Absorption, Distribution, Metabolism, Excretion, Toxicity Properties Research Articles

Synthesis of isomeric β-cycloalkoxyphosphonated hydrazones containing a dioxaphosphorinane ring: Configurational and conformational investigation and molecular docking analysis

A series of Z/E isomeric β-cycloalkoxyphosphonated hydrazones [R1R2C(CH2O)2P(=O)CH2-C{=NN(H)R5}C(H)R3R4] 3 bearing a six-membered dioxaphosphorinane ring was prepared, structurally analysed, and subjected to in silico studies to assess their Acetylcholinesterase (AChE) and Butyrylcholinesterase (BuChE) inhibition activity. Furthermore, their ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties were determined. Hydrazones 3 were prepared from their corresponding allenylphosphonates [R1R2C(CH2O)2P(=O)C(H)=C=CR3R4] 2 by addition of various hydrazines NH2NHR5. Since NMR studies indicated the occurrence of several isomeric species in solution, conformational and configurational studies, based on mechanistic elucidation and energy stability using Density functional theory (DFT) calculation at the B3LYP/6-311 G++ (d,p) level of theory, were performed to determine their relative predominance. The hydrazone 1-(2-oxo-1,3,2-dioxaphosphoranyl)-3-phenylpropanone 3c was crystallographically analysed by single-crystal X-ray diffraction (SCXRD), revealing the occurrence of strong intra- and intermolecular NH···O hydrogen bonding. These secondary interactions were further examined by a Hirshfeld surface analysis. The experimental SCXRD parameters are compared with the theoretical calculated ones and the preferred configuration of product 3c was determined. Furthermore, the crystal structure of the symmetric azine 2,2′-((2E,2′E)-hydrazine-1,2-diylidenebis(3-phenylpropan-1-yl-2-ylidene))bis(5-methyl-5-propyl-1,3,2-dioxaphosphinane 2-oxide) 4, resulting from the reaction between hydrazone 3a and allene 2a, has been determined. To predict whether hydrazones 3 are appropriate as anticholinesterase agents exhibiting a potential activity for treatment of the Alzheimer disease, in silico molecular docking simulations were employed to investigate the interaction modes between hydrazones 3 and the active sites of the BuChE and AChE enzymes. Additionally, an ADMET-properties prediction was carried out for selected hydrazones to determine their pharmacokinetics and drug-likeness properties.

A novel ACE inhibitory peptide from Pelodiscus sinensis Wiegmann meat water-soluble protein hydrolysate

Pelodiscus sinensis meat is a nutritional food and tonic with angiotensin-converting enzyme (ACE) inhibitory activities. To identify the bioactive substances responsible, several bioinformatics methods were integrated to enable a virtual screening for bioactive peptides in proteins identified within a water-soluble protein fraction of Pelodiscus sinensis meat by Shotgun proteomics. The peptides were generated from the identified proteins by in silico proteolysis using six proteases. A comparison of the numbers of proteins suitable for digestion with each enzyme and the iBAQ (intensity-based absolute quantification) values for these proteins revealed that bromelain and papain were the most suitable proteases for this sample. Next, the water solubility, toxicity, and ADMET (absorption/distribution/metabolism/excretion/toxicity) properties of these peptides were evaluated in silico. Finally, a novel ACE inhibitory peptide IEWEF with an IC50 value of 41.33 µM was identified. The activity of the synthesized peptide was verified in vitro, and it was shown to be a non-competitive ACE inhibitor. Molecular docking revealed that IEWEF could tightly bind to C-ACE, and N-ACE with energies less than 0 kJ mol−1, and the peptide IEWEF can form hydrogen bonds with C-ACE and N-ACE respectively. These results provide evidence that bioactive peptides in the water-soluble protein fraction account for (at least) some of the ACE inhibitory activities observed in Pelodiscus sinensis meat. Furthermore, our research provides a workflow for the efficient identification of novel ACE inhibitory peptides from complex protein mixtures.

Development of Novel Pyrrole Derivatives and Their Cinnamic Hybrids as Dual COX-2/LOX Inhibitors.

