Cytochrome P450 in Silico: An Integrative Modeling Approach

Structural insights into the potential binding sites of Cathepsin D using molecular modelling techniques

Alzheimer's disease (AD) is the most prevalent type of dementia caused by the accumulation of amyloid beta (Aβ) peptides. The extracellular deposition of Aβ peptides in human AD brain causes neuronal death. Therefore, it has been found that Aβ peptide degradation is a possible therapeutic target for AD. CathD has been known to breakdown amyloid beta peptides. However, the structural role of CathD is not yet clear. Hence, for the purpose of gaining a deeper comprehension of the structure of CathD, the present computational investigation was performed using virtual screening technique to predict CathD's active site residues and substrate binding mode. Ligand-based virtual screening was implemented on small molecules from ZINC database against crystal structure of CathD. Further, molecular docking was utilised to investigate the binding mechanism of CathD with substrates and virtually screened inhibitors. Localised compounds obtained through screening performed by PyRx and AutoDock 4.2 with CathD receptor and the compounds having highest binding affinities were picked as; ZINC00601317, ZINC04214975 and ZINCC12500925 as our top choices. The hydrophobic residues Viz. Gly35, Val31, Thr34, Gly128, Ile124 and Ala13 help stabilising the CathD-ligand complexes, which in turn emphasises substrate and inhibitor selectivity. Further, MM-GBSA approach has been used to calculate binding free energy between CathD and selected compounds. Therefore, it would be beneficial to understand the active site pocket of CathD with the assistance of these discoveries. Thus, the present study would be helpful to identify active site pocket of CathD, which could be beneficial to develop novel therapeutic strategies for the AD.

Multidisciplinary Insights into the Structure-Function Relationship of the CYP2B6 Active Site

Cytochrome P450 2B6 (CYP2B6) is a highly polymorphic human enzyme involved in the metabolism of many clinically relevant drugs, environmental toxins, and endogenous molecules with disparate structures. Over the last 20-plus years, in silico and in vitro studies of CYP2B6 using various ligands have provided foundational information regarding the substrate specificity and structure-function relationship of this enzyme. Approaches such as homology modeling, X-ray crystallography, molecular docking, and kinetic activity assays coupled with CYP2B6 mutagenesis have done much to characterize this originally neglected monooxygenase. However, a complete understanding of the structural details that make new chemical entities substrates of CYP2B6 is still lacking. Surprisingly little in vitro data has been obtained about the structure-function relationship of amino acids identified to be in the CYP2B6 active site. Since much attention has already been devoted to elucidating the function of CYP2B6 allelic variants, here we review the salient findings of in silico and in vitro studies of the CYP2B6 structure-function relationship with a deliberate focus on the active site. In addition to summarizing these complementary approaches to studying structure-function relationships, we note gaps/challenges in existing data such as the need for more CYP2B6 crystal structures, molecular docking results with various ligands, and data coupling CYP2B6 active site mutagenesis with kinetic parameter measurement under standard expression conditions. Harnessing in silico and in vitro techniques in tandem to understand the CYP2B6 structure-function relationship will likely offer further insights into CYP2B6-mediated metabolism. SIGNIFICANCE STATEMENT: The apparent importance of cytochrome P450 2B6 (CYP2B6) in the metabolism of various xenobiotics and endogenous molecules has grown since its discovery with many in silico and in vitro studies offering a partial description of its structure-function relationship. Determining the structure-function relationship of CYP2B6 is difficult but may be aided by thorough biochemical investigations of the CYP2B6 active site that provide a more complete pharmacological understanding of this important enzyme.

Antidiabetics Interactions with Herbs: A Compressive Review

Diabetes mellitus is a chronic illness with a variety of causes and pathophysiology. For the management of diabetes, various synthetic antidiabetic drugs are available. Still, people prefer complementary and alternative therapies as well as traditional herbal home remedies because they are perceived to be free of side effects and generally recognized as safe due to their natural origin. Hence, worldwide, the majority of the population is consuming herbs and/or herbal products in their daily routine. It has been observed that individuals with diabetes also consume herbs/herbal products either with or without medical supervision. This co-consumption of antidiabetic medications and herb/herbal products may result in herb-drug interactions, which might be potentially beneficial or harmful or, in some cases, even fatal. Most of the times, these interactions remain unnoticed or undiagnosed due to lack of knowledge and awareness about them. In this review, the authors have summarized some important aspects related to the herb-drug interaction (HDI), which include methods for prediction and mechanism of HDI (pharmacokinetic and pharmacodynamic) and also the clinical and experimental literature on herb-drug interactions (HDI) in the treatment of diabetes. Authors have attempted to categorize the interactions between oral hypoglycemic agents and various herbs as beneficial or harmful based on the results reported in the original research work.

