Comparison of drug-induced liver injury risk between propylthiouracil and methimazole: A quantitative systems toxicology approach.

Propylthiouracil (PTU) and methimazole (MMI), two classical antithyroid agents possess risk of drug-induced liver injury (DILI) with unknown mechanism of action. This study aimed to examine and compare their hepatic toxicity using a quantitative system toxicology approach. The impact of PTU and MMI on hepatocyte survival, oxidative stress, mitochondrial function and bile acid transporters were assessed in vitro. The physiologically based pharmacokinetic (PBPK) models of PTU and MMI were constructed while their risk of DILI was calculated by DILIsym, a quantitative systems toxicology (QST) model by integrating the results from in vitro toxicological studies and PBPK models. The simulated DILI (ALT >2 × ULN) incidence for PTU (300 mg/d) was 21.2%, which was within the range observed in clinical practice. Moreover, a threshold dose of 200 mg/d was predicted with oxidative stress proposed as an important toxic mechanism. However, DILIsym predicted a 0% incidence of hepatoxicity caused by MMI (30 mg/d), suggesting that the toxicity of MMI was not mediated through mechanism incorporated into DILIsym. In conclusion, DILIsym appears to be a practical tool to unveil hepatoxicity mechanism and predict clinical risk of DILI.

Antagonistic interaction between caffeine and ketamine in zebrafish: Implications for aquatic toxicity.

The coexistence of caffeine (CF) and ketamine (KET) in surface waters across Asia has been widely reported. Previous studies have implied that CF and KET may share a mechanism of action. However, the combined toxicity of these two chemicals on aquatic organisms remains unclear at environmental levels, and the underlying mechanisms are not well understood. Here we demonstrate that KET antagonizes the adverse effects of CF on zebrafish larvae by modulating the gamma-aminobutyric acid (GABA)ergic synapse pathway. Specifically, KET (10-250 ng L-1) ameliorates the locomotor hyperactivity and impaired circadian rhythms in zebrafish larvae induced by 2 mg L-1 of CF, showing a dose-dependent relationship. Additionally, the developmental abnormalities in zebrafish larvae exposed to CF are mitigated by KET, with an incidence rate reduced from 26.7% to 6.7%. The competition between CF and KET for binding sites on the GABA-A receptor (in situ and in silico) elucidates the antagonistic interactions between the two chemicals. Following a seven-day recovery period, the adverse outcomes of CF exposure persist in the fish, whereas the changes observed in the CF + KET groups are significantly alleviated, especially with KET at 10 ng L-1. Based on these results, it is imperative to further assess the environmental risks associated with CF and KET co-pollution. This pilot study underscores the utility of systems toxicology approaches in estimating the combined toxicity of environmental chemicals on aquatic organisms. Moreover, the nighttime behavioral functions of fish could serve as a sensitive biomarker for evaluating the toxicity of psychoactive substances.

A Network Toxicology Approach for Mechanistic Modelling of Nanomaterial Hazard and Adverse Outcomes.

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network-based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data-driven approaches for the hazard assessment of nanomaterials and other advanced materials.

Prediction of the liver safety profile of a first-in-class myeloperoxidase inhibitor using quantitative systems toxicology modeling.

The novel myeloperoxidase inhibitor verdiperstat was developed as a treatment for neuroinflammatory and neurodegenerative diseases. During development, a computational prediction of verdiperstat liver safety was performed using DILIsym v8A, a quantitative systems toxicology (QST) model of liver safety.A physiologically-based pharmacokinetic (PBPK) model of verdiperstat was constructed in GastroPlus 9.8, and outputs for liver and plasma time courses of verdiperstat were input into DILIsym. In vitro experiments measured the likelihood that verdiperstat would inhibit mitochondrial function, inhibit bile acid transporters, and generate reactive oxygen species (ROS); these results were used as inputs into DILIsym, with two alternate sets of parameters used in order to fully explore the sensitivity of model predictions. Verdiperstat dosing protocols up to 600 mg BID were simulated for up to 48 weeks using a simulated population (SimPops) in DILIsym.Verdiperstat was predicted to be safe, with only very rare, mild liver enzyme increases as a potential possibility in highly sensitive individuals. Subsequent Phase 3 clinical trials found that ALT elevations in the verdiperstat treatment group were generally similar to those in the placebo group. This validates the DILIsym simulation results and demonstrates the power of QST modelling to predict the liver safety profile of novel therapeutics.

