Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors

Hibiscus sabdariffa anthocyanins are potential modulators of estrogen receptor alpha activity with favourable toxicology: a computational analysis using molecular docking, ADME/Tox prediction, 2D/3D QSAR and molecular dynamics simulation

The estrogen hormone receptor (ER) mediated gene expression mainly regulate the development and physiology of the primary and secondary reproductive system alongside bone-forming, metabolism and behaviour. Over-expressed ER is associated with several pathological conditions and play a crucial role in breast cancer occurrence, progression and metastasis. Hibiscus sabdariffa L. or roselle is a rich source of naturally occurring polyphenolic compounds that reportedly have robust estrogenic activity. However, the estrogen-like ingredient of the plant remains ambiguous. This study has screened a library of already recorded and less-explored compounds of Hibiscus sabdariffa for their estrogen receptor binding affinity and safety using a suite of computational methods that include protein-ligand docking, ADME and Toxicity prediction, and 2D/3D QSAR. The study revealed that the estrogen-receptor binding potential of Pelargonidin, Delphinidin, Cyanidin, and Hibiscetin are more efficient than popular breast cancer drugs, Tamoxifen and Raloxifene. Besides, the compounds exhibited favourable toxicological parameters with potent bioactivity towards binding ER-α subunit. Thus, these compounds can serve as prototypes for designing novel Selective Estrogen Receptor Modulator molecules with a few structural modifications. This is the first report exploring the phytochemical basis of estrogenic activity of Hibiscus sabdariffa L.Communicated by Ramaswamy H. Sarma.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Computational Insights into Allosteric Conformational Modulation of P-Glycoprotein by Substrate and Inhibitor Binding

The ATP-binding cassette (ABC) transporter P-glycoprotein (P-gp) is a physiologically essential membrane protein that protects many tissues against xenobiotic molecules, but limits the access of chemotherapeutics into tumor cells, thus contributing to multidrug resistance. The atomic-level mechanism of how substrates and inhibitors differentially affect the ATP hydrolysis by P-gp remains to be elucidated. In this work, atomistic molecular dynamics simulations in an explicit membrane/water environment were performed to explore the effects of substrate and inhibitor binding on the conformational dynamics of P-gp. Distinct differences in conformational changes that mainly occurred in the nucleotide-binding domains (NBDs) were observed from the substrate- and inhibitor-bound simulations. The binding of rhodamine-123 can increase the probability of the formation of an intermediate conformation, in which the NBDs were closer and better aligned, suggesting that substrate binding may prime the transporter for ATP hydrolysis. By contrast, the inhibitor QZ-Leu stabilized NBDs in a much more separated and misaligned conformation, which may result in the deficiency of ATP hydrolysis. The significant differences in conformational modulation of P-gp by substrate and inhibitor binding provided a molecular explanation of how these small molecules exert opposite effects on the ATPase activity. A further structural analysis suggested that the allosteric communication between transmembrane domains (TMDs) and NBDs was primarily mediated by two intracellular coupling helices. Our computational simulations provide not only valuable insights into the transport mechanism of P-gp substrates, but also for the molecular design of P-gp inhibitors.

Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors

Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from non-inhibitors using molecular solvation theory with excellent accuracy and precision. The excess chemical potential and partial molar volume in various solvents are calculated for PgP± (PgP inhibitors and non-inhibitors) compounds with the statistical-mechanical based three-dimensional reference interaction site model with the Kovalenko-Hirata closure approximation (3D-RISM-KH molecular theory of solvation). The statistical importance analysis of descriptors identified the 3D-RISM-KH based descriptors as top molecular descriptors for classification. Among the constructed classification models, the support vector machine predicted the test set of Pgp± compounds with highest accuracy and precision of ~ 97% for test set. The validation of models confirms the robustness of state-of-the-art molecular solvation theory based descriptors in identification of the Pgp± compounds.

