Learning Chemotherapy Drug Action via Universal Physics-Informed Neural
  Networks
Lena Podina Ali Ghodsi Mohammad Kohandel
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.