTI). Retrieved from https://www.fda.gov/patients/learn-about-fda-patient-engagement/fda-patient-engagement-partnerships on July 11, 2023.

  21. Redman MW, Alegra CJ. The master protocol concept. Semin Oncol. 2015;42(5):724–30.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Murphy A, Bere N, Vamvakas S, Marvis M. The added value of patient engagement in early dialogue with the EMA: scientific advice as a case study. Front Med. 2022. https://doi.org/10.3389/fmed.2021.811855.

    Article  Google Scholar 

  23. MDGroup. The Complete Guide to Remarkable Patient Engagement With Clinical Trials. Retrieved from https://mdgroup.com/blog/the-complete-guide-to-remarkable-patient-engagement-with-clinical-trials/ on July 11, 2023; dated November 26, 2020.

  24. Lu C, Li X, Broglio K, et al. Practical considerations and recommendations for master protocol framework: basket, umbrella and platform trials. Therapeutic Innov Regulatory Sci. 2021;5:1145–54.

    Article  Google Scholar 

  25. Sudhop T, Brun N, Riedel C, et al. Master protocols in clinical trials: a universal Swiss army knife? Lancet Oncol Personal View. 2019;20(6):E336-342.

    Article  Google Scholar 

  26. FDA Website. IND Applications for Clinical Investigations: Regulatory and Administrative Components. March 07, 2022.

  27. World Medical Association. Declaration of Helsinki – Ethical Principles for Medical Research Involving Human Subjects, 64th WMA General Assembly, Frontoleza, Brazil. Retrieved from https://www.wma.net/wp-content/uploads/2016/11/DoH-Oct2008.pdf on July 11, 2023.

  28. FDA Website. Diversity Plans to Improve Enrollment of Participants From Underrepresented Racial and Ethnic Populations in Clinical Trials. Retrieved from https://www.fda.gov/media/157635/download; April 2022.

  29. Benderly B. Advocacy groups are crucial players in developing neurotherapeutics. NeuroRx. 2004;1(4):500–2.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Eva V, Finlay T, Schuitmaker-Warnaar T, et al. Evaluating the “Return on patient engagement initiatives” in medicines research and development: a literature review. Health Expert. 2020;23(1):5–18.

    Google Scholar 

  31. Collyar D. How have patient advocates in the United States benefitted cancer research? Nat Rev Cancer. 2005;5(1):73–8.

    Article  CAS  PubMed  Google Scholar 

  32. Cottler L, McCloskey D, Aguilar-Gaxiola S, et al. Community needs, concerns, and perceptions about health research: findings from the clinical and translational science award sentinel network. Am J Public Health. 2013;103(9):1685–92.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Huml R. Filling a Regulatory Void: Patient Advocates Submit Guidance for Duchenne Muscular Dystrophy. RAPS Regulatory Focus, 5pp, 2014.

  34. Katz M, Archer L, Peppercorn J, et al. Patient advocates’ role in clinical trials: perspectives from Cancer and Leukemia Group B investigators and advocates. Cancer. 2012;118(19):4801–5.

    Article  PubMed  Google Scholar 

  35. Huml R, Dawson J, Bailey M, et al. Accelerating rare disease drug development: lessons learned from muscular dystrophy patient advocacy groups. Therapeutic Innov Regulatory Sci. 2020;5:370–7.

    Google Scholar 

  36. Delgado J, Huang A. Improving the patient experience during musculoskeletal interventional procedures. Skeletal Radiol. 2023;52(5):889–95. https://doi.org/10.1007/s00256-022-04154-x.

    Article  PubMed  Google Scholar 

  37. Clinical Trials Transformation Initiative (CTTI). Master Protocol Studies. Retrieved from https://ctti-clinicaltrials.org/our-work/novel-clinical-trial-designs/master-protocol-studies/ on July 11, 2023.

  38. FDA Website. Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics Guidance for Industry. Retrieved from https://www.fda.gov/media/120721/download on July 11, 2023.

  39. Kim S, Bruinooge S, Roberts S, et al. Broadening eligibility criteria to make clinical trials more representative: American society of clinical oncology and friends of cancer research joint research statement. J Clin Oncol. 2017;35(33):3737–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kim S, Uldrick T, Schenkel C, et al. Continuing to broaden eligibility criteria to make clinical trials more representative and inclusive: ASCO-friends of cancer research joint research statement. Clin Cancer Res. 2021;27(9):2394–9.

    Article  PubMed  Google Scholar 

  41. Levitan B, Getz K, DiMasi J, et al. Assessing the financial value of patient engagement: a quantitative approach from CTTI’s patient groups and clinical trials project. Ther Innov Regul Sci. 2018;52(2):220–9.

