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

Consensus screening for a challenging target: the quest for P-glycoprotein inhibitors

ATP-binding cassette (ABC) transporters are a large family of proteins involved in membrane transport of a wide variety of substrates. Among them, ABCB1, also known as MDR-1 or P-glycoprotein (P-gp), is the most characterized. By exporting xenobiotics out of the cell, P-gp activity can affect the ADME properties of several drugs. Moreover, P-gp has been found to mediate multidrug resistance in cancer cells. Thus, the inhibition of P-gp activity may lead to increased absorption and/or intracellular accumulation of co-administered drugs, enhancing their effectiveness. Using the human-mouse chimeric cryoEM 3D structure of the P-gp in the inhibitor-bound intermediate form (PDBID: 6qee), approximately 200 000 commercially available natural compounds from the ZINC database were virtually screened. To build a model able to discriminate between substrate and inhibitors, two datasets of compounds with known activity, including P-gp inhibitors, substrates, and inactive molecules were also docked. The best docking pose of selected substrates and inhibitors were used to generate 3D common feature pharmacophoric models that were combined with the Autodock Vina binding energy values to prioritize compounds for visual inspection. With this consensus approach, 13 potential candidates were identified and then tested for their ability to inhibit P-gp, using zosuquidar, a third generation P-gp inhibitor, as a reference drug. Eight compounds were found to be active with 6 of them having an IC50 lower than 5 μM in a membrane-based ATPase activity assay. Moreover, the P-gp inhibitory activity was also confirmed by two different cell-based in vitro methods. Both retrospective and prospective results demonstrate the ability of the combined structure-based pharmacophore modeling and docking-based virtual screening approach to predict novel hit compounds with inhibitory activity toward P-gp. The resulting chemical scaffolds could serve as inspiration for the optimization of novel and more potent P-gp inhibitors.

Regulation of P-Glycoprotein during Oxidative Stress

P-glycoprotein (Pgp, ABCB1, MDR1) is an efflux transporter protein that removes molecules from the cells (outflow) into the extracellular space. Pgp plays an important role in pharmacokinetics, ensuring the absorption, distribution, and excretion of drugs and its substrates, as well as in the transport of endogenous molecules (steroid and thyroid hormones). It also contributes to tumor cell resistance to chemotherapy. In this review, we summarize the mechanisms of Pgp regulation during oxidative stress. The currently available data suggest that Pgp has a complex variety of regulatory mechanisms under oxidative stress, involving many transcription factors, the main ones being Nrf2 and Nf-kB. These factors often overlap, and some can be activated under certain conditions, such as the deposition of oxidation products, depending on the severity of oxidative stress. In most cases, the expression of Pgp increases due to increased transcription and translation, but under severe oxidative stress, it can also decrease due to the oxidation of amino acids in its molecule. At the same time, Pgp acts as a protector against oxidative stress, eliminating the causative factors and removing its by-products, as well as participating in signaling pathways.

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.

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.

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

Drug repurposing of propafenone to discover novel anti-tumor agents by impairing homologous recombination to delay DNA damage recovery of rare disease conjunctival melanoma

Conjunctival melanoma (CM), a rare and fatal malignant ocular tumor, lacks proper diagnostic biomarkers and therapy. Herein, we revealed the novel application of propafenone, an FDA-approved antiarrhythmic medication, which was identified effective in inhibiting CM cells viability and homologous recombination pathway. Detailed structure-activity relationships generated D34 as one of the most promising derivatives, which strongly suppressed the proliferation, viability, and migration of CM cells at submicromolar concentrations. Mechanically, D34 had the potential to increase γ-H2AX nuclear foci and aggravated DNA damage by suppressing homologous recombination pathway and its factors, particularly the complex of MRE11-RAD50-NBS1. D34 bound to human recombinant MRE11 protein and inhibited its endonuclease activity. Moreover, D34 dihydrochloride significantly suppressed tumor growth in the CRMM1 NCG xenograft model without obvious toxicity. Our finding shows that propafenone derivatives modulating the MRE11-RAD50-NBS1 complex will most likely provide an approach for CM targeted therapy, especially for improving chemo- and radio-sensitivity for CM patients.