An Energetically Favorable Ligand Entrance Gate of a Multidrug Transporter Revealed by Partial Nudged Elastic Band Simulations

P-glycoprotein (P-gp) is a multidrug transporter, which harnesses the chemical energy of ATP to power the efflux of diverse chemotherapeutics out of cells and thus contributes to the development of multidrug resistance (MDR) in cancer. It has been proved that the ligand-binding pocket of P-gp is located at the transmembrane domains (TMDs). However, the access of ligands into the binding pocket remains to be elucidated, which definitely hinder the development of P-gp inhibitors. Herein, the access pathways of a well-known substrate rhodamine-123 and a cyclopeptide inhibitor QZ-Leu were characterized by time-independent partial nudged elastic band (PNEB) simulations. The decreasing free energies along the PNEB-optimized access pathway indicated that TM4/6 cleft may be an energetically favorable entrance gate for ligand entry into the binding pocket of P-gp. The results can be reconciled with a range of experimental studies, further corroborating the reliability of the gate revealed by computational simulations. Our atomic level description of the ligand access pathway provides valuable insights into the gating mechanism for drug uptake and transport by P-gp and other multidrug transporters.

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P-gp contributes to the development of multidrug resistance in cancer.

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The entrance of drugs into P-gp binding pocket has yet to be elucidated.

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An energetically favorable entrance gate was revealed by PNEB simulations.

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The computational results were reconciled with the experimental data.

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The atomic simulations provide insights into the gating mechanism of P-gp.

Molecular Modeling of Drug-Transporter Interactions-An International Transporter Consortium Perspective

Membrane transporters play diverse roles in the pharmacokinetics and pharmacodynamics of small-molecule drugs. Understanding the mechanisms of drug-transporter interactions at the molecular level is, therefore, essential for the design of drugs with optimal therapeutic effects. This white paper examines recent progress, applications, and challenges of molecular modeling of membrane transporters, including modeling techniques that are centered on the structures of transporter ligands, and those focusing on the structures of the transporters. The goals of this article are to illustrate current best practices and future opportunities in using molecular modeling techniques to understand and predict transporter-mediated effects on drug disposition and efficacy.Membrane transporters from the solute carrier (SLC) and ATP-binding cassette (ABC) superfamilies regulate the cellular uptake, efflux, and homeostasis of many essential nutrients and significantly impact the pharmacokinetics of drugs; further, they may provide targets for novel therapeutics as well as facilitate prodrug approaches. Because of their often broad substrate selectivity they are also implicated in many undesirable and sometimes life-threatening drug-drug interactions (DDIs).^5,6.

In vitro and in silico evaluation of P-glycoprotein inhibition through 99m Tc-methoxyisobutylisonitrile uptake

P-glycoprotein (P-gp) is a multidrug resistance (MDR) transporter with unknown structural details. This macromolecule is normally responsible for extruding xenobiotics from normal cells. Overexpression of P-gp in tumor cells is a major obstacle in cancer chemotherapy. In this study, human 3D model of P-gp was built by homology modeling based on mouse P-gp crystallographic structure and stabilized through 1 ns molecular dynamics (MD) simulation. Stabilized human P-gp structure was used for flexible docking of 80 drugs into the putative active site of P-gp. Accordingly, digoxin, itraconazole, risperidone, ketoconazole, prazosin, verapamil, cyclosporine A, and ranitidine were selected for further in vitro assay. Subsequently, cell-based P-gp inhibition assay was performed on Caco-2 cells while ^99m Tc-methoxyisobutylisonitrile (MIBI) was used as a P-gp efflux substrate for calculating IC50 values. Results of the ^99m Tc-MIBI uptake in drug-treated Caco-2 cells were in agreement with the previously reported activities. This study for the first time described the relation between molecular dynamics and flexible docking with cellular experiments using ^99m Tc-MIBI radiotracer for evaluation of potencies of P-gp inhibitors. Finally, results showed that our radiotracer-cell-based assay is an accurate and fast screening tool for detecting P-gp inhibitors and non-inhibitors in drug development process.

