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Confounding Factors in Exposure-Response Analyses and Mitigation Strategies for Monoclonal Antibodies in Oncology
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  • Sonoko Kawakatsu,
  • Rene Bruno,
  • Matts Kågedal,
  • Chunze Li,
  • Sandhya Girish,
  • Amita Joshi,
  • Benjamin Wu
Sonoko Kawakatsu
Genentech Inc
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Rene Bruno
Genentech Inc
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Matts Kågedal
Genentech Inc
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Chunze Li
Genentech Inc
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Sandhya Girish
Genentech Inc
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Amita Joshi
Genentech Inc
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Benjamin Wu
Genentech Inc
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Abstract

Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on three different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (1) cox-proportional hazards modeling and case-matching, (2) tumor growth inhibition-overall survival (TGI-OS) modeling, and (3) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programs. Therefore, the strengths and weaknesses of the other two approaches are considered, and the favorable utility of TGI-OS modeling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials, and proposes that development programs be designed to study more dose levels in earlier trials.

Peer review status:UNDER REVIEW

17 Aug 2020Submitted to British Journal of Clinical Pharmacology
18 Aug 2020Assigned to Editor
18 Aug 2020Submission Checks Completed
24 Aug 2020Reviewer(s) Assigned