Quantitative Transcriptional Biomarkers of Xenobiotic Receptor Activation in Rat Liver for the Early Assessment of Drug Safety Liabilities

ICH S1 prospective evaluation study and weight of evidence assessments: commentary from industry representatives

Industry representatives on the ICH S1B(R1) Expert Working Group (EWG) worked closely with colleagues from the Drug Regulatory Authorities to develop an addendum to the ICH S1B guideline on carcinogenicity studies that allows for a weight-of-evidence (WoE) carcinogenicity assessment in some cases, rather than conducting a 2-year rat carcinogenicity study. A subgroup of the EWG composed of regulators have published in this issue a detailed analysis of the Prospective Evaluation Study (PES) conducted under the auspices of the ICH S1B(R1) EWG. Based on the experience gained through the Prospective Evaluation Study (PES) process, industry members of the EWG have prepared the following commentary to aid sponsors in assessing the standard WoE factors, considering how novel investigative approaches may be used to support a WoE assessment, and preparing appropriate documentation of the WoE assessment for presentation to regulatory authorities. The commentary also reviews some of the implementation challenges sponsors must consider in developing a carcinogenicity assessment strategy. Finally, case examples drawn from previously marketed products are provided as a supplement to this commentary to provide additional examples of how WoE criteria may be applied. The information and opinions expressed in this commentary are aimed at increasing the quality of WoE assessments to ensure the successful implementation of this approach.

From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

The human population will be approximately 9.7 billion by 2050, and food security has been identified as one of the key issues facing the global population. Agrochemicals are an important tool available to farmers that enable high crop yields and continued access to healthy foods, but the average new agrochemical active ingredient takes more than ten years, 350 million dollars, and 20,000 animals to develop and register. The time, monetary, and animal costs incentivize the use of New Approach Methodologies (NAMs) in early-stage screening to prioritize chemical candidates. This review outlines NAMs that are currently available or can be adapted for use in early-stage screening agrochemical programs. It covers new in vitro screens that are on the horizon in key areas of regulatory concern. Overall, early-stage screening with NAMs enables the prioritization of development for agrochemicals without human and environmental health concerns through a more directed, agile, and iterative development program before animal-based regulatory testing is even considered.

Integrating network pharmacology and drug side-effect data to explore mechanism of liver injury-induced by tyrosine kinase inhibitors

Tyrosine kinase inhibitors (TKIs) are highly efficient small-molecule anticancer drugs. Despite the specificity and efficacy of TKIs, they can produce off-target effects, leading to severe liver toxicity, and even some of them are labeled as black box hepatotoxicity. Thus, we focused on representative TKIs associated with severe hepatic adverse events, namely lapatinib, pazopanib, regorafenib, and sunitinib as objections of study, then integrated drug side-effect data from United State Food and Drug Administration (U.S. FDA) and network pharmacology to elucidate mechanism underlying TKI-induced liver injury. Based on network pharmacology, we constructed a specific comorbidity module of high risk of serious adverse effects and created drug-disease networks. Enrichment analysis of the networks revealed the depletion of all-trans-retinoic acid and the involvement of down-regulation of the HSP70 family-mediated endoplasmic reticulum (ER) stress as key factors in TKI-induced liver injury. These results were further verified by transcription data. Based on the target prediction results of drugs and reactive metabolites, we also shed light on the association between toxic metabolites and severe hepatic adverse reactions, and thinking HSPA8, HSPA1A, CYP1A1, CYP1A2 and CYP3A4 were potential therapeutic or preventive targets against TKI-induced liver injury. In conclusion, our research provides comprehensive insights into the mechanism underlying severe liver injury caused by TKIs, offering a better understanding of how to enhance patient safety and treatment efficacy.

Toxicogenomics Approaches to Address Toxicity and Carcinogenicity in the Liver

Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research.

Identifying multiscale translational safety biomarkers using a network-based systems approach

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.

