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Computational models for predicting drug transport mediated by P-glycoprotein
Abstract
P-glycoprotein (Pgp) is a transmembrane transporter which can, by transporting structurally diverse compounds, influence the absorption, distribution and efficacy of a number of drugs. Pgp overexpression in cells is a major contributing factor to the development of drug resistance. For these reasons, potential for compound efflux by Pgp should be assessed early on in the drug discovery process, preferably even prior to compound synthesis. To meet this demand, numerous computational models have been developed during the past decade, capable of predicting Pgp-mediated transport based solely on chemical structures. This paper summarizes the various approaches that have been used for model development, discusses their advantages and disadvantages and focuses on key factors that influence model reliability. The promiscuous nature of the transport can be seen as a major challenge for most computational chemistry methods. Nevertheless, the attained level of accuracy of literature models suggests that they can be useful in the drug discovery setting. Greater availability of experimental data and integration of predictions made by different modeling methods has the potential to further improve the reliability of computational predictions.
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P-glycoprotein (P-gp) is involved in the transport of xenobiotic compounds and responsible for the decrease of the drug accumulation in multi-drug-resistant cells. In this investigation we compare several docking algorithms in order to find the conditions that are able to discriminate between P-gp binders and nonbinders. We built a comprehensive dataset of binders and nonbinders based on a careful analysis of the experimental data available in the literature, trying to overcome the discrepancy noticeable in the experimental results. We found that Autodock Vina flexible docking is the best choice for the tested options. The results will be useful to filter virtual screening results in the rational design of new drugs that are not expected to be expelled by P-gp.
P-glycoprotein (P-gp) is a key player in the multidrug resistant phenotype in cancer. The protein confers resistance by mediating the ATP-dependent efflux of an astonishing array of anti-cancer drug. Its broad specificity has been the subject of numerous attempts to inhibit the protein and restore the efficacy of anti-cancer drugs. The general strategy has been to develop compounds that either compete with anti-cancer drugs for transport or act as direct inhibitors of P-gp. Despite considerable in vitro success, there are no compounds currently available to "block" P-gp mediated resistance in the clinic. The failure may be attributed to toxicity, adverse drug interaction and numerous pharmacokinetic issues. This review provides a description of several alternative approaches to overcome the activity of P-gp in drug resistant cells. These include (i) drugs that specifically target resistant cells, (ii) novel nano-technologies to provide high dose, targeted delivery of anticancer drugs, (iii) compounds that interfere with non-genomic transfer of resistance, and, (iv) approaches to reduce the expression of P-gp within tumours. Such approaches have been developed through the pursuit of greater understanding of resistance mediators such as P-gp and they show considerable potential for further application.
P-glycoprotein (Pgp, ABCB1) is an ATP-Binding Cassette (ABC) transporter that is associated with the development of multidrug resistance in cancer cells. Pgp transports a variety of chemically dissimilar amphipathic compounds using the energy from ATP hydrolysis. In the present study, to elucidate the binding sites on Pgp for substrates and modulators, we employed site-directed mutagenesis, cell- and membrane-based assays, molecular modeling and docking. We generated single, double and triple mutants with substitutions of the Y307, F343, Q725, F728, F978 and V982 residues at the proposed drug-binding site with cys in a cysless Pgp, and expressed them in insect and mammalian cells using a baculovirus expression system. All the mutant proteins were expressed at the cell surface to the same extent as the cysless wild-type Pgp. With substitution of three residues of the pocket (Y307, Q725 and V982) with cysteine in a cysless Pgp, QZ59S-SSS, cyclosporine A, tariquidar, valinomycin and FSBA lose the ability to inhibit the labeling of Pgp with a transport substrate, [(125)I]-Iodoarylazidoprazosin, indicating these drugs cannot bind at their primary binding sites. However, the drugs can modulate the ATP hydrolysis of the mutant Pgps, demonstrating that they bind at secondary sites. In addition, the transport of six fluorescent substrates in HeLa cells expressing triple mutant (Y307C/Q725C/V982C) Pgp is also not significantly altered, showing that substrates bound at secondary sites are still transported. The homology modeling of human Pgp and substrate and modulator docking studies support the biochemical and transport data. In aggregate, our results demonstrate that a large flexible pocket in the Pgp transmembrane domains is able to bind chemically diverse compounds. When residues of the primary drug-binding site are mutated, substrates and modulators bind to secondary sites on the transporter and more than one transport-active binding site is available for each substrate.
The recently determined C. elegans P-glycoprotein (Pgp) structure revealed significant deviations compared to the original mouse Pgp structure, which suggested possible misinterpretations in the latter model. To address this concern, we generated an experimental electron density map from single-wavelength anomalous dispersion phasing of an original mouse Pgp dataset to 3.8 Å resolution. The map exhibited significantly more detail compared to the original MAD map and revealed several regions of the structure that required de novo model building. The improved drug-free structure was refined to 3.8 Å resolution with a 9.4% and 8.1% decrease in Rwork and Rfree , respectively (Rwork =21.2%, Rfree =26.6%) and a significant improvement in protein geometry. The improved mouse Pgp model contains ~ 95% of residues in the favorable Ramachandran region compared to only 57% for the original model. The registry of six transmembrane helices was corrected, revealing amino acid residues involved in drug binding that were previously unrecognized. Registry shifts (rotations and translations) for TM4 and TM5 and the addition of three N-terminal residues were necessary, and were validated with new mercury labeling and anomalous fourier density. The corrected position of TM4, which forms the frame of a portal for drug entry, had backbone atoms shifted > 6 Å from their original positions. The drug translocation pathway of mouse Pgp is 96% identical to human Pgp and is enriched in aromatic residues that likely play a collective role in allowing a high degree of polyspecificity in substrate recognition.
