Quantitative structure-activity relationships (QSARs) within the cytochrome P450 system: QSARs describing substrate binding, inhibition and induction of P450s

Addressing chemically-induced obesogenic metabolic disruption: selection of chemicals for in vitro human PPARα, PPARγ transactivation, and adipogenesis test methods

Whilst western diet and sedentary lifestyles heavily contribute to the global obesity epidemic, it is likely that chemical exposure may also contribute. A substantial body of literature implicates a variety of suspected environmental chemicals in metabolic disruption and obesogenic mechanisms. Chemically induced obesogenic metabolic disruption is not yet considered in regulatory testing paradigms or regulations, but this is an internationally recognised human health regulatory development need. An early step in the development of relevant regulatory test methods is to derive appropriate minimum chemical selection lists for the target endpoint and its key mechanisms, such that the test method can be suitably optimised and validated. Independently collated and reviewed reference and proficiency chemicals relevant for the regulatory chemical universe that they are intended to serve, assist regulatory test method development and validation, particularly in relation to the OECD Test Guidelines Programme. To address obesogenic mechanisms and modes of action for chemical hazard assessment, key initiating mechanisms include molecular-level Peroxisome Proliferator-Activated Receptor (PPAR) α and γ agonism and the tissue/organ-level key event of perturbation of the adipogenesis process that may lead to excess white adipose tissue. Here we present a critical literature review, analysis and evaluation of chemicals suitable for the development, optimisation and validation of human PPARα and PPARγ agonism and human white adipose tissue adipogenesis test methods. The chemical lists have been derived with consideration of essential criteria needed for understanding the strengths and limitations of the test methods. With a weight of evidence approach, this has been combined with practical and applied aspects required for the integration and combination of relevant candidate test methods into test batteries, as part of an Integrated Approach to Testing and Assessment for metabolic disruption. The proposed proficiency and reference chemical list includes a long list of negatives and positives (20 chemicals for PPARα, 21 for PPARγ, and 11 for adipogenesis) from which a (pre-)validation proficiency chemicals list has been derived.

Novel saccharin analogs as promising antibacterial and anticancer agents: synthesis, DFT, POM analysis, molecular docking, molecular dynamic simulations, and cell-based assay

Saccharine is a pharmacologically significant active scaffold for various biological activities, including antibacterial and anticancer activities. Herein, saccharinyl hydrazide (1) was synthesized and converted into 2-[(2Z)-2-(1,1-dioxo-1,2-dihydro-3H-1λ⁶,2- benzothiazole-3-ylidene) hydrazinyl] acetohydrazide (5), which was employed as a key precursor for synthesizing a novel series of small molecules bearing different moieties of monosaccharides, aldehydes, and anhydrides. Potent biological activities were found against Staphylococcus and Escherichia coli, and the results indicated that compounds 6c and 10a were the most active analogs with an inhibition zone diameter of 30–35 mm. In cell-based anticancer assay over Ovcar-3 and M-14 cell lines, compound 10a was the most potent analog with IC₅₀ values of 7.64 ± 0.01 and 8.66 ± 0.01 µM, respectively. The Petra Orisis Molinspiration (POM) theoretical method was used to calculate the drug score of tested compounds and compare them with their experimental screening data. Theoretical DFT calculations were carried out in a gas phase in a set of B3LYP 6-311G (d,p). Molecular docking studies utilizing the MOE indicated the best binding mode with the highest energy interaction within the binding sites. The molecular docking for Ovcar-3 was carried out on the ovarian cancer protein (3W2S), while the molecular docking for M-14 melanoma was carried out on the melanoma cancer protein (2OPZ). The MD performed about 2ns simulations to validate selected compounds’ theoretical studies.

