Structure-based Drug Metabolism Predictions for Drug Design

Insights into Inhalation Drug Disposition: The Roles of Pulmonary Drug-Metabolizing Enzymes and Transporters

The past five decades have witnessed remarkable advancements in the field of inhaled medicines targeting the lungs for respiratory disease treatment. As a non-invasive drug delivery route, inhalation therapy offers numerous benefits to respiratory patients, including rapid and targeted exposure at specific sites, quick onset of action, bypassing first-pass metabolism, and beyond. Understanding the characteristics of pulmonary drug transporters and metabolizing enzymes is crucial for comprehending efficient drug exposure and clearance processes within the lungs. These processes are intricately linked to both local and systemic pharmacokinetics and pharmacodynamics of drugs. This review aims to provide a comprehensive overview of the literature on lung transporters and metabolizing enzymes while exploring their roles in exogenous and endogenous substance disposition. Additionally, we identify and discuss the principal challenges in this area of research, providing a foundation for future investigations aimed at optimizing inhaled drug administration. Moving forward, it is imperative that future research endeavors to focus on refining and validating in vitro and ex vivo models to more accurately mimic the human respiratory system. Such advancements will enhance our understanding of drug processing in different pathological states and facilitate the discovery of novel approaches for investigating lung-specific drug transporters and metabolizing enzymes. This deeper insight will be crucial in developing more effective and targeted therapies for respiratory diseases, ultimately leading to improved patient outcomes.

Role of Conformational Dynamics of Sulfotransferases SULT1A1 and SULT1A3 in Substrate Specificity

Sulfotransferases (SULTs) are phase II metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3'-Phosphoadenosine 5'-Phosphosulfate (PAPS) to a wide variety of endogenous compounds, drugs and natural products. Although SULT1A1 and SULT1A3 share 93% identity, SULT1A1, the most abundant SULT isoform in humans, exhibits a broad substrate range with specificity for small phenolic compounds, while SULT1A3 displays a high affinity toward monoamine neurotransmitters like dopamine. To elucidate the factors determining the substrate specificity of the SULT1 isoenzymes, we studied the dynamic behavior and structural specificities of SULT1A1 and SULT1A3 by using molecular dynamics (MD) simulations and ensemble docking of common and specific substrates of the two isoforms. Our results demonstrated that while SULT1A1 exhibits a relatively rigid structure by showing lower conformational flexibility except for the lip (loop L1), the loop L2 and the cap (L3) of SULT1A3 are extremely flexible. We identified protein residues strongly involved in the recognition of different substrates for the two isoforms. Our analyses indicated that being more specific and highly flexible, the structure of SULT1A3 has particularities in the binding site, which are crucial for its substrate selectivity.

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

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

Discovery of Hepatotoxic Equivalent Markers and Mechanism of Polygonum multiflorum Thunb. by Metabolomics Coupled with Molecular Docking

Polygonum multiflorum Thunb. (PMT), a commonly used Chinese herbal medicine for treating diseases such as poisoning and white hair, has attracted constant attention due to the frequent occurrence of liver injury incidents. To date, its hepatotoxic equivalent markers (HEMs) and potential hepatotoxic mechanisms are still unclear. In order to clarify the HEMs of PMT and further explore the potential mechanisms of hepatotoxicity, firstly, the chemical constituents in PMT extract were globally characterized, and the fingerprints of PMT extracts were established along with the detection of their hepatotoxicity in vivo. Then, the correlations between hepatotoxic features and component contents were modeled by chemometrics to screen HEMs of PMT, which were then further evaluated. Finally, the hepatotoxic mechanisms of PMT were investigated using liver metabolomics and molecular docking. The results show that the chemical combination of 2,3,5,4-tetrahydroxystilbene-2-O-β-D-glucoside (TSG) and emodin-8-O-glucoside (EG) was discovered as the HEMs of PMT through pre-screening and verifying process. Liver metabolomics revealed that PMT caused liver injury by interfering with purine metabolism, which might be related to mitochondrial function disorder and oxidative injury via the up-regulations of xanthosine and xanthine, and the down-regulation of 5' nucleotidase (NT5E) and adenylate kinase 2 (AK2). This study not only found that the HEMs of PMT were TSG and EG, but also clarified that PMT might affect purine metabolism to induce liver injury, which contributed to our understanding of the underlying mechanisms of PMT hepatotoxicity.

Effect of Binding Linkers on the Efficiency and Metabolite Profile of Biomimetic Reactions Catalyzed by Immobilized Metalloporphyrin

The investigation of liver-related metabolic stability of a drug candidate is a widely used key strategy in early-stage drug discovery. Metalloporphyrin-based biomimetic catalysts are good and well-described models of the function of CyP450 in hepatocytes. In this research, the immobilization of an iron porphyrin was performed on nanoporous silica particles via ionic interactions. The effect of the metalloporphyrin binding linkers was investigated on the catalytic efficiency and the metabolic profile of chloroquine as a model drug. The length of the amino-substituted linkers affects the chloroquine conversion as well as the ratio of human major and minor metabolites. While testing the immobilized catalysts in the continuous-flow reactor, results showed that the presented biomimetic system could be a promising alternative for the early-stage investigation of drug metabolites regarding analytical or synthetic goals as well.

