New methods in predictive metabolism

The triphenyltin carboxylate derivative triphenylstannyl 2-(benzylcarbamoyl)benzoate impedes prostate cancer progression via modulation of Akt/FOXO3a signaling

Prostate cancer (PCa) incidence is surging in United States and other parts of the world. Synthetic and natural compounds have been explored as potential modulators of PI3K/Akt signaling that is known to drive PCa growth. Here, we evaluated the efficacy of a series of triphenyltin (IV) carboxylate derivatives against PCa. From this library, triphenylstannyl 2-(benzylcarbamoyl)benzoate (Ch-319) resulted in reduced viability and induction of cell cycle arrest in PTEN^-/- PC3M and PTEN^+/- DU145 cells. In parallel, downregulation of PI3K p85/p110 subunits, dephosphorylation of Akt-1 and increase in FOXO3a expression were observed. In silico studies indicated binding interactions of Ch-319 within the ATP binding site of Akt-1 at Met²⁸¹, Phe⁴⁴² and Glu²³⁴ residues. Elevated po-pulation of apoptotic cells, activation of Bax and reduced Bcl2 expression indicated apoptosis by Ch-319. Pre-sensitization of PCa cells with Ch-319 augmented the effect of cabazitaxel, a commonly used taxane in patients with castration-resistant PCa. Next, in a prostate-specific PTEN^p-/- mice, Ch-319 showed reduced weights of genitourinary apparatus as compared to DMSO treated controls. Histological studies indicated absence of neoplastic foci in Ch-319 treated prostates. Consistently, dephosphorylation of Akt-1, reduced expression of PRAS40 and androgen receptor and increase in FOXO3a were observed in treated group. Notably, no overt organ toxicity was noted in Ch-319 treated animals. Our studies identify Akt/FOXO3a signaling as a target of triphenyltin (IV) carboxylate Ch-319 and provide a molecular basis of its growth inhibitory effect in PCa cells. We propose that Ch-319 has the potential to be developed as an anticancer agent against PCa.

4-Hydroxy-2-pyridone Derivatives and the δ-pyrone Isostere as Novel Agents Against Mycobacterium smegmatis Biofilm Inhibitors

BACKGROUND

The treatment of a bacterial infection when the bacterium is growing in a biofilm is a vexed issue. This is because the bacteria in a biofilm behaves differently compared to the individual planktonic free-form. As a result, traditional antibacterial agents lose their activity.

OBJECTIVE

Presently, there are not many drugs that are effective against bacteria growing in biofilms. Based on literature reports, we have sought to develop novel derivatives of 4-hydroxy-2- pyridone as both antimycobacterial and antibiofilm agents.

METHODS

The pyridone derivatives were synthesized by reacting 4-hydroxy-6-methyl-2H-pyran-2- one with appropriate amines and followed by reaction with substituted phenyl isocyanates as reported in the literature.

RESULTS

Four compounds in this series significantly inhibit the growth and formation of biofilm by Mycobacterium smegmatis (mc2 155 strain) at 50 µg/ml. Further, in silico evaluation of the ADME parameters shows that these compounds possess good drug-like properties and have the potential to be developed both as antibiofilm and as oral antimycobacterial agents.

CONCLUSION

This finding is of significance as presently very few small molecules are known to inhibit biofilm formation in mycobacteria. These compounds are unique in the sense that they are more potent against Mycobacterium smegmatis in the biofilm state compared to the planktonic form.