Identifying piRNAs that regulate BaP-induced lung injuries: A bottom-up approach from toxicity pathway investigation to animal validation.

PIWI-interacting RNAs (piRNAs) is an emerging class of small non-coding RNAs that has been recently reported to have functions in infertility, tumorigenesis, and multiple diseases in humans. Previously, 5 toxicity pathways were proposed from hundreds of toxicological studies that underlie BaP-induced lung injuries, and a "Bottom-up" approach was established to identify small non-coding RNAs that drive BaP-induced pulmonary effects by investigating the activation of these pathways in vitro, and the expression of the candidate microRNAs were validated in tissues of patients with lung diseases from publications. Here in this study, we employed the "Bottom-up" approach to identifying the roles of piRNAs and further validated the mechanisms in vivo using mouse acute lung injury model. Specifically, by non-coding RNA profiling in in vitro BaP exposure, a total of 3 suppressed piRNAs that regulate 5 toxicity pathways were proposed, including piR-004153 targeting CYP1A1, FGFR1, ITGA5, IL6R, NGRF, and SDHA, piR-020326 targeting CDK6, and piR-020388 targeting RASD1. Animal experiments demonstrated that tail vein injection of respective formulated agomir-piRNAs prior to BaP exposure could all alleviate acute lung injury that was shown by histopathological and biochemical evidences. Immunohistochemical evaluation focusing on NF-kB and Bcl-2 levels showed that exogenous piRNAs protect against BaP-induced inflammation and apoptosis, which further support that the inhibition of the 3 piRNAs had an important impact on BaP-induced lung injuries. This mechanism-driven, endpoint-supported result once again confirmed the plausibility and efficiency of the approach integrating in silico, in vitro, and in vivo evidences for the purpose of identifying key molecules.

A systems toxicology approach for identification of disruptions in cholesterol homeostasis after aggregated exposure to mixtures of perfluorinated compounds in humans.

Per- and polyfluoroalkyl substances (PFAS) are used in various household and industrial products. In humans, positive associations were reported between PFAS, including perfluorsulfonic acid and perfluorooctanoic acid, and cholesterol, a cardiometabolic risk factor. Animal studies show the opposite. Human-centered approaches are needed to better understand the effects of PFAS mixtures on cholesterol. Here, a systems toxicology approach is described, using a gene-centered cholesterol biokinetic model. PFAS exposure-gene expression relations from published data were introduced into the model. An existing PFAS physiologically based kinetic model was augmented with lung and dermal compartments and integrated with the cholesterol model to enable exposure-effect modeling. The final model was populated with data reflecting lifetime mixture exposure from: tolerable weekly intake values; the environment; high occupational exposures (ski waxing, PFAS industry). Results indicate that low level exposures (tolerable weekly intake, environmental) did not change cholesterol. In contrast, occupational exposures clearly resulted in internal PFAS exposure and disruption of cholesterol homeostasis, largely in line with epidemiological observations. Despite model limitations (eg, dynamic range, directionality), changes in cholesterol homeostasis were predicted for ski waxers, hitherto unknown from epidemiological studies. Here, future studies involving lipid metabolism could improve risk assessment.

Arsenic Contamination of Groundwater Is Determined by Complex Interactions between Various Chemical and Biological Processes.

At a great many locations worldwide, the safety of drinking water is not assured due to pollution with arsenic. Arsenic toxicity is a matter of both systems chemistry and systems biology: it is determined by complex and intertwined networks of chemical reactions in the inanimate environment, in microbes in that environment, and in the human body. We here review what is known about these networks and their interconnections. We then discuss how consideration of the systems aspects of arsenic levels in groundwater may open up new avenues towards the realization of safer drinking water. Along such avenues, both geochemical and microbiological conditions can optimize groundwater microbial ecology vis-à-vis reduced arsenic toxicity.