P-Glycoprotein (Pgp) is one of the best characterized ABC transporters, often involved in the multidrug-resistance phenotype expressed by several cancer cell lines. Experimental studies contributed to important knowledge concerning substrate polyspecificity, efflux mechanism and drug-binding sites. This information is, however, scattered through different perspectives, not existing a unifying model for all knowledge available for this transporter. Using a previously refined structure of murine Pgp, three putative drug-binding sites were hereby characterized by means of molecular docking. The modulator site (M-site) is characterized by cross interactions herein defined for the first time between both Pgp halves, having an important role in impairing conformational changes leading to substrate efflux. Two other binding sites, located next to the inner leaflet of the lipid bilayer, were identified as the substrate-binding H and R sites by matching docking and experimental results. A new classification model with the ability to discriminate substrates from modulators is also proposed, integrating a vast number of theoretical and experimental data.
The risk of acquisition of resistance to chemotherapy remains a major hurdle in the management of various types of cancer patients. Several cellular and noncellular mechanisms are involved in developing both intrinsic and acquired resistance in cancer cells toward chemotherapy. This review covers the various multidrug resistance (MDR) mechanisms observed in cancer cells as well as the various strategies developed to overcome these MDR mechanisms. Extensive studies have been conducted during the last several decades to enhance the efficacy of chemotherapy by suppressing or evading these MDR mechanisms including the use of new anticancer drugs that could escape from the efflux reaction, MDR modulators or chemosensitizers, multifunctional nanocarriers, and RNA interference (RNAi) therapy.
P-glycoprotein actively transports a wide variety of chemically diverse compounds out of the cell. Based on a comparison of a hundred compounds previously tested as P-glycoprotein substrates, we suggest that a set of well-defined structural elements is required for an interaction with P-glycoprotein. The recognition elements are formed by two (type I unit) or three electron donor groups (type II unit) with a fixed spatial separation. Type I units consist of two electron donor groups with a spatial separation of 2.5 ± 0.3 Å. Type II units contain either two electron donor groups with a spatial separation of 4.6 ± 0.6 Å or three electron donor groups with a spatial separation of the outer two groups of 4.6 ± 0.6 Å. All molecules that contain at least one type I or one type II unit are predicted to be P-glycoprotein substrates. The binding to P-glycoprotein increases with the strength and the number of electron donor or hydrogen bonding acceptor groups forming the type I and type II units. Correspondingly, a high percentage of amino acids with hydrogen bonding donor side chains is found in the transmembrane sequences of P-glycoprotein relevant for substrate interaction. Molecules that minimally contain one type II unit are predicted to be inducers of P-glycoprotein over-expression.
Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating biliary and renal elimination of structurally diverse drugs. Thus, identification of drugs or new molecular entities to be P-gp substrates is of vital importance for predicting the pharmacokinetics, efficacy, safety, or tissue levels of drugs or drug candidates. At present, publicly available, reliable in silico models predicting P-gp substrates are scarce. In this study, a support vector machine (SVM) method was developed to predict P-gp substrates and P-gp-substrate interactions, based on a training data set of 197 known P-gp substrates and non-substrates collected from the literature. We showed that the SVM method had a prediction accuracy of approximately 80% on an independent external validation data set of 32 compounds. A homology model of human P-gp based on the X-ray structure of mouse P-gp as a template has been constructed. We showed that molecular docking to the P-gp structures successfully predicted the geometry of P-gp-ligand complexes. Our SVM prediction and the molecular docking methods have been integrated into a free web server (http://pgp.althotas.com), which allows the users to predict whether a given compound is a P-gp substrate and how it binds to and interacts with P-gp. Utilization of such a web server may prove valuable for both rational drug design and screening.
P-glycoprotein (P-gp) is an ATP-dependent transport protein that is selectively expressed at entry points of xenobiotics where, acting as an efflux pump, it prevents their entering sensitive organs. The protein also plays a key role in the absorption and blood-brain barrier penetration of many drugs, while its overexpression in cancer cells has been linked to multidrug resistance in tumors. The recent publication of the mouse P-gp crystal structure revealed a large and hydrophobic binding cavity with no clearly defined sub-sites that supports an "induced-fit" ligand binding model. We employed flexible receptor docking to develop a new prediction algorithm for P-gp binding specificity. We tested the ability of this method to differentiate between binders and nonbinders of P-gp using consistently measured experimental data from P-gp efflux and calcein-inhibition assays. We also subjected the model to a blind test on a series of peptidic cysteine protease inhibitors, confirming the ability to predict compounds more likely to be P-gp substrates. Finally, we used the method to predict cellular metabolites that may be P-gp substrates. Overall, our results suggest that many P-gp substrates bind deeper in the cavity than the cyclic peptide in the crystal structure and that specificity in P-gp is better understood in terms of physicochemical properties of the ligands (and the binding site), rather than being defined by specific sub-sites.
