In Silico and in Vitro Assessment of OATP1B1 Inhibition in Drug Discovery

Coproporphyrin I as an in vitro fluorescent probe to measure OATP1B1 transport activity

The organic anion transporting polypeptide 1B1 (OATP1B1) is a major pharmacologically relevant hepatic uptake transporter that mediates the clearance of many drugs. The OATP1B1 substrate coproporphyrin I (CPI) has been continually shown to be a reliable and specific clinical biomarker of transport activity. These investigations, and others that have characterized OATP1B activity and transport kinetics, have largely relied on expensive, time-consuming, and nonfluorescent methods to detect CPI, such as liquid chromatography-tandem mass spectrometry. In consideration of porphyrin fluorescent properties, we hypothesized that CPI fluorescence can serve as a marker of OATP1B1 transport activity. Cellular accumulation of CPI specifically due to OATP1B1 was measured via fluorescence in the presence or absence of various Food and Drug Administration-approved tyrosine kinase inhibitors (TKIs). Our findings indicate that CPI fluorescence is an appropriate marker of OATP1B1 in vitro activity in overexpressing HEK293 cells. It was also observed that nilotinib, a TKI previously reported as an OATP1B1 inhibitor, could reduce 50% of CPI uptake at a concentration of 1.0 ± 0.5 μM. Using CPI and 8-(2-[Fluoresceinyl]aminoethylthio) adenosine- 3', 5'- cyclic monophosphate, 15 other TKIs were identified as potential OATP1B1 inhibitors, including tivozanib, which was observed to inhibit 50% of OATP1B1 activity at 4.0 ± 2.0 μM. Overall, our findings provide evidence to show that CPI fluorescence can be used as a method to assess OATP1B1-mediated transport in vitro and investigate the potential for drug-drug interactions. SIGNIFICANCE STATEMENT: This paper outlines a methodology for assessing coproporphyrin I accumulation in vitro specific to organic anion transporting polypeptide 1B1 (OATP1B1)-mediated transport. Coproporphyrin I is a reported sensitive clinical biomarker of OATP1B1 activity, and its fluorescent properties serve to provide a substrate with translational relevance in measuring drug-drug interactions in vitro with a low cost and time requirement. This method confirms nilotinib as an effective OATP1B1 inhibitor and identifies new inhibitors that can potentially promote life-threatening drug interactions using a clinically relevant biomarker.

Drug transporters in drug disposition - the year 2022 in review

On behalf of all the authors, I am pleased to share our third annual review on drug transporter science with an emphasis on articles published and deemed influential in signifying drug transporters' role in drug disposition in the year 2022. As the drug transporter field is rapidly evolving several key findings were noted including promising endogenous biomarkers, rhythmic activity, IVIVE approaches in transporter-mediated clearance, new modality interaction, and transporter effect on gut microbiome. As identified previously (Chothe et Cal. 2021, 2022) the goal of this review is to highlight key findings without a comprehensive overview of each article and to this end, each coauthor independently selected 1-3 peer-reviewed articles published or available online in the year 2022 (Table 1). Each article is summarized in synopsis and commentary with unbiased viewpoints by each coauthor. We strongly encourage readers to consult original articles for specifics of the study. Finally, I would like to thank all coauthors for their continued support in writing this annual review on drug transporters and invite anyone interested in contributing to future versions of this review.

Ethylmethylhydroxypyridine Succinate Is an Inhibitor but Not a Substrate of ABCB1 and SLCO1B1

2-Ethyl-6-methyl-3-hydroxypyridine succinate (EMHPS, Mexidol) is an original antioxidant and an anti-ischemic drug with the possibility of wide applications in the complex therapy of diseases, accompanied by the development of oxidative stress and ischemia; for example, ischemic stroke, chronic cerebral ischemia, and chronic heart failure. The use of EMHPS in the complex therapy of the above diseases may cause the development of drug-drug interactions, particularly pharmacokinetic interactions at the level of transporter proteins. In the present study, we evaluated the interaction of EMHPS with ABCB1 and SLCO1B1. In Caco-2 cells, it was shown that EMHPS is not a substrate of ABCB1 and that it does not affect its expression, but at the same time, it inhibits the activity of this transporter. Its inhibitory activity was inferior to verapamil-a classic inhibitor of ABCB1. In HEK293 and HEK293-SLCO1B1 cells, it was shown that EMHPS is not a substrate of SLCO1B1 either, but that it inhibited the activity of the transporter. However, its inhibitory activity was inferior to the classic inhibitor of SLCO1B1-rifampicin. Furthermore, it was found out that EMHPS does not affect SLCO1B1 expression in HepG2 cells. The approach proposed by the FDA (2020) and the International Transporter Consortium (2010) was used to assess the clinical significance of the study results. The effect of EMHPS on SLCO1B1 and the systemic inhibition of ABCB1 by EMPHS are not clinically significant, but ABCB1 inhibition by EMHPS in the gastrointestinal tract should be tested in vivo through clinical trials.