MetStabOn-Online Platform for Metabolic Stability Predictions

Metabolic stability is an important parameter to be optimized during the complex process of designing new active compounds. Tuning this parameter with the simultaneous maintenance of a desired compound's activity is not an easy task due to the extreme complexity of metabolic pathways in living organisms. In this study, the platform for in silico qualitative evaluation of metabolic stability, expressed as half-lifetime and clearance was developed. The platform is based on the application of machine learning methods and separate models for human, rat and mouse data were constructed. The compounds' evaluation is qualitative and two types of experiments can be performed-regression, which is when the compound is assigned to one of the metabolic stability classes (low, medium, high) on the basis of numerical value of the predicted half-lifetime, and classification, in which the molecule is directly assessed as low, medium or high stability. The results show that the models have good predictive power, with accuracy values over 0.7 for all cases, for Sequential Minimal Optimization (SMO), k-nearest neighbor (IBk) and Random Forest algorithms. Additionally, for each of the analyzed compounds, 10 of the most similar structures from the training set (in terms of Tanimoto metric similarity) are identified and made available for download as separate files for more detailed manual inspection. The predictive power of the models was confronted with the external dataset, containing metabolic stability assessment via the GUSAR software, leading to good consistency of results for SMOreg and Naïve Bayes (~0.8 on average). The tool is available online.

Design, synthesis, and pharmacological evaluation of fluorinated azoles as anti-tubercular agents

Design, synthesis, and biological screening of 2,2-dimethyl-2,3-dihydrobenzofuran tethered 1,3,4-oxadiazole derivatives as anti-tubercular agents were described. The synthesis of the target compounds was conducted by a series of reaction schemes. All the synthesized compounds were characterized by IR, ¹ H NMR, ¹³ C NMR, and mass spectrometry. The therapeutic potential of the synthesized compounds was confirmed by molecular docking studies. Among the synthesized compounds, 12a, 12c, 12d, 12e, 12g, and 12j were found to be more active against non-replicating than against replicating cultures of Mycobacterium tuberculosis H37Ra ex vivo and in vitro. These compounds exhibit minimum inhibitory concentration (MIC) values in the range of 2.31-23.91 μg/mL. The cytotoxicity study was conducted against the cell lines THP-1, A549 and PANC-1, and the compounds were observed to be non-toxic to host cells. Molecular docking was conducted with InhA (FabI/ENR) and suggested the antimycobacterial potential of the synthesized compounds. The investigation presented here was found to be adventitious for the development of new therapeutic agents against Mycobacterium infection.

A DFT Study of the Geometrical, Spectroscopical and Reactivity Properties of Diindolylmethane-Phenylboronic Acid Hybrids

The structure of the ortho-, meta- and para- hybrid diindolylmethane-phenylboronic acids and their interactions were optimized with by a quantum chemical method, using density functional theory at the (DFT) level. Thus, infrared bands were assigned based on the scaled theoretical wavenumbers by correlating the respective experimental data of the molecules. In addition, the corresponding ¹H-/¹³C-/¹¹B-NMR experimental and theoretical chemical shifts were correlated. The target molecules showed a poor treatment of the OH shifts in the GIAO method due to the absence of explicit solvent effects in these calculations; therefore, they were explicitly considered with acetone molecules. Moreover, the electron density at the hydrogen bond critical point increased, generating stabilization energy, from weak to moderate or weak to strong, serving as an indicator of the strength of the hydrogen bond between the different intermolecular interactions. Finally, some properties related to the reactive behavior of the target molecules associated with their cytotoxic effects and metabolic pathways were also calculated.

In silico Study of the Pharmacologic Properties and Cytotoxicity Pathways in Cancer Cells of Various Indolylquinone Analogues of Perezone

Several indolylquinone analogues of perezone, a natural sesquiterpene quinone, were characterized in this work by theoretical methods. In addition, some physicochemical, toxicological and metabolic properties were predicted using bioinformatics software. The predicted physicochemical properties are in agreement with the solubility and cLogP values, the penetration across the cell membrane, and absorption values, as well as with a possible apoptosis-activated mechanism of cytotoxic action. The toxicological predictions suggest no mutagenic, tumorigenic or reproductive effects of the four target molecules. Complementarily, the results of a performed docking study show high scoring values and hydrogen bonding values in agreement with the cytotoxicity IC50 value ranking, i.e: indolylmenadione > indolylperezone > indolylplumbagine > indolylisoperezone. Consequently, it is possible to suggest an appropriate apoptotic pathway for each compound. Finally, potential metabolic pathways of the molecules were proposed.

