The journal Food and Chemical Toxicology retracts the article “Long term toxicity of a Roundup herbicide and a Roundup-tolerant genetically modified maize,” which was published in this journal in November 2012. This retraction comes after a thorough and time-consuming analysis of the published article and the data it reports, along with an investigation into the peer-review behind the article. The Editor in-Chief deferred making any public statements regarding this article until this investigation was complete, and the authors were notified of the findings.

Industrial workers make up the segment of the population that is most vulnerable to chemical injury. To protect them from occupation-related harm, the American Conference of Governmental and Industrial Hygienists publishes annually revised threshold limit values (TLVs) (1), guidelines for permissible chemical exposure at the work place. TLV refers to concentrations of substances in parts per million or milligrams per cubic meter in the air to which most workers can be exposed on a daily basis without harm. These values apply to the work place only. They are not intended as guidelines for ambient air quality standards for the population at large. Obviously, genetic variations and diverse lifestyles (such as smoking, alcohol use, medication, and drug use) must be considered. Hypersensitive individuals may be adversely affected by exposure to certain chemicals even within the limits of the TLV. Thus, TLVs should be treated as guidelines only and not as fixed standards. The recommended goal is to minimize chemical exposure in the work place as much as possible. TLVs are expressed in three ways: 1. Time-weighted average (TLV–TWA) designates the average concentration of a chemical to which workers may safely be exposed for 8 h per day and 5 days per week. 2. Short-term exposure limit (TLV–STEL) designates permissible exposure for no more than 15 min, and no more than four times per day, with at least 60-min intervals between exposures. 3. Ceiling concentrations (TLV–C) are concentrations that should not be exceeded at any time. How protective the TLVs are is being questioned. The 1990 report that analyzed the scientific underpinnings of the TLVs revealed that at the exposure at or below the TLV, only few cases showed no adverse effect (2). In some cases even 100% of those exposed were affected. On the other hand, there was a good correlation between the TLVs and the measured exposure occurring in the work place. Thus, it appears that the TLVs represent levels of contaminants that may be encountered in the work place, rather than protective thresholds. Biological exposure indices (BEIs) provide another way of looking at exposure to chemicals.

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As the volume and complexity of nonclinical toxicology studies continue to increase, toxicologic pathology reporting faces persistent challenges, including fragmented sources of data (e.g., histopathology images, clinical pathology and other study data, adverse effects database, mechanistic literature), variable reporting timelines and heightened regulatory expectations. This white paper examines the emerging role of agentic artificial intelligence (AI) in addressing these issues through coordinated workflow orchestration, data integration, and pathologist-in-the-loop report generation. Based on a closed-door roundtable held during the 2025 Society of Toxicologic Pathology (STP) Annual Meeting and follow-on discussions, this paper synthesizes the perspectives of leading toxicologic pathologists, toxicologists, and AI developers. It outlines the key pain points in current reporting workflows, identifies realistic near-term use cases for agentic AI, and describes major adoption barriers including requirements for transparency, validation, and organizational readiness. A phased adoption roadmap and pilot design considerations are proposed to help support responsible evaluation and deployment of agentic AI system in nonclinical settings. The paper concludes by emphasizing the need for coordinated efforts across pharmaceutical organizations, CROs, academia, and regulators to establish shared standards, benchmarks, and governance frameworks that will lead to safe, transparent, and trustworthy integration of AI into toxicologic science.

ETHNOPHARMACOLOGICAL RELEVANCE Integrating network toxicology into traditional Chinese medicine toxicology boosts the efficiency and comprehensiveness of safety evaluations for these medicines. AIM OF THE STUDY This paper aims to deepen the comprehension of network toxicology and facilitate its application in the safety evaluation of traditional Chinese medicines. MATERIALS AND METHODS Using "network pharmacology," "network toxicology," and "traditional Chinese medicine" as keywords, relevant literature was searched in databases such as Web of Science, PubMed, and CNKI. The cited literature spans from 1999 to 2024. RESULTS Network toxicology constructs a "toxicity-gene-target-drug" network model to characterize the toxicological features of the research subject. This paper reviews existing toxicity prediction tools and databases, explores methodologies for toxicity prediction, and comprehensively summarizes the applications of network toxicology in the prediction of harmful components, analysis of toxicity mechanisms, integration of interdisciplinary approaches, and combination with omics technologies. Additionally, it analyzes the current challenges and limitations of network toxicology. CONCLUSIONS Network toxicology has been extensively utilized in the safety research of traditional Chinese medicine, particularly in identifying toxic components and elucidating their mechanisms. However, there remains significant space for advancement. Future research could investigate integrating network toxicology with cutting-edge technologies like artificial intelligence and multi-omics approaches, thereby offering robust theoretical foundations for developing a comprehensive traditional Chinese medicine safety evaluation system.

