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Confounding factors in exposure–response analyses and mitigation strategies for monoclonal antibodies in oncology
Author(s) -
Kawakatsu Sonoko,
Bruno René,
Kågedal Matts,
Li Chunze,
Girish Sandhya,
Joshi Amita,
Wu Benjamin
Publication year - 2021
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.14662
Subject(s) - confounding , medicine , oncology , monoclonal antibody , clinical trial , drug development , proportional hazards model , drug , pharmacology , immunology , antibody
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 3 different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (i) Cox‐proportional hazards modelling and case‐matching; (ii) tumour growth inhibition–overall survival modelling; and (iii) 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 programmes. Therefore, the strengths and weaknesses of the other 2 approaches are considered, and the favourable utility of tumour growth inhibition–overall survival modelling 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 programmes be designed to study more dose levels in earlier trials.