Prediction of hepatotoxicity in humans remains an unresolved challenge. Recently, an in vitro/in silico method was established to predict blood concentrations of test compounds with an increased risk of causing human hepatotoxicity. In the present study, we addressed the question whether gene expression data can improve the quality of hepatotoxicity prediction compared to cytotoxicity analysis alone. A particular challenge is that high-dimensional gene expression data must be sum­marized into variables that allow the determination of the lowest test compound concentration that causes altered gene expression. To address this challenge, we analyzed 60 hepatotoxic and non-hepatotoxic substances in a concentration-dependent manner for cytotoxicity and expression of 3,524 probes previously reported to be influenced by hepatotoxicants. The toxicity separation index (TSI) was applied to quantify how well specific summary variables of gene expression can differen­tiate between the set of hepatotoxic and non-hepatotoxic substances. The best TSI was obtained when the lowest concentration of a test compound that led to differential expression of two genes when compared to vehicle controls was considered positive. Furthermore, the best gene expression-based summary variable was superior to cytotoxicity-based variables alone, and the combination of the best summary variables of gene expression and cytotoxicity data further improved the TSI compared to each category alone. In conclusion, the method used to derive summary variables of gene expression is critical, and the best summary variables improve the prediction of hepatotoxic substances in relation to oral doses and blood concentrations in humans. Plain language summary Liver injury is a relevant side effect of many drugs. It represents the most common cause of acute liver failure in Western countries as well as the most common reason for late-stage drug development failure or withdrawal of drugs from the market. We show that a genome-wide gene expression analysis of cultured human hepatocytes exposed to drugs can identify hepatotoxic and non-hepato­toxic substances. To achieve this, we have developed a technique to summarize all gene expression variables to determine the lowest concentration at which a test substance begins to influence gene expression. We find that the cell test must include the highest concentration of the drug that is intended to be reached in the blood of patients at therapeutic doses. This approach can reduce the number of animals needed in pre-clinical safety studies as liver-damaging drug candidates would be detected and eliminated prior to studies in rodents.

Chemical risk assessment is evolving from traditional deterministic approaches to embrace proba­bilistic methodologies, where risk of hazard manifestation is understood as a more or less probable event depending on exposure, individual factors, and stochastic processes. This is driven by advancements in human stem cells, complex tissue engineering, high-performance computing, and cheminformatics, and is more recently facilitated by large-scale artificial intelligence models. These innovations enable a more nuanced understanding of chemical hazards, capturing the complexity of biological responses and variability within populations. However, each technology comes with its own uncertainties impacting on the estimation of hazard probabilities. This shift addresses the limitations of point estimates and thresholds that oversimplify hazard assessment, allowing for the integration of kinetic variability and uncertainty metrics into risk models. By leveraging modern technologies and expansive toxicological data, probabilistic approaches offer a comprehensive evaluation of chemical safety. This paper summarizes a workshop held in 2023 and discusses the technological and data-driven enablers, and the challenges faced in their implementation, with particular focus on perturbation of biology as the basis of hazard estimates. The future of toxico­logical risk assessment lies in the successful integration of these probabilistic models, promising more accurate and holistic hazard evaluations.

Most complex in vitro models (CIVM) and microphysiological systems (MPS) are composed of human cells, with the goal of evaluating diseases, efficacy, safety, and pharmacokinetic ques­tions specifically for humans. The hope with CIVM/MPS is that they will eventually improve our predictivity of clinical responses and reduce or replace animal use in research, supporting the 3Rs concept of only using animals in research when necessary. Given the potential of animal-based models to advance this field by comparing existing in vivo animal data with new animal-based MPS responses, there are currently few CIVM and MPS utilizing animal tissues. Animal-based MPS may also have specific utility for cross-species comparisons or species-specific mechanistic questions on zoonotic diseases, and therapies for animals. Animal-based MPS may help expand in-vitro-to-in-vivo correlations, advance the field, and establish confidence in the predictive nature of such platforms. The IQ MPS-FDA workshop provided an interactive venue for pharmaceutical companies and regulatory agencies such as the U.S. Food and Drug Administration (FDA), NC3Rs (UK), Health Canada, NIH/NCATS, NIHS and PMDA (Japan), Danish Medicines Agency, European Com­mission, NIEHS/NICEATM, HHS, NIST, EURL ECVAM, and the IQ MPS Affiliate, a collaboration of pharmaceutical companies, to jointly discuss considerations of animal-based MPS and applica­tions where animal-based MPS are of potential value.

