Exhaustive sampling of docking poses reveals binding hypotheses for propafenone type inhibitors of P-glycoprotein

Mechanistic insights into P-glycoprotein ligand transport and inhibition revealed by enhanced molecular dynamics simulations

P-glycoprotein (P-gp) plays a crucial role in cellular detoxification and drug efflux processes, transitioning between inward-facing (IF) open, occluded, and outward-facing (OF) states to facilitate substrate transport. Its role is critical in cancer therapy, where P-gp contributes to the multidrug resistance phenotype. In our study, classical and enhanced molecular dynamics (MD) simulations were conducted to dissect the structural and functional features of the P-gp conformational states. Our advanced MD simulations, including kinetically excited targeted MD (ketMD) and adiabatic biasing MD (ABMD), provided deeper insights into state transition and translocation mechanisms. Our findings suggest that the unkinking of TM4 and TM10 helices is a prerequisite for correctly achieving the outward conformation. Simulations of the IF-occluded conformations, characterized by kinked TM4 and TM10 helices, consistently demonstrated altered communication between the transmembrane domains (TMDs) and nucleotide binding domain 2 (NBD2), suggesting the implication of this interface in inhibiting P-gp's efflux function. A particular emphasis was placed on the unstructured linker segment connecting the NBD1 to TMD2 and its role in the transporter's dynamics. With the linker present, we specifically noticed a potential entrance of cholesterol (CHOL) through the TM4-TM6 portal, shedding light on crucial residues involved in accommodating CHOL. We therefore suggest that this entry mechanism could be employed for some P-gp substrates or inhibitors. Our results provide critical data for understanding P-gp functioning and developing new P-gp inhibitors for establishing more effective strategies against multidrug resistance.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling

The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

Synergistic Inhibitory Effect of Quercetin and Cyanidin-3O-Sophoroside on ABCB1

The human ABCB1 (P-glycoprotein, Pgp) protein is an active exporter expressed in the plasma membrane of cells forming biological barriers. In accordance with its broad substrate spectrum and tissue expression pattern, it affects the pharmacokinetics of numerous chemotherapeutic drugs and it is involved in unwanted drug-drug interactions leading to side effects or toxicities. When expressed in tumor tissues, it contributes to the development of chemotherapy resistance in malignancies. Therefore, the understanding of the molecular details of the ligand-ABCB1 interactions is of crucial importance. In a previous study, we found that quercetin (QUR) hampers both the transport and ATPase activity of ABCB1, while cyandin-3O-sophroside (C3S) stimulates the ATPase activity and causes only a weak inhibition of substrate transport. In the current study, when QUR and C3S were applied together, both a stronger ATPase inhibition and a robust decrease in substrate transport were observed, supporting their synergistic ABCB1 inhibitory effect. Similar to cyclosporine A, a potent ABCB1 inhibitor, co-treatment with QUR and C3S shifted the conformational equilibrium to the "inward-facing" conformer of ABCB1, as it was detected by the conformation-selective UIC2 mAb. To gain deeper insight into the molecular details of ligand-ABCB1 interactions, molecular docking experiments and MD simulations were also carried out. Our in silico studies support that QUR and C3S can bind simultaneously to ABCB1. The most favourable ligand-ABCB1 interaction is obtained when C3S binds to the central substrate binding site and QUR occupies the "access tunnel". Our results also highlight that the strong ABCB1 inhibitory effect of the combined treatment with QUR and C3S may be exploited in chemotherapy protocols for the treatment of multidrug-resistant tumors or for improving drug delivery through pharmacological barriers.

Surface plasmon resonance biosensor combined with lentiviral particle stabilization strategy for rapid and specific screening of P-Glycoprotein ligands

A novel surface plasmon resonance-based P-gp ligand screening system (SPR-PLSS) combined with lentiviral particle (LVP) stabilization strategy was constructed to screen out potential P-gp inhibitors from natural products. Firstly, we constructed LVPs with high and low expression levels of P-gp. The LVPs can ensure the natural conformation of P-gp based on the principle that LVPs germinated from packaging cells will contain cell membrane fragments and P-gp they carry. Then the LVPs with high P-gp expression for active channel and LVPs with low P-gp expression for reference channel were immobilized on CM5 chip respectively. The affinity detection was thus carried out with the signal reduction on the two channels. The P-gp inhibitors, Valspodar (Val) and cyclosporin (CsA), as positive compounds, were detected to characterize the chip's activity, and the K_D of Val and CsA were 14.09 μM and 16.41 μM, respectively. Forty compounds from natural product library were screened using the SPR CM5 chip, and magnolol (Mag), honokiol (Hon), and resveratrol (Res) were screened out as potential P-gp ligands, showing a significant response signal. This work presented a novel P-gp ligand screening system based on LVP-immobilized biosensor to rapidly screen out P-gp ligands from natural product library. Compared with traditional cell experiments which the screening time may take up to several days, our method only takes several hours. Furthermore, this study has also provided solid evidences to support that some complicated membrane proteins would apply to the lentivirus-based SPR screening system.

