Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor

O-linked N-acetylglucosamine (O-GlcNAc) is a unique intracellular post-translational glycosylation at the hydroxyl group of serine or threonine residues in nuclear, cytoplasmic and mitochondrial proteins. The enzyme O-GlcNAc transferase (OGT) is responsible for adding GlcNAc, and anomalies in this process can lead to the development of diseases associated with metabolic imbalance, such as diabetes and cancer. Repurposing approved drugs can be an attractive tool to discover new targets reducing time and costs in the drug design. This work focuses on drug repurposing to OGT targets by virtual screening of FDA-approved drugs through consensus machine learning (ML) models from an imbalanced dataset. We developed a classification model using docking scores and ligand descriptors. The SMOTE approach to resampling the dataset showed excellent statistical values in five of the seven ML algorithms to create models from the training set, with sensitivity, specificity and accuracy over 90% and Matthew's correlation coefficient greater than 0.8. The pose analysis obtained by molecular docking showed only H-bond interaction with the OGT C-Cat domain. The molecular dynamics simulation showed the lack of H-bond interactions with the C- and N-catalytic domains allowed the drug to exit the binding site. Our results showed that the non-steroidal anti-inflammatory celecoxib could be a potentially OGT inhibitor.

In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review

New approach methods (NAMs) have been developed to predict a wide range of toxicities through innovative technologies. Liver injury is one of the most extensively studied endpoints due to its severity and frequency, occurring among populations that consume drugs or dietary supplements. In this review, we focus on recent developments of in silico modeling for liver injury prediction using deep learning and in vitro data based on adverse outcome pathways (AOPs). Despite these models being mainly developed using datasets generated from drug-like molecules, they were also applied to the prediction of hepatotoxicity caused by herbal products. As deep learning has achieved great success in many different fields, advanced machine learning algorithms have been actively applied to improve the accuracy of in silico models. Additionally, the development of liver AOPs, combined with big data in toxicology, has been valuable in developing in silico models with enhanced predictive performance and interpretability. Specifically, one approach involves developing structure-based models for predicting molecular initiating events of liver AOPs, while others use in vitro data with structure information as model inputs for making predictions. Even though liver injury remains a difficult endpoint to predict, advancements in machine learning algorithms and the expansion of in vitro databases with relevant biological knowledge have made a huge impact on improving in silico modeling for drug-induced liver injury prediction.

Using a Graph Convolutional Neural Network Model to Identify Bile Salt Export Pump Inhibitors

The bile salt export pump (BSEP) is a key transporter involved in the efflux of bile salts from hepatocytes to bile canaliculi. Inhibition of BSEP leads to the accumulation of bile salts within the hepatocytes, leading to possible cholestasis and drug-induced liver injury. Screening for and identification of chemicals that inhibit this transporter aid in understanding the safety liabilities of these chemicals. Moreover, computational approaches to identify BSEP inhibitors provide an alternative to the more resource-intensive, gold standard experimental approaches. Here, we used publicly available data to develop predictive machine learning models for the identification of potential BSEP inhibitors. Specifically, we analyzed the utility of a graph convolutional neural network (GCNN)-based approach in combination with multitask learning to identify BSEP inhibitors. Our analyses showed that the developed GCNN model performed better than the variable-nearest neighbor and Bayesian machine learning approaches, with a cross-validation receiver operating characteristic area under the curve of 0.86. In addition, we compared GCNN-based single-task and multitask models and evaluated their utility in addressing data limitation challenges commonly observed in bioactivity modeling. We found that multitask models performed better than single-task models and can be utilized to identify active molecules for targets with limited data availability. Overall, our developed multitask GCNN-based BSEP model provides a useful tool for prioritizing hits during early drug discovery and in risk assessment of chemicals.

Using Jupyter Notebooks for re-training machine learning models

Machine learning (ML) models require an extensive, user-driven selection of molecular descriptors in order to learn from chemical structures to predict actives and inactives with a high reliability. In addition, privacy concerns often restrict the access to sufficient data, leading to models with a narrow chemical space. Therefore, we propose a framework of re-trainable models that can be transferred from one local instance to another, and further allow a less extensive descriptor selection. The models are shared via a Jupyter Notebook, allowing the evaluation and implementation of a broader chemical space by keeping most of the tunable parameters pre-defined. This enables the models to be updated in a decentralized, facile, and fast manner. Herein, the method was evaluated with six transporter datasets (BCRP, BSEP, OATP1B1, OATP1B3, MRP3, P-gp), which revealed the general applicability of this approach.