Curcumin Analogues as a Potential Drug against Antibiotic Resistant Protein, β-Lactamases and L, D-Transpeptidases Involved in Toxin Secretion in Salmonella typhi: A Computational Approach

Typhoid fever caused by the bacteria Salmonella typhi gained resistance through multidrug-resistant S. typhi strains. One of the reasons behind β-lactam antibiotic resistance is -lactamase. L, D-Transpeptidases is responsible for typhoid fever as it is involved in toxin release that results in typhoid fever in humans. A molecular modeling study of these targeted proteins was carried out by various methods, such as homology modeling, active site prediction, prediction of disease-causing regions, and by analyzing the potential inhibitory activities of curcumin analogs by targeting these proteins to overcome the antibiotic resistance. The five potent drug candidate compounds were identified to be natural ligands that can inhibit those enzymes compared to controls in our research. The binding affinity of both the Go-Y032 and NSC-43319 were found against β-lactamase was −7.8 Kcal/mol in AutoDock, whereas, in SwissDock, the binding energy was −8.15 and −8.04 Kcal/mol, respectively. On the other hand, the Cyclovalone and NSC-43319 had an equal energy of −7.60 Kcal/mol in AutoDock, whereas −7.90 and −8.01 Kcal/mol in SwissDock against L, D-Transpeptidases. After the identification of proteins, the determination of primary and secondary structures, as well as the gene producing area and homology modeling, was accomplished. The screened drug candidates were further evaluated in ADMET, and pharmacological properties along with positive drug-likeness properties were observed for these ligand molecules. However, further in vitro and in vivo experiments are required to validate these in silico data to develop novel therapeutics against antibiotic resistance.

Mechanism-Based Inactivation of Cytochrome P450 Enzymes: Computational Insights

Mechanism-based inactivation (MBI) refers to the metabolic bioactivation of a xenobiotic by cytochrome P450s to a highly reactive intermediate which subsequently binds to the enzyme and leads to the quasi-irreversible or irreversible inhibition. Xenobiotics, mainly drugs with specific functional units, are the major sources of MBI. Two possible consequences of MBI by medicinal compounds are drug-drug interaction and severe toxicity that are observed and highlighted by clinical experiments. Today almost all of these latent functional groups (e.g., thiophene, furan, alkylamines, etc.) are known, and their features and mechanisms of action, owing to the vast experimental and theoretical studies, are determined. In the past decade, molecular modeling techniques, mostly density functional theory, have revealed the most feasible mechanism that a drug undergoes by P450 enzymes to generate a highly reactive intermediate. In this review, we provide a comprehensive and detailed picture of computational advances toward the elucidation of the activation mechanisms of various known groups with MBI activity. To this aim, we briefly describe the computational concepts to carry out and analyze the mechanistic investigations, and then, we summarize the studies on compounds with known inhibition activity including thiophene, furan, alkylamines, terminal acetylene, etc. This study can be reference literature for both theoretical and experimental (bio)chemists in several different fields including rational drug design, the process of toxicity prevention, and the discovery of novel inhibitors and catalysts.

Assessment of CYP2C9 Structural Models for Site of Metabolism Prediction

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

Deciphering Reaction Determinants of Altered-Activity CYP2D6 Variants by Well-Tempered Metadynamics Simulation and QM/MM Calculations

