vNN Web Server for ADMET Predictions

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.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

In Silico Identification of Potential Natural Product Inhibitors of Human Proteases Key to SARS-CoV-2 Infection

Presently, there are no approved drugs or vaccines to treat COVID-19, which has spread to over 200 countries and at the time of writing was responsible for over 650,000 deaths worldwide. Recent studies have shown that two human proteases, TMPRSS2 and cathepsin L, play a key role in host cell entry of SARS-CoV-2. Importantly, inhibitors of these proteases were shown to block SARS-CoV-2 infection. Here, we perform virtual screening of 14,011 phytochemicals produced by Indian medicinal plants to identify natural product inhibitors of TMPRSS2 and cathepsin L. AutoDock Vina was used to perform molecular docking of phytochemicals against TMPRSS2 and cathepsin L. Potential phytochemical inhibitors were filtered by comparing their docked binding energies with those of known inhibitors of TMPRSS2 and cathepsin L. Further, the ligand binding site residues and non-covalent interactions between protein and ligand were used as an additional filter to identify phytochemical inhibitors that either bind to or form interactions with residues important for the specificity of the target proteases. This led to the identification of 96 inhibitors of TMPRSS2 and 9 inhibitors of cathepsin L among phytochemicals of Indian medicinal plants. Further, we have performed molecular dynamics (MD) simulations to analyze the stability of the protein-ligand complexes for the three top inhibitors of TMPRSS2 namely, qingdainone, edgeworoside C and adlumidine, and of cathepsin L namely, ararobinol, (+)-oxoturkiyenine and 3α,17α-cinchophylline. Interestingly, several herbal sources of identified phytochemical inhibitors have antiviral or anti-inflammatory use in traditional medicine. Further in vitro and in vivo testing is needed before clinical trials of the promising phytochemical inhibitors identified here.

Open access in silico tools to predict the ADMET profiling of drug candidates

INTRODUCTION

We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs.

AREAS COVERED

This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design.

EXPERT OPINION

The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction.

Cyanobacterial metabolites as promising drug leads against the Mpro and PLpro of SARS-CoV-2: an in silico analysis

A novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) has emerged as the causative agent behind the coronavirus disease 2019 (COVID-19) pandemic. Treatment efforts have been severely impeded due to the lack of specific effective antiviral drugs for the treatment of COVID-associated pathologies. In the present research endeavour the inhibitory prospects of cyanobacterial metabolites were assessed at the active binding pockets of the two vital SARS-CoV-2 proteases namely, main protease (M^pro) and the papain-like protease (PL^pro) that proteolytically process viral polyproteins and facilitate viral replication, employing an in silico molecular interaction-based approach. It was evident from our analysis based on the binding energy scores that the metabolites cylindrospermopsin, deoxycylindrospermopsin, carrageenan, cryptophycin 52, eucapsitrione, tjipanazole, tolyporphin and apratoxin A exhibited promising inhibitory potential against the SARS-CoV-2 M^pro. The compounds cryptophycin 1, cryptophycin 52 and deoxycylindrospermopsin were observed to display encouraging binding energy scores with the PL^pro of SARS-CoV-2. Subsequent estimation of physicochemical properties and potential toxicity of the metabolites followed by robust molecular dynamics simulations and analysis of MM-PBSA energy scoring function established deoxycylindrospermopsin as the most promising inhibitory candidate against both SARS-CoV-2 proteases. Present research findings bestow ample scopes to further exploit the potential of deoxycylindrospermopsin as a successful inhibitor of SARS-CoV-2 in vitro and in vivo and pave the foundation for the development of novel effective therapeutics against COVID-19.

Communicated by Ramaswamy H. Sarma

Identification of potential inhibitors of SARS-CoV-2 main protease from Aloe vera compounds: A molecular docking study

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A solution has to be found rapidly against COVD-19.

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From a set of 10 compounds of Aloe vera, 3 potential inhibitors of SARS-CoV-2 main protease were identified.

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The binding affinity of ligand-protein interactions and the Lipinski’s rule of five based-on ADME analysis were used to confirm the best candidate.