In silico prediction and a systematic toxicology-based in vivo investigation uncovering the mechanism of aquatic toxicity caused by beta-lactam antibiotics.

Recently, beta-lactam antibiotics have gained attention as significant contributors to public health and environmental issues due to their potential toxicity. Our study employed machine learning to develop a model for assessing the aquatic toxicity of beta-lactam antibiotics on zebrafish. Notably, aztreonam (AZT), a synthetic monobactam and a subclass of beta-lactam antibiotics, demonstrated developmental effects in zebrafish embryos comparable to cephalosporins, indicating a potential for toxicity. Using a systems toxicology-based approach, we identified apoptosis and metabolic disorders as the primary pathways affected by AZT and its impurity F exposure. During the administration of monobactams, we noted that ctsbb, nos2a, and dgat2, genes associated with apoptosis and the metabolic pathway, exhibited significant differential expression. Molecular docking studies were conducted to ascertain the binding affinity between monobactam compounds and their potential targets-Ctsbb, Nos2a, and Dgat2. Furthermore, our research revealed that monobactams influence pre-mRNA alternative splicing, resulting in disruptions in the expression of genes involved in hair cells, brain, spinal cord, and fin regeneration (e.g., krt4, krt5, krt17, cyt1). Notably, we observed a correlation between the levels of rpl3 and rps7 genes, both important ribosomal proteins, and the detected alternative splicing events. Overall, this study enhances our understanding of the toxicity of beta-lactam antibiotics in zebrafish by demonstrating the developmental effects of monobactams and uncovering the underlying mechanisms at the molecular level. It also identifies potential targets for further investigation into the mechanisms of toxicity and provides valuable insights for early assessment of biological toxicity associated with antibiotic pollutants.

Toxicometabolomics as a tool for next generation environmental risk assessment.

Traditionally applied methodology in environmental risk assessment (ERA) has fallen out of step with technological advancements and regulatory requirements, challenging effectiveness and accuracy of the assessments. Extensive efforts have been focused towards a transition to a more data-driven and mechanistically-based next generation risk assessment. Metabolomics can produce detailed and comprehensive molecular insight into affected biochemical processes. Combining metabolomics with environmental toxicology can help to understand the mechanisms and/or modes of action underlying toxicity of environmental pollutants and inform adverse outcome pathways, as well as facilitate identification of biomarkers to quantify effects and/or exposure. This Technical Report describes the activities and work performed within the frame of the European Food Risk Assessment Fellowship Programme (EU-FORA), implemented at the section 'Environmental Chemistry and Toxicology' at the Department of Environmental Science at Aarhus University in Denmark with synergies to an ongoing H2020 RIA project 'EndocRine Guideline Optimisation' (ERGO). In accordance with the 'training by doing' principles of the EU-FORA, the fellowship project combined the exploration of the status of scientific discussion on methodology in ERA through literature study with hands-on training, using the metabolomics analysis pipeline established at Aarhus University. For the hands-on training, an amphibian metamorphosis assay (OECD test no.231) was used as a proof-of-concept toxicometabolomics study case. Both a targeted biomarker - and an untargeted metabolomics approach was applied.

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population.

Genetically heterogenous cell lines from laboratory mice are promising tools for population-based screening as they offer power for genetic mapping, and potentially, predictive value for in vivo experimentation in genetically matched individuals. To explore this further, we derived a panel of fibroblast lines from a genetic reference population of laboratory mice (the Diversity Outbred, DO). We then used high-content imaging to capture hundreds of cell morphology traits in cells exposed to the oxidative stress-inducing arsenic metabolite monomethylarsonous acid (MMAIII). We employed dose-response modeling to capture latent parameters of response and we then used these parameters to identify several hundred cell morphology quantitative trait loci (cmQTL). Response cmQTL encompass genes with established associations with cellular responses to arsenic exposure, including Abcc4 and Txnrd1, as well as novel gene candidates like Xrcc2. Moreover, baseline trait cmQTL highlight the influence of natural variation on fundamental aspects of nuclear morphology. We show that the natural variants influencing response include both coding and non-coding variation, and that cmQTL haplotypes can be used to predict response in orthogonal cell lines. Our study sheds light on the major molecular initiating events of oxidative stress that are under genetic regulation, including the NRF2-mediated antioxidant response, cellular detoxification pathways, DNA damage repair response, and cell death trajectories.

