COMPARISON OF METHODS FOR THE PREDICTION OF THE METABOLIC SITES FOR CYP3A4-MEDIATED METABOLIC REACTIONS

Genetic variations of CYP3A4 on the metabolism of itraconazole in vitro

Itraconazole is a triazole anti-infective drug that has been proven to prevent and treat a variety of fungal and viral infections and has been considered to be a potential therapeutic remedy for COVID-19 treatment. In this study, we aimed to completely evaluate the impacts of Cytochrome P450 3A4 (CYP3A4) variant proteins and drug interactions on the metabolism of itraconazole in recombinant insect microsomes, and to characterize the potential mechanism of substrate selectivity. Incubations with itraconazole (0.2-15 μM) in the presence/absence of lopinavir or darunavir were assessed by CYP3A4 variants, and the metabolite hydroxyitraconazole concentrations were measured by UPLC-MS/MS. Our data showed that when compared with CYP3A4.1, 4 variants (CYP3A4.9, .10, .28 and .34) displayed no significant differences, and 3 variants (CYP3A4.14, .15 and .19) exhibited increased intrinsic clearance (CLint), whereas the remaining 17 variant proteins showed decreased enzyme activities for the catalysis of itraconazole. Moreover, the inhibitory effects of lopinavir and darunavir on itraconazole metabolism varied in different degrees. Furthermore, different changed trend of the kinetic parameters in ten variants (CYP3A4.5, .9, .10, .16, .19, .24, .28, .29, .31, and .33) were observed, especially CYP3A4.5 and CYP3A4.16, and this may be related to the metabolic site-heme iron atom distance. In the present study, we functionally analyzed the effects of 25 CYP3A4 protein variants on itraconazole metabolism for the first time, and provided comprehensive data on itraconazole metabolism in vitro. This may help to better assess the metabolism and elimination of itraconazole in clinic to improve the safety and efficacy of its clinical treatment and also provide new possibilities for the treatment of COVID-19.

In silico and in vitro ADME-Tox analysis and in vivo pharmacokinetic study of representative pan-PDE inhibitors from the group of 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione

Phosphodiesterase (PDE) inhibitors represent a wide class of chemically different compounds that have been extensively studied in recent years. Their anti-inflammatory and anti-fibrotic effects are particularly desirable in the treatment of chronic respiratory diseases, including asthma and chronic obstructive pulmonary disease (COPD). Due to diversified expression of individual PDEs within cells and/or tissues as well as PDE signaling compartmentalization, pan-PDE inhibitors (compounds capable of simultaneously blocking various PDE subtypes) are of particular interest. Recently, a large group of 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione (theophylline) was designed and synthesized. These compounds were characterized as potent pan-PDE inhibitors and their prominent anti-inflammatory and anti-fibrotic activity in vitro has been proved. Herein, we investigated a general in vitro safety profile and pharmacokinetic characteristics of two leading compounds from this group: a representative compound with N'-benzylidenebutanehydrazide moiety (38) and a representative derivative containing N-phenylbutanamide fragment (145). Both tested pan-PDE inhibitors revealed no cytotoxic, mutagenic, and genotoxic activity in vitro, showed moderate metabolic stability in mouse and human liver microsomes, as well as fell into the low or medium permeation category. Additionally, 38 and 145 revealed a lack of interaction with adenosine receptors, including A1, A2A, and A2B. Pharmacokinetic analysis revealed that both tested 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione were effectively absorbed from the peritoneal cavity. Simultaneously, they were extensively distributed to mouse lungs and after intraperitoneal (i.p.) administration were detected in bronchoalveolar lavage fluid. These findings provide evidence that investigated compounds represent a new drug candidates with a favorable in vitro safety profile and satisfactory pharmacokinetic properties after a single i.p. administration. As the next step, further pharmacokinetic studies after multiple i.p. and p.o. doses will be conducted to ensure effective 38 and 145 serum and lung concentrations for a longer period of time. In summary, 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione represent a promising compounds worth testing in animal models of chronic respiratory diseases, the etiology of which involves various PDE subtypes.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

BACKGROUND

Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly.

METHODS

To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status.

RESULTS

The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound.

CONCLUSION

It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.

