Toward predicting drug-induced liver injury: parallel computational approaches to identify multidrug resistance protein 4 and bile salt export pump inhibitors

Hepatic Gα13 ablation shifts region-specific colonic inflammatory status by modulating the bile acid synthetic pathway in mice

Inflammatory bowel disease is defined by inflammation and immune dysregulation. This study investigated the effects of Gα13 liver-specific knockout (LKO) on proximal and distal colons of dextran sodium sulfate (DSS)-induced mice in conjunction with a high-fat diet (HFD). HFD improved body weight gain and disease activity index scores. Gα13LKO exerted no improvement. In the proximal colon, HFD augmented the DSS effect on Il6, which was not observed in Gα13LKO mice. In the distal colon, HFD plus DSS oppositely fortified an increase in Tnfa and Cxcl10 mRNA in Gα13LKO but not WT. Il6 levels remained unchanged. Bioinformatic approaches using Gα13LKO livers displayed bile acid and cholesterol metabolism-related gene sets. Cholic acid and chenodeoxycholic acid levels were increased in the liver of mice treated with DSS, which was reversed by Gα13LKO. Notably, mice treated with DSS showed a reduction in hepatic ABCB11, CYP7B1, CYP7A1, and CYP8B1, which was reversed by Gα13LKO. Overall, feeding HFD augments the effect of DSS on Il6 in the proximal colon of WT, but not Gα13LKO mice, and enhances DSS effect on Tnfa and Cxcl10 in the distal colon of Gα13LKO mice, suggesting site-specific changes in the inflammatory cytokines, potentially resulting from changes in BA synthesis and excretion.

Beyond the hype and toward application: liver complex in vitro models in preclinical drug safety

INTRODUCTION

Drug induced Liver-Injury (DILI) is a leading cause of drug attrition and complex in vitro models (CIVMs), including three dimensional (3D) spheroids, 3D bio printed tissues and flow-based systems, could improve preclinical prediction. Although CIVMs have demonstrated good sensitivity and specificity in DILI detection their adoption remains limited.

AREAS COVERED

This article describes DILI, the challenges with its prediction and the current strategies and models that are being used. It reviews data from industry-FDA collaborations and strategic partnerships and finishes with an outlook of CIVMs in preclinical toxicity testing. Literature searches were performed using PubMed and Google Scholar while product information was collected from manufacturer websites.

EXPERT OPINION

Liver CIVMs are promising models for predicting DILI although, a decade after their introduction, routine use by the pharmaceutical industry is limited. To accelerate their adoption, several industry-regulator-developer partnerships or consortia have been established to guide the development and qualification. Beyond this, liver CIVMs should continue evolving to capture greater immunological mimicry while partnering with computational approaches to deliver systems that change the paradigm of predicting DILI.

In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review

New approach methods (NAMs) have been developed to predict a wide range of toxicities through innovative technologies. Liver injury is one of the most extensively studied endpoints due to its severity and frequency, occurring among populations that consume drugs or dietary supplements. In this review, we focus on recent developments of in silico modeling for liver injury prediction using deep learning and in vitro data based on adverse outcome pathways (AOPs). Despite these models being mainly developed using datasets generated from drug-like molecules, they were also applied to the prediction of hepatotoxicity caused by herbal products. As deep learning has achieved great success in many different fields, advanced machine learning algorithms have been actively applied to improve the accuracy of in silico models. Additionally, the development of liver AOPs, combined with big data in toxicology, has been valuable in developing in silico models with enhanced predictive performance and interpretability. Specifically, one approach involves developing structure-based models for predicting molecular initiating events of liver AOPs, while others use in vitro data with structure information as model inputs for making predictions. Even though liver injury remains a difficult endpoint to predict, advancements in machine learning algorithms and the expansion of in vitro databases with relevant biological knowledge have made a huge impact on improving in silico modeling for drug-induced liver injury prediction.