This corrects the article DOI: 10.14573/altex.2403151.

Next generation risk assessment of chemicals revolves around the use of mechanistic information without animal experimentation. In this regard, toxicogenomics has proven to be a useful tool to elucidate the mechanisms underlying the adverse effects of xenobiotics. In the present study, two widely used human hepatocyte culture systems, namely primary human hepatocytes (PHH) and human hepatoma HepaRG cells, were exposed to liver toxicants known to induce liver cholestasis, steatosis, or necrosis. Benchmark concentration (BMC) response modelling was applied to transcriptomics gene co-expression networks (modules) to derive BMCs and to gain mechanistic insight into the hepatotoxic effects. BMCs derived by concentration-response modelling of gene co-expression modules recapitulated concentration-response modelling of individual genes. Although PHH and HepaRG cells showed overlap in the genes and modules deregulated by the liver toxicants, PHH demonstrated a higher responsiveness, based on the lower BMCs of co-regulated gene modules. Such BMCs can be used as transcriptomics points of departure (tPOD) for assessing module-associated cellular (stress) pathways/processes. This approach identified clear tPODs of around maximum systemic concentration (Cmax) levels for the tested drugs, while for cosmetics ingredients the BMCs were 10-100-fold higher than the estimated plasma concentrations. This approach could serve next generation risk assessment practice to identify early responsive modules at low BMCs that could be linked to key events in liver adverse outcome pathways. In turn, this can assist in delineating potential hazards of new test chemicals using in vitro systems and be used in a risk assessment where BMCs are paired with chemical exposure assessment. Plain language summary Risk assessment of chemicals has traditionally been focused on animal experiments. In contrast, next generation risk assessment uses biological information obtained from experiments in cell culture models without animals to identify potential hazards. Since the liver is the main target organ of toxicity, many liver cell models have been developed and applied for hazard assessment. In this study, two widely used human liver cell models were exposed to liver toxic chemicals. Biological changes in gene expression were measured in a concentration range to identify the concentration at which a biological response started to be perturbed using a mathematical modelling approach. Genes belonging to the same bio­logical process were linked based on co-expression to derive an average concentration for this process. This animal-free approach could be applied to risk assessment by relating the biological response concentrations to the expected human exposure to identify the potential hazard of test chemicals.

The Environmental Protection Agency (EPA) needs to evaluate a large number of PFAS for potential human and ecological effects.Most of the hundreds of PFAS currently in commerce have limited or no toxicity data, and if EPA attempts to research them one at a time, it will be impossible for EPA to expeditiously understand, let alone address, the risks these substances may pose to human health and the environment.To address this data gap and fundamentally advance our understanding of these substances, EPA has developed this National PFAS Testing Strategy (Strategy) to deepen understanding of the impacts of PFAS, including potential hazards to human health and the environment.This Strategy will help EPA identify and select PFAS for which the Agency will require testing using Toxic Substances Control Act (TSCA) authorities.The Strategy develops categories of PFAS based on information about similarities in structure, physical-chemical properties, and existing test data on the toxicity of PFAS (both publicly available and submitted to EPA under TSCA).Consideration of the existing toxicity data prior to requiring further testing also ensures adherence to the TSCA goal of reducing animal testing.EPA will use the Strategy to identify important gaps in existing data and to select one or more candidate chemicals within identified categories for additional study.EPA expects to exercise its TSCA section 4 order authority to require PFAS manufacturers to conduct and fund the studies.EPA plans to issue the first round of test orders on selected PFAS by the end of 2021 with additional phases thereafter.