Annexin A1 exerts analgesic effect in a mouse model of medication overuse headache.

Medication overuse headache (MOH) is a serious global condition. The interaction between headache attacks and medication overuse complicates the understanding of its pathophysiology. In this study, we developed a preclinical MOH model that incorporates these two key factors by overusing rizatriptan benzoate (RIZ, 4 mg/kg, i.g.) in a glyceryl trinitrate (GTN, 10 mg/kg, i.p.) induced chronic migraine mouse model. We observed that RIZ overuse aggravated GTN-induced cutaneous allodynia and caused a prolonged state of latent sensitization. We also detected a significant upregulation of Annexin-A1 (ANXA1), a protein mainly expressed in the microglia of the spinal trigeminal nucleus caudalis (SPVC), in GTN+RIZ mice. Intracerebroventricular injection of ANXA1-derived peptide Ac2-26 trifluoroacetic acid (TFA) (5 µg/mouse) inhibited bright light stress (BLS) induced acute allodynia via the formyl peptide receptor (FPR) in GTN+RIZ mice. These results suggest that ANXA1 may have an analgesic effect in triptan-associated MOH and could potentially serve as a therapeutic target.

Systems toxicology of complex wood combustion aerosol reveals gaseous carbonyl compounds as critical constituents.

Epidemiological studies identified air pollution as one of the prime causes for human morbidity and mortality, due to harmful effects mainly on the cardiovascular and respiratory systems. Damage to the lung leads to several severe diseases such as fibrosis, chronic obstructive pulmonary disease and cancer. Noxious environmental aerosols are comprised of a gas and particulate phase representing highly complex chemical mixtures composed of myriads of compounds. Although some critical pollutants, foremost particulate matter (PM), could be linked to adverse health effects, a comprehensive understanding of relevant biological mechanisms and detrimental aerosol constituents is still lacking. Here, we employed a systems toxicology approach focusing on wood combustion, an important source for air pollution, and demonstrate a key role of the gas phase, specifically carbonyls, in driving adverse effects. Transcriptional profiling and biochemical analysis of human lung cells exposed at the air-liquid-interface determined DNA damage and stress response, as well as perturbation of cellular metabolism, as major key events. Connectivity mapping revealed a high similarity of gene expression signatures induced by wood smoke and agents prompting DNA-protein crosslinks (DPCs). Indeed, various gaseous aldehydes were detected in wood smoke, which promote DPCs, initiate similar genomic responses and are responsible for DNA damage provoked by wood smoke. Hence, systems toxicology enables the discovery of critical constituents of complex mixtures i.e. aerosols and highlights the role of carbonyls on top of particulate matter as an important health hazard.

Integrating Microbiome Analysis, Metabolomics, Bioinformatics, and Histopathology to Elucidate the Protective Effects of Pomegranate Juice against Benzo-alpha-pyrene-Induced Colon Pathologies.

Polycyclic aromatic hydrocarbons, e.g., benzo[a]pyrene (BaP), are common dietary pollutants with potential carcinogenic activity, while polyphenols are potential chemopreventive antioxidants. Although several health benefits are attributed to polyphenol-rich pomegranate, little is known about its interaction with BaP. This study integrates histochemical, microbiomic, and metabolomic approaches to investigate the protective effects of pomegranate juice from BaP-induced pathologies. To this end, 48 Sprague-Dawley rats received, for four weeks, either pomegranate, BaP, both, or neither (n = 12 rats per group). Whereas histochemical examination of the colon indicated tissue damage marked by mucin depletion in BaP-fed animals, which was partially restored by administration of pomegranate juice, the fecal microbiome and metabolome retained their resilience, except for key changes related to pomegranate and BaP biotransformation. Meanwhile, dramatic microbiome restructuring and metabolome shift were observed as a consequence of the elapsed time (age factor). Additionally, the analysis allowed a thorough examination of fecal microbiome-metabolome associations, which delineated six microbiome clusters (marked by a differential abundance of Lactobacillaceae and Prevotellaceae, Rumincococcaceae, and Erysipelotrichaceae) and two major metabolome clusters (a sugar- and amino-acids-dominated metabotype vs. a cluster of fatty acids and hydrocarbons), with sugar alcohols maintaining a unique signature. In conclusion, using paired comparisons to minimize inter-individual animal variations allowed the dissection of temporal vs. treatment-derived variations. Microbiome-metabolome association clusters may be further exploited for metabotype prediction and gut-health biomarker discovery.