Identification of flucloxacillin-modified hepatocellular proteins: implications in flucloxacillin-induced liver injury

Flucloxacillin is a β-lactam antibiotic associated with a high incidence of drug-induced liver injury. Although expression of HLA-B*57:01 is associated with increased susceptibility, little is known of the pathological mechanisms involved in the induction of the clinical phenotype. Irreversible protein modification is suspected to drive the reaction through the provision of flucloxacillin-modified peptides that are presented to T-cells by the protein encoded by the risk allele. In this study, we have shown that flucloxacillin binds to multiple proteins within human primary hepatocytes, including major hepatocellular proteins (hemoglobin and albumin) and mitochondrial proteins. Inhibition of membrane transporters multidrug resistance-associated protein 2 (MRP2) and P-glycoprotein (P-gp) appeared to reduce the levels of covalent binding. A diverse range of proteins with different functions was found to be targeted by flucloxacillin, including adaptor proteins (14-3-3), proteins with catalytic activities (liver carboxylesterase 1, tRNA-splicing endonuclease subunit Sen2, All-trans-retinol dehydrogenase ADH1B, Glutamate dehydrogenase 1 mitochondrial, Carbamoyl-phosphate synthase [ammonia] mitochondrial), and transporters (hemoglobin, albumin, and UTP-glucose-1-phosphate uridylyltransferase). These flucloxacillin-modified intracellular proteins could provide a potential source of neoantigens for HLA-B*57:01 presentation by hepatocytes. More importantly, covalent binding to critical cellular proteins could be the molecular initiating events that lead to flucloxacillin-induced cholestasis Data are available via ProteomeXchange with identifier PXD038581.

Transient Hepatotoxicity Induced by Vinblastine in a Young Girl with Chiasmatic Low Grade Glioma

BACKGROUND

Vinblastine (VBL) is a cytostatic drug frequently applied in children with lymphoma and progressive low-grade glioma (LGG), with hematotoxicity as the main side effect.

CASE REPORT

Here, the case of a 7-month-old girl with tumor progression of an LGG during standard chemotherapy with carboplatin and vincristine, is presented. Switching to VBL led to a 20-30- fold increase of transaminases (grade IV CTCAE 5.0), spontaneously resolving after the end of treatment. The toxicity is possibly age-related since it did not re-occur at the restart of VBL at 4 years old. This finding might have consequences for toxicity screening in future protocols, especially when including infants.

Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump

Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. We assessed the performance effects of different methods of chemical featurization, data set partitioning, and class labeling and identified the methods producing models that generalized best to novel chemical entities.

Current insights in the complexities underlying drug-induced cholestasis

Drug-induced cholestasis (DIC) poses a major challenge to the pharmaceutical industry and regulatory agencies. It causes both drug attrition and post-approval withdrawal of drugs. DIC represents itself as an impaired secretion and flow of bile, leading to the pathological hepatic and/or systemic accumulation of bile acids (BAs) and their conjugate bile salts. Due to the high number of mechanisms underlying DIC, predicting a compound's cholestatic potential during early stages of drug development remains elusive. A profound understanding of the different molecular mechanisms of DIC is, therefore, of utmost importance. Although many knowledge gaps and caveats still exist, it is generally accepted that alterations of certain hepatobiliary membrane transporters and changes in hepatocellular morphology may cause DIC. Consequently, liver models, which represent most of these mechanisms, are valuable tools to predict human DIC. Some of these models, such as membrane-based in vitro models, are exceptionally well-suited to investigate specific mechanisms (i.e. transporter inhibition) of DIC, while others, such as liver slices, encompass all relevant biological processes and, therefore, offer a better representation of the in vivo situation. In the current review, we highlight the principal molecular mechanisms associated with DIC and offer an overview and critical appraisal of the different liver models that are currently being used to predict the cholestatic potential of drugs.