    Article  PubMed  Google Scholar 

  42. FDA Website. Cancer Clinical Trial Eligibility Criteria: Available Therapy in Non-Curative Settings. Retrieved from https://www.fda.gov/media/150244/download on July 11, 2023; dated July 2022.

  43. Sacristan J, Aguaron A, Avendano-Sola C, et al. Patient involvement in clinical research: why, when and how. Patient Prefer Adherence. 2016;10:631–40.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Kinter E, Schmeding A, Rudolph I, et al. Identifying patient-relevant endpoints among individuals with schizophrenia: an application of patient-centered health technology assessment. Int J Technol Assess Health Care. 2009;25(1):35–41.

    Article  PubMed  Google Scholar 

  45. Kersting C, Kneer M, Barzel A. Patient-relevant outcomes: what are we talking about? A scoping review to improve conceptual clarity. BMC Health Serv Res. 2020;20(1):596.

    Article  PubMed  PubMed Central  Google Scholar 

  46. FDA Website. Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. Retrieved from https://www.fda.gov/media/77832/download on July 11, 2023; dated December 2009.

  47. FDA Website. Patient-Focused Drug Development Guidance Public Workshop: Incorporating Clinical Outcome Assessments into Endpoints for Regulatory Decision-Making. Retrieved from https://www.fda.gov/media/132505/download on July 11, 2023; dated December 6, 2019.

  48. FDA Website. Core Patient-Reported Outcomes in Cancer Clinical Trials. Retrieved from https://www.fda.gov/media/149994/download on July 11, 2023; dated June 2021.

  49. FDA Website. Principles for Selecting, Developing, Modifying, and Adapting Patient-Reported Outcome Instruments for Use in Medical Device Evaluation. Retrieved from https://www.fda.gov/media/141565/download on July 11, 2023; dated January 26, 2022.

  50. FDA Website. Patient Engagement in the Design and Conduct of Medical Device Clinical Studies. Retrieved from https://www.fda.gov/media/130917/download on July 11, 2023; dated January 26, 2022.

  51. Moore J, Goodson N, Wicks P, Reites J. What role can decentralized trial designs play to improve rare disease studies? Orphanet J Rare Dis. 2022;17:240.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. FDA Website. Patient-Focused Drug Development Guidance Public Workshop: Incorporating Clinical Outcome Assessments into Endpoints for Regulatory Decision-Making. Retrieved from https://www.fda.gov/media/132505/download on July 23, 2023; dated December 6, 2019.

  53. Gavrilovic M, Popovic D. A principal component analysis (PCA) based assessment of the gait performance. BiomedTech. 2021;66(5):449–57.

    Google Scholar 

  54. Apostolaros M, Babaian D, Corneli A, et al. Legal, regulatory, and practical issues to consider when adopting decentralized clinical trials: recommendations from the clinical trials transformation initiative. Ther Innov Regul Sci. 2020;54(4):779–87.

    Article  PubMed  Google Scholar 

  55. King-Kallimanis B, Howie L, et al. Patient reported outcomes in anti-PD-1/PD-L1 inhibitor immunotherapy registration trials: FDA analysis of data submitted and future directions. Clin Trials. 2019;16(3):322–6. https://doi.org/10.1177/1740774519836991.

    Article  PubMed  Google Scholar 

  56. Van Norman G. Decentralized Clinical Trials: The Future of Medicinal Product Development? Journal of American College of Cardiology: Basic to Translational Science, 6(14), 2021.

  57. Goodson N, Wicks P, Morgan J, et al. Opportunities and counterintuitive challenges for decentralized clinical trials to broaden participant inclusion. Nat Portfolio J Digital Medicine. 2022. https://doi.org/10.1038/s41746-022-00603-y.

    Article  Google Scholar 

  58. de Jong A, van Rissel T, Zuidgeest M, et al. Opportunities and challenges for decentralized clinical trials: European regulator’s perspective. Clin Pharmacol Ther. 2022;112(2):344–52.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Ghadessi M, Di J, Wang J, et al. Decentralized clinical trials and rare diseases: a DIA-innovative design scientific Working Group (DIA-IDSWG) perspective. Orphanet J Rare Dis. 2023. https://doi.org/10.1186/s13023-023-02693-7.

