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.