QSAR modeling and molecular docking studies of 2-oxo-1, 2-dihydroquinoline-4- carboxylic acid derivatives as p-glycoprotein inhibitors for combating cancer multidrug resistance

Multidrug resistance (MDR) proteins related to the ATP-binding cassette family are found in a very wide range of human tumors and result in therapeutic failure. The overexpression of efflux pumps such as ABCB1 is one of the mechanisms of MDR. This paper aims to develop a reliable quantitative structure-activity relationship (QSAR) model that best describes the correlation between the activity and the molecular structures in order to predict the inhibitory biological activity towards ABCB1. In this regard, a series of quinoline derivatives of 18 compounds were analyzed using different linear and non-linear machine learning (ML) regression methods including k-nearest neighbors (KNN), decision tree (DT), back propagation neural networks (BPNN) and gradient boosting-based (GB) methods. Their aim is to explain the origin of the activity of these investigated compounds and therefore, design new quinoline derivatives with higher effect on ABCB1. A total of 16 ML predictive models were developed on different number of 2D and 3D descriptors and were evaluated using the coefficient of determination (R²) and the root mean squared error (RMSE) statistical metrics. Among all developed models, A GB-based model in particular catboost achieved the highest predictive quality, with one descriptor, expressed by R² and RMSE of 95% and 0.283 respectively. Molecular docking studies against the target crystal structure of the outward-facing p-glycoprotein (6C0V) revealed significant binding affinities via both hydrophobic and H-bond interactions with the relevant compounds. The 17 has shown the highest binding energy of -9.22 kcal/mol. Therefore, it can suggest that 17 may prove to be a valuable potential lead structure for the design and synthesis of more potent P-glycoprotein inhibitors for combination used with anti-cancer drugs for cancer multidrug resistance management.

Ensemble of structure and ligand-based classification models for hERG liability profiling

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

P-glycoprotein: new insights into structure, physiological function, regulation and alterations in disease

The multidrug resistance phenomenon presents a major threat to the pharmaceutical industry. This resistance is a common occurrence in several diseases and is mediated by multidrug transporters that actively pump substances out of the cell and away from their target regions. The most well-known multidrug transporter is the P-glycoprotein transporter. The binding sites within P-glycoprotein can accommodate a variety of compounds with diverse structures. Hence, numerous drugs are P-glycoprotein substrates, with new ones being identified every day. For many years, the mechanisms of action of P-glycoprotein have been shrouded in mystery, and scientists have only recently been able to elucidate certain structural and functional aspects of this protein. Although P-glycoprotein is highly implicated in multidrug resistant diseases, this transporter also performs various physiological roles in the human body and is expressed in several tissues, including the brain, kidneys, liver, gastrointestinal tract, testis, and placenta. The expression levels of P-glycoprotein are regulated by different enzymes, inflammatory mediators and transcription factors; alterations in which can result in the generation of a disease phenotype. This review details the discovery, the recently proposed structure and the regulatory functions of P-glycoprotein, as well as the crucial role it plays in health and disease.

Extended-ensemble docking to probe dynamic variation of ligand binding sites during large-scale structural changes of proteins