Overexpression of the xenotoxin transporter P-glycoprotein (P-gp) represents one major reason for the development of multidrug resistance (MDR), leading to the failure of antibiotic and cancer therapies. Inhibitors of P-gp have thus been advocated as promising candidates for overcoming the problem of MDR. However, due to lack of a high-resolution structure the concrete mode of interaction of both substrates and inhibitors is still not known. Therefore, structure-based design studies have to rely on protein homology models. In order to identify binding hypotheses for propafenone-type P-gp inhibitors, five different propafenone derivatives with known structure-activity relationship (SAR) pattern were docked into homology models of the apo and the nucleotide-bound conformation of the transporter. To circumvent the uncertainty of scoring functions, we exhaustively sampled the pose space and analyzed the poses by combining information retrieved from SAR studies with common scaffold clustering. The results suggest propafenone binding at the transmembrane helices 5, 6, 7 and 8 in both models, with the amino acid residue Y307 playing a crucial role. The identified binding site in the non-energized state is overlapping with, but not identical to, known binding areas of cyclic P-gp inhibitors and verapamil. These findings support the idea of several small binding sites forming one large binding cavity. Furthermore, the binding hypotheses for both catalytic states were analyzed and showed only small differences in their protein-ligand interaction fingerprints, which indicates only small movements of the ligand during the catalytic cycle.
One of the major causes of chemotherapy failure in cancer treatment is multidrug resistance (MDR) which is mediated by the ABCB1/P-glycoprotein. Previously, through the use of an extensive screening process, we found that vardenafil, a phosphodiesterase 5 (PDE-5) inhibitor significantly reverses MDR in ABCB1 overexpressing cancer cells, and its efficacy was greater than that of tadalafil, another PDE-5 inhibitor. The present study was designed to determine the reversal mechanisms of vardenafil and tadalafil on ABC transporters-mediated MDR. Vardenafil or tadalafil alone, at concentrations up to 20 µM, had no significant toxic effects on any of the cell lines used in this study, regardless of their membrane transporter status. However, vardenafil when used in combination with anticancer substrates of ABCB1, significantly potentiated their cytotoxicity in ABCB1 overexpressing cells in a concentration-dependent manner, and this effect was greater than that of tadalafil. The sensitivity of the parenteral cell lines to cytotoxic anticancer drugs was not significantly altered by vardenafil. The differential effects of vardenafil and tadalafil appear to be specific for the ABCB1 transporter as both vardenafil and tadalafil had no significant effect on the reversal of drug resistance conferred by ABCC1 (MRP1) and ABCG2 (BCRP) transporters. Vardenafil significantly increased the intracellular accumulation of [(3)H]-paclitaxel in the ABCB1 overexpressing KB-C2 cells. In addition, vardenafil significantly stimulated the ATPase activity of ABCB1 and inhibited the photolabeling of ABCB1 with [(125)I]-IAAP. Furthermore, Western blot analysis indicated the incubation of cells with either vardenafil or tadalafil for 72 h did not alter ABCB1 protein expression. Overall, our results suggest that vardenafil reverses ABCB1-mediated MDR by directly blocking the drug efflux function of ABCB1.
A series of enantiomerically pure benzopyrano[3,4-b][1,4]oxazines have been synthesised and tested for their ability to inhibit P-glycoprotein. Reducing the conformational flexibility of the molecules leads to remarkable differences in the activity of diastereoisomers. Docking studies into a homology model of human P-gp provide first insights into potential binding areas for these compounds.
Previously, we reported sipholenol A, a sipholane triterpenoid from the Red Sea sponge Callyspongia siphonella, as a potent reversal of multidrug resistance (MDR) in cancer cells that overexpressed P-glycoprotein (P-gp). Through extensive screening of several related sipholane triterpenoids that have been isolated from the same sponge, we identified sipholenone E, sipholenol L and siphonellinol D as potent reversals of MDR in cancer cells. These compounds enhanced the cytotoxicity of several P-gp substrate anticancer drugs, including colchicine, vinblastine and paclitaxel, and significantly reversed the MDR-phenotype in P-gp-overexpressing MDR cancer cells KB-C2 in a dose-dependent manner. Moreover, these three sipholanes had no effect on the response to cytotoxic agents in cells lacking P-gp expression or expressing MRP1 (ABCC1) or MRP7 (ABCC10) or breast cancer resistance protein (BCRP/ABCG2). All three sipholanes (IC(50) >50 μM) were not toxic to all the cell lines that were used. [(3)H]-Paclitaxel accumulation and efflux studies demonstrated that all three triterpenoids time-dependently increased the intracellular accumulation of [(3)H]-paclitaxel by directly inhibiting P-gp-mediated drug efflux. Sipholanes also inhibited calcein-AM transport from P-gp-overexpressing cells. The Western blot analysis revealed that these three triterpenoids did not alter the expression of P-gp. However, they stimulated P-gp ATPase activity in a concentration-dependent manner and inhibited the photolabeling of this transporter with its transport substrate [(125)I]-iodoarylazidoprazosin. In silico molecular docking aided the virtual identification of ligand binding sites of these compounds. In conclusion, sipholane triterpenoids efficiently inhibit the function of P-gp through direct interactions and may represent potential reversal agents for the treatment of MDR.
Membrane transporters can be major determinants of the pharmacokinetic, safety and efficacy profiles of drugs. This presents several key questions for drug development, including which transporters are clinically important in drug absorption and disposition, and which in vitro methods are suitable for studying drug interactions with these transporters. In addition, what criteria should trigger follow-up clinical studies, and which clinical studies should be conducted if needed. In this article, we provide the recommendations of the International Transporter Consortium on these issues, and present decision trees that are intended to help guide clinical studies on the currently recognized most important drug transporter interactions. The recommendations are generally intended to support clinical development and filing of a new drug application. Overall, it is advised that the timing of transporter investigations should be driven by efficacy, safety and clinical trial enrolment questions (for example, exclusion and inclusion criteria), as well as a need for further understanding of the absorption, distribution, metabolism and excretion properties of the drug molecule, and information required for drug labelling.
Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described. In the discovery setting 'the rule of 5' predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the molecular weight (MWT) is greater than 500 and the calculated Log P (CLogP) is greater than 5 (or MlogP>4.15). Computational methodology for the rule-based Moriguchi Log P (MLogP) calculation is described. Turbidimetric solubility measurement is described and applied to known drugs. High throughput screening (FITS) leads tend to have higher MWT and Log P and lower turbidimetric solubility than leads in the pre-HTS era. In the development setting, solubility calculations focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with experimental thermodynamic solubility measurements. (C) 2012 Published by Elsevier B.V.
Chinese hamster ovary cells selected for resistance to colchicine display pleiotropic cross-resistance to a wide range of amphiphilic drugs. The drug-resistant phenotype is due to a membrane alteration which reduces the rate of drug permeation. Surface labelling studies reveal that drug-resistant Chinese hamster ovary cell membranes possess a carbohydrate-containing component of 170 000 daltons apparent molecular weight which is not observed in wild type cells. Through studies of the metabolic incorporation of carbohydrate and protein precursors, and through the use of selective proteolysis, this component is shown to be a cell surface glycoprotein. Since this glycoprotein appears unique to mutant cells displaying altered drug permeability, we have designated it the P glycoprotein. The relative amount of surface labelled P glycoprotein correlates with the degree of drug resistance in a number of independent mutant and revertant clones. A similar high molecular weight glycoprotein is also present in drug-resistant mutants from another hamster cell line. Observations on the molecular basis of pleiotropic drug resistance are interpreted in terms of a model wherein certain surface glycoproteins control drug permeation by modulating the properties of hydrophobic membrane regions...
Active drug efflux transporters of the ATP binding cassette (ABC)-containing family of proteins have a major impact on the pharmacological behavior of most of the drugs in use today. Pharmacological properties affected by ABC transporters include the oral bioavailability, hepatobiliary, direct intestinal, and urinary excretion of drugs and drug-metabolites and -conjugates. Moreover, the penetration of drugs into a range of important pharmacological sanctuaries, such as brain, testis, and fetus, and the penetration into specific cell- and tissue compartments can be extensively limited by ABC transporters. These interactions with ABC transporters determine to a large extent the clinical usefulness, side effects and toxicity risks of drugs. Many other xenotoxins, (pre-)carcinogens and endogenous compounds are also influenced by the ABC transporters, with corresponding consequences for the well-being of the individual. We aim to provide an overview of properties of the mammalian ABC transporters known to mediate significant transport of clinically relevant drugs.
ABCB1 (P-glycoprotein/P-gp) is an ATP-binding cassette transporter well known for its association with multidrug resistance in cancer cells. Powered by the hydrolysis of ATP, it effluxes structurally diverse compounds. In this chapter, we discuss current views on the molecular basis of the substrate polyspecificity of P-gp. One of the features that accounts for this property is the structural flexibility observed in P-gp. Several X-ray crystal structures of mouse P-gp have been published recently in the absence of nucleotide, with and without bound inhibitors. All the structures are in an inward-facing conformation exhibiting different degrees of domain separation, thus revealing a highly flexible protein. Biochemical and biophysical studies also demonstrate this flexibility in mouse as well as human P-gp. Site-directed mutagenesis has revealed the existence of multiple transport-active binding sites in P-gp for a single substrate. Thus, drugs can bind at either primary or secondary sites. Biochemical, molecular modeling, and structure-activity relationship studies suggest a large, common drug-binding pocket with overlapping sites for different substrates. We propose that in addition to the structural flexibility, the molecular or chemical flexibility also contributes to the binding of substrates to multiple sites forming the basis of polyspecificity.
© 2015 Elsevier Inc. All rights reserved.
P-glycoprotein (P-gp/ABCB1) and breast cancer resistance protein (BCRP/ABCG2) are two members of the adenosine triphosphate (ATP) binding cassette (ABC) family of transporters which function as membrane efflux transporters and display considerable substrate promiscuity. Both are known to significantly influence the absorption, distribution and elimination of drugs, mediate drug-drug interactions and contribute to multiple drug resistance (MDR) of cancer cells. Correspondingly, timely characterization of the interaction of novel leads and drug candidates with these two transporters is of great importance. In this study, several computational classification models for prediction of transport and inhibition of P-gp and BCRP, respectively, were developed based on newly compiled and critically evaluated experimental data. Artificial neural network (ANN) and support vector machine (SVM) ensemble based models were explored, as well as knowledge-based approaches to descriptor selection. The average overall classification accuracy of best performing models was 82% for P-gp transport, 88% for BCRP transport, 89% for P-gp inhibition and 87% for BCRP inhibition, determined across an array of different test sets. An analysis of substrate overlap between P-gp and BCRP was also performed. The accuracy, simplicity and interpretability of the proposed models suggest that they could be of significant utility in the drug discovery and development settings.
P-glycoprotein is the most crucial membrane transporter implicated in tumor resistance. Intensive efforts were paid to elucidate the complex mechanism of transport and to identify modulators of this transporter. However, the borderline between substrates and modulators is very thin and identification of the binding sites within P-glycoprotein is complex. Herein, we provide an intensive review of those issues and use molecular docking to assess its ability: first, to differentiate between three groups (substrates, modulators and non-substrates) and second to identify the binding sites. After thorough statistical analysis, we conclude despite the various challenges that molecular docking should not be underestimated as differences between the distinct groups were significant. However, when it comes to defining the binding site, care must be taken, since consensus throughout literature could not be reached.
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.