Novel 2-Hydroselenonicotinonitriles and Selenopheno[2, 3-b]pyridines: Efficient Synthesis, Molecular Docking-DFT Modeling, and Antimicrobial Assessment

Selenium containing heterocyclic compounds gained great interest as bioactive molecules as of late. This report explores the design, synthesis, characterization, and antimicrobial screening of new pyridine derivatives endowed with selenium moieties. A one-pot multicomponent system with a solvent-free, microwave irradiation environment was employed to afford this series. The spectroscopic techniques were exploited to verify the structures of the synthesized derivatives. Additionally, the agar diffusion method was employed to determine the antimicrobial activity of all the desired compounds. Of all the synthesized molecules, 9b, 12b, 14f, and 16d exhibited well to remarkable antibacterial and antifungal activities. Moreover, derivative 14f demonstrated the most potent antibacterial and antifungal performance. The results were also supported by molecular docking studies, utilizing the MOE (molecular operating environment) which revealed the best binding mode with the highest energy interaction within the binding pocket. Lastly, theoretical DFT calculations were carried out in a gas phase at B3LYP 6-311G (d,p) basis set to predict the molecular geometries and chemical reactivity descriptors. DFT results have been used to illustrate that molecular docking findings and biological activity assessments.

A New Family of Benzo[h]Chromene Based Azo Dye: Synthesis, In-Silico and DFT Studies with In Vitro Antimicrobial and Antiproliferative Assessment

The high biological activity of the chromene compounds coupled with the intriguing optical features of azo chromophores prompted our desire to construct novel derivatives of chromene incorporating azo moieties 4a-l, which have been prepared via a three-component reaction of 1-naphthalenol-4-[(4-ethoxyphenyl) azo], 1, with the benzaldehyde derivatives and malononitrile. The structural identities of the azo-chromene 4a-l were confirmed on the basis of their spectral data and elemental analysis, and a UV-visible study was performed in a Dimethylformamide (DMF) solution for these molecules. Additionally, the antimicrobial activity was investigated against four human pathogens (Gram-positive and Gram-negative bacteria) and four fungi, employing an agar well diffusion method, with their minimum inhibitory concentrations being reported. Molecules 4a, 4g, and 4h were discovered to be more efficacious against Syncephalastrum racemosum (RCMB 05922) in comparison to the reference drugs, while compounds 4b and 4h demonstrated the highest inhibitory activity against Escherichia coli (E. coli) in evaluation against the reference drugs. Moreover, their cytotoxicity was assessed against three different human cell lines, including human colon carcinoma (HCT-116), human hepatocellular carcinoma (HepG-2), and human breast adenocarcinoma (MCF-7) with a selection of molecules illustrating potency against the HCT-116 and MCF-7 cell lines. Furthermore, the molecular modeling results depicted the binding interactions of the synthesized compounds 3b and 3h in the active site of the E. coli DNA gyrase B enzyme with a clear SAR (structure-activity relationship) analysis. Lastly, the density functional theory's (DFTs) theoretical calculations were performed to quantify the energy levels of the Frontier Molecular Orbitals (FMOs) and their energy gaps, dipole moments, and molecular electrostatic potentials. These data were utilized in the chemical descriptor estimations to confirm the biological activity.

Profound implication of histological alterations, haematological responses and biocidal assessment of cationic amphiphiles unified with their molecular architecture

The interfacial properties depicting the micellization behaviour of the cationic amphiphiles (surfactants) belonging to the class of quaternary ammonium salts varying in degree of hydrophobicity were evaluated using tensiometry, conductivity and fluorescence spectrophotometric methods at 303.15 K. The impact of the amphiphilic nature of these amphiphiles as a function of their concentration is accounted against the selective microbial strains using the well-diffusion approach. Also, its influence on the histological (shrinkage/curling of lamellae, necrosis, haemorrhage, hyperplasia of villi in gills and intestine) alterations and haematological (blood parameters) changes in fingerling of Cirrhinus mrigala (C. mrigala) offers an insight into the stern damages reported as aquatic toxicity. The lesions exhibited moderate to severe alterations that are further correlated with the semi-quantitative mean alteration value (MAV). The in vitro and in vivo findings are explained significantly in terms of amphiphilic hydrophobicity which followed the order: C₁₆TAB > C₁₂TAB. All the observed outcomes are rationalized by the structural assessment of the selected amphiphiles as specified by the computational simulation approach using density functional theory (DFT) with B3LYP method and 3-21G basis source set. This work also portrays the biodegradability of these cationic amphiphiles and their fate on the environment. Graphical abstract Molecular architecture of cationic amphiphiles integrated with their in vitro and in vivo rejoinders.

