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An important aspect in the development of small molecules as drugs oragro-chemicals is their systemic availability after intravenous and oraladministration. The prediction of the systemic availability from the chemicalstructure of a potential candidate is highly desirable, as it allows to focusthe drug or agrochemical development on compounds with a favorable kineticprofile. However, such pre-dictions are challenging as the availability is theresult of the complex interplay between molecular properties, biology andphysiology and training data is rare. In this work we improve the hybrid modeldeveloped earlier [1]. We reduce the median fold change error for the totaloral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to1.62. This is achieved by training on a larger data set, improving the neuralnetwork architecture as well as the parametrization of mechanistic model.Further, we extend our approach to predict additional endpoints and to handledifferent covariates, like sex and dosage form. In contrast to a pure machinelearning model, our model is able to predict new end points on which it has notbeen trained. We demonstrate this feature by predicting the exposure over thefirst 24h, while the model has only been trained on the total exposure.

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