    Article  PubMed  PubMed Central  Google Scholar 

  60. FDA Website. Decentralized Clinical Trials for Drugs, Biological Products, and Devices. Draft Guidance; Retrieved from https://www.fda.gov/media/167696/download on July 11, 2023; dated May 2023. June 14

  61. FDA Website. Patient-Focused Drug Development: Collecting Comprehensive and Representative Input. Retrieved from https://www.fda.gov/media/139088/download; June 2018.

  62. FDA Website. FDA Patient-Focused Drug Development Guidance Series for Enhancing the Incorporation of the Patient’s Voice in Medical Product Development and Regulatory Decision-Making. Retrieved from https://www.fda.gov/drugs/development-approval-process-drugs/fda-patient-focused-drug-development-guidance-series-enhancing-incorporation-patients-voice-medical; April 2023.

  63. FDA Website. Patient Engagement in the Design and Conduct of Medical Device Clinical Studies. Retrieved from https://www.fda.gov/media/130917/download; January 2022.

  64. FDA Website. DCOA: Who We Are and What We Do. Retrieved from https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/division-clinical-outcome-assessment-dcoa; April 2022.

  65. FDA Website. Clinical Outcome Assessments (COAs) in Medical Device Decision-Making. Retrieved from https://www.fda.gov/about-fda/cdrh-patient-science-and-engagement-program/clinical-outcome-assessments-coas-medical-device-decision-making; June 29, 2022.

  66. FDA Website. Clinical Outcome Assessment (COA): Frequently Asked Questions. Retrieved from https://www.fda.gov/about-fda/clinical-outcome-assessment-coa-frequently-asked-questions; December 2020.

  67. FDA Website. Drug Development Tools: Fit-for-Purpose Initiative. Retrieved from https://www.fda.gov/drugs/development-approval-process-drugs/drug-development-tools-fit-purpose-initiative; August 2022.

  68. FDA Website. Core Patient-Reported Outcomes in Cancer Clinical Trials. Retrieved from https://www.fda.gov/media/149994/download; June 2021.

  69. European Organization for Research and Treatment of Cancer (EORTC). https://qol.eortc.org/questionnaire/eortc-qlq-c30/; accessed June 13, 2023.

  70. Functional Assessment of Cancer Therapy – General (FACT-G). https://www.facit.org/measures/FACT-G; accessed June 13, 2023.

  71. Haupt E. Adaptive Randomization and the I-SPY2 Trial Platform. Cancer Therapy Advisor; 2016.

  72. Woodcock J, LaVange L. Master protocols to study multiple therapies, multiple diseases, or both. N Eng J Med. 2017;377:62–70.

    Article  CAS  Google Scholar 

  73. Ramanathan T, Schmit C, Akshara M, et al. Public Health Law Brief: Federal Public Health Laws Supporting Data Use and Sharing. Retrieved from https://www.cdc.gov/phlp/docs/datasharing-laws.pdf; July 11, 2023.

  74. Chen C, Beckman R. Optimal cost-effective designs of phase II proof of concept trials and associated go-no go decisions. J Biopharm Stat. 2009;19:424–36.

    Article  PubMed  Google Scholar 

  75. Chen C, Beckman R. Optimal cost-effective go-no go decisions in late-stage oncology drug development. Stat Biopharm Stat. 2009;1:159–69.

    Article  Google Scholar 

  76. Beckman R, Clark J, Chen C. Integrating predictive biomarkers and classifiers into oncology clinical development programes. Nat Rev Drug Discovery. 2011;10:735–49.

    Article  CAS  PubMed  Google Scholar 

  77. Antonijevec Z, editor. Optimization of Pharmaceutical R&D Programs and Portfolios. Cham: Design and Investment Strategy. Springer Publishing; 2015.

    Google Scholar 

  78. Antonijevic Z. Impact of adaptive design on pharmaceutical portfolio optimization. Ther Innov Regul Sci. 2016;50(5):615–9.

    Article  PubMed  Google Scholar 

  79. McCoy M, Joffe S, Emanual E. Sharing patient data without exploiting patients. JAMA. 2020;323(6):505–6.

    Article  PubMed  Google Scholar 

  80. Kim J, Kim H, Bell E, et al. Patient perspectives about decisions to share medical data and biospecimens for research. JAMA Netw Open. 2019. https://doi.org/10.1001/jamanetworkopen.2019.9550.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Jason C. Ten Patient Data Sharing, Interoperability Principles for Providers. Integration & Interoperability News. Retrieved from https://ehrintelligence.com/news/10-patient-data-sharing-interoperability-principles-for-providers; July 11, 2023.

  82. Cole C, Sengupta S, Collins S, et al. Ten principles for data sharing and commercialization. JAMIA. 2021;28(3):646–9.

