MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database.

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.

Alternative Methods for Pulmonary-Administered Drugs Metabolism: A Breath of Change.

Prediction of pulmonary metabolites following inhalation of a locally acting pulmonary drug is essential to the successful development of novel inhaled medicines. The lungs present metabolic enzymes, therefore they influence drug disposal and toxicity. The present review provides an overview of alternative methods to evaluate the pulmonary metabolism for the safety and efficacy of pulmonary delivery systems. In vitro approaches for investigating pulmonary drug metabolism were described, including subcellular fractions, cell culture models and lung slices as the main available in vitro methods. In addition, in silico studies are promising alternatives that use specific software to predict pulmonary drug metabolism, determine whether a molecule will react with a metabolic enzyme, the site of metabolism (SoM) and the result of this interaction. They can be used in an integrated approach to delineate the major cytochrome P450 (CYP) isoforms to rationalize the use of in vivo methods. A case study about a combination of experimental and computational approaches was done using fluticasone propionate as an example. The results of three tested software, RSWebPredictor, SMARTCyp and XenoSite, demonstrated greater probability of the fluticasone propionate being metabolized by CYPs 3A4 at the S1 atom of 5-S-fluoromethyl carbothioate group. As the in vitro studies were not able to directly detect pulmonary metabolites, those alternatives in silico methods may reduce animal testing efforts, following the principle of 3Rs (Replacement, Reduction and Refinement), and contribute to the evaluation of pharmacological efficacy and safety profiles of new drugs in development.

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA.

BACKGROUND: The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A[Formula: see text]C). RESULTS: We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures. CONCLUSIONS: Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design.

The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning prediction models arrive at their decisions, which are called explainable artificial intelligence. First, we developed the prediction models for metabolic stability, CYP inhibition, and P-gp and BCRP substrate recognition using 265 physicochemical parameters for designing the molecular structures. Four important parameters, including the well-known indicator h_logD, are common in some in vitro studies; as such, these can be used to optimize compounds simultaneously to address multiple pharmacokinetic concerns. Next, we developed machine learning models that had been programmed to show structurally active sites. Many types of machine learning models were developed using the results of in vitro metabolic stability study of around 30000 in-house compounds. The metabolic sites of in-house compounds predicted using some prediction models matched experimentally identified metabolically active sites, with a ratio of number of metabolic sites (predicted/actual) of over 90%. These models can be applied to several screening projects. These two approaches can be employed for obtaining lead compounds with desirable pharmacokinetic profiles efficiently.

New Proluciferin Substrates for Human CYP4 Family Enzymes.

We report the synthesis of seven new proluciferins for convenient activity determination of enzymes belonging to the cytochrome P450 (CYP) 4 family. Biotransformation of these probe substrates was monitored using each of the twelve human CYP4 family members, and eight were found to act at least on one of them. For all substrates, activity of CYP4Z1 was always highest, while that of CYP4F8 was always second highest. Site of metabolism (SOM) predictions involving SMARTCyp and docking experiments helped to rationalize the observed activity trends linked to substrate accessibility and reactivity. We further report the first homology model of CYP4F8 including suggested substrate recognition residues in a catalytically competent conformation accessed by replica exchange solute tempering (REST) simulations.

PreMetabo: An in silico phase I and II drug metabolism prediction platform.

This study aimed to develop a drug metabolism prediction platform using knowledge-based prediction models. Site of Metabolism (SOM) prediction models for four cytochrome P450 (CYP) subtypes were developed along with uridine 5'-diphosphoglucuronosyltransferase (UGT) and sulfotransferase (SULT) substrate classification models. The SOM substrate for a certain CYP was determined using the sum of the activation energy required for the reaction at the reaction site of the substrate and the binding energy of the substrate to the CYP enzyme. Activation energy was calculated using the EaMEAD model and binding energy was calculated by docking simulation. Phase II prediction models were developed to predict whether a molecule is the substrate of a certain phase II conjugate protein, i.e., UGT or SULT. Using SOM prediction models, the predictability of the major metabolite in the top-3 was obtained as 72.5-84.5% for four CYPs, respectively. For internal validation, the accuracy of the UGT and SULT substrate classification model was obtained as 93.94% and 80.68%, respectively. Additionally, for external validation, the accuracy of the UGT substrate classification model was obtained as 81% in the case of 11 FDA-approved drugs. PreMetabo is implemented in a web environment and is available at https://premetabo.bmdrc.kr/.

Advances in the study of drug metabolism - symposium report of the 12th Meeting of the International Society for the Study of Xenobiotics (ISSX).

