Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks

TL;DR

This paper demonstrates the use of Universal Physics-Informed Neural Networks (UPINNs) to learn and fit parameters of differential equations modeling chemotherapy drug actions, reducing manual literature review and assumptions in pharmacodynamics modeling.

Contribution

It introduces UPINNs as a novel approach to automatically learn unknown components and parameters in pharmacodynamic models from synthetic data.

Findings

Successfully learned three chemotherapeutic drug actions from synthetic data

Fitted parameters for multiple datasets simultaneously

Learned the net proliferation rate in a doxorubicin pharmacodynamics model

Abstract

Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Parameters may need to be fit, and simplifying assumptions of the model need to be made. In this work, we apply Universal Physics-Informed Neural Networks (UPINNs) to learn unknown components of various differential equations that model chemotherapy pharmacodynamics. We learn three commonly employed chemotherapeutic drug actions (log-kill, Norton-Simon, and E_max) from synthetic data. Then, we use the UPINN method to fit the parameters for several synthetic datasets simultaneously. Finally, we learn the net proliferation rate in a model of doxorubicin (a chemotherapeutic) pharmacodynamics. As these are only toy examples, we highlight the usefulness of UPINNs in learning unknown terms in pharmacodynamic and pharmacokinetic models.