ABCG2/BCRP transport mechanism revealed through kinetically excited targeted molecular dynamics simulations

ABCG2/BCRP is an ABC transporter that plays an important role in tissue protection by exporting endogenous substrates and xenobiotics. ABCG2 is of major interest due to its involvement in multidrug resistance (MDR), and understanding its complex efflux mechanism is essential to preventing MDR and drug-drug interactions (DDI). ABCG2 export is characterized by two major conformational transitions between inward- and outward-facing states, the structures of which have been resolved. Yet, the entire transport cycle has not been characterized to date. Our study bridges the gap between the two extreme conformations by studying connecting pathways. We developed an innovative approach to enhance molecular dynamics simulations, ‘kinetically excited targeted molecular dynamics’, and successfully simulated the transitions between inward- and outward-facing states in both directions and the transport of the endogenous substrate estrone 3-sulfate. We discovered an additional pocket between the two substrate-binding cavities and found that the presence of the substrate in the first cavity is essential to couple the movements between the nucleotide-binding and transmembrane domains. Our study shed new light on the complex efflux mechanism, and we provided transition pathways that can help to identify novel substrates and inhibitors of ABCG2 and probe new drug candidates for MDR and DDI.

Δ-Quantum machine-learning for medicinal chemistry

Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.

In Vitro Evaluation of the Anti-Diabetic Potential of Aqueous Acetone Helichrysum petiolare Extract (AAHPE) with Molecular Docking Relevance in Diabetes Mellitus

Diabetes mellitus (DM) is a chronic metabolic condition that can lead to significant complications and a high fatality rate worldwide. Efforts are ramping up to find and develop novel α-glucosidase and α-amylase inhibitors that are both effective and potentially safe. Traditional methodologies are being replaced with new techniques that are less complicated and less time demanding; yet, both the experimental and computational strategies are viable and complementary in drug discovery and development. As a result, this study was conducted to investigate the in vitro anti-diabetic potential of aqueous acetone Helichrysum petiolare and B.L Burtt extract (AAHPE) using a 2-NBDG, 2-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl) amino)-2-deoxy-d-glucose uptake assay. In addition, we performed molecular docking of the flavonoid constituents identified and quantified by liquid chromatography-mass spectrometry (LC-MS) from AAHPE with the potential to serve as effective and safe α-amylase and α-glucosidase inhibitors, which are important in drug discovery and development. The results showed that AAHPE is a potential inhibitor of both α-amylase and α-glucosidase, with IC₅₀ values of 46.50 ± 6.17 (µg/mL) and 37.81 ± 5.15 (µg/mL), respectively. This is demonstrated by a significant increase in the glucose uptake activity percentage in a concentration-dependent manner compared to the control, with the highest AAHPE concentration of 75 µg/mL of glucose uptake activity being higher than metformin, a standard anti-diabetic drug, in the insulin-resistant HepG2 cell line. The molecular docking results displayed that the constituents strongly bind α-amylase and α-glucosidase while achieving better binding affinities that ranged from ΔG = -7.2 to -9.6 kcal/mol (compared with acarbose ΔG = -6.1 kcal/mol) for α-amylase, and ΔG = -7.3 to -9.0 kcal/mol (compared with acarbose ΔG = -6.3 kcal/mol) for α-glucosidase. This study revealed the potential use of the H. petiolare plant extract and its phytochemicals, which could be explored to develop potent and safe α-amylase and α-glucosidase inhibitors to treat postprandial glycemic levels in diabetic patients.

Computational Analysis of Chemical Space of Natural Compounds Interacting with Sulfotransferases

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

Insights into the substrate binding mechanism of SULT1A1 through molecular dynamics with excited normal modes simulations

Sulfotransferases (SULTs) are phase II drug-metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3′-phosphoadenosine 5′-phosphosulfate (PAPS) to a substrate. It has been previously suggested that a considerable shift of SULT structure caused by PAPS binding could control the capability of SULT to bind large substrates. We employed molecular dynamics (MD) simulations and the recently developed approach of MD with excited normal modes (MDeNM) to elucidate molecular mechanisms guiding the recognition of diverse substrates and inhibitors by SULT1A1. MDeNM allowed exploring an extended conformational space of PAPS-bound SULT1A1, which has not been achieved up to now by using classical MD. The generated ensembles combined with docking of 132 SULT1A1 ligands shed new light on substrate and inhibitor binding mechanisms. Unexpectedly, our simulations and analyses on binding of the substrates estradiol and fulvestrant demonstrated that large conformational changes of the PAPS-bound SULT1A1 could occur independently of the co-factor movements that could be sufficient to accommodate large substrates as fulvestrant. Such structural displacements detected by the MDeNM simulations in the presence of the co-factor suggest that a wider range of drugs could be recognized by PAPS-bound SULT1A1 and highlight the utility of including MDeNM in protein–ligand interactions studies where major rearrangements are expected.

