Generation of Bayesian prediction models for OATP-mediated drug-drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3

Survey of Pharmaceutical Industry's Best Practices around In Vitro Transporter Assessment and Implications for Drug Development: Considerations from the International Consortium for Innovation and Quality for Pharmaceutical Development Transporter Working Group

The International Consortium for Innovation and Quality in Pharmaceutical Development Transporter Working Group had a rare opportunity to analyze a crosspharma collation of in vitro data and assay methods for the evaluation of drug transporter substrate and inhibitor potential. Experiments were generally performed in accordance with regulatory guidelines. Discrepancies, such as not considering the impact of preincubation for inhibition and free or measured in vitro drug concentrations, may be due to the retrospective nature of the dataset and analysis. Lipophilicity was a frequent indicator of crosstransport inhibition (P-gp, BCRP, OATP1B, and OCT1), with high molecular weight (MW ≥500 Da) also common for OATP1B and BCRP inhibitors. A high level of overlap in in vitro inhibition across transporters was identified for BCRP, OATP1B1, and MATE1, suggesting that prediction of DDIs for these transporters will be common. In contrast, inhibition of OAT1 did not coincide with inhibition of any other transporter. Neutrals, bases, and compounds with intermediate-high lipophilicity tended to be P-gp and/or BCRP substrates, whereas compounds with MW <500 Da tended to be OAT3 substrates. Interestingly, the majority of in vitro inhibitors were not reported to be followed up with a clinical study by the submitting company, whereas those compounds identified as substrates generally were. Approaches to metabolite testing were generally found to be similar to parent testing, with metabolites generally being equally or less potent than parent compounds. However, examples where metabolites inhibited transporters in vitro were identified, supporting the regulatory requirement for in vitro testing of metabolites to enable integrated clinical DDI risk assessment. SIGNIFICANCE STATEMENT: A diverse dataset showed that transporter inhibition often correlated with lipophilicity and molecular weight (>500 Da). Overlapping transporter inhibition was identified, particularly that inhibition of BCRP, OATP1B1, and MATE1 was frequent if the compound inhibited other transporters. In contrast, inhibition of OAT1 did not correlate with the other drug transporters tested.

General Framework to Quantitatively Predict Pharmacokinetic Induction Drug-Drug Interactions Using In Vitro Data

INTRODUCTION

Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug-drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes.

METHODS

The area under the curve ratio (AUC_ratio) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUC_ratio (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer.

RESULTS

Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs.

CONCLUSION

This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development.

Machine Learning Models Identify New Inhibitors for Human OATP1B1

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

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.

DDIWAS: High-throughput electronic health record-based screening of drug-drug interactions

OBJECTIVE

We developed and evaluated Drug-Drug Interaction Wide Association Study (DDIWAS). This novel method detects potential drug-drug interactions (DDIs) by leveraging data from the electronic health record (EHR) allergy list.

MATERIALS AND METHODS

To identify potential DDIs, DDIWAS scans for drug pairs that are frequently documented together on the allergy list. Using deidentified medical records, we tested 616 drugs for potential DDIs with simvastatin (a common lipid-lowering drug) and amlodipine (a common blood-pressure lowering drug). We evaluated the performance to rediscover known DDIs using existing knowledge bases and domain expert review. To validate potential novel DDIs, we manually reviewed patient charts and searched the literature.

RESULTS

DDIWAS replicated 34 known DDIs. The positive predictive value to detect known DDIs was 0.85 and 0.86 for simvastatin and amlodipine, respectively. DDIWAS also discovered potential novel interactions between simvastatin-hydrochlorothiazide, amlodipine-omeprazole, and amlodipine-valacyclovir. A software package to conduct DDIWAS is publicly available.

CONCLUSIONS

In this proof-of-concept study, we demonstrate the value of incorporating information mined from existing allergy lists to detect DDIs in a real-world clinical setting. Since allergy lists are routinely collected in EHRs, DDIWAS has the potential to detect and validate DDI signals across institutions.

