Per- and polyfluoroalkyl substances (PFAS) are persistent and ubiquitous environmental contaminants associated with diverse adverse health outcomes. Despite prior extensive molecular analyses, mechanistic understanding of PFAS hepatotoxicity remains incomplete. In recent years, human liver spheroids have emerged as a useful tool in this space for investigating human-relevant responses to PFAS exposure across longer-term exposure periods. Here, we reprocessed publicly available PFAS RNA-sequencing data from human liver spheroids using the R-ODAF pipeline to generate a high-confidence set of differentially expressed genes (DEGs) and applied an integrated multimodal platform combining cheminformatic target prediction and transcription factor (TF) activity inference within a liver-specific prior knowledge network. While conventional pathway enrichment recapitulated known PFAS effects on SREBF- and PPAR-driven lipid metabolism, it provided limited insight for compounds with fewer DEGs, such as PFBS. Multimodal integration revealed compound-specific regulatory programs, with PFOS and PFOA exhibiting a time- and dose-dependent suppression of a conserved TF module comprising SREBF, CEBP, and HNF family members. Cheminformatic protein target predictions for the various PFAS compounds highlighted several common targets, including MAP3K5 and PLCG1, as well as several compound-specific target predictions. Incorporating these predictions with network biology analyses elucidated upstream cascades differentiating PFOA- versus PFOS-mediated regulation of lipid metabolism, with the PFOA-specific inhibition of PTPN7 being a discriminating feature, highlighting potential molecular bases for compound-specific toxicological profiles. Collectively, our findings demonstrate that integrating structural, transcriptomic, and network-biology approaches enhances mechanistic resolution beyond traditional enrichment analyses, providing testable hypotheses for PFAS-induced perturbations and guiding experimental validation of key targets.

Bulk tissue transcriptional profiling has played a key role in drug discovery and preclinical safety assessment by revealing mechanisms of toxicity. However, analyzing gene expression from whole tissues is challenging when toxicities are restricted to specific cell types or regions. Newer sequencing methods, including single-cell RNA sequencing and spatial transcriptomics, enable researchers to study cellular and spatial responses at higher resolution. These techniques provide unique insights into localized gene expression changes linked to drug toxicity. Despite their promise, spatial methods are not always the most practical option, as they present challenges related to cost, data complexity, and the need for specialized analytical expertise. Selection of an appropriate platform may depend on study objectives, tissue and species context, and the distribution of affected regions. Thoroughly assessing each platform’s analytical strengths, resolution, and practical limitations is crucial for effectively applying spatial transcriptomics in preclinical safety research. In this review, we evaluate spatial transcriptomic approaches to inform omics platform selection for preclinical safety studies, highlighting platform-specific strengths and considerations specific to drug development.

Toxicogenomic data provide key insights into molecular mechanisms underlying drug-induced organ toxicities. To simplify transcriptomic data interpretation, we applied weighted gene co-expression network analysis (WGCNA) to rat kidney transcriptomics data from TG-GATEs (TG) and DrugMatrix (DM), covering time- and dose-response data for 180 compounds. A total of 347 gene modules were incorporated into the rat kidney TXG-MAPr web-tool, that interactively visualizes and quantifies module activity using eigengene scores (EGSs). Several modules annotated for cellular stress, injury, and inflammation were associated with renal pathologies and included established and candidate biomarker genes. Many rat kidney modules were preserved across transcriptome datasets, suggesting potential applicability to other kidney injury contexts. Cross-species preservation analysis using human kidney data further supported the translational potential of these rat-derived modules. The TXG-MAPr platform facilitates upload and analysis of gene expression data in the context of rat kidney co-expression networks, which could identify mechanisms and safety liabilities of chemical or drug exposures.

Genotoxicity is a critical determinant for assessing the safety of pharmaceutical drugs, their metabolites, and impurities. Among genotoxicity tests, mechanistic assays such as the MultiFlow® DNA damage assay (MFA) allows the investigations on mode of action (MoA) of DNA damage through four mechanistic markers recorded at two time points. Previous studies have shown that machine learning (ML) can enhance precision on classifying the MoA of genotoxicants. Nevertheless, these approaches need to be tailored to specific chemical spaces and lab conditions for accurate risk assessment. In this study, we applied various state-of-the-art ML algorithms available in an open-source R package (caret) to build MFA-ML models using data from Bryce et al. (2016). The best model achieved 95% accuracy on the training dataset and correctly predicted genotoxicity in 16 out of 17 cases in the test dataset. Incorporating molecular descriptors properties from established in silico models demonstrated further improved performance of the approach to cover challenging examples of pharmaceuticals exhibiting a pharmacological mode of action that could interfere with the biomarker response. Further model validation on an external test set with 49 non-overlapped compounds showed a high model accuracy at 92%. Additionally, a tailored graphical user interface was developed using a freely available R package (shiny) to support visual analysis of MFA data including MoA predictions, facilitating broad usage by laboratory scientists. Lastly, a perspective on the integration of MoA predictions as additional evidence into a genotoxicity assessment workflow is proposed.

