Linking organic anion transporting polypeptide 1B1 and 1B3 (OATP1B1 and OATP1B3) interaction profiles to hepatotoxicity - The hyperbilirubinemia use case

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.

Hepatotoxicity of AKR1C3 Inhibitor BAY1128688: Findings from an Early Terminated Phase IIa Trial for the Treatment of Endometriosis

INTRODUCTION

BAY1128688 is a selective inhibitor of aldo-keto reductase family 1 member C3 (AKR1C3), an enzyme implicated in the pathology of endometriosis and other disorders. In vivo animal studies suggested a potential therapeutic application of BAY1128688 in treating endometriosis. Early clinical studies in healthy volunteers supported the start of phase IIa.

OBJECTIVE

This manuscript reports the results of a clinical trial (AKRENDO1) assessing the effects of BAY1128688 in adult premenopausal women with endometriosis-related pain symptoms over a 12-week treatment period.

METHODS

Participants in this placebo-controlled, multicenter phase IIa clinical trial (NCT03373422) were randomized into one of five BAY1128688 treatment groups: 3 mg once daily (OD), 10 mg OD, 30 mg OD, 30 mg twice daily (BID), 60 mg BID; or a placebo group. The efficacy, safety, and tolerability of BAY1128688 were investigated.

RESULTS

Dose-/exposure-dependent hepatotoxicity was observed following BAY1128688 treatment, characterized by elevations in serum alanine transferase (ALT) occurring at around 12 weeks of treatment and prompting premature trial termination. The reduced number of valid trial completers precludes conclusions regarding treatment efficacy. The pharmacokinetics and pharmacodynamics of BAY1128688 among participants with endometriosis were comparable with those previously found in healthy volunteers and were not predictive of the subsequent ALT elevations observed.

CONCLUSIONS

The hepatotoxicity of BAY1128688 observed in AKRENDO1 was not predicted by animal studies nor by studies in healthy volunteers. However, in vitro interactions of BAY1128688 with bile salt transporters indicated a potential risk factor for hepatotoxicity at higher doses. This highlights the importance of in vitro mechanistic and transporter interaction studies in the assessment of hepatoxicity risk and suggests further mechanistic understanding is required.

CLINICAL TRIAL REGISTRATION

NCT03373422 (date registered: November 23, 2017).

Fluorescence-based methods for studying activity and drug-drug interactions of hepatic solute carrier and ATP binding cassette proteins involved in ADME-Tox

In humans, approximately 70% of drugs are eliminated through the liver. This process is governed by the concerted action of membrane transporters and metabolic enzymes. Transporters mediating hepatocellular uptake of drugs belong to the SLC (Solute carrier) superfamily of transporters. Drug efflux either toward the portal vein or into the bile is mainly mediated by active transporters of the ABC (ATP Binding Cassette) family. Alteration in the function and/or expression of liver transporters due to mutations, disease conditions, or co-administration of drugs or food components can result in altered pharmacokinetics. On the other hand, drugs or food components interacting with liver transporters may also interfere with liver function (e.g., bile acid homeostasis) and may even cause liver toxicity. Accordingly, certain transporters of the liver should be investigated already at an early stage of drug development. Most frequently radioactive probes are applied in these drug-transporter interaction tests. However, fluorescent probes are cost-effective and sensitive alternatives to radioligands, and are gaining wider application in drug-transporter interaction tests. In our review, we summarize our current understanding about hepatocyte ABC and SLC transporters affected by drug interactions. We provide an update of the available fluorescent and fluorogenic/activable probes applicable in in vitro or in vivo testing of these ABC and SLC transporters, including near-infrared transporter probes especially suitable for in vivo imaging. Furthermore, our review gives a comprehensive overview of the available fluorescence-based methods, not directly relying on the transport of the probe, suitable for the investigation of hepatic ABC or SLC-type drug transporters.

Differences in the Hemolytic Behavior of Two Isomers in Ophiopogon japonicus In Vitro and In Vivo and Their Risk Warnings