P-glycoprotein (P-gp) actively transports a wide variety of chemically diverse compounds out of cells. It is highly associated with the ADMET properties of drugs and drug candidates, and moreover plays a major role in multidrug resistance (MDR) phenomenon, which leads to the failure of chemotherapy in cancer treatments. Therefore, the recognition of potential P-gp substrates at the early stages of drug discovery process is quite important. Here, we compiled an extensive dataset containing 423 P-gp substrates and 399 non-substrates, which is the largest P-gp substrate/non-substrate dataset yet published. Comparison of the distributions of eight important physicochemical properties for the substrates and non-substrates reveals that molecular weight and molecular solubility are the informative attributes differentiating P-gp substrates from non-substrates. Examination of the distributions of eight physicochemical properties for 735 P-gp inhibitors and 423 substrates gives the fact that inhibitors are significantly more hydrophobic than substrates while substrates tend to have more H-bond donors than inhibitors. Then, the classification models based on simple molecular properties, topological descriptors and molecular fingerprints were developed using the naïve Bayesian classification technique. The best naïve Bayesian classifier yields a Matthews correlation coefficient of 0.824 and a prediction accuracy of 91.2% for the training set from a 5-fold cross-validation procedure, and a Matthews correlation coefficient of 0.667 and a prediction accuracy of 83.5% for the test set containing 200 molecules. Analysis of the important structural fragments given by the Bayesian classifier shows that the essential H-bond acceptors arranged in distinct spatial patterns and flexibility are quite essential for P-gp substrate-likeness, which affords a deeper understanding on the molecular basis of substrate/P-gp interaction. Finally, the reasons of mispredictions were discussed. It turns out that the presented classifier could be used as a reliable virtual screening tool for identifying potential substrates of P-gp.
Drug export from cells is a major factor in the acquisition of cellular resistance to antimicrobial and cancer chemotherapy, and poses a significant threat to future clinical management of disease. Many of the proteins that catalyse drug efflux do so with remarkably low substrate specificity, a phenomenon known as multidrug transport. For these reasons we need a greater understanding of drug recognition and transport in multidrug pumps to inform research that attempts to circumvent their action. Structural and computational studies have been heralded as being great strides towards a full elucidation of multidrug recognition and transport. In this review we summarise these advances and ask how close we are to a molecular understanding of this remarkable phenomenon.
Herein, a combined molecular docking-based and pharmacophore-based target prediction strategy is presented, in which a probabilistic fusion method is suggested for target ranking. Establishment and validation of the combined strategy are described. A target database, termed TargetDB, was firstly constructed, which contains 1105 drug targets. Based on TargetDB, the molecular docking-based target prediction and pharmacophore-based target prediction protocols were established. A probabilistic fusion method was then developed by constructing probability assignment curves (PACs) against a set of selected targets. Finally the workflow for the combined molecular docking-based and pharmacophore-based target prediction strategy was established. Evaluations of the performance of the combined strategy were carried out against a set of structurally different single-target compounds and a well-known multi-target drug, 4H-tamoxifen, which results showed that the combined strategy consistently outperformed the sole use of docking-based and pharmacophore-based methods. Overall, this investigation provides a possible way for improving the accuracy of in silico target prediction and a method for target ranking.
P-glycoprotein (P-gp, MDR1) is a promiscuous drug efflux pump of substantial pharmacological importance. Taking advantage of large-scale cytotoxicity screening data involving 60 cancer cell lines, we correlated the differential biological activities of ~13 000 compounds against cellular P-gp levels. We created a large set of 934 high-confidence P-gp substrates or non-substrates by enforcing agreement with an orthogonal criterion involving P-gp overexpressing ADR-RES cells. A Support Vector Machine (SVM) was 86.7% accurate in discriminating P-gp substrates on independent test data, exceeding previous models. Two molecular features had an overarching influence: nearly all P-gp substrates were large (>35 atoms including H) and dense (specific volume <7.3 Å(3)/atom) molecules. Seven other descriptors and 24 molecular fragments ("effluxophores") were found enriched in the (non)substrates and incorporated into interpretable rule-based models. Biological experiments on an independent P-gp overexpressing cell line, the vincristine-resistant VK2, allowed us to re-classify six compounds previously annotated as substrates, validating our method's predictive ability. Models are freely available at http://pgp.biozyne.com.
P-glycoprotein (P-gp) is a plasma membrane efflux transporter belonging to ATP-binding cassette superfamily, responsible for multidrug resistance in tumor cells. Over-expression of P-gp in cancer cells limits the efficacy of many anticancer drugs. A clear understanding of P-gp substrate binding will be advantageous in early drug discovery process. However, substrate poly-specificity of P-gp is a limiting factor in rational drug design. In this investigation, we report a dynamic trans-membrane model of P-gp that accurately identified the substrate binding residues of known anticancer agents. The study included homology modeling of human P-gp based on the crystal structure of C. elegans P-gp, molecular docking, molecular dynamics analyses and binding free energy calculations. The model was further utilized to speculate substrate propensity of in-house anticancer compounds. The model demonstrated promising results with one anticancer compound (NSC745689). As per our observations, the molecule could be a potential lead for anticancer agents devoid of P-gp mediated multiple drug resistance. The in silico results were further validated experimentally using Caco-2 cell lines studies, where NSC745689 exhibited poor permeability (P
app 1.03 ± 0.16 × 10−6 cm/s) and low efflux ratio of 0.26.
