Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening.

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 µM), three OATP1B1 inhibitors (2.69 to 10 µM), and five OATP1B3 inhibitors (1.53 to 10 µM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

Computational methods to study enveloped viral entry.

The protein-membrane interactions that mediate viral infection occur via loosely ordered, transient assemblies, creating challenges for high-resolution structure determination. Computational methods and in particular molecular dynamics simulation have thus become important adjuncts for integrating experimental data, developing mechanistic models, and suggesting testable hypotheses regarding viral function. However, the large molecular scales of virus-host interaction also create challenges for detailed molecular simulation. For this reason, continuum membrane models have played a large historical role, although they have become less favored for high-resolution models of protein assemblies and lipid organization. Here, we review recent progress in the field, with an emphasis on the insight that has been gained using a mixture of coarse-grained and atomic-resolution molecular dynamics simulations. Based on successes and challenges to date, we suggest a multiresolution strategy that should yield the best mixture of computational efficiency and physical fidelity. This strategy may facilitate further simulations of viral entry by a broader range of viruses, helping illuminate the diversity of viral entry strategies and the essential common elements that can be targeted for antiviral therapies.

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.

A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19.

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces (APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, we present a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basis of two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeted download of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data for GLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.

Differences in Metformin and Thiamine Uptake between Human and Mouse Organic Cation Transporter 1: Structural Determinants and Potential Consequences for Intrahepatic Concentrations.

The most commonly used oral antidiabetic drug, metformin, is a substrate of the hepatic uptake transporter OCT1 (gene name SLC22A1). However, OCT1 deficiency leads to more pronounced reductions of metformin concentrations in mouse than in human liver. Similarly, the effects of OCT1 deficiency on the pharmacokinetics of thiamine were reported to differ between human and mouse. Here, we compared the uptake characteristics of metformin and thiamine between human and mouse OCT1 using stably transfected human embryonic kidney 293 cells. The affinity for metformin was 4.9-fold lower in human than in mouse OCT1, resulting in a 6.5-fold lower intrinsic clearance. Therefore, the estimated liver-to-blood partition coefficient is only 3.34 in human compared with 14.4 in mouse and may contribute to higher intrahepatic concentrations in mice. Similarly, the affinity for thiamine was 9.5-fold lower in human than in mouse OCT1. Using human-mouse chimeric OCT1, we showed that simultaneous substitution of transmembrane helices TMH2 and TMH3 resulted in the reversal of affinity for metformin. Using homology modeling, we suggest several explanations, of which a different interaction of Leu155 (human TMH2) compared with Val156 (mouse TMH2) with residues in TMH3 had the strongest experimental support. In conclusion, the contribution of human OCT1 to the cellular uptake of thiamine and especially of metformin may be much lower than that of mouse OCT1. This may lead to an overestimation of the effects of OCT1 on hepatic concentrations in humans when using mouse as a model. In addition, comparative analyses of human and mouse orthologs may help reveal mechanisms of OCT1 transport. SIGNIFICANCE STATEMENT: OCT1 is a major hepatic uptake transporter of metformin and thiamine, but this study reports strong differences in the affinity for both compounds between human and mouse OCT1. Consequently, intrahepatic metformin concentrations could be much higher in mice than in humans, impacting metformin actions and representing a strong limitation of using rodent animal models for predictions of OCT1-related pharmacokinetics and efficacy in humans. Furthermore, OCT1 transmembrane helices TMH2 and TMH3 were identified to confer the observed species-specific differences in metformin affinity.

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.

Effect of helical kink in antimicrobial peptides on membrane pore formation.

Every cell is protected by a semipermeable membrane. Peptides with the right properties, for example Antimicrobial peptides (AMPs), can disrupt this protective barrier by formation of leaky pores. Unfortunately, matching peptide properties with their ability to selectively form pores in bacterial membranes remains elusive. In particular, the proline/glycine kink in helical peptides was reported to both increase and decrease antimicrobial activity. We used computer simulations and fluorescence experiments to show that a kink in helices affects the formation of membrane pores by stabilizing toroidal pores but disrupting barrel-stave pores. The position of the proline/glycine kink in the sequence further controls the specific structure of toroidal pore. Moreover, we demonstrate that two helical peptides can form a kink-like connection with similar behavior as one long helical peptide with a kink. The provided molecular-level insight can be utilized for design and modification of pore-forming antibacterial peptides or toxins.