Physics-Informed Neural Networks for Chemotherapy Pharmacokinetics: Benchmarking the Clinical Estimator and Exposing Parameter Identifiability

2026-06-12 · Source: stat.ML updates on arXiv.org · Field: Science & Research — Life Sciences & Biology, Mathematics & Computational Sciences, Health & Medical Research · Depth: Expert, long

Summary

Physics-Informed Neural Networks (PINNs) were benchmarked against the standard clinical nonlinear least-squares (NLS) estimator and a data-only MLP for chemotherapy pharmacokinetics (PK) problems. On a linear two-compartment model, PINNs matched NLS performance, producing tissue curves, while the MLP failed on tissue by approximately 10x. NLS, being near-optimal, recovered k_e more cleanly. For a Michaelis-Menten extension with saturable elimination, NLS was mis-specified, yielding meaningless rate constants. The PINN, however, exposed structural non-identifiability from plasma data alone, converging to a k_12→0 basin. Adding two sparse tissue observations to the PINN largely resolved this, recovering k_21 within 1% of truth and V_max, K_m within one standard-deviation bar. The NLS estimator cannot incorporate tissue data. PINNs provide a uniform framework that performs comparably to NLS on well-specified problems, reveals hidden identifiability issues, and integrates diverse measurements.

Key takeaway

For research scientists developing pharmacokinetic models, especially for chemotherapy, you should consider Physics-Informed Neural Networks (PINNs) when dealing with nonlinear kinetics or unobserved tissue compartments. PINNs not only infer hidden states but also transparently expose structural non-identifiability, a critical limitation that traditional nonlinear least-squares methods can obscure. This capability is vital for more honest and reliable inference in drug development.

Key insights

PINNs offer a uniform framework for pharmacokinetics, matching traditional methods on linear problems and exposing identifiability issues in nonlinear models.

Principles

• Physics-informed neural networks expose structural non-identifiability.
• PINNs absorb heterogeneous measurements within a single loss.

Method

Train a neural network to satisfy ODEs via a residual loss, evaluated by automatic differentiation at collocation points, enforcing initial conditions as a loss term.

In practice

• Infer unobserved tissue concentrations using PINNs.
• Incorporate sparse tissue data to resolve parameter identifiability.

Topics

• Physics-Informed Neural Networks
• Pharmacokinetics Modeling
• Chemotherapy Dosing
• Parameter Identifiability
• Nonlinear Kinetics
• Drug Concentration Inference

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.