PINNs outperform standard models in chemotherapy PK modeling

Researchers Riya Bisht and Dhruv Agarwal published a paper on arXiv demonstrating how Physics-Informed Neural Networks (PINNs) can improve chemotherapy pharmacokinetic (PK) modeling compared to traditional clinical baselines. Their benchmark tests on two PK problems showed that PINNs matched the accuracy of nonlinear least-squares (NLS) methods on linear two-compartment models while also predicting tissue drug concentrations in a single training cycle. The breakthrough came in testing Michaelis-Menten kinetics, where standard biexponential models fail due to non-existent closed-form solutions. Here, PINNs exposed fundamental identifiability issues in the two-compartment model that conventional NLS methods completely missed.

The research highlights that while standard clinical estimators (NLS) perform near-optimally on textbook linear cases, they silently return meaningless rate constants when confronted with saturable elimination kinetics. By contrast, PINNs provided transparency by converging to basins that revealed the model's structural limitations. Adding just two sparse tissue observations improved identifiability dramatically, with PINNs recovering key parameters like k21 to within 1% of ground truth values and Vmax/Km within one standard deviation. This uniform approach offers clinicians a more reliable framework for chemotherapy dosing that can integrate heterogeneous measurement types within a single loss function.