Proteins can sample a broad landscape as they undergo conformational transition between different functional states. At the same time, as key players in almost all cellular processes, proteins are important drug targets. Considering the different conformational states of a protein is therefore central for a successful drug-design strategy. Here we introduce a novel docking protocol, termed extended-ensemble docking, pertaining to proteins that undergo large-scale (global) conformational changes during their function. In its application to multidrug ABC-transporter P-glycoprotein (Pgp), extensive non-equilibrium molecular dynamics simulations employing system-specific collective variables are first used to describe the transition cycle of the transporter. An extended set of conformations (extended ensemble) representing the full transition cycle between the inward- and the outward-facing states is then used to seed high-throughput docking calculations of known substrates, non-substrates, and modulators of the transporter. Large differences are predicted in the binding affinities to different conformations, with compounds showing stronger binding affinities to intermediate conformations compared to the starting crystal structure. Hierarchical clustering of the binding modes shows all ligands preferably bind to the large central cavity of the protein, formed at the apex of the transmembrane domain (TMD), whereas only small binding populations are observed in the previously described R and H sites present within the individual TMD leaflets. Based on the results, the central cavity is further divided into two major subsites, first preferably binding smaller substrates and high-affinity inhibitors, whereas the second one shows preference for larger substrates and low-affinity modulators. These central subsites along with the low-affinity interaction sites present within the individual TMD leaflets may respectively correspond to the proposed high- and low-affinity binding sites in Pgp. We propose further an optimization strategy for developing more potent inhibitors of Pgp, based on increasing its specificity to the extended ensemble of the protein, instead of using a single protein structure, as well as its selectivity for the high-affinity binding site. In contrast to earlier in silico studies using single static structures of Pgp, our results show better agreement with experimental studies, pointing to the importance of incorporating the global conformational flexibility of proteins in future drug-discovery endeavors.

Triterpenoids with modified A-ring as modulators of P-gp-dependent drug-resistance in cancer cells

Semi-synthetic A-cycle modified triterpenic derivatives with A-cycle condensed with a heterocyclic fragment (compound 1) and fragmented A-ring (compound 2) were tested for cytotoxicity against several tumor cell cultures and doxorubicin (Dox)-resistant cell lines. The equal cytotoxicity of the tested compounds to the parental tumor cell lines (HBL-100, K562) and their resistant subclones (HBL-100/Dox, K562/i-S9) was revealed. The overexpression of ABCB1 (MDR1) gene and P-glycoprotein (P-gp) was confirmed for both resistant subclones of tumor cells. Compounds 1 and 2 were shown to inhibit the ABC-transporter gene expression (MDR1, MRP, MVP, and BCRP) and the transport of well-known P-gp substrate Rhodamine 123 from resistant cells. The docking of triterpenoids 1 and 2 into the drug binding site of P-gp revealed a similarity between the conformation of the tested triterpenoids and that of classical inhibitor verapamil, thus assuming these compounds to be more likely the inhibitors than the substrates of P-gp. Any tested triterpenic derivatives, when combined at non-toxic concentrations with doxorubicin, improved cytotoxic effect of the therapeutic drug against resistant subclones of tumor cells.

Prediction of potential drug interactions between repurposed COVID-19 and antitubercular drugs: an integrational approach of drug information software and computational techniques data

Introduction:

Tuberculosis is a major respiratory disease globally with a higher prevalence in Asian and African countries than rest of the world. With a larger population of tuberculosis patients anticipated to be co-infected with COVID-19 infection, an ongoing pandemic, identifying, preventing and managing drug–drug interactions is inevitable for maximizing patient benefits for the current repurposed COVID-19 and antitubercular drugs.

Methods:

We assessed the potential drug–drug interactions between repurposed COVID-19 drugs and antitubercular drugs using the drug interaction checker of IBM Micromedex®. Extensive computational studies were performed at a molecular level to validate and understand the drug–drug interactions found from the Micromedex drug interaction checker database at a molecular level. The integrated knowledge derived from Micromedex and computational data was collated and curated for predicting potential drug–drug interactions between repurposed COVID-19 and antitubercular drugs.

Results:

A total of 91 potential drug–drug interactions along with their severity and level of documentation were identified from Micromedex between repurposed COVID-19 drugs and antitubercular drugs. We identified 47 pharmacodynamic, 42 pharmacokinetic and 2 unknown DDIs. The majority of our molecular modelling results were in line with drug–drug interaction data obtained from the drug information software. QT prolongation was identified as the most common type of pharmacodynamic drug–drug interaction, whereas drug–drug interactions associated with cytochrome P450 3A4 (CYP3A4) and P-glycoprotein (P-gp) inhibition and induction were identified as the frequent pharmacokinetic drug–drug interactions. The results suggest antitubercular drugs, particularly rifampin and second-line agents, warrant high alert and monitoring while prescribing with the repurposed COVID-19 drugs.

