Computational approaches for predicting CYP-related metabolism properties in the screening of new drugs

A Novel N-Tert-Butyl Derivatives of Pseudothiohydantoin as Potential Target in Anti-Cancer Therapy

Tumors are currently more and more common all over the world; hence, attempts are being made to explain the biochemical processes underlying their development. The search for new therapeutic pathways, with particular emphasis on enzymatic activity and its modulation regulating the level of glucocorticosteroids, may contribute to the development and implementation of new therapeutic options in the treatment process. Our research focuses on understanding the role of 11β-HSD1 and 11β-HSD2 as factors involved in the differentiation and proliferation of neoplastic cells. In this work, we obtained the 9 novel N-tert-butyl substituted 2-aminothiazol-4(5H)-one (pseudothiohydantoin) derivatives, differing in the substituents at C-5 of the thiazole ring. The inhibitory activity and selectivity of the obtained derivatives in relation to two isoforms of 11β-HSD were evaluated. The highest inhibitory activity for 11β-HSD1 showed compound 3h, containing the cyclohexane substituent at the 5-position of the thiazole ring in the spiro system (82.5% at a conc. 10 µM). On the other hand, the derivative 3f with the phenyl substituent at C-5 showed the highest inhibition of 11β-HSD2 (53.57% at a conc. of 10 µM). A low selectivity in the inhibition of 11β-HSD2 was observed but, unlike 18β-glycyrrhetinic acid, these compounds were found to inhibit the activity of 11β-HSD2 to a greater extent than 11β-HSD1, which makes them attractive for further research on their anti-cancer activity.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

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.

Predicting the binding modes and sites of metabolism of xenobiotics

Metabolism studies are an essential integral part of ADMET profiling of drug candidates to evaluate their safety and efficacy. Cytochrome P-450 (CYP) metabolizes a wide variety of xenobiotics/drugs. The binding modes of these compounds with CYP and their intrinsic reactivities decide the metabolic products. We report here a novel computational protocol, which comprises docking of ligands to heme-containing CYPs and prediction of binding energies through a newly developed scoring function, followed by analyses of the docked structures and molecular orbitals of the ligand molecules, for predicting the sites of metabolism (SOM) of ligands. The calculated binding free energies of 121 heme-containing protein-ligand docked complexes yielded a correlation coefficient of 0.84 against experiment. Molecular orbital analyses of the resultant top three unique poses of the docked complexes provided a success rate of 87% in identifying the experimentally known sites of metabolism of the xenobiotics. The SOM prediction methodology is freely accessible at .

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data

PURPOSE

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability.

METHODS

Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism).

RESULTS

"Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h.

CONCLUSIONS

Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

[Electrochemical methods for biomedical investigations]

In the review, authors discussed recently published experimental data concerning highly sensitive electrochemical methods and technologies for biomedical investigations in the postgenomic era. Developments in electrochemical biosensors systems for the analysis of various bio objects are also considered: cytochrome P450s, cardiac markers, bacterial cells, the analysis of proteins based on electro oxidized amino acids as a tool for analysis of conformational events. The electroanalysis of catalytic activity of cytochromes P450 allowed developing system for screening of potential substrates, inhibitors or modulators of catalytic functions of this class of hemoproteins. The highly sensitive quartz crystal microbalance (QCM) immunosensor has been developed for analysis of bio affinity interactions of antibodies with troponin I in plasma. The QCM technique allowed real-time monitoring of the kinetic differences in specific interactions and nonspecific sorption, with out multiple labeling procedures and separation steps. The affinity binding process was characterized by the association (ka) and the dissociation (kd) kinetic constants and the equilibrium association (K) constant, calculated using experimental data. Based on the electroactivity of bacterial cells, the electrochemical system for determination of sensitivity of the microbial cells to antibiotics cefepime, ampicillin, amikacin, and erythromycin was proposed. It was shown that the minimally detectable cell number corresponds to 106 CFU per electrode. The electrochemical method allows estimating the degree of E.coli JM109 cells resistance to antibiotics within 2-5 h. Electrosynthesis of polymeric analogs of antibodies for myoglobin (molecularly imprinted polymer, MIP) on the surface of graphite screen-printed electrodes as sensor elements with o- phenylenediamine as the functional monomer was developed. Molecularly imprinted polymers demonstrate selective complementary binding of a template protein molecule (myoglobin) by the "key-lock" principle.

