The traditional approach to drug development in oncology, with discrete phases of clinical testing, is becoming untenable owing to expansion of the precision medicine paradigm, whereby patients are stratified into multiple subgroups according to the underlying cancer biology. Seamless approaches to drug development in oncology hold great promise of accelerating the accessibility of novel therapeutic agents to the public but are also accompanied by important trade-offs, including the limited availability of information on the clinical benefit and safety of novel agents at the time of market entry. In this Perspectives article, we describe several opportunities, in the form of novel trial designs or modelling strategies, to improve the efficiency of drug development in oncology, as well as new mechanisms to obtain information about anticancer therapies throughout their life cycle, such as innovative functional imaging techniques or the use of real-world clinical data.
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The responsibility for the content of this article rests with the authors and does not necessarily represent the views of the National Academies of Sciences, Engineering and Medicine, its committees, its sponsors or its convening activities. The activities of the National Cancer Policy Forum are supported by its sponsoring members, which currently include the Centers for Disease Control and Prevention, the NIH/National Cancer Institute, the American Association for Cancer Research, the American Cancer Society, the American College of Radiology, ASCO, the American Society of Hematology, the Association of American Cancer Institutes, Bristol-Myers Squibb, the Cancer Support Community, the CEO Roundtable on Cancer, Flatiron Health, Helsinn Therapeutics (US), the LIVESTRONG Foundation, Merck, the National Comprehensive Cancer Network, Novartis Oncology, the Oncology Nursing Society and Pfizer. The authors thank the speakers and participants for their contributions to the workshop.
Nature Reviews Clinical Oncology thanks G. M. Blumenthal, F. Pignatti, E. D. Saad and H. J. West for their contribution to the peer review of this work.
S.J.N. receives partial salary support from the sponsors of the National Cancer Policy Forum. M.L.R. is an employee and stockholder of Pfizer. H.H. is a member of the board of directors of Ion Beam Applications. A.A. is an employee of Flatiron Health. K.A. is an advisory board member for Bristol-Myers Squibb (BMS), Celgene and Millennium Takeda, a scientific advisory board member of Gilead, and the National Cancer Institute and is a founder of C4 Therapeutics and Oncopep. A.W.G. receives salary support from the sponsors of the National Academies of Sciences, Engineering and Medicine (NASEM) Forum on Drug Discovery, Development and Translation. R.D.H. receives research funding that supports his salary from Abbvie, Amgen, Arqule, AstraZeneca, BMS, Calithera, Celgene, Corvus, Eli Lilly, Five Prime Therapeutics, Genmab, Halozyme, Ignyta, Incyte, Merck, Nektar, Pfizer, Regeneron, Rgenix, Sanofi, Syndax, Takeda and Vertex. R.L.S. is a principal investigator in the Targeted Agent and Profiling Utilization Registry (TAPUR) study, which receives grant support from AstraZeneca, Bayer, BMS, Genentech, Lilly, Merck and Pfizer. R.P., S.P., M.M.B. and D.S. declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
FDA Hematology/Oncology approvals and safety notifications: https://www.fda.gov/drugs/informationondrugs/approveddrugs/ucm279174.htm
National Cancer Policy Forum: nationalacademies.org/NCPF
- Bayesian learning process
The interpretation of information from accumulating data using Bayes’ theorem to modify a prior belief.
- Data and Safety Monitoring Boards
(DSMBs). Committees of impartial experts who can assess the risks and benefits for study participants on an ongoing basis and recommend whether the trial should continue or terminate early; the use of DSMBs is a universally employed method to monitor an ongoing clinical trial.
- Envelope simulation
A method of generating simulated data from a rectangular region (‘envelope’) of the dose–response plane to support a model that aids dose-finding studies.
- Surface design methods
Experimental designs in which two or more factors are varied to test their effects on a response. The response can then be plotted as a surface to look for peaks and valleys (high and low responses).
- Traditional type I and II error properties
Properties of many clinical trials in which the probability of type I errors (false-positive result) is set to 0.05 and that of type II errors (false-negative result) is set to 0.1–0.2. These choices are very common but not always appropriate for some questions.
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Nass, S.J., Rothenberg, M.L., Pentz, R. et al. Accelerating anticancer drug development — opportunities and trade-offs. Nat Rev Clin Oncol 15, 777–786 (2018). https://doi.org/10.1038/s41571-018-0102-3
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