Assessing network-based methods in the context of system toxicology.

Introduction: Network-based methods are promising approaches in systems toxicology because they can be used to predict the effects of drugs and chemicals on health, to elucidate the mode of action of compounds, and to identify biomarkers of toxicity. Over the years, the network biology community has developed a wide range of methods, and users are faced with the task of choosing the most appropriate method for their own application. Furthermore, the advantages and limitations of each method are difficult to determine without a proper standard and comparative evaluation of their performance. This study aims to evaluate different network-based methods that can be used to gain biological insight into the mechanisms of drug toxicity, using valproic acid (VPA)-induced liver steatosis as a benchmark. Methods: We provide a comprehensive analysis of the results produced by each method and highlight the fact that the experimental design (how the method is applied) is relevant in addition to the method specifications. We also contribute with a systematic methodology to analyse the results of the methods individually and in a comparative manner. Results: Our results show that the evaluated tools differ in their performance against the benchmark and in their ability to provide novel insights into the mechanism of adverse effects of the drug. We also suggest that aggregation of the results provided by different methods provides a more confident set of candidate genes and processes to further the knowledge of the drug's mechanism of action. Discussion: By providing a detailed and systematic analysis of the results of different network-based tools, we aim to assist users in making informed decisions about the most appropriate method for systems toxicology applications.

The Combination of a Human Biomimetic Liver Microphysiology System with BIOLOGXsym, a Quantitative Systems Toxicology (QST) Modeling Platform for Macromolecules, Provides Mechanistic Understanding of Tocilizumab- and GGF2-Induced Liver Injury.

Biologics address a range of unmet clinical needs, but the occurrence of biologics-induced liver injury remains a major challenge. Development of cimaglermin alfa (GGF2) was terminated due to transient elevations in serum aminotransferases and total bilirubin. Tocilizumab has been reported to induce transient aminotransferase elevations, requiring frequent monitoring. To evaluate the clinical risk of biologics-induced liver injury, a novel quantitative systems toxicology modeling platform, BIOLOGXsym™, representing relevant liver biochemistry and the mechanistic effects of biologics on liver pathophysiology, was developed in conjunction with clinically relevant data from a human biomimetic liver microphysiology system. Phenotypic and mechanistic toxicity data and metabolomics analysis from the Liver Acinus Microphysiology System showed that tocilizumab and GGF2 increased high mobility group box 1, indicating hepatic injury and stress. Tocilizumab exposure was associated with increased oxidative stress and extracellular/tissue remodeling, and GGF2 decreased bile acid secretion. BIOLOGXsym simulations, leveraging the in vivo exposure predicted by physiologically-based pharmacokinetic modeling and mechanistic toxicity data from the Liver Acinus Microphysiology System, reproduced the clinically observed liver signals of tocilizumab and GGF2, demonstrating that mechanistic toxicity data from microphysiology systems can be successfully integrated into a quantitative systems toxicology model to identify liabilities of biologics-induced liver injury and provide mechanistic insights into observed liver safety signals.

Advancing Computational Toxicology by Interpretable Machine Learning.

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction.

BACKGROUND: Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods. OBJECTIVES: This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets. METHODS: Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts. RESULTS: Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts. CONCLUSION: TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

System-wide health risk prediction for 4-methyl-2,4-bis(4-hydroxyphenyl)pent-1-ene(MBP), a major active metabolite of environmental pollutant and food contaminant - Bisphenol A.

