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

Interaction of systemic drugs causing ocular toxicity with organic cation transporter: an artificial intelligence prediction

Chronic disease patients (cancer, arthritis, cardiovascular diseases) undergo long-term systemic drug treatment. Membrane transporters in ocular barriers could falsely recognize these drugs and allow their trafficking into the eye from systemic circulation. Hence, despite their pharmacological activity, these drugs accumulate and cause toxicity at the non-target site, such as the eye. Since around 40% of clinically used drugs are organic cation in nature, it is essential to understand the role of organic cation transporter (OCT1) in ocular barriers to facilitate the entry of systemic drugs into the eye. We applied machine learning techniques and computer simulation models (molecular dynamics and metadynamics) in the current study to predict the potential OCT1 substrates. Artificial intelligence models were developed using a training dataset of a known substrates and non-substrates of OCT1 and predicted the potential OCT1 substrates from various systemic drugs causing ocular toxicity. Computer simulation studies was performed by developing the OCT1 homology model. Molecular dynamic simulations equilibrated the docked protein-ligand complex. And metadynamics revealed the movement of substrates across the transporter with minimum free energy near the binding pocket. The machine learning model showed an accuracy of about 80% and predicted the potential substrates for OCT1 among systemic drugs causing ocular toxicity - not known earlier, such as cyclophosphamide, bupivacaine, bortezomib, sulphanilamide, tosufloxacin, topiramate, and many more. However, further invitro and invivo studies are required to confirm these predictions.Communicated by Ramaswamy H. Sarma.

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.

Study Models of Drug-Drug Interactions Involving P-Glycoprotein: The Potential Benefit of P-Glycoprotein Modulation at the Kidney and Intestinal Levels

P-glycoprotein (P-gp) is a crucial membrane transporter situated on the cell's apical surface, being responsible for eliminating xenobiotics and endobiotics. P-gp modulators are compounds that can directly or indirectly affect this protein, leading to changes in its expression and function. These modulators can act as inhibitors, inducers, or activators, potentially causing drug-drug interactions (DDIs). This comprehensive review explores diverse models and techniques used to assess drug-induced P-gp modulation. We cover several approaches, including in silico, in vitro, ex vivo, and in vivo methods, with their respective strengths and limitations. Additionally, we explore the therapeutic implications of DDIs involving P-gp, with a special focus on the renal and intestinal elimination of P-gp substrates. This involves enhancing the removal of toxic substances from proximal tubular epithelial cells into the urine or increasing the transport of compounds from enterocytes into the intestinal lumen, thereby facilitating their excretion in the feces. A better understanding of these interactions, and of the distinct techniques applied for their study, will be of utmost importance for optimizing drug therapy, consequently minimizing drug-induced adverse and toxic effects.

Molecular basis for selective uptake and elimination of organic anions in the kidney by OAT1

In mammals, the kidney plays an essential role in maintaining blood homeostasis through the selective uptake, retention or elimination of toxins, drugs and metabolites. Organic anion transporters (OATs) are responsible for the recognition of metabolites and toxins in the nephron and their eventual urinary excretion. Inhibition of OATs is used therapeutically to improve drug efficacy and reduce nephrotoxicity. The founding member of the renal organic anion transporter family, OAT1 (also known as SLC22A6), uses the export of α-ketoglutarate (α-KG), a key intermediate in the Krebs cycle, to drive selective transport and is allosterically regulated by intracellular chloride. However, the mechanisms linking metabolite cycling, drug transport and intracellular chloride remain obscure. Here, we present cryogenic-electron microscopy structures of OAT1 bound to α-KG, the antiviral tenofovir and clinical inhibitor probenecid, used in the treatment of Gout. Complementary in vivo cellular assays explain the molecular basis for α-KG driven drug elimination and the allosteric regulation of organic anion transport in the kidney by chloride.