The xenobiotic metabolizing enzyme CYP2D6 is the P450 cytochrome family member with the highest rate of polymorphism. This causes changes in the enzyme activity and specificity, which can ultimately lead to adverse reactions during drug treatment. To avoid or lower CYP-related toxicity risks, prediction of the most likely positions within a molecule where a metabolic reaction might occur is paramount. In order to obtain accurate predictions, it is crucial to understand all phenomena within the active site of the enzyme that contribute to an efficient substrate recognition and the subsequent catalytic reaction together with their relative weight within the overall thermodynamic context. This study aims to define the weight of the driving forces upon the C-H bond activation within CYP2D6 wild-type and a clinically relevant allelic variant with increased activity (CYP2D6*53) featuring two amino acid mutations in close vicinity of the heme. First, we investigated the steric and electrostatic complementarity of the substrate bufuralol using well-tempered metadynamics simulations with the aim to obtain the free energy profiles for each site of metabolism (SoM) within the different active sites. Second, the stereoelectronic complementarity was determined for each SoM within the two different active-site environments. Relying on the well-tempered metadynamics simulation energy profiles of each SoM, we identified the binding mode that was closest to the preferred transition-state geometry for efficient C-H bond activation. The binding modes were then used as starting structures for the quantum mechanics/molecular mechanics calculations performed to quantify the corresponding activation barriers. Our results show the relevance of the steric component in orienting the SoM in an energetically accessible position toward the heme. However, the corresponding intrinsic reactivity and electronic complementarity within the active site must be accurately evaluated in order to obtain a meaningful reaction prediction, from which the predominant SoM can be determined. The F120I mutation lowered the activation barrier for the major site and one of the minor SoMs. However, it had an impact neither on the CYP2D6 enantioselectivity preference of the oxidation reaction nor on the stereoselectivity from the substrate point of view.

Curcumin analogs as the inhibitors of TLR4 pathway in inflammation and their drug like potentialities: a computer-based study

Toll-like receptor 4 (TLR4) pathway is one of the major pathways that mediate the inflammation in human body. There are different anti-inflammatory drugs available in the market which specifically act on different signaling proteins of TLR4 pathway but they do have few side effects and other limitations for intended use in human body. In this study, Curcumin and its different analogs have been analyzed as the inhibitors of signaling proteins, i.e. Cycloxygenase-2 (COX-2), inhibitor of kappaβ kinase (IKK) and TANK binding kinase-1 (TBK-1) of TLR4 pathway using different computational tools. Initially, three compounds were selected for respective target based on free binding energy among which different compounds were reported to have better binding affinity than commercially available drug (control). Upon continuous computational exploration with induced fit docking (IFD), 6-Gingerol, Yakuchinone A and Yakuchinone B were identified as the best inhibitors of COX-2, IKK, and TBK-1 respectively. Then their drug-like potentialities were analyzed in different experiments where they were also predicted to perform well. Hopefully, this study will uphold the efforts of researchers to identify anti-inflammatory drugs from natural sources.

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.

Structure-Guided Approach to Identify Potential Inhibitors of Large Envelope Protein to Prevent Hepatitis B Virus Infection

Hepatitis B virus (HBV) infection is one of the major causes of liver diseases, which can lead to hepatocellular carcinoma. The role of HBV envelope proteins is crucial in viral morphogenesis, infection, and propagation. Thus, blocking the pleiotropic functions of these proteins especially the PreS1 and PreS2 domains of the large surface protein (LHBs) is a promising strategy for designing efficient antivirals against HBV infection. Unfortunately, the structure of the LHBs protein has not been elucidated yet, and it seems that any structure-based drug discovery is critically dependent on this. To find effective inhibitors of LHBs, we have modeled and validated its three-dimensional structure and subsequently performed a virtual high-throughput screening against the ZINC database using RASPD and ParDOCK tools. We have identified four compounds, ZINC11882026, ZINC19741044, ZINC00653293, and ZINC15000762, showing appreciable binding affinity with the LHBs protein. The drug likeness was further validated using ADME screening and toxicity analysis. Interestingly, three of the four compounds showed the formation of hydrogen bonds with amino acid residues lying in the capsid binding region of the PreS1 domain of LHBs, suggesting the possibility of inhibiting the viral assembly and maturation process. The identification of potential lead molecules will help to discover more potent inhibitors with significant antiviral activities.

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.