Homology Modeling of the Human P-glycoprotein (ABCB1) and Insights into Ligand Binding through Molecular Docking Studies

The ABCB1 transporter also known as P-glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette super-family of transporters; it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping the compounds out of cells. P-gp contributes to a decrease of toxicity and possesses broad substrate specificity. It is involved in the failure of numerous anticancer and antiviral chemotherapies due to the multidrug resistance (MDR) phenomenon, where it removes the chemotherapeutics out of the targeted cells. Understanding the details of the ligand–P-gp interaction is therefore crucial for the development of drugs that might overcome the MRD phenomenon and for obtaining a more effective prediction of the toxicity of certain compounds. In this work, an in silico modeling was performed using homology modeling and molecular docking methods with the aim of better understanding the ligand–P-gp interactions. Based on different mouse P-gp structural templates from the PDB repository, a 3D model of the human P-gp (hP-gp) was constructed by means of protein homology modeling. The homology model was then used to perform molecular docking calculations on a set of thirteen compounds, including some well-known compounds that interact with P-gp as substrates, inhibitors, or both. The sum of ranking differences (SRD) was employed for the comparison of the different scoring functions used in the docking calculations. A consensus-ranking scheme was employed for the selection of the top-ranked pose for each docked ligand. The docking results showed that a high number of π interactions, mainly π–sigma, π–alkyl, and π–π type of interactions, together with the simultaneous presence of hydrogen bond interactions contribute to the stability of the ligand–protein complex in the binding site. It was also observed that some interacting residues in hP-gp are the same when compared to those observed in a co-crystallized ligand (PBDE-100) with mouse P-gp (PDB ID: 4XWK). Our in silico approach is consistent with available experimental results regarding P-gp efflux transport assay; therefore it could be useful in the prediction of the role of new compounds in systemic toxicity.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

Propafenone analogue with additional H-bond acceptor group shows increased inhibitory activity on P-glycoprotein

P-glycoprotein (P-gp) is an ATP-dependent efflux pump that has a marked impact on the absorption, distribution, and excretion of therapeutic drugs. As P-gp inhibition can result in drug-drug interactions and altered drug bioavailability, identifying molecular properties that are linked to inhibition is of great interest in drug development. In this study, we combined chemical synthesis, in vitro testing, quantitative structure-activity relationship analysis, and docking studies to investigate the role of hydrogen bond (H-bond) donor/acceptor properties in transporter-ligand interaction. In a previous work, it has been shown that propafenone analogs with a 4-hydroxy-4-piperidine moiety exhibit a generally 10-fold higher P-gp inhibitory activity than expected based on their lipophilicity. Here, we specifically expanded the data set by introducing substituents at position 4 of the 4-phenylpiperidine moiety to assess the importance of H-bond donor/acceptor features in this region. The results suggest that indeed an H-bond acceptor, such as hydroxy and methoxy, increases the affinity by forming a H-bond with Tyr310.

Rigorous sampling of docking poses unveils binding hypothesis for the halogenated ligands of L-type Amino acid Transporter 1 (LAT1)

L-type Amino acid Transporter 1 (LAT1) plays a significant role in the growth and propagation of cancer cells by facilitating the cross-membrane transport of essential nutrients, and is an attractive drug target. Several halogen-containing L-phenylalanine-based ligands display high affinity and high selectivity for LAT1; nonetheless, their molecular mechanism of binding remains unclear. In this study, a combined in silico strategy consisting of homology modeling, molecular docking, and Quantum Mechanics-Molecular Mechanics (QM-MM) simulation was applied to elucidate the molecular basis of ligand binding in LAT1. First, a homology model of LAT1 based on the atomic structure of a prokaryotic homolog was constructed. Docking studies using a set of halogenated ligands allowed for deriving a binding hypothesis. Selected docking poses were subjected to QM-MM calculations to investigate the halogen interactions. Collectively, the results highlight the dual nature of the ligand-protein binding mode characterized by backbone hydrogen bond interactions of the amino acid moiety of the ligands and residues I63, S66, G67, F252, G255, as well as hydrophobic interactions of the ligand’s side chains with residues I139, I140, F252, G255, F402, W405. QM-MM optimizations indicated that the electrostatic interactions involving halogens contribute to the binding free energy. Importantly, our results are in good agreement with the recently unraveled cryo-Electron Microscopy structures of LAT1.