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.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

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

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Meat Proteins as Dipeptidyl Peptidase IV Inhibitors and Glucose Uptake Stimulating Peptides for the Management of a Type 2 Diabetes Mellitus In Silico Study

Diabetes mellitus is a non-communicable disease entity currently constituting one of the most significant health problems. The development of effective therapeutic strategies for the prevention and/or treatment of diabetes mellitus based on the selection of methods to restore and maintain blood glucose homeostasis is still in progress. Among the different courses of action, inhibition of dipeptidyl peptidase IV (DPP-IV) can improve blood glucose control in diabetic patients. Pharmacological therapy offering synthetic drugs is commonly used. In addition to medication, dietary intervention may be effective in combating metabolic disturbances caused by diabetes mellitus. Food proteins as a source of biologically active sequences are a potential source of anti-diabetic peptides (DPP-IV inhibitors and glucose uptake stimulating peptides). This study showed that in silico pork meat proteins digested with gastrointestinal enzymes are a potential source of bioactive peptides with a high potential to control blood glucose levels in patients with type 2 diabetes mellitus. Analysis revealed that the sequences released during in silico digestion were small dipeptides (with an average weight of 270.07 g mol^-1), and most were poorly soluble in water. The selected electron properties of the peptides with the highest bioactivity index (i.e., GF, MW, MF, PF, PW) were described using the DFT method. The contribution of hydrophobic amino acids, in particular Phe and Trp, in forming the anti-diabetic properties of peptides released from pork meat was emphasized.

Aldehyde oxidase and its role as a drug metabolizing enzyme

Aldehyde oxidase (AO) is a cytosolic enzyme that belongs to the family of structurally related molybdoflavoproteins like xanthine oxidase (XO). The enzyme is characterized by broad substrate specificity and marked species differences. It catalyzes the oxidation of aromatic and aliphatic aldehydes and various heteroaromatic rings as well as reduction of several functional groups. The references to AO and its role in metabolism date back to the 1950s, but the importance of this enzyme in the metabolism of drugs has emerged in the past fifteen years. Several reviews on the role of AO in drug metabolism have been published in the past decade indicative of the growing interest in the enzyme and its influence in drug metabolism. Here, we present a comprehensive monograph of AO as a drug metabolizing enzyme with emphasis on marketed drugs as well as other xenobiotics, as substrates and inhibitors. Although the number of drugs that are primarily metabolized by AO are few, the impact of AO on drug development has been extensive. We also discuss the effect of AO on the systemic exposure and clearance these clinical candidates. The review provides a comprehensive analysis of drug discovery compounds involving AO with the focus on developmental candidates that were reported in the past five years with regards to pharmacokinetics and toxicity. While there is only one known report of AO-mediated clinically relevant drug-drug interaction (DDI), a detailed description of inhibitors and inducers of AO known to date has been presented here and the potential risks associated with DDI. The increasing recognition of the importance of AO has led to significant progress in predicting the site of AO-mediated metabolism using computational methods. Additionally, marked species difference in expression of AO makes it is difficult to predict human clearance with high confidence. The progress made towards developing in vivo, in vitro and in silico approaches for predicting AO metabolism and estimating human clearance of compounds that are metabolized by AO have also been discussed.

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.

A comprehensive evaluation of anti-diabetic drugs on nuclear receptor PXR platform

Pregnane & Xenobiotic Receptor (PXR), one of the members of nuclear receptor superfamily, acts as a 'master-regulator' of drug metabolism and disposition machinery (DMD). Activation of PXR enables detoxification and elimination of toxic xenobiotics/endobiotics, and defends our body against chemical insults. On the contrary, PXR activation also imposes a serious concern for drug-drug interactions (DDIs). Such DDIs could either decrease the efficacy or lead to accumulation of co-administered drugs at toxic level. Therefore, it is desirable that during drug development process the small drug molecules are screened on PXR-platform prior to their clinical trial and prevent late stage failures. In view of this, we have selected a group of anti-diabetic drug molecules to examine if the success and potential failure of small molecule modulators can be pre-assessed and judiciously correlated on PXR platform. For this purpose, we have examined the PXR activation potential of the selected anti-diabetic drugs. Subsequent to screening of these anti-diabetic drugs, we elaborated the study further with rosiglitazone and pioglitazone (thiazolidinediones, TZDs) which are oral anti-diabetic formulations and have been in controversy owing to their association with cardiotoxicity and bladder cancer respectively. Our study revealed that some of the selected anti-diabetic drugs possess PXR activation potential, implying that these can up-regulate the expression of CYP3A4, UGT1A1, MDR1 and thereby can be predicted to inflict undesirable consequences.

Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database

Even though glucuronidations are the most frequent metabolic reactions of conjugation, both in quantitative and qualitative terms, they have rather seldom been investigated using computational approaches. To fill this gap, we have used the manually collected MetaQSAR metabolic reaction database to generate two models for the prediction of UGT-mediated metabolism, both based on molecular descriptors and implementing the Random Forest algorithm. The first model predicts the occurrence of the reaction and was internally validated with a Matthew correlation coefficient (MCC) of 0.76 and an area under the ROC curve (AUC) of 0.94, and further externally validated using a test set composed of 120 additional xenobiotics (MCC of 0.70 and AUC of 0.90). The second model distinguishes between O- and N-glucuronidations and was optimized by the random undersampling procedure to improve the predictive accuracy during the internal validation, with the recall measure of the minority class increasing from 0.55 to 0.78.

Catalytic α-Selective Deuteration of Styrene Derivatives

We report an operationally simple protocol for the catalytic α-deuteration of styrenes. This process proceeds via the base-catalyzed reversible addition of methanol to styrenes in DMSO -d₆ solvent. The concentration of methanol is shown to be critical for high yields and selectivities over multiple competing side reactions. The synthetic utility of α-deuterated styrenes for accessing deuterium-labeled chiral benzylic stereocenters is demonstrated.

Deuterium isotope effects in drug pharmacokinetics II: Substrate-dependence of the reaction mechanism influences outcome for cytochrome P450 cleared drugs

Two chemotypes were examined in vitro with CYPs 3A4 and 2C19 by molecular docking, metabolic profiles, and intrinsic clearance deuterium isotope effects with specifically deuterated form to assess the potential for enhancement of pharmacokinetic parameters. The results show the complexity of deuteration as an approach for pharmacokinetic enhancement when CYP enzymes are involved in metabolic clearance. With CYP3A4 the rate limiting step was chemotype-dependent. With one chemotype no intrinsic clearance deuterium isotope effect was observed with any deuterated form, whereas with the other chemotype the rate limiting step was isotopically sensitive, and the magnitude of the intrinsic clearance isotope effect was dependent on the position(s) and extent of deuteration. Molecular docking and metabolic profiles aided in identifying sites for deuteration and predicted the possibility for metabolic switching. However, the potential for an isotope effect on the intrinsic clearance cannot be predicted and must be established by examining select deuterated versions of the chemotypes. The results show how in a deuteration strategy molecular docking, in-vitro metabolic profiles, and intrinsic clearance assessments with select deuterated versions of new chemical entities can be applied to determine the potential for pharmacokinetic enhancement in a discovery setting. They also help explain the substantial failures reported in the literature of deuterated versions of drugs to elicit a systemic enhancement on pharmacokinetic parameters.

Stable cellular models of nuclear receptor PXR for high-throughput evaluation of small molecules

Pregnane & Xenobiotic Receptor (PXR) is one of the 48 members of the ligand-modulated transcription factors belonging to nuclear receptor superfamily. Though PXR is now well-established as a 'xenosensor', regulating the central detoxification and drug metabolizing machinery, it has also emerged as a key player in several metabolic disorders. This makes PXR attractive to both, researchers and pharmaceutical industry since clinical success of small drug molecules can be pre-evaluated on PXR platform. At the early stages of drug discovery, cell-based assays are used for high-throughput screening of small molecules. The future success or failure of a drug can be predicted by this approach saving expensive resources and time. In view of this, we have developed human liver cell line-based, dual-level screening and validation protocol on PXR platform having application to assess small molecules. We have generated two different stably transfected cell lines, (i) a stable promoter-reporter cell line (HepXREM) expressing PXR and a commonly used CYP3A4 promoter-reporter i.e. XREM-luciferase; and (ii) two stable cell lines integrated with proximal PXR-promoter-reporter (Hepx-1096/+43 and Hepx-497/+43). Employing HepXREM, Hepx-1096/+43 and Hepx-497/+43 stable cell lines > 25 anti-cancer herbal drug ingredients were screened for examining their modulatory effects on a) PXR transcriptional activity and, b) PXR-promoter activity. In conclusion, the present report provides a convenient and economical, dual-level screening system to facilitate the identification of superior therapeutic small molecules.

Exploring the functional impact of mutational drift in LRRK2 gene and identification of specific inhibitors for the treatment of Parkinson disease

Parkinson's disease (PD) is a disorder of the central nervous system that is caused due to the death of the dopaminergic neurons in the region of the brain called substantia nigra. Mutations in LRRK2 genes are associated with disease condition and it's been reported as crucial factor for drug resistance. Identification of deleterious mutations and studying the structural and functional impact of such mutations may lead to the identification of potential selective inhibitors. In this study, we analyzed 52 PD associated mutations, among that 20 were identified as highly deleterious. The deleterious mutations G2019S and I2020T in the kinase domain were playing a key role in causing resistance to drug levedopa. Molecular docking analyses have been performed to understand the binding affinity of levodapa with LRRK2 in wild and mutant condition. Molecular docking results show that levedopa binds differentially and obtained less number of hydrogen bonds in compared with wild type LRRK2. In addition, molecular dynamics simulations were performed to study the efficacy of docked complexes and it was observed that the efficacy of the mutant complexes (G2019S-Levodopa and I2020T-Levodopa) affected in the presence of mutation. Finally, through virtual screening approach specific inhibitors SCHEMBL6473053 and SCHEMBL1278779 have been identified that could potentially inhibit LLRK2 mutants G2019S and I2020T respectively. Over all this computational investigation correlates the impact of genotypic modulation in structure and function of drug target which enhanced in the identification of precision medicine to treat PD.

