A Deep Neural Network -- Mechanistic Hybrid Model to Predict Pharmacokinetics in Rat
This work addresses the problem of predicting pharmacokinetics for drug and agro-chemical development, but it is incremental as it builds on a prior hybrid model.
The authors tackled the challenge of predicting systemic availability of small molecules in rats by improving a hybrid model, reducing median fold change errors from 2.85 to 2.35 for oral exposure and from 1.95 to 1.62 for intravenous administration.
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 potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such pre-dictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier [1]. We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional 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 new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24h, while the model has only been trained on the total exposure.