A novel adaptive ensemble classification framework for ADME prediction

AECF is a GA based ensemble method. It includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble.

Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates

Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.

Screening dietary flavonoids for the reversal of P-glycoprotein-mediated multidrug resistance in cancer

P-Glycoprotein (P-gp) serves as a therapeutic target for the development of inhibitors to overcome multidrug resistance in cancer cells. Although various screening procedures have been practiced so far to develop first three generations of P-gp inhibitors, their toxicity and drug interaction profiles are still a matter of concern. To address the above important problem of developing safe and effective P-gp inhibitors, we have made systematic computational and experimental studies on the interaction of natural phytochemicals with human P-gp. Molecular docking and QSAR studies were carried out for 40 dietary phytochemicals in the drug-binding site of the transmembrane domains (TMDs) of P-gp. Dietary flavonoids exhibit better interactions with homology modeled human P-gp. Based on the computational analysis, selected flavonoids were tested for their inhibitory potential against P-gp transport function in drug resistant cell lines using calcein-AM and rhodamine 123 efflux assays. It has been found that quercetin and rutin were the highly desirable flavonoids for the inhibition of P-gp transport function and they significantly reduced resistance in cytotoxicity assays to paclitaxel in P-gp overexpressing MDR cell lines. Hence, quercetin and rutin may be considered as potential chemosensitizing agents to overcome multidrug resistance in cancer.

Enzastaurin inhibits ABCB1-mediated drug efflux independently of effects on protein kinase C signalling and the cellular p53 status

The PKCβ inhibitor enzastaurin was tested in parental neuroblastoma and rhabdomyosarcoma cell lines, their vincristine-resistant sub-lines, primary neuroblastoma cells, ABCB1-transduced, ABCG2-transduced, and p53-depleted cells. Enzastaurin IC₅₀s ranged from 3.3 to 9.5 μM in cell lines and primary cells independently of the ABCB1, ABCG2, or p53 status. Enzastaurin 0.3125 μM interfered with ABCB1-mediated drug transport. PKCα and PKCβ may phosphorylate and activate ABCB1 under the control of p53. However, enzastaurin exerted similar effects on ABCB1 in the presence or absence of functional p53. Also, enzastaurin inhibited PKC signalling only in concentrations ≥ 1.25 μM. The investigated cell lines did not express PKCβ. PKCα depletion reduced PKC signalling but did not affect ABCB1 activity. Intracellular levels of the fluorescent ABCB1 substrate rhodamine 123 rapidly decreased after wash-out of extracellular enzastaurin, and enzastaurin induced ABCB1 ATPase activity resembling the ABCB1 substrate verapamil. Computational docking experiments detected a direct interaction of enzastaurin and ABCB1. These data suggest that enzastaurin directly interferes with ABCB1 function. Enzastaurin further inhibited ABCG2-mediated drug transport but by a different mechanism since it reduced ABCG2 ATPase activity. These findings are important for the further development of therapies combining enzastaurin with ABC transporter substrates.

Advances in plant-based inhibitors of P-glycoprotein

Multidrug resistance (MDR) has emerged as the main problem in anti-cancer therapy. Although MDR involves complex factors and processes, the main pivot is the expression of multidrug efflux pumps. P-glycoprotein (P-gp) belongs to the family of adenosine triphosphate (ATP)-binding cassette (ABC) transporters. It functions in cellular detoxification, pumping a wide range of xenobiotic compounds out of the cell. An attractive therapeutic strategy for overcoming MDR is to inhibit the transport function of P-gp and thus, increase intracellular concentration of drugs. Recently, various types of P-gp inhibitors have been found and used in experiments. However, none of them has passed clinical trials due to their high side-effects. Hence, the search for alternatives, such as plant-based P-gp inhibitors have gained attention recently. Therefore, we give an overview of the source, function, structure and mechanism of plant-based P-gp inhibitors and give more attention to cancer-related studies. These products could be the future potential drug candidates for further research as P-gp inhibitors.

