Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates

Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism

The cytochrome P450 (CYP450) enzyme system is responsible for the metabolism of more than two-thirds of xenobiotics. This review summarizes reports of a series of in silico tools for CYP450 enzyme-drug interaction predictions, including the prediction of sites of metabolism (SOM) of a drug and the identification of inhibitor/substrates for CYP subtypes. We also evaluated four prediction tools to identify CYP inhibitors utilizing 52 of the most frequently prescribed drugs. ADMET Predictor and CYPlebrity demonstrated the best performance. We hope that this review provides guidance for choosing appropriate enzyme prediction tools from a variety of in silico platforms to meet individual needs. Such predictions are useful for medicinal chemists to prioritize their designed compounds for further drug discovery.

Prediction of Sites of Metabolism of CYP3A4 Substrates Utilizing Docking-Derived Geometric Features

Cytochrome P450 3A4 (CYP3A4) is one of the major drug-metabolizing enzymes in the human body and is responsible for the metabolism of ∼50% of clinically used drugs. Therefore, the identification of the compound's sites of metabolism (SOMs) mediated by CYP3A4 is of utmost importance in the early stage of drug discovery and development. Herein, docking-based approaches incorporating geometric features were used for SOMs prediction of CYP3A4 substrates. The cross-docking poses of a relatively large data set containing 474 substrates were analyzed in depth, and a widely observed geometric pattern called the close proximity of SOMs was derived from the poses. On the basis of the close proximity, several structure-based models have been constructed, which demonstrated better performance than those structure-based models using the criterion of Fe-SOM distance. For further improving the prediction performance, the structure-based models were also combined with the well-known ligand-based model SMARTCyp. One combined model exhibited good performance on the SOMs prediction of an external substrate set containing kinase inhibitors, PROTACs, approved drugs, and some lead compounds.

Molecular docking and in vitro evaluations reveal the role of human cytochrome P450 3A4 in the cross-coupling metabolism of phenolic xenobiotics

Metabolism generally transforms xenobiotics into more polar and hydrophilic products, facilitating their elimination from the body. Recently, a new metabolic pathway that transforms phenolic xenobiotics into more lipophilic and bioactive dimer products was discovered. To elucidate the role of cytochrome P450 (CYP) enzymes in mediating this cross-coupling metabolism, we used high-throughput screening to identify the metabolites generated from the coupling of 20 xenobiotics with four endogenous metabolites in liver microsomes. Endogenous vitamin E (VE) was the most reactive metabolite, as VE reacted with seven phenolic xenobiotics containing various structures (e.g., an imidazoline ring or a diphenol group) to generate novel lipophilic ethers such as bakuchiol-O-VE, phentolamine-O-VE, phenylethyl resorcinol-O-VE, 2-propanol-O-VE, and resveratrol-O-VE. Seven recombinant CYP enzymes were successfully expressed and purified in Escherichia coli. Integration of the results of recombinant human CYP incubation and molecular docking identified the central role of CYP3A4 in the cross-coupling metabolic pathway. Structural analysis revealed the π-π interactions, hydrogen bonds, and hydrophobic interactions between reactive xenobiotics and VE in the malleable active sites of CYP3A4. The consistency between the molecular docking results and the in vitro human cytochrome P450 evaluation shows that docking calculations can be used to screen molecules participating in cross-coupling metabolism. The results of this study provide supporting evidence for the overlooked toxicological effects induced by direct reactions between xenobiotics and endogenous metabolites during metabolic processes.

Assessment of CYP2C9 Structural Models for Site of Metabolism Prediction

Structure-based prediction of a compound's potential sites of metabolism (SOMs) mediated by cytochromes P450 (CYPs) is highly advantageous in the early stage of drug discovery. However, the accuracy of the SOMs prediction can be influenced by several factors. CYP2C9 is one of the major drug-metabolizing enzymes in humans and is responsible for the metabolism of ∼13 % of clinically used drugs. In this study, we systematically evaluated the effects of protein crystal structure models, scoring functions, heme forms, conserved active-site water molecules, and protein flexibility on SOMs prediction of CYP2C9 substrates. Our results demonstrated that, on average, ChemScore and GlideScore outperformed four other scoring functions: Vina, GoldScore, ChemPLP, and ASP. The performance of the crystal structure models with pentacoordinated heme was generally superior to that of the hexacoordinated iron-oxo heme (referred to as Compound I) models. Inclusion of the conserved active-site water molecule improved the prediction accuracy of GlideScore, but reduced the accuracy of ChemScore. In addition, the effect of the conserved water on SOMs prediction was found to be dependent on the receptor model and the substrate. We further found that one of snapshots from molecular dynamics simulations on the apo form can improve the prediction accuracy when compared to the crystal structural model.

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.

