Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs

A comprehensive review on structural and therapeutical insight of Cerebroside sulfotransferase (CST) - An important target for development of substrate reduction therapy against metachromatic leukodystrophy

This review is an effort towards the development of substrate reduction therapy using cerebroside sulfotransferase (CST) as a target protein for the development of inhibitors intended to treat pathophysiological condition resulting from the accumulation of sulfatide, a product from the catalytic action of CST. Accumulation of sulfatides leads to progressive impairment and destruction of the myelin structure, disruption of normal physiological transmission of electrical impulse between nerve cells, axonal loss in the central and peripheral nervous system and cumulatively gives a clinical manifestation of metachromatic leukodystrophy. Thus, there is a need to develop specific and potent CST inhibitors to positively control sulfatide accumulation. Structural similarity and computational studies revealed that LYS85, SER172 and HIS141 are key catalytic residues that determine the catalytic action of CST through the transfer of sulfuryl group from the donor PAPS to the acceptor galactosylceramide. Computational studies revealed catalytic site of CST consists two binding site pocket including PAPS binding pocket and substrate binding pocket. Specific substrate site residues in CST can be targeted to develop specific CST inhibitors. This review also explores the challenges of CST-directed substrate reduction therapy as well as the opportunities available in natural products for inhibitor development.

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning

1. Unexpected metabolism could lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Thus, it is critical to predict and study the dominant routes of metabolism in the early stages of research. In this study, we describe the development and validation of a 'WhichEnzyme' model that accurately predicts the enzyme families most likely to be responsible for a drug-like molecule's metabolism. Furthermore, we combine this model with our previously published regioselectivity models for Cytochromes P450, Aldehyde Oxidases, Flavin-containing Monooxygenases, UDP-glucuronosyltransferases and Sulfotransferases - the most important Phase I and Phase II drug metabolising enzymes - and a 'WhichP450' model that predicts the Cytochrome P450 isoform(s) responsible for a compound's metabolism. The regioselectivity models are based on a mechanistic understanding of these enzymes' actions, and use quantum mechanical simulations with machine learning methods to accurately predict sites of metabolism and the resulting metabolites. We train heuristic based on the outputs of the 'WhichEnzyme', 'WhichP450', and regioselectivity models to determine the most likely routes of metabolism and metabolites to be observed experimentally. Finally, we demonstrate that this combination delivers high sensitivity in identifying experimentally reported metabolites and higher precision than other methods for predicting in vivo metabolite profiles.