Challenges in Permeability Assessment for Oral Drug Product Development

Drug permeation across the intestinal epithelium is a prerequisite for successful oral drug delivery. The increased interest in oral administration of peptides, as well as poorly soluble and poorly permeable compounds such as drugs for targeted protein degradation, have made permeability a key parameter in oral drug product development. This review describes the various in vitro, in silico and in vivo methodologies that are applied to determine drug permeability in the human gastrointestinal tract and identifies how they are applied in the different stages of drug development. The various methods used to predict, estimate or measure permeability values, ranging from in silico and in vitro methods all the way to studies in animals and humans, are discussed with regard to their advantages, limitations and applications. A special focus is put on novel techniques such as computational approaches, gut-on-chip models and human tissue-based models, where significant progress has been made in the last few years. In addition, the impact of permeability estimations on PK predictions in PBPK modeling, the degree to which excipients can affect drug permeability in clinical studies and the requirements for colonic drug absorption are addressed.

Structural and functional annotation of solute carrier transporters: implication for drug discovery

INTRODUCTION

Solute carriers (SLCs) represent the largest group of membrane transporters in the human genome. They play a central role in controlling the compartmentalization of metabolism and most of this superfamily is linked to human disease. Despite being in general considered druggable and attractive therapeutic targets, many SLCs remain poorly annotated, both functionally and structurally.

AREAS COVERED

The aim of this review is to provide an overview of functional and structural parameters of SLCs that play important roles in their druggability. To do this, the authors provide an overview of experimentally solved structures of human SLCs, with emphasis on structures solved in complex with chemical modulators. From the functional annotations, the authors focus on SLC localization and SLC substrate annotations.

EXPERT OPINION

Recent progress in the structural and functional annotations allows to refine the SLC druggability index. Particularly the increasing number of experimentally solved structures of SLCs provides insights into mode-of-action of a significant number of chemical modulators of SLCs.

Regulation of Human Endogenous Metabolites by Drug Transporters and Drug Metabolizing Enzymes: An Analysis of Targeted SNP-Metabolite Associations

Drug transporters and drug-metabolizing enzymes are primarily known for their role in the absorption, distribution, metabolism, and excretion (ADME) of small molecule drugs, but they also play a key role in handling endogenous metabolites. Recent cross-tissue co-expression network analyses have revealed a "Remote Sensing and Signaling Network" of multispecific, oligo-specific, and monospecific transporters and enzymes involved in endogenous metabolism. This includes many proteins from families involved in ADME (e.g., SLC22, SLCO, ABCC, CYP, UGT). Focusing on the gut-liver-kidney axis, we identified the endogenous metabolites potentially regulated by this network of ~1000 proteins by associating SNPs in these genes with the circulating levels of thousands of small, polar, bioactive metabolites, including free fatty acids, eicosanoids, bile acids, and other signaling metabolites that act in part via G-protein coupled receptors (GPCRs), nuclear receptors, and kinases. We identified 77 genomic loci associated with 7236 unique metabolites. This included metabolites that were associated with multiple, distinct loci, indicating coordinated regulation between multiple genes (including drug transporters and drug-metabolizing enzymes) of specific metabolites. We analyzed existing pharmacogenomic data and noted SNPs implicated in endogenous metabolite handling (e.g., rs4149056 in SLCO1B1) also affecting drug ADME. The overall results support the existence of close relationships, via interactions with signaling metabolites, between drug transporters and drug-metabolizing enzymes that are part of the Remote Sensing and Signaling Network, and with GPCRs and nuclear receptors. These analyses highlight the potential for drug-metabolite interactions at the interfaces of the Remote Sensing and Signaling Network and the ADME protein network.

