Identification of Novel Inhibitors of Organic Anion Transporting Polypeptides 1B1 and 1B3 (OATP1B1 and OATP1B3) Using a Consensus Vote of Six Classification Models

MONSTROUS: a web-based chemical-transporter interaction profiler

Transporters are membrane proteins that are critical for normal cellular function and mediate the transport of endogenous and exogenous chemicals. Chemical interactions with these transporters have the potential to affect the pharmacokinetic properties of drugs. Inhibition of transporters can cause adverse drug-drug interactions and toxicity, whereas if a drug is a substrate of a transporter, it could lead to reduced therapeutic effects. The importance of transporters in drug efficacy and toxicity has led regulatory agencies, such as the U.S. Food and Drug Administration and the European Medicines Agency, to recommend screening of new molecular entities for potential transporter interactions. To aid in the rapid screening and prioritization of drug candidates without transporter liability, we developed a publicly available, web-based transporter profiler, MOlecular traNSporT inhibitoR and substrate predictOr Utility Server (MONSTROUS), that predicts the potential of a chemical to interact with transporters recommended for testing by regulatory agencies. We utilized publicly available data and developed machine learning or similarity-based classification models to predict inhibitors and substrates for 12 transporters. We used graph convolutional neural networks (GCNNs) to develop predictive models for transporters with sufficient bioactivity data, and we implemented two-dimensional similarity-based approach for those without sufficient data. The GCNN inhibitor models have an average five-fold cross-validated receiver operating characteristic area under the curve (ROC-AUC) of 0.85 ± 0.07, and the GCNN substrate models have an average ROC-AUC of 0.79 ± 0.08. We implemented the models along with applicability domain calculations in an easy-to-use web interface and made it publicly available at https://monstrous.bhsai.org/.

Using Jupyter Notebooks for re-training machine learning models

Machine learning (ML) models require an extensive, user-driven selection of molecular descriptors in order to learn from chemical structures to predict actives and inactives with a high reliability. In addition, privacy concerns often restrict the access to sufficient data, leading to models with a narrow chemical space. Therefore, we propose a framework of re-trainable models that can be transferred from one local instance to another, and further allow a less extensive descriptor selection. The models are shared via a Jupyter Notebook, allowing the evaluation and implementation of a broader chemical space by keeping most of the tunable parameters pre-defined. This enables the models to be updated in a decentralized, facile, and fast manner. Herein, the method was evaluated with six transporter datasets (BCRP, BSEP, OATP1B1, OATP1B3, MRP3, P-gp), which revealed the general applicability of this approach.

Influence of Tyrosine Kinase Inhibition on Organic Anion Transporting Polypeptide 1B3-Mediated Uptake

The organic anion transporting polypeptide family member (OATP) 1B3 is a hepatic uptake transporter that has a broad substrate recognition and plays a significant role in regulating elimination of endogenous biomolecules or xenobiotics. OATP1B3 works in tandem with OATP1B1, with which it shares approximately 80% sequence homology and a high degree of substrate overlap. Despite some substrates being recognized solely by OATP1B3, its ability to compensate for loss of OATP1B1-mediated elimination and recognition by regulatory agencies, little is known about OATP1B3 regulatory factors and how they are involved with drug-drug interaction. It was recently discovered that OATP1B1 function is mediated by the activity of a particular tyrosine kinase that is sensitive to a variety of tyrosine kinase inhibitors (TKIs). This study reports that OATP1B3 is similarly regulated, as at least 50% of its activity is reduced by 20 US Food and Drug Administration -approved TKIs. Nilotinib was assessed as the most potent OATP1B3 inhibitor among the investigated TKIs, which can occur at clinically relevant concentrations and acted predominantly through noncompetitive inhibition without impacting membrane expression. Finally, OATP1B3 function was determined to be sensitive to the knockdown of the Lck/Yes novel tyrosine kinase that is sensitive to nilotinib and has been previously implicated in mediating OATP1B1 activity. Collectively, our findings identify tyrosine kinase activity as a major regulator of OATP1B3 function which is sensitive to kinase inhibition. Given that OATP1B1 is similarly regulated, simultaneous disruption of these transporters can have drastic effects on systemic drug concentrations, which would promote adverse events. SIGNIFICANCE STATEMENT: The organic anion transporting polypeptide family member (OATP) 1B3 is a facilitator of hepatic drug elimination, although much is unknown of how OATP1B3 activity is mediated, or how such regulators contribute to drug-drug interactions. This study reports that OATP1B3 activity is dependent on the Lck/Yes novel tyrosine kinase, which is sensitive to numerous tyrosine kinase inhibitors. These findings provide insight into the occurrence of many clinical drug-drug interactions, and a rationale for future study of tyrosine kinases regulating drug disposition.