Current Therapeutic Landscape and Safety Roadmap for Targeting the Aryl Hydrocarbon Receptor in Inflammatory Gastrointestinal Indications

Target modulation of the AhR for inflammatory gastrointestinal (GI) conditions holds great promise but also the potential for safety liabilities both within and beyond the GI tract. The ubiquitous expression of the AhR across mammalian tissues coupled with its role in diverse signaling pathways makes development of a “clean” AhR therapeutically challenging. Ligand promiscuity and diversity in context-specific AhR activation further complicates targeting the AhR for drug development due to limitations surrounding clinical translatability. Despite these concerns, several approaches to target the AhR have been explored such as small molecules, microbials, PROTACs, and oligonucleotide-based approaches. These various chemical modalities are not without safety liabilities and require unique de-risking strategies to parse out toxicities. Collectively, these programs can benefit from in silico and in vitro methodologies that investigate specific AhR pathway activation and have the potential to implement thresholding parameters to categorize AhR ligands as “high” or “low” risk for sustained AhR activation. Exploration into transcriptomic signatures for AhR safety assessment, incorporation of physiologically-relevant in vitro model systems, and investigation into chronic activation of the AhR by structurally diverse ligands will help address gaps in our understanding regarding AhR-dependent toxicities. Here, we review the role of the AhR within the GI tract, novel therapeutic modality approaches to target the AhR, key AhR-dependent safety liabilities, and relevant strategies that can be implemented to address drug safety concerns. Together, this review discusses the emerging therapeutic landscape of modalities targeting the AhR for inflammatory GI indications and offers a safety roadmap for AhR drug development.

A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies

There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making.

Tox-GAN: An AI Approach Alternative to Animal Studies-a Case Study with Toxicogenomics

Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide effort in toxicology towards "reducing, refining and replacing" (3Rs) animal use. Here, we proposed a deep generative adversarial network (GAN)-based framework capable of deriving new animal results from existing animal studies without additional experiments. To prove the concept, we employed this Tox-GAN framework to generate both gene activities and expression profiles for multiple doses and treatment durations in toxicogenomics (TGx). Using the pre-existing rat liver TGx data from the Open TG-GATEs, we generated Tox-GAN transcriptomic profiles with high similarity (0.997 ± 0.002 in intensity and 0.740 ± 0.082 in fold change) to the corresponding real gene expression profiles. Consequently, Tox-GAN showed an outstanding performance in two critical TGx applications, gaining a molecular understanding of underlying toxicological mechanisms and gene expression-based biomarker development. For the former, over 87% agreement in Gene Ontology was found between Tox-GAN results and real gene expression data. For the latter, the concordance of biomarkers between real and generated data was high in both predictive performance and biomarker genes. We also demonstrated that the Tox-GAN models constructed with TG-GATEs data were capable of generating transcriptomic profiles reported in DrugMatrix. Finally, we demonstrated potential utility for Tox-GAN in aiding chemical-based read-across. To the best of our knowledge, the proposed Tox-GAN model is novel in its ability to generate in vivo transcriptomic profiles at different treatment conditions from chemical structures. Overall, Tox-GAN holds great promise for generating high-quality toxicogenomic profiles without animal experimentation.

Evaluating adaptive stress response gene signatures using transcriptomics

Stress response pathways (SRPs) mitigate the cellular effects of chemicals, but excessive perturbation can lead to adverse outcomes. Here, we investigated a computational approach to evaluate SRP activity from transcriptomic data using gene set enrichment analysis (GSEA). We extracted published gene signatures for DNA damage response (DDR), unfolded protein response (UPR), heat shock response (HSR), response to hypoxia (HPX), metal-associated response (MTL), and oxidative stress response (OSR) from the Molecular Signatures Database (MSigDB). Next, we used a gene-frequency approach to build consensus SRP signatures of varying lengths from 50 to 477 genes. We then prepared a reference dataset from perturbagens associated with SRPs from the literature with their transcriptomic profiles retrieved from public repositories. Lastly, we used receiver-operator characteristic analysis to evaluate the GSEA scores from matching transcriptomic reference profiles to SRP signatures. Our consensus signatures performed better than or as well as published signatures for 4 out of the 6 SRPs, with the best consensus signature area under the curve (% performance relative to median of published signatures) of 1.00 for DDR (109%), 0.86 for UPR (169%), 0.99 for HTS (103%), 1.00 for HPX (104%), 0.74 for MTL (150%) and 0.83 for OSR (148%). The best matches between transcriptomic profiles and SRP signatures correctly classified perturbagens in 78% and 88% of the cases by first and second rank, respectively. We believe this approach can characterize SRP activity for new chemicals using transcriptomics with further evaluation.