Molecular docking and pharmacokinetic evaluation of natural compounds as targeted inhibitors against Crz1 protein in Rhizoctonia solani

Crz1p regulates Calcineurin, a serine-threonine-specific protein phosphatase, in Rhizoctonia solani. It has attracted consideration as a novel target of antifungal therapy based on studies in numerous pathogenic fungi, including, Cryptococcus neoformans, Candida albicans and Aspergillus fumigatus. To investigate whether Calcineurin can be a useful target for the treatment of Crz1 protein in R. solani causing wet root rot in Chickpea. The work presented here reports the in-silico studies of Crz1 protein against natural compounds. This study Comprises of quantitative structure-toxicity relationship (QSTR) and quantitative structure-activity relationship (QSAR). All compounds showed high binding energy for Crz1 protein through molecular docking. Further, a pharmacokinetic study revealed that these compounds had minimal side effects. Biological activity spectrum prediction of these compounds showed potential antifungal properties by showing significant interaction with Crz1. Hence, these compounds can be used for the prevention and treatment of wet root rot in Chickpea.

Understanding the relevance of herb-drug interaction studies with special focus on interplays: a prerequisite for integrative medicine

Integrative medicine refers to the blending of conventional and evidence-based complementary medicines and therapies with the aim of using the most appropriate of either or both modalities for ultimate patient benefits. One of the major hurdles for the same is the chances of potential herb–drug interactions (HDIs). These HDIs could be beneficial or harmful, or even fatal; therefore, a thorough understanding of the eventualities of HDIs is essential so that a successful integration of the modern and complementary alternative systems of medicine could be achieved. Here, we summarize all the important points related to HDIs, including types, tools/methods for study, and prediction of the HDIs, along with a special focus on interplays between drug metabolizing enzymes and transporters. In addition, this article covers future perspective, with a focus on background endogenous players of interplays and approaches to predict the drug–disease–herb interactions so as to fetch the desired effects of these interactions.

Can the inhibition of cytochrome P450 in aquatic invertebrates due to azole fungicides be estimated with in silico and in vitro models and extrapolated between species?

Azole fungicides, designed to halt fungal growth by specific inhibition of fungal cytochrome P450 (CYP51), inhibit cytochrome P450s involved in the metabolism of xenobiotics in several non-target organisms thus raising environmental concern. The present study investigates the degree by which inhibition strengths of azoles toward cytochrome P450 in rat liver, the insect Chironomus riparius larvae and the snail Lymnaea stagnalis can be extrapolated from estimated in silico affinities. Azoles' affinities toward human cytochrome P450 isoforms involved in xenobiotic metabolism (CYP3A4, CYP2C9 and CYP2D6) as well as fungal CYP51 were estimated with a ligand-protein docking model based on the ChemScore scoring function. Estimated affinities toward the selected enzymatic structures correlated strongly with measured inhibition strengths in rat liver (ChemScore vs. logIC₅₀ among cytochrome P450 isoforms: -0.662 < r < -0.891, n = 17 azoles), while weaker correlations were found for C. riparius larvae (-0.167 < r < -0.733, n = 9) and L. stagnalis (-0.084 < r < -0.648, n = 8). Inhibition strengths toward C. riparius and rat liver activities were found to be highly correlated to each other (r: 0.857) while no significant relationship was found between either of the species and L. stagnalis. The inhibition of cytochrome P450 due to azole fungicides could be estimated in vitro and to a lesser extent in silico for C. riparius but not for L. stagnalis, possibly due to different enzymatic susceptibility toward azole inhibition among the species.

RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules

Background

Experimental approaches for determining the metabolic properties of the drug candidates are usually expensive, time-consuming and labor intensive. There is a great deal of interest in developing computational methods to accurately and efficiently predict the metabolic decomposition of drug-like molecules, which can provide decisive support and guidance for experimentalists.

Results

Here, we developed an integrated, low false positive and reaction types extensive metabolism prediction approach called RD-Metabolizer (Reaction Database-based Metabolizer). RD-Metabolizer firstly employed the detailed reaction SMARTS patterns to encode different metabolism reaction types with the aim of covering larger chemical reaction space. 2D fingerprint similarity calculation model was built to calculate the metabolic probability of each site in a molecule. RDKit was utilized to act on pre-written reaction SMARTS patterns to correct the metabolic ranking of each site in a molecule generated by the 2D fingerprint similarity calculation model as well as generate corresponding structures of metabolites, thus helping to reduce the false positive metabolites. Two test sets were adopted to evaluate the performance of RD-Metabolizer in predicting SOMs and structures of metabolites. The results indicated that RD-Metabolizer was better than or at least as good as several widely used SOMs prediction methods. Besides, the number of false positive metabolites was obviously reduced compared with MetaPrint2D-React.

