Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data

Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery.

Cell Painting, gene expression, and chemical structural data are used to examine the differences between mitochondrial toxicants and non-toxicants and enhance the detection of mitotoxic compounds for future drug discovery.

Potential inhibitors of the main protease of SARS-CoV-2 and modulators of arachidonic acid pathway: Non-steroidal anti-inflammatory drugs against COVID-19

The main protease of SARS-CoV-2 is one of the key targets to develop and design antiviral drugs. There is no general agreement on the use of non-steroidal anti-inflammatory drugs (NSAIDs) in COVID-19. In this study, we investigated NSAIDs as potential inhibitors for chymotrypsin-like protease (3CLpro) and the main protease of the SARS-CoV-2 to find out the best candidates, which can act as potent inhibitors against the main protease. We also predicted the effect of NSAIDs on the arachidonic pathway and evaluated the hepatotoxicity of the compounds using systems biology techniques. Molecular docking was conducted via AutoDock Vina to estimate the interactions and binding affinities between selected NSAIDs and the main protease. Molecular docking results showed the presence of 10 NSAIDs based on lower binding energy (kcal/mol) toward the 3CLpro inhibition site compared to the co-crystal native ligand Inhibitor N3 (-6.6 kcal/mol). To validate the docking results, molecular dynamic (MD) simulations on the top inhibitor, Talniflumate, were performed. To obtain differentially-expressed genes under the 27 NSAIDs perturbations, we utilized the L1000 final Z-scores from the NCBI GEO repository (GSE92742). The obtained dataset included gene expression profiling signatures for 27 NSAIDs. The hepatotoxicity of NSAIDs was studied by systems biology modeling of Disturbed Metabolic Pathways. This study highlights the new application of NSAIDs as anti-viral drugs used against COVID-19. NSAIDs may also attenuate the cytokine storm through the downregulation of inflammatory mediators in the arachidonic acid pathway.

Phytochemical Screening and Protective Effects of Prunus persica Seeds Extract on Carbon Tetrachloride-Induced Hepatic Injury in Rats

BACKGROUND AND PURPOSE

Carbon tetrachloride (CCl4) is a dynamic environmental toxin released from chemical factories and its concentration in atmosphere is accelerating at an alarming proportion. Potential presence of CCl4 in human body causes liver injury via free radical stimulated inflammatory responses.

OBJECTIVES

In this study, protective effects of hydromethanolic seeds extract of Prunus persica (PPHM) were evaluated for free radical scavenging potential in CCl4 mediated acute liver toxicity in murine model.

EXPERIMENTAL APPROACH

Followed by acute oral toxicity analysis, liver cells of Sprague-dawley (SD) rats were treated with CCl4 and subsequently chemoprophylactic effect of extract (400 mg/Kg dose) was evaluated using in vivo studies including silymarin as positive control. Biochemical parameters, staining (hematoxylin and eosin (H & E) and masson's trichome) and quantitative gene expression analysis via real-time PCR was used to evaluate hepatic damage control.

RESULTS

The results illustrated that PPHM extract exhibit strong antioxidant activity comparable to positive control, gallic acid. Research study results also demonstrated that extract treatment at 400 mg/Kg concentration is highly effective in protecting liver damage due to CCl4 exposure. Mechanistic investigations indicated the therapeutic action of PPHM was correlated with the increase in Nrf2, NQO-1 and decrease in collagen III mRNA genes expression as compared to CCl4 treated group.

CONCLUSIONS AND IMPLICATIONS

Accordingly, our research study indicated that PPHM alleviated CCl4-mediated oxidative stress through Nrf2/NQO-1 pathway, thereby protecting liver damage against environmental toxins. Our findings provide supportive evidence to suggest PPHM as a novel nontoxic hepatoprotective agent.

Biopharmaceutical and pharmacokinetic activities of oxymatrine determined by a sensitive UHPLC-MS/MS method

BACKGROUND

Oxymatrine is one of the most promising alkaloids from Sophora flavescens for its excellent pharmacological effects.

OBJECTIVE

The aim of this research is to assess the biopharmaceutical and pharmacokinetic activities of oxymatrine, and clarify its mechanisms of absorption and metabolism.

METHODS

The biological characteristics of oxymatrine were systematically investigated by UHPLC-MS/MS. The mechanisms of absorption and metabolism of oxymatrine were further clarified through incubation in rat liver microsomes and transport across Caco-2 monolayer cell absorption model.