Molecular hybridization has emerged as a promising approach in the treatment of diseases exhibiting multifactorial etiology. With regard to this, dual cyclooxygenase-2/lipoxygenase (COX-2/LOX) inhibitors could be considered a safe alternative to traditional non-steroidal anti-inflammatory drugs (tNSAIDs) and selective COX-2 inhibitors (coxibs) for the treatment of inflammatory conditions. Taking this into account, six novel pyrrole derivatives and pyrrole-cinnamate hybrids were developed as potential COX-2 and soybean LOX (sLOX) inhibitors with antioxidant activity. In silico calculations were performed to predict their ADMET (absorption, distribution, metabolism, excretion, toxicity) properties and drug-likeness, while lipophilicity was experimentally determined as RM values. All synthesized compounds (1-4, 5-8) could be described as drug-like. The results from the docking studies on COX-2 were in accordance with the in vitro studies. According to molecular docking studies on soybean LOX, the compounds displayed allosteric interactions with the enzyme. Pyrrole 2 appeared to be the most potent s-LOX inhibitor (IC50 = 7.5 μM). Hybrids 5 and 6 presented a promising combination of in vitro LOX (IC50 for 5 = 30 μM, IC50 for 6 = 27.5 μM) and COX-2 (IC50 for 5 = 0.55 μM, IC50 for 6 = 7.0 μM) inhibitory activities, and therefore could be used as the lead compounds for the synthesis of more effective multi-target agents.

Elucidating the Anti-Inflammatory Properties of Garcinia kola and Vernonia amygdalina through in silico Molecular Biology Techniques

Introduction: Inflammation is implicated in many disorders, including communicable and noncommunicable diseases. Cyclooxygenase-2 (Cox-2) is a key enzyme involved in the production of prostaglandins implicated in inflammatory disorders. Garcinia kola and Vernonia amygdalina are medicinal plants being used for treating various ailments in many parts of the world and extensive in vitro and in vivo studies have been conducted on them. Five phytochemicals were selected from the two plants; aspirin and celecoxib were used as reference drugs. This study investigated the interactions of the seven ligands with the Cox-2 enzyme, using in silico molecular biology techniques. Materials and Methods: The 3-D structures of the seven ligands were retrieved from the PubChem database in their Structure Data Format (SDF). Cox-2 was retrieved in its Protein Data Bank (PDB) format. The ligands and the protein were converted to their pdbqt formats through the open babel software. The Cox-2 was docked with the ligands using the Auto-Dock Vina software. The binding energies and the root mean square deviation values were noted. Pharmacophore modeling was visualized by using the Biovia Discovery Studio Visualizer. One of the ligands (luteolin) was further subjected to molecular dynamics simulation using the desmond maestro software. Results: While celecoxib had the best binding property with Cox-2 (-10.8 kcal/mol, 3 H bonds), the five ligands from the two plants had better binding properties than aspirin (which had -6.5kcal/mol, 1 H bond). Kolaviron, from G. kola (-9.1 kcal/mol, 3 H bonds) and luteolin, from V. amygdalina (-8.5kcal/mol, 2 H bonds) demonstrated the best binding properties among the five phytochemicals. Additional interactions of H bonds and hydrophobic bonds were noticed post molecular dynamics simulation of luteolin with Cox-2, indicating dynamic forces’ fluctuations. MD simulations showed that Ser530 and Tyr385 were the best amino acid side chains that interacted with luteolin for the stabilization of the protein-ligand complex. Conclusion: The energy values and protein-ligand interactions indicate affinity and stability of the complex. Luteolin can be taken as a promising drug target and subjected to ADMET (absorption, distribution, metabolism, excretion, toxicity) properties analysis and clinical trials. This is especially important in view of the various side effects associated with both nonselective and selective Cox-2 inhibitors, including aspirin and celecoxib.

Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates

Breast cancer is presently one of the most common malignancies worldwide, with a higher fatality rate. In this study, a quantitative structure-activity relationship (QSAR) model of compound biological activity and ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties prediction model were performed using estrogen receptor alpha (ERα) antagonist information collected from compound samples. We first utilized grey relation analysis (GRA) in conjunction with the random forest (RF) algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity, and then we used Spearman correlation analysis to identify 16 independent variables. Second, a QSAR model of the compound were developed based on BP neural network (BPNN), genetic algorithm optimized BP neural network (GA-BPNN), and support vector regression (SVR). The BPNN, the SVR, and the logistic regression (LR) models were then used to identify and predict the ADMET properties of substances, with the prediction impacts of each model compared and assessed. The results reveal that a SVR model was used in QSAR quantitative prediction, and in the classification prediction of ADMET properties: the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene) properties, the LR model predicts the cytochrome P450 enzyme 3A4 subtype (CYP3A4) and Micronucleus (MN) properties, and the BPNN model predicts the Human Oral Bioavailability (HOB) properties. Finally, information entropy theory is used to validate the rationality of variable screening, and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability, which can be used as a reference for screening probable active compounds and drug discovery.

Inhibitory potentials of phytocompounds from Ocimum gratissimum against anti-apoptotic BCL-2 proteins associated with cancer: an integrated computational study

ABSTRACT The anti-apoptotic Bcl-2 family is intrinsically involved in regulating apoptosis. Over expression of these proteins is associated with cancer. Thus, inhibitors of these proteins will enhance the development of anti-apoptotic drugs. Herein, previously reported 103 Ocimum gratissimum derived phytochemicals were screened against five anti-apoptotic BCL-2 proteins (BCL-2, MCL-1, BCL-B BCL-XL and BFL-1) to identify potential inhibitors of multiple anti-apoptotic Bcl-2 proteins, using static and dynamic docking simulations, molecular dynamics (MD) simulations, clustering and Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) filtering analysis. Based on the minimal binding energy and a comparative reference inhibitors binding mode analysis, five lead phytochemicals (FLP) (ursolic acid, beta-sitosterol, luteolin, basilimoside and apigenin 7,4’,dimethyl ether) were identified. Ursolic acid, β-sitosterol and luteolin exhibited higher binding tendencies to the BH3 binding groove of multiple targets. Ursolic acid-Bcl-2 and luteolin-BCL-XL, complexes demonstrated structural stability in the simulated MD environment. Also, the FLP demonstrated favorable ADMET properties. Evidences from previously reported antiproliferative activities of ursolic acid, β-sitosterol and luteolin and results from this study suggest that the anti-proliferative activity of O. gratissimum may be as a result of the synergistic activities of, at least, the FLP. They are recommended for further study as natural-inhibitors against cancers defined by over expression of Bcl-2 family protein.

Searching for Novel Anaplastic Lymphoma Kinase Inhibitors: Structure-Guided Screening of Natural Compounds for a Tyrosine Kinase Therapeutic Target in Cancers.

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase molecular target with broad importance for drug discovery, especially in the field of cancer therapeutics. ALK belongs to the insulin receptor superfamily that is involved in various malignancies, including non-small cell lung cancer, anaplastic large cell lymphoma, and neuroblastoma. ALK has been shown to play a role in cancer progression and metastasis, making it one of the prime targets to develop novel anticancer therapeutics. In this context, natural compounds can be an important resource to unravel novel ALK inhibitors. In this study, we report a structure-based virtual screening of natural compounds from the ZINC database, with an eye to potential inhibitors of ALK. Molecular docking was performed on a natural compound library, and top hits holding good binding affinity, docking score, and specificity toward ALK were selected. The hits were further evaluated based on the PAINS (pan-assay interference compounds) filter, ADMET (absorption, distribution, metabolism, excretion, toxicity) properties, PASS (prediction of activity spectra for substances) analysis, and two-dimensional interaction of protein-ligand complexes. Importantly, two natural compounds (ZINC03845566 and ZINC03999625) were identified as potential candidates for ALK, having appreciable affinity and specificity toward the ALK binding pocket and depicting drug-like properties as predicted from ADMET analysis and their physicochemical parameters. An all-atom molecular dynamics simulation for 100 ns on ALK promised stable ALK-ligand complexes. Hence, we conclude that ZINC03845566 and ZINC03999625 can act as potential ALK inhibitors against cancers where ALK plays a role, for example, in lung cancer, among others. All in all, these findings inform future discovery and translational research for ALK inhibitors as anticancer drugs.