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Acute poly-substance intoxication requires rapid, life-saving decisions under substantial uncertainty, as clinicians must rely on incomplete ingestion details and nonspecific symptoms. Effective diagnostic reasoning in this chaotic environment requires fusing unstructured, non-medical narratives (e.g. paramedic scene descriptions and unreliable patient self-reports or known histories), with structured medical data like vital signs. While Large Language Models (LLMs) show potential for processing such heterogeneous inputs, they struggle in this setting, often underperforming simple baselines that rely solely on patient histories. To address this, we present DeToxR (Decision-support for Toxicology with Reasoning), the first adaptation of Reinforcement Learning (RL) to emergency toxicology. We design a robust data-fusion engine for multi-label prediction across 14 substance classes based on an LLM finetuned with Group Relative Policy Optimization (GRPO). We optimize the model's reasoning directly using a clinical performance reward. By formulating a multi-label agreement metric as the reward signal, the model is explicitly penalized for missing co-ingested substances and hallucinating absent poisons. Our model significantly outperforms its unadapted base LLM counterpart and supervised baselines. Furthermore, in a preliminary clinical validation study, the model indicates a clinical advantage by achieving higher micro-F1 (0.644 vs 0.473) and recall in identifying the correct poisons. These results demonstrate the potential of RL-aligned LLMs to synthesize unstructured pre-clinical narratives and structured medical data for decision support in high-stakes environments.

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This paper presents a quasi-sequential optimal design framework for toxicology experiments, specifically applied to sea urchin embryos. The authors propose a novel approach combining robust optimal design with adaptive, stage-based testing to improve efficiency in toxicological studies, particularly where traditional uniform designs fall short. The methodology uses statistical models to refine dose levels across experimental phases, aiming for increased precision while reducing costs and complexity. Key components include selecting an initial design, iterative dose optimization based on preliminary results, and assessing various model fits to ensure robust, data-driven adjustments. Through case studies, we demonstrate improved statistical efficiency and adaptability in toxicology, with potential applications in other experimental domains.

The increase in plastic debris and its environmental impact has been a major concern for scientists. Physical destruction, chemical reactions, and microbial activity can degrade plastic waste into particles smaller than 5 mm, known as microplastics (MPs). MPs may eventually enter aquatic ecosystems through surface runoff. The accumulation of MPs in aquatic environments poses a potential threat to finfish, shellfish, and the ecological balance. This study investigated the effect of MP exposure on freshwater and marine fish. MPs could cause significant harm to fish, including physical damage, death, inflammation, oxidative stress, disruption of cell signalling and cellular biochemical processes, immune system suppression, genetic damage, and reduction in fish growth and reproduction rates. The activation of the detoxification system of fish exposed to MPs may be associated with the toxicity of MPs and chemical additives to plastic polymers. Furthermore, MPs can enhance the bioavailability of other xenobiotics, allowing these harmful substances to more easily enter and accumulate in fish. Accumulation of MPs and associated chemicals in fish can have adverse effects on the fish and humans who consume them, with these toxic substances magnifying as they move up the food chain. Changes in migration and reproduction patterns and disruptions in predator-prey relationships in fish exposed to MPs can significantly affect ecological dynamics. These interconnected changes can lead to cascading effects throughout aquatic ecosystems. Thus, implementing solutions like reducing plastic production, enhancing recycling efforts, using biodegradable materials, and improving waste management is essential to minimize plastic waste and its environmental impact.

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The separate evaluation for males and females is the recent standard in in-vivo toxicology for dose or treatment effects using Dunnett tests. The alternative pre-test for sex-by-treatment interaction is problematic. Here a joint test is proposed considering the two sex-specific and the pooled Dunnett-type comparisons. The calculation of either simultaneous confidence intervals or adjusted p-values with the R-package multcomp is demonstrated using a real data example.

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 emphasises 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, we developed a network-based approach to characterise toxicological responses in the context of a biological system, inferring biological system specific networks. We directly linked molecular alterations to the adverse outcome pathway (AOP) framework, establishing connections with toxicologically relevant phenotypic events. We applied this framework on a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrated how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve our understanding of toxicological mechanisms from a systems biology perspective, guiding the hazard assessment of nanomaterials and other advanced materials.