Human cytochrome P450 and personalized medicine

Personalized medicine has become a hot topic ascribed to the development of Human Genome Project. And currently, bioinformatics methodology plays an essential role in personal drug design. Here in this review we mainly focused on the basic introduction of the SNPs of human drug metabolic enzymes and their relationships with personalized medicine. Some common bioinformatics analysis methods and latest progresses and applications in personal drug design have also been discussed. Thus bioinformatics studies on SNPs of human CYP450 genes will contribute to indicate the most possible genes that are associated with human diseases and relevant therapeutic targets, identify and predict the drug efficacy and adverse drug response, investigate individual gene specific properties and then provide personalized and optimal clinic therapies.

OpenVirtualToxLab--a platform for generating and exchanging in silico toxicity data

The VirtualToxLab is an in silico technology for estimating the toxic potential--endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity--of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The toxic potential of a compound--its ability to trigger adverse effects--is derived from its computed binding affinities toward these very proteins: the computationally demanding simulations are executed in client-server model on a Linux cluster of the University of Basel. The graphical-user interface supports all computer platforms, allows building and uploading molecular structures, inspecting and downloading the results and, most important, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. Access to the VirtualToxLab is available free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

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

PURPOSE

Predicting atoms in a potential drug compound that are susceptible to oxidation by cytochrome P450 (CYP) enzymes is of great interest to the pharmaceutical community. We aimed to develop a computational approach combining ligand- and structure-based design principles to accurately predict sites of metabolism (SoMs) in a series of CYP2C9 substrates.

METHODS

We employed the reactivity model, SMARTCyp, ensemble docking, and pseudo-receptor modeling based on quantitative structure-activity relationships (QSAR) to account for influences of both the inherent reactivity of each atom and the physical structure of the CYP2C9 binding site.

RESULTS

We tested ligand-based prediction alone (i.e. SMARTCyp), structure-based prediction alone (i.e. AutoDock Vina docking), the linear combination of the SMARTCYP and docking scores, and finally a pseudo-receptor QSAR model based on the docked compounds in combination with SMARTCyp. We found that by using the latter combined approach we were able to accurately predict 88% and 96% of the true SoMs, within the top-1 and top-2 predictions, respectively.

CONCLUSIONS

We have outlined a novel combination approach for accurately predicting SoMs in CYP2C9 ligands. We believe that this method may be applied to other CYP2C9 ligands as well as to other CYP systems.

Combined use of pharmacophoric models together with drug metabolism and genotoxicity "in silico" studies in the hit finding process

In this study we propose a virtual screening strategy based on the generation of a pharmacophore hypothesis, followed by an in silico evaluation of some ADME-TOX properties with the aim to apply it to the hit finding process and, specifically, to characterize new chemical entities with potential to control inflammatory processes mediated by T lymphocytes such as multiple sclerosis, systemic lupus erithematosus or rheumatoid arthritis. As a result, three compounds with completely novel scaffolds were selected as final hits for future hit-to-lead optimization due to their anti-inflammatory profile. The biological results showed that the selected compounds increased the intracellular cAMP levels and inhibited cell proliferation in T lymphocytes. Moreover, two of these compounds were able to increase the production of IL-4, an immunoregulatory cytokine involved in the selective deviation of T helper (Th) immune response Th type 2 (Th2), which has been proved to have anti-inflammatory properties in several animal models for autoimmune pathologies as multiple sclerosis or rheumatoid arthritis. Thus our pharmacological strategy has shown to be useful to find molecules with biological activity to control immune responses involved in many inflammatory disorders. Such promising data suggested that this in silico strategy might be useful as hit finding process for future drug development.