SARS-CoV-2 is the pathogen agent of the new corona virus disease that appeared at the end of 2019 in China. There is, currently, no effective treatment against COVID-19. We report in this study a molecular docking study of ten Aloe vera molecules with the main protease (3CLpro) responsible for the replication of coronaviruses. The outcome of their molecular simulation and ADMET properties reveal three potential inhibitors of the enzyme (ligands 6, 1 and 8) with a clear preference of ligand 6 that has the highest binding energy (−7.9 kcal/mol) and fully obeys the Lipinski’s rule of five.

Natural compounds from Clerodendrum spp. as possible therapeutic candidates against SARS-CoV-2: An in silico investigation

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has rattled global public health, with researchers struggling to find specific therapeutic solutions. In this context, the present study employed an in silico approach to assess the inhibitory potential of the phytochemicals obtained from GC-MS analysis of twelve Clerodendrum species against the imperative spike protein, main protease enzyme M^pro and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2. An extensive molecular docking investigation of the phytocompounds at the active binding pockets of the viral proteins revealed promising inhibitory potential of the phytochemicals taraxerol, friedelin and stigmasterol. Decent physicochemical attributes of the compounds in accordance with Lipinski’s rule of five and Veber’s rule further established them as potential therapeutic candidates against SARS-CoV-2. Molecular mechanics-generalized Born surface area (MM-GBSA) binding free energy estimation revealed that taraxerol was the most promising candidate displaying the highest binding efficacy with all the concerned SARS-CoV-2 proteins included in the present analysis. Our observations were supported by robust molecular dynamics simulations of the complexes of the viral proteins with taraxerol for a timescale of 40 nanoseconds. It was striking to note that taraxerol exhibited better binding energy scores with the concerned viral proteins than the drugs that are specifically targeted against them. The present results promise to provide new avenues to further evaluate the potential of the phytocompound taraxerol in vitro and in vivo towards its successful deployment as a SARS-CoV-2 inhibitor and combat the catastrophic COVID-19.

Communicated by Ramaswamy H. Sarma

Involvement of the gabaergic, serotonergic and glucocorticoid mechanism in the anxiolytic-like effect of mastoparan-L

Mastoparan-L (mast-L) is a cell-penetrating tetradecapeptide and stimulator of monoamine exocytosis. In the present study, we evaluated the anxiolytic-like effect of mast-L. Preliminary pharmacological tests were conducted to determine the most appropriate route of administration, extrapolate dose and detect potential toxic effects of this peptide. Oral and intracerebroventricular administration of mast-L (0.1, 0.3 or 0.9 mg.kg^-1), diazepam (1 or 5 mg.kg^-1), buspirone (10 mg.kg^-1) or vehicle 10 mL.kg^-1 was carried out prior to the exposure of mice to the anxiety models: open field, light-dark box and elevated plus-maze. To characterize the mechanism underlying the antianxiety-like effect of mast-L, pharmacological antagonism, blood plasma analysis, molecular docking, and receptor binding assays were performed. The absence of a neurotoxic sign, animal's death as well as lack of significant changes in the relative organ weight, hematological and biochemical parameters suggest that mast-L is relatively safe. The anxiolytic-like effect of mast-L was attenuated by flumazenil (antagonist of benzodiazepine binding site) and WAY100635 (selective antagonist of 5-HT_1A receptors) pretreatments. Mast-L reduced plasma corticosterone and lowered the scoring function at GABA_A -18.48 kcal/mol (Ki = 155 nM), 5-HT_1A -22.39 kcal/mol (Ki = 130 nM), corticotropin-releasing factor receptor subtype 1 (CRF1) -11.95 kcal/mol (Ki = 299 nM) and glucocorticoid receptors (GR) -14.69 kcal/mol (Ki = 3552 nM). These data fit the binding affinity (Ki) and demonstrate the involvement of gabaergic, serotonergic and glucocorticoid mechanisms in the anxiolytic-like property of mast-L.

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

New role for crinamine as a potent, safe and selective inhibitor of human monoamine oxidase B: In vitro and in silico pharmacology and modeling

ETHNOPHARMACOLOGICAL RELEVANCE

The development of selective inhibitors of monoamine oxidase B (MAO-B) has been essential in treating Parkinson's disease. However, the apparent hepatotoxicity and drug-drug interactions of current inhibitors accentuate the need for the development of novel pharmacotherapies. Crossyne guttata (L.) D. & U. Müll-Doblies is used frequently by Rastafarian bush doctors to treat alcoholism, a disorder which is also accentuated by MAO.