Investigation of substrate recognition for cytochrome P450 1A2 mediated by water molecules using docking and molecular dynamics simulations

The role of water molecules in the active site of cytochrome P450 1A2 (CYP1A2) was investigated using an explicit water model to simulate biological environments. Moreover, differences in ligand recognition between the inhibitor α-naphthoflavone (ANF) and the substrate 7-ethoxyresorufin (7ER) in the CYP1A2 complex were examined. More than 200-ns molecular dynamics (MD) simulations were performed for each complex structure of CYP1A2. In the complex structure with 7ER obtained after MD simulation, some water molecules existed in the active site and formed hydrogen bonds between 7ER and some residues. However, in the complex structure with ANF, the hydrogen bond network differed. These results suggest that CYP1A2 requires water molecules in its active site for substrate recognition. The observed differences in the hydrogen bond network in the complex with ANF or 7ER may be due to the fact that ANF is an inhibitor.

In silico investigation of lavandulyl flavonoids for the development of potent fatty acid synthase-inhibitory prototypes

BACKGROUND

Inhibition of fatty acid synthase (FAS) is regarded as a sensible therapeutic strategy for the development of optimal anti-cancer agents. Flavonoids exhibit potent anti-neoplastic properties.

METHODS

The MeOH extract of Sophora flavescens was subjected to chromatographic analyses such as VLC and HPLC for the purification of active flavonoids. The DP4 chemical-shift analysis protocol was employed to investigate the elusive chirality of the lavandulyl moiety of the purified polyphenols. Induced Fit docking protocols and per-residue analyses were utilized to scrutinize structural prerequisites for hampering FAS activity. The FAS-inhibitory activity of the purified flavonoids was assessed via the incorporation of [³H] acetyl-CoA into palmitate.

RESULTS

Six flavonoids, including lavandulyl flavanones, were purified and evaluated for FAS inhibition. The lavandulyl flavanone sophoraflavanone G (2) exhibited the highest potency (IC₅₀ of 6.7±0.2μM), which was more potent than the positive controls. Extensive molecular docking studies revealed the structural requirements for blocking FAS. Per-residue interaction analysis demonstrated that the lavandulyl functional group in the active flavonoids (1-3 and 5) significantly contributed to increasing their binding affinity towards the target enzyme.

CONCLUSION

This research suggests a basis for the in silico design of a lavandulyl flavonoid-based architecture showing anti-cancer effects via enhancement of the binding potential to FAS.

GENERAL SIGNIFICANCE

FAS inhibition by flavonoids and their derivatives may offer significant potential as an approach to lower the risk of various cancer diseases and related fatalities. In silico technologies with available FAS crystal structures may be of significant use in optimizing preliminary leads.

Characterization of endoproteolytic processing of dynorphins by proprotein convertases using mouse spinal cord S9 fractions and mass spectrometry

Dynorphins are important neuropeptides with a central role in nociception and pain alleviation. Many mechanisms regulate endogenous dynorphin concentrations, including proteolysis. Proprotein convertases (PCs) are widely expressed in the central nervous system and specifically cleave at C-terminal of either a pair of basic amino acids, or a single basic residue. The proteolysis control of endogenous big dynorphin (BDyn) and dynorphin A (Dyn A) levels has a profound impact on pain perception and the role of PCs remain unclear. The objective of this study was to decipher the role of PC1 and PC2 in the proteolysis control of BDyn and Dyn A levels using cellular fractions of spinal cords from wild-type (WT), PC1(-/+) and PC2(-/+) animals and mass spectrometry. Our results clearly demonstrate that both PC1 and PC2 are involved in the proteolysis regulation of BDyn and Dyn A with a more important role for PC1. C-terminal processing of BDyn generates specific peptide fragments dynorphin 1-19, dynorphin 1-13, dynorphin 1-11 and dynorphin 1-7, and C-terminal processing of Dyn A generates dynorphin 1-13, dynorphin 1-11 and dynorphin 1-7, all these peptide fragments are associated with PC1 or PC2 processing. Moreover, the proteolysis of BDyn leads to the formation of Dyn A and Leu-Enk, two important opioid peptides. The rate of formation of both is significantly reduced in cellular fractions of spinal cord mutant mice. As a consequence, even the partial inhibition of PC1 or PC2 may impair the endogenous opioid system.