Antimalarial phytochemicals as potential inhibitors of SARS-CoV-2 guanine N7-methyltransferase (nsp 14): an integrated computational approach

The inhibition of capping enzymes such as guanine-N7-methyltransferase (GMT) is an attractive target for regulating viral replication, transcription, virulence, and pathogenesis. Thus, compounds that target the Severe Acute Respiratory Syndrome Corona Virus 2 GMT (S2GMT) will enhance drug development against COVID-19. In this study, an in-house library of 249 phytochemicals from African medicinal plants was screened using computational approaches including homology modeling, molecular docking, molecular dynamic simulations, binding free energy calculations based on molecular mechanics/Poisson-Boltzmann surface area (MMPBSA) and Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) analysis for inhibitors of S2GMT. The top-ten ranked phytochemicals (TTRP) obtained from the docking analysis to S2GMT were further docked to SARS-COV N7-MTase. Among the TTRP, the top-four ranked phytocompounds (TFRP) viz: 3 alkaloids (Isocryptolepine, 10’–Hydroxyusambarensine and Isostrychnopentamine) and a flavonoid (Mulberrofuran F) interacted strongly with critical catalytic residues whose interference either reduce or completely abolish N7-MTase activity, indicating their potential as capping machinery disruptors. The interactions of TFRP with the catalytic residues of S2GMT were preserved in a 100 ns simulated dynamic environment, thereby, demonstrating high degree of structural stability. The MMPBSA binding free energy calculations corroborated the docking scores with biscryptolepine having the highest binding free energy to S2GMT. The TFRP showed favourable drug-likeness and ADMET properties over a wide range of molecular descriptors. Therefore, the TFRP can be further explored as potential S2GMT inhibitors in in vitro and in vivo experiments. Communicated by Ramaswamy H. Sarma

SARS-CoV-2 host cell entry: an in silico investigation of potential inhibitory roles of terpenoids

BackgroundTargeting viral cell entry proteins is an emerging therapeutic strategy for inhibiting the first stage of SARS-CoV-2 infection. In this study, 106 bioactive terpenoids from African medicinal plants were screened through molecular docking analysis against human angiotensin-converting enzyme 2 (hACE2), human transmembrane protease serine 2 (TMPRSS2), and the spike (S) proteins of SARS-CoV-2, SARS-CoV, and MERS-CoV. In silico absorption-distribution-metabolism-excretion-toxicity (ADMET) and drug-likeness prediction, molecular dynamics (MD) simulation, binding free energy calculations, and clustering analysis of MD simulation trajectories were performed on the top docked terpenoids to respective protein targets. ResultsThe results revealed eight terpenoids with high binding tendencies to the catalytic residues of different targets. Two pentacyclic terpenoids (24-methylene cycloartenol and isoiguesteri) interacted with the hACE2 binding hotspots for the SARS-CoV-2 spike protein, while the abietane diterpenes were found accommodated within the S1-specificity pocket, interacting strongly with the active site residues TMPRSS2. 3-benzoylhosloppone and cucurbitacin interacted with the RBD and S2 subunit of SARS-CoV-2 spike protein respectively. These interactions were preserved in a simulated dynamic environment, thereby, demonstrating high structural stability. The MM-GBSA binding free energy calculations corroborated the docking interactions. The top docked terpenoids showed favorable drug-likeness and ADMET properties over a wide range of molecular descriptors. ConclusionThe identified terpenoids from this study provides core structure that can be exploited for further lead optimization to design drugs against SARS-CoV-2 cell-mediated entry proteins. They are therefore recommended for further in vitro and in vivo studies towards developing entry inhibitors against the ongoing COVID-19 pandemic.

Virtual Screening of Natural Products against Type II Transmembrane Serine Protease (TMPRSS2), the Priming Agent of Coronavirus 2 (SARS-CoV-2).