Conclusion:

Predicting these potential drug–drug interactions, particularly related to CYP3A4, P-gp and the human Ether-à-go-go-Related Gene proteins, could be used in clinical settings for screening and management of drug–drug interactions for delivering safer chemotherapeutic tuberculosis and COVID-19 care. The current study provides an initial propulsion for further well-designed pharmacokinetic-pharmacodynamic-based drug–drug interaction studies.

Plain Language Summary

Artificial intelligence in drug design: algorithms, applications, challenges and ethics

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

Scaffold fragmentation and substructure hopping reveal potential, robustness, and limits of computer-aided pattern analysis (C@PA)

Computer-aided pattern analysis (C@PA) was recently presented as a powerful tool to predict multitarget ABC transporter inhibitors. The backbone of this computational methodology was the statistical analysis of frequently occurring molecular features amongst a fixed set of reported small-molecules that had been evaluated toward ABCB1, ABCC1, and ABCG2. As a result, negative and positive patterns were elucidated, and secondary positive substructures could be suggested that complemented the multitarget fingerprints. Elevating C@PA to a non-statistical and exploratory level, the concluded secondary positive patterns were extended with potential positive substructures to improve C@PA's prediction capabilities and to explore its robustness. A small-set compound library of known ABCC1 inhibitors with a known hit rate for triple ABCB1, ABCC1, and ABCG2 inhibition was taken to virtually screen for the extended positive patterns. In total, 846 potential broad-spectrum ABCB1, ABCC1, and ABCG2 inhibitors resulted, from which 10 have been purchased and biologically evaluated. Our approach revealed 4 novel multitarget ABCB1, ABCC1, and ABCG2 inhibitors with a biological hit rate of 40%, but with a slightly lower inhibitory power than derived from the original C@PA. This is the very first report about discovering novel broad-spectrum inhibitors against the most prominent ABC transporters by improving C@PA.

Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists

The α2a adrenoceptor is a medically relevant subtype of the G protein-coupled receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads for this receptor have been largely unsuccessful because of the complex pharmacology of adrenergic receptors. As such, cutting-edge in silico ligand- and structure-based assessment and de novo deep learning methods are well positioned to provide new insights into protein-ligand interactions and potential active compounds. In this work, we (i) collect a dataset of α2a adrenoceptor agonists and provide it as a resource for the drug design community; (ii) use the dataset as a basis to generate candidate-active structures via deep learning; and (iii) apply computational ligand- and structure-based analysis techniques to gain new insights into α2a adrenoceptor agonists and assess the quality of the computer-generated compounds. We further describe how such assessment techniques can be applied to putative chemical probes with a case study involving proposed medetomidine-based probes.

Identification of novel inhibitors of the ABC transporter BmrA

The resistance of microbes to commonly used antibiotics has become a worldwide health problem. A major underlying mechanism of microbial antibiotic resistance is the export of drugs from bacterial cells. Drug efflux is mediated through the action of multidrug resistance efflux pumps located in the bacterial cell membranes. The critical role of bacterial efflux pumps in antibiotic resistance has directed research efforts to the identification of novel efflux pump inhibitors that can be used alongside antibiotics in clinical settings. Here, we aimed to find potential inhibitors of the archetypical ATP-binding cassette (ABC) efflux pump BmrA of Bacillus subtilis via virtual screening of the Mu.Ta.Lig. Chemotheca small molecule library. Molecular docking calculations targeting the nucleotide-binding domain of BmrA were performed using AutoDock Vina. Following a further drug-likeness filtering step based on Lipinski's Rule of Five, top 25 scorers were identified. These ligands were then clustered into separate groups based on their contact patterns with the BmrA nucleotide-binding domain. Six ligands with distinct contact patterns were used for further in vitro inhibition assays based on intracellular ethidium bromide accumulation. Using this methodology, we identified two novel inhibitors of BmrA from the Chemotheca small molecule library.