Biotransformation of 4-fluoro-N-(1-{2-[(propan-2-yl)phenoxy]ethyl}-8-azabicyclo[3.2.1]octan-3-yl)-benzenesulfonamide, a novel potent 5-HT7 receptor antagonist with antidepressant-like and anxiolytic properties: In vitro and in silico approach

The aim of the study was to investigate the metabolism of 4-fluoro-N-(1-{2-[(propan-2-yl)phenoxy]ethyl}-8-azabicyclo[3.2.1]octan-3-yl)-benzenesulfonamide (PZ-1150), a novel 5-HT₇ receptor antagonist with antidepressant-like and anxiolytic properties, by the following three ways: in vitro with microsomes; in vitro employing Cunninghamella echinulata, and in silico using MetaSite. Biotransformation of PZ-1150 with microsomes resulted in five metabolites, while transformation with C. echinulata afforded two metabolites. In both models, the predominant metabolite occurred due to hydroxylation of benzene ring. In silico data coincide with in vitro experiments, as three MetaSite metabolites matched compounds identified in microsomal samples. In human liver microsomes PZ-1150 exhibited in vitro half-life of 64 min, with microsomal intrinsic clearance of 54.1 μL/min/mg and intrinsic clearance of 48.7 mL/min/kg. Therefore, PZ-1150 is predicted to be a high-clearance agent. The study demonstrated the applicability of using microsomal model coupled with microbial model to elucidate the metabolic pathways of compounds and comparison with in silico metabolite predictions.

Exploration of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one derivatives as JAK inhibitors using various in silico techniques

This study focuses on understanding the structural features of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one (dpp) derivatives to computationally identify new JAK inhibiting compounds. For the purpose, a novel virtual screening strategy, with 2D and 3D-QSAR (CoMFA and CoMSIA), data mining, pharmacophore modeling, ADMET prediction, multi-targeted protein-based docking and inverse QSAR, was employed. The 2D-QSAR equations developed for the JAK3, JAK2 and JAK1 involved five physicochemical descriptors. These descriptors correlate with the anti-RA activity with R² values for JAK3, JAK2 and JAK1 are 0.9811, 0.8620 and 0.9740, respectively. The 3D-QSAR studies such as CoMFA and CoMSIA carried out through PLS analysis of the training set of JAK3, JAK2 and JAK1, gave Q² values as 0.369, 0.476 and 0.490; [Formula: see text] values as 0.863, 0.684 and 0.724 and, F values as 23.098, 28.139 and 31.438, respectively. The contour maps produced by the CoMFA and CoMSIA models were used to understand the importance of hydrogen bond donor, acceptor, hydrophobic, steric and electrostatic interactions. The molecular docking studies of these selected compounds with various JAK proteins were carried out and the protein-ligand interactions were also studied. The study concluded that dpp15(s) is a highly potent JAK inhibitor with a very good predicted IC₅₀ value.

Computational strategies to explore antimalarial thiazine alkaloid lead compounds based on an Australian marine sponge Plakortis Lita

In this work, an attempt was made to propose new leads based on the natural scaffold Thiaplakortone-A active against malaria. The 2D QSAR studies suggested that three descriptors correlate with the anti-malarial activity with an R² value of 0.814. Robustness, reliability, and predictive power of the model were tested by internal validation, external validation, Y-scrambling, and applicability domain analysis. HQSAR studies were carried out as an additional tool to find the sub-structural fingerprints. The CoMFA and CoMSIA models gave Q² values of 0.813 and 0.647, and [Formula: see text] values of 0.994 and 0.984, respectively. Using the 2D-QSAR equation, the activity values of the seven modified compounds were calculated and it was found that three molecules showed good anti-malarial activity. Molecular docking of the 42 Thiaplakortone-A derivatives with Plasmodium falciparum calcium-dependent protein kinase 1 (PfCDPK1) was carried out to find out protein-ligand interactions. Data mining of the bioassay data-set AID: 504850 using the classifier based on Random Forest of Weka suggested that all of the eight molecules selected and three out of the seven virtual molecules were anti-malarial active. Both the virtual molecules and drug molecules were docked with CYP3A4, indicating that the virtual molecules could metabolize easily. Toxicity studies using Osiris shows that three molecules showed no toxic characters.

In vitro mutagenic, antimutagenic, and antioxidant activities evaluation and biotransformation of some bioactive 4-substituted 1-(2-methoxyphenyl)piperazine derivatives

In vitro mutagenic, antimutagenic, and antioxidant potency evaluation and biotransformation of six novel 4-substituted 1-(2-methoxyphenyl)piperazine derivatives demonstrating antidepressant-like activity were investigated. Mutagenic and antimutagenic properties were assessed using the Ames test; free radical scavenging activity was evaluated with 2,2-diphenyl-1-picrylhydrazyl radical scavenging assay and biotransformation was performed with liver microsomes. It was found that all tested compounds are not mutagenic in bacterial strains TA100 and TA1535 and exhibit antimutagenic effects in the Ames test. Noteworthy, compounds possessing propyl linker between phenoxyl and N-(2-methoxyphenyl)piperazine displayed more pronounced antimutagenic properties than derivatives with ethoxyethyl linker. Additionally, compounds 2 and 6 in vitro biotransformation showed that primarily their hydroxylated or O-dealkylated metabolites are formed. Some of the compounds exhibited intrinsic clearance values lower than those reported previously for antidepressant imipramine. To sum up, the results of the present study might represent a valuable step in designing and planning future studies with piperazine derivatives.