Interaction of Antifungal Drugs with CYP3A- and OATP1B-Mediated Venetoclax Elimination

Venetoclax, a BCL-2 inhibitor used to treat certain hematological cancers, exhibits low oral bioavailability and high interpatient pharmacokinetic variability. Venetoclax is commonly administered with prophylactic antifungal drugs that may result in drug interactions, of which the underlying mechanisms remain poorly understood. We hypothesized that antifungal drugs may increase venetoclax exposure through inhibition of both CYP3A-mediated metabolism and OATP1B-mediated transport. Pharmacokinetic studies were performed in wild-type mice and mice genetically engineered to lack all CYP3A isoforms, or OATP1B2 that received venetoclax alone or in combination with ketoconazole or micafungin. In mice lacking all CYP3A isoforms, venetoclax AUC was increased by 1.8-fold, and pretreatment with the antifungal ketoconazole further increased venetoclax exposure by 1.6-fold, despite the absence of CYP3A. Ensuing experiments demonstrated that the deficiency of OATP1B-type transporters is also associated with increases in venetoclax exposure, and that many antifungal drugs, including micafungin, posaconazole, and isavuconazole, are inhibitors of this transport mechanism both in vitro and in vivo. These studies have identified OATP1B-mediated transport as a previously unrecognized contributor to the elimination of venetoclax that is sensitive to inhibition by various clinically-relevant antifungal drugs. Additional consideration is warranted when venetoclax is administered together with agents that inhibit both CYP3A-mediated metabolism and OATP1B-mediated transport.

The role of DMPK science in improving pharmaceutical research and development efficiency

The successful regulatory authority approval rate of drug candidates in the drug development pipeline is crucial for determining pharmaceutical research and development (R&D) efficiency. Regulatory authorities include the US Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceutical and Food Safety Bureau Japan (PFSB), among others. Optimal drug metabolism and pharmacokinetics (DMPK) properties influence the progression of a drug candidate from the preclinical to the clinical phase. In this review, we provide a comprehensive assessment of essential concepts, methods, improvements, and challenges in DMPK science and its significance in drug development. This information provides insights into the association of DMPK science with pharmaceutical R&D efficiency.

Data-Driven Ensemble Docking to Map Molecular Interactions of Steroid Analogs with Hepatic Organic Anion Transporting Polypeptides

Hepatic organic anion transporting polypeptides—OATP1B1, OATP1B3, and OATP2B1—are expressed at the basolateral membrane of hepatocytes, being responsible for the uptake of a wide range of natural substrates and structurally unrelated pharmaceuticals. Impaired function of hepatic OATPs has been linked to clinically relevant drug–drug interactions leading to altered pharmacokinetics of administered drugs. Therefore, understanding the commonalities and differences across the three transporters represents useful knowledge to guide the drug discovery process at an early stage. Unfortunately, such efforts remain challenging because of the lack of experimentally resolved protein structures for any member of the OATP family. In this study, we established a rigorous computational protocol to generate and validate structural models for hepatic OATPs. The multistep procedure is based on the systematic exploration of available protein structures with shared protein folding using normal-mode analysis, the calculation of multiple template backbones from elastic network models, the utilization of multiple template conformations to generate OATP structural models with various degrees of conformational flexibility, and the prioritization of models on the basis of enrichment docking. We employed the resulting OATP models of OATP1B1, OATP1B3, and OATP2B1 to elucidate binding modes of steroid analogs in the three transporters. Steroid conjugates have been recognized as endogenous substrates of these transporters. Thus, investigating this data set delivers insights into mechanisms of substrate recognition. In silico predictions were complemented with in vitro studies measuring the bioactivity of a compound set on OATP expressing cell lines. Important structural determinants conferring shared and distinct binding patterns of steroid analogs in the three transporters have been identified. Overall, this comparative study provides novel insights into hepatic OATP-ligand interactions and selectivity. Furthermore, the integrative computational workflow for structure-based modeling can be leveraged for other pharmaceutical targets of interest.