Can Bile Salt Export Pump Inhibition Testing in Drug Discovery and Development Reduce Liver Injury Risk? An International Transporter Consortium Perspective

Bile salt export pump (BSEP) inhibition has emerged as an important mechanism that may contribute to the initiation of human drug‐induced liver injury (DILI). Proactive evaluation and understanding of BSEP inhibition is recommended in drug discovery and development to aid internal decision making on DILI risk. BSEP inhibition can be quantified using in vitro assays. When interpreting assay data, it is important to consider in vivo drug exposure. Currently, this can be undertaken most effectively by consideration of total plasma steady state drug concentrations (C_ss,plasma). However, because total drug concentrations are not predictive of pharmacological effect, the relationship between total exposure and BSEP inhibition is not causal. Various follow‐up studies can aid interpretation of in vitro BSEP inhibition data and may be undertaken on a case‐by‐case basis. BSEP inhibition is one of several mechanisms by which drugs may cause DILI, therefore, it should be considered alongside other mechanisms when evaluating possible DILI risk.

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance

Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, the vast amount of gene expression data provides us valuable information for distinguishing DILI on a genomic scale. Moreover, the deep learning algorithm is a powerful strategy to automatically learn important features from raw and noisy data and shows great success in the field of medical diagnosis. In this study, a gene expression data based deep learning model was developed to predict DILI in advance by using gene expression data associated with DILI collected from ArrayExpress and then optimized by feature gene selection and parameters optimization. In addition, the previous machine learning algorithm support vector machine (SVM) was also used to construct another prediction model based on the same data sets, comparing the model performance with the optimal DL model. Finally, the evaluation test using 198 randomly selected samples showed that the optimal DL model achieved 97.1% accuracy, 97.4% sensitivity, 96.8% specificity, 0.942 matthews correlation coefficient, and 0.989 area under the ROC curve, while the performance of SVM model only reached 88.9% accuracy, 78.8% sensitivity, 99.0% specificity, 0.794 matthews correlation coefficient, and 0.901 area under the ROC curve. Furthermore, external data sets verification and animal experiments were conducted to assess the optimal DL model performance. Finally, the predicted results of the optimal DL model were almost consistent with experiment results. These results indicated that our gene expression data based deep learning model could systematically and accurately predict DILI in advance. It could be a useful tool to provide safety information for drug discovery and clinical rational drug use in early stage and become an important part of drug safety assessment.

MRP4/ABCC4 As a New Therapeutic Target: Meta-Analysis to Determine cAMP Binding Sites as a Tool for Drug Design

MRP4 transports multiple endogenous and exogenous substances and is critical not only for detoxification but also in the homeostasis of several signaling molecules. Its dysregulation has been reported in numerous pathological disorders, thus MRP4 appears as an attractive therapeutic target. However, the efficacy of MRP4 inhibitors is still controversial. The design of specific pharmacological agents with the ability to selectively modulate the activity of this transporter or modify its affinity to certain substrates represents a challenge in current medicine and chemical biology. The first step in the long process of drug rational design is to identify the therapeutic target and characterize the mechanism by which it affects the given pathology. In order to develop a pharmacological agent with high specific activity, the second step is to systematically study the structure of the target and identify all the possible binding sites. Using available homology models and mutagenesis assays, in this review we recapitulate the up-to-date knowledge about MRP structure and aligned amino acid sequences to identify the candidate MRP4 residues where cyclic nucleotides bind. We have also listed the most relevant MRP inhibitors studied to date, considering drug safety and specificity for MRP4 in particular. This meta-analysis platform may serve as a basis for the future development of inhibitors of MRP4 cAMP specific transport.

Membrane transporter data to support kinetically-informed chemical risk assessment using non-animal methods: Scientific and regulatory perspectives

Humans are continuously exposed to low levels of thousands of industrial chemicals, most of which are poorly characterised in terms of their potential toxicity. The new paradigm in chemical risk assessment (CRA) aims to rely on animal-free testing, with kinetics being a key determinant of toxicity when moving from traditional animal studies to integrated in vitro-in silico approaches. In a kinetically informed CRA, membrane transporters, which have been intensively studied during drug development, are an essential piece of information. However, how existing knowledge on transporters gained in the drug field can be applied to CRA is not yet fully understood. This review outlines the opportunities, challenges and existing tools for investigating chemical-transporter interactions in kinetically informed CRA without animal studies. Various environmental chemicals acting as substrates, inhibitors or modulators of transporter activity or expression have been shown to impact TK, just as drugs do. However, because pollutant concentrations are often lower in humans than drugs and because exposure levels and internal chemical doses are not usually known in contrast to drugs, new approaches are required to translate transporter data and reasoning from the drug sector to CRA. Here, the generation of in vitro chemical-transporter interaction data and the development of transporter databases and classification systems trained on chemical datasets (and not only drugs) are proposed. Furtheremore, improving the use of human biomonitoring data to evaluate the in vitro-in silico transporter-related predicted values and developing means to assess uncertainties could also lead to increase confidence of scientists and regulators in animal-free CRA. Finally, a systematic characterisation of the transportome (quantitative monitoring of transporter abundance, activity and maintenance over time) would reinforce confidence in the use of experimental transporter/barrier systems as well as in established cell-based toxicological assays currently used for CRA.