In Vitro Evidence of Potential Interactions between CYP2C8 and Candesartan Acyl-β-D-glucuronide in the Liver

Growing evidence suggests that certain glucuronides function as potent inhibitors of CYP2C8. We previously reported the possibility of drug-drug interactions between candesartan cilexetil and paclitaxel. In this study, we evaluated the effects of candesartan N2-glucuronide and candesartan acyl-β-D-glucuronide on pathways associated with the elimination of paclitaxel, including those involving organic anion-transporting polypeptide (OATP) 1B1, OATP1B3, CYP2C8, and CYP3A4. UDP-glucuronosyltransferase (UGT) 1A10 and UGT2B7 were found to increase candesartan N2-glucuronide and candesartan acyl-β-D-glucuronide formation in a candesartan concentration-dependent manner. Additionally, the uptake of candesartan N2-glucuronide and candesartan acyl-β-D-glucuronide by cells stably expressing OATPs is a saturable process with K _m of 5.11 and 12.1 μM for OATP1B1 and 28.8 and 15.7 μM for OATP1B3, respectively; both glucuronides exhibit moderate inhibition of OATP1B1/1B3. Moreover, the hydroxylation of paclitaxel was evaluated using recombinant CYP3A4 and CYP3A5. Results show that candesartan, candesartan N2-glucuronide, and candesartan acyl-β-D-glucuronide inhibit the CYP2C8-mediated metabolism of paclitaxel, with candesartan acyl-β-D-glucuronide exhibiting the strongest inhibition (IC₅₀ is 18.9 µM for candesartan acyl-β-D-glucuronide, 150 µM for candesartan, and 166 µM for candesartan N2-glucuronide). However, time-dependent inhibition of CYP2C8 by candesartan acyl-β-D-glucuronide was not observed. Conversely, the IC₅₀ values of all the compounds are comparable for CYP3A4. Taken together, these data suggest that candesartan acyl-β-D-glucuronide is actively transported by OATPs into hepatocytes, and drug-drug interactions may occur with coadministration of candesartan and CYP2C8 substrates, including paclitaxel, as a result of the inhibition of CYP2C8 function. SIGNIFICANCE STATEMENT: This study demonstrates that the acyl glucuronidation of candesartan to form candesartan acyl-β-D-glucuronide enhances CYP2C8 inhibition while exerting minimal effects on CYP3A4, organic anion-transporting polypeptide (OATP) 1B1, and OATP1B3. Thus, candesartan acyl-β-D-glucuronide might represent a potential mediator of drug-drug interactions between candesartan and CYP2C8 substrates, such as paclitaxel, in clinical settings. This work adds to the growing knowledge regarding the inhibitory effects of glucuronides on CYP2C8.

Drug interaction study of flavonoids toward OATP1B1 and their 3D structure activity relationship analysis for predicting hepatoprotective effects

Organic anion transporting polypeptide 1B1 (OATP1B1), a liver-specific uptake transporter, was associated with drug induced liver injury (DILI). Screening and identifying potent OATP1B1 inhibitors with little toxicity is of great value in reducing OATP1B1-mediated DILI. Flavonoids are a group of polyphenols ubiquitously present in vegetables, fruits and herbal products, some of them were reported to produce transporter-mediated DDI. Our objective was to investigate potential inhibitors of OATP1B1 from 99 flavonoids, and to assess the hepatoprotective effects on bosentan induced liver injury. Eight flavonoids, including biochanin A, hispidulin, isoliquiritigenin, isosinensetin, kaempferol, licochalcone A, luteolin and sinensetin exhibited significant inhibition (>50 %) on OATP1B1 in OATP1B1-HEK293 cells, which reduced the OATP1B1-mediated influx of methotrexate, accordingly decreased its cytotoxicity in OATP1B1-HEK293 cells and increased its AUC_0-t in different extents in rats, from 28.27%-82.71 %. In bosentan-induced rat liver injury models, 8 flavonoids reduced the levels of serum total bile acid (TBA) and the liver concentration of bosentan in different degrees. Among them, kaempferol decreased the concentration most significantly, by 54.17 %, which indicated that flavonoids may alleviate bosentan-induced liver injury by inhibiting OATP1B1-mediated bosentan uptake. Furthermore, the pharmacophore model indicated the hydrogen bond acceptors and hydrogen bond donors may play critical role in the potency of flavonoids inhibition on OATP1B1. Taken together, our findings would provide helpful information for predicting the potential risks of flavonoid-containing food/herb-drug interactions in humans and alleviating bosentan -induced liver injury by OATP1B1 regulation.

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.

Proteomics of human liver membrane transporters: a focus on fetuses and newborn infants

BACKGROUND

Hepatic membrane transporters are involved in the transport of many endogenous and exogenous compounds, including drugs. We aimed to study the relation of age with absolute transporter protein expression in a cohort of 62 mainly fetus and newborn samples.