Identification of Organic Anion Transporter 2 Inhibitors: Screening, Structure-Based Analysis, and Clinical Drug Interaction Risk Assessment

Organic anion transporter 2 (OAT2 or SLC22A7) plays an important role in the hepatic uptake and renal secretion of several endogenous compounds and drugs. The goal of this work is to understand the structure activity of OAT2 inhibition and assess clinical drug interaction risk. A single-point inhibition assay using OAT2-transfected HEK293 cells was employed to screen about 150 compounds; and concentration-dependent inhibition potency (IC₅₀) was measured for the identified "inhibitors". Acids represented about 65% of all inhibitors, and the frequency of bases-plus-zwitterions approximately doubled for "non-inhibitors". Interestingly, 9 of 10 most potent inhibitors (low IC₅₀) are acids (pK_a ∼ 3-5). Additionally, inhibitors are significantly larger and lipophilic than non-inhibitors. In silico (binary) models were developed to identify inhibitors and non-inhibitors. Finally, in vivo risk assessed via static drug-drug interaction models identified several inhibitors with potential for renal and hepatic OAT2 inhibition at clinical doses. This is the first study assessing the global pattern of OAT2-ligand interactions.

Studying the right transporter at the right time: an in vitro strategy for assessing drug-drug interaction risk during drug discovery and development

INTRODUCTION

Transporters are significant in dictating drug pharmacokinetics, thus inhibition of transporter function can alter drug concentrations resulting in drug-drug interactions (DDIs). Because they can impact drug toxicity, transporter DDIs are a regulatory concern for which prediction of clinical effect from in vitro data is critical to understanding risk.

AREA COVERED

The authors propose in vitro strategies to assist mitigating/removing transporter DDI risk during development by frontloading specific studies, or managing patient risk in the clinic. An overview of clinically relevant drug transporters and observed DDIs are provided, alongside presentation of key considerations/recommendations for in vitro study design evaluating drugs as inhibitors or substrates. Guidance on identifying critical co-medications, clinically relevant disposition pathways and using mechanistic static equations for quantitative prediction of DDI is compiled.

EXPERT OPINION

The strategies provided will facilitate project teams to study the right transporter at the right time to minimise development risks associated with DDIs. To truly alleviate or manage clinical risk, the industry will benefit from moving away from current qualitative basic static equation approaches to transporter DDI hazard assessment towards adopting the use of mechanistic models to enable quantitative DDI prediction, thereby contextualising risk to ascertain whether a transporter DDI is simply pharmacokinetic or clinically significant requiring intervention.

Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design

The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning prediction models arrive at their decisions, which are called explainable artificial intelligence. First, we developed the prediction models for metabolic stability, CYP inhibition, and P-gp and BCRP substrate recognition using 265 physicochemical parameters for designing the molecular structures. Four important parameters, including the well-known indicator h_logD, are common in some in vitro studies; as such, these can be used to optimize compounds simultaneously to address multiple pharmacokinetic concerns. Next, we developed machine learning models that had been programmed to show structurally active sites. Many types of machine learning models were developed using the results of in vitro metabolic stability study of around 30000 in-house compounds. The metabolic sites of in-house compounds predicted using some prediction models matched experimentally identified metabolically active sites, with a ratio of number of metabolic sites (predicted/actual) of over 90%. These models can be applied to several screening projects. These two approaches can be employed for obtaining lead compounds with desirable pharmacokinetic profiles efficiently.

Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model

We constructed machine learning-based pharmacokinetic prediction models with very high performance. The models were trained on 26138 and 16613 compounds involved in metabolic stability and cytochrome P450 inhibition, respectively. Because the compound features largely differed between the publicly available and in-house compounds, the models learned on the public data could not predict the in-house compounds, suggesting that outside of a certain applicability domain (AD), the prediction results are unreliable and can mislead the design of novel compounds. To exclude the uncertain prediction results, we constructed another machine learning model that determines whether the newly designed compound is inside or outside the AD. The AD was evaluated multi-dimensionally with some explanatory variables: The structural similarities and the probability obtained from the pharmacokinetic prediction model. The accuracy of predicting metabolic stability was 79.9% on the test set, increasing significantly to 93.6% after excluding the low-reliability compounds. The model properly classified the reliability of the compounds. After learning on the in-house compounds, the reliability model classified almost all (90%) of the public compounds as low reliability, indicating that the AD was properly determined by the model.