ABCB transporters in a leaf beetle respond to sequestered plant toxins

Phytophagous insects can tolerate and detoxify toxic compounds present in their host plants and have evolved intricate adaptations to this end. Some insects even sequester the toxins for their defence. This necessitates specific mechanisms, especially carrier proteins that regulate uptake and transport to specific storage sites or protect sensitive tissues from noxious compounds. We identified three ATP-binding cassette subfamily B (ABCB) transporters from the transcriptome of the cardenolide-sequestering leaf beetle Chrysochus auratus and analysed their functional role in the sequestration process. These were heterologously expressed and tested for their ability to interact with various potential substrates: verapamil (standard ABCB substrate), the cardenolides digoxin (commonly used), cymarin (present in the species's host plant) and calotropin (present in the ancestral host plants). Verapamil stimulated all three ABCBs and each was activated by at least one cardenolide, however, they differed as to which they were activated by. While the expression of the most versatile transporter fits with a protective role in the blood-brain barrier, the one specific for cymarin shows an extreme abundance in the elytra, coinciding with the location of the defensive glands. Our data thus suggest a key role of ABCBs in the transport network needed for cardenolide sequestration.

Semisynthetic quercetin-quinone mitigates BV-2 microglia activation through modulation of Nrf2 pathway

During brain ageing, microglia, the resident immune cells of the CNS, are immunologically activated and contribute to neuroinflammation, a vicious cycle that supports development of neurological disorders. Therapeutic approaches focus mainly on downregulation of their pro-inflammatory activated state that is associated with health benefits. Electrophilic compounds, such as natural quinones and their reduced pro-electrophilic precursors, flavonoids, represent a wide group of diverse substances with important biological effects. They can cause considerable cytotoxicity when used at higher dosages, but on the other hand, they have versatile health benefits at lower dosages. In this study, we investigated the cytotoxicity and prooxidant profile of synthetic conjugate of two electrophilic compounds, quercetin and 1,4-naphthoquinone, 4'-O-(2-chloro-1,4-naphthoquinone-3-yloxy) quercetin (CHNQ), and its attenuation of inflammatory responses and modulation of Nrf2 pathway in BV-2 microglial cells. CHNQ showed higher cytotoxicity than its precursors, accompanied by promotion of production of reactive oxygen species along with G2/M cell cycle arrest at higher concentrations tested. Nevertheless, at a lower non-toxic concentration, CHNQ, more significantly than did its precursors, downregulated LPS-stimulated microglia cells as documented by decreased iNOS, COX-2 and TNFα protein levels. Moreover, CHNQ most effectively upregulated expression of phase II antioxidant enzyme HO-1 and β5 subunit of constitutive proteasome. The enhanced anti-inflammatory effect of CHNQ was accompanied by prominent increase in cytosolic expression of Nrf2 and c-Jun, however, induction effect on nuclear Nrf2 translocation was comparable to QUER. Moreover, a conditioned medium from activated BV-2 cells co-treated with quercetin and CHNQ maintained viability of neuron-like PC12 cells. The compounds tested did not show any disturbance of phagocytosis of live or dead PC12 cells. The present experimental data predict a preventive and therapeutic potential of semisynthetic derivative CHNQ in ageing and related pathologies, mediated by activation of proteins of the antioxidant response.

