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.

本研究利用Universal Physics-Informed Neural Networks（UPINNs）从合成数据中学习化疗药物动力学的三种常用作用方式（log-kill、Norton-Simon和E_max），并使用UPINN方法同时拟合多个合成数据集的参数。最后，学习了一种多柔比星（一种化疗药物）动力学模型中的净增殖速率，突出了UPINNs在学习药效动力学和药物代谢动力学模型中未知项的有用性。

通过通用的物理信息神经网络学习化疗药物作用机制