Confounding Factors in Exposure-Response Analyses and Mitigation
Strategies for Monoclonal Antibodies in Oncology
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.