An essential aspect of the EU’s Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation is the European Chemicals Agency’s (ECHA) evaluation of testing proposals submitted by registrants to address data gaps. Registrants may propose adaptations, such as read-across, to waive standard testing; however, it is widely believed that ECHA often finds justifications for read-across hypotheses inadequate. From 2008 to August 2023, 2,630 testing proposals were submitted to ECHA; of these, 1,538 had published decisions that were systematically evaluated in this study. Each document was manually reviewed and information extracted for further analyses, focusing on 17 assessment elements (AEs) from the Read-Across Assessment Framework (RAAF) and testing proposal evaluations (TPE). Each submission was classified as to the AEs relied upon by the registrants and by ECHA. Data was analyzed for patterns and associations. Adaptations were included in 23% (350) of proposals, with analogue (168) and group (136) read-across being most common. Of the 304 read-across hypotheses, 49% were accepted, with group read-across showing significantly higher odds of acceptance. Data analysis examined factors such as tonnage band (Annex), test guidelines, hypothesis AEs, and structural similarities of target and source sub­stances. While decisions were often context-specific, several significant associations influencing acceptance emerged. Overall, this analysis provides a comprehensive overview of 15 years of experience with testing proposal-specific read-across adaptations by both registrants and ECHA. These data will inform future submissions as they identify most critical AEs to increase the odds of read-across acceptance. Plain language summary The European Union’s law requires companies to provide safety data on chemicals they produce or import. To avoid unnecessary testing in animals, companies can propose alternatives like “read-across”, which uses data from similar substances. However, the European Chemicals Agency often rejects these proposals if the reasoning is not convincing. 1,538 decisions on testing proposals submitted between 2008 and 2023 were reviewed for this study. We analyzed the factors that influ­enced the agency’s decisions, focusing on 17 criteria. About 23% of proposals included read-across adaptations, with nearly half of these (49%) found acceptable. Group read-across proposals were more likely to succeed than analogue approaches. The study identified patterns and key factors, such as the similarity between substances and the type of tests proposed, that affected approval rates. This research offers valuable insights to help companies improve their proposals and increase the chances of approval for alternatives to animal testing.

Dysregulation of vascular endothelial growth factor (VEGF) and its receptor (VEGFR) contributes to atherosclerosis and cardiovascular disease (CVD), making it a potential target for CVD risk assessment. High-throughput screening (HTS) approaches have resulted in large-scale in vitro data, providing mechanistic information that can help assess chemical toxicity and identify molecular ini­tiating events (MIEs) of adverse outcome pathways (AOPs). AOPs represent a logical sequence of biological responses contributing to toxicity and are valuable tools to inform chemical risk assessment. Here, we used HTS data to formulate an AOP relating VEGF signaling perturbation to atheroscle­rosis. ToxCast, Tox21, and PubChem data were evaluated to obtain bioprofiles of 4165 compounds active in assays targeting VEGFR. Cheminformatics analysis identified 109 enriched structural fin­gerprints. Applying a subspace clustering approach based on chemical structure bioactivity yielded 12 primary targets, whose relevance to CVD was confirmed by an AI-assisted literature review. An AOP was hypothesized by coupling mechanistic relationships highlighted by HTS data with literature review findings, linking serotonin receptor (HTR), estrogen receptor alpha (ERα), and vasopressin receptor (AVPR) targets with VEGFR activity, angiogenic signaling, and atherosclerosis. Several endocrine disrupting chemicals (EDCs), e.g., bisphenols, triclosan, dichlorodiphenyltrichloroethane (DDT), and polychlorinated biphenyls (PCBs), were identified as relevant chemical stressors. Sub­space clustering of these chemicals evaluated potential MIEs and highlighted associations with use case classes. By applying computational methods to profile HTS data and hypothesize a mechanistic AOP, this study proposes a data-driven approach to evaluating environmental cardiotoxicity, which could eventually supplement and reduce the need for animal testing in toxicological assessments. Plain language summaryThis study explores how exposure of humans to chemicals in the environment can contribute to athero­sclerosis, the buildup of plaques in arteries that can lead to diseases of the heart and blood vessels. Using existing data on thousands of chemicals from non-animal screening methods, the study links certain chemicals, e.g., bisphenols and DDT, to biological changes related to the signaling receptor VEGFR and the development of atherosclerosis. An adverse outcome pathway (AOP) framework is used to map the sequence of biological events initiated upon exposure to chemicals to the manifes­tation of adverse health outcomes. This approach helps understand the risks posed by environmental chemicals and protects public health while reducing animal experiments.