Previous in vitro and clinical research have indicated that a wide variety of drug transporters as well as metabolic enzymes dominate the pharmacokinetics of drugs and that some drugs modified the expression/function of drug transporters in humans, which lead to the altered pharmacokinetics and subsequent pharmacological/toxicological effects. Thus, regulatory authorities in US and EU have recently emphasized the needs to evaluate the risk of transporter-mediated drug-drug interactions (DDIs) in the (draft) guidance for pharmaceutical industries. The revised guidance includes the key transporters governing pharmacokinetics of drugs and decision trees to determine whether NMEs are substrates or inhibitors of each key transporter and when an in vivo clinical study is needed. In the evaluation of the potency of clinical DDIs, estimation of the inhibitor concentration at the target site is essential, but difficult since its direct measurement is almost impossible. Thus, people are now discussing what kind of inhibitor concentration should be used and how much is the appropriate cutoff value of the ratio of plasma AUC in the presence of inhibitor drugs to that in its absence (AUCR) to avoid false-negative predictions and maximize prediction accuracy. This minireview briefly summarizes the current status of the criteria for risk management of transporter-mediated DDIs in the regulatory guidelines, and describes scientific achievements that may affect regulatory decisions. Target transporters include OATP1B1 (SLCO1B1) and OATP1B3 (SLCO1B3) in the liver, and OAT1 (SLC22A6), OAT3 (SLC22A8), OCT2 (SLC22A2), MATE1 (SLC47A1), and MATE2-K (SLC47A2) in the kidney, and MDR1 (ABCB1) in the intestine.
Enhanced drug extrusion from cells due to the overexpression of the ATP-binding cassette (ABC) drug transporters inhibits the cytotoxic effects of structurally diverse and mechanistically unrelated anticancer agents and is a major cause of multidrug resistance (MDR) of human malignancies. Multiple compounds can suppress the activity of these efflux transporters and sensitize resistant tumor cells, but despite promising preclinical and early clinical data, they have yet to find a role in oncologic practice. Based on the knowledge of the structure, function, and distribution of MDR-related ABC transporters and the results of their preclinical and clinical evaluation, we discuss probable reasons why these inhibitors have not improved the outcome of therapy for cancer patients. We also outline new MDR-reversing strategies that directly target ABC transporters or circumvent relevant signaling pathways.
P-Glycoprotein (Pgp) is involved in the elimination and in the disposition of a significant portion of marketed drugs. So far, publicly available data sets used for modeling Pgp transport included compounds tested in different assays, different cell lines, and different protocols. In this work, we present a collection of 478 Efflux Ratios (ERs) in MDCK-MDR1 cell lines, and from this collection we define a data set of 187 compounds that were tested in the Borst-derived MDCK-MDR1 cell lines. Of the 23 models resulting from the use of different descriptors, classification algorithms, and variable selection techniques, the 4 most accurate in external validation (∼0.86) are based on VolSurf+ (VS+) descriptors. Two of these models are Naïve Bayes (NB) classifiers using 4 descriptors that were selected through a new technique hereby first time extensively described.
In this work, we present a novel approach for the development of models for prediction of aqueous solubility, based on the implementation of an algorithm for the automatic adjustment of descriptor's relative importance (AARI) in counter-propagation artificial neural networks (CPANN). Using this approach, the interpretability of the models based on artificial neural networks, which are traditionally considered as "black box" models, was significantly improved. For the development of the model, a data set consisting of 374 diverse drug-like molecules, divided into training (n=280) and test (n=94) sets using self-organizing maps, was used. Heuristic method was applied in preselecting a small number of the most significant descriptors to serve as inputs for CPANN training. The performances of the final model based on 7 descriptors for prediction of solubility were satisfactory for both training (RMSEP(train)=0.668) and test set (RMSEP(test)=0.679). The model was found to be a highly interpretable in terms of solubility, as well as rationalizing structural features that could have an impact on the solubility of the compounds investigated. Therefore, the proposed approach can significantly enhance model usability by giving guidance for structural modifications of compounds with the aim of improving solubility in the early phase of drug discovery.
As part of the ATP-binding cassette transporter superfamily P-glycoprotein (ABCB1) acts as xenotoxic exporter and consequently is strongly involved in multidrug resistance (MDR) and drug-drug interactions. In this work we focus on our in-house developed SIBAR approach for prediction of ABCB1 substrates. SIBAR values were calculated on basis of three different descriptor sets: 2D-MOE descriptors, VSA descriptors and 3D Autocorrelation vectors using in total four reference sets. In order to compare linear with non-linear classification methods we used binary QSAR and a support vector machine (SVM), respectively. Results demonstrate that with 2D-MOE and VSA-descriptors prediction of non substrates performs better, whereas autocorrelation vectors show higher accuracy for substrates. With respect to the different reference sets used in this case selection on basis of maximum diversity yielded better results than a set derived from the training set compounds. In general, the models show distinct differences in their performance depending on the combination of method and descriptor type.
P-Glycoprotein (P-gp), a transmembrane, ATP-dependent drug efflux transporter, has attracted considerable interest both with respect to its role in tumour cell multidrug resistance and in absorption-distribution and elimination of drugs. Although known since more than 30 years, the understanding of the molecular basis of drug/transporter interaction is still limited, which is mainly due to the lack of structural information available. However, within the past decade X-ray structures of several bacterial homologues as well as very recently also of mouse P-gp have become available. Within this review we give an overview on the current status of structural information available and on its impact for structure-based drug design.