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands.

Discovery of Potent Inhibitors for the Large Neutral Amino Acid Transporter 1 (LAT1) by Structure-Based Methods

The large neutral amino acid transporter 1 (LAT1) is a promising anticancer target that is required for the cellular uptake of essential amino acids that serve as building blocks for cancer growth and proliferation. Here, we report a structure-based approach to identify chemically diverse and potent inhibitors of LAT1. First, a homology model of LAT1 that is based on the atomic structures of the prokaryotic homologs was constructed. Molecular docking of nitrogen mustards (NMs) with a wide range of affinity allowed for deriving a common binding mode that could explain the structure−activity relationship pattern in NMs. Subsequently, validated binding hypotheses were subjected to molecular dynamics simulation, which allowed for extracting a set of dynamic pharmacophores. Finally, a library of ~1.1 million molecules was virtually screened against these pharmacophores, followed by docking. Biological testing of the 30 top-ranked hits revealed 13 actives, with the best compound showing an IC₅₀ value in the sub-μM range.

Interspecies comparison of putative ligand binding sites of human, rat and mouse P-glycoprotein

Prior to the clinical phases of testing, safety, efficacy and pharmacokinetic profiles of lead compounds are evaluated in animal studies. These tests are primarily performed in rodents, such as mouse and rats. In order to reduce the number of animal experiments, computational models that predict the outcome of these studies and thus aid in prioritization of preclinical candidates are heavily needed. However, although computational models for human off-target interactions with decent quality are available, they cannot easily be transferred to rodents due to lack of respective data. In this study, we assess the transferability of human P-glycoprotein activity data for development of in silico models to predict in vivo effects in rats and mouse using a structure-based approach. P-glycoprotein (P-gp) is an ATP-dependent efflux transporter that transports xenobiotic compounds such as toxins and drugs out of cells and has a broad substrate and inhibitor specificity. Being mostly expressed at barriers, it influences the bioavailability of drugs and thus contributes also to toxicity. Comparison of the binding site interaction profiles of human, rat and mouse P-gp derived from docking studies with a set of common inhibitors suggests that the inhibitors share potentially similar binding modes. These findings encourage the use of in vitro human P-gp data for predicting in vivo effects in rodents and thus contributes to the 3Rs (Replace, Reduce and Refine) of animal experiments.

Protein arginine methyltransferase 1 may be involved in pregnane x receptor-activated overexpression of multidrug resistance 1 gene during acquired multidrug resistant

PURPOSE

Pregnane x receptor (PXR) - activated overexpression of the multidrug resistance 1 (MDR1) gene is an important way for tumor cells to acquire drug resistance. However, the detailed mechanism still remains unclear. In the present study, we aimed to investigate whether protein arginine methyl transferase 1(PRMT1) is involved in PXR - activated overexpression of MDR1 during acquired multidrug resistant.

EXPERIMENTAL DESIGN

Arginine methyltransferase inhibitor 1 (AMI-1) was used to pharmacologically block PRMT1 in resistant breast cancer cells (MCF7/adr). The mRNA and protein levels of MDR1 were detected by real-time PCR and western blotting analysis. Immunofluorescence microscopy and co-immunoprecipitation were used to investigate the physical interaction between PXR and PRMT1. Then, 136 candidate compounds were screened for PRMT1 inhibitors. Lastly, luciferase reporter gene and nude mice bearing resistant breast cancer xenografts were adopted to investigate the anti-tumor effect of PRMT1 inhibitors when combined with adriamycin.

RESULTS

AMI-1 significantly suppressed the expression of MDR1 in MCF7/adr cells and increased cells sensitivity of MCF7/adr to adriamycin. Physical interaction between PRMT1 and PXR exists in MCF7/adr cells, which could be disrupted by AMI-1. Those results suggest that PRMT1 may be involved in PXR-activated overexpression of MDR1 in resistant breast cancer cells, and AMI-1 may suppress MDR1 by disrupting the interaction between PRMT1 and PXR. Then, five compounds including rutin, isoquercitrin, salvianolic acid A, naproxen, and felodipline were identified to be PRMT1 inhibitors. Finally, those PRMT1 inhibitors were observed to significantly decrease MDR1 promoter activity in vitro and enhance the antitumor effect of adriamycin in nude mice that bearing resistant breast cancer xenografts.