Reliable prediction and prevention of adverse drug reactions (ADRs) remains a key challenge in the development of new medicines. Advanced mathematical and computational modelling approaches, which incorporate cutting-edge mechanistic understanding of ADRs in concert with systematically collected data addressing knowledge gaps, are integral components of model-informed drug discovery and development (MID3). These approaches provide a precise, quantitative framework for predicting and mitigating safety risks in the earliest phases of drug development. Here, we highlight recent developments in the burgeoning field of quantitative systems toxicology (QST), including insights into the current state-of-the-art, as well as outcomes from the Innovative Medicines Initiative (IMI) 2 TransQST project. QST models that describe the disruption of cardiovascular, gastrointestinal, hepatic and renal physiological functions following drug exposure are presented, along with recommendations for their application in drug discovery and development.

Nephrotoxicity caused by drug or chemical exposure involves complex mechanisms as well as a temporal integration of injury and repair responses in different nephron segments. Distinct cellular transcriptional programs regulate the time-dependent tissue injury and regeneration responses. Whole kidney transcriptome analysis cannot dissect the complex spatio-temporal injury and regeneration responses in the different nephron segments. Here, we used laser capture microdissection of formalin-fixed paraffin embedded sections followed by whole genome targeted RNA-sequencing-TempO-Seq and co-expression gene-network (module) analysis to determine the spatial-temporal responses in rat kidney glomeruli (GM), cortical proximal tubules (CPT) and outer-medulla proximal tubules (OMPT) comparison with whole kidney, after a single dose of the nephrotoxicant cisplatin. We demonstrate that cisplatin induced early onset of DNA damage in both CPT and OMPT, but not GM. Sustained DNA damage response was strongest in OMPT coinciding with OMPT specific inflammatory signaling, actin cytoskeletal remodeling and increased glycolytic metabolism with suppression of mitochondrial activity. Later responses reflected regeneration-related cell cycle pathway activation and ribosomal biogenesis in the injured OMPT regions. Activation of modules containing kidney injury biomarkers was strongest in OMPT, with OMPT Clu expression highly correlating with urinary clusterin biomarker measurements compared the correlation of Kim1. Our findings also showed that whole kidney responses were less sensitive than OMPT. In conclusion, our LCM-TempO-Seq method reveals a detailed spatial mechanistic understanding of renal injury/regeneration after nephrotoxicant exposure and identifies the most representative mechanism-based nephron segment specific renal injury biomarkers.

Genotoxicity is a critical determinant for assessing the safety of pharmaceutical drugs, their metabolites, and impurities. Among genotoxicity tests, mechanistic assays such as the MultiFlow (R) DNA damage assay (MFA) allows the investigations on mode of action (MoA) of DNA damage through four mechanistic markers recorded at two time points. Previous studies have shown that machine learning (ML) can enhance precision on classifying the MoA of genotoxicants. Nevertheless, these approaches need to be tailored to specific chemical spaces and lab conditions for accurate risk assessment. In this study, we applied various state-of-the-art ML algorithms available in an open-source R package (caret) to build MFA-ML models using data from Bryce et al. (2016). The best model achieved 95% accuracy on the training dataset and correctly predicted genotoxicity in 16 out of 17 cases in the test dataset. Incorporating molecular descriptors properties from established in silico models demonstrated further improved performance of the approach to cover challenging examples of pharmaceuticals exhibiting a pharmacological mode of action that could interfere with the biomarker response. Further model validation on an external test set with 49 non-overlapped compounds showed a high model accuracy at 92%. Additionally, a tailored graphical user interface was developed using a freely available R package (shiny) to support visual analysis of MFA data including MoA predictions, facilitating broad usage by laboratory scientists. Lastly, a perspective on the integration of MoA predictions as additional evidence into a genotoxicity assessment workflow is proposed.

We present a simple methodology that allows the building and sharing of predictive models without compromising the confidentiality of the structures of the training series. Multiple shared models can be used to obtain ensemble models, providing better coverage of the chemical space and better predictions than the original one. This approach is demonstrated in a collaborative exercise where four pharmaceutical and chemical companies developed predictive models for the AMES mutagenicity end point and shared them to build ensemble models using logical and machine learning algorithms. The results were systematically analyzed and compared, obtaining clear benefits in predictive quality. The method has the advantage of being very simple to execute, using only open-source software, and the possibility to audit the whole process and interchange files to guarantee that no confidential information is exported from the company facilities.

Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human in vitro models can address the species translation but might not replicate in vivo complexity. Herein, we propose a network-based method addressing these translational multiscale problems that derives in vivo liver injury biomarkers applicable to in vitro human early safety screening. We applied weighted correlation network analysis (WGCNA) to a large rat liver transcriptomic dataset to obtain co-regulated gene clusters (modules). We identified modules statistically associated with liver pathologies, including a module enriched for ATF4-regulated genes as associated with the occurrence of hepatocellular single-cell necrosis, and as preserved in human liver in vitro models. Within the module, we identified TRIB3 and MTHFD2 as a novel candidate stress biomarkers, and developed and used BAC-eGFPHepG2 reporters in a compound screening, identifying compounds showing ATF4-dependent stress response and potential early safety signals.

Toxicogenomic data represent a valuable source of biological information at molecular and cellular level to understand unanticipated organ toxicities. Weighted gene co-expression networks analysis can reduce the complexity of gene-level transcriptomic data to a set of biological response-networks useful for providing insights into mechanisms of drug-induced adverse outcomes. In this study, we have built co-regulated gene networks (modules) from the TG-GATEs rat kidney datasets consisting of time- and dose-response data for 41 compounds, including nephrotoxicants. Data from the 347 modules were incorporated into the rat kidney TXG-MAPr web tool, a user-friendly interface that enables visualization and analysis of module perturbations, quantified by a module eigengene score (EGS) for each treatment condition. Several modules annotated for cellular stress, renal injury and inflammation were statistically associated with concurrent renal pathologies, including modules that contain both well-known and novel renal biomarker genes. In addition, many rat kidney modules contain well annotated, robust gene networks that are preserved in other transcriptome datasets, suggesting that these biological networks translate to other (drug-induced) kidney injury cases. Moreover, preservation analysis of human kidney transcriptomic data provided a quantitative metric to assess the likelihood that rat kidney modules, and the associated biological interpretation, translate from non-clinical species to human. In conclusion, the rat kidney TXG-MAPr enables uploading and analysis of kidney gene expression data in the context of rat kidney co-expression networks, which could identify possible safety liabilities and/or mechanisms that can lead to adversity for chemical or drug candidates.Translational Statement Gene co-expression networks (modules) were generated using rat kidney toxicogenomics data, which reduced data complexity and retained quantitative mechanisms to enhance safety assessment. Several stress, injury and inflammation modules were statistically associated with renal pathologies, useful for biomarker identification. Moreover, many rat kidney modules contained well-annotated, robust gene-networks that were preserved in human patients transcriptome data after renal transplantation, suggesting that these biological networks translate to human relevant kidney-injury. So, the rat kidney TXG-MAPr tool enables transcriptome analysis in the context of kidney co-expression networks, which could identify chemical-induced safety liabilities and/or mechanisms leading to adversity, relevant for human risk-assessment.### Competing Interest StatementJSR reports funding from GSK, Pfizer and Sanofi and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer and Grunenthal. PT is a Sanofi employee and may hold shares and/or stock options in the company. All the other authors have declared no competing interests.* AKI : Acute kidney injury APL : Allopurinol ATF : Activating transcription factor AvAbsEGS : Average absolute eigengene score BUN Blood urea nitrogen corEG : Correlation eigengene score CRE : Creatinine CSP : Cisplatin DIKI : Drug-induced kidney injury DM : DrugMatrix EGS : Eigengene score ER : Endoplasmic reticulum FC : Fold change GO : Gene ontology IRI : Ischemia reperfusion injury KIM-1 : Kidney injury molecule-1 NRF2 : Nuclear Factor Erythroid 2-Related Factor 2 ORA : Over Representation Analysis PAN : Puromycin aminonucleoside PTC : Proximal tubular cells RMA : Robust Multi-array Average TG : TG-GATEs TF : Transcription factor TXG : Toxicogenomics WGCNA : Weighted gene co-expression network analysis

Thirteen pharmaceutical companies have shared and integrated preclinical and clinical data for creating computational resources that enhance translational drug safety assessment.