Conclusions

The accuracy and efficiency of RD-Metabolizer was further illustrated by a metabolism prediction case of AZD9291, which is a mutant-selective EGFR inhibitor. RD-Metabolizer will serve as a useful toolkit for the early metabolic properties assessment of drug-like molecules at the preclinical stage of drug discovery.Graphical abstract

Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors Based on Deep Belief Network

The cytochrome P450 (CYP) superfamily, exists in the human liver, is responsible for more than 90% of the metabolism of clinical drugs. So it is necessary to adopt a new kind of computer simulation methods that can predict the rejection capability of compounds for a concrete CYPs isoform. In this work, a model is presented for classification of CYP450 1A2 inhibitors and noninhibitors based on a multi-tiered deep belief network (DBN) on a large dataset. The dataset composed of more than 13,000 heterogeneous compounds was acquired from PubChem. Firstly, 139 2D and 53 3D descriptors are calculated and preprocessed. Then, the unsupervised learning method is used to train DBN model to automatically extract multiple levels of distributed representation from the descriptors of training set. Finally, by using testing set and external validation set, we evaluate the classified performance of DBN for the inhibition of CYP1A2. Meanwhile, the proposed model is compared with shallow machine learning models (support vector machine (SVM) and artificial neural network (ANN)). We also discussed the performance of DBN by comparing it with different features combination. The experimental results showed that DBN has a better prediction ability compared with SVM and ANN. And these models combined with the features of 2D and 3D obtain the best forecast accuracy.

Functional analysis of CYP6ER1, a P450 gene associated with imidacloprid resistance in Nilaparvata lugens

The cytochrome P450 CYP6ER1 has been reported to play an important role in imidacloprid resistance of the brown planthopper (BPH), Nilaparvata lugens, and is overexpressed in most resistant populations. In the present study, we confirmed that CYP6ER1 expression can be induced by certain levels of imidacloprid. Developmental expression analysis revealed that CYP6ER1 was expressed highly in the adult stage, and tissue distribution analysis showed that CYP6ER1 was expressed mainly in the fat body and midgut. RNA interference (RNAi) of CYP6ER1 and transgenic expression of CYP6ER1 in Drosophila melanogaster both suggested that the expression of CYP6ER1 is sufficient to confer imidacloprid resistance. Furthermore, we analyzed the interaction of imidacloprid and CYP6ER1 monooxygenase by using dynamic simulations and molecular docking. We found that Nitrogen atoms in the heterocycle of the imidacloprid molecule may bind to iron atoms in the center of the homology model of CYP6ER1 via 4,5-dihedro-1H-imidazole. This finding contributes to a better understanding of how CYP6ER1 takes part in the insecticide metabolism.

Synthesis, antiamoebic and molecular docking studies of furan-thiazolidinone hybrids

In continuation of our previous work, a series of furan-thiazolidinone hybrids was prepared by Knoevenagel condensation of 3-(furan-2-ylmethyl)-2-(phenylimino)-1, 3-thiazolidin-4-one with different aryl aldehydes in presence of strong base. Some members of the series exhibited remarkable antiamoebic activity and cell viability. Three compounds (3, 6 and 11) showed excellent binding energy for Entamoeba histolytica O-acetyle-l-serine sulfohydrolase and Entamoeba histolytica thioredoxin reductase. These compounds demonstrated significant inhibition of O-acetyle-l-serine sulfohydrolase. The promising antiamoebic activity and enzymatic assay of 3, 6 and 11 make them promising molecules for further lead optimization in the development of novel antiamoebic agents.

Quantum Chemical and Docking Insights into Bioavailability Enhancement of Curcumin by Piperine in Pepper

We combine quantum chemical and molecular docking techniques to provide new insights into how piperine molecule in various forms of pepper enhances bioavailability of a number of drugs including curcumin in turmeric for which it increases its bioavailability by a 20-fold. We have carried out docking studies of quantum chemically optimized piperine structure binding to curcumin, CYP3A4 in cytochrome P450, p-Glycoprotein and UDP-glucuronosyltransferase (UGT), the enzyme responsible for glucuronosylation, which increases the solubility of curcumin. All of these studies establish that piperine binds to multiple sites on the enzymes and also intercalates with curcumin forming a hydrogen bonded complex with curcumin. The conjugated network of double bonds and the presence of multiple charge centers of piperine offer optimal binding sites for piperine to bind to enzymes such as UDP-GDH, UGT, and CYP3A4. Piperine competes for curcumin's intermolecular hydrogen bonding and its stacking propensity by hydrogen bonding with enolic proton of curcumin. This facilitates its metabolic transport, thereby increasing its bioavailability both through intercalation into curcumin layers through intermolecular hydrogen bonding, and by inhibiting enzymes that cause glucuronosylation of curcumin.