The 12th International Society for the Study of Xenobiotics (ISSX) meeting, held in Portland, OR, USA from July 28 to 31, 2019, was attended by diverse members of the pharmaceutical sciences community. The ISSX New Investigators Group provides learning and professional growth opportunities for student and early career members of ISSX. To share meeting content with those who were unable to attend, the ISSX New Investigators herein elected to highlight the "Advances in the Study of Drug Metabolism" symposium, as it engaged attendees with diverse backgrounds. This session covered a wide range of current topics in drug metabolism research including predicting sites and routes of metabolism, metabolite identification, ligand docking, and medicinal and natural products chemistry, and highlighted approaches complemented by computational modeling. In silico tools have been increasingly applied in both academic and industrial settings, alongside traditional and evolving in vitro techniques, to strengthen and streamline pharmaceutical research. Approaches such as quantum mechanics simulations facilitate understanding of reaction energetics toward prediction of routes and sites of drug metabolism. Furthermore, in tandem with crystallographic and orthogonal wet lab techniques for structural validation of drug metabolizing enzymes, in silico models can aid understanding of substrate recognition by particular enzymes, identify metabolic soft spots and predict toxic metabolites for improved molecular design. Of note, integration of chemical synthesis and biosynthesis using natural products remains an important approach for identifying new chemical scaffolds in drug discovery. These subjects, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are discussed in this review.

Structure-Based Site of Metabolism (SOM) Prediction of Ligand for CYP3A4 Enzyme: Comparison of Glide XP and Induced Fit Docking (IFD).

Metabolism is one of the prime reasons where most of drugs fail to accomplish their clinical trials. The enzyme CYP3A4, which belongs to the superfamily of cytochrome P450 enzymes (CYP), helps in the metabolism of a large number of drugs in the body. The enzyme CYP3A4 catalyzes oxidative chemical processes and shows a very broad range of ligand specificity. The understanding of the compound's structure where oxidation would take place is crucial for the successful modification of molecules to avoid unwanted metabolism and to increase its bioavailability. For this reason, it is required to know the site of metabolism (SOM) of the compounds, where compounds undergo enzymatic oxidation. It can be identified by predicting the accessibility of the substrate's atom toward oxygenated Fe atom of heme in a CYP protein. The CYP3A4 enzyme is highly flexible and can take significantly different conformations depending on the ligand with which it is being bound. To predict the accessibility of substrate atoms to the heme iron, conventional protein-rigid docking methods failed due to the high flexibility of the CYP3A4 protein. Herein, we demonstrated and compared the ability of the Glide extra precision (XP) and Induced Fit docking (IFD) tool of Schrodinger software suite to reproduce the binding mode of co-crystallized ligands into six X-ray crystallographic structures. We extend our studies toward the prediction of SOM for compounds whose experimental SOM is reported but the ligand-enzyme complex crystal structure is not available in the Protein Data Bank (PDB). The quality and accuracy of Glide XP and IFD was determined by calculating RMSD of docked ligands over the corresponding co-crystallized bound ligand and by measuring the distance between the SOM of the ligand and Fe atom of heme. It was observed that IFD reproduces the exact binding mode of available co-crystallized structures and correctly predicted the SOM of experimentally reported compounds. Our approach using IFD with multiple conformer structures of CYP3A4 will be one of the effective methods for SOM prediction.

In Silico Prediction of Metabolic Epoxidation for Drug-like Molecules via Machine Learning Methods.

Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxidation experimentally. In silico models based on big data and machine learning methods are hence valuable approaches to predict whether a compound would undergo epoxidation. In this study, we collected 884 epoxidation data manually from various sources, and finally got 829 unique sites of epoxidation. Three types of molecular fingerprints with different lengths (1024, 2048 or 4096 bits) were used to describe the reaction sites. Six machine learning methods were used to build the classification models. The training set and test set were randomly divided into 8 : 2, and 54 models were constructed and evaluated. Four best models were selected for feature selection. The features were then chosen and verified by external validation set. The resulted optimal model had the accuracy and AUC (area under the curve) values at 0.873 and 0.944 for the test set, 0.838 and 0.987 for the external validation set, respectively. The models built in this study could accurately predict whether a compound will undergo epoxidation and which part is most susceptible to epoxidation, which is of great significance for drug design.

Novel Electrotopological Atomic Descriptors for the Prediction of Xenobiotic Cytochrome P450 Reactions.

Cytochrome P450 (CYP) is an enzyme family that plays a crucial role in metabolism, mainly metabolizing xenobiotics to produce non-toxic structures, however, some metabolized products can cause hepatotoxicity. Hence, predicting the structures of CYP products is an important task in designing non-hepatotoxic drugs. Here, we have developed novel atomic descriptors to predict the sites of metabolism (SoM) in CYP substrates. We proposed descriptors that describe topological and electrostatic characteristics of CYP substrates using Gasteiger charge. The proposed descriptors were applied to CYP3A4 data analysis as a case study. As a result of the descriptor selection, we obtained a gradient boosting decision tree-based SoM classification model that used 139 existing descriptors and the proposed 45 descriptors, and the model performed well in terms of the Matthews correlation coefficient. We also developed a structure converter to predict CYP products. This converter correctly generated 51 structural formulas of experimentally observed CYP3A4 products according to a manual evaluation.