Search for ABCB1 Modulators Among 2-Amine-5-Arylideneimidazolones as a New Perspective to Overcome Cancer Multidrug Resistance

Multidrug resistance (MDR) is a severe problem in the treatment of cancer with overexpression of glycoprotein P (Pgp, ABCB1) as a reason for chemotherapy failure. A series of 14 novel 5-arylideneimidazolone derivatives containing the morpholine moiety, with respect to two different topologies (groups A and B), were designed and obtained in a three- or four-step synthesis, involving the Dimroth rearrangement. The new compounds were tested for their inhibition of the ABCB1 efflux pump in both sensitive (parental (PAR)) and ABCB1-overexpressing (MDR) T-lymphoma cancer cells in a rhodamine 123 accumulation assay. Their cytotoxic and antiproliferative effects were investigated by a thiazolyl blue tetrazolium bromide (MTT) assay. For active compounds, an insight into the mechanisms of action using either the luminescent Pgp-Glo™ Assay in vitro or docking studies to human Pgp was performed. The safety profile in vitro was examined. Structure–activity relationship (SAR) analysis was discussed. The most active compounds, representing both 2-substituted- (11) and Dimroth-rearranged 3-substituted (18) imidazolone topologies, displayed 1.38–1.46 fold stronger efflux pump inhibiting effects than reference verapamil and were significantly safer than doxorubicin in cell-based toxicity assays in the HEK-293 cell line. Results of mechanistic studies indicate that active imidazolones are substrates with increasing Pgp ATPase activity, and their dye-efflux inhibition via competitive action on the Pgp verapamil binding site was predicted in silico.

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

Considerations for Determining Direct Versus Indirect Functional Effects of Solubilizing Excipients on Drug Transporters for Enhancing Bioavailability

Excipients used in drug formulations at clinically safe levels have been considered to be pharmacologically inert; however, numerous studies have suggested that many solubilizing agents may modulate drug transporter activities and intestinal absorption. Here, the reported interactions between various solubilizing excipients and drug transporters are evaluated to consider various potential underlying mechanisms. This forms the basis for debate in the field in regard to whether or not the effects are based on "direct" interactions or "indirect" consequences arising from the role of the excipients. For example, an increase in apparent drug solubility can give rise to saturation of transporters according to Michaelis-Menten kinetics. This is also drawing the attention of regulatory agencies as they seek to understand the role of formulation additives. The continued application of excipients as a tool in solubility enhancement is crucial in the drug development process, creating a need for additional data to verify the proposed mechanism behind these changes. A literature review is provided here with some guidance on other factors that should be considered to delineate the effects that arise from direct physiological interactions or indirect effects. The results of such studies may aid the rational design of bioavailability-enhancing formulations.

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

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

PgpRules: a decision tree based prediction server for P-glycoprotein substrates and inhibitors

Summary

P-glycoprotein (P-gp) is a member of ABC transporter family that actively pumps xenobiotics out of cells to protect organisms from toxic compounds. P-gp substrates can be easily pumped out of the cells to reduce their absorption; conversely P-gp inhibitors can reduce such pumping activity. Hence, it is crucial to know if a drug is a P-gp substrate or inhibitor in view of pharmacokinetics. Here we present PgpRules, an online P-gp substrate and P-gp inhibitor prediction server with ruled-sets. The two models were built using classification and regression tree algorithm. For each compound uploaded, PgpRules not only predicts whether the compound is a P-gp substrate or a P-gp inhibitor, but also provides the rules containing chemical structural features for further structural optimization.

Availability and implementation

PgpRules is freely accessible at https://pgprules.cmdm.tw/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Discovery of a non-toxic [1,2,4]triazolo[1,5-a]pyrimidin-7-one (WS-10) that modulates ABCB1-mediated multidrug resistance (MDR)

Multidrug resistance (MDR) has been shown to reduce the effectiveness of chemotherapy. Strategies to overcoming MDR have been widely explored in the last decades, leading to a generation of numerous small molecules targeting ABC and MRP transporters. Among the ABC family, ABCB1 plays key roles in the development of drug resistance and is the most well studied. In this work, we report the discovery of non-toxic [1,2,4]triazolo[1,5-a]pyrimidin-7-one (WS-10) from our structurally diverse in-house compound collection that selectively modulates ABCB1-mediated multidrug resistance. WS-10 enhanced the intracellular accumulation of paclitaxel in SW620/Ad300 cells, but did not affect the expression of ABCB1 Protein and ABCB1 localization. The cellular thermal shift assay (CETSA) showed that WS-10 was able to bind to ABCB1, which could be responsible for the reversal effect of WS-10 toward paclitaxel and doxorubicin in SW620/Ad300 cells. Docking simulations were performed to show the possible binding modes of WS-10 within ABCB1 transporter. To conclude, WS-10 could be used as a template for designing new ABCB1 modulators to overcome ABCB1-mediated multidrug resistance.