Multiple Mutations on the Second Acetylcholinesterase Gene Associated With Dimethoate Resistance in the Melon Aphid, Aphis gossypii (Hemiptera: Aphididae)

The melon aphid, Aphis gossypii Glover (Hemiptera: Aphididae), is an important cosmopolitan and extremely polyphagous species capable of causing direct and indirect damage to various crops. Insecticide resistance in melon aphids is of particular concern. To determine the basis of resistance, organophosphate (OP)-resistant strains of A. gossypii were obtained by continuous selection with dimethoate in the laboratory, and resistance mechanisms were investigated along with susceptible strains. Three resistant strains LKR-1, LKR-2, and LKR-3 exhibiting 270-, 243-, and 210-fold resistance obtained after 30 generations of selection with dimethoate, respectively, were utilized in this study. The role of acetylcholinesterase (AChE), a target enzyme for OPs and carbamates (CMs), was investigated. AChE enzyme assay revealed that there was no significant change in the activities of AChE in resistant and susceptible strains. However, AChE inhibitory assay showed that 50% of the enzyme activity in resistant strains was inhibited at significantly higher concentration of dimethoate (131.87, 158.65, and 99.29 µmolL(−1)) as compared with susceptible strains (1.75 and 2.01 µmolL(−1)), indicating AChE insensitivity owing to altered AChE. Molecular diagnostic tool polymerase chain reaction-restriction fragment length polymorphism revealed the existence of two consistent non-synonymous point mutations, single-nucleotide polymorphism, viz., A302S (equivalent to A201 in Torpedo californica Ayres) and S431F (equivalent to F331 in T. californica), in the AChE gene Ace2 of resistant strains. Further, cloning and sequencing of a partial fragment of Ace2 (897 bp) gene from susceptible and resistant strains revealed an additional novel mutation G221A in resistant strains, LKR-1 and LKR-2. Susceptible Ace2 genes shared 99.6 and 98.9% identity at the nucleic acid and amino acid levels with resistant ones, respectively. Functional analysis of these point mutations was assessed by in silico docking studies using the modeled wild-type and naturally mutated AChE2. Computational analysis showed that the conformational changes in AChE2 active site due to structural gene substitutions (A302S, S431F, and G221A) significantly reduced the level of ligand (OP-dimethoate, omethoate, and CM-pirimicarb) binding, suggesting that they are potentially associated with resistance development. These results unambiguously suggested that multiple mutations located in the enzyme active site are responsible for AChE insensitivity to dimethoate and are likely the molecular basis for dimethoate resistance in these selected field populations of A. gossypii.

Thienopyrimidine sulphonamide hybrids: design, synthesis, antiprotozoal activity and molecular docking studies

A series of hybrid compounds containing the thienopyrimidine scaffold with sulphonamide piperazine skeleton were synthesized and evaluated against K1 strain of Plasmodium falciparum and the HM1:1MSS strain of Entamoeba histolytica, respectively

A Mechanism-Based Model for the Prediction of the Metabolic Sites of Steroids Mediated by Cytochrome P450 3A4

Early prediction of xenobiotic metabolism is essential for drug discovery and development. As the most important human drug-metabolizing enzyme, cytochrome P450 3A4 has a large active cavity and metabolizes a broad spectrum of substrates. The poor substrate specificity of CYP3A4 makes it a huge challenge to predict the metabolic site(s) on its substrates. This study aimed to develop a mechanism-based prediction model based on two key parameters, including the binding conformation and the reaction activity of ligands, which could reveal the process of real metabolic reaction(s) and the site(s) of modification. The newly established model was applied to predict the metabolic site(s) of steroids; a class of CYP3A4-preferred substrates. 38 steroids and 12 non-steroids were randomly divided into training and test sets. Two major metabolic reactions, including aliphatic hydroxylation and N-dealkylation, were involved in this study. At least one of the top three predicted metabolic sites was validated by the experimental data. The overall accuracy for the training and test were 82.14% and 86.36%, respectively. In summary, a mechanism-based prediction model was established for the first time, which could be used to predict the metabolic site(s) of CYP3A4 on steroids with high predictive accuracy.