Homology Modeling of Transporter Proteins

Membrane transporter proteins are divided into channels/pores and carriers and constitute protein families of physiological and pharmacological importance. Several presently used therapeutic compounds elucidate their effects by targeting membrane transporter proteins, including anti-arrhythmic, anesthetic, antidepressant, anxiolytic and diuretic drugs. The lack of three-dimensional structures of human transporters hampers experimental studies and drug discovery. In this chapter, the use of homology modeling for generating structural models of membrane transporter proteins is reviewed. The increasing number of atomic resolution structures available as templates, together with improvements in methods and algorithms for sequence alignments, secondary structure predictions, and model generation, in addition to the increase in computational power have increased the applicability of homology modeling for generating structural models of transporter proteins. Different pitfalls and hints for template selection, multiple-sequence alignments, generation and optimization, validation of the models, and the use of transporter homology models for structure-based virtual ligand screening are discussed.

Machine Learning Models Identify New Inhibitors for Human OATP1B1

The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [3H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug-drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.

DrugMAP: molecular atlas and pharma-information of all drugs

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

Transporters in Regulatory Science: Notable Contributions from Dr. Giacomini in the Past Two Decades

Transporters govern the access of molecules to cells or their exit from cells, thereby controlling the overall distribution of drugs to their intracellular site of action. Clinically relevant drug-drug interactions mediated by transporters are of increasing interest in drug development. Drug transporters, acting alone or in concert with drug metabolizing enzymes, can play an important role in modulating drug absorption, distribution, metabolism, and excretion, thus affecting the pharmacokinetics and/or pharmacodynamics of a drug. Dr. Kathy Giacomini from the University of California, San Francisco is one of the world leaders in transporters and pharmacogenetics with key contributions to transporter science. Her contributions to transporter science are noteworthy. This review paper will summarize Dr. Giacomini's key contributions and influence on transporters in regulatory science in the past two decades. Regulatory science research highlighted in this review covers various aspects of transporter science including understanding the effect of renal impairment on transporters, transporter ontogeny, biomarkers for transporters, and interactions of excipients with transporters affecting drug absorption. Significance Statement This review paper highlights Dr. Giacomini's key contributions and influence on transporters in regulatory science in the past two decades. She has been at the cutting edge of science pertaining to drug transport, drug disposition, and regulatory science, leading to new era of translational sciences pertaining to drug disposition and transporter biology. Her research has and will continue to bring enormous impact on gaining new knowledge in guiding drug development and inspire scientists from all sectors in the field.

Increased/Targeted Brain (Pro)Drug Delivery via Utilization of Solute Carriers (SLCs)

Membrane transporters have a crucial role in compounds’ brain drug delivery. They allow not only the penetration of a wide variety of different compounds to cross the endothelial cells of the blood–brain barrier (BBB), but also the accumulation of them into the brain parenchymal cells. Solute carriers (SLCs), with nearly 500 family members, are the largest group of membrane transporters. Unfortunately, not all SLCs are fully characterized and used in rational drug design. However, if the structural features for transporter interactions (binding and translocation) are known, a prodrug approach can be utilized to temporarily change the pharmacokinetics and brain delivery properties of almost any compound. In this review, main transporter subtypes that are participating in brain drug disposition or have been used to improve brain drug delivery across the BBB via the prodrug approach, are introduced. Moreover, the ability of selected transporters to be utilized in intrabrain drug delivery is discussed. Thus, this comprehensive review will give insights into the methods, such as computational drug design, that should be utilized more effectively to understand the detailed transport mechanisms. Moreover, factors, such as transporter expression modulation pathways in diseases that should be taken into account in rational (pro)drug development, are considered to achieve successful clinical applications in the future.

New and Emerging Research on Solute Carrier and ATP Binding Cassette Transporters in Drug Discovery and Development: Outlook from the International Transporter Consortium