Protein kinase inhibitors' classification using K-Nearest neighbor algorithm

Protein kinases are enzymes acting as a source of phosphate through ATP to regulate protein biological activities by phosphorylating groups of specific amino acids. For that reason, inhibiting protein kinases with an active small molecule plays a significant role in cancer treatment. To achieve this aim, computational drug design, especially QSAR model, is one of the best economical approaches to reduce time and save in costs. In this respect, active inhibitors are attempted to be distinguished from inactive ones using hybrid QSAR model. Therefore, genetic algorithm and K-Nearest Neighbor method were suggested as a dimensional reduction and classification model, respectively. Finally, to evaluate the proposed model's performance, support vector machine and Naïve Bayesian algorithm were examined. The outputs of the proposed model demonstrated significant superiority to other QSAR models.

In Silico Studies for Bacterystic Evaluation against Staphylococcus aureus of 2-Naphthoic Acid Analogues

Background:

Staphylococcus aureus is a gram-positive spherical bacterium commonly present in nasal fossae and in the skin of healthy people; however, in high quantities, it can lead to complications that compromise health. The pathologies involved include simple infections, such as folliculitis, acne, and delay in the process of wound healing, as well as serious infections in the CNS, meninges, lung, heart, and other areas.

Aim:

This research aims to propose a series of molecules derived from 2-naphthoic acid as a bioactive in the fight against S. aureus bacteria through in silico studies using molecular modeling tools.

Methods:

A virtual screening of analogues was done in consideration of the results that showed activity according to the prediction model performed in the KNIME Analytics Platform 3.6, violations of the Lipinski rule, absorption rate, cytotoxicity risks, energy of binder-receptor interaction through molecular docking, and the stability of the best profile ligands in the active site of the proteins used (PDB ID 4DXD and 4WVG).

Results:

Seven of the 48 analogues analyzed showed promising results for bactericidal action against S. aureus.

Conclusion:

It is possible to conclude that ten of the 48 compounds derived from 2-naphthoic acid presented activity based on the prediction model generated, of which seven presented no toxicity and up to one violation to the Lipinski rule.

Membrane transporter data to support kinetically-informed chemical risk assessment using non-animal methods: Scientific and regulatory perspectives

Humans are continuously exposed to low levels of thousands of industrial chemicals, most of which are poorly characterised in terms of their potential toxicity. The new paradigm in chemical risk assessment (CRA) aims to rely on animal-free testing, with kinetics being a key determinant of toxicity when moving from traditional animal studies to integrated in vitro-in silico approaches. In a kinetically informed CRA, membrane transporters, which have been intensively studied during drug development, are an essential piece of information. However, how existing knowledge on transporters gained in the drug field can be applied to CRA is not yet fully understood. This review outlines the opportunities, challenges and existing tools for investigating chemical-transporter interactions in kinetically informed CRA without animal studies. Various environmental chemicals acting as substrates, inhibitors or modulators of transporter activity or expression have been shown to impact TK, just as drugs do. However, because pollutant concentrations are often lower in humans than drugs and because exposure levels and internal chemical doses are not usually known in contrast to drugs, new approaches are required to translate transporter data and reasoning from the drug sector to CRA. Here, the generation of in vitro chemical-transporter interaction data and the development of transporter databases and classification systems trained on chemical datasets (and not only drugs) are proposed. Furtheremore, improving the use of human biomonitoring data to evaluate the in vitro-in silico transporter-related predicted values and developing means to assess uncertainties could also lead to increase confidence of scientists and regulators in animal-free CRA. Finally, a systematic characterisation of the transportome (quantitative monitoring of transporter abundance, activity and maintenance over time) would reinforce confidence in the use of experimental transporter/barrier systems as well as in established cell-based toxicological assays currently used for CRA.

Induction of Mild Hyperbilirubinemia: Hype or Real Therapeutic Opportunity?

Observational epidemiological studies showed that mild hyperbilirubinemia has beneficial effects on the prevention of cardiovascular disease, type 2 diabetes mellitus, and metabolic syndrome. In mammals, bilirubin plays a major role as a potent antioxidant. Uridine 5'-diphospho-glucuronosyl transferase (UGT)1A1 variants coding for bilirubin UDP-glucuronosyl transferase resulting in mild hyperbilirubinemia (as in Gilbert syndrome (GS)) may confer a strong genetic advantage. Strategies to boost bioavailability of bilirubin or to mimic GS represent an attractive approach to prevent many oxidative stress and inflammation-mediated diseases. Even a tiny, micromolar increase in serum bilirubin concentrations substantially decreases the risk of oxidative stress-mediated diseases. There are several possible ways to achieve this, including lifestyle changes, changes in dietary patterns, regular physical activities, or use of chemical drug or of specific plant products either in the form of regular food items or nutraceuticals. Further basic and experimental research is required to fully uncover this promising therapeutic field.