Antileishmanial potential of fused 5-(pyrazin-2-yl)-4H-1,2,4-triazole-3-thiols: Synthesis, biological evaluations and computational studies

A series of newer 1,2,4-triazole-3-thiol derivatives 5(a-m) and 6(a-i) containing a triazole fused with pyrazine moiety of pharmacological significance have been synthesized. All the synthesized compounds were screened for their in vitro antileishmanial and antioxidant activities. Compounds 5f (IC₅₀=79.0µM) and 6f (IC₅₀=79.0µM) were shown significant antileishmanial activity when compared with standard sodium stibogluconate (IC₅₀=490.0µM). Compounds 5b (IC₅₀=13.96µM) and 6b (IC₅₀=13.96µM) showed significant antioxidant activity. After performing molecular docking study and analyzing overall binding modes it was found that the synthesized compounds had potential to inhibit L. donovani pteridine reductase 1 enzyme. In silico ADME and metabolic site prediction studies were also held out to set an effective lead candidate for the future antileishmanial and antibacterial drug discovery initiatives.

Carboxylate derivatives of tributyltin (IV) complexes as anticancer and antileishmanial agents

Background

Tributyltin (IV) compounds are promising candidates for drug development. In the current study, we evaluated in-vitro and in-silico profile of carboxylate derivatives of tributyltin (IV) complexes.

Methods

ADMET and drug-likeliness properties were predicted using MetaPrint2D React, preADMET, SwissADME and Molsoft tools. SwissTargetPrediction predicted molecular targets for compounds. In-vitro bioactivity was evaluated by quantifying cytotoxicity against HepG2, THP-1 cell lines, isolated lymphocytes and leishmania promastigotes as well as measuring protein kinase (PK) inhibition activity.

Results

Results indicate partial compliance of compounds with drug-likeliness rules. Ch-409 complies with WDI and Lipinski rules. ADMET profile prediction shows strong plasma protein binding except for Ch-409, low to high GI absorption and BBB penetration (C_brain/C_blood = 0.942–11; caco-2 cells permeability 20.13–26.75 nm/sec), potential efflux by P-glycoprotein, metabolism by CYP3A4, medium inhibition of hERG, mutagenicity and capacity to be detoxified by glutathionation and glucuronidation. Molecular targets include proteases, enzymes, membrane receptors, transporters and ion channels where Ch-409 targets membrane receptors only. Compounds are significantly (p < 0.05) cytotoxic against HepG2 cell line and leishmania as compared with normal isolated lymphocytes. Ch-459 indicates highest toxicity against leishmania (mortality 97.9 ± 3.99%; LC50 0.323 ± 0.002 μg/mL) whereas Ch-409 possesses maximum cytotoxicity against HepG2 cell line (IC50 0.08 ± 0.001 μg/mL) as well as 97.5 ± 1.98% (LC50 0.954 ± 0.158 μg/mL) mortality of leishmania promastigotes. It was observed that antileishmanial effect was reduced by 16.38%–34.38% and 15–38.2% in the presence of NaN₃ and mannitol respectively. PK inhibition and reactive oxygen species production are possible mechanisms for cytotoxicity.

Conclusions

Selected carboxylate derivatives of tributyltin (IV) complexes possess significant antileishmanial and cytotoxic potential. These are promising compounds for the development of antileishmanial and anticancer drugs.

Graphical Abstract

Structure–metabolism-relationships in the microsomal clearance of piperazin-1-ylpyridazines

In this study, we provide insight into the metabolic profile of a series of piperazin-1-ylpyridazines suffering from rapid in vitro intrinsic clearance in a metabolic stability assay using liver microsomes.