OBJECTIVE

The study sought to isolate, identify and characterise the biologically active constituents of C. guttata based on their ability to inhibit the MAO enzymes.

MATERIALS AND METHODS

Column chromatography was used to isolate the biologically active alkaloids of C. guttata. The ability of the alkaloids to inhibit the biotransformation of 4-aminoantipyrine by the MAO enzymes was evaluated in vitro. In silico docking was conducted using AutoDock Vina server while the pharmacokinetic properties of the compounds were evaluated using SwissADME.

RESULTS

Chromatographic separation of an ethanolic fraction of C. guttata yielded the alkaloids crinamine 1 and epibuphanisine 2. 1 and 2 along with structurally related alkaloids haemanthamine 3 and haemanthidine 4 were evaluated for their ability to inhibit the action of isozymes of MAO in vitro. Alkaloids effected submicromolar IC₅₀ values against MAO-B, the most potent of which being crinamine 1 (0.014 μM) > haemanthidine 4 (0.017 μM) > epibuphanisine 2 (0.039 μM) > haemanthamine 3 (0.112 μM). Binding energies of the alkaloids correlated well with their inhibitory potential with crinamine displaying the best binding efficacy and binding energy score with MAO-B.

DISCUSSION AND CONCLUSION

Crinamine and epibuphanisine exhibited potent and selective inhibitory activity towards MAO-B. After comprehensive in silico investigations encompassing robust molecular docking analysis, the drug-like attributes and safety of the alkaloids suggest the crinamine is a potentially safe drug for human application.

ADMET properties of novel 5-O-benzoylpinostrobin derivatives

Background Prediction of the properties of absorption, distribution, metabolism, excretion, and toxicity (ADMET) from a compound is essential, especially for modified novel compounds. Previous research has successfully designed several modified compounds of 5-O-benzoyl derivatives from pinostrobin, a flavanone that has cytotoxic activity. This study aims to describe the properties of ADMET from the 5-O-benzoylpinostrobin derivative. Methods Prediction of the properties of ADMET was carried out using three web servers consisting of SwissADME, pkCSM, and ProTox-II. The observed parameters are divided into ADMET parameters. Results In general, absorption parameters indicate that the 5-O-benzoylpinostrobin derivative has lower water solubility than the parent pinostrobin. Distribution parameters show mixed results for distribution through the blood-brain barrier. Metabolism parameters showed different results with generally inhibitory activity shown in CYP2C19, CYP2C9, and CYP3A4. The excretion parameters showed a higher total clearance than pinostrobin except in the trifluoromethyl derivative. The toxicity parameters showed both pinostrobin and the 5-O-benzoylpinostrobin derivatives, including the class IV toxicity category with the lowest LD50 value indicated by the nitro derivative of 1500, with the possible target of the androgen receptor and prostaglandin G/H synthase 1. Conclusions Overall, the 5-O-benzoylpinostrobin derivative has the predicted ADMET profile that is relatively similar to pinostrobin, with the most noticeable difference being shown in the absorption parameters where all 5-O-benzoylpinostrobin derivatives have lower water solubility than pinostrobin.

Chromatographic and computational screening of anisotropic lipophilicity and pharmacokinetics of newly synthesized 1-aryl-3-ethyl-3-methylsuccinimides