VirtualToxLab - a platform for estimating the toxic potential of drugs, chemicals and natural products

The VirtualToxLab is an in silico technology for estimating the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of proteins, known or suspected to trigger adverse effects. The toxic potential, a non-linear function ranging from 0.0 (none) to 1.0 (extreme), is derived from the individual binding affinities of a compound towards currently 16 target proteins: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, and thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, and 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The interface to the technology allows building and uploading molecular structures, viewing and downloading results and, most importantly, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. The VirtualToxLab has been used to predict the toxic potential for over 2500 compounds: the results are posted on http://www.virtualtoxlab.org. The free platform - the OpenVirtualToxLab - is accessible (in client-server mode) over the Internet. It is free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

Identification and characterization of reactive metabolites in natural products-driven drug discovery

Toxicity of natural products arising from their metabolic biotransformation into reactive chemical intermediates is an important reason for high attrition rates in early drug discovery efforts. Screening promising natural products for their likelihood to form such metabolites is therefore an important step in identifying potential liabilities in the drug development process. However, such screening is complicated by the need to have test methods that are sensitive, reliable, accurate, efficient, and cost-effective enough to allow for routine identification and characterization of the reactive metabolites. These metabolites are typically formed in minute quantities, usually through minor metabolic pathways, and, due to their highly reactive and therefore transient chemical nature, pose considerable analytical challenges in attempts to determine their properties. Understanding the formation of reactive metabolites may be used as the basis for synthetic chemical modification of parent natural products aimed at bypassing such harmful bioactivation. This paper highlights the general principles and protocols commonly used to predict and study the formation of reactive metabolites in vitro and how the data obtained from such studies can be used in the development of safer drugs from natural products.

Accessing, using, and creating chemical property databases for computational toxicology modeling

Toxicity data is expensive to generate, is increasingly seen as precompetitive, and is frequently used for the generation of computational models in a discipline known as computational toxicology. Repositories of chemical property data are valuable for supporting computational toxicologists by providing access to data regarding potential toxicity issues with compounds as well as for the purpose of building structure-toxicity relationships and associated prediction models. These relationships use mathematical, statistical, and modeling computational approaches and can be used to understand the mechanisms by which chemicals cause harm and, ultimately, enable prediction of adverse effects of these chemicals to human health and/or the environment. Such approaches are of value as they offer an opportunity to prioritize chemicals for testing. An increasing amount of data used by computational toxicologists is being published into the public domain and, in parallel, there is a greater availability of Open Source software for the generation of computational models. This chapter provides an overview of the types of data and software available and how these may be used to produce predictive toxicology models for the community.

Molecular docking, 3D-QSAR studies, and in silico ADME prediction of p-aminosalicylic acid derivatives as neuraminidase inhibitors

Neuraminidase (NA) is a major glycoprotein of influenza virus which is essential for viral infection. It offers a potential target for antiviral drug design and discovery. To develop novel potent neuraminidase inhibitors (NAI), Surflex-Dock was employed to dock 40 hydrophobic p-aminosalicylic acid derivatives into the active site of NA. The 3D-quantitative structure-activity relationship studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out on 40 molecules. Both CoMFA (q(2) = 0.628, r(2) = 0.697) and CoMSIA (q(2) = 0.746, r(2) = 0.816) gave reasonable results. A preliminary pharmacokinetic profile of these NAI was also performed on the basis of Volsurf predictions. The results obtained from this study will be useful in the design of novel potent NAI.

Potentially increasing the metabolic stability of drug candidates via computational site of metabolism prediction by CYP2C9: The utility of incorporating protein flexibility via an ensemble of structures

Cytochrome P450 enzymes are responsible for metabolizing many endogenous and xenobiotic molecules encountered by the human body. It has been estimated that 75% of all drugs are metabolized by cytochrome P450 enzymes. Thus, predicting a compound's potential sites of metabolism (SOM) is highly advantageous early in the drug development process. We have combined molecular dynamics, AutoDock Vina docking, the neighboring atom type (NAT) reactivity model, and a solvent-accessible surface-area term to form a reactivity-accessibility model capable of predicting SOM for cytochrome P450 2C9 substrates. To investigate the importance of protein flexibility during the ligand-binding process, the results of SOM prediction using a static protein structure for docking were compared to SOM prediction using multiple protein structures in ensemble docking. The results reported here indicate that ensemble docking increases the number of ligands that can be docked in a bioactive conformation (ensemble: 96%, static: 85%) but only leads to a slight improvement (49% vs. 44%) in predicting an experimentally known SOM in the top-1 position for a ligand library of 75 CYP2C9 substrates. Using ensemble docking, the reactivity-accessibility model accurately predicts SOM in the top-1 ranked position for 49% of the ligand library and considering the top-3 predicted sites increases the prediction success rate to approximately 70% of the ligand library. Further classifying the substrate library according to K(m) values leads to an improvement in SOM prediction for substrates with low K(m) values (57% at top-1). While the current predictive power of the reactivity-accessibility model still leaves significant room for improvement, the results illustrate the usefulness of this method to identify key protein-ligand interactions and guide structural modifications of the ligand to increase its metabolic stability.