CONCLUSIONS

PRMT1 may be an important co-activator of PXR in activating MDR1 gene during acquired resistance, and PRMT1 inhibitor combined with chemotherapy drugs may be a new strategy for overcoming tumor MDR.

Selected anthraquinones as potential free radical scavengers and P-glycoprotein inhibitors

In this article, we estimated the scavenger capacity of six selected anthraquinones toward free radicals and their efficacy as inhibitors of P-glycoproteins.

Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP

The bile salt export pump (BSEP) actively transports conjugated monovalent bile acids from the hepatocytes into the bile. This facilitates the formation of micelles and promotes digestion and absorption of dietary fat. Inhibition of BSEP leads to decreased bile flow and accumulation of cytotoxic bile salts in the liver. A number of compounds have been identified to interact with BSEP, which results in drug-induced cholestasis or liver injury. Therefore, in silico approaches for flagging compounds as potential BSEP inhibitors would be of high value in the early stage of the drug discovery pipeline. Up to now, due to the lack of a high-resolution X-ray structure of BSEP, in silico based identification of BSEP inhibitors focused on ligand-based approaches. In this study, we provide a homology model for BSEP, developed using the corrected mouse P-glycoprotein structure (PDB ID: 4M1M). Subsequently, the model was used for docking-based classification of a set of 1212 compounds (405 BSEP inhibitors, 807 non-inhibitors). Using the scoring function ChemScore, a prediction accuracy of 81% on the training set and 73% on two external test sets could be obtained. In addition, the applicability domain of the models was assessed based on Euclidean distance. Further, analysis of the protein-ligand interaction fingerprints revealed certain functional group-amino acid residue interactions that could play a key role for ligand binding. Though ligand-based models, due to their high speed and accuracy, remain the method of choice for classification of BSEP inhibitors, structure-assisted docking models demonstrate reasonably good prediction accuracies while additionally providing information about putative protein-ligand interactions.

Empowering pharmacoinformatics by linked life science data

With the public availability of large data sources such as ChEMBLdb and the Open PHACTS Discovery Platform, retrieval of data sets for certain protein targets of interest with consistent assay conditions is no longer a time consuming process. Especially the use of workflow engines such as KNIME or Pipeline Pilot allows complex queries and enables to simultaneously search for several targets. Data can then directly be used as input to various ligand- and structure-based studies. In this contribution, using in-house projects on P-gp inhibition, transporter selectivity, and TRPV1 modulation we outline how the incorporation of linked life science data in the daily execution of projects allowed to expand our approaches from conventional Hansch analysis to complex, integrated multilayer models.

Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors

P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors.

From linked open data to molecular interaction: studying selectivity trends for ligands of the human serotonin and dopamine transporter

Retrieval of congeneric and consistent SAR data sets for protein targets of interest is still a laborious task to do if no appropriate in-house data set is available. However, combining integrated open data sources (such as the Open PHACTS Discovery Platform) with workflow tools now offers the possibility of querying across multiple domains and tailoring the search to the given research question. Starting from two phylogenetically related protein targets of interest (the human serotonin and dopamine transporters), the whole chemical compound space was explored by implementing a scaffold-based clustering of compounds possessing biological measurements for both targets. In addition, potential hERG blocking liabilities were included. The workflow allowed studying the selectivity trends of scaffold series, identifying potentially harmful compound series, and performing SAR, docking studies and molecular dynamics (MD) simulations for a consistent data set of 56 cathinones. This delivered useful insights into driving determinants for hDAT selectivity over hSERT. With respect to the scaffold-based analyses it should be noted that the cathinone data set could be retrieved only when Murcko scaffold analyses were combined with similarity searches such as a common substructure search.

Homology modelling of human P-glycoprotein

P-glycoprotein (P-gp) is an ATP-binding cassette transporter that exports a huge range of compounds out of cells and is thus one of the key proteins in conferring multi-drug resistance in cancer. Understanding how it achieves such a broad specificity and the series of conformational changes that allow export to occur form major, on-going, research objectives around the world. Much of our knowledge to date has been derived from mutagenesis and assay data. However, in recent years, there has also been great progress in structural biology and although the structure of human P-gp has not yet been solved, there are now a handful of related structures on which homology models can be built to aid in the interpretation of the vast amount of experimental data that currently exists. Many models for P-gp have been built with this aim, but the situation is complicated by the apparent flexibility of the system and by the fact that although many potential templates exist, there is large variation in the conformational state in which they have been crystallized. In this review, we summarize how homology modelling has been used in the past, how models are typically selected and finally illustrate how MD simulations can be used as a means to give more confidence about models that have been generated via this approach.

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.