Within the last decades, the detailed knowledge on the impact of membrane bound drug efflux transporters of the ATP binding cassette (ABC) protein family on the pharmacological profile of drugs has enormously increased. Especially, ABCB1 (P-glycoprotein, P-gp, MDR1) has attracted particular interest in medicinal chemistry, since it determines the clinical efficacy, side effects and toxicity risks of drug candidates. Based on this, the development of in silico models that provide rapid and cost-effective screening tools for the classification of substrates and nonsubstrates of ABCB1 is an urgent need in contemporary ADMET profiling. A characteristic hallmark feature of this transporter is its polyspecific ligand recognition pattern. In this study we describe a method for classifying ABCB1 ligands in terms of simple, conjunctive rules (RuleFit) based on interpretable ADMET features. The retrieved results showed that models based on large, very diverse data sets gave better classification performance than models based on smaller, more homogenous training sets. The best model achieved gave a correct classification rate of 0.90 for an external validation set. Furthermore, from the interpretation of the best performing model it could be concluded that in comparison to nonsubstrates ABCB1 substrates generally show a higher number of hydrogen-bond acceptors, are more flexible and exhibit higher logP values.
Purpose. MDR1 P-glycoprotein (P-gp) plays an important role in determining drug disposition. The purpose of the present study was to establish in vitro models to predict the in vivo function of P-gp.
Methods. As an in vitro method, the transcellular transport of 12 compounds across the monolayer of Caco-2- and MDR1-transfected cells was examined. The ability of these compounds to stimulate the ATP hydrolysis was also determined using the isolated membrane fraction expressing P-gp. As a parameter to describe the in vivo P-gp function, we calculated the brain-to-plasma concentration ratio of compounds in mdr1a/1b knockout mice divided by the same ratio in wild type mice.
Results. A good correlation was observed between the in vitro flux ratio across the monolayer and in vivo P-gp function for 12 compounds. Although all compounds that stimulated ATP hydrolysis were significantly transported by P-gp, some compounds were transported by P-gp without significantly affecting ATP hydrolysis.
Conclusion. Collectively, the in vitro flux ratio across monolayers of P-gp-expressing cells may be used to predict in vivo P-gp function. The extent of ATP-hydrolysis in vitro may also be a useful parameter for in vivo prediction, particularly for eliminating P-gp substrates in high-throughput screening procedures.
The field of drug transporters has exploded over the past 30 years. It is now known that transporters are located in virtually
every organ and tissue in the body and are involved in every aspect of drug absorption, distribution, excretion, and even
influence drug metabolism by regulating access to drug metabolizing enzymes. Because of their ubiquity and function, drug
transporters profoundly influence drug pharmacokinetics and pharmacodynamics. Moreover, factors which influence drug transporter
function, such as genetic polymorphisms or transporter based drug interactions, can have a major impact on drug efficacy and
safety. This chapter summarizes the role of transporters in drug disposition, gives examples of their clinical relevance,
and describes the emerging role of drug transporters in drug discovery and development.
With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.
In the pharmaceutical industry today, many of the potent compounds discovered using expensive technologies are eventually rejected because of poor physicochemical or absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) properties. This problem can be addressed by placing fast and accurate computational technologies at the heart of drug discovery. Chemically diverse and potent compounds generated by de novo design algorithms are scored for ADME/Tox properties using rigorously validated statistical models. Every molecule passing through this in silico pipeline is thus associated with a wealth of predicted properties, thereby allowing for rapid assessment to determine which molecule should be further developed. Critical to this idea is a platform that allows for the efficient exchange of in silico and experimental data between all scientists regardless of specialization. By bridging the gap between the in silico and experimental cultures in this fashion, an information-driven, cost-effective drug discovery program can be realized.
We have studied the interaction between verapamil and other modulators of the P-glycoprotein ATPase from membranes of CR1R12 Chinese hamster ovary cells. Four major categories of interaction were identified. (i) Non-competitive inhibition of verapamil's stimulation of enzyme activity was found with vanadate. (ii) Competitive inhibition of the ATPase was found for the pair verapamil and cyclosporin A. (iii) Allosteric inhibition with an increase in the Hill number for verapamil was found in the cases of daunorubicin, epirubicin, gramicidin S and D, vinblastine, amiodarone, and colchicine. (iv) Cooperative stimulation of verapamil-induced ATPase activity was found with progesterone, diltiazem, amitriptyline, and propranolol. At high levels, progesterone and verapamil mutually enhanced each other's inhibitory action on the ATPase. Our data show that the substrate binding behavior of P-glycoprotein is complex with more than one binding site being present. This information could form the basis for the development of improved modulators of P-glycoprotein.
The impact of P-glycoprotein (P-gp) on the multidrug resistance and pharmacokinetics of clinically important drugs has been widely recognized. Here, we review in silico approaches and computational models for identifying substrates or inhibitors of P-gp. The advances in the datasets for model building and available computational models are summarized and the advantages and drawbacks of these models are outlined. We also discuss the impact of the recently reported crystal structures of P-gp on potential breakthroughs in the computational modeling of P-gp substrates. Finally, the challenges of developing reliable prediction models for P-gp inhibitors or substrates, as well as the strategies to surmount these challenges, are reviewed.
Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.
P-glycoprotein (P-gp) is one of the major ABC transporters and involved in many essential processes such as lipid and steroid transport across cell membranes but also in the uptake of drugs such as HIV protease and reverse transcriptase inhibitors. Despite its importance, reliable models predicting substrates of P-gp are scarce. In this study, we have built several computational models to predict whether or not a compound is a P-gp substrate, based on the largest data set yet published, employing 332 distinct structures. Each molecule is represented by ADRIANA.Code, MOE, and ECFP_4 fingerprint descriptors. The models are computed using a support vector machine based on a training set which includes 131 substrates and 81 nonsubstrates that were evaluated by 5-, 10-fold, and leave-one-out (LOO) cross-validation. The best model gives a Matthews Correlation Coefficient of 0.73 and a prediction accuracy of 0.88 on the test set. Examination of the model based on ECFP_4 fingerprints revealed several substructures which could have significance in separating substrates and nonsubstrates of P-gp, such as the nitrile and sulfoxide functional groups which have a higher frequency in nonsubstrates than in substrates. In addition structural isomerism in sugars was found to result in remarkable differences regarding the likelihood of a compound to be a substrate for P-gp.