The present study is focused on a series of newly synthesized 1-aryl-3-ethyl-3-methylsuccinimide derivatives, as potential anticonvulsants. The retention behavior of eleven succinimide derivatives was determined by using reversed phase high performance liquid chromatography (RP-HPLC) and reversed phase high performance thin layer chromatography (RP-HPTLC). The estimated retention behavior was correlated with partition (logP) and distribution coefficients (logD). These high correlations pointed out that the determined retention parameters (logk₀ and R_M⁰) can be considered chromatographic (anisotropic) lipophilicity of the studied succinimide derivatives. The structural properties, which dominantly affect the chromatographic lipophilicity, were determined as well. The significant correlations between the chromatographic lipophilicity and plasma protein binding (PPB), Madin-Darby Canine Kidney (MDCK) cells permeability, volume of distribution (Vd) and absorption constant (Ka) indicate the strong influence of lipophilicity on pharmacokinetics of 1-aryl-3-ethyl-3-methylsuccinimide derivatives. These derivatives have also been tested applying Comprehensive Medicinal Chemistry (CMC) drug-like rules which confirmed their drug-like properties. Besides, their blood-brain penetration (BBB) ability has been estimated applying the set of Clark's rules and by using Pre-ADMET software. Regarding toxicity, it was predicted that only one compound from the set might have toxic effects by blocking the hERG potassium channel. The present study reveals which molecular features in the structure of novel succinimide derivatives could be crucial for their lipophilicity, and consequently for their pharmacokinetic properties. The results indicate that the newly synthesized series of succinimide derivatives should be further considered in design of novel anticonvulsants.

A curated knowledgebase on endocrine disrupting chemicals and their biological systems-level perturbations

Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can perturb the hormonal homeostasis leading to adverse health effects. In this work, we have developed a detailed workflow to identify EDCs with supporting evidence of endocrine disruption in published experiments in humans or rodents. Thereafter, this workflow was used to manually evaluate more than 16,000 published research articles and identify 686 potential EDCs with published evidence in humans or rodents. Importantly, we have compiled the observed adverse effects or endocrine-specific perturbations along with the dosage information for the potential EDCs from their supporting published experiments. Subsequently, the potential EDCs were classified based on the type of supporting evidence, their environmental source and their chemical properties. Additional compiled information for potential EDCs include their chemical structure, physicochemical properties, predicted ADMET properties and target genes. In order to enable future research based on this compiled information on potential EDCs, we have built an online knowledgebase, Database of Endocrine Disrupting Chemicals and their Toxicity profiles (DEDuCT), accessible at: https://cb.imsc.res.in/deduct/. After building this comprehensive resource, we have performed a network-centric analysis of the chemical space and the associated biological space of target genes of EDCs. Specifically, we have constructed two networks of EDCs using our resource based on similarity of chemical structures or target genes. Ensuing analysis revealed a lack of correlation between chemical structure and target genes of EDCs. Though our detailed results highlight potential challenges in developing predictive models for EDCs, the compiled information in our resource will undoubtedly enable future research in the field, especially, those focussed towards mechanistic understanding of the systems-level perturbations caused by EDCs.

Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.

A drug-likeness toolbox facilitates ADMET study in drug discovery

Undesirable pharmacokinetic (PK) properties or unacceptable toxicity are the main causes of the failure of drug candidates at the clinical trial stage. Since the concept of drug-likeness was first proposed, it has become an important consideration in the selection of compounds with desirable bioavailability during the early phases of drug discovery. Over the past decade, online resources have effectively facilitated drug-likeness studies in an economical and time-efficient manner. Here, we provide a comprehensive summary and comparison of current accessible online resources, in terms of their key features, application fields, and performance for in silico drug-likeness studies. We hope that the assembled toolbox will provide useful guidance to facilitate future in silico drug-likeness research.

Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models

The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and the estimation of overall CYP inhibitor properties might serve as valuable tools during the early phases of drug discovery. Herein, we present a large data set of inhibitors against five major metabolic CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) for the evaluation of important physicochemical properties and ligand efficiency metrics to define property trends across various activity levels (active, efficient and inactive). Decision tree models for CYP inhibition were developed with an accuracy >90% for both the training set and 10-folds cross validation. Overall, molecular weight (MW), hydrogen bond acceptors/donors (HBA/HBD) and lipophilicity (clogP/logPo/w) represent important physicochemical descriptors for CYP450 inhibitors. However, highly efficient CYP inhibitors show mean MW, HBA, HBD and logP values between 294.18–482.40,5.0–8.2,1–7.29 and 1.68–2.57, respectively. Our results might help in optimization of toxicological profiles associated with new chemical entities (NCEs), through a better understanding of inhibitor properties leading to CYP-mediated interactions.