Ligand-based modeling of diverse aryalkylamines yields new potent P-glycoprotein inhibitors

The P-glycoprotein (P-gp) efflux pump has an important role as a natural detoxification system in many types of normal and cancer cells. P-gp is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents, and therefore, it is clinically validated target for cancer therapy. Accordingly, in this study we combined exhaustive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent P-gp inhibitors employing 130 known P-gp ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) or multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of new promising P-gp inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. Several potent hits were captured. The most potent hit decreased the IC50 of doxorubicin from 0.906 to 0.190 μM on doxorubicin resistant MCF7 cell-line.

Development of in Silico Models for Predicting P-Glycoprotein Inhibitors Based on a Two-Step Approach for Feature Selection and Its Application to Chinese Herbal Medicine Screening

P-glycoprotein (P-gp) is regarded as an important factor in determining the ADMET (absorption, distribution, metabolism, elimination, and toxicity) characteristics of drugs and drug candidates. Successful prediction of P-gp inhibitors can thus lead to an improved understanding of the underlying mechanisms of both changes in the pharmacokinetics of drugs and drug-drug interactions. Therefore, there has been considerable interest in the development of in silico modeling of P-gp inhibitors in recent years. Considering that a large number of molecular descriptors are used to characterize diverse structural moleculars, efficient feature selection methods are required to extract the most informative predictors. In this work, we constructed an extensive available data set of 2428 molecules that includes 1518 P-gp inhibitors and 910 P-gp noninhibitors from multiple resources. Importantly, a two-step feature selection approach based on a genetic algorithm and a greedy forward-searching algorithm was employed to select the minimum set of the most informative descriptors that contribute to the prediction of P-gp inhibitors. To determine the best machine learning algorithm, 18 classifiers coupled with the feature selection method were compared. The top three best-performing models (flexible discriminant analysis, support vector machine, and random forest) and their ensemble model using respectively only 3, 9, 7, and 14 descriptors achieve an overall accuracy of 83.2%-86.7% for the training set containing 1040 compounds, an overall accuracy of 82.3%-85.5% for the test set containing 1039 compounds, and a prediction accuracy of 77.4%-79.9% for the external validation set containing 349 compounds. The models were further extensively validated by DrugBank database (1890 compounds). The proposed models are competitive with and in some cases better than other published models in terms of prediction accuracy and minimum number of descriptors. Applicability domain then was addressed by developing an ensemble classification model to obtain more reliable predictions. Finally, we employed these models as a virtual screening tool for identifying potential P-gp inhibitors in Traditional Chinese Medicine Systems Pharmacology (TCMSP) database containing a total of 13 051 unique compounds from 498 herbs, resulting in 875 potential P-gp inhibitors and 15 inhibitor-rich herbs. These predictions were partly supported by a literature search and are valuable not only to develop novel P-gp inhibitors from TCM in the early stages of drug development, but also to optimize the use of herbal remedies.

Computational modeling to predict the functions and impact of drug transporters

Transport proteins are important mediators of cellular drug influx and efflux and play crucial roles in drug distribution, disposition and clearance. Drug-drug interactions have increasingly been found to occur at the transporter level and, hence, computational tools for studying drug-transporter interactions have gained in interest. In this short review, we present the most important transport proteins for drug influx and efflux. Computational tools for predicting and understanding the substrate and inhibitor interactions with these membrane-bound proteins are discussed. We have primarily focused on ligand-based and structure-based modeling, for which the state-of-the-art and future challenges are also discussed.

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools

In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Computational models for predicting the interaction with ABC transporters

There is strong evidence that ATP-binding cassette (ABC) transporters play a critical role in the pharmacokinetic and pharmacodynamic properties of many drugs and xenobiotics. Due to their pharmacological role, several computational approaches have been developed to understand and predict the interaction between ABC transporters and their ligands. Here, we provide an overview of the current state of the art of the ligand-based models that, derived from the transport and inhibitory activities of a set of ligands, have been published for ABC transporters.