Anti-inflammatory properties of pterocarpanquinone LQB-118 in mice

Pterocarpanquinone (+/-)-LQB-118 presents antineoplastic and antiparasitic properties and also shows great inhibitory effect on TNF-α release in vitro. Here, its anti-inflammatory activity was evaluated in a lipopolysaccharide (LPS)-induced lung inflammation model in C57BL/6 mice. LPS inhalation induced a marked neutrophil infiltration to the lungs which was reduced by intraperitoneal treatment with (+/-)-LQB-118 in a similar manner to that of dexamethasone and even better than that of acetylsalicylic acid. Moreover, (+/-)-LQB-118 administration resulted in decrease of NF-κB activation and KC level in lungs, with a pronounced inhibitory effect on TNF-α release, measured in bronchoalveolar lavage fluid. Trying to understand the anti-inflammatory mechanism by which (+/-)-LQB-118 acts, we performed a molecular modeling analysis, including docking to estrogen receptors α and β. Results suggested that (+/-)-LQB-118 may bind to both receptors, with a similar orientation to 17-β-estradiol. Together, these results showed that (+/-)-LQB-118 exhibits an anti-inflammatory effect, most likely by inhibiting TNF-α release and NF-κB activation, which may be related to the estrogen receptor binding.

Novel amalgamation of phthalazine-quinolines as biofilm inhibitors: One-pot synthesis, biological evaluation and in silico ADME prediction with favorable metabolic fate

A facile and highly efficient one-pot synthesis of phthalazine-quinoline derivatives is reported via four component reaction of phthalic anhydride, hydrazine hydrate, 5,5-dimethyl 1,3 cyclohexanedione and various quinoline aldehydes using PrxCoFe2-xO4 (x=0.1) nanoparticles as a catalyst. The synthesized compounds have been evaluated for anti-biofilm activity against Pseudomonas aeruginosa and Candida albicans. The compounds 12a (IC50=30.0μM) and 12f (IC50=34.5μM) had shown promising anti-biofilm activity against P. aeruginosa and C. albicans, respectively, when compared with standards without affecting the growth of cells (and thus behave as anti-quorum sensing agents). Compounds 12a (MIC=45.0μg/mL) and 12f (MIC=57.5μg/mL) showed significant potent antimicrobial activity against P. aeruginosa and C. albicans, respectively. Thus, the active derivatives were not only potent biofilm inhibitors but also efficient antimicrobial agents. In silico ADME and metabolic site prediction studies were also held out to set an effective lead candidate for the future antimicrobial drug discovery initiatives.

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

Simple chalcones and bis-chalcones ethers as possible pleiotropic agents

The synthesis, the antioxidative properties and the lipoxygenase (LOX) and acetylcholinesterase (AChE) inhibition of a number of 4-hydroxy-chalcones diversely substituted as well as of a series of bis-chalcones ether derivatives are reported. The chalcones derivatives were readily produced using a Claisen-Schmidt condensation in a ultra sound bath in good yields. The structures of the synthesized compounds were confirmed by spectral and elemental analysis. Their lipophilicity is experimentally determined by reversed-phase thin-layer chromatography method. Most of them are potent in vitro inhibitors of lipid peroxidation and of LOX. Compounds b2 and b3 were found to be the most potent LOX and AChE inhibitors among the tested derivatives with a significant anti-lipid peroxidation profile. The results led us to propose these enone derivatives as new multifunctional compounds against Alzheimer's disease. The results are discussed in terms of structural and physicochemical characteristics of the compounds. Moreover, the pharmacokinetic profile of these compounds was investigated using computational methods.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse

Background

Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive.

Results

Here a fast and reliable method to analyse ligands and visualise potential metabolic sites is presented which is based on annotated metabolic data, described by circular fingerprints. The method is available via the graphical workbench Bioclipse, which is equipped with advanced features in cheminformatics.

Conclusions

Due to the speed of predictions (less than 50 ms per molecule), scientists can get real time decision support when editing chemical structures. Bioclipse is a rich client, which means that all calculations are performed on the local computer and do not require network connection. Bioclipse and MetaPrint2D are free for all users, released under open source licenses, and available from http://www.bioclipse.net.