FXR expression in rats of hilar cholangiocarcinoma

The study objective was to detect the expression of farnesoid X receptor (FXR) in a rat model of hilar cholangiocarcinoma to provide a new therapeutic target for gene therapy in hilar cholangiocarcinoma. Sixty male Wistar rats (weighing 190 ± 8 g) were randomly divided into three groups (experimental group, control group and sham operation group, 20 rats in each group). The three groups were fed a standard diet. The QBC939 cell suspension of cholangiocarcinoma was injected into the hilar bile duct in the experimental group with a microsyringe. The control group was injected with normal saline, and the sham operation group was not injected with any drugs. A modified tail suspension test (TST) was used to evaluate the mental state and physical activity of rats every day. At 5 weeks, one rat in the experimental group was euthanized, and the changes in the hilar bile duct were recorded. The procedure was repeated at one and half months. After one and half months, hilar cholangiocarcinoma only occurred in the experimental group. Pathological examination confirmed the formation of tumours, and hilar bile duct tissues were taken from the three groups. FXR expression in the hilar bile duct was detected by real-time polymerase chain reaction (RT-PCR) and immunohistochemistry. After two weeks, the rats in the experimental group ate less, and their weight was significantly reduced. One and half months later, hilar cholangiocarcinoma was detected in 16 rats in the experimental group. The levels of alanine aminotransferase and aspartate transaminase in the experimental group were higher than those in the other two groups. The ratio of FXR/GAPDH mRNA was significantly different among the hilar cholangiocarcinoma, control and sham operation groups. Under the light microscope, FXR protein reacted with anti-FXR antibody and showed granular expression. Every pathological section included 4800 cells. A total of 1856 positive cells were in the experimental group, 3279 positive cells were in the control group, and 3371 positive cells were in the sham operation group. FXR expression in the hilar cholangiocarcinoma of rats was significantly lower than that in normal hilar bile duct tissues, suggesting that drugs targeting FXR may be a new strategy for the treatment of hilar cholangiocarcinoma.

NTCP Change in Rats of Hilar Cholangiocarcinoma and Therapeutic Significance

Background: The study aims to detect the expression of Na⁺/taurocholate cotransporter polypeptide in hilar cholangiocarcinoma of rat model, to provide a new therapeutic target for gene therapy of hilar cholangiocarcinoma. Methods: 60 male Wistar rats (weighing 190 ± 8 g) were randomly divided into 3 groups (experimental group, control group, and sham operation group; 20 rats in each group). The 3 groups were fed with standard diet. The QBC939 cell suspension of cholangiocarcinoma was injected into the hilar bile duct in the experimental group with a micro syringe. The control group was injected with normal saline, and the sham operation group was not injected with any drugs. Comprehensive behavior score and Basso Beattie Bresnahan were used to evaluate the mental state and exercise of rats every day. At 5 weeks, one rat in the experimental group was killed, and the changes in hilar bile duct were recorded. The procedure was repeated at one and half months. After one and half months, hilar cholangiocarcinoma only occurred in the experimental group. Pathological examination confirmed the formation of tumor, and hilar bile duct tissues were taken from the 3 groups. Na⁺/taurocholate cotransporter polypeptide expression in hilar bile duct was detected by real-time polymerase chain reaction and immunohistochemistry. Results: After 2 weeks, the rats in experimental group ate less, and their weight was significantly reduced compared with the other 2 groups. One and half months later, hilar cholangiocarcinoma was detected in 16 rats in the experimental group. The levels of alanine aminotransferase and aspartate transaminase in the experimental group were higher than those in the other 2 groups. The ratio of Na⁺/taurocholate cotransporter polypeptide/GAPDH mRNA in hilar cholangiocarcinoma, control group, and sham operation group was significantly different. Under the light microscope, Na⁺/taurocholate cotransporter polypeptide protein reacted with anti-Na⁺/taurocholate cotransporter polypeptide antibody and showed granular expression. Every pathological section included 4800 cells. 3823 positive cells were in the experimental group, 1765 positive cells were in the control group, and 1823 positive cells were in the sham operation group. Conclusions: Na⁺/taurocholate cotransporter polypeptide expression in hilar cholangiocarcinoma of rats was significantly higher than normal hilar bile duct tissues, suggesting that drugs targeting Na⁺/taurocholate cotransporter polypeptide may be a new strategy for the treatment of hilar cholangiocarcinoma.

Hybrid In Silico Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC50 ≤ 15 μM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown to inhibit the entry of pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.