Design, synthesis and biological evaluation of novel estrone phosphonates as high affinity organic anion-transporting polypeptide 2B1 (OATP2B1) inhibitors

Organic anion-transporting polypeptide 2B1 (OATP2B1) is a multispecific membrane transporter mediating the cellular uptake of various exo- and endobiotics, including drugs and steroid hormones. Increased uptake of steroid hormones by OATP2B1 may increase tumor proliferation. Therefore, understanding OATP2B1's substrate/inhibitor recognition and inhibition of its function, e.g., in hormone-dependent tumors, would be highly desirable. To identify the crucial structural features that correlate with OATP2B1 inhibition, here we designed modifications at four positions of the estrane skeleton. 13α- or 13β-estrone phosphonates modified at ring A or ring D were synthesized. Hirao and Cu(I)-catalyzed azide-alkyne click reactions served in the syntheses as key steps. 13β-Derivatives displayed outstanding OATP2B1 inhibitory action with IC₅₀ values in the nanomolar range (41-87 nM). A BODIPY-13α-estrone conjugate was additionally synthesized, modified at C-3-O of the steroid, containing a four-carbon linker between the triazole moiety and the BODIPY core. The fluorescent conjugate displayed efficient, submicromolar OATP2B1 inhibitory potency. The newly identified inhibitors and the structure-activity relationships specified here promote our understanding about drug recognition of OATP2B1.

Regulation of OATP1B1 Function by Tyrosine Kinase-mediated Phosphorylation

PURPOSE

OATP1B1 (SLCO1B1) is the most abundant and pharmacologically relevant uptake transporter in the liver and a key mediator of xenobiotic clearance. However, the regulatory mechanisms that determine OATP1B1 activity remain uncertain, and as a result, unexpected drug-drug interactions involving OATP1B1 substrates continue to be reported, including several involving tyrosine kinase inhibitors (TKI).

EXPERIMENTAL DESIGN

OATP1B1-mediated activity in overexpressing HEK293 cells and hepatocytes was assessed in the presence of FDA-approved TKIs, while rosuvastatin pharmacokinetics in the presence of an OATP1B1 inhibiting TKI were measured in vivo. Tyrosine phosphorylation of OATP1B1 was determined by LC/MS-MS-based proteomics and transport function was measured following exposure to siRNAs targeting 779 different kinases.

RESULTS

Twenty-nine of 46 FDA-approved TKIs studied significantly inhibit OATP1B1 function. Inhibition of OATP1B1 by TKIs, such as nilotinib, is predominantly noncompetitive, can increase systemic concentrations of rosuvastatin in vivo, and is associated with reduced phosphorylation of OATP1B1 at tyrosine residue 645. Using genetic screens and functional validation studies, the Src kinase LYN was identified as a potential regulator of OATP1B1 activity that is highly sensitive to inhibition by various TKIs at clinically relevant concentrations.

CONCLUSIONS

A novel kinase-dependent posttranslational mechanism of OATP1B1 activation was identified and interference with this process by TKIs can influence the elimination of a broad range of xenobiotic substrates.

Role of OATP1B1 and OATP1B3 in Drug-Drug Interactions Mediated by Tyrosine Kinase Inhibitors

Failure to recognize important features of a drug's pharmacokinetic characteristics is a key cause of inappropriate dose and schedule selection, and can lead to reduced efficacy and increased rate of adverse drug reactions requiring medical intervention. As oral chemotherapeutic agents, tyrosine kinase inhibitors (TKIs) are particularly prone to cause drug-drug interactions as many drugs in this class are known or suspected to potently inhibit the hepatic uptake transporters OATP1B1 and OATP1B3. In this article, we provide a comprehensive overview of the published literature and publicly-available regulatory documents in this rapidly emerging field. Our findings indicate that, while many TKIs can potentially inhibit the function of OATP1B1 and/or OATP1B3 and cause clinically-relevant drug-drug interactions, there are many inconsistencies between regulatory documents and the published literature. Potential explanations for these discrepant observations are provided in order to assist prescribing clinicians in designing safe and effective polypharmacy regimens, and to provide researchers with insights into refining experimental strategies to further predict and define the translational significance of TKI-mediated drug-drug interactions.