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

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

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

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

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

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

Molecular characterization of CYP2B6 substrates

CYP2B6 has not been as fully characterized at the molecular level as other members of the human cytochrome P450 family. As more widely used in vitro probes for characterizing the involvement of this enzyme in the metabolism of xenobiotics have become available, the number of molecules identified as CYP2B6 substrates has increased. In this study we have analyzed the available kinetic data generated by multiple laboratories with human recombinant expressed CYP2B6 and along with calculated molecular properties derived from the ChemSpider database, we have determined the molecular features that appear to be important for CYP2B6 substrates. In addition we have applied 2D and 3D QSAR methods to generate predictive pharmacophore and 2D models. For 28 molecules with K(m) data, the molecular weight (mean +/- SD) is 253.78+/-74.03, ACD/logP is 2.68+/-1.51, LogD(pH 5.5) is 1.51+/-1.43, LogD(pH 7.4) is 2.02+/-1.25, hydrogen bond donor (HBD) count is 0.57 +/-0.57, hydrogen bond acceptor (HBA) count is 2.57+/-1.37, rotatable bonds is 3.50+/-2.71 and total polar surface area (TPSA) is 27.63+/-19.42. A second set of 15 molecules without K(m) data possessed similar mean molecular property values. These properties are comparable to those of a set of 21 molecules used in a previous pharmacophore modeling study (Ekins et al., J Pharmacol Exp Ther 288 (1), 21-29, 1999). Only the LogD and HBD values were statistically significantly different between these different datasets. We have shown that CYP2B6 substrates are generally small hydrophobic molecules that are frequently central nervous system active, which may be important for drug discovery research.

Pharmacokinetics in drug discovery

The aim of this current review is to summarize the present status of pharmacokinetics in Drug Discovery. The review is structured into four sections. The first section is a general overview of what we understand by pharmacokinetics and the different LADMET aspects: Liberation, Absorption, Distribution, Metabolism, Excretion, and Toxicity. The second section highlights the different computational or in silico approaches to estimate/predict one or several aspects of the pharmacokinetic profile of a discovery lead compound. The third section discusses the most commonly used in vitro methodologies. The fourth and last section examines the various approaches employed towards the pharmacokinetic assessment of discovery molecules; including all the LADME processes, discussing the different mathematical methodologies available to establish the PK profile of a test compound; what the main differences are and what should be the criteria for using one or another mathematical approach. The major conclusion of this review is that the use of the appropriate preclinical assays has a key role in the long-term viability of a pharmaceutical company since applying the right tools early in discovery will play a key role in determining the company's ability to discover novel safe and effective therapeutics to patients as quickly as possible.

State-of-the-art tools for computational site of metabolism predictions: comparative analysis, mechanistical insights, and future applications

In drug design, it is crucial to have reliable information on how a chemical entity behaves in the presence of metabolizing enzymes. This requires substantial experimental efforts. Consequently, being able to predict the likely site/s of metabolism in any compound, synthesized or virtual, would be highly beneficial and time efficient. In this work, six different methodologies for predictions of the site of metabolism (SOM) have been compared and validated using structurally diverse data sets of drug-like molecules with well-established metabolic pattern in CYP3A4, CYP2C9, or both. Three of the methods predict the SOM based on the ligand's chemical structure, two additional methods use structural information of the enzymes, and the sixth method combines structure and ligand similarity and reactivity. The SOM is correctly predicted in 50 to 90% of the cases, depending on method and enzyme, which is an encouraging rate. We also discuss the underlying mechanisms of cytochrome P450 metabolism in the light of the results from this comparison.

Reaction site mapping of xenobiotic biotransformations

Predictive metabolism methods can be used in drug discovery projects to enhance the understanding of structure-metabolism relationships. The present study uses data mining methods to exploit biotransformation data that have been recorded in the MDL Metabolite database. Reacting center fingerprints were derived from a comparison of substrates and their corresponding products listed in the database. This process yields two fingerprint databases: all atoms in all substrates and all reacting centers. The metabolic reaction data are then mined by submitting a new molecule and searching for fingerprint matches to every atom in the new molecule in both databases. An "occurrence ratio" is derived from the fingerprint matches between the submitted compound and the reacting center and substrate fingerprint databases. Normalization of the occurrence ratio within each submitted molecule enables the results of the search to be rank-ordered as a measure of the relative frequency of a reaction occurring at a specific site within the submitted molecule. Predictive performance that would allow this method to be used by drug discovery teams to generate useful hypotheses regarding structure metabolism relationships was observed.

