Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks

Abstract

Objective: 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.

Methods: Using UPINNs, 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.

Results: We show that the UPINN can successfully learn the hidden terms and unknown parameters in a variety of differential equations (with differing time and variable scales) that model the effect of chemotherapeutics on cancer cells.

Conclusions: As the examples we study are only toy examples, we highlight the usefulness of UPINNs in learning unknown terms in pharmacodynamic and pharmacokinetic models. UPINNs can be used to find these terms and analyze them further to understand new chemotherapeutics and biological mechanisms that interact with them.

Keywords: Chemotherapy drug action; Differential equations; Machine learning; Physics-informed neural networks; Quantitative systems pharmacology.