Chlorophenoxy aminoalkyl derivatives as histamine H(3)R ligands and antiseizure agents

A series of twenty new chlorophenoxyalkylamine derivatives (9-28) was synthesized and evaluated on their binding properties at the human histamine H3 receptor (hH3R). The spacer alkyl chain contained five to seven carbon atoms. The highest affinities have shown the 4-chloro substituted derivatives 10 and 25 (Ki=133 and 128 nM, respectively) classified as antagonists in cAMP accumulation assay (EC50=72 and 75 nM, respectively). Synthesized compounds were also evaluated for anticonvulsant activity in Antiepileptic Screening Program (ASP) at National Institute of Neurological Disorders and Stroke (USA). Two compounds (4-chloro substituted derivatives: 20 and 26) were the most promising and showed in the MES seizure model in rats (after ip administration) ED50 values of 14 mg/kg and 13.18 mg/kg, respectively. Protective indexes (PI=TD50/ED50) were 3.2 for 20 and 3.8 for 26. Moreover, molecular modeling and docking studies were undertaken to explain affinity at hH3R of target compounds, and the experimentally and in silico estimation of properties like lipophilicity and metabolism was performed. Antiproliferative effects have been also investigated in vitro for selected compounds (10 and 25). These compounds neither possessed significant antiproliferative and antitumor activity, nor modulated CYP3A4 activity up to concentration of 10 μM.

Directed evolution of cytochrome P450 enzymes for biocatalysis: exploiting the catalytic versatility of enzymes with relaxed substrate specificity

Cytochrome P450 enzymes are renowned for their ability to insert oxygen into an enormous variety of compounds with a high degree of chemo- and regio-selectivity under mild conditions. This property has been exploited in Nature for an enormous variety of physiological functions, and representatives of this ancient enzyme family have been identified in all kingdoms of life. The catalytic versatility of P450s makes them well suited for repurposing for the synthesis of fine chemicals such as drugs. Although these enzymes have not evolved in Nature to perform the reactions required for modern chemical industries, many P450s show relaxed substrate specificity and exhibit some degree of activity towards non-natural substrates of relevance to applications such as drug development. Directed evolution and other protein engineering methods can be used to improve upon this low level of activity and convert these promiscuous generalist enzymes into specialists capable of mediating reactions of interest with exquisite regio- and stereo-selectivity. Although there are some notable successes in exploiting P450s from natural sources in metabolic engineering, and P450s have been proven repeatedly to be excellent material for engineering, there are few examples to date of practical application of engineered P450s. The purpose of the present review is to illustrate the progress that has been made in altering properties of P450s such as substrate range, cofactor preference and stability, and outline some of the remaining challenges that must be overcome for industrial application of these powerful biocatalysts.

DR-predictor: incorporating flexible docking with specialized electronic reactivity and machine learning techniques to predict CYP-mediated sites of metabolism

Computational methods that can identify CYP-mediated sites of metabolism (SOMs) of drug-like compounds have become required tools for early stage lead optimization. In recent years, methods that combine CYP binding site features with CYP/ligand binding information have been sought in order to increase the prediction accuracy of such hybrid models over those that use only one representation. Two challenges that any hybrid ligand/structure-based method must overcome are (1) identification of the best binding pose for a specific ligand with a given CYP and (2) appropriately incorporating the results of docking with ligand reactivity. To address these challenges we have created Docking-Regioselectivity-Predictor (DR-Predictor)--a method that incorporates flexible docking-derived information with specialized electronic reactivity and multiple-instance-learning methods to predict CYP-mediated SOMs. In this study, the hybrid ligand-structure-based DR-Predictor method was tested on substrate sets for CYP 1A2 and CYP 2A6. For these data, the DR-Predictor model was found to identify the experimentally observed SOM within the top two predicted rank-positions for 86% of the 261 1A2 substrates and 83% of the 100 2A6 substrates. Given the accuracy and extendibility of the DR-Predictor method, we anticipate that it will further facilitate the prediction of CYP metabolism liabilities and aid in in-silico ADMET assessment of novel structures.

Software aided approaches to structure-based metabolite identification in drug discovery and development

Technological advances in mass spectrometry (MS) such as accurate mass high resolution instrumentation have fundamentally changed the approach to systematic metabolite identification over the past decade. Despite technological break-through on the instrumental side, metabolite identification still requires tedious manual data inspection and interpretation of huge analytical datasets. The process of metabolite identification has become largely facilitated and partly automated by cheminformatics approaches such as knowledge base metabolite prediction using, for example, Meteor, MetaDrug, MetaSite and StarDrop that are typically applied pre-acquisition. Likewise, emerging new technologies in postacquisition data analysis like mass defect filtering (MDF) have moved the technology driven analytical methodology to metabolite identification toward generic, structure-based workflows. The biggest challenge for automation however remains the structural assignment of drug metabolites. Software-guided approaches for the unsupervised metabolite identification still cannot compete with expert user manual data interpretation yet. Recently MassMetaSite has been introduced for the automated ranked output of metabolite structures based on the combination of metabolite prediction and interrogation of analytical mass spectrometric data. This approach and others are promising milestones toward an unsupervised process to metabolite identification and structural characterization moving away from a sample focused per-compound approach to a structure-driven generic workflow.