Enabled by a plethora of new technologies, research in membrane transporters has exploded in the past decade. The goal of this state‐of‐the‐art article is to describe recent advances in research on membrane transporters that are particularly relevant to drug discovery and development. This review covers advances in basic, translational, and clinical research that has led to an increased understanding of membrane transporters at all levels. At the basic level, we describe the available crystal structures of membrane transporters in both the solute carrier (SLC) and ATP binding cassette superfamilies, which has been enabled by the development of cryogenic electron microscopy methods. Next, we describe new research on lysosomal and mitochondrial transporters as well as recently deorphaned transporters in the SLC superfamily. The translational section includes a summary of proteomic research, which has led to a quantitative understanding of transporter levels in various cell types and tissues and new methods to modulate transporter function, such as allosteric modulators and targeted protein degraders of transporters. The section ends with a review of the effect of the gut microbiome on modulation of transporter function followed by a presentation of 3D cell cultures, which may enable in vivo predictions of transporter function. In the clinical section, we describe new genomic and pharmacogenomic research, highlighting important polymorphisms in transporters that are clinically relevant to many drugs. Finally, we describe new clinical tools, which are becoming increasingly available to enable precision medicine, with the application of tissue‐derived small extracellular vesicles and real‐world biomarkers.

Pharmacoproteomics of Brain Barrier Transporters and Substrate Design for the Brain Targeted Drug Delivery

One of the major reasons why central nervous system (CNS)-drug development has been challenging in the past, is the barriers that prevent substances entering from the blood circulation into the brain. These barriers include the blood-brain barrier (BBB), blood-spinal cord barrier (BSCB), blood-cerebrospinal fluid barrier (BCSFB), and blood-arachnoid barrier (BAB), and they differ from each other in their transporter protein expression and function as well as among the species. The quantitative expression profiles of the transporters in the CNS-barriers have been recently revealed, and in this review, it is described how they affect the pharmacokinetics of compounds and how these expression differences can be taken into account in the prediction of brain drug disposition in humans, an approach called pharmacoproteomics. In recent years, also structural biology and computational resources have progressed remarkably, enabling a detailed understanding of the dynamic processes of transporters. Molecular dynamics simulations (MDS) are currently used commonly to reveal the conformational changes of the transporters and to find the interactions between the substrates and the protein during the binding, translocation in the transporter cavity, and release of the substrate on the other side of the membrane. The computational advancements have also aided in the rational design of transporter-utilizing compounds, including prodrugs that can be actively transported without losing potency towards the pharmacological target. In this review, the state-of-art of these approaches will be also discussed to give insights into the transporter-mediated drug delivery to the CNS.

Identification of Key Amino Acids that Impact Organic Solute Transporter α/β (OSTα/β)

Organic solute transporter α/β (OSTα/β) is a bidirectional bile acid transporter localized on the basolateral membrane of hepatic, intestinal, and renal epithelial cells. OSTα/β plays a critical role in intestinal bile acid reabsorption and is upregulated in hepatic diseases characterized by elevated bile acids, whereas genetic variants in SLC51A/B have been associated with clinical cholestasis. OSTα/β also transports and is inhibited by commonly used medications. However, there is currently no high-resolution structure of OSTα/β, and structure-function data for OSTα, the proposed substrate-binding subunit, are lacking. The present study addressed this knowledge gap and identified amino acids in OSTα that are important for bile acid transport. This was accomplished using computational modeling and site-directed mutagenesis of the OSTα subunit to generate OSTα/β mutant cell lines. Out of the 10 OSTα/β mutants investigated, four (S228K, T229S, Q269E, Q269K) exhibited decreased [3H]-taurocholate (TCA) uptake (ratio of geometric means relative to OSTα/β wild type (WT) of 0.76, 0.75, 0.79, and 0.13, respectively). Three OSTα/β mutants (S228K, Q269K, E305A) had reduced [3H]-TCA efflux % (ratio of geometric means relative to OSTα/β WT of 0.86, 0.65, and 0.79, respectively). Additionally, several OSTα/β mutants demonstrated altered expression and cellular localization when compared with OSTα/β WT. In summary, we identified OSTα residues (Ser228, Thr229, Gln269, Glu305) in predicted transmembrane domains that affect expression of OSTα/β and may influence OSTα/β-mediated bile acid transport. These data advance our understanding of OSTα/β structure/function and can inform future studies designed to gain further insight into OSTα/β structure or to identify additional OSTα/β substrates and inhibitors. SIGNIFICANCE STATEMENT: OSTα/β is a clinically important transporter involved in enterohepatic bile acid recycling with currently no high-resolution protein structure and limited structure-function data. This study identified four OSTα amino acids (Ser228, Thr229, Gln269, Glu305) that affect expression of OSTα/β and may influence OSTα/β-mediated bile acid transport. These data can be utilized to inform future investigation of OSTα/β structure and refine molecular modeling approaches to facilitate the identification of substrates and/or inhibitors of OSTα/β.