Ophiopogonin D (OPD) and Ophiopogonin D′ (OPD′) are two bioactive ingredients in Ophiopogon japonicus. Previously published studies have often focused on the therapeutic effects related to OPD's antioxidant capacity but underestimated the cytotoxicity-related side effects of OPD′, which may result in unpredictable risks. In this study, we reported another side effect of OPD′, hemolysis, and what was unexpected was that this side effect also appeared with OPD. Although hemolysis effects for saponins are familiar to researchers, the hemolytic behavior of OPD or OPD′ and the interactions between these two isomers are unique. Therefore, we investigated the effects of OPD and OPD′ alone or in combination on the hemolytic behavior in vitro and in vivo and adopted chemical compatibility and proteomics methods to explain the potential mechanism. Meanwhile, to explain the drug-drug interactions (DDIs), molecular modeling was applied to explore the possible common targets. In this study, we reported that OPD′ caused hemolysis both in vitro and in vivo, while OPD only caused hemolysis in vivo. We clarified the differences and DDIs in the hemolytic behavior of the two isomers. An analysis of the underlying mechanism governing this phenomenon showed that hemolysis caused by OPD or OPD′ was related to the destruction of the redox balance of erythrocytes. In vivo, in addition to the redox imbalance, the proteomics data demonstrated that lipid metabolic disorders and mitochondrial energy metabolism are extensively involved by hemolysis. We provided a comprehensive description of the hemolysis of two isomers in Ophiopogon japonicus, and risk warnings related to hemolysis were presented. Our research also provided a positive reference for the development and further research of such bioactive components.

Combining In Vivo Data with In Silico Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction

Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of fatty acids in hepatocytes. In this study, we developed reliable in silico models for predicting hepatic steatosis on the basis of an in vivo data set of 1041 compounds measured in rodent studies with repeated oral exposure. The imbalanced nature of the data set (1:8, with the "steatotic" compounds belonging to the minority class) required the use of meta-classifiers-bagging with stratified under-sampling and Mondrian conformal prediction-on top of the base classifier random forest. One major goal was the investigation of the influence of different descriptor combinations on model performance (tested by predicting an external validation set): physicochemical descriptors (RDKit), ToxPrint features, as well as predictions from in silico nuclear receptor and transporter models. All models based upon descriptor combinations including physicochemical features led to reasonable balanced accuracies (BAs between 0.65 and 0.69 for the respective models). Combining physicochemical features with transporter predictions and further with ToxPrint features gave the best performing model (BAs up to 0.7 and efficiencies of 0.82). Whereas both meta-classifiers proved useful for this highly imbalanced toxicity data set, the conformal prediction framework also guarantees the error level and thus might be favored for future studies in the field of predictive toxicology.

Prediction of Drug-Induced Hyperbilirubinemia by In Vitro Testing

Bilirubin, the end product of heme catabolism, is produced continuously in the body and may reach toxic levels if accumulates in the serum and tissues; therefore, a highly efficient mechanism evolved for its disposition. Normally, unconjugated bilirubin enters hepatocytes through the uptake transporters organic anion transporting polypeptide (OATP) 1B1 and 1B3, undergoes glucuronidation by the Phase II enzyme UDP glucuronosyltransferase 1A1 (UGT1A1), and conjugated forms are excreted into the bile by the canalicular export pump multidrug resistance protein 2 (MRP2). Any remaining conjugated bilirubin is transported back to the blood by MRP3 and passed on for uptake and excretion by downstream hepatocytes or the kidney. The bile salt export pump BSEP as the main motor of bile flow is indirectly involved in bilirubin disposition. Genetic mutations and xenobiotics that interfere with this machinery may impede bilirubin disposition and cause hyperbilirubinemia. Several pharmaceutical compounds are known to cause hyperbilirubinemia via inhibition of OATP1Bs, UGT1A1, or BSEP. Herein we briefly review the in vitro prediction methods that serve to identify drugs with a potential to induce hyperbilirubinemia. In vitro assays can be deployed early in drug development and may help to minimize late-stage attrition. Based on current evidence, drugs that behave as mono- or multispecific inhibitors of OATP1B1, UGT1A1, and BSEP in vitro are at risk of causing clinically significant hyperbilirubinemia. By integrating inhibition data from in vitro assays, drug serum concentrations, and clinical reports of hyperbilirubinemia, predictor cut-off values have been established and are provisionally suggested in this review. Further validation of in vitro readouts to clinical outcomes is expected to enhance the predictive power of these assays.

Vienna LiverTox Workspace-A Set of Machine Learning Models for Prediction of Interactions Profiles of Small Molecules With Transporters Relevant for Regulatory Agencies

Transporters expressed in the liver play a major role in drug pharmacokinetics and are a key component of the physiological bile flow. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury. Therefore, predicting the interaction profile of small molecules with transporters expressed in the liver may help medicinal chemists and toxicologists to prioritize compounds in an early phase of the drug development process. Based on a comprehensive analysis of the data available in the public domain, we developed a set of classification models which allow to predict—for a small molecule—the inhibition of and transport by a set of liver transporters considered to be relevant by FDA, EMA, and the Japanese regulatory agency. The models were validated by cross-validation and external test sets and comprise cross validated balanced accuracies in the range of 0.64–0.88. Finally, models were implemented as an easy to use web-service which is freely available at https://livertox.univie.ac.at.