    PubMed  Google Scholar 

  83. Files D, Matthay M, Calfee C, et al. I-SPY COVID adaptive platform trial for COVID-19 acute respiratory failure: rationale, design and operations. BMJ Open. 2022. https://doi.org/10.1136/bmjopen-2021-060664.

    Article  PubMed  Google Scholar 

  84. Quantum Leap Healthcare Collaborative. I-SPY Trials: An unprecedented and streamlined approach to clinical trial design. Retrieved from https://www.quantumleaphealth.org/portfolio/i-spy-clinical-trials; accessed June 10, 2023.

  85. McMillan G, Mayer C, Tang R, et al. Planning for the next pandemic: ethics and innovation today for improved clinical trials tomorrow. Stat Biopharm Res. 2022;14:22–7.

    Article  PubMed  Google Scholar 

  86. Trusheim M, Shrier A, Antonijevic Z, et al. Pipelines: creating comparable clinical knowledge efficiently by linking trial platforms. Clin Pharmacol Ther. 2016;100:713–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Fons-Martinez J, Ferrer-Albero C, Diez-Domingo J. Co-creation of information materials within the assent process: From theory to practice. Health Expect. 2023;26(1):429–39. https://doi.org/10.1111/hex.13675.

    Article  PubMed  Google Scholar 

  88. Applied Clinical Trials Website. Phogat P, Vashisht V. Demands for Plain Language Summaries for Clinical Trial Results that can be Understood by Anyone Could Create New Challenges for Sponsors, May 22, 2018.

  89. James LC, Collyar D, Tood A et al. Medical Writing for Patients: When and How, Volume 29, Number 4. Retrieved from https://journal.emwa.org/writing-for-patients/writing-for-patients-when-and-how/article/7226/writing-for-patients-when-and-how.pdf; December 2020.

  90. Snapinn A, Chen MG, Koutsoukos T. Assessment of futility in clinical trials. Pharm Stat. 2006;5(4):273–81.

    Article  PubMed  Google Scholar 

  91. Higgins T, Larson E, Schnall R. Unraveling the meaning of patient engagement: A concept analysis. Patient Educ Couns. 2017;100(1):30–6. https://doi.org/10.1016/j.pec.2016.09.002.

    Article  PubMed  Google Scholar 

  92. FDA Website. Focus Area: Patient-Reported Outcomes and other Clinical Outcome Assessments. Retrieved from https://www.fda.gov/science-research/focus-areas-regulatory-science-report/focus-area-patient-reported-outcomes-and-other-clinical-outcome-assessments; September 2022.

  93. European Commission. Summaries of Clinical Trial Results for Laypersons. Retrieved from https://health.ec.europa.eu/system/files/2020-02/2017_01_26_summaries_of_ct_results_for_laypersons_0.pdf; January 2017.

  94. Yuan Y, Huang X, Lui S. A Bayesian response-adaptive covariate-balanced randomization design with application to a leukemia clinical trial. Stat Med. 2011;30(11):1218–29.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors wish to thank Dr. Jill Dawson, independent communications consultant, and Alice Miller, Content Manager for Syneos Health Clinical Solutions, for their editorial suggestions and thoughtful insights.

Funding

No funding was solicited or obtained to produce this manuscript.

Author information

Authors and Affiliations

  1. Syneos Health Clinical Solutions, Morrisville, NC, USA

    Raymond A. Huml MS, DVM, RAC

  2. Patient Advocates in Research, Danville, CA, USA

    Deborah Collyar

  3. Statistical Consulting, Abond CRO Inc., Allendale, MI, USA

    Zoran Antonijevic MS

  4. Departments of Oncology and of Biostatistics, Bioinformatics, & Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, District of Columbia (DC), Washington, USA

    Robert A. Beckman MD

  5. Health Economics & Outcomes Research, Regeneron Pharmaceuticals, Tarrytown, NY, USA

    Ruben G. W. Quek PhD

  6. Data Science and Operational Excellent, Global Statistics and Data Sciences, BeiGene, Ltd., Washington, DC, USA

    Jingjing Ye MS, PhD

Authors
  1. Raymond A. Huml MS, DVM, RAC
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deborah Collyar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zoran Antonijevic MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert A. Beckman MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruben G. W. Quek PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jingjing Ye MS, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raymond A. Huml MS, DVM, RAC.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix – Glossary of Select Terms

Appendix – Glossary of Select Terms

Term

Definition

Adaptive Design

An adaptive design is defined as a clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial

Basket Trials [15]