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.

Design, Synthesis, and Pharmacological Evaluation of Potent Positive Allosteric Modulators of the Glucagon-like Peptide-1 Receptor (GLP-1R)

The therapeutic success of peptidic GLP-1 receptor agonists for treatment of type 2 diabetes mellitus (T2DM) motivated our search for orally bioavailable small molecules that can activate the GLP-1 receptor (GLP-1R) as a well-validated target for T2DM. Here, the discovery and characterization of a potent and selective positive allosteric modulator (PAM) for GLP-1R based on a 3,4,5,6-tetrahydro-1H-1,5-epiminoazocino[4,5-b]indole scaffold is reported. Optimization of this series from HTS was supported by a GLP-1R ligand binding model. Biological in vitro testing revealed favorable ADME and pharmacological profiles for the best compound 19. Characterization by in vivo pharmacokinetic and pharmacological studies demonstrated that 19 activates GLP-1R as positive allosteric modulator (PAM) in the presence of the much less active endogenous degradation product GLP1(9-36)NH₂ of the potent endogenous ligand GLP-1(7-36)NH₂. While these data suggest the potential of small molecule GLP-1R PAMs for T2DM treatment, further optimization is still required towards a clinical candidate.

Sorafenib Population Pharmacokinetics and Skin Toxicities in Children and Adolescents with Refractory/Relapsed Leukemia or Solid Tumor Malignancies

PURPOSE

To determine the pharmacokinetics and skin toxicity profile of sorafenib in children with refractory/relapsed malignancies.

PATIENTS AND METHODS

Sorafenib was administered concurrently or sequentially with clofarabine and cytarabine to patients with leukemia or with bevacizumab and cyclophosphamide to patients with solid tumor malignancies. The population pharmacokinetics (PPK) of sorafenib and its metabolites and skin toxicities were evaluated.

RESULTS

In PPK analysis, older age, bevacizumab and cyclophosphamide regimen, and higher creatinine were associated with decreased sorafenib apparent clearance (CL/f; P < 0.0001 for all), and concurrent clofarabine and cytarabine administration was associated with decreased sorafenib N-oxide CL/f (P = 7e-4). Higher bilirubin was associated with decreased sorafenib N-oxide and glucuronide CL/f (P = 1e-4). Concurrent use of organic anion-transporting polypeptide 1B1 inhibitors was associated with increased sorafenib and decreased sorafenib glucuronide CL/f (P < 0.003). In exposure-toxicity analysis, a shorter time to development of grade 2-3 hand-foot skin reaction (HFSR) was associated with concurrent (P = 0.0015) but not with sequential (P = 0.59) clofarabine and cytarabine administration, compared with bevacizumab and cyclophosphamide, and with higher steady-state concentrations of sorafenib (P = 0.0004) and sorafenib N-oxide (P = 0.0275). In the Bayes information criterion model selection, concurrent clofarabine and cytarabine administration, higher sorafenib steady-state concentrations, larger body surface area, and previous occurrence of rash appeared in the four best two-predictor models of HFSR. Pharmacokinetic simulations showed that once-daily and every-other-day sorafenib schedules would minimize exposure to sorafenib steady-state concentrations associated with HFSR.

CONCLUSIONS

Sorafenib skin toxicities can be affected by concurrent medications and sorafenib steady-state concentrations. The described PPK model can be used to refine exposure-response relations for alternative dosing strategies to minimize skin toxicity.

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.

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.

Glycyrrhizin has a high likelihood to be a victim of drug-drug interactions mediated by hepatic organic anion-transporting polypeptide 1B1/1B3

BACKGROUND AND PURPOSE

Intravenous glycyrrhizin, having anti-inflammatory and hepatoprotective properties, is incorporated into the management of liver diseases in China. This investigation was designed to elucidate the molecular mechanism underlying hepatobiliary excretion of glycyrrhizin and to investigate its potential for drug-drug interactions on organic anion-transporting polypeptide (OATP)1B.

