Dynamic optimization of chemo–immunotherapy sequencing reveals phenotype-dependent regimens across tumor immune microenvironments
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Cytotoxic chemotherapy and immune checkpoint inhibitors (ICIs) have transformed the management of advanced cancers, yet durable responses remain restricted to subsets of patients and strongly depend on the tumor immune microenvironment (TIME). Distinct “hot” and “cold” TIMEs differ in pre-existing effector T-cell activity and treatment-induced immunogenicity, suggesting that combination regimens should be tailored to microenvironmental context under realistic clinical constraints. Here we develop a deterministic dynamic optimization framework that jointly designs chemotherapy and ICI dosing schedules on a mechanistic tumor–immune model. The model tracks sensitive and resistant tumor cells, effector CD8 + T cells and circulating drug concentrations with pharmacodynamic couplings encoding immunogenic cell death and checkpoint inhibition. We formulate a continuous-time optimal control problem that balances tumor burden against drug exposure while enforcing cumulative dose limits, end-of-interval concentration bounds and bounds on stepwise changes in infusion rates. Using gradient-based optimization with embedded forward sensitivities, we solve the resulting nonlinear programs for four representative TIME phenotypes (extremely cold, hot, cold and cold with high antigenic resistance). The optimizer recovers qualitatively distinct and biologically interpretable regimens, including chemotherapy-dominated schedules in extremely cold TIMEs, aggressive early ICI administration in hot TIMEs and minimal ICI pulses in cold TIMEs. Alternative weightings of treatment objectives reveal trade-offs between tumor eradication and effector preservation. These results show that constrained dynamic optimization can systematically derive phenotype-specific, mechanistically consistent combination strategies and provide a reusable computational module for integrating TIME-resolved models into in silico trial and quantitative systems pharmacology workflows.
Author summary
Immune checkpoint inhibitors (ICI) have transformed cancer therapy, yet their benefit varies dramatically across tumor immune microenvironments (TIMEs). In the clinic, ICI is often combined with chemotherapy, and clinicians must decide not only whether to use ICI, but also how to schedule both agents over months of treatment. We develop a reusable dynamic optimization framework that takes any ordinary differential equation model of tumor–immune–therapy interactions as input, enforces clinically motivated dosing and toxicity constraints, and outputs optimal chemo–ICI regimens. Using a mechanistic model and four virtual patients representing hot, cold, and extremely cold TIMEs, we show that the framework automatically suppresses futile ICI dosing in immune deserts, while allocating aggressive combination therapy to highly ICI-responsive tumors.