Electrochemical simulation of metabolism for antitumor-active imidazoacridinone C-1311 and in silico prediction of drug metabolic reactions.

The metabolism of antitumor-active 5-diethylaminoethylamino-8-hydroxyimidazoacridinone (C-1311) has been investigated widely over the last decade but some aspects of molecular mechanisms of its metabolic transformation are still not explained. In the current work, we have reported a direct and rapid analytical tool for better prediction of C-1311 metabolism which is based on electrochemistry (EC) coupled on-line with electrospray ionization mass spectrometry (ESI-MS). Simulation of the oxidative phase I metabolism of the compound was achieved in a simple electrochemical thin-layer cell consisting of three electrodes (ROXY™, Antec Leyden, the Netherlands). We demonstrated that the formation of the products of N-dealkylation reactions can be easily simulated using purely instrumental approach. Newly reported products of oxidative transformations like hydroxylated or oxygenated derivatives become accessible. Structures of the electrochemically generated metabolites were elucidated on the basis of accurate mass ion data and tandem mass spectrometry experiments. In silico prediction of main sites of C-1311 metabolism was performed using MetaSite software. The compound was evaluated for cytochrome P450 1A2-, 3A4-, and 2D6-mediated reactions. The results obtained by EC were also compared and correlated with those of reported earlier for conventional in vitro enzymatic studies in the presence of liver microsomes and in the model peroxidase system. The in vitro experimental approach and the in silico metabolism findings showed a quite good agreement with the data from EC/ESI-MS analysis. Thus, we conclude here that the electrochemical technique provides the promising platform for the simple evaluation of drug metabolism and the reaction mechanism studies, giving first clues to the metabolic transformation of pharmaceuticals in the human body.

Prediction of metabolites of epoxidation reaction in MetaTox.

Biotransformation is a process of the chemical modifications which may lead to the reactive metabolites, in particular the epoxides. Epoxide reactive metabolites may cause the toxic effects. The prediction of such metabolites is important for drug development and ecotoxicology studies. Epoxides are formed by some oxidation reactions, usually catalysed by cytochromes P450, and represent a large class of three-membered cyclic ethers. Identification of molecules, which may be epoxidized, and indication of the specific location of epoxide functional group (which is called SOE - site of epoxidation) are important for prediction of epoxide metabolites. Datasets from 355 molecules and 615 reactions were created for training and validation. The prediction of SOE is based on a combination of LMNA (Labelled Multilevel Neighbourhood of Atom) descriptors and Bayesian-like algorithm implemented in PASS software and MetaTox web-service. The average invariant accuracy of prediction (AUC) calculated in leave-one-out and 20-fold cross-validation procedures is 0.9. Prediction of epoxide formation based on the created SAR model is included as the component of MetaTox web-service ( http://www.way2drug.com/mg ).

Structure-metabolism relationships in human-AOX: Chemical insights from a large database of aza-aromatic and amide compounds.

Aldehyde oxidase (AOX) is a metabolic enzyme catalyzing the oxidation of aldehyde and aza-aromatic compounds and the hydrolysis of amides, moieties frequently shared by the majority of drugs. Despite its key role in human metabolism, to date only fragmentary information about the chemical features responsible for AOX susceptibility are reported and only "very local" structure-metabolism relationships based on a small number of similar compounds have been developed. This study reports a more comprehensive coverage of the chemical space of structures with a high risk of AOX phase I metabolism in humans. More than 270 compounds were studied to identify the site of metabolism and the metabolite(s). Both electronic [supported by density functional theory (DFT) calculations] and exposure effects were considered when rationalizing the structure-metabolism relationship.

Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics.

BACKGROUND: The knowledge of drug metabolite structures is essential at the early stage of drug discovery to understand the potential liabilities and risks connected with biotransformation. The determination of the site of a molecule at which a particular metabolic reaction occurs could be used as a starting point for metabolite identification. The prediction of the site of metabolism does not always correspond to the particular atom that is modified by the enzyme but rather is often associated with a group of atoms. To overcome this problem, we propose to operate with the term "reacting atom", corresponding to a single atom in the substrate that is modified during the biotransformation reaction. The prediction of the reacting atom(s) in a molecule for the major classes of biotransformation reactions is necessary to generate drug metabolites. RESULTS: Substrates of the major human cytochromes P450 and UDP-glucuronosyltransferases from the Biovia Metabolite database were divided into nine groups according to their reaction classes, which are aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. Each training set consists of positive and negative examples of structures with one labelled atom. In the positive examples, the labelled atom is the reacting atom of a particular reaction that changed adjacency. Negative examples represent non-reacting atoms of a particular reaction. We used Labelled Multilevel Neighbourhoods of Atoms descriptors for the designation of reacting atoms. A Bayesian-like algorithm was applied to estimate the structure-activity relationships. The average invariant accuracy of prediction obtained in leave-one-out and 20-fold cross-validation procedures for five human isoforms of cytochrome P450 and all isoforms of UDP-glucuronosyltransferase varies from 0.86 to 0.99 (0.96 on average). CONCLUSIONS: We report that reacting atoms may be predicted with reasonable accuracy for the major classes of metabolic reactions-aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. The proposed method is implemented as a freely available web service at http://www.way2drug.com/RA and may be used for the prediction of the most probable biotransformation reaction(s) and the appropriate reacting atoms in drug-like compounds.Graphical abstract.