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.

Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.

Utility of protein structures in overcoming ADMET-related issues of drug-like compounds

The number of solved X-ray structures of proteins relevant for ADMET processes of drug molecules has increased remarkably over recent years. In principle, this development offers the possibility to complement the quantitative structure-property relationship (QSPR)-dominated repertoire of in silico ADMET methods with protein-structure-based approaches. However, the complex nature and the weak nonspecific ligand-binding properties of ADMET proteins take structural biology methods and current docking programs to the limit. In this review we discuss the utility of protein-structure-based design and docking approaches aimed at overcoming issues related to plasma protein binding, active transport via P-glycoprotein, hERG channel mediated cardiotoxicity and cytochrome P450 inhibition, metabolism and induction.

[Sensor systems for medical application based on hemoproteins and nanocomposite materials]

Recent advances in nanotechnologies stimulate the development of sensor systems based on nanocomposite materials. This review discusses the prospects and challenges of sensors coupled with functionally important for medicine hemoproteins and nanoscale materials. Authors summarized their own experimental results and literature data on hemoprotein-based sensor systems. Mechanisms and the main function principles of electrochemical nanosensors are also discussed.

Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule

BACKGROUND

Different isoforms of Cytochrome P450 (CYP) metabolized different types of substrates (or drugs molecule) and make them soluble during biotransformation. Therefore, fate of any drug molecule depends on how they are treated or metabolized by CYP isoform. There is a need to develop models for predicting substrate specificity of major isoforms of P450, in order to understand whether a given drug will be metabolized or not. This paper describes an in-silico method for predicting the metabolizing capability of major isoforms (e.g. CYP 3A4, 2D6, 1A2, 2C9 and 2C19).

RESULTS

All models were trained and tested on 226 approved drug molecules. Firstly, 2392 molecular descriptors for each drug molecule were calculated using various softwares. Secondly, best 41 descriptors were selected using general and genetic algorithm. Thirdly, Support Vector Machine (SVM) based QSAR models were developed using 41 best descriptors and achieved an average accuracy of 86.02%, evaluated using fivefold cross-validation. We have also evaluated the performance of our model on an independent dataset of 146 drug molecules and achieved average accuracy 70.55%. In addition, SVM based models were developed using 26 Chemistry Development Kit (CDK) molecular descriptors and achieved an average accuracy of 86.60%.

CONCLUSIONS

This study demonstrates that SVM based QSAR model can predict substrate specificity of major CYP isoforms with high accuracy. These models can be used to predict isoform responsible for metabolizing a drug molecule. Thus these models can used to understand whether a molecule will be metabolized or not. This is possible to develop highly accurate models for predicting substrate specificity of major isoforms using CDK descriptors. A web server MetaPred has been developed for predicting metabolizing isoform of a drug molecule http://crdd.osdd.net/raghava/metapred/.

SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism

SMARTCyp is an in silico method that predicts the sites of cytochrome P450-mediated metabolism of druglike molecules. The method is foremost a reactivity model, and as such, it shows a preference for predicting sites that are metabolized by the cytochrome P450 3A4 isoform. SMARTCyp predicts the site of metabolism directly from the 2D structure of a molecule, without requiring calculation of electronic properties or generation of 3D structures. This is a major advantage, because it makes SMARTCyp very fast. Other advantages are that experimental data are not a prerequisite to create the model, and it can easily be integrated with other methods to create models for other cytochrome P450 isoforms. Benchmarking tests on a database of 394 3A4 substrates show that SMARTCyp successfully identifies at least one metabolic site in the top two ranked positions 76% of the time. SMARTCyp is available for download at http://www.farma.ku.dk/p450.