High-throughput, computer assisted, specific MetID. A revolution for drug discovery

One of the key factors in drug discovery is related to the metabolic properties of the lead compound, which may influence the bioavailability of the drug, its therapeutic window, and unwanted side-effects of its metabolites. Therefore, it is of critical importance to enable the fast translation of the experimentally determined metabolic information into design knowledge. The elucidation of the metabolite structure is the most structurally rich and informative end-point in the available range of metabolic assays. A methodology is presented to partially automate the analysis of this experimental information, making the process more efficient. The computer assisted method helps in the chromatographic peak selection and the metabolite structure assignment, enabling automatic data comparison for qualitative applications (kinetic analysis, cross species comparison).

Moderate correlations of in vitro versus in vivo pharmacokinetics questioning the need of early microsomal stability testing

BACKGROUND/AIMS

Putative in vitro-in vivo correlations of pharmacokinetic (PK) parameters are regarded as a prerequisite to filter hits derived from high-throughput screening (HTS) approaches for subsequent murine in vivo PK studies.

METHODS

In this study, we assessed stabilities in rat and human microsomes of 121 compounds from an early, academic drug discovery programme targeting the (pro)renin receptor and correlated the respective data with single-dose, in vivo PK parameters of 22 hits administered intravenously in rats.

RESULTS

After transformation of in vitro half-lives to predicted in vivo hepatic clearances, r(2) regarding in vitro-in vivo clearance correlations were 0.31 and 0.27 for the rat and human species, respectively.

CONCLUSIONS

Our data concerning structurally diverse real-world compounds indicate that microsomal stability testing is not a tool to triage early compounds for in vivo PK testing.

Investigating metabolite-protein interactions: an overview of available techniques

Metabolites comprise the molar majority of chemical substances in living cells, and metabolite-protein interactions are expected to be quite common. Many interactions have already been identified and have been shown to be involved in the regulation of different types of cellular processes including signaling events, enzyme activities, protein localizations and interactions. Recent technological advances have greatly facilitated the detection of metabolite-protein interactions at high sensitivity and some of these have been applied on a large scale. In this manuscript, we review the available in vitro, in silico and in vivo technologies for mapping small-molecule-protein interactions. Although some of these were developed for drug-protein interactions they can be applied for mapping metabolite-protein interactions. Information gained from the use of these approaches can be applied to the manipulation of cellular processes and therapeutic applications.

Probe ADME and test hypotheses: a PATH beyond clearance in vitro-in vivo correlations in early drug discovery

INTRODUCTION

In vitro cytochrome P450 (CYP450) metabolic profiling is pursued extensively to optimize drug properties. Still, the in vivo clearance of half of all new chemical entities (NCEs) remains poorly predicted by CYP450 metabolism, based on Novartis rat pharmacokinetic data. The conventional route to illuminating key drivers of in vivo clearance beyond hepatic metabolism is, frequently, the process of elimination, a time-consuming and sometimes resource-intensive practice. A more nimble and efficient diagnosis of drug clearance is imperative to support today's chemistry optimization.

AREAS COVERED

This article reviews in vitro-in vivo clearance correlation (IVIVC) analysis of drugs and NCEs including in silico advances, in vitro opportunities for clearance characterization and guidance for proper interpretation of clearance data. Potential mechanisms for under- and overestimation of in vivo clearance obtained from in vitro approaches are reviewed. The article offers insight into a practical PATH (Probe ADME and Test Hypotheses) for discovery data analysis that can enrich IVIVC development and guide more efficient use of the ADME-PK toolbox.

EXPERT OPINION

In vitro hepatic CYP450 stability measurements remain the most practical way to triage for high metabolic liabilities. Clearance is a complex process involving multiple mechanisms and many factors tend to be overlooked in routine correlation analyses. Equilibrium protein binding, intrinsic permeability and ionization may yield insight into distribution-limited clearance. In addition, hydrophobic character and transporter interaction can be valuable in diagnosing dominant clearance pathways. An integrated ADME approach to clearance interrogation is expected to help refine the in vitro-in silico strategies that guide medicinal chemistry.

Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans

The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CL_int) obtained from in vivo animal studies and (ii) to incorporate it with human physiology in a PBPK model for predicting the inhalation pharmacokinetics of VOCs. CL_int, calculated as the ratio of the in vivo V_max (μmol/h/kg bw rat) to the K_m (μM), was obtained for 26 VOCs from the literature. The QPPR model resulting from stepwise linear regression analysis passed the validation step (R² = 0.8; leave-one-out cross-validation Q² = 0.75) for CL_int normalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation of CL_int (L PL/h/kg bw rat) from the input data on log P_ow, log blood: water PC and ionization potential. The predictions of the QPPR as lower and upper bounds of the 95% mean confidence intervals (LMCI and UMCI, resp.) were then integrated within a human PBPK model. The ratio of the maximum (using LMCI for CL_int) to minimum (using UMCI for CL_int) AUC predicted by the QPPR-PBPK model was 1.36 ± 0.4 and ranged from 1.06 (1,1-dichloroethylene) to 2.8 (isoprene). Overall, the integrated QPPR-PBPK modeling method developed in this study is a pragmatic way of characterizing the impact of the lack of knowledge of CL_int in predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty of CL_int on human pharmacokinetics of VOCs.

Predicting drug metabolism by cytochrome P450 2C9: comparison with the 2D6 and 3A4 isoforms

By the use of knowledge gained through modeling of drug metabolism mediated by the cytochrome P450 2D6 and 3A4 isoforms, we constructed a 2D-based model for site-of-metabolism prediction for the cytochrome P450 2C9 isoform. The similarities and differences between the models for the 2C9 and 2D6 isoforms are discussed through structural knowledge from the X-ray crystal structures and trends in experimental data. The final model was validated on an independent test set, resulting in an area under the curve value of 0.92, and a site of metabolism was found among the top two ranked atoms for 77% of the compounds.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Toward in silico structure-based ADMET prediction in drug discovery

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.

RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

From known knowns to known unknowns: predicting in vivo drug metabolites

'It is better to be useful than perfect'. This review attempts to critically cover and assess the currently available approaches and tools to answer the crucial question: Is it possible (and if it is, to what extent is it possible) to predict in vivo metabolites and their abundances on the basis of in vitro and preclinical animal studies? In preclinical drug development, it is possible to produce metabolite patterns from a candidate drug by virtual means (i.e., in silico models), but these are not yet validated. However, they may be useful to cover the potential range of metabolites. In vitro metabolite patterns and apparent relative abundances are produced by various in vitro systems employing tissue preparations (mainly liver) and in most cases using liquid chromatography-mass spectrometry analytical techniques for tentative identification. The pattern of the metabolites produced depends on the enzyme source; the most comprehensive source of drug-metabolizing enzymes is cultured human hepatocytes, followed by liver homogenate fortified with appropriate cofactors. For specific purposes, such as the identification of metabolizing enzyme(s), recombinant enzymes can be used. Metabolite data from animal in vitro and in vivo experiments, despite known species differences, may help pinpoint metabolites that are not apparently produced in in vitro human systems, or suggest alternative experimental approaches. The range of metabolites detected provides clues regarding the enzymes attacking the molecule under study. We also discuss established approaches to identify the major enzymes. The last question, regarding reliability and robustness of metabolite extrapolations from in vitro to in vivo, both qualitatively and quantitatively, cannot be easily answered. There are a number of examples in the literature suggesting that extrapolations are generally useful, but there are only a few systematic and comprehensive studies to validate in vitro-in vivo extrapolations. In conclusion, extrapolation from preclinical metabolite data to the in vivo situation is certainly useful, but it is not known to what extent.

The challenges of in silico contributions to drug metabolism in lead optimization

IMPORTANCE OF THE FIELD

The site of metabolism (SOM) predictions by CYP 3A4 are extremely important during the drug discovery process especially during the lead discovery or library design phases. With the ability to rapidly characterize metabolites from these enzymes, the challenges facing in silico contribution change during the drug optimization phase. Some of the challenges are addressed in this article. Some aspects of the SOM prediction software and methodology are discussed in this opinion article and examples of software utility in overcoming metabolic instability in drug optimization are shown.

AREAS COVERED IN THIS REVIEW

SOM prediction by various approaches is discussed. Two ways of overcoming metabolic instability, blocking the metabolic softspots and rational modification of the instable molecule to avoid interaction with the CYP pocket, are discussed. The contribution plot in MetaSite and its use are discussed.

WHAT THE READER WILL GAIN

The reader will gain an understanding of possible approaches to either blocking the metabolic softspot or rationally modifying the molecule using MetaSite software or docking approaches. Blocking metabolism using fluorination has risks especially introducing multifluorinated benzene rings in the molecule.

TAKE HOME MESSAGE

During the lead optimization phase of drug discovery, when metabolic instability is an issue in a series, in silico approaches can be used to modify the molecule in order to decrease clearance due to metabolism, even that due to CYP3A4.