Drug-induced liver injury (DILI) is the most prevalent adversity encountered in drug development and clinical settings leading to urgent needs to understand the underlying mechanisms. In this study, we have systematically investigated the dynamics of the activation of cellular stress response pathways and cell death outcomes upon exposure of a panel of liver toxicants using live cell imaging of fluorescent reporter cell lines. We established a comprehensive temporal dynamic response profile of a large set of BAC-GFP HepG2 cell lines representing the following components of stress signaling: i) unfolded protein response (UPR) [ATF4, XBP1, BIP and CHOP]; ii) oxidative stress [NRF2, SRXN1, HMOX1]; iii) DNA damage [P53, P21, BTG2, MDM2]; and iv) NF-κB pathway [A20, ICAM1]. We quantified the single cell GFP expression as a surrogate for endogenous protein expression using live cell imaging over > 60 h upon exposure to 14 DILI compounds at multiple concentrations. Using logic-based ordinary differential equation (Logic-ODE), we modelled the dynamic profiles of the different stress responses and extracted specific descriptors potentially predicting the progressive outcomes. We identified the activation of ATF4-CHOP axis of the UPR as the key pathway showing the highest correlation with cell death upon DILI compound perturbation. Knocking down main components of the UPR provided partial protection from compound-induced cytotoxicity, indicating a complex interplay among UPR components as well as other stress pathways. Our results suggest that a systematic analysis of the temporal dynamics of ATF4-CHOP axis activation can support the identification of DILI risk for new candidate drugs.

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

Toxicogenomics studies typically reveal a group of genes relevant to the pathophysiology of drug-induced organ injury. In recent years, network-based methods have become an attractive analytical approach as they can capture not only the global changes of regulatory gene networks but also the relationships between their components. Among them, a causal reasoning approach additionally depicts the mechanisms of regulation that connect upstream regulators in signaling networks towards their downstream gene targets. In this work, we applied CARNIVAL, a causal network contextualisation tool, to infer upstream regulatory signaling networks based on gene expression microarray data from the TG-GATEs database. We focussed on six compounds that induce observable histopathologies linked to drug-induced liver injury (DILI) from repeated dosing experiments in rats. We compared responses in vitro and in vivo to identify potential cross-platform concordances in rats as well as network preservations between rat and human. Our results showed similarities of enriched pathways and network motifs between compounds. These pathways and motifs induce the same pathology in rats but not in humans. In particular, the causal interactions “LCK activates SOCS3, which in turn inhibits TFDP1” was commonly identified as a regulatory path among the fibrosis-inducing compounds. This potential pathology-inducing regulation illustrates the value of our approach to generate hypotheses that can be further validated experimentally.

Kidney fibrosis is characterized by expansion and activation of platelet-derived growth factor receptor-β (PDGFR-β)-positive mesenchymal cells. To study the consequences of PDGFR-β activation, we developed a model of primary renal fibrosis using transgenic mice with PDGFR-β activation specifically in renal mesenchymal cells, driving their pathological proliferation and phenotypic switch toward myofibroblasts. This resulted in progressive mesangioproliferative glomerulonephritis, mesangial sclerosis, and interstitial fibrosis with progressive anemia due to loss of erythropoietin production by fibroblasts. Fibrosis induced secondary tubular epithelial injury at later stages, coinciding with microinflammation, and aggravated the progression of hypertensive and obstructive nephropathy. Inhibition of PDGFR activation reversed fibrosis more effectively in the tubulointerstitium compared to glomeruli. Gene expression signatures in mice with PDGFR-β activation resembled those found in patients. In conclusion, PDGFR-β activation alone is sufficient to induce progressive renal fibrosis and failure, mimicking key aspects of chronic kidney disease in humans. Our data provide direct proof that fibrosis per se can drive chronic organ damage and establish a model of primary fibrosis allowing specific studies targeting fibrosis progression and regression.

SUMMARY:The molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large datasets is increasing steadily. A useful way to extract mechanistic insight from the data is by integrating them with a prior knowledge network of signalling to obtain dynamic models. CellNOpt is a collection of Bioconductor R packages for building logic models from perturbation data and prior knowledge of signalling networks. We have recently developed new components and refined the existing ones to keep up with the computational demand of increasingly large datasets, including (i) an efficient integer linear programming, (ii) a probabilistic logic implementation for semi-quantitative datasets, (iii) the integration of a stochastic Boolean simulator, (iv) a tool to identify missing links, (v) systematic post-hoc analyses and (vi) an R-Shiny tool to run CellNOpt interactively.AVAILABILITY AND IMPLEMENTATION:R-package(s): https://github.com/saezlab/cellnopt.SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Omics data have been increasingly generated with limited demonstrated value in drug safety assessment. The TransQST consortium was launched to use omics and other data in mechanistic-based quantitative systems toxicology (QST) models to evaluate their potential use in species translation.