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

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.

Reproducibility and robustness of high-throughput S1500+ transcriptomics on primary rat hepatocytes for chemical-induced hepatotoxicity assessment

•

TempO-Seq assays of rat hepatocytes in collagen sandwich are highly reproducible.

•

Gene expression analysis shows S1500+ is representative of the whole transcriptome.

•

Connectivity mapping shows consistency between TempO-Seq and Affymetrix data.

•

Gene set enrichment shows consistency between S1500+ and the whole transcriptome.

•

Gene set enrichment using hallmark gene sets informs hepatotoxicity.

Cell-based in vitro models coupled with high-throughput transcriptomics (HTTr) are increasingly utilized as alternative methods to animal-based toxicity testing. Here, using a panel of 14 chemicals with different risks of human drug-induced liver injury (DILI) and two dosing concentrations, we evaluated an HTTr platform comprised of collagen sandwich primary rat hepatocyte culture and the TempO-Seq surrogate S1500+ (ST) assay. First, the HTTr platform was found to exhibit high reproducibility between technical and biological replicates (r greater than 0.85). Connectivity mapping analysis further demonstrated a high level of inter-platform reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project. Second, the TempO-Seq ST assay was shown to be a robust surrogate to the whole transcriptome (WT) assay in capturing chemical-induced changes in gene expression, as evident from correlation analysis, PCA and unsupervised hierarchical clustering. Gene set enrichment analysis (GSEA) using the Hallmark gene set collection also demonstrated consistency in enrichment scores between ST and WT assays. Lastly, unsupervised hierarchical clustering of hallmark enrichment scores broadly divided the samples into hepatotoxic, intermediate, and non-hepatotoxic groups. Xenobiotic metabolism, bile acid metabolism, apoptosis, p53 pathway, and coagulation were found to be the key hallmarks driving the clustering. Taken together, our results established the reproducibility and performance of collagen sandwich culture in combination with TempO-Seq S1500+ assay, and demonstrated the utility of GSEA using the hallmark gene set collection to identify potential hepatotoxicants for further validation.

Human-relevant mechanisms and risk factors for TAK-875-Induced liver injury identified via a gene pathway-based approach in Collaborative Cross mice

Development of TAK-875 was discontinued when a small number of serious drug-induced liver injury (DILI) cases were observed in Phase 3 clinical trials. Subsequent studies have identified hepatocellular oxidative stress, mitochondrial dysfunction, altered bile acid homeostasis, and immune response as mechanisms of TAK-875 DILI and the contribution of genetic risk factors in oxidative response and mitochondrial pathways to the toxicity susceptibility observed in patients. We tested the hypothesis that a novel preclinical approach based on gene pathway analysis in the livers of Collaborative Cross mice could be used to identify human-relevant mechanisms of toxicity and genetic risk factors at the level of the hepatocyte as reported in a human genome-wide association study. Eight (8) male mice (4 matched pairs) from each of 45 Collaborative Cross lines were treated with a single oral (gavage) dose of either vehicle or 600 mg/kg TAK-875. As expected, liver injury was not detected histologically and few changes in plasma biomarkers of hepatotoxicity were observed. However, gene expression profiling in the liver identified hundreds of transcripts responsive to TAK-875 treatment across all strains reflecting alterations in immune response and bile acid homeostasis and the interaction of treatment and strain reflecting oxidative stress and mitochondrial dysfunction. Fold-change expression values were then used to develop pathway-based phenotypes for genetic mapping which identified candidate risk factor genes for TAK-875 toxicity susceptibility at the level of the hepatocyte. Taken together, these findings support our hypothesis that a gene pathway-based approach using Collaborative Cross mice could inform sensitive strains, human-relevant mechanisms of toxicity, and genetic risk factors for TAK-875 DILI. This novel preclinical approach may be helpful in understanding, predicting, and ultimately preventing clinical DILI for other drugs.