Designing novel possible kinase inhibitor derivatives as therapeutics against Mycobacterium tuberculosis: An in silico study

Rv2984 is one of the polyphosphate kinases present in Mycobacterium tuberculosis involved in the catalytic synthesis of inorganic polyphosphate, which plays an essential role in bacterial virulence and drug resistance. Consequently, the structure of Rv2984 was investigated and an 18 membered compound library was designed by altering the scaffolds of computationally identified inhibitors. The virtual screening of these altered inhibitors was performed against Rv2984 and the top three scoring inhibitors were selected, exhibiting the free energy of binding between 8.2–9 kcal mol⁻¹ and inhibition constants in the range of 255–866 nM. These selected molecules showed relatively higher binding affinities against Rv2984 compared to the first line drugs Isoniazid and Rifampicin. Furthermore, the docked complexes were further analyzed in explicit water conditions using 100 ns Molecular Dynamics simulations. Through the assessment of obtained trajectories, the interactions between the protein and selected inhibitors including first line drugs were evaluated using MM/PBSA technique. The results validated the higher efficiency of the designed molecules compared to 1^st line drugs with total interaction energies observed between −100 kJ mol⁻¹ and −1000 kJ mol⁻¹. This study will facilitate the process of drug designing against M. tuberculosis and can be used in the development of potential therapeutics against drug-resistant strains of bacteria.

Toward steroidal anticancer drugs: Non-parametric and 3D-QSAR modeling of 17-picolyl and 17-picolinylidene androstanes with antiproliferative activity on breast adenocarcinoma cells

The present study is aimed to analyze lipophilicity and ADMET profiles, and to develop field based 3D-QSAR and ligand-based pharmacophore hypothesis for a series of 17α-picolyl and 17(E)-picolinylidene androstane derivatives in order to give detailed structural insights and to highlight important binding features of novel androstane derivatives, as compounds with antiproliferative activity toward breast adenocarcinoma cells. This study can provide guidelines for the rational design of novel potent compounds. Sum of ranking differences (SRD), as a non-parametric method, was applied for compounds ranking. 3D-QSAR methods, including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), were applied to predict the antiproliferative effect on breast adenocarcinoma cells and provide the regions in space where interactive fields may influence the activity. The compounds are ranked so the compounds with the most favorable ADME and lipophilicity features together with significant anticancer activity can be distinguished. The established 3D-QSAR model could be used for design of new compounds with antiproliferative activity on the human ER- breast adenocarcinoma cells. The pharmacophore model is able to accurately predict antiproliferative activity. Generally, the present study provides significant guidelines for further selection, synthesis and rational design of new highly potential androstane derivatives as anticancer drugs.

ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database

Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/ .

Molecular designing, virtual screening and docking study of novel curcumin analogue as mutation (S769L and K846R) selective inhibitor for EGFR

The somatic mutations in ATP binding cleft of the tyrosine kinase binding domain of EGFR are known to occur in 15-40% of non-small cell lung cancer (NSCLC) patients. Although first and second generation anti-EGFR inhibitors are widely used to treat these patients, their therapeutic efficacy is modest and often results in adverse effects or drug resistance. Therefore, there is a need to develop novel as well as safe anti-EGFR drugs. The rapid emergence of computational drug designing provided a great opportunity to both discover and predict the efficacy of novel EGFR inhibitors from plant sources. In the present study, we designed several chemical analogues of edible curcumin (CUCM) compound and assessed their drug likeliness, ADME and toxicity properties using a diverse range of advanced computational methods. We also have examined the structural plasticity and binding characteristics of EGFR wild-type and mutant forms (S769L and K846R) against ligand molecules like Gefitinib, native CUCM, and different CUCM analogues. Through multidimensional experimental approaches, we conclude that CUCM-36 ((1E,4Z,6E)-1-(3,4-Diphenoxyphenyl)-5-hydroxy-7-(4-hydroxy-3-phenoxyphenyl)-1,4,6-heptatrien-3-one) is the best anti-EGFR compound with high drug-likeness, ADME properties, and low toxicity properties. CUCM-36 compound has demonstrated better affinity towards both wild-type (ΔG is -8.5 kcal/Mol) and mutant forms (V769L & K846R; ΔG for both is >-9.20 kcal/Mol) compared to natural CUCM and Gefitinib inhibitor. This study advises the future laboratory assays to develop CUCM-36 as a novel drug compound for treating EGFR positive non-small cell lung cancer patients.