Design, Synthesis, Molecular Modeling, Anticancer Studies, and Density Functional Theory Calculations of 4-(1,2,4-Triazol-3-ylsulfanylmethyl)-1,2,3-triazole Derivatives

New conjugates of substituted 1,2,3-triazoles linked to 1,2,4-triazoles were synthesized starting from the appropriate S-propargylated 1,2,4-triazoles 7 and 8. Ligation of 1,2,4-triazoles to the 1,2,3-triazole core was performed through Cu(I)-catalyzed cycloaddition of 1,2,4-triazole-based alkyne side chain 7 and/or 8 with several un/functionalized alkyl- and/or aryl-substituted azides 9-15 to afford the desired 1,4-disubstituted 1,2,3-triazoles 16-27, using both classical and microwave methods. After their spectroscopic characterization (infrared, 1H, 13C nuclear magnetic resonance, and elemental analyses), an anticancer screening was carried out against some cancer cell lines including human colon carcinoma (Caco-2 and HCT116), human cervical carcinoma (HeLa), and human breast adenocarcinoma (MCF-7). The outcomes of this exploration revealed that compounds 17, 22, and 25 had a significant anticancer activity against MCF-7 and Caco-2 cancer cell lines with IC50 values of 0.31 and 4.98 μM, respectively, in relation to the standard reference drug, doxorubicin. Enzyme-docking examination was executed onto cyclin-dependent kinase 2; a promising aim for cancer medication. Synthesized compounds acquiring highest potency showcased superior interactions with the active site residue of the target protein and exhibited minimum binding energy. Finally, the density functional theory (DFT) calculations were carried out to confirm the outcomes of the molecular docking and the experimental findings. The chemical reactivity descriptors such as softness (δ), global hardness (η), electronegativity (χ), and electrophilicity were calculated from the levels of the predicted frontier molecular orbitals and their energy gap. The DFT results and the molecular docking calculation results explained the activity of the most expectedly active compounds 17, 22, and 25.

Modelling the Anti-Methicillin-Resistant Staphylococcus Aureus (MRSA) Activity of Cannabinoids: A QSAR and Docking Study

Twenty-four cannabinoids active against MRSA SA1199B and XU212 were optimized at WB97XD/6-31G(d,p), and several molecular descriptors were obtained. Using a multiple linear regression method, several mathematical models with statistical significance were obtained. The robustness of the models was validated, employing the leave-one-out cross-validation and Y-scrambling methods. The entire data set was docked against penicillin-binding protein, iso-tyrosyl tRNA synthetase, and DNA gyrase. The most active cannabinoids had high affinity to penicillin-binding protein (PBP), whereas the least active compounds had low affinities for all of the targets. Among the cannabinoid compounds, Cannabinoid 2 was highlighted due to its suitable combination of both antimicrobial activity and higher scoring values against the selected target; therefore, its docking performance was compared to that of oxacillin, a commercial PBP inhibitor. The 2D figures reveal that both compounds hit the protein in the active site with a similar type of molecular interaction, where the hydroxyl groups in the aromatic ring of cannabinoids play a pivotal role in the biological activity. These results provide some evidence that the anti-Staphylococcus aureus activity of these cannabinoids may be related to the inhibition of the PBP protein; besides, the robustness of the models along with the docking and Quantitative Structure–Activity Relationship (QSAR) results allow the proposal of three new compounds; the predicted activity combined with the scoring values against PBP should encourage future synthesis and experimental testing.

