Modeling and simulation with evolving cancer therapies: combinations, cancer immunotherapy, and more

Aims: Recent break-through and success in oncology including immuno-oncology and combination therapies has generated many excitement. With newer medicines being more effective to prolong patient’s life, finding the optimal dose regimens that best balance efficacy and safety profile is critical for chronic anti-cancer treatment. The aim of this presentation is to illustrate the impact of multiple modeling and simulation (M&S) approaches for dosing optimization in oncology with case examples including combinations and cancer immunotherapy.

Methods: M&S approaches to be highlighted include preclinical-to-clinical translational modeling, early-to-late translational modeling, exposure-response and longitudinal Pharmacokinetics/Pharmacodynamics (PK/PD) for efficacy and safety endpoints, literature meta-analysis, and Physiologically-Based Pharmacokinetics (PBPK) modeling. 

Results: Combination therapy has become common practice in oncology with multiple mechanisms of action and/or simultaneous inhibition of multiple targets to enhance activity in broader population with less resistance. Typical combo strategy includes combination of an investigational agent with Standard of Care (SOC) or combination of 2 investigational agents, each has their unique considerations in dosing optimization. Recent immune-oncology therapies have shifted the oncology development paradigm with their unique mechanism of actions and pronounced efficacy in many cancer types. These drug development challenges offer special opportunities for innovative study design, mechanism related effective measurements (pathway and disease biomarkers, imaging), and broad application of M&S throughout the development cycle. Case examples will include application of PBPK and population PK approaches to evaluate PK drug-drug interaction (DDI) and inform trial design for small molecule combinations, application of translational and clinical PK/PD to evaluate PD DDI and optimize dosing based on therapeutic windows, with highlight of special considerations for cancer immunotherapy. Meta-analysis of literature summary-level data and internal individual-level data offer unique insights regarding SOC benchmarking, decision making based on early trial endpoints, and the underlying response mechanisms to ensure right patient selection.

Conclusion: Modeling and Simulation has shown growing impact with evolving cancer therapies to provide data driven quantitative analyses and projection to guide study design and decisionmaking in drug development