Prediction of drug-ABC-transporter interaction--Recent advances and future challenges

With the discovery of P-glycoprotein (P-gp), it became evident that ABC-transporters play a vital role in bioavailability and toxicity of drugs. They prevent intracellular accumulation of toxic compounds, which renders them a major defense mechanism against xenotoxic compounds. Their expression in cells of all major barriers (intestine, blood–brain barrier, blood–placenta barrier) as well as in metabolic organs (liver, kidney) also explains their influence on the ADMET properties of drugs and drug candidates. Thus, in silico models for the prediction of the probability of a compound to interact with P-gp or analogous transporters are of high value in the early phase of the drug discovery process. Within this review, we highlight recent developments in the area, with a special focus on the molecular basis of drug–transporter interaction. In addition, with the recent availability of X-ray structures of several ABC-transporters, also structure-based design methods have been applied and will be addressed.

Identification of Possible Binding Sites for Morphine and Nicardipine on the Multidrug Transporter P-Glycoprotein Using Umbrella Sampling Techniques

The multidrug transporter P-glycoprotein (P-gp) is central to the development of multidrug resistance in cancer. While residues essential for transport and binding have been identified, the location, composition, and specificity of potential drug binding sites are uncertain. Here molecular dynamics simulations are used to calculate the free energy profile for the binding of morphine and nicardipine to P-gp. We show that morphine and nicardipine primarily interact with key residues implicated in binding and transport from mutational studies, binding at different but overlapping sites within the transmembrane pore. Their permeation pathways were distinct but involved overlapping sets of residues. The results indicate that the binding location and permeation pathways of morphine and nicardipine are not well separated and cannot be considered as unique. This has important implications for our understanding of substrate uptake and transport by P-gp. Our results are independent of the choice of starting structure and consistent with a range of experimental studies.

A binding mode hypothesis of tiagabine confirms liothyronine effect on γ-aminobutyric acid transporter 1 (GAT1)

Elevating GABA levels in the synaptic cleft by inhibiting its reuptake carrier GAT1 is an established approach for the treatment of CNS disorders like epilepsy. With the increasing availability of crystal structures of transmembrane transporters, structure-based approaches to elucidate the molecular basis of ligand–transporter interaction also become feasible. Experimental data guided docking of derivatives of the GAT1 inhibitor tiagabine into a protein homology model of GAT1 allowed derivation of a common binding mode for this class of inhibitors that is able to account for the distinct structure–activity relationship pattern of the data set. Translating essential binding features into a pharmacophore model followed by in silico screening of the DrugBank identified liothyronine as a drug potentially exerting a similar effect on GAT1. Experimental testing further confirmed the GAT1 inhibiting properties of this thyroid hormone.

P-glycoprotein induction in Caco-2 cells by newly synthetized thioxanthones prevents paraquat cytotoxicity

The induction of P-glycoprotein (P-gp), an ATP-dependent efflux pump, has been proposed as a strategy against the toxicity induced by P-gp substrates such as the herbicide paraquat (PQ). The aim of this study was to screen five newly synthetized thioxanthonic derivatives, a group known to interact with P-gp, as potential inducers of the pump's expression and/or activity and to evaluate whether they would afford protection against PQ-induced toxicity in Caco-2 cells. All five thioxanthones (20 µM) caused a significant increase in both P-gp expression and activity as evaluated by flow cytometry using the UIC2 antibody and rhodamine 123, respectively. Additionally, it was demonstrated that the tested compounds, when present only during the efflux of rhodamine 123, rapidly induced an activation of P-gp. The tested compounds also increased P-gp ATPase activity in MDR1-Sf9 membrane vesicles, indicating that all derivatives acted as P-gp substrates. PQ cytotoxicity was significantly reduced in the presence of four thioxanthone derivatives, and this protective effect was reversed upon incubation with a specific P-gp inhibitor. In silico studies showed that all the tested thioxanthones fitted onto a previously described three-feature P-gp induction pharmacophore. Moreover, in silico interactions between thioxanthones and P-gp in the presence of PQ suggested that a co-transport mechanism may be operating. Based on the in vitro activation results, a pharmacophore model for P-gp activation was built, which will be of further use in the screening for new P-gp activators. In conclusion, the study demonstrated the potential of the tested thioxanthonic compounds in protecting against toxic effects induced by P-gp substrates through P-gp induction and activation.