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused about 2 million infections and is responsible for more than 100,000 deaths worldwide. To date, there is no specific drug registered to combat the disease it causes, named coronavirus disease 2019 (COVID-19). In the current study, we used an in silico approach to screen natural compounds to find potent inhibitors of the host enzyme transmembrane protease serine 2 (TMPRSS2). This enzyme facilitates viral particle entry into host cells, and its inhibition blocks virus fusion with angiotensin-converting enzyme 2 (ACE2). This, in turn, restricts SARS-CoV-2 pathogenesis. A three-dimensional structure of TMPRSS2 was built using SWISS-MODEL and validated by RAMPAGE. The natural compounds library Natural Product Activity and Species Source (NPASS), containing 30,927 compounds, was screened against the target protein. Two techniques were used in the Molecular Operating Environment (MOE) for this purpose, i.e., a ligand-based pharmacophore approach and a molecular docking-based screening. In total, 2140 compounds with pharmacophoric features were retained using the first approach. Using the second approach, 85 compounds with molecular docking comparable to or greater than that of the standard inhibitor (camostat mesylate) were identified. The top 12 compounds with the most favorable structural features were studied for physicochemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. The low-molecular-weight compound NPC306344 showed significant interaction with the active site residues of TMPRSS2, with a binding energy score of −14.69. Further in vitro and in vivo validation is needed to study and develop an anti-COVID-19 drug based on the structures of the most promising compounds identified in this study.

Potential Cyclooxygenase (COX-2) enzyme inhibitors from Myrica nagi-from in-silico to in-vitro investigation

Introduction: Myrica nagi Thunb. (family Myricaceae) are actinorhizal plants showing symbiotic interaction with Frankia. Inhibition of cyclooxygenase-2 (COX-2) enzyme is known to be significant in preventing inflammation and in therapeutics. Objectives: Our principal focus was to identify COX-2 enzyme inhibitors, safer and natural anti-inflammatory compounds from M. nagi. Protein–ligand interaction has a significant role in structure-based drug design. Materials and Methods: Sixty-eight phytochemicals were therefore screened and evaluated for their binding energies with COX-2. These phytoconstituents were screened and analyzed for drug Likeliness along with Lipinski's rule of five. The X-ray crystallographic structure of the target COX-2 (protein data bank [PDB] ID: 4PH9), obtained from PDB, was docked with PubChem structures of phytochemicals using AutoDock 4.2 that uses Lamarckian genetic algorithm. Further, myricetin was subjected to in vitro anti-inflammatory assay using RAW-264.7 cell lines and inhibitory concentration (IC50) value was also determined. Results: The myricetin, myricitrin, and corchoionoside-C inhibited COX-2 with − 6.52, −4.94, and − 4.94 Kcal/mol binding energies, respectively, comparable to ibuprofen. Eventually, bioactivity score and absorption distribution metabolism excretion-toxicity properties showed considerable biological activities as G protein-coupled receptor, nuclear receptor, protease inhibitor, and enzyme inhibitors for myricetin, myricitrin, and corchoionoside-C phytochemicals. Molecular docking revealed hydrophobic interactions followed by four, nine, and four numbers of hydrogen bonds between myricetin, myricitrin, and corchoionoside-C, respectively, within the binding site of COX-2. Flavonol myricetin showed 112 μg/mL as IC50 value when it was subjected to in vitro cytotoxicity assay. These results clearly demonstrated that myricetin, myricitrin, and corchoionoside-C could act as highly potential COX-2 inhibitors. Therefore, in silico and in vitro studies revealed that of three best phytochemicals, myricetin could be promising candidate.

Synthesis, Reactivity, Functionalization, and ADMET Properties of Silicon-Containing Nitrogen Heterocycles.

Silicon-containing compounds have been largely ignored in drug design and development, despite their potential to improve not only the potency but also the physicochemical and ADMET ( absorption, distribution, metabolism, excretion, toxicity) properties of drug-like candidates because of the unique characteristics of silicon. This deficiency is in large part attributable to a lack of general methods for synthesizing diverse organosilicon structures. Accordingly, a new building block strategy has been developed that diverges from traditional approaches to incorporation of silicon into drug candidates. Flexible, multi-gram-scale syntheses of silicon-containing tetrahydroquinoline and tetrahydroisoquinoline building blocks are described, along with methods by which diversely functionalized silicon-containing nitrogen heterocycles can be rapidly built using common reactions optimized to accommodate the properties of silicon. Furthermore, to better clarify the liabilities and advantages of silicon incorporation, select compounds and their carbon analogues were challenged in ADMET-focused biological studies.

IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics

Phytochemicals of medicinal plants encompass a diverse chemical space for drug discovery. India is rich with a flora of indigenous medicinal plants that have been used for centuries in traditional Indian medicine to treat human maladies. A comprehensive online database on the phytochemistry of Indian medicinal plants will enable computational approaches towards natural product based drug discovery. In this direction, we present, IMPPAT, a manually curated database of 1742 Indian Medicinal Plants, 9596 Phytochemicals, And 1124 Therapeutic uses spanning 27074 plant-phytochemical associations and 11514 plant-therapeutic associations. Notably, the curation effort led to a non-redundant in silico library of 9596 phytochemicals with standard chemical identifiers and structure information. Using cheminformatic approaches, we have computed the physicochemical, ADMET (absorption, distribution, metabolism, excretion, toxicity) and drug-likeliness properties of the IMPPAT phytochemicals. We show that the stereochemical complexity and shape complexity of IMPPAT phytochemicals differ from libraries of commercial compounds or diversity-oriented synthesis compounds while being similar to other libraries of natural products. Within IMPPAT, we have filtered a subset of 960 potential druggable phytochemicals, of which majority have no significant similarity to existing FDA approved drugs, and thus, rendering them as good candidates for prospective drugs. IMPPAT database is openly accessible at: https://cb.imsc.res.in/imppat.

ANTIMICROBIAL AND IN SILICO ADMET SCREENING OF NOVEL (E)-N-(2-(1H-INDOL-3-YL-AMINO) VINYL)-3-(1-METHYL-1H-INDOL-3-YL)-3-PHENYLPROPANAMIDE DERIVATIVES

<p><strong>Objective: </strong>Synthesis, <em>in silico</em> absorption, distribution, metabolism, excretion, toxicity (ADMET) and <em>in vitro</em> antimicrobial screening of (<em>E</em>)-<em>N</em>-(2-(1<em>H</em>-indol-3-ylamino) vinyl)-3-(1-methyl-1<em>H</em>-indol-3-yl)-3-phenylpropanamide derivatives.<strong></strong></p><p><strong>Methods: </strong>(<em>E</em>)-<em>N</em>-(2-(1<em>H</em>-indol-3-ylamino) vinyl)-3-(1-methyl-1<em>H</em>-indol-3-yl)-3 phenylpropane-amide derivatives were synthesized by combining indole ethanolamine and substituted Meldrum’s adduct. The synthesized compounds were subjected to <em>in vitro</em> antimicrobial study by cup plate method and <em>in silico</em> ADMET properties using ACD/I-Lab 2.0.</p><p><strong>Results: </strong>The <em>in vitro </em>antimicrobial screening against precarious pathogenic microorganisms <em>viz</em>, <em>Pseudomonas aureginosa</em>, <em>Staphylococcus aureus,</em> <em>Escherichia coli, </em><em>Vibrio cholerae</em>, and the antifungal activity against <em>Candida albicans, </em><em>Aspergillus niger</em>, <em>Penicillin chrysogenum</em> and <em>Cladosporium oxysporum</em> strains. The results revealed that compounds 5b, 5c, 5d and 5e showed good antimicrobial property and obeyed the <em>in silico</em> pharmacokinetic parameters.</p><p><strong>Conclusion: </strong>The encouraging results exhibited by the compounds (<em>E</em>)-<em>N</em>-(2-(1<em>H</em>-indol-3-ylamino) vinyl)-3-(1-methyl-1<em>H</em>-indol-3-yl)-3-phenyl propanamide derivatives, 5(a-e) can be explored as possible hits in antimicrobial therapy. The molecules obey the Lipinski rule of five when tested <em>in silico </em>and can be used in understanding the quantitative structure-activity relationship (QSAR) parameters.</p>

IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data.

Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process.Description We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data.ConclusionIDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database.Graphical abstractFDA approved drug data integration for predictive modelingElectronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0141-7) contains supplementary material, which is available to authorized users.

Investigating drug-target association and dissociation mechanisms using metadynamics-based algorithms.