Validating interfacial behaviour of surface-active ionic liquids (SAILs) with computational study integrated with biocidal and cytotoxic assessment

Surface-active ionic liquids (SAILs) belonging to the series of N-alkylmethylimidazolium halides [C₈mimX] (X = Br, Cl, and BF₄) and [C_nmimBr] (n = 10, 12, 14, and 16) were employed to understand the influence of hydrophobicity of alkyl chain length and the chaotropicity of counter-ions of SAILs on the micellization, antimicrobial action and cytotoxicity properties. The micellization phenomenon of SAILs in an aqueous environment was examined employing tensiometry and steady-state fluorescence spectrophotometry. The corresponding interfacial parameters viz., critical micelle concentration (CMC), effectiveness (γ_CMC), surface pressure (П_CMC), maximum surface excess concentration (Г_max), and the minimum area engaged per molecule (A_min) at the air-water interface were evaluated at 303.15 K. These experimental findings were monitored and geometrically optimized theoretically using Gaussian software to highlight the recent advances in this field of theoretical calculations for putative structure. The simulation descriptors correlated the micellization behavior as a function of hydrophobicity which may contribute to obtaining awareness on their ecological behavior and fate. In addition, the biological screening of all the examined SAILs was undertaken with a combined experimental and theoretical (optimized) method against bacteria and fungus. Results revealed that SAILs with the alkyl chain-length greater than C₈- act as a fair antimicrobial agent against the selected microbial strain which is attributed to the enhanced degree of SAILs hydrophobicity. The cytotoxicity of these imidazolium-based SAILs was also assessed on the cervical human cell line (HeLa) using the MTT cell viability assay and the data thus obtained were subjected to statistical analysis.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

The utilisation of structural descriptors to predict metabolic constants of xenobiotics in mammals

Quantitative structure-activity relationships (QSARs) were developed to predict the Michaelis-Menten constant (Km) and the maximum reaction rate (Vmax) of xenobiotics metabolised by four enzyme classes in mammalian livers: alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), flavin-containing monooxygenase (FMO), and cytochrome P450 (CYP). Metabolic constants were gathered from the literature and a genetic algorithm was employed to select at most six predictors from a pool of over 2000 potential molecular descriptors using two-thirds of the xenobiotics in each enzyme class. The resulting multiple linear models were cross-validated using the remaining one-third of the compounds. The explained variances (R(2)adj) of the QSARs were between 50% and 80% and the predictive abilities (R(2)ext) between 50% and 60%, except for the Vmax QSAR of FMO with both R(2)adj and R(2)ext less than 30%. The Vmax values of FMO were independent of substrate chemical structure because the rate-limiting step of its catalytic cycle occurs before compound oxidation. For the other enzymes, Vmax was predominantly determined by functional groups or fragments and electronic properties because of the strong and chemical-specific interactions involved in the metabolic reactions. The most relevant predictors for Km were functional groups or fragments for the enzymes metabolising specific compounds (ADH, ALDH and FMO) and size and shape properties for CYP, likely because of the broad substrate specificity of CYP enzymes. The present study can be helpful to predict the Km and Vmax of four important oxidising enzymes in mammals and better understand the underlying principles of chemical transformation by liver enzymes.

Mechanistically-based QSARs to describe metabolic constants in mammals

Biotransformation is one of the processes which influence the bioaccumulation of chemicals. The enzymatic action of metabolism involves two processes, i.e. the binding of the substrate to the enzyme followed by a catalytic reaction, which are described by the Michaelis-Menten constant (Km) and the maximum rate (Vmax). Here, we developed Quantitative Structure-Activity Relationships (QSARs) for Log(1/Km) and LogVmax for substrates of four enzyme classes. We focused on oxidations catalysed by alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), flavin-containing monooxygenase (FMO) and cytochrome P450 (CYP) in mammals. The chemicals investigated were xenobiotics, including alcohols, aldehydes, pesticides and drugs. We applied general linear models for this purpose, employing descriptors related to partitioning, geometric characteristics, and electronic properties of the substrates, which can be interpreted mechanistically. The explained variance of the QSARs varied between 20% and 70%, and it was larger for Log(1/Km) than for LogVmax. The increase of 1/Km with compound logP and size suggests that weak interactions are important, e.g. by substrate binding via desolvation processes. The importance of electronic factors for 1/Km was described in relation to the catalytic mechanism of the enzymes. Vmax was particularly influenced by electronic properties, such as dipole moment and energy of the lowest unoccupied molecular orbital. This can be explained by the nature of the catalysis, characterised by the cleavage and formation of covalent or ionic bonds (strong interactions). The present study may be helpful to understand the underlying principles of the chemical specific activity of four important oxidising enzymes.