Modelling three-dimensional protein structures for applications in drug design

A structural perspective of drug target and anti-target proteins, and their molecular interactions with biologically active molecules, largely advances many areas of drug discovery, including target validation, hit and lead finding and lead optimisation. In the absence of experimental 3D structures, protein structure prediction often offers a suitable alternative to facilitate structure-based studies. This review outlines recent methodical advances in homology modelling, with a focus on those techniques that necessitate consideration of ligand binding. In this context, model quality estimation deserves special attention because the accuracy and reliability of different structure prediction techniques vary considerably, and the quality of a model ultimately determines its usefulness for structure-based drug discovery. Examples of G-protein-coupled receptors (GPCRs) and ADMET-related proteins were selected to illustrate recent progress and current limitations of protein structure prediction. Basic guidelines for good modelling practice are also provided.

The ability of molecular docking to unravel the controversy and challenges related to P-glycoprotein--a well-known, yet poorly understood drug transporter

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.

Reversible dimers of the atypical antipsychotic quetiapine inhibit p-glycoprotein-mediated efflux in vitro with increased binding affinity and in situ at the blood-brain barrier

The multidrug resistance transporter P-glycoprotein (P-gp) is highly expressed in the capillary endothelial cells of the blood-brain barrier (BBB) where it functions to limit the brain penetration of many drugs, including antipsychotic agents used to treat schizophrenia. Therefore, in an effort to inhibit the transporter, we designed dimers of the antipsychotic drug and P-gp substrate quetiapine (QT), linked by variable length tethers. In P-gp overexpressing cells and in human brain capillary endothelial hCMEC/D3 cells, the dimer with the shortest tether length (QT2C2) (1) was the most potent inhibitor showing >80-fold better inhibition of P-gp-mediated transport than monomeric QT. The dimers, which are linked via ester moieties, are designed to revert to the therapeutic monomer once inside the target cells. We demonstrated that the addition of two sterically blocking methyl groups to the linker (QT2C2Me2, 8) increased the half-life of the molecule in plasma 10-fold as compared to the dimer lacking methyl groups (QT2C2, 1), while retaining inhibitory potency for P-gp transport and sensitivity to cellular esterases. Experiments with purified P-gp demonstrated that QT2C2 (1) and QT2C2Me2 (8) interacted with both the H- and R-binding sites of the transporter with binding affinities 20- to 30-fold higher than that of monomeric QT. Using isolated rat brain capillaries, QT2C2Me2 (8) was a more potent inhibitor of P-gp transport than QT. Lastly, we showed that QT2C2Me2 (8) increased the accumulation of the P-gp substrate verapamil in rat brain in situ three times more than QT. Together, these results indicate that the QT dimer QT2C2Me2 (8) strongly inhibited P-gp transport activity in human brain capillary endothelial cells, in rat brain capillaries, and at the BBB in an animal model.

In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions

P-glycoprotein (P-gp) is an ATP (adenosine triphosphate)-binding cassette transporter that causes multidrug resistance of various chemotherapeutic substances by active efflux from mammalian cells. P-gp plays a pivotal role in limiting drug absorption and distribution in different organs, including the intestines and brain. Thus, the prediction of P-gp–drug interactions is of vital importance in assessing drug pharmacokinetic and pharmacodynamic properties. To find the strongest P-gp blockers, we performed an in silico structure-based screening of P-gp inhibitor library (1,300 molecules) by the gradient optimization method, using polynomial empirical scoring (POLSCORE) functions. We report a strong correlation (r²=0.80, F=16.27, n=6, P<0.0157) of inhibition constants (Ki_exp or pKi_exp; experimental Ki or negative decimal logarithm of Ki_exp) converted from experimental IC₅₀ (half maximal inhibitory concentration) values with POLSCORE-predicted constants (Ki_POLSCORE or pKi_POLSCORE), using a linear regression fitting technique. The hydrophobic interactions between P-gp and selected drug substances were detected as the main forces responsible for the inhibition effect. The results showed that this scoring technique might be useful in the virtual screening and filtering of databases of drug-like compounds at the early stage of drug development processes.

Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors

The ABC transporter P-glycoprotein (P-gp) actively transports a wide range of drugs and toxins out of cells, and is therefore related to multidrug resistance and the ADME profile of therapeutics. Thus, development of predictive in silico models for the identification of P-gp inhibitors is of great interest in the field of drug discovery and development. So far in silico P-gp inhibitor prediction was dominated by ligand-based approaches because of the lack of high-quality structural information about P-gp. The present study aims at comparing the P-gp inhibitor/noninhibitor classification performance obtained by docking into a homology model of P-gp, to supervised machine learning methods, such as Kappa nearest neighbor, support vector machine (SVM), random fores,t and binary QSAR, by using a large, structurally diverse data set. In addition, the applicability domain of the models was assessed using an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and noninhibitors, correctly predicting 73/75% of the external test set compounds. Classification based on the docking experiments using the scoring function ChemScore resulted in the correct prediction of 61% of the external test set. This demonstrates that ligand-based models currently remain the methods of choice for accurately predicting P-gp inhibitors. However, structure-based classification offers information about possible drug/protein interactions, which helps in understanding the molecular basis of ligand-transporter interaction and could therefore also support lead optimization.