Bsep expression in hilar cholangiocarcinoma of rat model

Develop a rat model of hilar cholangiocarcinoma for detecting bile salt export pump (Bsep) expression in hilar cholangiocarcinoma tissues, in order to provide a new therapeutic target for the gene therapy of hilar cholangiocarcinoma. Sixty male Wistar rats (body weight, 190 ± 8 g) were randomly divided into three groups (the experimental group, the control group and the sham operation group, n = 20 each) as follows: The three groups were fed a standard diet, the experimental group was injected by cholangiocarcinoma QBC939 cell suspension along the hilar bile duct into the bile duct bifurcation with microsyringe, the control group was injected by normal saline, the sham operation group did not inject anything. Every day assess the rats’ mental state, diet, and motion by using Basso–Beattie–Bresnahan and combined behavioral score. At 4 weeks, one rat of the experimental group was sacrificed after it was administered anesthesia, and we recorded changes in hilar bile duct size, texture, and form. This procedure was repeated at 6 weeks. After 6 weeks, hilar cholangiocarcinoma developed only in the experimental group, thereby establishing an experimental model for studying QBC939-induced hilar cholangiocarcinoma. Tumor formation was confirmed by pathological examination, and hilar bile duct tissues were harvested from both the groups. A real-time polymerase chain reaction assay and an immunohistochemical assay were used to analyze the expression of Bsep in hilar bile duct tissues of each group. From the second week, the rats in experimental group began to eat less, and their body mass decreased compared with control group and sham operation group. After 6 weeks, we detected hilar cholangiocarcinoma in the hilar bile duct tissues of 18 rats (90%) in the experimental group. In the experimental group with hilar cholangiocarcinoma, we found that the levels of total cholesterol, total bilirubin, and direct bilirubin were higher compared with those in the control group and sham operation group. Simultaneously, muddy stones emerged from the bile ducts of rats in the experimental group. The Bsep/Gapdh mRNA ratio in hilar cholangiocarcinoma, control group and sham operation group differed markedly. Light microscopy revealed a granular pattern of Bsep protein expression which reacted with the anti-Bsep antibody. Each section was randomly divided into six regions, with 80 cells were observed in every region. Sections with > 10% positive cells were designated positive, Sections with < 10% positive cells were designated negative. Each group included 4800 cells. In the experimental group, 1200 cells (25%) were positive, in the control group, 3648 cells (76%) were positive and in the sham operation group 3598 cells (75%) were positive, and this difference was statistically significant. Bsep expression significantly decreased in hilar cholangiocarcinoma of rats than those in control group and sham operation group, suggesting that drugs targeting Bsep are a new strategy for hilar cholangiocarcinoma.

Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump

Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. We assessed the performance effects of different methods of chemical featurization, data set partitioning, and class labeling and identified the methods producing models that generalized best to novel chemical entities.

The Bile Salt Export Pump: Molecular Structure, Study Models and Small-Molecule Drugs for the Treatment of Inherited BSEP Deficiencies

The bile salt export pump (BSEP/ABCB11) is responsible for the transport of bile salts from hepatocytes into bile canaliculi. Malfunction of this transporter results in progressive familial intrahepatic cholestasis type 2 (PFIC2), benign recurrent intrahepatic cholestasis type 2 (BRIC2) and intrahepatic cholestasis of pregnancy (ICP). Over the past few years, several small molecular weight compounds have been identified, which hold the potential to treat these genetic diseases (chaperones and potentiators). As the treatment response is mutation-specific, genetic analysis of the patients and their families is required. Furthermore, some of the mutations are refractory to therapy, with the only remaining treatment option being liver transplantation. In this review, we will focus on the molecular structure of ABCB11, reported mutations involved in cholestasis and current treatment options for inherited BSEP deficiencies.

In silico toxicity evaluation of Salubrinal and its analogues

This paper reports on a comprehensive in silico toxicity assessment of Salubrinal and its analogues containing a cinnamic acid residue or quinoline ring using the online servers admetSAR, ADMETlab, ProTox, ADVERPred, Pred-hERG and Vienna LiverTox. Apart from rare exceptions, in all 55 studied structures, mild or practical absence of acute toxicity was predicted for rats (III or IV toxicity class). Cardiotoxic, hepatotoxic and immunotoxic effects were predicted for Salubrinal and its analogues. We constructed models of the main predicted anti-targets hERG, BSEP, MRP3, MRP4 and AhR using the principle of homologous modeling. Molecular docking studies were carried out with the obtained models. We carried out molecular docking for all targets using AutoDock Vina, implemented in the PyRx 0.8 software package. According to the results of molecular docking, the compounds analyzed are potential moderate or weak hERG blockers. Induction of cholestasis and, as a consequence, liver damage by these drugs, directly related to inhibition of BSEP, MRP3 and MRP4, most likely will not be observed. Interaction with AhR for the studied compounds is impossible for steric reasons and, as a consequence, toxic effects on the immune and other organ systems associated with the activation of the AhR signaling pathway are excluded.