Methods for Predicting Human Drug Metabolism

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

Classification of Metabolites with Kernel-Partial Least Squares (K-PLS)

Numerous experimental and computational approaches have been developed to predict human drug metabolism. Since databases of human drug metabolism information are widely available, these can be used to train computational algorithms and generate predictive approaches. In turn, they may be used to assist in the identification of possible metabolites from a large number of molecules in drug discovery based on molecular structure alone. In the current study we have used a commercially available database (MetaDrug) and extracted a fraction of the human drug metabolism data. These data were used along with augmented atom descriptors in a predictive machine learning model, kernel-partial least squares (K-PLS). A total of 317 molecules, including parent drugs and their primary and secondary (sequential) metabolites, were used to build these models corresponding to individual metabolism rules, representing the formation of discrete metabolites, e.g., N-dealkylation. Each model was internally validated to assess the capability to classify other molecules that were left out. Using receiver operator curve statistics models for N-dealkylation, O-dealkylation, aromatic hydroxylation, aliphatic hydroxylation, O-glucuronidation, and O-sulfation gave area under the curve values from 0.75 to 0.84 and were able to predict between 61 and 79% active molecules upon leave-one-out testing. This preliminary study indicates that K-PLS and possibly other similar machine learning methods (such as support vector machines) can be applied to predicting human drug metabolite formation in a classification manner. Improvements can be achieved using considerably larger datasets that contain more positive examples for the less frequently occurring metabolite rules, as well as the external evaluation of novel molecules.

Designing better drugs: predicting cytochrome P450 metabolism

Many 3D ligand-based and structure-based computational approaches have been used to predict, and thus help explain, the metabolism catalyzed by the enzymes of the cytochrome P450 superfamily (P450s). P450s are responsible for >90% of the metabolism of all drugs, so the computational prediction of metabolism can help to design out drug-drug interactions in the early phases of the drug discovery process. Computational methodologies have focused on a few P450s that are directly involved in drug metabolism. The recently derived crystal structures for human P450s enable better 3D modelling of these important metabolizing enzymes. Models derived for P450s have evolved from simple comparisons of known substrates to more-elaborate experiments that require considerable computer power involving 3D overlaps and docking experiments. These models help to explain and, more importantly, predict the involvement of P450s in the metabolism of specific compounds and guide the drug-design process.

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

The site of biotransformation, the extent and rate of metabolism and the number of active metabolic pathways are among the most important characteristics of the pharmacokinetics of a drug. The catalytic activity of drug metabolizing enzymes is likely the most influential determinant of the pharmacokinetic variability. Metabolic stability is the prerequisite for sustaining the therapeutically relevant concentrations. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. A variety of computational approaches are currently available for predicting different cytochrome P450 (CYP)-related metabolism endpoints. The present review will describe these approaches and their impact on drug development process. Indications on the available software for the implementation will also be given.

A COMBINED APPROACH TO DRUG METABOLISM AND TOXICITY ASSESSMENT

The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods, and, more recently, computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction. In the field of drug metabolism and toxicology, we have seen the growth, in recent years, of computational quantitative structure-activity relationships (QSARs), as well as empirical data from microarrays. In the current study we have further developed a novel computational approach, MetaDrug, that 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various absorption, distribution, metabolism, excretion, and toxicity models, 3) incorporates the predictions with human cell signaling and metabolic pathways and networks, and 4) integrates networks and metabolites, with relevant toxicogenomic or other high throughput data. We have demonstrated the utility of such an approach using recently published data from in vitro metabolism and microarray studies for aprepitant, 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694), trovofloxacin, 4-hydroxytamoxifen, and artemisinin and other artemisinin analogs to show the predicted interactions with cytochromes P450, pregnane X receptor, and P-glycoprotein, and the metabolites and the networks of genes that are affected. As a comparison, we used a second computational approach, MetaCore, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSARs and systems biology methods.

