Selectivity profiling of BCRP versus P-gp inhibition: from automated collection of polypharmacology data to multi-label learning

Additional file 1. The list of descriptor names, instructions on how to run the python script, the distribution plots for the important descriptors, the heat map of activities for the dense dataset, the structure of the over-represented scaffolds in the sparse dataset, a 2D representation of a PCA run on Morgan fingerprints (ECFP-like) for both dense and sparse datasets, and the structures of the 9 misclassified compounds

Structural dissection of 13-epiestrones based on the interaction with human Organic anion-transporting polypeptide, OATP2B1

Human OATP2B1 encoded by the SLCO2B1 gene is a multispecific transporter mediating the cellular uptake of large, organic molecules, including hormones, prostaglandins and bile acids. OATP2B1 is ubiquitously expressed in the human body, with highest expression levels in pharmacologically relevant barriers, like enterocytes, hepatocytes and endothelial cells of the blood-brain-barrier. In addition to its endogenous substrates, OATP2B1 also recognizes clinically applied drugs, such as statins, antivirals, antihistamines and chemotherapeutic agents and influences their pharmacokinetics. On the other hand, OATP2B1 is also overexpressed in various tumors. Considering that elevated hormone uptake by OATP2B1 results in increased cell proliferation of hormone dependent tumors (e.g. breast or prostate), inhibition of OATP2B1 can be a good strategy to inhibit the growth of these tumors. 13-epiestrones represent a potential novel strategy in the treatment of hormone dependent cancers by the suppression of local estrogen production due to the inhibition of the key enzyme of estrone metabolism, 17ß-hydroxysteroid-dehydrogenase type 1 (HSD17ß1). Recently, we have demonstrated that various phosphonated 13-epiestrones are dual inhibitors also suppressing OATP2B1 function. In order to gain better insights into the molecular determinants of OATP2B1 13-epiestrone interaction we investigated the effect of C-2 and C-4 halogen or phenylalkynyl modified epiestrones on OATP2B1 transport function. Potent inhibitors (with EC50 values in the low micromolar range) as well as non-inhibitors of OATP2B1 function were identified. Based on the structure-activity relationship (SAR) of the various 13-epiestrone derivatives we could define structural elements important for OATP2B1 inhibition. Our results may help to understand the drug/inhibitor interaction profile of OATP2B1, and also may be a useful strategy to block steroid hormone entry into tumors

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 OATP2B1are 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 normalmode 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

Adverse outcome pathways:opportunities, limitations and open questions

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field

AI3SD Video: Data-Driven Molecular Design in Computational Toxicology

Timely drug discovery and toxicology approaches have seen a rise in strategies which use data as a basis for decisions at various stages. Such approaches include (automated) data integration and curation efforts, predictive machine learning approaches, as well as structure-based molecular design strategies that make use of the wealth of publicly available data sources and data types. In this talk, various computational workflows which have been developed in my lab for addressing research questions related to toxicology will be presented. In one project, ligand- and structure-based methods have been combined in an effective data-driven manner to decipher the molecular basis of ligand recognition and selectivity for hepatic Organic Anion Transporting Polypeptides (OATPs). In the framework of this successful project, novel highly potent inhibitors of these SLC uptake transporters have been identified by an AI-driven virtual screening approach. At the other end of the spectrum, we are using target-agnostic information if the underlying mechanism of toxicity is insufficiently understood. Such approaches allow to leverage in vivo data for building predictive machine learning models but they also make the incorporation of in vitro bioactivity data possible. Another example will illustrate how data integration strategies can be used to consolidate Adverse Outcome Pathway (AOP) hypotheses, which are effective tools in toxicology and risk assessment to capture mechanistic knowledge of critical toxicological effects that span over different layers of biological organization