Improved ligand-protein binding affinity predictions using multiple binding modes

Accurate ligand-protein binding affinity prediction, for a set of similar binders, is a major challenge in the lead optimization stage in drug development. In general, docking and scoring functions perform unsatisfactorily in this application. Docking calculations, followed by molecular dynamics simulations and free energy calculations can be applied to improve the predictions. However, for targets with large, flexible binding sites, with no experimentally determined binding modes for a set of ligands, insufficient sampling can decrease the accuracy of the free energy calculations. Cytochrome P450s, a protein family of major importance for drug metabolism, is an example of a challenging target for binding affinity predictions. As a result, the choice of starting structure from the docking solutions becomes crucial. In this study, an iterative scheme is introduced that includes multiple independent molecular dynamics simulations to obtain weighted ensemble averages to be used in the linear interaction energy method. The proposed scheme makes the initial pose selection less crucial for further simulation, as it automatically calculates the relative weights of the various poses. It also properly takes into account the possibility that multiple binding modes contribute similarly to the overall affinity, or of similar compounds occupying very different poses. The method was applied to a set of 12 compounds binding to cytochrome P450 2C9 and it displayed a root mean-square error of 2.9 kJ/mol.

Site of metabolism prediction on cytochrome P450 2C9: a knowledge-based docking approach

A novel structure-based approach for site of metabolism prediction has been developed. This knowledge-based method consists of three steps: (1) generation of possible metabolites, (2) docking the predicted metabolites to the CYP binding site and (3) selection of the most probable metabolites based on their complementarity to the binding site. As a proof of concept we evaluated our method by using MetabolExpert for metabolite generation and Glide for docking into the binding site of the CYP2C9 crystal structure. Our method could identify the correct metabolite among the three best-ranked compounds in 69% of the cases. The predictive power of our knowledge-based method was compared to that achieved by substrate docking and two alternative literature approaches.

Predicting activation enthalpies of cytochrome-P450-mediated hydrogen abstractions. 2. Comparison of semiempirical PM3, SAM1, and AM1 with a density functional theory method

Predicting the biotransformation of xenobiotics is important in the chemical and pharmaceutical industries, as well as in toxicology. Here, we extend and evaluate the rapid methodology of Korzekwa, Jones, and Gillette (J. Am. Chem. Soc. 1990, 112, 7042-7046 ) to estimate the activation enthalpy (DeltaH) of hydrogen-abstraction by cytochrome P450 (CYP) enzymes, using the p-nitrosophenoxy radical (PNPO) as a simple surrogate for the CYP active oxygen species. The DeltaH is estimated with a linear regression model using the reaction enthalpy and ionization energy (of the substrate radical) as predictor variables, calculated by semiempirical (SE) methods. While Korzekwa et al. used the SE method AM1, we applied PM3 and SAM1 and compared the results of the three methods. For 24 substrates, the AM1-, PM3-, and SAM1-derived regression models showed R(2) values of 0.89, 0.90, and 0.93, respectively, for the correlation between calculated and predicted DeltaH. Furthermore, we compared the DeltaH() calculated semiempirically using PNPO radical with density functional theory (DFT) B3LYP activation energies calculated by Olsen et al. (J. Med. Chem. 2006, 49, 6489-6499 ) using a more realistic iron-oxo-porphine model, and the results revealed limitations of the PNPO radical model. Thus, predictive models developed using SE predictors provide rapid and generally internally consistent results, but they should be interpreted and used cautiously.

Polymorphism of human cytochrome P450 enzymes and its clinical impact

Pharmacogenetics is the study of how interindividual variations in the DNA sequence of specific genes affect drug response. This article highlights current pharmacogenetic knowledge on important human drug-metabolizing cytochrome P450s (CYPs) to understand the large interindividual variability in drug clearance and responses in clinical practice. The human CYP superfamily contains 57 functional genes and 58 pseudogenes, with members of the 1, 2, and 3 families playing an important role in the metabolism of therapeutic drugs, other xenobiotics, and some endogenous compounds. Polymorphisms in the CYP family may have had the most impact on the fate of therapeutic drugs. CYP2D6, 2C19, and 2C9 polymorphisms account for the most frequent variations in phase I metabolism of drugs, since almost 80% of drugs in use today are metabolized by these enzymes. Approximately 5-14% of Caucasians, 0-5% Africans, and 0-1% of Asians lack CYP2D6 activity, and these individuals are known as poor metabolizers. CYP2C9 is another clinically significant enzyme that demonstrates multiple genetic variants with a potentially functional impact on the efficacy and adverse effects of drugs that are mainly eliminated by this enzyme. Studies into the CYP2C9 polymorphism have highlighted the importance of the CYP2C9*2 and *3 alleles. Extensive polymorphism also occurs in other CYP genes, such as CYP1A1, 2A6, 2A13, 2C8, 3A4, and 3A5. Since several of these CYPs (e.g., CYP1A1 and 1A2) play a role in the bioactivation of many procarcinogens, polymorphisms of these enzymes may contribute to the variable susceptibility to carcinogenesis. The distribution of the common variant alleles of CYP genes varies among different ethnic populations. Pharmacogenetics has the potential to achieve optimal quality use of medicines, and to improve the efficacy and safety of both prospective and currently available drugs. Further studies are warranted to explore the gene-dose, gene-concentration, and gene-response relationships for these important drug-metabolizing CYPs.

