Investigation of in silico studies for cytochrome P450 isoforms specificity

Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.

Mechanistic Insights into the Regio- and Stereoselectivities of Testosterone and Dihydrotestosterone Hydroxylation Catalyzed by CYP3A4 and CYP19A1

The hydroxylation of nonreactive C-H bonds can be easily catalyzed by a variety of metalloenzymes, especially cytochrome P450s (P450s). The mechanism of P450 mediated hydroxylation has been intensively studied, both experimentally and theoretically. However, understanding the regio- and stereoselectivities of substrates hydroxylated by P450s remains a great challenge. Herein, we use a multi-scale modeling approach to investigate the selectivity of testosterone (TES) and dihydrotestosterone (DHT) hydroxylation catalyzed by two important P450s, CYP3A4 and CYP19A1. For CYP3A4, two distinct binding modes for TES/DHT were predicted by dockings and molecular dynamics simulations, in which the experimentally identified sites of metabolism of TES/DHT can access to the catalytic center. The regio- and stereoselectivities of TES/DHT hydroxylation were further evaluated by quantum mechanical and ONIOM calculations. For CYP19A1, we found that sites 1β, 2β and 19 can access the catalytic center, with the intrinsic reactivity 2β>1β>19. However, our ONIOM calculations indicate that the hydroxylation is favored at site 19 for both TES and DHT, which is consistent with the experiments and reflects the importance of the catalytic environment in determining the selectivity. Our study unravels the mechanism underlying the selectivity of TES/DHT hydroxylation mediated by CYP3A4 and CYP19A1 and is helpful for understanding the selectivity of other substrates that are hydroxylated by P450s.

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

Alignment-Based Prediction of Sites of Metabolism

Prediction of metabolically labile atom positions in a molecule (sites of metabolism) is a key component of the simulation of xenobiotic metabolism as a whole, providing crucial information for the development of safe and effective drugs. In 2008, an exploratory study was published in which sites of metabolism were derived based on molecular shape- and chemical feature-based alignment to a molecule whose site of metabolism (SoM) had been determined by experiments. We present a detailed analysis of the breadth of applicability of alignment-based SoM prediction, including transfer of the approach from a structure- to ligand-based method and extension of the applicability of the models from cytochrome P450 2C9 to all cytochrome P450 isozymes involved in drug metabolism. We evaluate the effect of molecular similarity of the query and reference molecules on the ability of this approach to accurately predict SoMs. In addition, we combine the alignment-based method with a leading chemical reactivity model to take reactivity into account. The combined model yielded superior performance in comparison to the alignment-based approach and the reactivity models with an average area under the receiver operating characteristic curve of 0.85 in cross-validation experiments. In particular, early enrichment was improved, as evidenced by higher BEDROC scores (mean BEDROC = 0.59 for α = 20.0, mean BEDROC = 0.73 for α = 80.5).