Computational predictions of the site of metabolism of cytochrome P450 2D6 substrates: comparative analysis, molecular docking, bioactivation and toxicological implications.

Cytochrome P450 2D6 (CYP2D6) is a polymorphic enzyme responsible for metabolizing approximately 25% of all drugs. CYP2D6 is highly expressed in the brain and plays a role as the major CYP in the metabolism of numerous brain-penetrant drugs, including antipsychotics and antidepressants. CYP2D6 activity and inhibition have been associated with numerous undesirable effects in patients, such as bioactivation, drug-associated suicidality and prolongation of the QTc interval. Several in silico tools have been developed in recent years to assist safety assessment scientists in predicting the structural identity of CYP2D6-derived metabolites. The first goal of this study was to perform a comparative evaluation on the ability of four commonly used in silico tools (MetaSite, StarDrop, SMARTCyp and RS-WebPredictor) to correctly predict the CYP2D6-derived site of metabolism (SOM) for 141 compounds, including 10 derived from the Genentech small molecule library. The second goal was to evaluate if a bioactivation prediction model, based on an indicator of chemical reactivity (ELUMO-EHOMO) and electrostatic potential, could correctly predict five representative compounds known to be bioactivated by CYP2D6. Such a model would be of great utility in safety assessment since unforeseen toxicities of CYP2D6 substrates may in part be due to bioactivation mechanisms. The third and final goal was to investigate whether molecular docking, using the crystal structure of human CYP2D6, had the potential to compliment or improve the results obtained from the four SOM in silico programs.

Prediction of cytochrome P450 mediated metabolism.

Cytochrome P450 enzymes (CYPs) form one of the most important enzyme families involved in the metabolism of xenobiotics. CYPs comprise many isoforms, which catalyze a wide variety of reactions, and potentially, a large number of different metabolites can be formed. However, it is often hard to rationalize what metabolites these enzymes generate. In recent years, many different in silico approaches have been developed to predict binding or regioselective product formation for the different CYP isoforms. These comprise ligand-based methods that are trained on experimental CYP data and structure-based methods that consider how the substrate is oriented in the active site or/and how reactive the part of the substrate that is accessible to the heme group is. We will review key aspects for various approaches that are available to predict binding and site of metabolism (SOM), what outcome can be expected from the predictions, and how they could potentially be improved.

Effects of protein flexibility on the site of metabolism prediction for CYP2A6 substrates.

Structure-based prediction for the site of metabolism (SOM) of a compound metabolized by human cytochrome P450s (CYPs) is highly beneficial in drug discovery and development. However, the flexibility of the CYPs' active site remains a huge challenge for accurate SOM prediction. Compared with other CYPs, the active site of CYP2A6 is relatively small and rigid. To address the impact of the flexibility of CYP2A6 active site residues on the SOM prediction for substrates, in this work, molecular dynamics (MD) simulations and molecular docking were used to predict the SOM of 96 CYP2A6 substrates. Substrates with known SOM were docked into the snapshot structures from MD simulations and the crystal structures of CYP2A6. Compared to the crystal structures, the protein structures obtained from MD simulations showed more accurate prediction for SOM. Our results indicated that the flexibility of the active site of CYP2A6 significantly affects the SOM prediction results. Further analysis for the 40 substrates with definite Km values showed that the prediction accuracy for the low Km substrates is comparable to that by ligand-based methods.

Enrichment of True Positives from Structural Alerts Through the Use of Novel Atomic Fragment Based Descriptors.

To enhance the discrimination rate for methods applying structural alerts and biotransformation rules in the prediction of toxicity and drug metabolism we have developed a set of novel fragment based atomic descriptors. These atomic descriptors encode the properties of the fragments separating an atom from the closest end of a branch or the molecule. The end of a branch and the end of a molecule, as well as the selection of the fragments, are made by an algorithm that uses only the distance matrix of the molecule. The novel descriptors are applied to a small set of biotransformation rules and are shown to be able to reduce the number of unconfirmed positives by up to 58 %.