Delay-aware adaptive chemotherapy for tumor-immune dynamics using proximal policy optimization

The design of adaptive chemotherapy is complicated by nonlinear tumor–immune interactions, delayed drug transport and uncertainty in state evolution. This study develops a delay-aware computational framework that couples a normalized normal–tumor–immune model with central and peripheral pharmacokinetic compartments. A proximal policy optimization (PPO) agent observes the population states, their rates of change and both drug concentrations, and selects a bounded continuous infusion action. Pharmacodynamic killing is driven by peripheral exposure, thereby distinguishing systemic administration from the concentration acting at the tumor site. Positivity and boundedness of the deterministic biological–pharmacokinetic system are established for non-negative initial states and bounded infusion. The learning objective jointly penalizes tumor burden, positive tumor growth, cumulative exposure, abrupt normalized-action variation and depletion of normal and immune populations. Numerical experiments compare untreated and periodic regimens, one- and two-compartment environments, nominal and stochastic transitions, and PPO with two actor–critic baselines. Explicit distribution dynamics change the learned policy from a sustained high-action plateau to an early loading phase followed by gradual tapering. Moderate transition randomization yields the smallest reported tumor-tracking errors and reduced trajectory dispersion, whereas stronger perturbations substantially widen the uncertainty bands. These results identify pharmacokinetic representation as a consequential component of learning-based treatment design. The framework is an in-silico mechanism study rather than a clinical dosing recommendation and requires drug-specific calibration, hard toxicity constraints, complete statistical evaluation and biological validation.