Accounting For Dose Modifications In Exposure-Response Analyses In Oncology: The Case Example Of Brigimadlin
Authors: Kang Jia , Wiens Matthew , Rogers James , Baron Kyle , Choy Steve , Jayadeva Girish , Perez-Pitarch Alejandro , Busse David 
Published: February 2025

Objectives: Model-based exposure-response (ER) analyses are a cornerstone of dose optimization in the Project Optimus era in oncology drug development. Yet, during cancer clinical trials, it is often necessary to perform dose modifications (dose interruption, reduction, or discontinuation) due to safety and tolerability issues [1]. This leads to feedback in the dose-exposure-safety relationship, where safety outcomes have an impact on dose. Failure to account for this feedback in standard model-based ER analyses may lead to unrealistic simulations (i.e., too high exposure), reducing the credibility of model-based inferences. Based on the case example of safety-based dose modifications of brigimadlin, a potent, oral murine double minute 2 homolog-tumor protein 53 antagonist, we aimed to:- Characterize the relationship between safety endpoints and dose modifications- Perform dynamic simulations of exposure and safety that account for dose modificationsMethods: Simulations for time-varying treatments were used to obtain an unbiased estimate of the relationship between initial dose and safety in the presence of intercurrent events [2]. A Bayesian model of the probability of dose modification as a function of platelet and neutrophil counts was developed to enable dynamic and probabilistic dosing decisions. This model was a composite of a categorical model for the dosing decision and a time-to-event model for the length of the dose delay. The dose decision model used four categories: (i) no dose change and no delay, (ii) a delay with no change, (iii) a dose reduction without delay, (iv) and a dose reduction with delay. Subsequently, a dynamic simulation was conducted using mrgsolve [3] which simulated the loop of dose to exposure with a PK model, exposure to platelets and neutrophils with PKPD models, and platelets and neutrophils to dose decision using the dose modification model. Results: Dose delays and reductions were estimated to happen more frequently with lower platelet and neutrophil counts; additionally, the delays were longer with lower counts. Simulated patient profiles with dynamic dosing regimens adequately captured the qualitative trajectories of dose decisions, exposure, platelet, and neutrophil counts. Under the observed initial dose, the predicted rate of thrombocytopenia was 21.6%, compared to the observed rate of 24.6%, while for neutropenia the predicted rate was 13.8% compared to the observed rate 17.5%. Conclusions: A dose modification model was successfully integrated into a dynamic simulation framework accounting for the impact of safety signals on dose. This framework was able to adequately predict the observed safety outcomes and may serve as a basis to support realistic simulations in other oncology drug development programs.Citations: [1] (2022), ACCP Abstract Booklet. Clinical Pharmacology in Drug Development, 11: 1-112. https://doi.org/10.1002/cpdd.1151[2] Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.[3] Baron KT, Gillespie B, Margossian C, Pastoor D, Denney B, Singh D, Le Louedec F, Waterhouse T. mrgsolve: Simulate from ODE-Based Models [Internet]. Metrum Research Group; 2021. Available from: https://cran.r-project.org/package=mrgsolve