Prediction of cytochrome P450 mediated metabolism

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

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

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.

Epoxyalcohols: bioactivation and conjugation required for skin sensitization

Allylic alcohols, such as geraniol 1, are easily oxidized by varying mechanisms, including the formation of both 2,3-epoxides and/or aldehydes. These epoxides, aldehydes, and epoxy-aldehydes can be interconverted to each other, and the reactivity of them all must be considered when considering the sensitization potential of the parent allylic alcohol. An in-depth study of the possible metabolites and autoxidation products of allylic alcohols is described, covering the formation, interconversion, reactivity, and sensitizing potential thereof, using a combination of in vivo, in vitro, in chemico, and in silico methods. This multimodal study, using the integration of diverse techniques to investigate the sensitization potential of a molecule, allows the identification of potential candidate(s) for the true culprit(s) in allergic responses to allylic alcohols. Overall, the sensitization potential of the investigated epoxyalcohols and unsaturated alcohols was found to derive from metabolic oxidation to the more potent aldehyde where possible. Where this is less likely, the compound remains weakly or nonsensitizing. Metabolic activation of a double bond to form a nonconjugated, nonterminal epoxide moiety is not enough to turn a nonsensitizing alcohol into a sensitizer, as such epoxides have low reactivity and low sensitizing potency. In addition, even an allylic 2,3-epoxide moiety is not necessarily a potent sensitizer, as shown for 2, where formation of the epoxide weakens the sensitization potential.

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.

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

Quantum mechanics/molecular mechanics modeling of regioselectivity of drug metabolism in cytochrome P450 2C9

Cytochrome P450 enzymes (P450s) are important in drug metabolism and have been linked to adverse drug reactions. P450s display broad substrate reactivity, and prediction of metabolites is complex. QM/MM studies of P450 reactivity have provided insight into important details of the reaction mechanisms and have the potential to make predictions of metabolite formation. Here we present a comprehensive study of the oxidation of three widely used pharmaceutical compounds (S-ibuprofen, diclofenac, and S-warfarin) by one of the major drug-metabolizing P450 isoforms, CYP2C9. The reaction barriers to substrate oxidation by the iron-oxo species (Compound I) have been calculated at the B3LYP-D/CHARMM27 level for different possible metabolism sites for each drug, on multiple pathways. In the cases of ibuprofen and warfarin, the process with the lowest activation energy is consistent with the experimentally preferred metabolite. For diclofenac, the pathway leading to the experimentally observed metabolite is not the one with the lowest activation energy. This apparent inconsistency with experiment might be explained by the two very different binding modes involved in oxidation at the two competing positions. The carboxylate of diclofenac interacts strongly with the CYP2C9 Arg108 side chain in the transition state for formation of the observed metabolite—but not in that for the competing pathway. We compare reaction barriers calculated both in the presence and in the absence of the protein and observe a marked improvement in selectivity prediction ability upon inclusion of the protein for all of the substrates studied. The barriers calculated with the protein are generally higher than those calculated in the gas phase. This suggests that active-site residues surrounding the substrate play an important role in controlling selectivity in CYP2C9. The results show that inclusion of sampling (particularly) and dispersion effects is important in making accurate predictions of drug metabolism selectivity of P450s using QM/MM methods.

Substrate selectivity of human intestinal UDP-glucuronosyltransferases (UGTs): in silico and in vitro insights

The current drug development process aims to produce safe, effective drugs within a reasonable time and at a reasonable cost. Phase II metabolism (glucuronidation) can affect drug action and pharmacokinetics to a considerable extent and so its studies and prediction at initial stages of drug development are very imperative. Extensive glucuronidation is an obstacle to oral bioavailability because the first-pass glucuronidation [or premature clearance by UDP-glucuronosyltransferases (UGTs)] of orally administered agents frequently results in poor oral bioavailability and lack of efficacy. Modeling of new chemical entities/drugs for UGTs and their kinetic data can be useful in understanding the binding patterns to be used in the design of better molecules. This review concentrates on first-pass glucuronidation by intestinal UGTs, including their topology, expression profile, and pharmacogenomics. In addition, recent advances are discussed with respect to substrate selectivity at the binding pocket, structural requirements, and mechanism of enzyme actions.

