A Deep Neural Network -- Mechanistic Hybrid Model to Predict Pharmacokinetics in Rat

An important aspect in the development of small molecules as drugs or agro-chemicals is their systemic availability after intravenous and oral administration.The prediction of the systemic availability from the chemical structure of a poten-tial candidate is highly desirable, as it allows to focus the drug or agrochemicaldevelopment on compounds with a favorable kinetic profile. However, such pre-dictions are challenging as the availability is the result of the complex interplaybetween molecular properties, biology and physiology and training data is rare.In this work we improve the hybrid model developed earlier [34]. We reducethe median fold change error for the total oral exposure from 2.85 to 2.35 andfor intravenous administration from 1.95 to 1.62. This is achieved by trainingon a larger data set, improving the neural network architecture as well as theparametrization of mechanistic model. Further, we extend our approach to predictadditional endpoints and to handle different covariates, like sex and dosage form.In contrast to a pure machine learning model, our model is able to predict newend points on which it has not been trained. We demonstrate this feature by1predicting the exposure over the first 24h, while the model has only been trainedon the total exposure.