Fast prediction of cytochrome P450 mediated drug metabolism

Cytochrome P450 mediated metabolism of drugs is one of the major determinants of their kinetic profile, and prediction of this metabolism is therefore highly relevant during the drug discovery and development process. A new rule-based method, based on results from density functional theory calculations, for predicting activation energies for aliphatic and aromatic oxidations by cytochromes P450 is developed and compared with several other methods. Although the applicability of the method is currently limited to a subset of P450 reactions, these reactions describe more than 90 % of the metabolites. The rules employed are relatively few and general, and when combined with solvent-accessible surface area calculations to account for steric accessibility, the method gives a major P450 metabolite as first-ranked position for 75 % of the substrates, and ranked in the top three for 90 % of substrates for a set of 20 substrates. In combination with docking, it can predict isoform-specific metabolism, and we apply this on CYP1A2 with very good results on 81 substrates, for which we find a major metabolite ranked in the top three for 90 % of the substrates (100 % in the training set and 87 % in the larger test set).

QSAR of cytochrome inhibitors

Cytochrome P450 (CYP450) enzymes are predominantly involved in the Phase I metabolism of xenobiotics. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. Metabolic stability is a prerequisite for sustaining the therapeutically relevant concentrations, and very often drug candidates are sacrificed due to poor metabolic profiles. Computational tools such as quantitative structure-activity relationships are widely used to study different metabolic end points successfully to accelerate the drug discovery process. There are a lot of computational studies on clinically important CYPs already reported in recent years. But other clinically significant families are to yet be explored computationally. Powerfulness of quantitative structure-activity relationship will drive computational chemists to develop new potent and selective inhibitors of different classes of CYPs for the treatment of different diseases with least drug-drug interactions. Furthermore, there is a need to enhance the accuracy, interpretability and confidence in the computational models in accelerating the drug discovery pathways.

In Silico Toxicology in Drug Discovery — Concepts Based on Three-dimensional Models

Animal testing is still compulsory worldwide, for the approval of drugs and chemicals produced in large quantities. Computer-assisted ( in silico) technologies are considered to be efficient alternatives to in vivo experiments, and are therefore endorsed by many regulatory agencies, e.g. for use in the European REACH initiative. Advantages of in silico methods include: the possible study of hypothetical compounds; their low cost; and the fact that such virtual experiments are typically based on human data, thus making the question of interspecies transferability obsolete. Since the mid-1990s, computer-based technologies have become an indispensable tool in drug discovery — used primarily to identify small molecules displaying a stereospecific and selective binding to a regulatory macromolecule. Since toxic effects are still responsible for some 20% of the late-stage failures, there is a continuing need for in silico concepts which can be used to estimate a compound's ADMET ( adsorption, distribution, metabolism, elimination, toxicity) properties — in particular, toxicity. The aim of this paper is to provide an insight into computational technologies that allow for the prediction of toxic effects triggered by pharmaceuticals. As most adverse and toxic effects are mediated by unwanted interactions with macromolecules involved in biological regulatory systems, we have focused on methodologies that are based on three-dimensional models of small molecules binding to such entities, and discuss the results at the molecular level.

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.

CypScore: Quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory

CypScore is an in silico approach for predicting the likely sites of cytochrome P450-mediated metabolism of druglike organic molecules. It consists of multiple models for the most important P450 oxidation reactions such as aliphatic hydroxylation, N-dealkylation, O-dealkylation, aromatic hydroxylation, double-bond oxidation, N-oxidation, and S-oxidation. Each of these models is based on atomic reactivity descriptors derived from surface-based properties calculated with ParaSurf and based on AM1 semiempirical molecular orbital theory. The models were trained with data derived from Bayer Schering Pharma's in-house MajorMetabolite Database with more than 2300 transformations and more than 800 molecules collected from the primary literature. The models have been balanced to allow the treatment of relative intramolecular, intra-chemotype, and inter-chemotype reactivities of the labile sites toward oxidation. The models were evaluated with promising hit rates on three public datasets of varying quality in the annotation of the experimental positions. For 39 well-characterized compounds from 14 in-house lead optimization programs, we could detect at least one major metabolite for the three highest-ranked positions in 87 % of the compounds and overall more than 62 % of all major metabolites, with promising true- to false-positive ratios of 0.9.

