Harnessing Quantitative Medicine to Advance Oncology Drug Development.

Authors

Benjamin Wu, Kenta Yoshida, Pascal Chanu, Chunze Li

Published in

Clinical and translational science. Volume 19. Issue 10. Pages e70733.

Abstract

Oncology drug development continues to have a high attrition rate despite major advances in therapeutic modalities such as antibody-drug conjugates, bispecific antibodies, cell therapies, and cancer vaccines. Critical development decisions are often made under substantial uncertainty, creating a need for quantitative approaches that integrate diverse sources of evidence. Quantitative medicine (QM) and model-informed drug development (MIDD) provide a framework to support decision-making throughout the oncology drug development lifecycle by leveraging pharmacology, disease biology, clinical data, biomarkers, and computational modeling. This review highlights three potential applications of QM that address drug development challenges. First, tumor growth inhibition-overall survival (TGI-OS) modeling links longitudinal tumor dynamics with survival outcomes, enabling earlier assessment of treatment benefit and supporting Phase III go/no-go decisions using Phase Ib/II data. Second, pan-molecule modeling across multiple antibody-drug conjugates that share a common linker-payload construct. This QM approach characterizes the class-specific exposure-toxicity relationships and informs dose optimization strategies, as illustrated by peripheral neuropathy risk modeling for vc-MMAE-containing agents. Third, population pharmacokinetic modeling and clinical trial simulation can facilitate intravenous-to-subcutaneous bridging by predicting pharmacokinetic non-inferiority, optimizing dose selection, and reducing development risk, as demonstrated for pertuzumab/trastuzumab and atezolizumab. Collectively, these examples demonstrate how QM can improve confidence in critical development decisions, optimize benefit-risk assessment, and enhance development efficiency. Continued integration of quantitative approaches, including emerging artificial intelligence and mechanistic modeling methodologies, has the potential to improve the probability of success and accelerate the delivery of effective oncology therapies to patients.

PMID:
42806861
Bibliographic data and abstract were imported from PubMed on 29 Sep 2026.

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