Protocol Design and Performance Benchmarks by Phase and by Oncology and Rare Disease Subgroups.
Clinical trial performance benchmarks
Oncology protocols
Protocol complexity
Protocol design
Protocol scope
Rare disease protocols
Journal
Therapeutic innovation & regulatory science
ISSN: 2168-4804
Titre abrégé: Ther Innov Regul Sci
Pays: Switzerland
ID NLM: 101597411
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
received:
31
01
2022
accepted:
20
07
2022
pubmed:
13
8
2022
medline:
20
12
2022
entrez:
12
8
2022
Statut:
ppublish
Résumé
Benchmark data characterizing protocol design practices and performance informs clinical trial design decisions and serves as important baseline measures for assessing protocol design behaviors and their impact during and post-pandemic. Tufts CSDD, in collaboration with a working group of 20 major and mid-sized pharmaceutical companies and CROs, gathered phase I-III data from protocols completed just prior to the start of the global pandemic. Data for 187 protocols were analyzed to derive benchmarks overall and for two primary subgroups: oncology vs. non-oncology protocols and rare disease vs. non-rare disease protocols. The results show a continuing upward trend across all protocol design variables. Phase II and III protocols average more endpoints, eligibility criteria, protocol pages; investigative sites; countries and datapoints collected. Oncology and rare disease protocols' enrolled-to-completion rates are much lower, involve a much higher average number of countries and investigative sites, require more planned patient visits and generate considerably more clinical research data. As such, oncology and rare disease clinical trial cycle times are longer-most notably at time periods occurring after study startup and prior to database lock-due to intense patient recruitment and retention challenges. The results of this study present valuable design insights and comparative baseline measures. The implications of these results and the expected impact of decentralized clinical trials on protocol design practices and performance is discussed.
Sections du résumé
BACKGROUND
Benchmark data characterizing protocol design practices and performance informs clinical trial design decisions and serves as important baseline measures for assessing protocol design behaviors and their impact during and post-pandemic.
METHODS
Tufts CSDD, in collaboration with a working group of 20 major and mid-sized pharmaceutical companies and CROs, gathered phase I-III data from protocols completed just prior to the start of the global pandemic.
RESULTS
Data for 187 protocols were analyzed to derive benchmarks overall and for two primary subgroups: oncology vs. non-oncology protocols and rare disease vs. non-rare disease protocols. The results show a continuing upward trend across all protocol design variables. Phase II and III protocols average more endpoints, eligibility criteria, protocol pages; investigative sites; countries and datapoints collected. Oncology and rare disease protocols' enrolled-to-completion rates are much lower, involve a much higher average number of countries and investigative sites, require more planned patient visits and generate considerably more clinical research data. As such, oncology and rare disease clinical trial cycle times are longer-most notably at time periods occurring after study startup and prior to database lock-due to intense patient recruitment and retention challenges.
CONCLUSIONS
The results of this study present valuable design insights and comparative baseline measures. The implications of these results and the expected impact of decentralized clinical trials on protocol design practices and performance is discussed.
Identifiants
pubmed: 35960455
doi: 10.1007/s43441-022-00438-5
pii: 10.1007/s43441-022-00438-5
pmc: PMC9373886
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
49-56Informations de copyright
© 2022. The Author(s), under exclusive licence to The Drug Information Association, Inc.
Références
Sampler S. Tracking protocol complexity. GCPJ. 2000;7(2):6–8.
Vogel J, Getz K. Factors driving the increases use of contractors in drug development. Clin Res Reg Affairs. 1997;14(4):177–90.
doi: 10.3109/10601339709080077
Maloy J, Getz K, Hovde M. Clinical research in transition. Monitor. 2001;15:31–6.
Kahn M, Broverman C, Wu N, Farnsworth W, Manlapaz-Espiritu L. Improving protocol quality. Appl Clin Trials. 2002;11:40–50.
Rai S. Drug companies cut costs with foreign clinical trials. The New York Times. 2005. http://www.nytimes.com/2005/02/24/business/24clinic.html?8bl .
Getz K, Brown C, Stergiopoulos S, Beltre C. Baseline assessment of a global clinical investigator landscape poised for structural change. TIRS. 2017;51(5):575–81.
Woodcock J. The concept of pharmaceutical quality. Am Pharm Rev. 2004;1:1–3.
Rathore A, Winkle H. Quality by design for biopharmaceuticals. Nat Biotechnol. 2009;27:26–34.
doi: 10.1038/nbt0109-26
Getz K. Protocol design trends and their effect on clinical trial performance. RegAffairs. 2008;19:315–6.
Bhatt D, Mehta C. Adaptive designs for clinical trials. NEJM. 2016;375:65–74.
doi: 10.1056/NEJMra1510061
Pallmann P, Bedding A, Choodari-Oskooei B, Dimairo M, Flight L, Hampson L, Holmes J, Mander A, Odondi L, Sydes M, Villar S, Wason J, Weir C, Wheeler G, Yap C, Jaki T. Adaptive designs in clinical trials: why use them, and how to run them. BMC Med. 2018;16(29):2–15.
Meyer E, Mesenbrink P, Dunger-Baldauf C, Fülle H, Glimm E, Li Y, Posch M, Konig F. The evolution of master protocol clinical trial designs. ClinTher. 2020;42(7):1330–60.
Getz K, Campo R. New benchmarks characterizing growth in protocol design complexity. TIRS. 2018;52:22–8.
Lamberti M, Kubick W, Awatin J, McCormick J, Carrol J, Getz K. The use of real world evidence and data in clinical research band post-approval safety studies. TIRS. 2018;52:778–83.
Getz K. Improving protocol design feasibility to drive drug development economics and performance. Int J Environ Res Public Health. 2014;11:5069–80.
doi: 10.3390/ijerph110505069
Getz K, Campo RA. New benchmarks characterizing growth in protocol design complexity. TIRS. 2018;52(1):22–8.
Getz K, Stergiopoulos S, Short M, Surgeon L, Krauss R, Pretorius S, Desmond J, Dunn D. The impact of protocol amendments on clinical trial performance and cost. TIRS. 2016;50(4):436–41.
Smith Z, Bilke R, Pretorius S, Getz K. Protocol design variables highly correlated with, and predictive of clinical trial performance. TIRS. 2021. https://doi.org/10.1007/s43441-021-00370-0 .
doi: 10.1007/s43441-021-00370-0
Apostolates M, Barbadian D, Cornelli A, Forrest A, Hamre G, Hewitt J, Podolsky L, Popat V, Randall P. Legal, regulatory and practical issues to consider when adopting decentralized clinical trials. TIRS. 2020;54:779–87.