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

Exploring the potential of P-glycoprotein inhibitors in the targeted delivery of anti-cancer drugs: A comprehensive review

Due to the high prevalence of cancer, progress in the management of cancer is the need of the hour. Most cancer patients develop chemotherapeutic drug resistance, and many remain insidious due to overexpression of Multidrug Resistance Protein 1 (MDR1), also known as Permeability-glycoprotein (P-gp) or ABCB1 transporter (ATP-binding cassette subfamily B member 1). P-gp, a transmembrane protein that protects vital organs from outside chemicals, expels medications from malignant cells. The blood-brain barrier (BBB), gastrointestinal tract (GIT), kidneys, liver, pancreas, and cancer cells overexpress P-gp on their apical surfaces, making treatment inefficient and resistant. Compounds that compete with anticancer medicines for transportation or directly inhibit P-gp may overcome biological barriers. Developing nanotechnology-based formulations may help overcome P-gp-mediated efflux and improve bioavailability and cell chemotherapeutic agent accumulation. Nanocarriers transport pharmaceuticals via receptor-mediated endocytosis, unlike passive diffusion, which bypasses ABCB1. Anticancer drugs and P-gp inhibitors in nanocarriers may synergistically increase drug accumulation and chemotherapeutic agent toxicity. The projection of desirable binding and effect may be procured initially by molecular docking of the inhibitor with P-gp, enabling the reduction of preliminary trials in formulation development. Here, P-gp-mediated efflux and several possible outcomes to overcome the problems associated with currently prevalent cancer treatments are highlighted.

Recent developments of P-glycoprotein inhibitors and its structure-activity relationship (SAR) studies

P-glycoprotein (P-gp) over-expression is a key factor in multi-drug resistance (MDR), which is a major factor in the failure of cancer treatment. P-gp inhibitors have been demonstrated to have powerful pharmacological properties and may be used as a therapeutic approach to overcome the MDR in cancer cells. Combining clinical investigations with biochemical and computational research may potentially lead to a clearer understanding of the pharmacological properties and the mechanisms of action of these P-gp inhibitors. The task of turning these discoveries into effective therapeutic candidates for a variety of malignancies, including resistant and metastatic kinds, falls on medicinal chemists. A variety of P-gp inhibitors with great potency, high selectivity, and minimal toxicity have been identified in recent years. The latest advances in drug design, characterization, structure-activity relationship (SAR) research, and modes of action of newly synthesized, powerful small molecules P-gp inhibitors over the previous ten years are highlighted in this review. P-gp transporter over-expression has been linked to MDR, therefore the development of P-gp inhibitors will expand our understanding of the processes and functions of P-gp-mediated drug efflux, which will be helpful for drug discovery and clinical cancer therapies.

Molecular Modeling Studies to Probe the Binding Hypothesis of Novel Lead Compounds against Multidrug Resistance Protein ABCB1

The expression of drug efflux pump ABCB1/P-glycoprotein (P-gp), a transmembrane protein belonging to the ATP-binding cassette superfamily, is a leading cause of multidrug resistance (MDR). We previously curated a dataset of structurally diverse and selective inhibitors of ABCB1 to develop a pharmacophore model that was used to identify four novel compounds, which we showed to be potent and efficacious inhibitors of ABCB1. Here, we dock the inhibitors into a model structure of the human transporter and use molecular dynamics (MD) simulations to report the conformational dynamics of human ABCB1 induced by the binding of the inhibitors. The binding hypotheses are compared to the wider curated dataset and those previously reported in the literature. Protein-ligand interactions and MD simulations are in good agreement and, combined with LipE profiling, statistical and pharmacokinetic analyses, are indicative of potent and selective inhibition of ABCB1.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.

Comparative study on ABCB1-dependent efflux of anthracyclines and their metabolites: consequences for cancer resistance

1. ABCB1 (P-glycoprotein, MDR1) is one of the most important transporter involved in cancer multi-drug resistance. It also plays a significant role in cancer resistance against anthracyclines, an anticancer group of drugs, including doxorubicin and daunorubicin. Several intracellular enzymes metabolise anthracyclines to carbonyl-reduced, hydroxy metabolites, which have impaired cytotoxic properties. However, metabolite efflux by ABCB1 transporter is not well characterised, while it may be the mechanism responsible for the metabolites' lack of activity.2. In this study recombinant ABCB1 ATPase transporter assay; anthracyclines accumulation assay in resistant cells overexpressing ABCB1; and molecular modelling were used to investigate anthracyclines: doxorubicin and daunorubicin and their carbonyl-reduced metabolites (doxorubicinol, daunorubicinol) susceptibility for ABCB1-dependent efflux.3. Based on the kinetics parameters of ATPase activity of ABCB1, it was found that daunorubicinol exerted an exceptionally high potential for being effluxed by the ABCB1 transporter. ABCB1 significantly affected the accumulation pattern of studied chemicals in resistant cancer cells. Doxorubicin and daunorubicinol accumulation were influenced by the activity of ABCB1 modulator - valspodar.4. Results indicate that ABCB1 activity affects not only anthracyclines but also their metabolites. Therefore crosstalk between the process of anthracyclines metabolism and metabolite efflux may be the mechanism of impairing anticancer properties of anthracyclines metabolites.

