Impact of the Recent Mouse P-Glycoprotein Structure for Structure-Based Ligand Design

The Alternating Access Mechanism in Mammalian Multidrug Resistance Transporters and Their Bacterial Homologs

Multidrug resistance (MDR) proteins belonging to the ATP-Binding Cassette (ABC) transporter group play a crucial role in the export of cytotoxic drugs across cell membranes. These proteins are particularly fascinating due to their ability to confer drug resistance, which subsequently leads to the failure of therapeutic interventions and hinders successful treatments. One key mechanism by which multidrug resistance (MDR) proteins carry out their transport function is through alternating access. This mechanism involves intricate conformational changes that enable the binding and transport of substrates across cellular membranes. In this extensive review, we provide an overview of ABC transporters, including their classifications and structural similarities. We focus specifically on well-known mammalian multidrug resistance proteins such as MRP1 and Pgp (MDR1), as well as bacterial counterparts such as Sav1866 and lipid flippase MsbA. By exploring the structural and functional features of these MDR proteins, we shed light on the roles of their nucleotide-binding domains (NBDs) and transmembrane domains (TMDs) in the transport process. Notably, while the structures of NBDs in prokaryotic ABC proteins, such as Sav1866, MsbA, and mammalian Pgp, are identical, MRP1 exhibits distinct characteristics in its NBDs. Our review also emphasizes the importance of two ATP molecules for the formation of an interface between the two binding sites of NBD domains across all these transporters. ATP hydrolysis occurs following substrate transport and is vital for recycling the transporters in subsequent cycles of substrate transportation. Specifically, among the studied transporters, only NBD2 in MRP1 possesses the ability to hydrolyze ATP, while both NBDs of Pgp, Sav1866, and MsbA are capable of carrying out this reaction. Furthermore, we highlight recent advancements in the study of MDR proteins and the alternating access mechanism. We discuss the experimental and computational approaches utilized to investigate the structure and dynamics of MDR proteins, providing valuable insights into their conformational changes and substrate transport. This review not only contributes to an enhanced understanding of multidrug resistance proteins but also holds immense potential for guiding future research and facilitating the development of effective strategies to overcome multidrug resistance, thus improving therapeutic interventions.

Recent Advances on P-Glycoprotein (ABCB1) Transporter Modelling with In Silico Methods

ABC transporters play a critical role in both drug bioavailability and toxicity, and with the discovery of the P-glycoprotein (P-gp), this became even more evident, as it plays an important role in preventing intracellular accumulation of toxic compounds. Over the past 30 years, intensive studies have been conducted to find new therapeutic molecules to reverse the phenomenon of multidrug resistance (MDR) ), that research has found is often associated with overexpression of P-gp, the most extensively studied drug efflux transporter; in MDR, therapeutic drugs are prevented from reaching their targets due to active efflux from the cell. The development of P-gp inhibitors is recognized as a good way to reverse this type of MDR, which has been the subject of extensive studies over the past few decades. Despite the progress made, no effective P-gp inhibitors to reverse multidrug resistance are yet on the market, mainly because of their toxic effects. Computational studies can accelerate this process, and in silico models such as QSAR models that predict the activity of compounds associated with P-gp (or analogous transporters) are of great value in the early stages of drug development, along with molecular modelling methods, which provide a way to explain how these molecules interact with the ABC transporter. This review highlights recent advances in computational P-gp research, spanning the last five years to 2022. Particular attention is given to the use of machine-learning approaches, drug-transporter interactions, and recent discoveries of potential P-gp inhibitors that could act as modulators of multidrug resistance.