Inhibition of cytochrome P450 enzymes and biochemical aspects of mechanism-based inactivation (MBI)

Mechanism-based inactivation (MBI) often involves metabolic bioactivation of the xenobiotic by cytochrome P450s (CYPs) to an electrophilic reactive intermediate and results in quasi-irreversible or irreversible inactivation. Such reactive intermediate can cause quasi-irreversible inhibition through coordination to the ferrous state, Fe(II), of the P450 enzyme forming a tight noncovalent bond leading to the formation of metabolic-intermediate complex (MIC). By contrast, irreversible inactivation is one of the most common causes for the observed drug–drug interaction (DDI) and usually implies the formation of a covalent bond between the metabolite and the enzyme via alkylation of either the heme or the P450 apoprotein. Here we illustrate the important points of the current literature understanding of the mechanisms of inhibition of CYP enzymes with emphasis on general mechanistic aspects of MBI for some drugs/moieties associated with the phenomenon. Additionally, the utility of computational and in silico approaches to address bioactivation issues will be briefly discussed.

RS-Predictor models augmented with SMARTCyp reactivities: robust metabolic regioselectivity predictions for nine CYP isozymes

RS-Predictor is a tool for creating pathway-independent, isozyme-specific, site of metabolism (SOM) prediction models using any set of known cytochrome P450 (CYP) substrates and metabolites. Until now, the RS-Predictor method was only trained and validated on CYP 3A4 data, but in the present study, we report on the versatility the RS-Predictor modeling paradigm by creating and testing regioselectivity models for substrates of the nine most important CYP isozymes. Through curation of source literature, we have assembled 680 substrates distributed among CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1, and 3A4, the largest publicly accessible collection of P450 ligands and metabolites released to date. A comprehensive investigation into the importance of different descriptor classes for identifying the regioselectivity mediated by each isozyme is made through the generation of multiple independent RS-Predictor models for each set of isozyme substrates. Two of these models include a density functional theory (DFT) reactivity descriptor derived from SMARTCyp. Optimal combinations of RS-Predictor and SMARTCyp are shown to have stronger performance than either method alone, while also exceeding the accuracy of the commercial regioselectivity prediction methods distributed by Optibrium and Schrödinger, correctly identifying a large proportion of the metabolites in each substrate set within the top two rank-positions: 1A2 (83.0%), 2A6 (85.7%), 2B6 (82.1%), 2C19 (86.2%), 2C8 (83.8%), 2C9 (84.5%), 2D6 (85.9%), 2E1 (82.8%), 3A4 (82.3%), and merged (86.0%). Comprehensive datamining of each substrate set and careful statistical analyses of the predictions made by the different models revealed new insights into molecular features that control metabolic regioselectivity and enable accurate prospective prediction of likely SOMs.

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.

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.

Identification of cytochrome P450 2D6 and 2C9 substrates and inhibitors by QSAR analysis

This paper presents four new QSAR models for CYP2C9 and CYP2D6 substrate recognition and inhibitor identification based on human clinical data. The models were used to screen a large data set of environmental chemicals for CYP activity, and to analyze the frequency of CYP activity among these compounds. A large fraction of these chemicals were found to be CYP active, and thus potentially capable of affecting human physiology. 20% of the compounds within applicability domain of the models were predicted to be CYP2C9 substrates, and 17% to be inhibitors. The corresponding numbers for CYP2D6 were 9% and 21%. Where the majority of CYP2C9 active compounds were predicted to be both a substrate and an inhibitor at the same time, the CYP2D6 active compounds were primarily predicted to be only inhibitors. It was demonstrated that the models could identify compound classes with a high occurrence of specific CYP activity. An overrepresentation was seen for poly-aromatic hydrocarbons (group of procarcinogens) among CYP2C9 active and mutagenic compounds compared to CYP2C9 inactive and mutagenic compounds. The mutagenicity was predicted with a QSAR model based on Ames in vitro test data.