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.

Renal Drug Transporters and Drug Interactions

Transporters in proximal renal tubules contribute to the disposition of numerous drugs. Furthermore, the molecular mechanisms of tubular secretion have been progressively elucidated during the past decades. Organic anions tend to be secreted by the transport proteins OAT1, OAT3 and OATP4C1 on the basolateral side of tubular cells, and multidrug resistance protein (MRP) 2, MRP4, OATP1A2 and breast cancer resistance protein (BCRP) on the apical side. Organic cations are secreted by organic cation transporter (OCT) 2 on the basolateral side, and multidrug and toxic compound extrusion (MATE) proteins MATE1, MATE2/2-K, P-glycoprotein, organic cation and carnitine transporter (OCTN) 1 and OCTN2 on the apical side. Significant drug-drug interactions (DDIs) may affect any of these transporters, altering the clearance and, consequently, the efficacy and/or toxicity of substrate drugs. Interactions at the level of basolateral transporters typically decrease the clearance of the victim drug, causing higher systemic exposure. Interactions at the apical level can also lower drug clearance, but may be associated with higher renal toxicity, due to intracellular accumulation. Whereas the importance of glomerular filtration in drug disposition is largely appreciated among clinicians, DDIs involving renal transporters are less well recognized. This review summarizes current knowledge on the roles, quantitative importance and clinical relevance of these transporters in drug therapy. It proposes an approach based on substrate-inhibitor associations for predicting potential tubular-based DDIs and preventing their adverse consequences. We provide a comprehensive list of known drug interactions with renally-expressed transporters. While many of these interactions have limited clinical consequences, some involving high-risk drugs (e.g. methotrexate) definitely deserve the attention of prescribers.

The in silico identification of human bile salt export pump (ABCB11) inhibitors associated with cholestatic drug-induced liver injury

Drug-induced liver injury (DILI) is one of the major causes of drug attrition and failure. Currently, there is increasing evidence that direct inhibition of the human bile salt export pump (BSEP/ABCB11) by drugs and/or metabolites is one of the most important mechanisms of cholestatic DILI. In the present study, we employ two in silico methods, random forest (RF) and the pharmacophore method, to recognize potential BSEP inhibitors that could cause cholestatic DILI, with the aim of mitigating the risk of cholestatic DILI to some extent. The RF model achieved the best prediction performance, producing AUC (area under receiver operating characteristic curve) values of 0.901, 0.929 and 0.996 for leave-one-out cross-validation, the test set and the external test set, respectively, indicating that the built RF model has a satisfactory identification ability. As a complement to the RF model, the pharmacophore model was also built and was proved to be reliable with good predictive performance based on the internal and external validation results. Further analysis indicates that hydrophobicity, molecular size and polarity are important factors that influence the inhibitory activity of BSEP. Furthermore, the two models are applied to screen FDA-approved small molecule drugs, among which the drugs with the potential risk of cholestatic DILI are reported. In conclusion, the RF and pharmacophore models that we present can be considered as integrated screening tools to indicate the potential risk of cholestatic DILI by inhibition of BSEP.