SPORCalc: A development of a database analysis that provides putative metabolic enzyme reactions for ligand-based drug design

Understanding both the enzyme reactions that contribute to intermediate metabolism and the biochemical fate of candidate therapeutic and toxic agents are essential for drug design. Traditional metabolic databases indicate whether reactions have been observed but do not provide the likelihoods of reactions occurring, for example those of mixed function oxygenases and oxidases, during phase I metabolism. The desire for more quantitative predictions motivated the development of the recently introduced Substrate Product Occurrence Ratio Calculator (SPORCalc) that identifies metabolically labile atom positions in candidate compounds. This paper describes a further development and provides a clearer explanation of SPORCalc for the computational pharmacology, medicinal chemistry and drug design communities interested in metabolic prediction of xenobiotics using chemical databases of biotransformations. Examples of reaction centre detection in Metabolite are described followed by a demonstration of almokalant, an anti-arrhythmic agent, undergoing phase I metabolism. In general, occurrence ratio (OR) values are calculated throughout a compound and its transformed metabolites to give propensity (p) values at each atom position. The OR values from substrates and products in the database are essential for addition and elimination reactions. For almokalant, the resulting p values ranged from 10(-1) to 10(-5) and their order of magnitude reflected the known and experimentally observed metabolites. SPORCalc depends entirely on the level of detail from isoform- or species-specific reaction classes in Metabolite. Labile atom positions (sites of metabolism) are identified in both the candidate compound and its metabolites. In general, the likelihood of one enzyme isoform-dependent reaction occurring relative to another and the putative metabolic routes from different isoforms can be investigated. SPORCalc can be developed further to include suitable three-dimensional, structure-activity and physiochemical information.

Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450

BACKGROUND

Early in-vitro consideration of metabolism and inhibition of cytochrome P450 has proven its merits over the last 15 years. Simultaneously, many computational drug-design methods have been developed, and are being applied to study the interactions between drug candidates and cytochrome P450 enzymes (P450s).

OBJECTIVE

This review discusses the recent advances of these methods and the implications that are specific for P450s.

METHODS

Mainly focusing on the prediction of binding affinity and ligand selectivity, we outline the applicability of the different methods to answer specific questions. Special emphasis is put on the different levels of theory that are being used in recent computational descriptions of ligand-P450 interactions.

CONCLUSION

P450s offer an additional challenge for computational methods, considering the ambiguities of the catalytic cycle and the significant flexibility of the active site. Different computational methods display different limitations, which is crucial to take into account when choosing the method appropriate to each application.

Substrate binding to cytochromes P450

P450s have attracted tremendous attention owing to not only their involvement in the metabolism of drug molecules and endogenous substrates but also the unusual nature of the reaction they catalyze, namely, the oxidation of unactivated C-H bonds. The binding of substrates to P450s, which is usually viewed as the first step in the catalytic cycle, has been studied extensively via a variety of biochemical and biophysical approaches. These studies were directed towards answering different questions related to P450s, including mechanism of oxidation, substrate properties, unusual substrate oxidation kinetics, function, and active-site features. Some of the substrate binding studies extending over a period of more than 40 years of dedicated work have been summarized in this review and categorized by the techniques employed in the binding studies.

Regioselectivity prediction of CYP1A2-mediated phase I metabolism

A kinetic, reactivity-binding model has been proposed to predict the regioselectivity of substrates meditated by the CYP1A2 enzyme, which is responsible for the metabolism of planar-conjugated compounds such as caffeine. This model consists of a docking simulation for binding energy and a semiempirical molecular orbital calculation for activation energy. Possible binding modes of CYP1A2 substrates were first examined using automated docking based on the crystal structure of CYP1A2, and binding energy was calculated. Then, activation energies for CYP1A2-mediated metabolism reactions were calculated using the semiempirical molecular orbital calculation, AM1. Finally, the metabolic probability obtained from two energy terms, binding and activation energies, was used for predicting the most probable metabolic site. This model predicted 8 out of 12 substrates accurately as the primary preferred site among all possible metabolic sites, and the other four substrates were predicted into the secondary preferred site. This method can be applied for qualitative prediction of drug metabolism mediated by CYP1A2 and other CYP450 family enzymes, helping to develop drugs efficiently.

Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9

Cytochromes P450 3A4, 2D6, and 2C9 metabolize a large fraction of drugs. Knowing where these enzymes will preferentially oxidize a molecule, the regioselectivity, allows medicinal chemists to plan how best to block its metabolism. We present QSAR-based regioselectivity models for these enzymes calibrated against compiled literature data of drugs and drug-like compounds. These models are purely empirical and use only the structures of the substrates, in contrast to those models that simulate a specific mechanism like hydrogen radical abstraction, and/or use explicit models of active sites. Our most predictive models use three substructure descriptors and two physical property descriptors. Descriptor importances from the random forest QSAR method show that other factors than the immediate chemical environment and the accessibility of the hydrogen affect regioselectivity in all three isoforms. The cross-validated predictions of the models are compared to predictions from our earlier mechanistic model (Singh et al. J. Med. Chem. 2003, 46, 1330-1336) and predictions from MetaSite (Cruciani et al. J. Med. Chem. 2005, 48, 6970-6979).

Exploration of Enzyme−Ligand Interactions in CYP2D6 & 3A4 Homology Models and Crystal Structures Using a Novel Computational Approach

New crystal structures of human CYP2D6 and CYP3A4 have recently been reported, and in this study, we wanted to compare them with previously used homology models with respect to predictions of site of metabolism and ligand-enzyme interactions. The data set consisted of a family of synthetic opioid analgesics with the aim to cover both CYP2D6 and CYP3A4, as most of these compounds are metabolized by both isoforms. The program MetaSite was used for the site of metabolism predictions, and the results were validated by experimental assessment of the major metabolites formed with recombinant CYP450s. This was made on a selection of 14 compounds in the data set. The prediction rates for MetaSite were 79-100% except for the CYP3A4 homology model, which picked the correct site in half of the cases. Despite differences in orientation of some important amino acids in the active sites, the MetaSite-predicted sites were the same for the different structures, with the exception of the CYP3A4 homology model. Further exploration of interactions with ligands was done by docking substrates/inhibitors in the different structures with the docking program GLUE. To address the challenge in interpreting patterns of enzyme-ligand interactions for the large number of different docking poses, a new computational tool to handle the results from the dockings was developed, in which the output highlights the relative importance of amino acids in CYP450-substrate/inhibitor interactions. The method is based on calculations of the interaction energies for each pose with the surrounding amino acids. For the CYP3A4 structures, this method was compared with consensus principal component analysis (CPCA), a commonly used method for structural comparison to evaluate the usefulness of the new method. The results from the two methods were comparable with each other, and the highlighted amino acids resemble those that were identified to have a different orientation in the compared structures. The new method has clear advantages over CPCA in that it is far simpler to interpret and there is no need for protein alignment. The methodology enables structural comparison but also gives insights on important amino acid substrate/inhibitor interactions and can therefore be very useful when suggesting modifications of new chemical entities to improve their metabolic profiles.

Predicting the Oxidative Metabolism of Statins: An Application of the MetaSite® Algorithm

PURPOSE

This study was undertaken to examine the MetaSite algorithm by comparing its predictions with experimentally characterized metabolites of statins produced by cytochromes P450 (CYPs).

METHODS

Seven statins were investigated, namely atorvastatin, cerivastatin, fluvastatin, pitavastatin and pravastatin which are (or were) used in their active hydroxy-acid form, and lovastatin and simvastatin which are used as the lactone prodrug. But given the fast lactone-hydroxy-acid equilibrium undergone by statins, both forms were investigated for each of the seven drugs. The MetaSite version 2.5.3 used here contains the homology 3D-models of CYP1A2, CYP2C19, CYP2C9, CYP2D6 and CYP3A4. In addition, we also used the crystallographic 3D-structure of human CYP2C9 and CYP3A4. To allow a better interpretation of results, the probability function PsMi calculated by MetaSite (namely the probability of atom i to be a site of metabolism) was explicitly decomposed into its two components, namely a recognition score Ei (the accessibility of atom i) and the chemical reactivity Ri of atom i toward oxidation reactions.

RESULTS

The current version of MetaSite is known to work best with prior experimental knowledge of the cytochrome(s) P450 involved. And indeed, experimentally confirmed sites of oxidation were correctly given a high priority by MetaSite. In particular 77% of correct predictions (including false positive but, as discussed, this is not necessarily a shortcoming) were obtained when considering the first five metabolites indicated by MetaSite.

CONCLUSION

To the best of our knowledge, this is the first independent report on the software. It is expected to contribute to the development of improved versions, but above all it demonstrates that the usefulness of such softwares critically depends on human experts.

Methods for Predicting Human Drug Metabolism

Drug metabolism information is a necessary component of drug discovery and development. The key issues in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism. Methods for predicting human drug metabolism from in vitro and computational methodologies and determining relationships between the structure and metabolic activity of molecules are also critically important for understanding potential drug interactions and toxicity. There are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have their own limitations. It is apparent that few of the computational tools for metabolism prediction alone provide the major integrated functions needed to assist in drug discovery. Similarly the different in vitro methods for human drug metabolism themselves have implicit limitations. The utilization of these methods for pharmaceutical and other applications as well as their integration is discussed as it is likely that hybrid methods will provide the most success.