Refers to designs in which a targeted therapy is evaluated in multiple diseases that have common molecular alterations or otherwise common pathophysiology. A total of 49 examples are listed in Reference 15 including AcSe (NCT02304809), Ado-trastuzumab Basket Trial (NCT02675829), & AGADIR (NCT03915678)

Clinical Trial Participants

Refers to those patients enrolled in a clinical trial randomized to a control arm or receiving a specific intervention or candidate therapy according to the protocol (and SAP) created by the investigators

Efficacy

Refers to whether a drug or candidate therapy demonstrates a health benefit over placebo, other intervention, or standard of care when evaluated in a clinical trial

Efficiency

The ability of a clinical trial or program design to reach a scientific conclusion using the minimum number of trial participants. Also, can be used to refer to the ability of an optimal resource allocation to reach multiple scientific conclusions concerning a portfolio of drugs while minimizing the required number of trial participants

Futility [90]

Refers to the inability of a clinical trial to achieve its objectives or stopping a clinical trial when the interim results suggest that it is unlikely to achieve statistical significance

Master Protocol (MP)

According to the FDA, an MP is: “A protocol designed with multiple sub-studies, which may have different objectives (goals) and involve coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure.” A MP design can be implemented in all phases of drug development (Phase I, II and III)

Optimal/Optimize/Optimization

Mathematical optimization refers to finding the “best available” values of some objective function given a defined domain or input, including a variety of different types of objective functions and different types of domains

Patient Advocacy Groups (PAGs)

Organizations set-up to represent and support patients with a specific condition and their families (e.g., Facioscapulohumeral muscular dystrophy [FSHD] Society or Parent Project Muscular Dystrophy) or an umbrella group that represents multiple conditions (e.g., the National Organization for Rare Disorders [NORD])

Patient Engagement (PE) [91]

According to Higgin T et al. (Patient Educ Couns 2017), PE is defined as “the desire and capability to actively choose to participate in care in a way uniquely appropriate to the individual, in cooperation with a healthcare provider or institution, for the purposes of maximizing outcomes or improving experiences of care.”

Patient-Reported Outcomes (PRO) [92]

According to the FDA, PROs are defined as “measures of a patient’s health status as directly reported from the patient without added interpretation by a healthcare worker or anyone else, such as a pain scale.”

Patient

According to FDA’s Patient-Focused Drug Development Glossary (FDA.gov), a patient is defined as “Any individual with or at risk of a specific health condition, whether or not he or she currently receives any therapy to prevent or treat that condition.”

Plain Language Summary (PLS) [93]

The Plain Writing Act was signed into US law on October 13, 2010, requiring all federal agencies use “clear government communication that the public can understand and use.” This concept is also being voluntarily adopted by biopharmaceutical sponsors, contract research organizations and the ASA/DIA MP Working Group. The European Union (EU) Clinical Trials Regulation (CTR) No 536/2014 was released in 2014, requiring sponsors to provide “lay summary” results of clinical trials

Platform Trials [15]

Trials can evaluate several interventions against a common control group and can be perpetual. This design can drop or add interventions based on pre-specified criteria. A common operational platform is used across all interventions

A total of 16 examples cited including CREATE (NCT01524926), EBOLA (NCT02380625) & GBM AGILE (NCT03970447)

Pooling

Describes the practice of gathering small sets of data that are assumed to have a similar value of a characteristic (e.g., a mean) to form a combined larger set (the “pool”) to obtain a more precise estimate of that characteristic

Pruning

Pruning in this article refers to removing study arms at interim analysis in a basket trial

Response-Adaptive Randomization (RAR) [94]

In RAR randomization ratio of trial participants assigned to multiple experimental treatment arms over time to randomly assigning a higher proportion of patients to the arms that are doing better. RAR may also randomize trial participants with different biomarker status to different therapies

Statistical Analysis Plan (SAP)

The SAP describes the intended clinical trial data analyses. It is a technical document that describes the methods to compare outcome measurements between the intervention and control area, at baseline and at subsequent follow-up assessments

Umbrella Trials [15]

Refers to evaluating multiple targeted therapies for a single disease that is stratified into subgroups by molecular alteration. A total of 18 examples cited including BATTLE (NCT00409968), CheckMate 370 (NCT02574078), & FUTURE (NCT03805399)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huml, R.A., Collyar, D., Antonijevic, Z. et al. Aiding the Adoption of Master Protocols by Optimizing Patient Engagement. Ther Innov Regul Sci 57, 1136–1147 (2023). https://doi.org/10.1007/s43441-023-00570-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s43441-023-00570-w

Keywords

Search

Navigation