Comparative antioxidant status in freshwater fish Carassius auratus exposed to eight imidazolium bromide ionic liquids: a combined experimental and theoretical study

Imidazolium bromide ionic liquids such as 1-alkyl-3-methylimidazolium bromides ([AMIm]Br) and 1-alkyl-2,3-dimethylimidazolium bromides ([AMMIm]Br) are common-use organic salts. However, data on comparative toxicological effects of these ILs are lacking for fish. In this study, a combined experimental and theoretical approach was applied to compare and analyze the effects of these ILs on biochemical biomarkers in liver of Carassius auratus treated with different concentrations (2 and 20mg/L) for 3 and 16d. Changes in the activities of superoxide dismutase, catalase, glutathione peroxidase, and in the levels of reduced glutathione and malondialdehyde were detected, indicating that these ILs exhibit potential biotoxicity. The integrated biomarker response (IBR) index suggested that 1-hexyl-3-methylimidazolium bromide ([HMIm]Br), 1-octyl-3-methylimidazolium bromide ([OMIm]Br), 1-hexyl-2,3-dimethylimidazolium bromide ([HMMIm]Br), and 1-octyl-2,3-dimethylimidazolium bromide ([OMMIm]Br) showed the highest biotoxicity under different concentrations or exposure time, while 1-ethyl-3-methylimidazolium bromide ([EMIm]Br) always showed the least stressful power towards the test organism. Quantum chemical calculations (electronic parameters, frontier molecular orbitals, and Wiberg bond order) were also conducted to interpret the experimental results. Notably, some descriptors were correlated with the toxicity order. In addition, theoretical calculations provided some valuable information on metabolic pathways of these ILs, which may help to get better understanding on their environmental behavior and fate. In general, the toxicological determination and analysis of these ILs were performed with a combined experimental and theoretical method, which may contribute to the future ecotoxicological studies.

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.

Insights on cytochrome p450 enzymes and inhibitors obtained through QSAR studies

The cytochrome P450 (CYP) superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the treatment of several disease conditions such as numerous cancers and fungal infections in addition to their critical role in drug-drug interactions. Structure activity relationships (SAR), and three-dimensional quantitative structure activity relationships (3D-QSAR) represent important tools in understanding the interactions of the inhibitors with the active sites of the CYP enzymes. A comprehensive account of the QSAR studies on the major human CYPs 1A1, 1A2, 1B1, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and a few other CYPs are detailed in this review which will provide us with an insight into the individual/common characteristics of the active sites of these enzymes and the enzyme-inhibitor interactions.

Cytochrome P450 1A1-mediated anticancer drug discovery: in silico findings

INTRODUCTION

Target-specific drugs may offer fewer side/adverse effects in comparison with other anticancer agents and thus save normal healthy cells to a greater extent. The selective overexpression of cytochrome P450 1A1 (CYP1A1) in tumor cells induces the metabolism of benzothiazole and aminoflavone compounds to their reactive species, which are responsible for DNA adduct formation and cell death. This review encompasses the novelty of CYP1A1 as an anticancer drug target and explores the possible in silico strategies that would be applicable in the discovery and development of future antitumor compounds.

AREAS COVERED

This review highlights the various ligand-based and target-based in silico methodologies that were efficiently used in exploration of CYP1A1 as a novel antitumor target. These methodologies include electronic structure analysis, CoMFA studies, homology modeling, molecular docking, molecular dynamics analysis, pharmacophore mapping and quantitative structure activity relationship (QSAR) studies. It also focuses on the various approaches used in the development of the lysyl amide prodrug of 5F-203 (NSC710305) and dimethanesulfonate salt of 5-aminoflavone (NSC710464) as clinical candidates from their less potent analogues.