Human exposure to plastic contaminated foods and environmental micro/nano plastic derived chemicals necessitates system-wide health risk assessment. Hence, current study intend to explore the mode of action (MoA) based adverse outcome pathways of 4-methyl-2,4-bis(4-hydroxyphenyl)pent-1-ene (MBP), the major active metabolite of bisphenol A (BPA). The computational study employed broad range of target prediction, systems biology tools and molecular docking protocols. Further, validation of MBP targets was done using protein-ligand fluorescence quenching assay, endothelial cell culture and chicken embryo vascular angiogenesis models. Interestingly, the current results illustrate that various physiological signaling pathways (MAPK and VEGF related angiogenesis signaling) and disease progression pathways (hypertension, cancer and endocrine disorders) were enriched as potential targets of MBP. Further, docking studies highlights the possible binding mechanism of MBP with important targets including endothelial nitric oxide synthase (eNOS) and serum albumin (BSA). In addition, the validation studies on MBP-BSA interaction (fluorescence quenching), eNOS derived nitric oxide (NOx) generation in endothelial cells and chicken embryo angiogenesis support the system-wide impacts of MBP with highlights on cardiovascular pathogenesis. Thus, the current observation provides novel insights into the system wide impacts of MBP for the futuristic health risk assessment of plastic derived chemicals.

Toxicoproteomics reveals an effect of clozapine on autophagy in human liver spheroids.

Background: Clozapine is an atypical antipsychotic drug used to treat treatment-resistant schizophrenia. Its side effects, including liver enzyme abnormalities, experienced by many patients preclude its more common use as a first-line therapy for schizophrenia. Toxicoproteomic approaches have been demonstrated to effectively guide the identification of toxicological mechanisms.Methods: To further our understanding of the molecular effects of clozapine, we performed a data-independent acquisition (DIA)-based quantitative proteomics investigation of clozapine-treated human liver spheroid cultures.Results: In total, we quantified 4479 proteins across the five treatment groups (vehicle; 15 µM, 30 µM, and 60 µM clozapine; and 10 ng/mL TNFα + IL-1ß). Clozapine (60 µM) treatment yielded 36 differentially expressed proteins (FDR < 0.05). Gene-set enrichment analysis indicated perturbation of several gene sets, including interferon gamma signaling (e.g. interferon gamma receptor 1) and prominent autophagy-related processes (e.g. upregulation of sequestosome-1 (SQSTM1), MAP1LC3B/LC3B2, GABARAPL2, and nuclear receptor coactivator 4). The effects of clozapine on autophagy were confirmed by targeted mass spectrometry and western blotting using conventional SQSTM1 and LC3B markers.Conclusions: Combined with prior literature, our work suggests a broad contribution of autophagy to both the therapeutic and side effects of clozapine. Overall, this study demonstrates how proteomics can contribute to the elucidation of physiological and toxicological mechanisms of drugs.

Metabolomics- and systems toxicology-based hepatotoxicity mechanism of Sophorae Tonkinensis Radix et Rhizoma in rats.

Drug-induced liver injury (DILI) is a major challenge to the development and clinical application of drugs, especially limits the global application of Chinese herbal medicines, because the material basis and mechanisms of some Chinese herbal medicines are not well clear. In this study, a comprehensive method integrating metabolomics and systems toxicology (SysT) was used to investigate how the main substances in Sophorae Tonkinensis Radix et Rhizoma (STRER) influence the metabolic pathways and molecular mechanisms of hepatotoxicity. Through a 28-day continuous oral administration toxicity study combined with serum metabolomics analyses, the aqueous, ethanol-precipitation and dichloromethane extracts of STRER exhibited significant hepatotoxic effects. In addition, 19 differential metabolites with a time-dose-effect relationship were identified in rats. The primary bile acid biosynthesis pathway was significantly altered, which was consistent with the findings of the SysT analysis. Furthermore, through the quantification of bile acids in serum, 16 differential bile acids were identified as being significantly changed; moreover, 21 relevant targets which intersected with the hepatotoxic targets of STRER were identified. Molecular docking was used to confirm the validation of bindings between targets and corresponding compounds, and finally, six important compounds and 14 potential targets were identified to be involved in STRER-induced liver injury in relation to bile acid metabolism.