Integrative Data Mining, Scaffold Analysis, and Sequential Binary Classification Models for Exploring Ligand Profiles of Hepatic Organic Anion Transporting Polypeptides

Hepatocellular organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are important for proper liver function and the regulation of the drug elimination process. Understanding their roles in different conditions of liver toxicity and cancer requires an in-depth investigation of hepatic OATP–ligand interactions and selectivity. However, such studies are impeded by the lack of crystal structures, the promiscuous nature of these transporters, and the limited availability of reliable bioactivity data, which are spread over different data sources in the open domain. To this end, we integrated ligand bioactivity data for hepatic OATPs from five open data sources (ChEMBL, the UCSF–FDA TransPortal database, DrugBank, Metrabase, and IUPHAR) in a semiautomatic KNIME workflow. Highly curated data sets were analyzed with respect to enriched scaffolds, and their activity profiles and interesting scaffold series providing indication for selective, dual-, or pan-inhibitory activity toward hepatic OATPs could be extracted. In addition, a sequential binary modeling approach revealed common and distinctive ligand features for inhibitory activity toward the individual transporters. The workflows designed for integrating data from open sources, data curation, and subsequent substructure analyses are freely available and fully adaptable. The new data sets for inhibitors and substrates of hepatic OATPs as well as the insights provided by the feature and substructure analyses will guide future structure-based studies on hepatic OATP–ligand interactions and selectivity.

Membrane Transporters for Bilirubin and Its Conjugates: A Systematic Review

Background: Bilirubin is a highly-hydrophobic tetrapyrrole which binds to plasma albumin. It is conjugated in the liver to glucuronic acid, and the water-soluble glucuronides are excreted in urine and bile. The membrane transporters of bilirubin diglucuronide are well-known. Still undefined are however the transporters performing the uptake of bilirubin from the blood into the liver, a process known to be fast and not rate-limited. The biological importance of this process may be appraised by considering that in normal adults 200–300 mg of bilirubin are produced daily, as a result of the physiologic turnover of hemoglobin and cellular cytochromes. Nevertheless, research in this field has yielded controversial and contradicting results. We have undertaken a systematic review of the literature, believing in its utility to improve the existing knowledge and promote further advancements.

Methods: We have sourced the PubMed database until 30 June 2017 by applying 5 sequential searches. Screening and eligibility criteria were applied to retain research articles reporting results obtained by using bilirubin molecules in membrane transport assays in vitro or by assessing serum bilirubin levels in in vivo experiments.

Results: We have identified 311 articles, retaining 44, reporting data on experimental models having 6 incremental increases of complexity (isolated proteins, membrane vesicles, cells, organ fragments, in vivo rodents, and human studies), demonstrating the function of 19 membrane transporters, encoded by either SLCO or ABC genes. Three other bilirubin transporters have no gene, though one, i.e., bilitranslocase, is annotated in the Transporter Classification Database.

Conclusions: This is the first review that has systematically examined the membrane transporters for bilirubin and its conjugates. Paradoxically, the remarkable advancements in the field of membrane transport of bilirubin have pointed to the elusive mechanism(s) enabling bilirubin to diffuse into the liver as if no cellular boundary existed.

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.

Curated human hyperbilirubinemia data and the respective OATP1B1 and 1B3 inhibition predictions

Hyperbilirubinemia is a pathological condition, very often indicative of underlying liver condition that is characterized by excessive accumulation of conjugated or unconjugated bilirubin in sinusoidal blood. In literature there are several indications associating the inhibition of the basolateral hepatic transporters Organic anion transporting polypeptide 1B1 and 1B3 (OATP1B1 and 1B3) with hyperbilirubinemia. In this article, we present a curated human hyperbilirubinemia dataset and the respective OATP1B1 and 1B3 inhibition predictions obtained from an effort to generate a classification model for hyperbilirubinemia. These data originate from the research article "Linking organic anion transporting polypeptide 1b1 and 1b3 (oatp1b1 and oatp1b3) interaction profiles to hepatotoxicity- the hyperbilirubinemia use case" (E. Kotsampasakou, S.E. Escher, G.F. Ecker, 2017) [1]. We further provide the full list of descriptors used for generating the hyperbilirubinemia classification models as well as the calculated descriptors for each compound of the dataset that was used to build the classification model.

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.