Drug Induced Liver Injury (DILI). Mechanisms and Medicinal Chemistry Avoidance/Mitigation Strategies

Adverse drug reactions (ADRs) are a common cause of attrition in drug discovery and development and drug-induced liver injury (DILI) is a leading cause of preclinical and clinical drug terminations. This perspective outlines many of the known DILI mechanisms and assessment methods used to evaluate and mitigate DILI risk. Literature assessments and retrospective analyses using verified DILI-associated drugs from the Liver Tox Knowledge Base (LTKB) have been used to derive the predictive value of each end point, along with combination approaches of multiple methods. In vitro assays to assess inhibition of the bile salt export pump (BSEP), mitotoxicity, reactive metabolite (RM) formation, and hepatocyte cytolethality, along with physicochemical properties and clinical dose provide useful DILI predictivity. This Perspective also highlights some of the strategies used by medicinal chemists to reduce DILI risk during the optimization of drug candidates.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

Can Bile Salt Export Pump Inhibition Testing in Drug Discovery and Development Reduce Liver Injury Risk? An International Transporter Consortium Perspective

Bile salt export pump (BSEP) inhibition has emerged as an important mechanism that may contribute to the initiation of human drug‐induced liver injury (DILI). Proactive evaluation and understanding of BSEP inhibition is recommended in drug discovery and development to aid internal decision making on DILI risk. BSEP inhibition can be quantified using in vitro assays. When interpreting assay data, it is important to consider in vivo drug exposure. Currently, this can be undertaken most effectively by consideration of total plasma steady state drug concentrations (C_ss,plasma). However, because total drug concentrations are not predictive of pharmacological effect, the relationship between total exposure and BSEP inhibition is not causal. Various follow‐up studies can aid interpretation of in vitro BSEP inhibition data and may be undertaken on a case‐by‐case basis. BSEP inhibition is one of several mechanisms by which drugs may cause DILI, therefore, it should be considered alongside other mechanisms when evaluating possible DILI risk.

Discovery of Potent Inhibitors for the Large Neutral Amino Acid Transporter 1 (LAT1) by Structure-Based Methods

The large neutral amino acid transporter 1 (LAT1) is a promising anticancer target that is required for the cellular uptake of essential amino acids that serve as building blocks for cancer growth and proliferation. Here, we report a structure-based approach to identify chemically diverse and potent inhibitors of LAT1. First, a homology model of LAT1 that is based on the atomic structures of the prokaryotic homologs was constructed. Molecular docking of nitrogen mustards (NMs) with a wide range of affinity allowed for deriving a common binding mode that could explain the structure−activity relationship pattern in NMs. Subsequently, validated binding hypotheses were subjected to molecular dynamics simulation, which allowed for extracting a set of dynamic pharmacophores. Finally, a library of ~1.1 million molecules was virtually screened against these pharmacophores, followed by docking. Biological testing of the 30 top-ranked hits revealed 13 actives, with the best compound showing an IC₅₀ value in the sub-μM range.

Interspecies comparison of putative ligand binding sites of human, rat and mouse P-glycoprotein

Prior to the clinical phases of testing, safety, efficacy and pharmacokinetic profiles of lead compounds are evaluated in animal studies. These tests are primarily performed in rodents, such as mouse and rats. In order to reduce the number of animal experiments, computational models that predict the outcome of these studies and thus aid in prioritization of preclinical candidates are heavily needed. However, although computational models for human off-target interactions with decent quality are available, they cannot easily be transferred to rodents due to lack of respective data. In this study, we assess the transferability of human P-glycoprotein activity data for development of in silico models to predict in vivo effects in rats and mouse using a structure-based approach. P-glycoprotein (P-gp) is an ATP-dependent efflux transporter that transports xenobiotic compounds such as toxins and drugs out of cells and has a broad substrate and inhibitor specificity. Being mostly expressed at barriers, it influences the bioavailability of drugs and thus contributes also to toxicity. Comparison of the binding site interaction profiles of human, rat and mouse P-gp derived from docking studies with a set of common inhibitors suggests that the inhibitors share potentially similar binding modes. These findings encourage the use of in vitro human P-gp data for predicting in vivo effects in rodents and thus contributes to the 3Rs (Replace, Reduce and Refine) of animal experiments.