Predicting liver toxicity on basis of transporter interaction profiles

Arzneimittelinduzierte Leberschädigung (DILI) ist eine der großen Herausforderungen für die Pharmaindustrie und stellt einer der Hauptgründe für das Scheiterns neuer Substanzen während klinischer und präklinischer Phasen dar. Weiters ist DILI der primäre Grund für den Rückruf vom Markt. Es besteht daher die dringende Notwendigkeit, eine potenzielle Hepatotoxizität möglichst früh zu erkennen. Allerdings ist die Vorhersage ist aufgrund der Komplexität des klinischen Endpunkts und möglicher idiosynkratischer Lebertoxizität eine schwierige Aufgabe. In den letzten Jahren rückten Transporter in der Leber durch ihre Rolle bei der Entwicklung von arzneimittelinduzierter Hepatotoxizität ins Zentrum der Aufmerksameit. Die Literatur berichtet über mehrere Transporter, unter anderem die Gallensalz-Export-Pumpe (BSEP), das Brustkrebs-Resistenzprotein (BCRP), P-Glykoprotein (P-gp), und die Organo-Anion-Transporter 1B1 und 1B3 (OATP1B1 und OATP1B3) . Die Hauptthemen der vorliegenden Arbeit beinhalten die Modellierung klinischer Endpunkte von Lebertoxizität und allgemeiner arzneimittelinduzierter Hepatotoxizität durch Kombination von Informationen über die Hemmung der zuvor genannten Leber-Transporter mit molekularen Deskriptoren. Anstelle der fehlenden in vitro Daten wurden Vorhersagen der Transporterhemmung verwendet. Nachdem für BSEP, BCRP und P-gp lagen bereits in silico Modelle in der Arbeitsgruppe vorhanden waren, wurden zuerst Klassifikationsmodelle für die Hemmung von OATP1B1 und OATP1B3 entwickelt. Die untersuchten Endpunkte waren medikamenteninduzierte Leberschädigung, Hyperbilirubinämie und Cholestase. Für den Einfluss der Hemmung von Transportern wurden hauptsächlich menschliche, in einigen Fällen auch tierische Daten aus vordergründig öffentlichen Quellen verwendet, was sorgfältige Kuration erforderte. Ein Teil der tierischen in vivo Daten wurde aus dem eTOX Konsortium zur Verfügung gestellt. Aufbauend auf diesen Daten wurden mehrere Modelle sowohl für Transporter als auch für toxische Endpunkte entwickelt. Die Modellierung der Transporter war die vergleichsweise leichtere Aufgabe und führte mit einem einfachen Klassifikationsschema zu guten Ergebnissen. Für toxische Endpunkte mit einer klaren mechanistischen Basis, wie beispielsweise Cholestase, konnte ebenfalls eine Assoziation zwischen Transporter-Hemmung und Toxizität gezeigt werden. Komplexere Formen der Toxizität, wie DILI, ergaben keinen klaren Trend. Die Berücksichtigung weiterer hepatischer Transporter und auch Enzyme, die wichtige Rollen in diesem Zusammenhang spielen, wird daher für zukünftige Studien von Interesse sein.Drug-induced liver injury (DILI) is currently a major challenge for drug development in pharmaceutical industry: it is one of the main causes for attrition during clinical and pre-clinical studies and the primary reason for drug withdrawal from the market. Subsequently, there is great need for recognizing or foreseeing potential hepatotoxicity issues as early as possible. Unfortunately, predicting hepatotoxicity is not an easy task, due to the complexity of the endpoint and potential idiosyncratic phenomena. In recent years, liver transporters attracted lots of attention regarding their role in development of drug induced hepatotoxicity. There are many reports in literature for several transporters, including among others bile salt export pump (BSEP), breast cancer resistance protein (BCRP), P-glycoprotein (P-gp) and organic anion transporting polypeptide 1B1 and 1B3 (OATP1B1 and OATP1B3). Main topic of the current thesis is modeling liver toxicity endpoints, as well as general drug-induced hepatotoxicity, by combining information of the liver transporters’ inhibition aforementioned and molecular descriptors. Due to lack of in vitro data, predictions of transporters’ inhibition were used instead. For this cause, classification models for OATP1B1 and OATP1B3 inhibition were initially developed, while for the rest of BSEP, BCRP and P-gp in silico models already available in-house were used. The studied endpoints were drug-induced liver injury, hyperbilirubinemia and cholestasis. Apart from modeling, also the role of hepatic transporters’ inhibition was investigated for the cases of the toxicity endpoint. Mainly human, - and in some cases also animal - data were used. They come primarily from public sources – thus, extended careful curation was provided - while some of the animal in vivo data were provided from the eTOX consortium. Several models were developed, both for transporters and toxicity endpoints, with some of them yielding very satisfactory performance. In general, the modeling of the transporters was a comparably easier task and gave better results with simpler classification schema. For toxicity endpoints with a more straightforward mechanistic basis, like cholestasis, association between transporter inhibition and toxicity was also shown. For more general forms of toxicity, like DILI, there was no clear trend. Of course, there are more hepatic transporters, as well as enzymes, playing an important role and their inclusion in a further study would be interesting

Predicting Drug-Induced Cholestasis with the Help of Hepatic TransportersAn <i>in Silico</i> 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 <i>k</i>-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)

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

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 gener ated 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

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

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.© 2017 The Author

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

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.© The Author(s) 201