CONSPECTUS: This Account highlights recent advances and discusses major challenges in the field of drug-target recognition, binding, and unbinding studied using metadynamics-based approaches, with particular emphasis on their role in structure-based design. Computational chemistry has significantly contributed to drug design and optimization in an extremely broad range of areas, including prediction of target druggability and drug likeness, de novo design, fragment screening, ligand docking, estimation of binding affinity, and modulation of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. Computationally driven drug discovery must continuously adapt to keep pace with the evolving knowledge of the factors that modulate the pharmacological action of drugs. There is thus an urgent need for novel computational approaches that integrate the vast amount of complex information currently available for small (bio)organic compounds, biologically relevant targets and their complexes, while also accounting accurately for the thermodynamics and kinetics of drug-target association, the intrinsic dynamical behavior of biomolecular systems, and the complexity of protein-protein networks. Understanding the mechanism of drug binding to and unbinding from biological targets is fundamental for optimizing lead compounds and designing novel biologically active ones. One major challenge is the accurate description of the conformational complexity prior to and upon formation of drug-target complexes. Recently, enhanced sampling methods, including metadynamics and related approaches, have been successfully applied to investigate complex mechanisms of drugs binding to flexible targets. Metadynamics is a family of enhanced sampling techniques aimed at enhancing the rare events and reconstructing the underlying free energy landscape as a function of a set of order parameters, usually referred to as collective variables. Studies of drug binding mechanisms have predicted the most probable association and dissociation pathways and the related binding free energy profile. In addition, the availability of an efficient open-source implementation, running on cost-effective GPU (i.e., graphical processor unit) architectures, has considerably decreased the learning curve and the computational costs of the methods, and increased their adoption by the community. Here, we review the recent contributions of metadynamics and other enhanced sampling methods to the field of drug-target recognition and binding. We discuss how metadynamics has been used to search for transition states, to predict binding and unbinding paths, to treat conformational flexibility, and to compute free energy profiles. We highlight the importance of such predictions in drug discovery. Major challenges in the field and possible solutions will finally be discussed.

Simultaneous Modeling of Antimycobacterial Activities and ADMET Profiles: A Chemoinformatic Approach to Medicinal Chemistry

Mycobacteria represent a group of pathogens which cause serious diseases in mammals, including the lethal tuberculosis (Mycobacterium tuberculosis). Despite the mortality of this community-acquired and nosocomial disease mentioned above, other mycobacteria may cause similar infections, acting as dangerous opportunistic pathogens. Additionally, resistant strains belonging to Mycobacterium spp. have emerged. Thus, the design of novel antimycobacterial agents is a challenge for the scientific community. In this sense, chemoinformatics has played a vital role in drug discovery, helping to rationalize chemical synthesis, as well as the evaluation of pharmacological and ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles in both medicinal and pharmaceutical chemistry. Until now, there is no in silico methodology able to assess antimycobacterial activity and ADMET properties at the same time. This work introduces the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous prediction of antimycobacterial activities and ADMET profiles of drugs/chemicals under diverse experimental conditions. The mtk-QSBER model was constructed by using a large and heterogeneous dataset of compounds (more than 34600 cases), displaying accuracies higher than 90% in both, training and prediction sets. To illustrate the utility of the present model, several molecular fragments were selected and their contributions to different biological effects were calculated and analyzed. Also, many properties of the investigational drug TMC-207 were predicted. Results confirmed that, from one side, TMC-207 can be a promising antimycobacterial drug, and on the other hand, this study demonstrates that the present mtk-QSBER model can be used for virtual screening of safer antimycobacterial agents.

Tyrosine kinase inhibitors as modulators of ATP binding cassette multidrug transporters: substrates, chemosensitizers or inducers of acquired multidrug resistance?