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

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

Structural and functional aspects of P-glycoprotein and its inhibitors

P-glycoprotein (P-gp) is a member of ATP-binding cassette (ABC) superfamily which extrudes chemotherapeutic agents out of the cell. Suppression of this efflux activity has been the subject of numerous attempts to develop P-gp inhibitors. The aim of this review is to present up-to-date information on the structural and functional aspects of P-gp and its known inhibitors. The data presented also provide some information on drug discovery approaches for candidate P-gp inhibitors. Nucleotide-binding domains (NBDs) and drug-binding domains (DBDs) have been extensively studied to gain more information about P-gp inhibition and it looks that the ATPase activity of this pump has been the most attractive target for designing inhibitors. Hydrophobic and π-π (aromatic) interactions between P-gp binding domains and inhibitors are dominant intermolecular forces that have been reported in many studies using different methods. Many synthetic and natural products have been found to possess inhibitory or modulatory effects on drug transporter proteins. Log P value is an important factor in studying these inhibitors and has a crucial role on absorption, distribution, metabolism, and excretion (ADME) properties of candidate P-gp inhibitors.

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.

Unearthing new genomic markers of drug response by improved measurement of discriminative power

Background

Oncology drugs are only effective in a small proportion of cancer patients. Our current ability to identify these responsive patients before treatment is still poor in most cases. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs. Screening these drugs against a large panel of cancer cell lines has led to the discovery of new genomic markers of in vitro drug response. However, while the identification of such markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking.

Methods

Here we present a new non-parametric method to measuring the discriminative power of a drug-gene association. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers.

Results

The application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium.

Conclusions

Discovering markers using more than one statistical test and testing them on independent data is unusual. We found this helpful to discard statistically significant drug-gene associations that were actually spurious correlations. This approach also revealed new, independently validated, in vitro markers of drug response such as Temsirolimus-CDKN2A (resistance) and Gemcitabine-EWS_FLI1 (sensitivity).

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0336-z) contains supplementary material, which is available to authorized users.

A novel adaptive ensemble classification framework for ADME prediction

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

Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function

In recent years, an epidemic of the highly pathogenic avian influenza H7N9 virus has persisted in China, with a high mortality rate. To develop novel anti-influenza therapies, we have constructed a machine-learning-based scoring function (RF-NA-Score) for the effective virtual screening of lead compounds targeting the viral neuraminidase (NA) protein. RF-NA-Score is more accurate than RF-Score, with a root-mean-square error of 1.46, Pearson’s correlation coefficient of 0.707, and Spearman’s rank correlation coefficient of 0.707 in a 5-fold cross-validation study. The performance of RF-NA-Score in a docking-based virtual screening of NA inhibitors was evaluated with a dataset containing 281 NA inhibitors and 322 noninhibitors. Compared with other docking–rescoring virtual screening strategies, rescoring with RF-NA-Score significantly improved the efficiency of virtual screening, and a strategy that averaged the scores given by RF-NA-Score, based on the binding conformations predicted with AutoDock, AutoDock Vina, and LeDock, was shown to be the best strategy. This strategy was then applied to the virtual screening of NA inhibitors in the SPECS database. The 100 selected compounds were tested in an in vitro H7N9 NA inhibition assay, and two compounds with novel scaffolds showed moderate inhibitory activities. These results indicate that RF-NA-Score improves the efficiency of virtual screening for NA inhibitors, and can be used successfully to identify new NA inhibitor scaffolds. Scoring functions specific for other drug targets could also be established with the same method.