Computational Insight Into Vitamin K1 ω-Hydroxylation by Cytochrome P450 4F2

Vitamin K₁ (VK1) plays an important role in the modulation of bleeding disorders. It has been reported that ω-hydroxylation on the VK1 aliphatic chain is catalyzed by cytochrome P450 4F2 (CYP4F2), an enzyme responsible for the metabolism of eicosanoids. However, the mechanism of VK1 ω-hydroxylation by CYP4F2 has not been disclosed. In this study, we employed a combination of quantum mechanism (QM) calculations, homology modeling, molecular docking, molecular dynamics (MD) simulations, and combined quantum mechanism/molecular mechanism (QM/MM) calculations to investigate the metabolism profile of VK1 ω-hydroxylation. QM calculations based on the truncated VK1 model show that the energy barrier for ω-hydroxylation is about 6-25 kJ/mol higher than those at other potential sites of metabolism. However, results from the MD simulations indicate that hydroxylation at the ω-site is more favorable than at the other potential sites, which is in accordance with the experimental observation. The evaluation of MD simulations was further endorsed by the QM/MM calculation results. Our studies thus suggest that the active site residues of CYP4F2 play a determinant role in the ω-hydroxylation. Our results provide structural insights into the mechanism of VK1 ω-hydroxylation by CYP4F2 at the atomistic level and are helpful not only for characterizing the CYP4F2 functions but also for looking into the ω-hydroxylation mediated by other CYP4 enzymes.

Computational Assessment of Pharmacokinetics and Biological Effects of Some Anabolic and Androgen Steroids

PURPOSE

The aim of this study is to use computational approaches to predict the ADME-Tox profiles, pharmacokinetics, molecular targets, biological activity spectra and side/toxic effects of 31 anabolic and androgen steroids in humans.

METHODS

The following computational tools are used: (i) FAFDrugs4, SwissADME and admetSARfor obtaining the ADME-Tox profiles and for predicting pharmacokinetics;(ii) SwissTargetPrediction and PASS online for predicting the molecular targets and biological activities; (iii) PASS online, Toxtree, admetSAR and Endocrine Disruptomefor envisaging the specific toxicities; (iv) SwissDock to assess the interactions of investigated steroids with cytochromes involved in drugs metabolism.

RESULTS

Investigated steroids usually reveal a high gastrointestinal absorption and a good oral bioavailability, may inhibit someof the human cytochromes involved in the metabolism of xenobiotics (CYP2C9 being the most affected) and reflect a good capacity for skin penetration. There are predicted numerous side effects of investigated steroids in humans: genotoxic carcinogenicity, hepatotoxicity, cardiovascular, hematotoxic and genitourinary effects, dermal irritations, endocrine disruption and reproductive dysfunction.

CONCLUSIONS

These results are important to be known as an occupational exposure to anabolic and androgenic steroids at workplaces may occur and because there also is a deliberate human exposure to steroids for their performance enhancement and anti-aging properties.

Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System

During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)—a novel concept, which integrates information about site of metabolism (SOM) and enzyme—was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic C-hydroxylation, aromatic C-hydroxylation, N-dealkylation and O-dealkylation, were involved. For validation, the overall accuracies of all four biotransformations exceeded 0.95. For receiver operating characteristic (ROC) analysis, each of these models gave a significant area under curve (AUC) value >0.98. In addition, an external test was performed based on dataset published previously. As a result, 87.7% of the potential SOMs were correctly identified by our four models. In summary, four MMRS-based models were established, which can be used to predict the metabolism mediated by CYP3A4, 2D6, and 2C9 with high accuracy.

The Next Era: Deep Learning in Pharmaceutical Research

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Structure Prediction of a Novel Exo-β-1,3-Glucanase: Insights into the Cold Adaptation of Psychrophilic Yeast Glaciozyma antarctica PI12

We report a detailed structural analysis of the psychrophilic exo-β-1,3-glucanase (GaExg55) from Glaciozyma antarctica PI12. This study elucidates the structural basis of exo-1,3-β-1,3-glucanase from this psychrophilic yeast. The structural prediction of GaExg55 remains a challenge because of its low sequence identity (37 %). A 3D model was constructed for GaExg55. Threading approach was employed to determine a suitable template and generate optimal target-template alignment for establishing the model using MODELLER9v15. The primary sequence analysis of GaExg55 with other mesophilic exo-1,3-β-glucanases indicated that an increased flexibility conferred to the enzyme by a set of amino acids substitutions in the surface and loop regions of GaExg55, thereby facilitating its structure to cold adaptation. A comparison of GaExg55 with other mesophilic exo-β-1,3-glucanases proposed that the catalytic activity and structural flexibility at cold environment were attained through a reduced amount of hydrogen bonds and salt bridges, as well as an increased exposure of the hydrophobic side chains to the solvent. A molecular dynamics simulation was also performed using GROMACS software to evaluate the stability of the GaExg55 structure at varying low temperatures. The simulation result confirmed the above findings for cold adaptation of the psychrophilic GaExg55. Furthermore, the structural analysis of GaExg55 with large catalytic cleft and wide active site pocket confirmed the high activity of GaExg55 to hydrolyze polysaccharide substrates.

Effects of protein flexibility and active site water molecules on the prediction of sites of metabolism for cytochrome P450 2C19 substrates

Structure-based prediction of sites of metabolism (SOMs) mediated by cytochrome P450s (CYPs) is of great interest in drug discovery and development.

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.