Introduction: Anticancer tyrosine kinase inhibitors (TKIs) are small molecule hydrophobic compounds designed to arrest aberrant signaling pathways in malignant cells. Multidrug resistance (MDR) ATP binding cassette (ABC) transporters have recently been recognized as important determinants of the general ADME-Tox (absorption, distribution, metabolism, excretion, toxicity) properties of small molecule TKIs, as well as key factors of resistance against targeted anticancer therapeutics.Areas covered: The article summarizes MDR-related ABC transporter interactions with imatinib, nilotinib, dasatinib, gefitinib, erlotinib, lapatinib, sunitinib and sorafenib, including in vitro and in vivo observations. An array of methods developed to study such interactions is presented. Transporter–TKI interactions relevant to the ADME-Tox properties of TKI drugs, primary or acquired cancer TKI resistance, and drug–drug interactions are also reviewed.Expert opinion: Based on the concept presented in this review, TKI anticancer drugs are considered as compounds recognized by the cellular mechanisms handling xenobiotics. Accordingly, novel anticancer therapies should equally focus on the effectiveness of target inhibition and exploration of potential interactions of the designed molecules by membrane transporters. Thus, targeted hydrophobic small molecule compounds should also be screened to evade xenobiotic-sensing cellular mechanisms.

Role of Transmembrane Domain/Transmembrane Domain Interfaces of PGlycoprotein (ABCB1) in Solute Transport. Convergent Information from Photoaffinity Labeling, Site Directed Mutagenesis and in Silico Importance Prediction

Human P-glycoprotein (P-gp, ABCB1) plays an important role in the development of resistance to anticancer therapy. This ABC-transporter (ATP-binding cassette transporter) intercepts drugs at the level of the plasma membrane and effluxes them before they are able to reach their intracellular target structures. Inhibition of P-gp by low molecular weight compounds has been advocated as a concept for resensitization of cells to anticancer agents and several clinical studies in oncological patients have advanced to phase III. Even more importantly, P-glycoprotein also represents an antitarget. Its expression in cells lining the intestinal tract, the canalicular side of hepatocytes, renal tubuli and the blood brain barrier lead to interference with pharmacokinetics of compounds that are recognized as pump substrates. An early prediction of ADMET (Absorption-Distribution-Metabolism-Excretion-Toxicity) properties is important during drug development, since interference of a compound with P-gp might compromise its future development into a drug. Despite considerable efforts, the mechanism by which P-gp binds and transports its solutes remains unclear. Generation of homology models of the protein allowed integration of data obtained by photoaffinity labeling, in silico prediction of functional importance by evolutionary tracing and site directed mutagenesis. An integral view of data indicates that these three lines of evidence converge to indicate two pseudosymmetric P-gp drug binding pockets located at the two transmembrane domain interfaces.

Multivariate modeling of cytochrome P450 3A4 inhibition

In the early phases of current pharmaceutical research projects, huge numbers of compounds are tested on their biological activity with respect to a certain target by experimental or virtual screening campaigns. To reduce the attrition rate in later stages of a project, other relevant properties such as physicochemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) properties should be assessed as early as possible in lead discovery and optimization. The present study describes the development of in silico models to predict the inhibition of human cytochrome P450 3A4 (CYP3A4) from calculated molecular descriptors. The models were trained and validated using a set of 967 structural diverse drug-like research compounds with an experimentally determined CYP3A4 inhibition potency (IC 50 value) which was carefully split into a training and a test set. For classification models, the data sets were further subdivided into strong, medium, and weak inhibitors. Different descriptor sets were used to cover various aspects of molecular properties, including properties derived from the 2D structure, the interaction of the molecule with its environment, and properties derived from quantum–mechanical calculations. The descriptors were related to the CYP3A4 inhibition potency by multivariate data analysis methods such as partial least-squares projection to latent structures (PLS), PLS discriminant analysis (PLS-DA), and soft independent class modeling (SIMCA). The squared correlation between experimental and predicted IC 50 values of the previously unseen test set compounds was Q ext 2 = 0.6 for the best PLS models, corresponding to a root mean squared error (RMSE) of RMSE = 0.45 (logarithm of IC 50). The best PLS-DA models were able to correctly classify more than 60% of the test set compounds, whereas almost no strong inhibitors were wrongly classified as weak inhibitors and vice versa. Furthermore, relevant molecular properties were identified which are closely related to the CYP3A4 inhibition potency of a compound. The results presented here are very encouraging since our models could, for instance, serve to flag problematic compounds or to guide further synthesis efforts.