Development and Application of a Transcriptomic Signature of Bioactivation in an Advanced In Vitro Liver Model to Reduce Drug-induced Liver Injury Risk Early in the Pharmaceutical Pipeline

Early risk assessment of drug-induced liver injury (DILI) potential for drug candidates remains a major challenge for pharmaceutical development. We have previously developed a set of rat liver transcriptional biomarkers in short-term toxicity studies to inform the potential of drug candidates to generate a high burden of chemically reactive metabolites that presents higher risk for human DILI. Here, we describe translation of those NRF1-/NRF2-mediated liver tissue biomarkers to an in vitro assay using an advanced micropatterned coculture system (HEPATOPAC) with primary hepatocytes from male Wistar Han rats. A 9-day, resource-sparing and higher throughput approach designed to identify new chemical entities with lower reactive metabolite-forming potential was qualified for internal decision making using 93 DILI-positive and -negative drugs. This assay provides 81% sensitivity and 90% specificity in detecting hepatotoxicants when a positive test outcome is defined as the bioactivation signature score of a test drug exceeding the threshold value at an in vitro test concentration that falls within 3-fold of the estimated maximum drug concentration at the human liver inlet following highest recommended clinical dose administrations. Using paired examples of compounds from distinct chemical series and close structural analogs, we demonstrate that this assay can differentiate drugs with lower DILI risk. The utility of this in vitro transcriptomic approach was also examined using human HEPATOPAC from a single donor, yielding 68% sensitivity and 86% specificity when the aforementioned criteria are applied to the same 93-drug test set. Routine use of the rat model has been adopted with deployment of the human model as warranted on a case-by-case basis. This in vitro transcriptomic signature-based strategy can be used early in drug discovery to derisk DILI potential from chemically reactive metabolites by guiding structure-activity relationship hypotheses and candidate selection.

Universal Toxicity Gene Signatures for Early Identification of Drug-Induced Tissue Injuries in Rats

A new safety testing paradigm that relies on gene expression biomarker panels was developed to easily and quickly identify drug-induced injuries across tissues in rats prior to drug candidate selection. Here, we describe the development, qualification, and implementation of gene expression signatures that diagnose tissue degeneration/necrosis for use in early rat safety studies. Approximately 400 differentially expressed genes were first identified that were consistently regulated across 4 prioritized tissues (liver, kidney, heart, and skeletal muscle), following injuries induced by known toxicants. Hundred of these "universal" genes were chosen for quantitative PCR, and the most consistent and robustly responding transcripts selected, resulting in a final 22-gene set from which unique sets of 12 genes were chosen as optimal for each tissue. The approach was extended across 4 additional tissues (pancreas, gastrointestinal tract, bladder, and testes) where toxicities are less common. Mathematical algorithms were generated to convert each tissue's 12-gene expression values to a single metric, scaled between 0 and 1, and a positive threshold set. For liver, kidney, heart, and skeletal muscle, this was established using a training set of 22 compounds and performance determined by testing a set of approximately 100 additional compounds, resulting in 74%-94% sensitivity and 94%-100% specificity for liver, kidney, and skeletal muscle, and 54%-62% sensitivity and 95%-98% specificity for heart. Similar performance was observed across a set of 15 studies for pancreas, gastrointestinal tract, bladder, and testes. Bundled together, we have incorporated these tissue signatures into a 4-day rat study, providing a rapid assessment of commonly seen compound liabilities to guide selection of lead candidates without the necessity to perform time-consuming histopathologic analyses.