Current industrial practices in assessing CYP450 enzyme induction: preclinical and clinical

Induction of drug metabolizing enzymes, such as the cytochromes P450 (CYP) is known to cause drug-drug interactions due to increased elimination of co-administered drugs. This increased elimination may lead to significant reduction or complete loss of efficacy of the co-administered drug. Due to the significance of such drug interactions, many pharmaceutical companies employ screening and characterization models which predict CYP enzyme induction to avoid or attenuate the potential for drug interactions with new drug candidates. The most common mechanism of CYP induction is transcriptional gene activation. Activation is mediated by nuclear receptors, such as AhR, CAR, and PXR that function as transcription factors. Early high throughput screening models utilize these nuclear hormone receptors in ligand binding or cell-based transactivation/reporter assays. In addition, immortalized hepatocyte cell lines can be used to assess enzyme induction of specific drug metabolizing enzymes. Cultured primary human hepatocytes, the best established in vitro model for predicting enzyme induction and most accepted by regulatory agencies, is the predominant assay used to evaluate induction of a wide variety of drug metabolizing enzymes. These in vitro models are able to appropriately predict enzyme induction in patients when compared to clinical drug-drug interactions. Finally, transgenic animal models and the cynomolgus monkey have also been shown to recapitulate human enzyme induction and may be appropriate in vivo animal models for predicting human drug interactions.

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.

Ligand-Based Models for the Isoform Specificity of Cytochrome P450 3A4, 2D6, and 2C9 Substrates

A data set of 379 drugs and drug analogs that are metabolized by human cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9, respectively, was studied. A series of descriptor sets directly calculable from the constitution of these drugs was systematically investigated as to their power into classifying a compound into the CYP isoform that metabolizes it. In a four-step build-up process eventually 303 different descriptor components were investigated for 146 compounds of a training set by various model building methods, such as multinomal logistic regression, decision tree, or support vector machine (SVM). Automatic variable selection algorithms were used in order to decrease the number of descriptors. A comprehensive scheme of cross-validation (CV) experiments was applied to assess the robustness and reliability of the four models developed. In addition, the predictive power of the four models presented in this paper was inspected by predicting an external validation data set with 233 compounds. The best model has a leave-one-out (LOO) cross-validated predictivity of 89% and gives 83% correct predictions for the external validation data set. For our favored model we showed the strong influence on the predictivity of the way a data set is split into a training and test data set.

Membrane transporter proteins: a challenge for CNS drug development

Drug transporters are membrane proteins present in various tissues such as the lymphocytes, intestine, liver, kidney, testis, placenta, and central nervous system. These transporters play a significant role in drug absorption and distribution to organic systems, particularly if the organs are protected by blood-organ barriers, such as the blood-brain barrier or the maternal-fetal barrier. In contrast to neurotransmitters and receptor-coupled transporters or other modes of interneuronal transmission, drug transporters are not directly involved in specific neuronal functions, but provide global protection to the central nervous system. The lack of capillary fenestration, the low pinocytic activity, and the tight junctions between brain capillary and choroid plexus endothelial cells represent further gatekeepers limiting the entrance of endogenous and exogenous compounds into the central nervous system. Drug transport is a result of the concerted action of efflux and influx pumps (transporters) located both in the basolateral and apical membranes of brain capillary and choroid plexus endothelial cells. By regulating efflux and influx of endogenous or exogenous substances, the blood-brain barrier and, to a lesser extent, the blood-cerebrospinal barrier in the ventricles, represents the main interface between the central nervous system and the blood, ie, the rest of the body. As drug distribution to organs is dependent on the affinity of a substrate for a specific transport system, membrane transporter proteins are increasingly recognized as a key determinant of drug disposition. Many drug transporters are members of the adenosine triphosphate (ATP)-binding cassette (ABC) transporter superfamily or the solute-linked carrier (SLC) class. The multidrug resistance protein MDR1 (ABCB1), also called P-glycoprotein, the multidrug resistance-associated proteins MRP1 (ABCC1) and MRP2 (ABCC2), and the breast cancer-resistance protein BCRP (ABCG2) are ATP-dependent efflux transporters expressed in the blood-brain barrier. They belong to the superfamily of ABC transporters, which export drugs from the intracellular to the extracellular milieu. Members of the SLC class of solute carriers include, for example, organic ion transporting peptides, organic cation transporters, and organic ion transporters. They are ATP-independent polypeptides principally expressed at the basolateral membrane of brain capillary and choroid plexus endothelial cells that also mediate drug transport through central nervous system barriers.