Predicting drug-induced liver injury: The importance of data curation

Drug-induced liver injury (DILI) is a major issue for both patients and pharmaceutical industry due to insufficient means of prevention/prediction. In the current work we present a 2-class classification model for DILI, generated with Random Forest and 2D molecular descriptors on a dataset of 966 compounds. In addition, predicted transporter inhibition profiles were also included into the models. The initially compiled dataset of 1773 compounds was reduced via a 2-step approach to 966 compounds, resulting in a significant increase (p-value < 0.05) in model performance. The models have been validated via 10-fold cross-validation and against three external test sets of 921, 341 and 96 compounds, respectively. The final model showed an accuracy of 64% (AUC 68%) for 10-fold cross-validation (average of 50 iterations) and comparable values for two test sets (AUC 59%, 71% and 66%, respectively). In the study we also examined whether the predictions of our in-house transporter inhibition models for BSEP, BCRP, P-glycoprotein, and OATP1B1 and 1B3 contributed in improvement of the DILI mode. Finally, the model was implemented with open-source 2D RDKit descriptors in order to be provided to the community as a Python script.

Drug-induced liver injury: Do we know everything?

Interest in drug-induced liver injury (DILI) has dramatically increased over the past decade, and it has become a hot topic for clinicians, academics, pharmaceutical companies and regulatory bodies. By investigating the current state of the art, the latest scientific findings, controversies, and guidelines, this review will attempt to answer the question: Do we know everything? Since the first descriptions of hepatotoxicity over 70 years ago, more than 1000 drugs have been identified to date, however, much of our knowledge of diagnostic and pathophysiologic principles remains unchanged. Clinically ranging from asymptomatic transaminitis and acute or chronic hepatitis, to acute liver failure, DILI remains a leading causes of emergent liver transplant. The consumption of unregulated herbal and dietary supplements has introduced new challenges in epidemiological assessment and clinician management. As such, numerous registries have been created, including the United States Drug-Induced Liver Injury Network, to further our understanding of all aspects of DILI. The launch of LiverTox and other online hepatotoxicity resources has increased our awareness of DILI. In 2013, the first guidelines for the diagnosis and management of DILI, were offered by the Practice Parameters Committee of the American College of Gastroenterology, and along with the identification of risk factors and predictors of injury, novel mechanisms of injury, refined causality assessment tools, and targeted treatment options have come to define the current state of the art, however, gaps in our knowledge still undoubtedly remain.

Identification of novel MRP3 inhibitors based on computational models and validation using an in vitro membrane vesicle assay

INTRODUCTION

Multidrug resistance-associated protein 3 (MRP3), an efflux transporter on the hepatic basolateral membrane, may function as a compensatory mechanism to prevent the accumulation of anionic substrates (e.g., bile acids) in hepatocytes. Inhibition of MRP3 may disrupt bile acid homeostasis and is one hypothesized risk factor for the development of drug-induced liver injury (DILI). Therefore, identifying potential MRP3 inhibitors could help mitigate the occurrence of DILI.

METHODS

Bayesian models were developed using MRP3 transporter inhibition data for 86 structurally diverse drugs. The compounds were split into training and test sets of 57 and 29 compounds, respectively, and six models were generated based on distinct inhibition thresholds and molecular fingerprint methods. The six Bayesian models were validated against the test set and the model with the highest accuracy was utilized for a virtual screen of 1470 FDA-approved drugs from DrugBank. Compounds that were predicted to be inhibitors were selected for in vitro validation. The ability of these compounds to inhibit MRP3 transport at a concentration of 100μM was measured in membrane vesicles derived from stably transfected MRP3-over-expressing HEK-293 cells with [³H]-estradiol-17β-d-glucuronide (E₂17G; 10μM; 5min uptake) as the probe substrate.

RESULTS

A predictive Bayesian model was developed with a sensitivity of 73% and specificity of 71% against the test set used to evaluate the six models. The area under the Receiver Operating Characteristic (ROC) curve was 0.710 against the test set. The final selected model was based on compounds that inhibited substrate transport by at least 50% compared to the negative control, and functional-class fingerprints (FCFP) with a circular diameter of six atoms, in addition to one-dimensional physicochemical properties. The in vitro screening of predicted inhibitors and non-inhibitors resulted in similar model performance with a sensitivity of 64% and specificity of 70%. The strongest inhibitors of MRP3-mediated E₂17G transport were fidaxomicin, suramin, and dronedarone. Kinetic assessment revealed that fidaxomicin was the most potent of these inhibitors (IC₅₀=1.83±0.46μM). Suramin and dronedarone exhibited IC₅₀ values of 3.33±0.41 and 47.44±4.41μM, respectively.

