Using Quantitative Approaches to Optimize Dosages for New Combinations and Subsequent Indications for Oncology Drugs
Open accessAbstract
Ongoing efforts to optimize the dosages of oncology drugs have largely focused on the initial indication, with emphasis placed on maximizing the utility of all available evidence to improve dose finding, dose selection, and trial design; however, optimizing dosages for new combinations or subsequent indications is more complex and warrants further discussion. For example, the dose–response (DR) or exposure–response (ER) relationships can change when multiple drugs are used (combination therapies) and can differ between tumor types, patient populations, and treatment settings (subsequent indications). Quantitative approaches can help address the challenges of optimizing dosages for new combinations or subsequent indications. To further this dialogue, the US Food and Drug Administration's Office of Clinical Pharmacology and the International Society of Pharmacometrics co‐sponsored a workshop to discuss the development of investigational and approved drugs in new combinations or for subsequent indications using model‐informed approaches to investigate, support, and select optimized dosages for oncology drugs.
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Examples of QSP contexts of use in early clinical development include first-in-human dose projections, recommended phase 2 or 3 dose optimization/justification, mechanistic biomarker-based patient stratification, and mechanistic insight into combination treatment or dose optimization across indications.
“In this setting, examples of QSP CoU could be first in human (FIH) dose projections, recommended phase 2 or 3 dose (RP2D/RP3D) optimization/justification; mechanistic biomarker-based patient stratification or providing mechanistic insight into combination treatment or dose optimization across indications (8) (9).”
From Best Practices on QSP Model Reporting for Regulatory Use: perspectives from ISoP QSP SIG Working Group (2026) · cites this paper as [8]
The section ‘Using model-informed approaches to identify dosages and inform trial design’ (page unavailable) describes QSP-guided dosing for mosunetuzumab and its use in a subsequent indication. The paper also discusses model-informed approaches to combination dosing, but the passages do not establish QSP uses for first-in-human dose projections, RP2D/RP3D justification, or mechanistic biomarker-based patient stratification. (LLM, conf=0.85)
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Zaph, S., Shtylla, B., Chang, S., Cheng, Y., Gong, J. Q. X., Gulati, A., Hansson, E., Kulesza, A., Ratushny, A. V., Reali, F., et al. (2026) · arXiv · doi:10.48550/arxiv.2604.07811
- Authors
- Gao, W., Liu, J., Musante, C. J., Zhu, H., Thompson, M., Shah, M., Cao, Y., Ivaturi, V., Conaway, M. R., Bottino, D., Yin, D., Semiond, D., Oganesian, A., Ratain, M. J., Li, C., Zhu, L., Ou, Y. C., Jiang, X., Vallejo, J., Madabushi, R., Liu, Q., Theoret, M., Rahman, A., Booth, B., Okusanya, O., Johnson‐Williams, B. E., & Shord, S. S.
- Affiliations
- EMD Serono, Inc. (United States) (US)
- United States Food and Drug Administration (US)
- Pfizer (United States) (US)
- University of North Carolina at Chapel Hill (US)
- Manipal Academy of Higher Education (IN)
- University of Virginia (US)
- Takeda (United States) (US)
- Pharmacokinetics, Dynamics, and Metabolism (PKDM) Department, Sanofi Cambridge Massachusetts USA
- Taiho Oncology (United States) (US)
- University of Chicago (US)
- Genentech (US)
- Bristol-Myers Squibb (United States) (US)
- Clinical Pharmacology, Beigene San Francisco California USA
- Office of Clinical Pharmacology (OCP), Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER) Food and Drug Administration (FDA) Silver Spring Maryland USA
- Corresponding author
- Shord, Stacy S.
- ORCID
- Gao, Wei
- Liu, Jiang
- Musante, Cynthia J.
- Zhu, Hao
- Thompson, Matthew
- Shah, Mirat
- Cao, Yanguang
- Ivaturi, Vijay
- Conaway, Mark R.
- Bottino, Dean
- Oganesian, Aram
- Ratain, Mark J.
- Li, Chunze
- Zhu, Li
- Ou, Ying C.
- Jiang, Xiling
- Madabushi, Rajanikanth
- Liu, Qi
- Theoret, Marc
- Booth, Brian
- Okusanya, Olanrewaju
- Johnson‐Williams, Bernadette E.
- Shord, Stacy S.
- Venue
- Clinical Pharmacology & Therapeutics
- Publisher
- Wiley
- Published
- 2025-06-05
- Volume
- 118
- Issue
- 4
- Pages
- 772-777
- ISSN
- 0009-9236 (ISSN-L)
- 1532-6535
- OpenAlex
- W4411063450
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 2
- FWCI
- 0.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- onlinelibrary.wiley.com/termsAndConditions#vor (vor)
- Locations
- Clinical Pharmacology & Therapeutics (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Keywords
- combination therapy, dose-response relationship, exposure-response relationship, model-informed drug development, dose selection, quantitative pharmacology
- MeSH terms
- Antineoplastic Agents, Antineoplastic Combined Chemotherapy Protocols, Dose-Response Relationship, Drug, Humans, Neoplasms, United States, United States Food and Drug Administration, Drug Dosage Calculations
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