Advancing Autologous CAR T-cell Therapy through Real-Time Patient Health Data Integration: A Simulation-Based Approach

Abstract

Autologous chimeric antigen receptor (CAR) T-cell therapy presents promising treatment outcomes for various cancers. However, its potential is restrained by unique supply chain challenges, including dynamic patient health conditions and extended turnaround time. These challenges often lead to missed optimal treatment windows, impeding the effective delivery of life-saving treatments. This paper presents SimPAC (Simulation-based decision support for Patient-centric manufacturing of Autologous Cell therapies). SimPAC is designed to model and incorporate real-time patient health conditions into the supply chain decisions of autologous CAR T-cell therapy. SimPAC integrates system dynamics and agent-based simulation techniques, facilitating the adaptation of manufacturing processes and production schedules based on real-time patient health conditions. SimPAC can model various patient disease progressions using parametric functions, non-parametric functions, or tabular data. Additionally, SimPAC offers easy configuration options to model various cell therapy supply chains. We provide two case studies to demonstrate the capabilities of SimPAC and highlight the benefits of patient-centric manufacturing, including improved survival rates and potential economic advantages. However, while the benefits are significant, our study also emphasizes the importance of balancing improved patient outcomes, economic viability, and ethical considerations in the context of personalized medicine. SimPAC can be used to explore applications of this approach to diverse therapeutic contexts and supply chain configurations.