A Symbolic Regression Model for the Prediction of Drug Binding to Human Liver Microsomes

It is common practice in the early drug discovery process to conduct in vitro screening experiments using liver microsomes in order to obtain an initial assessment of test compound metabolic stability. Compounds which bind to liver microsomes are unavailable for interaction with the drug metabolizing enzymes. As such, assessment of the unbound fraction of compound available for biotransformation is an important factor for interpretation of in vitro experimental results and to improve prediction of the in vivo metabolic clearance. Various in silico methods have been proposed for the prediction of test compound binding to microsomes, from various simple lipophilicity-based models with moderate performance to sophisticated machine learning models which demonstrate superior performance at the cost of increased complexity and higher data requirements. In this work, we attempt to strike a middle ground by developing easily implementable equations with improved predictive performance. We employ a symbolic regression approach based on a medium-size in-house data set of fraction unbound in human liver microsomes measurements allowing the identification of novel equations with improved performance. We validate the model performance on an in-house held-out test set and an external validation set.

Automated summary

In the early drug discovery process, liver microsomes are commonly used for in vitro screening experiments to evaluate the metabolic stability of test compounds. Predicting the unbound fraction of compounds available for biotransformation is important for interpreting in vitro results and improving the prediction of in vivo metabolic clearance. Different in silico methods have been proposed for predicting compound binding to microsomes, ranging from simple lipophilicity-based models to complex machine learning models with higher data requirements. This study aims to develop easily implementable equations with improved predictive performance using a symbolic regression approach and a medium-sized in-house data set.