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.

How Open Data Shapes In Silico Transporter Modeling

Chemical compound bioactivity and related data are nowadays easily available from open data sources and the open medicinal chemistry literature for many transmembrane proteins. Computational ligand-based modeling of transporters has therefore experienced a shift from local (quantitative) models to more global, qualitative, predictive models. As the size and heterogeneity of the data set rises, careful data curation becomes even more important. This includes, for example, not only a tailored cutoff setting for the generation of binary classes, but also the proper assessment of the applicability domain. Powerful machine learning algorithms (such as multi-label classification) now allow the simultaneous prediction of multiple related targets. However, the more complex, the less interpretable these models will get. We emphasize that transmembrane transporters are very peculiar, some of which act as off-targets rather than as real drug targets. Thus, careful selection of the right modeling technique is important, as well as cautious interpretation of results. We hope that, as more and more data will become available, we will be able to ameliorate and specify our models, coming closer towards function elucidation and the development of safer medicine.

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.

In silico prediction of chronic toxicity with chemical category approaches

Chemical chronic toxicity, referring to the toxic effect of a chemical following long-term or repeated sub lethal exposures, is an important toxicological end point in drug design and environmental risk assessment.

Prediction of luciferase inhibitors by the high-performance MIEC-GBDT approach based on interaction energetic patterns

The MIEC-GBDT model can be used as a powerful tool to identify potential interference compounds in luciferase-based high-throughput screening.

Using the Variable-Nearest Neighbor Method To Identify P-Glycoprotein Substrates and Inhibitors

Permeability glycoprotein (Pgp) is an essential membrane-bound transporter that efficiently extracts compounds from a cell. As such, it is a critical determinant of the pharmacokinetic properties of drugs. Multidrug resistance in cancer is often associated with overexpression of Pgp, which increases the efflux of chemotherapeutic agents from the cell. This, in turn, may prevent an effective treatment by reducing the effective intracellular concentrations of such agents. Consequently, identifying compounds that can either be transported out of the cell by Pgp (substrates) or impair Pgp function (inhibitors) is of great interest. Herein, using publically available data, we developed quantitative structure-activity relationship (QSAR) models of Pgp substrates and inhibitors. These models employed a variable-nearest neighbor (v-NN) method that calculated the structural similarity between molecules and hence possessed an applicability domain, that is, they used all nearest neighbors that met a minimum similarity constraint. The performance characteristics of these v-NN-based models were comparable or at times superior to those of other model constructs. The best v-NN models for identifying either Pgp substrates or inhibitors showed overall accuracies of >80% and κ values of >0.60 when tested on external data sets with candidate Pgp substrates and inhibitors. The v-NN prediction model with a well-defined applicability domain gave accurate and reliable results. The v-NN method is computationally efficient and requires no retraining of the prediction model when new assay information becomes available-an important feature when keeping QSAR models up-to-date and maintaining their performance at high levels.

Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA

The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acctrain-diverse = 84%; accinternal-test = 92% and accexternal-test = 100%). For ligand promiscuity between P-gp and NorA, perceptual maps and pharmacophore models were generated for the detection of rules and features. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening in an attempt to restore drug sensitivity in cancer cells and bacteria.

Structural and dynamic perspectives on the promiscuous transport activity of P-glycoprotein

The multidrug transporter P-glycoprotein (P-gp) is expressed in the blood-brain barrier endothelium where it effluxes a range of drug substrates, preventing their accumulation within the brain. P-gp has been studied extensively for 40 years because of its crucial role in the absorption, distribution, metabolism and elimination of a range of pharmaceutical compounds. Despite this, many aspects of the structure-function mechanism of P-gp are unresolved. Here we review the emerging role of molecular dynamics simulation techniques in our understanding of the membrane-embedded conformation of P-gp. We discuss its conformational plasticity in the presence and absence of ATP, and recent efforts to characterize the drug binding sites and uptake pathways.