CONCLUSION

Bayesian models are a useful screening approach to identify potential inhibitors of transport proteins. Novel MRP3 inhibitors were identified by virtual screening using the selected Bayesian model, and MRP3 inhibition was confirmed by an in vitro transporter inhibition assay. Information generated using this modeling approach may be valuable in predicting the potential for DILI and/or MRP3-mediated drug-drug interactions.

The pathogenesis of drug-induced liver injury

Drugs can induce liver injury when taken as an over-dose, or even at therapeutic doses in susceptible individuals. Although severe drug-induced liver injury (DILI) is a relatively uncommon clinical event, it is a potentially life threatening adverse drug reaction and is the most common indication for the drug withdrawal. Areas covered: However, the diagnosis of DILI remains a significant challenge, because the establishment of causality is very difficult, and the histopathologic findings of DILI may be indistinguishable from those of other hepatic disorders, such as viral and alcoholic hepatitis. In this review, we provide an overview of recent advances in identification of serologic markers of diagnosis and prognosis, etiologic factors for susceptibility and diagnostic evaluation of DILI, with a focus on its pathogenic mechanisms and the role of liver biopsy. Expert commentary: Further studies of divergent research platforms, using a systems biology approach such as genomics and transcriptomics, may provide a deeper understanding of human drug metabolism and the causes, risk factors, and pathogenesis of DILI.

Rho-kinase/myosin light chain kinase pathway plays a key role in the impairment of bile canaliculi dynamics induced by cholestatic drugs

Intrahepatic cholestasis represents a frequent manifestation of drug-induced liver injury; however, the mechanisms underlying such injuries are poorly understood. In this study of human HepaRG and primary hepatocytes, we found that bile canaliculi (BC) underwent spontaneous contractions, which are essential for bile acid (BA) efflux and require alternations in myosin light chain (MLC2) phosphorylation/dephosphorylation. Short exposure to 6 cholestatic compounds revealed that BC constriction and dilation were associated with disruptions in the ROCK/MLCK/myosin pathway. At the studied concentrations, cyclosporine A and chlorpromazine induced early ROCK activity, resulting in permanent MLC2 phosphorylation and BC constriction. However, fasudil reduced ROCK activity and caused rapid, substantial and permanent MLC2 dephosphorylation, leading to BC dilation. The remaining compounds (1-naphthyl isothiocyanate, deoxycholic acid and bosentan) caused BC dilation without modulating ROCK activity, although they were associated with a steady decrease in MLC2 phosphorylation via MLCK. These changes were associated with a common loss of BC contractions and failure of BA clearance. These results provide the first demonstration that cholestatic drugs alter BC dynamics by targeting the ROCK/MLCK pathway; in addition, they highlight new insights into the mechanisms underlying bile flow failure and can be used to identify new predictive biomarkers of drug-induced cholestasis.

Hepatic drug transporters: the journey so far

INTRODUCTION

The key role of transporter biology in both the manifestation and treatment of disease is now firmly established. Experiences of sub-optimal drug exposure due to drug-transporter interplay have supported incorporation of studies aimed at understanding the interactions between compounds and drug transporters much earlier in drug discovery. While drug transporters can impact the most pivotal pharmacokinetic parameter with respect to human dose and exposure projections, clearance, at a renal or hepatobiliary level, the latter will form the focus of this perspective.

AREAS COVERED

A synopsis of guidelines on which transporters to study together with an overview of the currently available toolkit is presented. A perspective on when to conduct studies with various hepatic transporters is also provided together with structural "alerts" which should prompt early investigation.

EXPERT OPINION

Great progress has been made in individual laboratories and via consortia to understand the role of drug transporters in disease, drug disposition, drug-drug interactions and toxicity. A systematic analysis of the value posed by the available approaches and an inter-lab comparison now seems warranted. The emerging ability to use physico-chemical properties to guide future screening cascades promises to revolutionise the efficiency of early drug discovery.