EXPERIMENTAL APPROACH

Human transporters mediating hepatobiliary excretion of glycyrrhizin were characterized at the cellular and vesicular levels and compared with rat hepatic transporters. The role of Oatp1b2 in glycyrrhizin's elimination and pharmacokinetics was evaluated in rats using the inhibitor rifampin. A physiologically based pharmacokinetic (PBPK) model for glycyrrhizin, incorporating transporter-mediated hepatobiliary excretion, was established and applied to predict potential drug-drug interactions related to glycyrrhizin in humans.

KEY RESULTS

Hepatobiliary excretion of glycyrrhizin involved human OATP1B1/1B3 (Oatp1b2 in rats)-mediated hepatic uptake from blood and human multidrug resistance-associated protein (MRP)2/breast cancer resistance protein (ABCP)/bile salt export pump (BSEP)/multidrug resistance protein 1 (Mrp2/Abcp/Bsep in rats)-mediated hepatic efflux into bile. In rats, rifampin impaired hepatic uptake of glycyrrhizin significantly increasing its systemic exposure. Glomerular-filtration-based renal excretion of glycyrrhizin was slow due to extensive protein binding in plasma. Quantitative analysis using the PBPK model demonstrated that OATP1B1/1B3 have critical roles in the pharmacokinetics of glycyrrhizin, which is highly likely to be a victim of drug-drug interactions when co-administered with potent dual inhibitors of these transporters.

CONCLUSIONS AND IMPLICATIONS

Transporter-mediated hepatobiliary excretion governs glycyrrhizin's elimination and pharmacokinetics. Understanding glycyrrhizin's potential drug-drug interactions on OATP1B1/1B3 should enhance the therapeutic outcome of glycyrrhizin-containing drug combinations on liver diseases.

Reviewing Hit Discovery Literature for Difficult Targets: Glutathione Transferase Omega-1 as an Example

Early stage drug discovery reporting on relatively new or difficult targets is often associated with insufficient hit triage. Literature reviews of such targets seldom delve into the detail required to critically analyze the associated screening hits reported. Here we take the enzyme glutathione transferase omega-1 (GSTO1-1) as an example of a relatively difficult target and review the associated literature involving small-molecule inhibitors. As part of this process we deliberately pay closer-than-usual attention to assay interference and hit quality aspects. We believe this Perspective will be a useful guide for future development of GSTO1-1 inhibitors, as well serving as a template for future review formats of new or difficult targets.

Comparing the performance of meta-classifiers-a case study on selected imbalanced data sets relevant for prediction of liver toxicity

Abstract

Cheminformatics datasets used in classification problems, especially those related to biological or physicochemical properties, are often imbalanced. This presents a major challenge in development of in silico prediction models, as the traditional machine learning algorithms are known to work best on balanced datasets. The class imbalance introduces a bias in the performance of these algorithms due to their preference towards the majority class. Here, we present a comparison of the performance of seven different meta-classifiers for their ability to handle imbalanced datasets, whereby Random Forest is used as base-classifier. Four different datasets that are directly (cholestasis) or indirectly (via inhibition of organic anion transporting polypeptide 1B1 and 1B3) related to liver toxicity were chosen for this purpose. The imbalance ratio in these datasets ranges between 4:1 and 20:1 for negative and positive classes, respectively. Three different sets of molecular descriptors for model development were used, and their performance was assessed in 10-fold cross-validation and on an independent validation set. Stratified bagging, MetaCost and CostSensitiveClassifier were found to be the best performing among all the methods. While MetaCost and CostSensitiveClassifier provided better sensitivity values, Stratified Bagging resulted in high balanced accuracies.

Graphical Abstract

Electronic supplementary material

The online version of this article (10.1007/s10822-018-0116-z) contains supplementary material, which is available to authorized users.

Drug-Induced Liver Injury: An Institutional Case Series and Review of Literature

Drug-induced liver injury (DILI) is the most common cause of acute liver failure in the USA. DILI can be broadly classified as Intrinsic and Idiosyncratic. Identifying predictors and at-risk patients are challenging but can have a substantial clinical implication. This case report series demonstrates the importance of valproic acid, fluconazole, and amiodarone as potential hepatoxic agents of drug-induced liver injury leading to acute hepatic failure. The causality in all cases was established by Roussel Uclaf Causality Assessment Method/Council for International Organizations of Medical Sciences score and Naranjo Algorithm. Obesity, hypo-perfusion state, and concurrent hepatotoxic agent might identify at-risk patients. Further studies are required to understand the risk factors.