EXPERT OPINION

Selective overexpression of CYP1A1 in cancer cells offers tumor-specific drug design to ameliorate the current adverse effects associated with existing antitumor agents. Medicinal chemistry and in vitro driven approaches, in combination with knowledge-based drug design and by using the currently available tools of in silico methodologies, would certainly make it possible to design and develop novel anticancer compounds targeting CYP1A1.

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

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

Compound lipophilicity as a descriptor to predict binding affinity (1/K(m)) in mammals

In bioaccumulation models, biotransformation is one of the processes decreasing the concentration of chemicals in an organism. In order to be metabolized, a compound needs to bind to an enzyme. In this study, we derived relationships between binding affinity and lipophilicity, expressed as Log (1/K(m)) and Log K(ow), respectively. We focused on oxidations in mammals catalyzed by alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), flavin-containing monooxygenase (FMO), and cytochrome P450 (CYP) enzymes. For all regressions, 1/K(m) increased with compound K(ow), which can be understood from the tendency to biotransform lipophilic compounds into more polar, thus more easily excretable metabolites. Lipophilicity was relevant to the binding of most of the substrate classes of ADH, ALDH, and CYP. The resulting slopes had 95% Confidence Intervals covering the value of 0.63, typically noted in protein-water distribution (Log K(pw)) and Log K(ow) regressions. A reduced slope (0.2-0.3) was found for FMO: this may be due to a different reaction mechanism involving a nucleophilic attack. The general patterns of metabolism were mechanistically interpreted in terms of partitioning theory. Information on the overall principles determining biotransformation may be helpful in predicting metabolic rates.

QSAR models of cytochrome P450 enzyme 1A2 inhibitors using CoMFA, CoMSIA and HQSAR

Quantitative structure-activity relationship (QSAR) studies were conducted on an in-house database of cytochrome P450 enzyme 1A2 inhibitors using the comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA) and hologram QSAR (HQSAR) approaches. The database consisted of 36 active molecules featuring varied core structures. The model based on the naphthalene substructure alignment incorporating 19 molecules yielded the best model with a CoMFA cross validation value q(2) of 0.667 and a Pearson correlation coefficient r(2) of 0.976; a CoMSIA q(2) value of 0.616 and r(2) value of 0.985; and a HQSAR q(2) value of 0.652 and r(2) value of 0.917. A second model incorporating 34 molecules aligned using the benzene substructure yielded an acceptable CoMFA model with q(2) value of 0.5 and r(2) value of 0.991. Depending on the core structure of the molecule under consideration, new CYP1A2 inhibitors will be designed based on the results from these models.

QSARs for PBPK modelling of environmental contaminants

Physiologically-based pharmacokinetic (PBPK) models are increasingly finding use in risk assessment applications of data-rich compounds. However, it is a challenge to determine the chemical-specific parameters for these models, particularly in time- and resource-limiting situations. In this regard, SARs, QSARs and QPPRs are potentially useful for computing the chemical-specific input parameters of PBPK models. Based on the frequency of occurrence of molecular fragments (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure) and exposure conditions, the available QSAR-PBPK models facilitate the simulation of tissue and blood concentrations for some inhaled volatile organic chemicals. The application domain of existing QSARs for developing PBPK models is limited, due to lack of relevant data for diverse chemicals and mechanisms. Even though this approach is conceptually applicable to non-volatile and high molecular weight organics as well, it is more challenging to predict the other PBPK model parameters required for modelling the kinetics of these chemicals (particularly tissue diffusion coefficients, association constants for binding and oral absorption rates). As the level of our understanding of the mechanistic basis of toxicokinetic processes improves, QSARs to provide a priori predictions of key chemical-specific PBPK parameters can be developed to expedite the internal dose-based health risk assessments in data-poor situations.