METHODS

Protein expressions of BCRP, BSEP, GLUT1, MCT1, MDR1, MRP1, MRP2, MRP3, NTCP, OCT1, OATP1B1, OATP1B3, OATP2B1 and ATP1A1 were quantified with LC-MS/MS in isolated crude membrane fractions of snap-frozen post-mortem fetal and pediatric, and surgical adult liver samples. mRNA expression was quantified using RNA sequencing, and genetic variants with TaqMan assays. We explored relationships between protein expression and age (gestational age [GA], postnatal age [PNA], and postmenstrual age); between protein and mRNA expression; and between protein expression and genotype.

RESULTS

We analyzed 36 fetal (median GA 23.4 weeks [range 15.3-41.3]), 12 premature newborn (GA 30.2 weeks [24.9-36.7], PNA 1.0 weeks [0.14-11.4]), 10 term newborn (GA 40.0 weeks [39.7-41.3], PNA 3.9 weeks [0.3-18.1]), 4 pediatric (PNA 4.1 years [1.1-7.4]) and 8 adult liver samples. A relationship with age was found for BCRP, BSEP, GLUT1, MDR1, MRP1, MRP2, MRP3, NTCP, OATP1B1 and OCT1, with the strongest relationship for postmenstrual age. For most transporters mRNA and protein expression were not correlated. No genotype-protein expression relationship was detected.

DISCUSSION AND CONCLUSION

Various developmental patterns of protein expression of hepatic transporters emerged in fetuses and newborns up to four months of age. Postmenstrual age was the most robust factor predicting transporter expression in this cohort. Our data fill an important gap in current pediatric transporter ontogeny knowledge.

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.

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

Organic anion transporting polypeptides 1B1 and 1B3 are transporters selectively expressed on the basolateral membrane of the hepatocyte. Several studies reveal that they are involved in drug–drug interactions, cancer, and hyperbilirubinemia. In this study, we developed a set of classification models for OATP1B1 and 1B3 inhibition based on more than 1700 carefully curated compounds from literature, which were validated via cross-validation and by use of an external test set. After combining several sets of descriptors and classifiers, the 6 best models were selected according to their statistical performance and were used for virtual screening of DrugBank. Consensus scoring of the screened compounds resulted in the selection and purchase of nine compounds as potential dual inhibitors and of one compound as potential selective OATP1B3 inhibitor. Biological testing of the compounds confirmed the validity of the models, yielding an accuracy of 90% for OATP1B1 and 80% for OATP1B3, respectively. Moreover, at least half of the new identified inhibitors are associated with hyperbilirubinemia or hepatotoxicity, implying a relationship between OATP inhibition and these severe side effects.

Toxicokinetic Triage for Environmental Chemicals

Toxicokinetic (TK) models link administered doses to plasma, blood, and tissue concentrations. High-throughput TK (HTTK) performs in vitro to in vivo extrapolation to predict TK from rapid in vitro measurements and chemical structure-based properties. A significant toxicological application of HTTK has been "reverse dosimetry," in which bioactive concentrations from in vitro screening studies are converted into in vivo doses (mg/kg BW/day). These doses are predicted to produce steady-state plasma concentrations that are equivalent to in vitro bioactive concentrations. In this study, we evaluate the impact of the approximations and assumptions necessary for reverse dosimetry and develop methods to determine whether HTTK tools are appropriate or may lead to false conclusions for a particular chemical. Based on literature in vivo data for 87 chemicals, we identified specific properties (eg, in vitro HTTK data, physico-chemical descriptors, and predicted transporter affinities) that correlate with poor HTTK predictive ability. For 271 chemicals we developed a generic HT physiologically based TK (HTPBTK) model that predicts non-steady-state chemical concentration time-courses for a variety of exposure scenarios. We used this HTPBTK model to find that assumptions previously used for reverse dosimetry are usually appropriate, except most notably for highly bioaccumulative compounds. For the thousands of man-made chemicals in the environment that currently have no TK data, we propose a 4-element framework for chemical TK triage that can group chemicals into 7 different categories associated with varying levels of confidence in HTTK predictions. For 349 chemicals with literature HTTK data, we differentiated those chemicals for which HTTK approaches are likely to be sufficient, from those that may require additional data.