We present a probabilistic framework for interpreting structure-based virtual screening that returns a quantitative likelihood of observing bioactivity and can be quantitatively combined with ligand-based screening methods to yield a cumulative prediction that consistently outperforms any single screening metric. The approach has been developed and validated on more than 30 different protein targets. Transforming structure-based in silico screening results into robust probabilities of activity enables the general fusion of multiple structure- and ligand-based approaches and returns a quantitative expectation of success that can be used to prioritize (or deprioritize) further discovery activities. This unified probabilistic framework offers a paradigm shift in how docking and scoring results are interpreted, which can enhance early lead-finding efforts by maximizing the value of in silico computational tools.
Multiple drug resistance (multidrug resistance; MDR), a phenomenon whereby human tumours that acquire resistance to one type of therapy are found to be resistant to several other drugs that are often quite different in both structure and mode of action, has been recognised clinically for several decades. An important advance in our understanding of MDR came with the identification of P-glycoprotein and other related transporters that were expressed in some cancer cells and could recognise and catalyse the efflux of diverse anticancer drugs from cells. A second advance came from an understanding of the mechanism of programmed cell death or apoptosis, leading to MDR mediated by increased to resistance to anticancer drug-induced apoptosis. A third advance came with the finding that the proliferation of human tumours was driven by a small population of self-renewing tumour cells, focussing attention on the MDR properties of these so-called tumour stem cells rather than on the cells that comprised the majority of the tumour population. A fourth advance was the delineation of features of the tumour microenvironment, including immunosuppression, which essentially provided tumour stem cells with an MDR phenotype. Most published work on the overcoming of MDR has concentrated on inhibition of drug transporters but the complexity of mechanisms contributing demands a broad strategy for the development of methods to overcome MDR in a clinical setting.
Transporter proteins are expressed throughout the human body in different vital organs. They play an important role to various extents in determining absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties of therapeutic molecules. Over the past decade, numerous drug transporters have been cloned and considerable progress has been made toward understanding the molecular characteristics of individual transporters. In this chapter several in vitro and in silico techniques are described with applications to understand transporter behavior. These include employing new techniques to rapidly identify novel ligands for transporters. Ultimately these methods should lead to a greater overall appreciation of the role of transporters in vivo.
Multidrug resistance is a major challenge in the therapy of cancer and pathogenic fungal infections. More than three decades ago, P-glycoprotein was the first identified multidrug transporter. It has been studied extensively at the genetic and biochemical levels ever since. Pdr5, the most abundant ATP-binding cassette transporter in Saccharomyces cerevisiae, is highly homologous to azole-resistance-mediating multidrug transporters in fungal pathogens, and a focus of clinical drug resistance research. Despite functional equivalences, P-glycoprotein and Pdr5 exhibit striking differences in their architecture and mechanisms. In this minireview, we discuss the mechanisms of substrate selection and multidrug transport by comparing the fraternal twins P-glycoprotein and Pdr5. We propose that substrate selection in eukaryotic multidrug ATP-binding cassette transporters is not solely determined by structural features of the transmembrane domains but also by their dynamic behavior.
One of the Holy Grails of ATP-binding cassette transporter research is a structural understanding of drug binding and transport in a eukaryotic multidrug resistance pump. These transporters are front-line mediators of drug resistance in cancers and represent an important therapeutic target in future chemotherapy. Although there has been intensive biochemical research into the human multidrug pumps, their 3D structure at atomic resolution remains unknown. The recent determination of the structure of a mouse P-glycoprotein at subatomic resolution is complemented by structures for a number of prokaryotic homologues. These structures have provided advances into our knowledge of the ATP-binding cassette exporter structure and mechanism, and have provided the template data for a number of homology modelling studies designed to reconcile biochemical data on these clinically important proteins.
Chemotherapy remains the mainstay in the treatment and management of many cancers. However, this treatment modality is fraught with difficulties associated with toxicity and also the emergence of chemotherapy resistance is a considerable problem. Cancer scientists and oncologists have worked together for some time to find ways of understanding anticancer drug resistance and also to develop pharmacological strategies to overcome that resistance. The greatest focus has been on the reversal of the multidrug resistance (MDR) phenotype by inhibition of the ATP-binding cassette (ABC) drug transporters. Inhibitors of ABC transporters--termed MDR modulators--have in the past been numerous and have occupied industry and academia in drug discovery programs. The field has been fraught with difficulties and disappointments but, nonetheless, we are currently considering the fourth generation of MDR modulator development with much data pending from the clinical trials with the third-generation modulators. First-generation MDR modulator compounds were very diverse and broad spectrum pharmacological agents which fuelled the excitement surrounding the research into the MDR phenotype in cancer at the time. Second-generation agents were very heavily evaluated in mechanistic studies and formed the basis for a number of oncology portfolios of big pharmaceutical companies. Given this input, a number of clinical trials were carried out, the results of which were somewhat disappointing. Even with the modest evidence of active combinations, trial data were considered promising enough to warrant development of the third-generation of modulators. A number of key molecules have been identified with potent, long lasting MDR reversal properties, and minimal pharmacokinetic interaction with the co-administered cytotoxic agent. The results from a number of these trials are eagerly awaited and there are many in the cancer research community who remain committed to this area of anticancer drug discovery.