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

TL;DR

This paper enhances a hybrid mechanistic-neural network model to better predict rat pharmacokinetics, achieving more accurate exposure predictions and extending capabilities to new endpoints and covariates.

Contribution

The authors improve a hybrid model for pharmacokinetic prediction by training on larger data, refining neural network architecture, and extending endpoint predictions.

Findings

Median fold change error reduced for oral exposure from 2.85 to 2.35.

Median fold change error reduced for intravenous exposure from 1.95 to 1.62.

Model can predict new endpoints like 24h exposure without direct training.

Abstract

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.