Lactandrate: a D-homo-aza-androsterone alkylator in the treatment of breast cancer

The sensitivity of breast neoplasms to hormonal control provides the basis of novel investigational treatments with steroidal alkylators. An androsterone D-lactam steroidal ester, the 3beta-hydroxy-13alpha-amino-13,17-seco- 5alpha-androstan-17-oic-13,17-lactam, p-bis(2-chloroethyl)amino phenyl acetate (lactandrate) was synthesized and tested for antitumor activity against six human breast cancer cell lines in vitro and against two murine and one xenograft mammary tumors in vivo. A docking study on the binding interactions of lactandrate with the ligand-binding domain (LBD) of estrogen receptor-alpha (ERalpha) was inquired. In vitro testing of lactandrate cytostatic and cytotoxic activity was performed on T47D, MCF7, MDA-MB-231, BT-549, Hs578T, MDA-MB-435 breast adenocarcinoma human cell lines. In vivo testing was performed on two murine mammary tumors, the MXT tumor and CD8F1 adenocarcinoma, as well as on human mammary carcinoma MX-1 xenograft. Molecular modeling techniques were adopted to predict a possible location and interaction mode of the molecule into LBD. Lactandrate induced significantly high antitumor effect against all tested in vitro and in vivo models. The cell lines with positive ER expression found to be significantly more sensitive to lactandrate. Moreover, lactandrate found to be positioned inside the binding cavity with its steroidal moiety, whilst the alkylating moiety protrudes out of receptor's pocket. Lactandrate produced important anticancer activity on breast cancer in vitro and in vivo. Some correlation between ER and lactandrate effect was demonstrated. Docking studies provide the basis for the structure-based design of improved steroidal alkylating esters for the treatment of estrogen-related cancers.

Computational prediction of human drug metabolism

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.

Systems-ADME/Tox: resources and network approaches

The increasing cost of drug development is partially due to our failure to identify undesirable compounds at an early enough stage of development. The application of higher throughput screening methods have resulted in the generation of very large datasets from cells in vitro or from in vivo experiments following the treatment with drugs or known toxins. In recent years the development of systems biology, databases and pathway software has enabled the analysis of the high-throughput data in the context of the whole cell. One of the latest technology paradigms to be applied alongside the existing in vitro and computational models for absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) involves the integration of complex multidimensional datasets, termed toxicogenomics. The goal is to provide a more complete understanding of the effects a molecule might have on the entire biological system. However, due to the sheer complexity of this data it may be necessary to apply one or more different types of computational approaches that have as yet not been fully utilized in this field. The present review describes the data generated currently and introduces computational approaches as a component of ADME/Tox. These methods include network algorithms and manually curated databases of interactions that have been separately classified under systems biology methods. The integration of these disparate tools will result in systems-ADME/Tox and it is important to understand exactly what data resources and technologies are available and applicable. Examples of networks derived with important drug transporters and drug metabolizing enzymes are provided to demonstrate the network technologies.

Integrating virtual screening in lead discovery

Target- and ligand-based virtual screening have emerged as resource-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Eight confirmed virtual screening hits have recently been described and are discussed in this review, with focus on the workflow. These are then evaluated in the light of pharmacokinetics prediction (e.g. Caco-2 permeability, cytochrome P450 inhibition and hERG binding). We anticipate problems for five of these hits (e.g. cardiac toxicity), which warrant further experiments. Future challenges include dynamic tautomer/protonation treatment for both ligands and targets and improved pre- and post- virtual screening filters.

Computational design strategies for combinatorial libraries

Medicinal chemistry principles are being increasingly applied to the design of smaller, high purity, information-rich libraries. Recent computational advances in statistical methodology, the design of libraries to reduce ADMET problems, targeting protein families and revisiting natural products as sources of inspiration for scaffolds and reagents are all areas of progressive research.