Speaker
Description
Drug development in oncology faces high failure rates and significant costs due to methodological and operational challenges. Modeling and simulation approaches increasingly support decision-making by integrating biological knowledge and existing data throughout development programs. Nonlinear joint modeling of longitudinal and survival data characterizes the dynamic relationship between biomarker evolution and event occurrence, enabling precise parameter estimation and reduced bias in treatment effect predictions [1]. Increasingly adopted in oncology, it supports trial simulations to explore dosing regimens, identify prognostic factors, or support regulatory decisions [2]. We illustrate the impact of joint PK/PD modeling through four case studies. Joint modeling of serum M-protein (surrogate of tumor burden) and progression-free survival (PFS) supported isatuximab dose selection in relapsed refractory multiple myeloma patients and regulatory approval in combination with dexamethasone in Japan, avoiding a dedicated trial [2]. Remarkably, phase 3 PFS outcomes were accurately predicted using joint models built on early phase 1–2 data alone. This predictive capability was shown across multiple indications: multiple myeloma with isatuximab-pomalidomide-dexamethasone [3], breast cancer with amcenestrant [4], and lung cancer with tusamitamab ravtansine [5]. Beyond efficacy, we developed a novel framework to assess benefit-risk balance, accounting for efficacy-safety interactions and the impact of dose modifications due to adverse events [5]. These case studies demonstrate how joint modeling enhances decision-making in drug development. Future applications include prospective implementation with virtual patient approaches to inform trial designs.
References
[1] Desmée S, Mentré F, Veyrat-Follet C, Guedj J. Nonlinear Mixed-effect Models for Prostate-specific Antigen Kinetics and Link with Survival in the Context of Metastatic Prostate Cancer: A Comparison by Simulation of Two-stage and Joint Approaches. AAPS J. 2015 May;17(3):691-9
[2] Thai HT, Koiwai K, Shitara Y, Kazama H, Fau JB, Semiond D, Veyrat-Follet C. Model-based simulation to support the approval of isatuximab alone or with dexamethasone for the treatment of relapsed/refractory multiple myeloma in Japanese patients. CPT Pharmacometrics Syst Pharmacol 2023;12(12):1846-58 doi 10.1002/psp4.12947.
[3] Pitoy A, Desmée S, Riglet F, Thai HT, Klippel Z, Semiond D, Veyrat-Follet C, Bertrand J. Isatuximab-dexamethasone-pomalidomide combination effects on serum M protein and PFS in myeloma: Development of a joint model using phase I/II data. CPT Pharmacometrics Syst Pharmacol. 2024 Dec;13(12):2087-2101.
[4] Cerou M, Thai HT, Deyme L, Fliscounakis-Huynh S, Comets E, Cohen P, Cartot-Cotton S, Veyrat-Follet C. Joint modeling of tumor dynamics and progression-free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I-II trials. CPT Pharmacometrics Syst Pharmacol. 2024 Jun;13(6):941-953.
[5] Cerou M, Veyrat-Follet C, Fliscounakis-Huynh S, Pouzin C, Fagniez N, Mihaljevic F, Chadjaa M, Comets E, Thai HT. Novel Drug-Disease Modeling Framework for Oncology Benefit-Risk Evaluation: Application to Tusamitamab Ravtansine. CPT Pharmacometrics Syst Pharmacol. 2026 Feb;15(2):e70190