Effect of inhalation exposure to toluene on the activity of organic anion transporting polypeptide (Oatp) using pravastatin as a probe drug in rats

1. Toluene, used as a pure substance or in solvent mixtures, is the cause of occupational exposures of large numbers of workers in the world. The organic anion transporting polypeptides (OATP: human; Oatp: rodents) are drug carriers which have been frequently associated to drug-drug interactions. The objective of this study was to evaluate the influence of inhalation exposure to toluene in Oatp in vivo activity using pravastatin as a probe drug in rats. 2. Male Wistar rats ((n = 6 per sampling time) were exposed to 85 mg/m³ toluene by inhalation or air in a nose only exposure system for 6 h/d, 5 d/week during 4 weeks, in order to simulate the occupational exposure to toluene at level slightly above the occupational exposure limit proposed by the American Conference of Governmental Industrial Hygienists (ACGIH). After 4 weeks of exposure, animals received a single dose of 20 mg/kg pravastatin orally. 3. Areas under concentration × time curves extrapolated to infinite (AUC^0-∞) were calculated by Gauss Laguerre quadrature. Non-exposed animals showed AUC^0-∞ of 726.0 (261.8) ng h/mL for pravastatin and rats exposed to toluene 85 mg/m3 showed AUC^0-∞ of 681.8 (80.1) ng h/mL [data presented as mean (standard error of the mean)]. No significant difference was observed in pravastatin kinetic disposition between groups in terms of 95% confidence interval for the difference between means. 4. Toluene exposure by inhalation did not change the in vivo activity of Oatp evaluated by pravastatin kinetic disposition in rats.

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.

Predicting Drug-Induced Cholestasis with the Help of Hepatic Transporters-An in Silico Modeling Approach

Cholestasis represents one out of three types of drug induced liver injury (DILI), which comprises a major challenge in drug development. In this study we applied a two-class classification scheme based on k-nearest neighbors in order to predict cholestasis, using a set of 93 two-dimensional (2D) physicochemical descriptors and predictions of selected hepatic transporters' inhibition (BSEP, BCRP, P-gp, OATP1B1, and OATP1B3). In order to assess the potential contribution of transporter inhibition, we compared whether the inclusion of the transporters' inhibition predictions contributes to a significant increase in model performance in comparison to the plain use of the 93 2D physicochemical descriptors. Our findings were in agreement with literature findings, indicating a contribution not only from BSEP inhibition but a rather synergistic effect deriving from the whole set of transporters. The final optimal model was validated via both 10-fold cross validation and external validation. It performs quite satisfactorily resulting in 0.686 ± 0.013 for accuracy and 0.722 ± 0.014 for area under the receiver operating characteristic curve (AUC) for 10-fold cross-validation (mean ± standard deviation from 50 iterations).

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.

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

Hyperbilirubinemia is a pathological condition of excessive accumulation of conjugated or unconjugated bilirubin in blood. It has been associated with neurotoxicity and non-neural organ dysfunctions, while it can also be a warning of liver side effects. Hyperbilirubinemia can either be a result of overproduction of bilirubin due to hemolysis or dyserythropoiesis, or the outcome of impaired bilirubin elimination due to liver transporter malfunction or inhibition. There are several reports in literature that inhibition of organic anion transporting polypeptides 1B1 and 1B3 (OATP1B1 and OATP1B3) might lead to hyperbilirubinemia. In this study we created a set of classification models for hyperbilirubinemia, which, besides physicochemical descriptors, also include the output of classification models of human OATP1B1 and 1B3 inhibition. Models were based on either human data derived from public toxicity reports or animal data extracted from the eTOX database VITIC. The generated models showed satisfactory accuracy (68%) and area under the curve (AUC) for human data and 71% accuracy and 70% AUC for animal data. However, our results did not indicate strong association between OATP inhibition and hyperbilirubinemia, neither for humans nor for animals.

Insights into solute carriers: physiological functions and implications in disease and pharmacokinetics

SLCs transport many endogenous and exogenous compounds including drugs; SLCs dysfunction has implications in pharmacokinetics, drug toxicity or lack of efficacy.