Biotransformation of 8:2 fluorotelomer alcohol by recombinant human cytochrome P450s, human liver microsomes and human liver cytosol

Biotransformation of 8:2 fluorotelomer alcohol (8:2 FTOH) can form potentially more toxic metabolites. However, the responsible cytochrome P450 (CYP) isoform(s) and phase II metabolism have not been studied in humans. Here, we characterized the in vitro metabolism of 8:2 FTOH by recombinant human CYPs, human liver microsomes, and human liver cytosol. The results showed that among the 11 isoforms investigated, CYP2C19 was the only enzyme capable of catalyzing 8:2 FTOH with Km and Vmax values of 18.8 μM and 8.52 pmol min(-1) pmol(-1) P450, respectively. The phase I metabolite was identified as 8:2 fluorotelomer aldehyde (8:2 FTAL). HLMs also catalyzed 8:2 FTOH transformation, with the Vmax and intrinsic clearance (CLint) values similar to those of CYP2C19 after the protein content is taken into account. Molecular docking showed that the hydroxyl group of 8:2 FTOH accesses the heme iron-oxo of CYP2C19 in an energetically favored orientation. 8:2 FTOH was also transformed by phase II enzymes to form O-glucuronide and O-sulfate conjugates. The CLint values follow the order of sulfation > oxidation > glucuronidation, suggesting that conjugation is the major metabolic pathway, which explains the low yield of perfluoroalkyl acids (PFCAs). These results provide new insight into fluorotelomer alcohol biotransformation and indirect human exposure to PFCAs.

Physiological, pharmacokinetic and liver metabolism comparisons between 3-, 6-, 12- and 18-month-old male Sprague Dawley rats under ketamine-xylazine anesthesia

The main objective of this study was to compare the physiological changes (withdrawal and corneal reflexes, respiratory and cardiac frequency, blood oxygen saturation, and rectal temperature) following intraperitoneal administration of ketamine (80 mg/kg) and xylazine (10 mg/kg) to 3-, 6-, 12- and 18-month-old male Sprague Dawley rats (n=6/age group). Plasma pharmacokinetics, liver metabolism, and blood biochemistry were examined for a limited number of animals to better explain anesthetic drug effects. Selected organs were collected for histopathology. The results for the withdrawal and corneal reflexes suggest a shorter duration and decreased depth of anesthesia with aging. Significant cardiac and respiratory depression, as well as decreased blood oxygen saturation, occurred in all age groups however, cardiac frequency was the most affected parameter with aging, since the 6-, 12-, and 18-month-old animals did not recuperate to normal values during recovery from anesthesia. Pharmacokinetic parameters (T_1/2 and AUC) increased and drug clearance decreased with aging, which strongly suggests that drug exposure is associated with the physiological results. The findings for liver S9 fractions of 18-month-old rats compared with the other age groups suggest that following a normal ketamine anesthetic dose (80 mg/kg), drug metabolism is impaired, leading to a significant increase of drug exposure. In conclusion, age and related factors have a substantial effect on ketamine and xylazine availability, which is reflected by significant changes in pharmacokinetics and liver metabolism of these drugs, and this translates into shorter and less effective anesthesia with increasing age.

Screening alpha-glucosidase and alpha-amylase inhibitors from natural compounds by molecular docking in silico

The alpha-glucosidase inhibitor is a common oral anti-diabetic drug used for controlling carbohydrates normally converted into simple sugars and absorbed by the intestines. However, some adverse clinical effects have been observed. The present study seeks an alternative drug that can regulate the hyperglycemia by down-regulating alpha-glucosidase and alpha-amylase activity by molecular docking approach to screen the hyperglycemia antagonist against alpha-glucosidase and alpha-amylase activities from the 47 natural compounds. The docking data showed that Curcumin, 16-hydroxy-cleroda-3,13-dine-16,15-olide (16-H), Docosanol, Tetracosanol, Antroquinonol, Berberine, Catechin, Quercetin, Actinodaphnine, and Rutin from 47 natural compounds had binding ability towards alpha-amylase and alpha-glucosidase as well. Curcumin had a better biding ability of alpha-amylase than the other natural compounds. Analyzed alpha-glucosidase activity reveals natural compound inhibitors (below 0.5 mM) are Curcumin, Actinodaphnine, 16-H, Quercetin, Berberine, and Catechin when compared to the commercial drug Acarbose (3 mM). A natural compound with alpha-amylase inhibitors (below 0.5 mM) includes Curcumin, Berberine, Docosanol, 16-H, Actinodaphnine/Tetracosanol, Catechin, and Quercetin when compared to Acarbose (1 mM). When taken together, the implication is that molecular docking is a fast and effective way to screen alpha-glucosidase and alpha-amylase inhibitors as lead compounds of natural sources isolated from medicinal plants.

Synthesis, spectroscopic investigation and computational study of 3-(1-(((methoxycarbonyl)oxy)imino)ethyl)-2H-chromen-2-one

The molecular structure and vibrational modes of 3-acetylcoumarin oxime carbonate (abbreviated as 3-ACOC) have been investigated by FT-IR, FT-Raman, NMR spectra and also by computational methods using HF and B3LYP with 6-311++G(d,p) basis set. The optimized geometric parameters (bond lengths, bond angles and dihedral angles) were in good agreement with the corresponding experimental values of 3-ACOC. The calculated vibrational frequencies of normal modes from DFT method matched well with the experimental values. The complete assignments were made on the basis of the total energy distribution (TED) of the vibrational modes. NMR ((1)H and (13)C) chemical shifts were calculated by GIAO method and the results were compared with the experimental values. The other parameters like dipole moment, polarizability, first order hyperpolarizability, zero-point vibrational energy, E(HOMO), E(LUMO), heat capacity and entropy have also been computed.