Molecular Properties of Drugs Handled by Kidney OATs and Liver OATPs Revealed by Chemoinformatics and Machine Learning: Implications for Kidney and Liver Disease

In patients with liver or kidney disease, it is especially important to consider the routes of metabolism and elimination of small-molecule pharmaceuticals. Once in the blood, numerous drugs are taken up by the liver for metabolism and/or biliary elimination, or by the kidney for renal elimination. Many common drugs are organic anions. The major liver uptake transporters for organic anion drugs are organic anion transporter polypeptides (OATP1B1 or SLCO1B1; OATP1B3 or SLCO1B3), whereas in the kidney they are organic anion transporters (OAT1 or SLC22A6; OAT3 or SLC22A8). Since these particular OATPs are overwhelmingly found in the liver but not the kidney, and these OATs are overwhelmingly found in the kidney but not liver, it is possible to use chemoinformatics, machine learning (ML) and deep learning to analyze liver OATP-transported drugs versus kidney OAT-transported drugs. Our analysis of >30 quantitative physicochemical properties of OATP- and OAT-interacting drugs revealed eight properties that in combination, indicate a high propensity for interaction with "liver" transporters versus "kidney" ones based on machine learning (e.g., random forest, k-nearest neighbors) and deep-learning classification algorithms. Liver OATPs preferred drugs with greater hydrophobicity, higher complexity, and more ringed structures whereas kidney OATs preferred more polar drugs with more carboxyl groups. The results provide a strong molecular basis for tissue-specific targeting strategies, understanding drug-drug interactions as well as drug-metabolite interactions, and suggest a strategy for how drugs with comparable efficacy might be chosen in chronic liver or kidney disease (CKD) to minimize toxicity.

Nicotinamide N-Methyltransferase: An Emerging Protagonist in Cancer Macro(r)evolution

Nicotinamide N-methyltransferase (NNMT) has progressed from being considered merely a Phase II metabolic enzyme to one with a central role in cell function and energy metabolism. Over the last three decades, a significant body of evidence has accumulated which clearly demonstrates a central role for NNMT in cancer survival, metastasis, and drug resistance. In this review, we discuss the evidence supporting a role for NNMT in the progression of the cancer phenotype and how it achieves this by driving the activity of pro-oncogenic NAD+-consuming enzymes. We also describe how increased NNMT activity supports the Warburg effect and how it promotes oncogenic changes in gene expression. We discuss the regulation of NNMT activity in cancer cells by both post-translational modification of the enzyme and transcription factor binding to the NNMT gene, and describe for the first time three long non-coding RNAs which may play a role in the regulation of NNMT transcription. We complete the review by discussing the development of novel anti-cancer therapeutics which target NNMT and provide insight into how NNMT-based therapies may be best employed clinically.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