Multiple transport-active binding sites are available for a single substrate on human P-glycoprotein (ABCB1)

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, [¹²⁵I]-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.

Drug rescue distinguishes between different structural models of human P-glycoprotein

There is no high-resolution crystal structure of the human P-glycoprotein (P-gp) drug pump. Homology models of human P-gp based on the crystal structures of mouse or Caenorhabditis elegans P-gps show large differences in the orientation of transmembrane segment 5 (TM5). TM5 is one of the most important transmembrane segments involved in drug–substrate interactions. Drug rescue of P-gp processing mutants containing an arginine at each position in TM5 was used to identify positions facing the lipid or internal aqueous chamber. Only the model based on the C. elegans P-gp structure was compatible with the drug rescue results.

Interactions of the multidrug resistance modulators tariquidar and elacridar and their analogues with P-glycoprotein

Tariquidar and elacridar are among the most potent inhibitors of the multidrug resistance transporter P-glycoprotein (P-gp), but how they interact with the protein is yet unknown. In this work, we describe a possible way in which these inhibitors interact with P-gp. We rely on structure-activity relationship analysis of a small group of tariquidar and elacridar analogues that was purposefully selected, designed, and tested. Structural modifications of the compounds relate to the presence or absence of functional groups in the tariquidar and elacridar scaffolds. The activity of the compounds was evaluated by their effects on the accumulation of P-gp substrates rhodamine 123 and Hoechst 33342 in resistant tumor cells. The data allow estimation of the ability of the compounds to interact with the experimentally proposed R- and H-sites to which rhodamine 123 and Hoechst 33342 bind, respectively. Using an inward-facing homology model of human P-gp based on the crystallographic structure of mouse P-gp, we demonstrate that these binding sites may overlap with the binding sites of the QZ59 ligands co-crystallized with mouse P-gp. Based on this SAR analysis, and using flexible alignment and docking, we propose possible binding modes for tariquidar and elacridar. Our results suggest the possibility for the studied compounds to bind to sites that coincide or overlap with the binding sites of rhodamine 123 and Hoechst 33342. These results contribute to further understanding of structure-function relationships of P-gp and can help in the design of selective and potent P-gp inhibitors with potential clinical use.

On the origin of large flexibility of P-glycoprotein in the inward-facing state

P-glycoprotein (Pgp) is one of the most biomedically relevant transporters in the ATP binding cassette (ABC) superfamily due to its involvement in developing multidrug resistance in cancer cells. Employing molecular dynamics simulations and double electron-electron resonance spectroscopy, we have investigated the structural dynamics of membrane-bound Pgp in the inward-facing state and found that Pgp adopts an unexpectedly wide range of conformations, highlighted by the degree of separation between the two nucleotide-binding domains (NBDs). The distance between the two NBDs in the equilibrium simulations covers a range of at least 20 Å, including, both, more open and more closed NBD configurations than the crystal structure. The double electron-electron resonance measurements on spin-labeled Pgp mutants also show wide distributions covering both longer and shorter distances than those observed in the crystal structure. Based on structural and sequence analyses, we propose that the transmembrane domains of Pgp might be more flexible than other structurally known ABC exporters. The structural flexibility of Pgp demonstrated here is not only in close agreement with, but also helps rationalize, the reported high NBD fluctuations in several ABC exporters and possibly represents a fundamental difference in the transport mechanism between ABC exporters and ABC importers. In addition, during the simulations we have captured partial entrance of a lipid molecule from the bilayer into the lumen of Pgp, reaching the putative drug binding site. The location of the protruding lipid suggests a putative pathway for direct drug recruitment from the membrane.

A salt bridge in intracellular loop 2 is essential for folding of human p-glycoprotein

There is no high-resolution structure of the human P-glycoprotein (P-gp, ABCB1) drug pump. Homology models based on the crystal structures of mouse and Caenorhabditis elegans P-gps show extensive contacts between intracellular loop 2 (ICL2, in the first transmembrane domain) and the second nucleotide-binding domain. Human P-gp modeled on these P-gp structures yields different ICL2 structures. Only the model based on the C. elegans P-gp structure predicts the presence of a salt bridge. We show that the Glu256–Arg276 salt bridge was critical for P-gp folding.

