A multistate modeling and simulation framework to learn dose–response of oncology drugs: Application to bintrafusp alfa in non‐small cell lung cancer

Abstract

The dose/exposure‐efficacy analyses are often conducted separately for oncology end points like best overall response, progression‐free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose‐end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition‐specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF‐β and PD‐L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non‐small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose‐specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2‐months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

“…Whereas initial oncology disease models were driven by landmark measures of tumor shrinkage as the predictor of survival hazard, longitudinal models that dynamically link the time course of tumor size to survival hazard are increasingly used, including the emergence of multistate modeling frameworks that can powerfully integrate the totality of tumor size, response categories, and overall survival (OS) states in a longitudinal dataset while also protecting from the impact of immortal time bias. 48 , 49 , 50 , 51 , 52 , 53 , 54 …”

Multistate modeling for survival analysis in critically ill patients treated with meropenem

“…Multistate modeling, introducing multiple intermediate states and transition‐dependent hazard functions, has recently gained popularity for describing the natural progression of diseases like cancer (e.g., stable disease, response or progression, and death), and helps to mitigate the bias introduced by confounding factors (e.g., second‐line treatment after disease progression) 11,12 . Compared to single hazard TTE analysis, multistate modeling allows to simultaneously perform related TTE analysis (e.g., transition from stable disease to response/progression, or transition from stable disease to death/censoring) 13–15 . Moreover, achieved values of relevant PK/PD indices and other covariates can be tested on all hazard functions and characterized as predictors in a prospective and transition‐specific way (e.g., PK/PD target attainment on in‐hospital death).…”

“… 11 , 12 Compared to single hazard TTE analysis, multistate modeling allows to simultaneously perform related TTE analysis (e.g., transition from stable disease to response/progression, or transition from stable disease to death/censoring). 13 , 14 , 15 Moreover, achieved values of relevant PK/PD indices and other covariates can be tested on all hazard functions and characterized as predictors in a prospective and transition‐specific way (e.g., PK/PD target attainment on in‐hospital death).…”