Potential Effects of Dietary Isoflavones on Drug-Induced Liver Injury

Numerous prescribed drugs and herbal and dietary supplements have been reported to cause drug-induced acute liver injury, which is a frequent cause of acute liver failure (ALF). It is a tremendous challenge with ever-increasing drug application in the medication system for huge populations. Drug-induced acute liver injury can lead to diverse pathologies similar to acute and chronic hepatitis, acute liver failure, biliary obstruction, fatty liver disease, and so on. Recently, extensive work demonstrated that isoflavones play an essential and protecting role in drug-induced liver injury (DILI). The isoflavones mediated hepatoprotection by modulating specific genes linked with control of cellular redox homeostasis and inflammatory responses. Isoflavones upregulate oxidative stress-responsive nuclear factor erythroid 2-like 2 (Nrf2), downregulate inflammatory nuclear factor-κB (NF-κB) signaling pathways, and modulate a balance between cell survival and death. Moreover, isoflavones actively inhibit the expression of cytochromes P450 (CYPs) enzyme during drug metabolism. Moreover, isoflavones are also linked with farnesoid X receptor (FXR) activation and signal transducer and activator of transcription factor 3 (STAT3) phosphorylation in hepatoprotection DILI. In vivo and in vitro studies clearly stated that isoflavones bear strong antioxidant potential and promising agents for hepatotoxicity prevention and stressed their potential role as therapeutic supplements in DILI. The current review will elaborate on isoflavones’ preventive and therapeutic potential concisely and highlight various molecular targets to exert a protective effect on DILI.

An Overview of Cell-Based Assay Platforms for the Solute Carrier Family of Transporters

The solute carrier (SLC) superfamily represents the biggest family of transporters with important roles in health and disease. Despite being attractive and druggable targets, the majority of SLCs remains understudied. One major hurdle in research on SLCs is the lack of tools, such as cell-based assays to investigate their biological role and for drug discovery. Another challenge is the disperse and anecdotal information on assay strategies that are suitable for SLCs. This review provides a comprehensive overview of state-of-the-art cellular assay technologies for SLC research and discusses relevant SLC characteristics enabling the choice of an optimal assay technology. The Innovative Medicines Initiative consortium RESOLUTE intends to accelerate research on SLCs by providing the scientific community with high-quality reagents, assay technologies and data sets, and to ultimately unlock SLCs for drug discovery.

Recent advances in understanding prodrug transport through the SLC15 family of proton-coupled transporters

Solute carrier (SLC) transporters play important roles in regulating the movement of small molecules and ions across cellular membranes. In mammals, they play an important role in regulating the uptake of nutrients and vitamins from the diet, and in controlling the distribution of their metabolic intermediates within the cell. Several SLC families also play an important role in drug transport and strategies are being developed to hijack SLC transporters to control and regulate drug transport within the body. Through the addition of amino acid and peptide moieties several novel antiviral and anticancer agents have been developed that hijack the proton-coupled oligopeptide transporters, PepT1 (SCL15A1) and PepT2 (SLC15A2), for improved intestinal absorption and renal retention in the body. A major goal is to understand the rationale behind these successes and expand the library of prodrug molecules that utilise SLC transporters. Recent co-crystal structures of prokaryotic homologues of the human PepT1 and PepT2 transporters have shed important new insights into the mechanism of prodrug recognition. Here, I will review recent developments in our understanding of ligand recognition and binding promiscuity within the SLC15 family, and discuss current models for prodrug recognition.

Identification of Novel High-Affinity Substrates of OCT1 Using Machine Learning-Guided Virtual Screening and Experimental Validation

OCT1 is the most highly expressed cation transporter in the liver and affects pharmacokinetics and pharmacodynamics. Newly marketed drugs have previously been screened as potential OCT1 substrates and verified by virtual docking. Here, we used machine learning with transport experiment data to predict OCT1 substrates based on classic molecular descriptors, pharmacophore features, and extended-connectivity fingerprints and confirmed them by in vitro uptake experiments. We virtually screened a database of more than 1000 substances. Nineteen predicted substances were chosen for in vitro testing. Sixteen of the 19 newly tested substances (85%) were confirmed as, mostly strong, substrates, including edrophonium, fenpiverinium, ritodrine, and ractopamine. Even without a crystal structure of OCT1, machine learning algorithms predict substrates accurately and may contribute not only to a more focused screening in drug development but also to a better molecular understanding of OCT1 in general.

Targeting Solute Carrier Transporters through Functional Mapping

Solute carrier (SLC) transporters are emerging drug targets. Identifying the molecular determinants responsible for their specific and selective transport activities and describing key interactions with their ligands are crucial steps towards the design of potential new drugs. A general functional mapping across more than 400 human SLC transporters would pave the way to the rational and systematic design of molecules modulating cellular transport.