Discovery of the inhibitory effect of a phosphatidylinositol derivative on P-glycoprotein by virtual screening followed by in vitro cellular studies

P-glycoprotein is capable of effluxing a broad range of cytosolic and membrane penetrating xenobiotic substrates, thus leading to multi-drug resistance and posing a threat for the therapeutic treatment of several diseases, including cancer and central nervous disorders. Herein, a virtual screening campaign followed by experimental validation in Caco-2, MDKCII, and MDKCII mdr1 transfected cell lines has been conducted for the identification of novel phospholipids with P-gp transportation inhibitory activity. Phosphatidylinositol-(1,2-dioctanoyl)-sodium salt (8∶0 PI) was found to significantly inhibit transmembrane P-gp transportation in vitro in a reproducible-, cell line-, and substrate-independent manner. Further tests are needed to determine whether this and other phosphatidylinositols could be co-administered with oral drugs to successfully increase their bioavailability. Moreover, as phosphatidylinositols and phosphoinositides are present in the human diet and are known to play an important role in signal transduction and cell motility, our finding could be of substantial interest for nutrition science as well.

Protein contacts and ligand binding in the inward-facing model of human P-glycoprotein

The primary aim of this work was to analyze the contacts between residues in the nucleotide binding domains (NBDs) and at the interface between the transmembrane domains (TMDs) and the NBDs in the inward-open homology model of human P-glycoprotein (P-gp). The analysis revealed communication nets through hydrogen bonding in the NBD and at the NBD-TMD interface of each half involving residues from the adenosine triphosphate (ATP) motifs and the coupling helices of the intracellular loops. Similar networks have been identified in P-gp conformations generated by molecular dynamics simulation. Differences have been recorded in the networking between both halves of P-gp. Many of the residue contacts have also been observed in the X-ray crystal structures of other ATP binding cassette (ABC) transporters, which confirms their validity. Next, possible binding pockets involving residues of importance for the TMD-NBD communication were identified. By studying these pockets, binding sites were suggested for rhodamine 123 (R-site) and prazosin (regulatory site) at the NBD-TMD interface that agreed with the experimental data on their location. Additionally, one more R-site in the protein cavity was proposed, in accordance with the available biochemical data. Together with the previously suggested Hoechst 33342 site (H-site), all sites were interpreted with respect to their effects on the protein ATPase activity, in correspondence with the experimental observations. Several residues involved in key contacts in the P-gp NBDs were proposed for further targeted mutagenesis experiments.

Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors

P-Glycoprotein (P-gp, ABCB1) plays a significant role in determining the ADMET properties of drugs and drug candidates. Substrates of P-gp are not only subject to multidrug resistance (MDR) in tumor therapy, they are also associated with poor pharmacokinetic profiles. In contrast, inhibitors of P-gp have been advocated as modulators of MDR. However, due to the polyspecificity of P-gp, knowledge on the molecular basis of ligand-transporter interaction is still poor, which renders the prediction of whether a compound is a P-gp substrate/non-substrate or an inhibitor/non-inhibitor quite challenging. In the present investigation, we used a set of fingerprints representing the presence/absence of various functional groups for machine learning based classification of a set of 484 substrates/non-substrates and a set of 1935 inhibitors/non-inhibitors. Best models were obtained using a combination of a wrapper subset evaluator (WSE) with random forest (RF), kappa nearest neighbor (kNN) and support vector machine (SVM), showing accuracies >70%. Best P-gp substrate models were further validated with three sets of external P-gp substrate sources, which include Drug Bank (n = 134), TP Search (n = 90) and a set compiled from literature (n = 76). Association rule analysis explores the various structural feature requirements for P-gp substrates and inhibitors.

Structure-activity relationships, ligand efficiency, and lipophilic efficiency profiles of benzophenone-type inhibitors of the multidrug transporter P-glycoprotein

The drug efflux pump P-glycoprotein (P-gp) has been shown to promote multidrug resistance (MDR) in tumors as well as to influence ADME properties of drug candidates. Here we synthesized and tested a series of benzophenone derivatives structurally analogous to propafenone-type inhibitors of P-gp. Some of the compounds showed ligand efficiency and lipophilic efficiency (LipE) values in the range of compounds which entered clinical trials as MDR modulators. Interestingly, although lipophilicity plays a dominant role for P-gp inhibitors, all compounds investigated showed LipE values below the threshold for promising drug candidates. Docking studies of selected analogues into a homology model of P-glycoprotein suggest that benzophenones show an interaction pattern similar to that previously identified for propafenone-type inhibitors.