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

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

Applying machine learning techniques for ADME-Tox prediction: a review

INTRODUCTION

Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety were considered some of the major causes of clinical failures in the development of new chemical entities. In this context, machine learning (ML) techniques have been often used in ADME-Tox studies due to the existence of compounds with known pharmacokinetic properties available for generating predictive models.

AREAS COVERED

This review examines the growth in the use of some ML techniques in ADME-Tox studies, in particular supervised and unsupervised techniques. Also, some critical points (e.g., size of the data set and type of output variable) must be considered during the generation of models that relate ADME-Tox properties and biological activity.

EXPERT OPINION

ML techniques have been successfully employed in pharmacokinetic studies, helping the complex process of designing new drug candidates from the use of reliable ML models. An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure-activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.

Recent progress on structural bioinformatics research of cytochrome P450 and its impact on drug discovery

Cytochrome P450 is predominantly responsible for human drug metabolism, which is of critical importance for drug discovery and development. Structural bioinformatics focuses on analysis and prediction of three-dimentional structure of biological macromolecules and elucidation of structure-function relationship as well as identification of important binding interactions. Rapid advancement of structural bioinformatics has been made over the last decade. With more information available for CYP structures, the methods of structural bioinformatics may be used in the CYP field. In this review, we demonstrate three previous studies on CYP using the methods of structural bioinformatics, including the investigation of reasons for decrease of enzymatic activity of CYP1A2 caused by a peripheral mutation, the construction of a pharmacophore model specific to active site of CYP1A2 and the prediction of the functional consequences of single residue mutation in CYP. By illustrating these studies we attempt to show the potential role of structural bioinformatics in CYP research and help better understanding the importance of structural bioinformatics in drug designing.

Precursors for cytochrome P450 profiling breath tests from an in silico screening approach

The family of cytochrome P450 enzymes (CYPs) is a major player in the metabolism of drugs and xenobiotics. Genetic polymorphisms and transcriptional regulation give a complex patient-individual CYP activity profile for each human being. Therefore, personalized medicine demands easy and non-invasive measurement of the CYP phenotype. Breath tests detect volatile organic compounds (VOCs) in the patients' exhaled air after administration of a precursor molecule. CYP breath tests established for individual CYP isoforms are based on the detection of (13)CO2 or (14)CO2 originating from CYP-catalyzed oxidative degradation reactions of isotopically labeled precursors.We present an in silico work-flow aiming at the identification of novel precursor molecules, likely to result in VOCs other than CO2 upon oxidative degradation as we aim at label-free precursor molecules. The ligand-based work-flow comprises five parts: (1) CYP profiling was encoded as a decision tree based on 2D molecular descriptors derived from established models in the literature and validated against publicly available data extracted from the DrugBank. (2) Likely sites of metabolism were identified by reactivity and accessibility estimation for abstractable hydrogen radical. (3) Oxidative degradation reactions (O- and N-dealkylations) were found to be most promising in the release of VOCs. Thus, the CYP-catalyzed oxidative degradation reaction was encoded as SMIRKS (a programming language style to implement reactions based on the SMARTS description) to enumerate possible reaction products. (4) A quantitative structure property relation (QSPR) model aiming to predict the Henry constant H was derived from data for 488 organic compounds and identifies potentially VOCs amongst CYP reaction products. (5) A blacklist of naturally occurring breath components was implemented to identify marker molecules allowing straightforward detection within the exhaled air.Evident oxidative degradation reactions served as test case for the screening approach. Comparisons to metabolism data from literature support the results' plausibility. Thus, a large scale screening for potential novel breath test precursor using the presented five stage work-flow is promising.

Studies of metabolite-protein interactions: a review

The study of metabolomics can provide valuable information about biochemical pathways and processes at the molecular level. There have been many reports that have examined the structure, identity and concentrations of metabolites in biological systems. However, the binding of metabolites with proteins is also of growing interest. This review examines past reports that have looked at the binding of various types of metabolites with proteins. An overview of the techniques that have been used to characterize and study metabolite-protein binding is first provided. This is followed by examples of studies that have investigated the binding of hormones, fatty acids, drugs or other xenobiotics, and their metabolites with transport proteins and receptors. These examples include reports that have considered the structure of the resulting solute-protein complexes, the nature of the binding sites, the strength of these interactions, the variations in these interactions with solute structure, and the kinetics of these reactions. The possible effects of metabolic diseases on these processes, including the impact of alterations in the structure and function of proteins, are also considered.