RESULTS

It was found that the absolute oral bioavailability of oxymatrine was 26.43% and the pharmacokinetic parameters Cmax, Tmax, and t1/2 were 605.5 ng/mL, 0.75 h, and 4.181 h after oral administration, indicating that oxymatrine can be absorbed quickly. The tissue distribution tests showed that oxymatrine distributed throughout all the organs, with the small intestine accumulating the highest level followed by kidney, stomach and spleen. The Papp in Caco-2 cell line absorption model was over 1 × 10-5 and PDR 1.064, t1/2 of oxymatrine in rat liver microsome in vitro was 1.042 h, indicating that oxymatrine can be absorbed easily through passive diffusion and CYP450 enzymes could be involved in its metabolism. The plasma protein binding rate of oxymatrine was 2.78 ± 0.85%.

CONCLUSION

Oxymatrine can be absorbed into blood easily through passive diffusion, mainly distributed in the intestine, stomach, liver and spleen in vivo, and CYP450 enzymes in liver could be involved in its metabolism.

High-Content Screening for the Detection of Drug-Induced Oxidative Stress in Liver Cells

Drug-induced liver injury (DILI) remains a major cause of drug development failure, post-marketing warnings and restriction of use. An improved understanding of the mechanisms underlying DILI is required for better drug design and development. Enhanced reactive oxygen species (ROS) levels may cause a wide spectrum of oxidative damage, which has been described as a major mechanism implicated in DILI. Several cell-based assays have been developed as in vitro tools for early safety risk assessments. Among them, high-content screening technology has been used for the identification of modes of action, the determination of the level of injury and the discovery of predictive biomarkers for the safety assessment of compounds. In this paper, we review the value of in vitro high-content screening studies and evaluate how to assess oxidative stress induced by drugs in hepatic cells, demonstrating the detection of pre-lethal mechanisms of DILI as a powerful tool in human toxicology.

Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes

Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.

An integrative machine learning approach for prediction of toxicity-related drug safety

Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.

Generating Modeling Data From Repeat-Dose Toxicity Reports

Over the past decades, pharmaceutical companies have conducted a large number of high-quality in vivo repeat-dose toxicity (RDT) studies for regulatory purposes. As part of the eTOX project, a high number of these studies have been compiled and integrated into a database. This valuable resource can be queried directly, but it can be further exploited to build predictive models. As the studies were originally conducted to investigate the properties of individual compounds, the experimental conditions across the studies are highly heterogeneous. Consequently, the original data required normalization/standardization, filtering, categorization and integration to make possible any data analysis (such as building predictive models). Additionally, the primary objectives of the RDT studies were to identify toxicological findings, most of which do not directly translate to in vivo endpoints. This article describes a method to extract datasets containing comparable toxicological properties for a series of compounds amenable for building predictive models. The proposed strategy starts with the normalization of the terms used within the original reports. Then, comparable datasets are extracted from the database by applying filters based on the experimental conditions. Finally, carefully selected profiles of toxicological findings are mapped to endpoints of interest, generating QSAR-like tables. In this work, we describe in detail the strategy and tools used for carrying out these transformations and illustrate its application in a data sample extracted from the eTOX database. The suitability of the resulting tables for developing hazard-predicting models was investigated by building proof-of-concept models for in vivo liver endpoints.

New animal-free concepts and test methods for developmental toxicity and peripheral neurotoxicity

The complex toxicological fields of repeat dose organ toxicity (RDT) and developmental and reproductive toxicity (DART) still require new concepts and approaches to achieve a fully animal-free safety assessment of chemicals. One novel approach is the generation of relevant human cell types from pluripotent stem cells, and the use of such cells for the establishment of phenotypic test methods. Due to their broad endpoints, such tests capture multiple types of toxicants, i.e. they are a readout for the activation of many adverse outcome pathways (AOPs). The 2016 Lush Science Prize was awarded for the development of one such assay, the PeriTox test, which uses human peripheral neurons generated from stem cells. The assay endpoints measure various cell functions, and these give information on the potential neurotoxicity and developmental neurotoxicity hazard of test compounds. The PeriTox test method has a high predictivity and sensitivity for peripheral neurotoxicants, and thus addresses the inherent challenges in pesticide testing and drug development. Data from the test can be obtained quickly and at a relatively high-throughput, and thus, the assay has the potential to replace animal-based safety assessment during early product development or for screening potential environmental toxicants.

Legacy data sharing to improve drug safety assessment: the eTOX project

The sharing of legacy preclinical safety data among pharmaceutical companies and its integration with other information sources offers unprecedented opportunities to improve the early assessment of drug safety. Here, we discuss the experience of the eTOX project, which was established through the Innovative Medicines Initiative to explore this possibility.