Food dyes as P-glycoprotein modulators

The drug transporter P-glycoprotein (P-gp) is often investigated in drug-interaction studies because the activity is modulated by a wide variety of xenobiotics including drugs, herbal products, and food components. In this study, we tested six common arylsulfonate food dyes-allura red, carmoisine, ponceau 4R, quinolone yellow, sunset yellow, and tartrazine-as activators and inhibitors of P-gp activity in vitro. The dyes were studied as P-gp activators by measuring ATPase activity in P-gp-expressing membranes. Compared to verapamil, a known activator of P-gp, the six food dyes showed no stimulatory activity. The potential for these six food dyes to act as P-gp inhibitors was tested in an intracellular efflux assay with P-gp-expressing cells. Compared to GF120918, a known P-gp inhibitor, there was no inhibitory activity for these six food dyes. The six food dyes tested do not interact with P-gp in vitro and, therefore, are unlikely cause clinical drug-food dye interactions. Further investigation is necessary to determine whether these food dyes could interact with other drug transporters.

Toward a Systematic Structural and Functional Annotation of Solute Carriers Transporters-Example of the SLC6 and SLC7 Families

SLC transporters are emerging key drug targets. One important step for drug development is the profound understanding of the structural determinants defining the substrate selectivity of each transporter. Recently, the improvement of computational power and experimental methods such as X-ray and cryo-EM crystallography permitted to conduct structure-based studies on specific transporters having important pharmacological impact. However, a lot remains to be discovered regarding their dynamics, transport modulation and ligand recognition. A detailed functional characterization of transporters would provide opportunities to develop new compounds targeting these key drug targets. Here, we are giving an overview of two major human LeuT-fold families, SLC6 and SLC7, with an emphasis on the most relevant members of each family for drug development. We gather the most recent understanding on the structural determinants of selectivity within and across the two families. We then use this information to discuss the benefits of a more generalized structural and functional annotation of the LeuT fold and the implications of such mapping for drug discovery.

SLC16 Family: From Atomic Structure to Human Disease

The solute carrier 16 (SLC16) family represents a diverse group of membrane proteins mediating the transport of monocarboxylates across biological membranes. Family members show a variety of functional roles ranging from nutrient transport and intracellular pH regulation to thyroid hormone homeostasis. Changes in the expression levels and transport function of certain SLC16 transporters are manifested in severe health disorders including cancer, diabetes, and neurological disorders. L-Lactate-transporting SLC16 family members play essential roles in the metabolism of certain tumors and became validated drug targets. This review illuminates the SLC16 family under a new light using structural information obtained from a SLC16 homolog. Furthermore, the role of these transporters in cancer metabolism and how their inhibition can contribute to anticancer therapy are discussed.

Acetaminophen Overdose as a Potential Risk Factor for Parkinson's Disease

Four complementary approaches were used to investigate acetaminophen overdose as a risk factor for Parkinson's disease (PD). Circulating microRNAs (miRNAs) serum profiles from acetaminophen-overdosed patients were compared with patients with terminal PD, revealing four shared miRNAs. Similarities were found among molecular structures of dopamine (DA), acetaminophen, and two known PD inducers indicating affinity for dopaminergic transport. Potential interactions between acetaminophen and the human DA transporter were confirmed by molecular docking modeling and binding free energy calculations. Thus, it is plausible that acetaminophen is taken up by the dopaminergic transport system into the substantia nigra (SN). A ChEMBL query identified proteins that are similarly targeted by DA and acetaminophen. Here, we highlight CYP3A4, present in the SN, a predominant metabolizer of acetaminophen into its toxic metabolite N-acetyl-p-benzoquinone imine and shown to be regulated in PD. Overall, based on our results, we hypothesize that overdosing of acetaminophen is a potential risk factor for parkinsonism.

Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword "bioinformatics"). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature.