Towards structural systems pharmacology to study complex diseases and personalized medicine

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

Exposition and reactivity optimization to predict sites of metabolism in chemicals

Chemical modifications of drugs induced by phase I biotransformations significantly affect their pharmacokinetic properties. Because the metabolites produced can themselves have a pharmacological effect and an intrinsic toxicity, medicinal chemists need to accurately predict the sites of metabolism (SoM) of drugs as early as possible. However, site of metabolism prediction is rarely accompanied by a prediction of the relative abundance of the various metabolites. Such a prediction would be a great help in the study of drug– drug interactions and in the process of reducing the toxicity of potential drug candidates. The aim of this paper is to present recent developments in the prediction of xenobiotic metabolism and to use concrete examples to explain the computational mechanism employed.

Structure-based ligand design to overcome CYP inhibition in drug discovery projects

Cytochrome P450 (CYP) enzymes are key players in xenobiotic metabolism, and inhibition of CYPs can therefore result in unwanted drug-drug interactions. Within drug discovery, CYP inhibition can cause delays in the progression of candidate drugs, or even premature closure of projects. During the past decade, a massive effort in the pharmaceutical industry and academic research has produced a wealth of structural information in the CYP field. In this short review, we will describe how structure-based approaches can be used in the pharmaceutical industry to work away from CYP inhibition, with a focus on the opportunities and challenges. We will show two examples from our own work where structural information on CYP2C9 and CYP3A4 inhibitor complexes have been successfully exploited in ongoing drug discovery projects.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Correlating structure and function of drug-metabolizing enzymes: progress and ongoing challenges

This report summarizes a symposium sponsored by the American Society for Pharmacology and Experimental Therapeutics at Experimental Biology held April 20-24 in Boston, MA. Presentations discussed the status of cytochrome P450 (P450) knowledge, emphasizing advances and challenges in relating structure with function and in applying this information to drug design. First, at least one structure of most major human drug-metabolizing P450 enzymes is known. However, the flexibility of these active sites can limit the predictive value of one structure for other ligands. A second limitation is our coarse-grain understanding of P450 interactions with membranes, other P450 enzymes, NADPH-cytochrome P450 reductase, and cytochrome b5. Recent work has examined differential P450 interactions with reductase in mixed P450 systems and P450:P450 complexes in reconstituted systems and cells, suggesting another level of functional control. In addition, protein nuclear magnetic resonance is a new approach to probe these protein/protein interactions, identifying interacting b5 and P450 surfaces, showing that b5 and reductase binding are mutually exclusive, and demonstrating ligand modulation of CYP17A1/b5 interactions. One desired outcome is the application of such information to control drug metabolism and/or design selective P450 inhibitors. A final presentation highlighted development of a CYP3A4 inhibitor that slows clearance of human immunodeficiency virus drugs otherwise rapidly metabolized by CYP3A4. Although understanding P450 structure/function relationships is an ongoing challenge, translational advances will benefit from continued integration of existing and new biophysical approaches.

In silico mechanistic profiling to probe small molecule binding to sulfotransferases

Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.

Molecular dynamics simulation study and molecular docking descriptors in structure-based QSAR on acetylcholinesterase (AChE) inhibitors

In this study we present an approach for predicting the inhibitory activity of acetylcholinesterase (AChE) inhibitors by combining molecular dynamics (MD) simulation and docking studies in a structure-based quantitative structure-activity relationship (QSAR) model. The MD simulation was performed on AChE to obtain enzyme conformation in a water environment. The resulting conformation of the enzyme was used for docking with the most potent inhibitor (26a). Docking analysis revealed that hydrophobic interactions play important roles in the AChE-inhibitor complex. Then, all inhibitors that could bind simultaneously at the catalytic site and at the peripheral anionic site of AChE were docked into the enzyme and their interactions with AChE were used as new interpretable descriptors in a structure-based QSAR model. The least squares support vector regression was constructed using the four most relevant docking descriptors and one molecular structure descriptor. The Q(2) value of the model was found to be 0.790. Furthermore, to study the enzyme conformation stability, a second MD simulation was performed on AChE-inhibitor 26a complex. In MD simulation, the topological parameters of the inhibitor were derived from the PRODRG server, and partial atomic charges were modified using the B3LYP/6-31G level of theory. The radius of gyration for the complex showed that AChE conformation did not change in the presence of the inhibitors.

Advances in molecular modeling of human cytochrome P450 polymorphism

Cytochrome P450 (CYP) is a supergene family of metabolizing enzymes involved in the phase I metabolism of drugs and endogenous compounds. CYP oxidation often leads to inactive drug metabolites or to highly toxic or carcinogenic metabolites involved in adverse drug reactions (ADR). During the last decade, the impact of CYP polymorphism in various drug responses and ADR has been demonstrated. Of the drugs involved in ADR, 56% are metabolized by polymorphic phase I metabolizing enzymes, 86% among them being CYP. Here, we review the major CYP polymorphic forms, their impact for drug response and current advances in molecular modeling of CYP polymorphism. We focus on recent studies exploring CYP polymorphism performed by the use of sequence-based and/or protein-structure-based computational approaches. The importance of understanding the molecular mechanisms related to CYP polymorphism and drug response at the atomic level is outlined.