Exploring the molecular determinants for subtype-selectivity of 2-amino-1,4,5,6-tetrahydropyrimidine-5-carboxylic acid analogs as betaine/GABA transporter 1 (BGT1) substrate-inhibitors

We have previously identified 2-amino-1,4,5,6-tetrahydropyrimidine-5-carboxylic acid (ATPCA) as the most potent substrate-inhibitor of the betaine/GABA transporter 1 (BGT1) (IC₅₀ 2.5 µM) reported to date. Herein, we characterize the binding mode of 20 novel analogs and propose the molecular determinants driving BGT1-selectivity. A series of N₁-, exocyclic-N-, and C₄-substituted analogs was synthesized and pharmacologically characterized in radioligand-based uptake assays at the four human GABA transporters (hGATs) recombinantly expressed in mammalian cells. Overall, the analogs retained subtype-selectivity for hBGT1, though with lower inhibitory activities (mid to high micromolar IC₅₀ values) compared to ATPCA. Further characterization of five of these BGT1-active analogs in a fluorescence-based FMP assay revealed that the compounds are substrates for hBGT1, suggesting they interact with the orthosteric site of the transporter. In silico-guided mutagenesis experiments showed that the non-conserved residues Q299 and E52 in hBGT1 as well as the conformational flexibility of the compounds potentially contribute to the subtype-selectivity of ATPCA and its analogs. Overall, this study provides new insights into the molecular interactions governing the subtype-selectivity of BGT1 substrate-inhibitors. The findings may guide the rational design of BGT1-selective pharmacological tool compounds for future drug discovery.

Exploring movement and energy in human P-glycoprotein conformational rearrangement

Human P-glycoprotein (P-gp), a kind of ATP-Binding Cassette transporter, can export a diverse variety of anti-cancer drugs out of the tumor cell. Its overexpression is one of the main reasons for the multidrug resistance (MDR) of tumor cells. It has been confirmed that during the substrate transport process, P-gp experiences a large-scale structural rearrangement from the inward- to outward-facing states. However, the mechanism of how the nucleotide-binding domains (NBDs) control the transmembrane domains (TMDs) to open towards the periplasm in the outward-facing state has not yet been fully characterized. Herein, targeted molecular dynamics simulations were performed to explore the conformational rearrangement of human P-gp. The results show that the allosteric process proceeds in a coupled way, and first the transition is driven by the NBDs, and then transmitted to the cytoplasmic parts of TMDs, finally to the periplasmic parts. The trajectories show that besides the translational motions, the NBDs undergo a rotation movement, which mainly occurs in xy plane and ensures the formation of the correct ATP-binding pockets. The analyses on the interaction energies between the six structure segments (cICLs) from the TMDs and NBDs reveal that their subtle energy differences play an important role in causing the periplasmic parts of the transmembrane helices to separate from each other in the established directions and in appropriate amplitudes. This conclusion can explain the two experimental phenomena about human P-gp in some extent. These studies have provided a detailed exploration into human P-gp rearrangement process and given an energy insight into the TMD reorientation during P-gp transition.

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.

Toward a unifying strategy for the structure-based prediction of toxicological endpoints

Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.

Computational classification models for predicting the interaction of drugs with P-glycoprotein and breast cancer resistance protein

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.

Modulation of P-glycoprotein efflux pump: induction and activation as a therapeutic strategy

P-glycoprotein (P-gp) is an ATP-dependent efflux pump encoded by the MDR1 gene in humans, known to mediate multidrug resistance of neoplastic cells to cancer therapy. For several decades, P-gp inhibition has drawn many significant research efforts in an attempt to overcome this phenomenon. However, P-gp is also constitutively expressed in normal human epithelial tissues and, due to its broad substrate specificity, to its cellular polarized expression in many excretory and barrier tissues, and to its great efflux capacity, it can play a crucial role in limiting the absorption and distribution of harmful xenobiotics, by decreasing their intracellular accumulation. Such a defense mechanism can be of particular relevance at the intestinal level, by significantly reducing the intestinal absorption of the xenobiotic and, consequently, avoiding its access to the target organs. In this review, the current knowledge on this important efflux pump is summarized, and a new focus is brought on the therapeutic interest of inducing and/or activating P-gp for limiting the toxicity caused by its substrates. Several in vivo and in vitro studies validating the use of such a therapeutic strategy are discussed. An extensive literature search for reported P-gp inducers/activators and for the experimental models used in their characterization was conducted. Those studies demonstrate that effective antidotal pathways can be achieved by efficiently promoting the P-gp-mediated efflux of deleterious xenobiotics, resulting in a significant reduction in their intracellular levels and, consequently, in a significant reduction of their toxicity.