Mechanistic Investigations Support Liver Safety of Ubrogepant

Small-molecule calcitonin gene-related peptide (CGRP) receptor antagonists have demonstrated therapeutic efficacy for the treatment of migraine. However, previously investigated CGRP receptor antagonists, telcagepant and MK-3207, were discontinued during clinical development because of concerns about drug-induced liver injury. A subsequent effort to identify novel CGRP receptor antagonists less likely to cause hepatotoxicity led to the development of ubrogepant. The selection of ubrogepant, following a series of mechanistic studies conducted with MK-3207 and telcagepant, was focused on key structural modifications suggesting that ubrogepant was less prone to forming reactive metabolites than previous compounds. The potential for each drug to cause liver toxicity was subsequently assessed using a quantitative systems toxicology approach (DILIsym) that incorporates quantitative assessments of mitochondrial dysfunction, disruption of bile acid homeostasis, and oxidative stress, along with estimates of dose-dependent drug exposure to and within liver cells. DILIsym successfully modeled liver toxicity for telcagepant and MK-3207 at the dosing regimens used in clinical trials. In contrast, DILIsym predicted no hepatotoxicity during treatment with ubrogepant, even at daily doses up to 1000 mg (10-fold higher than the approved clinical dose of 100 mg). These predictions are consistent with clinical trial experience showing that ubrogepant has lower potential to cause hepatotoxicity than has been observed with telcagepant and MK-3207.

Application of a Rat Liver Drug Bioactivation Transcriptional Response Assay Early in Drug Development That Informs Chemically Reactive Metabolite Formation and Potential for Drug-induced Liver Injury

Drug-induced liver injury is a major reason for drug candidate attrition from development, denied commercialization, market withdrawal, and restricted prescribing of pharmaceuticals. The metabolic bioactivation of drugs to chemically reactive metabolites (CRMs) contribute to liver-associated adverse drug reactions in humans that often goes undetected in conventional animal toxicology studies. A challenge for pharmaceutical drug discovery has been reliably selecting drug candidates with a low liability of forming CRM and reduced drug-induced liver injury potential, at projected therapeutic doses, without falsely restricting the development of safe drugs. We have developed an in vivo rat liver transcriptional signature biomarker reflecting the cellular response to drug bioactivation. Measurement of transcriptional activation of integrated nuclear factor erythroid 2-related factor 2 (NRF2)/Kelch-like ECH-associated protein 1 (KEAP1) electrophilic stress, and nuclear factor erythroid 2-related factor 1 (NRF1) proteasomal endoplasmic reticulum (ER) stress responses, is described for discerning estimated clinical doses of drugs with potential for bioactivation-mediated hepatotoxicity. The approach was established using well benchmarked CRM forming test agents from our company. This was subsequently tested using curated lists of commercial drugs and internal compounds, anchored in the clinical experience with human hepatotoxicity, while agnostic to mechanism. Based on results with 116 compounds in short-term rat studies, with consideration of the maximum recommended daily clinical dose, this CRM mechanism-based approach yielded 32% sensitivity and 92% specificity for discriminating safe from hepatotoxic drugs. The approach adds new information for guiding early candidate selection and informs structure activity relationships (SAR) thus enabling lead optimization and mechanistic problem solving. Additional refinement of the model is ongoing. Case examples are provided describing the strengths and limitations of the approach.

A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays

Chemical-induced liver cancer occurs in rodents through well-characterized adverse outcome pathways. We hypothesized that measurement of the 6 most common molecular initiating events (MIEs) in liver cancer adverse outcome pathways in short-term assays using only gene expression will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in 2-year bioassays. We tested this hypothesis using transcript data from a rat liver microarray compendium consisting of 2013 comparisons of 146 chemicals administered at doses with previously established effects on rat liver tumor induction. Five MIEs were measured using previously characterized gene expression biomarkers composed of gene sets predictive for genotoxicity and activation of 1 or more xenobiotic receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Because chronic injury can be important in tumorigenesis, we also developed a biomarker for cytotoxicity that had a 96% balanced accuracy. Characterization of the genes in each biomarker set using the unsupervised TXG-MAP network model demonstrated that the genes were associated with distinct functional coexpression modules. Using the Toxicological Priority Index to rank chemicals based on their ability to activate the MIEs showed that chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Balanced accuracies using thresholds derived from either TG-GATES or DrugMatrix data sets to predict tumorigenicity in independent sets of chemicals were up to 93%. These results show that a MIE-directed approach using only gene expression biomarkers could be used in short-term assays to identify chemicals and their doses that cause tumors.