ABSTRACT This study introduces a novel drug‐disease modeling framework designed to assess the benefit–risk balance of antibody‐drug conjugates (ADC) in oncology. The framework integrates dose levels, pharmacokinetics, tumor growth dynamics, progression‐free survival (PFS), and dose‐adjusted adverse events. We demonstrated this through its application to tusamitamab ravtansine (Tusa), an ADC targeting Carcinoembryonic Antigen‐Related Cell Adhesion Molecule 5 in non‐squamous non‐small cell lung cancer (nsq NSCLC). We developed our model using phase I trial safety data from 254 patients (doses: 5–190 mg/m 2 ) and efficacy data from 88 nsq NSCLC patients (dose 100 mg/m 2 ). This model accurately predicted phase III outcomes for the Tusa arm via an iterative simulation. Using phase III baseline characteristics, simulations of Tusa doses comparing three dose levels (80, 100, and 120 mg/m 2 every 2 weeks) revealed a critical trade‐off: while higher doses increased response rates, they also substantially increased corneal toxicity without improving survival. These findings demonstrate how early‐phase data can inform optimal dose selection by quantifying benefit–risk. This robust framework and methodology is generalizable beyond Tusa, offering value to support dose selection and trial decision‐making in oncology drug development.
Cerou et al. (Thu,) studied this question.