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.

Substrate versus inhibitor dynamics of P-glycoprotein

By far the most studied multidrug resistance protein is P-glycoprotein. Despite recent structural data, key questions about its function remain. P-glycoprotein (P-gp) is flexible and undergoes large conformational changes as part of its function and in this respect, details not only of the export cycle, but also the recognition stage are currently lacking. Given the flexibility, molecular dynamics (MD) simulations provide an ideal tool to examine this aspect in detail. We have performed MD simulations to examine the behaviour of P-gp. In agreement with previous reports, we found that P-gp undergoes large conformational changes which tended to result in the nucleotide-binding domains coming closer together. In all simulations, the approach of the NBDs was asymmetrical in agreement with previous observations for other ABC transporter proteins. To validate the simulations, we make extensive comparison to previous cross-linking data. Our results show very good agreement with the available data. We then went on to compare the influence of inhibitor compounds bound with simulations of a substrate (daunorubicin) bound. Our results suggest that inhibitors may work by keeping the NBDs apart, thus preventing ATP-hydrolysis. On the other hand, repeat simulations of daunorubicin (substrate) in one particular binding pose suggest that the approach of the NBDs is not impaired and that the structure would be still be competent to perform ATP hydrolysis, thus providing a model for inhibition or substrate transport. Finally we compare the latter to earlier QSAR data to provide a model for the first part of substrate transport within P-gp.

Localization and substrate selectivity of sea urchin multidrug (MDR) efflux transporters

In this study, we cloned, expressed and functionally characterized Stronglycentrotus purpuratus (Sp) ATP-binding cassette (ABC) transporters. This screen identified three multidrug resistance (MDR) transporters with functional homology to the major types of MDR transporters found in humans. When overexpressed in embryos, the apical transporters Sp-ABCB1a, ABCB4a, and ABCG2a can account for as much as 87% of the observed efflux activity, providing a robust assay for their substrate selectivity. Using this assay, we found that sea urchin MDR transporters export canonical MDR susbtrates such as calcein-AM, bodipy-verapamil, bodipy-vinblastine, and mitoxantrone. In addition, we characterized the impact of nonconservative substitutions in the primary sequences of drug binding domains of sea urchin versus murine ABCB1 by mutation of Sp-ABCB1a and treatment of embryos with stereoisomeric cyclic peptide inhibitors (QZ59 compounds). The results indicated that two substitutions in transmembrane helix 6 reverse stereoselectivity of Sp-ABCB1a for QZ59 enantiomers compared with mouse ABCB1a. This suggests that subtle changes in the primary sequence of transporter drug binding domains could fine-tune substrate specificity through evolution.

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

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.

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

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.

A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields

P-glycoprotein (Pgp or ABCB1) is an ABC transporter protein involved in intestinal absorption, drug metabolism, and brain penetration, and its inhibition can seriously alter a drug's bioavailability and safety. In addition, inhibitors of Pgp can be used to overcome multidrug resistance. Given this dual purpose, reliable in silico procedures to predict Pgp inhibition are of great interest. A large and accurate literature collection yielded more than 1200 structures; a model was then constructed using various molecular interaction field-based technologies, considering pharmacophoric features and those physicochemical properties related to membrane partitioning. High accuracy was demonstrated internally with two different validation sets and, moreover, using a number of molecules, for which Pgp inhibition was not experimentally available but was evaluated in-house. All of the validations confirmed the robustness of the model and its suitability to help medicinal chemists in drug discovery. The information derived from the model was rationalized as a pharmacophore for competitive Pgp inhibition.