Recent advances in the exploration of the bile salt export pump (BSEP/ABCB11) function

INTRODUCTION

The bile salt export pump (BSEP/ABCB11), residing in the apical membrane of hepatocyte, mediates the secretion of bile salts into the bile. A range of human diseases is associated with the malfunction of BSEP, including fatal hereditary liver disorders and mild cholestatic conditions. Manifestation of these diseases primarily depends on the mutation type; however, other factors such as hormonal changes and drug interactions can also trigger or influence the related diseases.

AREAS COVERED

Here, we summarize the recent knowledge on BSEP by covering its transport properties, cellular localization, regulation and major mutations/polymorphisms, as well as the hereditary and acquired diseases associated with BSEP dysfunction. We discuss the different model expression systems employed to understand the function of the BSEP variants, their drug interactions and the contemporary therapeutic interventions.

EXPERT OPINION

The limitations of the available model expression systems for BSEP result in controversial conclusions, and obstruct our deeper insight into BSEP deficiencies and BSEP-related drug interactions. The knowledge originating from different methodologies, such as clinical studies, molecular genetics, as well as in vitro and in silico modeling, should be integrated and harmonized. Increasing availability of robust molecular biological tools and our better understanding of the mechanism of BSEP deficiencies should make the personalized, mutation-based therapeutic interventions more attainable.

The Art and Science of Diagnosing and Managing Drug-induced Liver Injury in 2015 and Beyond

Drug-induced liver injury (DILI) remains a leading reason why new compounds are dropped from further study or are the subject of product warnings and regulatory actions. Hy's Law of drug-induced hepatocellular jaundice causing a case-fatality rate or need for transplant of 10% or higher has been validated in several large national registries, including the ongoing, prospective U.S. Drug-Induced Liver Injury Network. It serves as the basis for stopping rules in clinical trials and in clinical practice. Because DILI can mimic all known causes of acute and chronic liver disease, establishing causality can be difficult. Histopathologic findings are often nonspecific and rarely, if ever, considered pathognomonic. A daily drug dose >50-100 mg is more likely to be hepatotoxic than does <10 mg, especially if the compound is highly lipophilic or undergoes extensive hepatic metabolism. The quest for a predictive biomarker to replace alanine aminotransferase is ongoing. Markers of necrosis and apoptosis such as microRNA-122 and keratin 18 may prove useful in identifying patients at risk for severe injury when they initially present with a suspected acetaminophen overdose. Although a number of drugs causing idiosyncratic DILI have HLA associations that may allow for pre-prescription testing to prevent hepatotoxicity, the cost and relatively low frequency of injury among affected patients limit the current usefulness of such genome-wide association studies. Alanine aminotransferase monitoring is often recommended but has rarely been shown to be an effective method to prevent serious DILI. Guidelines on the diagnosis and management of DILI have recently been published, although specific therapies remain limited. The LiverTox Web site has been introduced as an interactive online virtual textbook that makes the latest information on more than 650 agents available to clinicians, regulators, and drug developers alike.

The Pharmacological and Physiological Role of Multidrug-Resistant Protein 4

Multidrug-resistant protein 4 (MRP4), a member of the C subfamily of ATP-binding cassette transporters, is distributed in a variety of tissues and a number of cancers. As a drug transporter, MRP4 is responsible for the pharmacokinetics and pharmacodynamics of numerous drugs, especially antiviral drugs, antitumor drugs, and diuretics. In this regard, the functional role of MRP4 is affected by a number of factors, such as genetic mutations; tissue-specific transcriptional regulations; post-transcriptional regulations, including miRNAs and membrane internalization; and substrate competition. Unlike other C family members, MRP4 is in a pivotal position to transport cellular signaling molecules, through which it is tightly connected to the living activity and physiologic processes of cells and bodies. In the context of several cancers in which MRP4 is overexpressed, MRP4 inhibition shows striking effects against cancer progression and drug resistance. In this review, we describe the role of MRP4 more specifically in both healthy conditions and disease states, with an emphasis on its potential as a drug target.