QSAR of cytochrome inhibitors

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

Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity

The description of quantitative structure-activity relationship (QSAR) models has been a topic for scientific research for more than 40 years and a topic within the regulatory framework for more than 20 years. At present, efforts on QSAR development are increasing because of their promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments. However, their acceptance in risk assessment seems to require a more standardized and scientific underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for QSAR model development recently proposed by the Organization for Economic Cooperation and Development (OECD) [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and Assessment No. 69, Paris] are expected to help increase the acceptability of QSAR models for regulatory purposes. The guidelines recommend that QSAR models should be associated with (i) a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv) appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic interpretation, if possible [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR model development and their rationale, as well as the promises and pitfalls of using QSAR approaches and these guidelines for predicting metabolism and toxicity of new and existing chemicals.

Comparative QSAR studies of CYP1A2 inhibitor flavonoids using 2D and 3D descriptors

Comparative Quantitative Structure Activity Relationship (QSAR) analyses have been performed with 21 naturally occurring flavonoids for their inhibitory effects on cytochrome P450 1A2 enzyme using two-dimensional (topological, structural, and thermodynamic) and three-dimensional (spatial) descriptors. The chemometric tools used for the analyses are stepwise multiple linear regression, partial least squares, genetic function approximation, and genetic partial least squares. The data set was divided into a training set (n = 15) and test set (n = 6), based on K-means clustering technique applied on standardized two-dimensional descriptor matrix, and models were developed from the training set compounds. The best model (genetic partial least squares model using two-dimensional descriptors) was selected based on the highest external predictive R(2) (R(2)(pred)) value (0.840) and the lowest root mean square error of prediction value (0.351). The developed QSAR equations suggest the importance of the double bond present at 2 and 3 positions and requirement of absence of hydroxyl substituent or glycosidic linkage at 3 position of the 1,4-benzopyrone nucleus. Furthermore, the phenyl ring present at 2 position of the 1,4-benzopyrone ring should not be substituted with hydroxyl group. Moreover, hydroxyl groups present at 5 and 7 positions of the benzopyran nucleus should not be glycosylated for good cytochrome P450 1A2 enzyme inhibitory activity.

Quantitative structure-activity relationships within a homologous series of 7-alkoxyresorufins exhibiting activity towards CYP1A and CYP2B enzymes: molecular modelling studies on key members of the resorufin series with CYP2C5-derived models of human CYP1A1, CYP1A2, CYP2B6 and CYP3A4

1. The results of quantitative structure-activity relationships (QSARs) within a homologous series of 7-n-alkoxyresorufins are reported. They are consistent with homology modelling of the relevant P450s involved in their metabolism. 2. QSARs were generated for activities involving CYP1A and CYP2B enzymes with structural descriptors relating to compound planarity and other shape parameters, together with certain features of the n-alkoxyresorufin electronic structure, especially electron densities and superdelocalizabilities. 3. A quadratic relationship between compound lipophilicity and binding to CYP2B enzymes was apparent, and which indicated maximal interaction for 7-pentoxyresorufin. Such indications help to explain enzyme selectivity in terms of optimal alkyl chain length for fitting within the relevant active site region. 4. Calculation of the binding affinities for methoxy-, ethoxy-, pentoxy- and benzyloxy-resorufins towards either CYP1A2 or CYP2B6 enzymes, depending on the 7-alkoxyresorufin agree favourably with experimental values obtained from K(m) determinations.

Quantitative structure-activity relationships (QSARs) within cytochromes P450 2B (CYP2B) subfamily enzymes: the importance of lipophilicity for binding and metabolism