Advances and Challenges in Rational Drug Design for SLCs

There are over 420 human solute carrier (SLC) transporters from 65 families that are expressed ubiquitously in the body. The SLCs mediate the movement of ions, drugs, and metabolites across membranes and their dysfunction has been associated with a variety of diseases, such as diabetes, cancer, and central nervous system (CNS) disorders. Thus, SLCs are emerging as important targets for therapeutic intervention. Recent technological advances in experimental and computational biology allow better characterization of SLC pharmacology. Here we describe recent approaches to modulate SLC transporter function, with an emphasis on the use of computational approaches and computer-aided drug design (CADD) to study nutrient transporters. Finally, we discuss future perspectives in the rational design of SLC drugs.

Current Advances in Studying Clinically Relevant Transporters of the Solute Carrier (SLC) Family by Connecting Computational Modeling and Data Science

Organic anion and cation transporting proteins (OATs, OATPs, and OCTs), as well as the Multidrug and Toxin Extrusion (MATE) transporters of the Solute Carrier (SLC) family are playing a pivotal role in the discovery and development of new drugs due to their involvement in drug disposition, drug-drug interactions, adverse drug effects and related toxicity. Computational methods to understand and predict clinically relevant transporter interactions can provide useful guidance at early stages in drug discovery and design, especially if they include contemporary data science approaches. In this review, we summarize the current state-of-the-art of computational approaches for exploring ligand interactions and selectivity for these drug (uptake) transporters. The computational methods discussed here by highlighting interesting examples from the current literature are ranging from semiautomatic data mining and integration, to ligand-based methods (such as quantitative structure-activity relationships, and combinatorial pharmacophore modeling), and finally structure-based methods (such as comparative modeling, molecular docking, and molecular dynamics simulations). We are focusing on promising computational techniques such as fold-recognition methods, proteochemometric modeling or techniques for enhanced sampling of protein conformations used in the context of these ADMET-relevant SLC transporters with a special focus on methods useful for studying ligand selectivity.

Structural basis for prodrug recognition by the SLC15 family of proton-coupled peptide transporters

Poor oral bioavailability is one of the leading causes of compound failure in drug development and a major challenge for the pharmaceutical industry. A successful approach to address this challenge has been the development of prodrugs that target the intestinal peptide transporter, PepT1 (SLC15A1). PepT1 exhibits a remarkably promiscuous binding site and is known to transport many different drug molecules, making it an excellent target for prodrug design and delivery. However, the structural basis for drug recognition remains largely unknown. Here we present the structure of a bacterial homolog of PepT1 bound to both an antiviral prodrug, valacyclovir, and anticancer drug 5-aminolevulinic acid. These structures enable a pharmacophore model to be developed that will aid future prodrug design.

A major challenge in drug development is the optimization of intestinal absorption and cellular uptake. A successful strategy has been to develop prodrug molecules, which hijack solute carrier (SLC) transporters for active transport into the body. The proton-coupled oligopeptide transporters, PepT1 and PepT2, have been successfully targeted using this approach. Peptide transporters display a remarkable capacity to recognize a diverse library of di- and tripeptides, making them extremely promiscuous and major contributors to the pharmacokinetic profile of several important drug classes, including beta-lactam antibiotics and antiviral and antineoplastic agents. Of particular interest has been their ability to recognize amino acid and peptide-based prodrug molecules, thereby providing a rational approach to improving drug transport into the body. However, the structural basis for prodrug recognition has remained elusive. Here we present crystal structures of a prokaryotic homolog of the mammalian transporters in complex with the antiviral prodrug valacyclovir and the peptide-based photodynamic therapy agent, 5-aminolevulinic acid. The valacyclovir structure reveals that prodrug recognition is mediated through both the amino acid scaffold and the ester bond, which is commonly used to link drug molecules to the carrier’s physiological ligand, whereas 5-aminolevulinic acid makes far fewer interactions compared with physiological peptides. These structures provide a unique insight into how peptide transporters interact with xenobiotic molecules and provide a template for further prodrug development.