Perspectives on drug repurposing to overcome cancer multidrug resistance mediated by ABCB1 and ABCG2

The overexpression of the human ATP-binding cassette (ABC) transporters in cancer cells is a common mechanism involved in developing multidrug resistance (MDR). Unfortunately, there are currently no approved drugs specifically designed to treat multidrug-resistant cancers, making MDR a significant obstacle to successful chemotherapy. Despite over two decades of research, developing transporter-specific inhibitors for clinical use has proven to be a challenging endeavor. As an alternative approach, drug repurposing has gained traction as a more practical method to discover clinically effective modulators of drug transporters. This involves exploring new indications for already-approved drugs, bypassing the lengthy process of developing novel synthetic inhibitors. In this context, we will discuss the mechanisms of ABC drug transporters ABCB1 and ABCG2, their roles in cancer MDR, and the inhibitors that have been evaluated for their potential to reverse MDR mediated by these drug transporters. Our focus will be on providing an up-to-date report on approved drugs tested for their inhibitory activities against these drug efflux pumps. Lastly, we will explore the challenges and prospects of repurposing already approved medications for clinical use to overcome chemoresistance in patients with high tumor expression of ABCB1 and/or ABCG2.

The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity

The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy, and toxicity as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biologic membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiologic functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms. The data collected contributes toward safer and more effective pharmacological treatments. SIGNIFICANCE STATEMENT: The experimental structure of ATP-binding cassette and solute carrier transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms.

P-glycoprotein mechanical functional analysis using in silico molecular modeling: Pharmacokinetic variability according to ABCB1 c.2677G > T/A genetic polymorphisms

P-glycoprotein (P-gp) is a widely membrane-expressed multi-drug transporter. It is unclear whether the pharmacokinetic diversity of P-gp substrates is highly dependent on ABCB1 polymorphisms encoding P-gp. The purpose of this study is to analyze the mechanistic function of P-gp through in silico molecular modeling and to approach the resolution of controversy over pharmacokinetic differences according to ABCB1 polymorphisms. P-gp conformations of apo, ligand-docked, and outward-facing states can be modeled based on structural information of human P-gp. And polymorphic P-gp structures were constructed through homology modeling. ABCB1 c.2677G > T/A (Ala893Ser/Thr), did not correspond to P-gp's nucleotide-binding-domain (NBD) or drug-binding-pocket (DBP) or involve mechanical conformational changes. Although amino acid substitution by ABCB1 c.2677G > T/A caused a 30 % increased strain in an α-helix hinge between the NBD and DBP in P-gp's internal tunnel, there were no overall structural changes compared to wild-type. ABCB1 c.2677G > T/A may increase torsional energy, impacting conformational change rate, but this does not significantly affect P-gp's general functioning. Fexofenadine docking into P-gp's DBP explained the substrate interaction, but no effect by ABCB1 c.2677G > T/A was confirmed. Our findings provide additional insights useful in resolving the debate about the influence of ABCB1 polymorphisms on the interindividual pharmacokinetic variability of P-gp substrates.

Bioinformatic analysis of highly consumed phytochemicals as P-gp binders to overcome drug-resistance

Background and purpose

P-glycoprotein (P-gp) is an adenosine triphosphate (ATP)-dependent membrane efflux pump for protecting cells against xenobiotic compounds. Unfortunately, overexpressed P-gp in neoplastic cells prevents cell entry of numerous chemotherapeutic agents leading to multidrug resistance (MDR). MDR cells may be re-sensitized to chemotherapeutic drugs via P-gp inhibition/modulation. Side effects of synthetic P-gp inhibitors encouraged the development of natural products.

Experimental approach

Molecular docking and density functional theory (DFT) calculations were used as fast and accurate computational methods to explore a structure binding relationship of some dietary phytochemicals inside distinctive P-gp binding sites (modulatory/inhibitory). For this purpose, top-scored docked conformations were subjected to per-residue energy decomposition analysis in the B3LYP level of theory with a 6-31g (d, p) basis set by Gaussian98 package.

Findings/Results

Consecutive application of computational techniques revealed binding modes/affinities of nutritive phytochemicals within dominant binding sites of P-gp. Blind docking scores for best-ranked compounds were superior to verapamil and rhodamine-123. Pairwise amino acid decomposition of superior docked conformations revealed Tyr303 as an important P-gp binding residue. DFT-based induced polarization analysis revealed major electrostatic fluctuations at the atomistic level and confirmed larger effects for amino acids with energy-favored binding interactions. Conformational analysis exhibited that auraptene and 7,4',7'',4'''-tetra-O-methylamentoflavone might not necessarily interact to P-gp binding sites through minimum energy conformations.

Conclusion and implications

Although there are still many hurdles to overcome, obtained results may propose a few nutritive phytochemicals as potential P-gp binding agents. Moreover; top-scored derivatives may have the chance to exhibit tumor chemo-sensitizing effects.