The results of qualitative structure-activity relationship (QSAR) analysis are reported for several series of compounds which act as substrates for mammalian CYP2B subfamily enzymes, together with a homologous series of aliphatic primary amines which are known to inhibit CYP2B enzymes. It is found that the compound lipophilicity in the form of the log P value (where P is the octanol/water partition coefficient) is related, either linearly or quadratically, to equilibrium constants of inhibition (Ki), binding (Ks) or metabolism (Km) depending on the series of compounds in question. In some instances, the difference between frontier orbital energy levels (deltaE) also features in several of the log P expressions with biological activity. Also present in a small number of correlations are parameters which are likely to be related to logP: namely, Rm, which is the partitioning factor derived from thin layer chromatography (TLC) retention times, and also the compound molecular weight (Mr). All of these three parameters ((log P, Rm and Mr) are thought to be related to the compound's ability to desolvate the P450 active site when they bind to the enzyme. Although the linear relationships between lipophilicity and CYP2B-related activity point to a major role for desolvation of the enzyme binding site in the overall interaction, it is noted that there may be an optimal log P value displayed by preferred substrates as shown by parabolic relationships with this lipophilic parameter. In addition, there is a remarkable similarity in the coefficients for the log P term of any QSAR expression, which suggests that the hydrophobicity of CYP2B active sites may be broadly equivalent between the various mammalian species.

Theoretical quantitative structure–activity relationships of flavone ligands interacting with cytochrome P450 1A1 and 1A2 isozymes

Theoretical descriptors obtained from quantum mechanical calculations on isolated ligands in different media and molecular dynamics simulations of ligand-enzyme complexes have been used to obtain a quantitative rationalization of the inhibition of CYP1A2 and CYP1A2 by three series of flavonoids. Predictive models obtained through one-descriptor QSAR studies and mechanistic explanations have been obtained for recognition and selectivity.

Bioinformatics in drug development and assessment

Bioinformatics is playing an increasingly important role in nearly all aspects of drug discovery, drug assessment, and drug development. This growing importance lies not only in the role that bioinformatics plays in handling large volumes of data, but also in the utility of bioinformatics tools to predict, analyze, or help interpret clinical and preclinical findings. This review focuses on describing and evaluating some of the newer or more important bioinformatics resources (i.e., databases and software) that are of growing importance to understanding or predicting drug metabolism, especially with respect to the absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of both existing drugs and potential drug leads. Detailed descriptions and critical assessments of a number of potentially useful bioinformatics/cheminformatics databases and predictive ADMET software tools are provided. Additionally, several pharmaceutically important applications of both the databases and software are highlighted. Given the rapid growth in this area and the rapid changes that are taking place, a special emphasis is placed on freely available or Web-accessible resources.

Predictive ADMET studies, the challenges and the opportunities

Predictive ADMET is the new 'hip' area in drug discovery. The aim is to use large databases of ADMET data associated with structures to build computational models that link structural changes with changes in response, from which compounds with improved properties can be designed and predicted. These databases also provide the means to enable predictions of human ADMET properties to be made from human in vitro and animal in vivo ADMET measurements. Both methods are limited by the amount of data available to build such predictive models, the limitations of modelling methods and our understanding of the systems we wish to model. The current failures, successes and opportunities are reviewed.

Quantitative structure–activity relationships (QSARs) for substrates of human cytochromes P450 CYP2 family enzymes

The results of quantitative structure-activity relationship (QSAR) studies on substrates of human CYP2 family enzymes are reported, together with those of a small number of CYP2A6, CYP2C19 and CYP2D6 inhibitors. In general, there are good correlations (R = 0.90-0.99) between binding affinity (based on Km or KD values) and various parameters relating to active site interactions such as hydrogen bonding and pi-pi stacking. There is also evidence for the role of compound lipophilicity (as determined by either log P or log D7.4 values) in overall substrate binding affinity, and this could reflect the desolvation energy involved in substrate interaction within the enzyme active site. It is possible to estimate the substrate binding energy for a given P450 from a combination of energy terms relating to hydrogen bonding, pi-pi stacking, desolvation and loss in rotatable bond energy, which agree closely (R = 0.98) with experimental data based on either Km or KD values. Consequently, it is likely that active site interactions represent the major contributory factors to the overall binding affinities for human CYP2 family substrates and, therefore, their estimation is of potential importance for the development of new chemical entities (NCEs) as this can facilitate an assessment of likely metabolic clearance.