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A new method for constructing absolutely continuous two--dimensional copulas by differential equations is presented. The copulas are symmetric with respect to reflection in the opposite diagonal. The support of the copula density may be prescribed to arbitrary opposite symmetric hypographs of invertible functions, containing the diagonal. The method is applied to toxicological probit modeling, where new compatibility conditions for the probit parameters are derived.

Polymeric nanoparticles (NPs) are particles within the size range from 1 to 1000 nm and can be loaded with active compounds entrapped within or surface-adsorbed onto the polymeric core. The term “nanoparticle” stands for both nanocapsules and nanospheres, which are distinguished by the morphological structure. Polymeric NPs have shown great potential for targeted delivery of drugs for the treatment of several diseases. In this review, we discuss the most commonly used methods for the production and characterization of polymeric NPs, the association efficiency of the active compound to the polymeric core, and the in vitro release mechanisms. As the safety of nanoparticles is a high priority, we also discuss the toxicology and ecotoxicology of nanoparticles to humans and to the environment.

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Here, we present TOX-FM, a family of non-clinical PFMs trained on large-scale diverse histopathology datasets of over one million whole slide images consisting of over 60 organs across six animal species from over 14,000 toxicology studies across six decades. We introduce a comprehensive benchmark of 18 non-clinical downstream tasks, incorporate an established benchmark of 9 clinical downstream tasks, and evaluate TOX-FM against state-of-the-art clinical PFMs across both non-clinical and clinical domains. Notably, we explore open questions surrounding domain-specific training and cross-domain generalization, offering insights into the opportunities and challenges of applying PFMs in the non-clinical domain.

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies. Investigative toxicology tools and strategies are used in pharmaceutical companies to reduce safety-related attrition in drug development. This Perspective article summarizes the key goals of investigative toxicology, highlights current approaches and discusses selected emerging technologies that have the potential to improve the current safety-testing paradigm.

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For toxicology studies, the validation of the concurrent control group by historical control data (HCD) has become requirements. This validation is usually done by historical control limits (HCL), which should cover the observations of the concurrent control with a predefined level of confidence. In many applications, HCL are applied to dichotomous data, e.g. the number of rats with a tumor vs. the number of rats without a tumor (carcinogenicity studies) or the number of cells with a micronucleus out of a total number of cells. Dichotomous HCD may be overdispersed and can be heavily right- (or left-) skewed, which is usually not taken into account in the practical applications of HCL. To overcome this problem, four different prediction intervals (two frequentist, two Bayesian), that can be applied to such data, are proposed. Based on comprehensive Monte-Carlo simulations, the coverage probabilities of the proposed prediction intervals were compared to heuristical HCL typically used in daily toxicological routine (historical range, limits of the np-chart, mean plus minus 2 SD). Our simulations reveal, that frequentist bootstrap calibrated prediction intervals control the type-1-error best, but, also prediction intervals calculated based on Bayesian generalized linear mixed models appear to be practically applicable. Contrary, all heuristics fail to control the type-1-error. The application of HCL is demonstrated based on a real life data set containing historical controls from long-term carcinogenicity studies run on behalf of the U.S. National Toxicology Program. The proposed frequentist prediction intervals are publicly available from the R package predint, whereas R code for the computation of the two Bayesian prediction intervals is provided via GitHub.

Nickel is a transition element extensively distributed in the environment, air, water, and soil. It may derive from natural sources and anthropogenic activity. Although nickel is ubiquitous in the environment, its functional role as a trace element for animals and human beings has not been yet recognized. Environmental pollution from nickel may be due to industry, the use of liquid and solid fuels, as well as municipal and industrial waste. Nickel contact can cause a variety of side effects on human health, such as allergy, cardiovascular and kidney diseases, lung fibrosis, lung and nasal cancer. Although the molecular mechanisms of nickel-induced toxicity are not yet clear, mitochondrial dysfunctions and oxidative stress are thought to have a primary and crucial role in the toxicity of this metal. Recently, researchers, trying to characterize the capability of nickel to induce cancer, have found out that epigenetic alterations induced by nickel exposure can perturb the genome. The purpose of this review is to describe the chemical features of nickel in human beings and the mechanisms of its toxicity. Furthermore, the attention is focused on strategies to remove nickel from the environment, such as phytoremediation and phytomining.

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There are very different statistical methods for demonstrating a trend in pharmacological experiments. Here, the focus is on sparse models with only one parameter to be estimated and interpreted: the increase in the regression model and the difference to control in the contrast model. This provides both p-values and confidence intervals for an appropriate effect size. A combined test consisting of the Tukey regression approach and the multiple contrast test according to Williams is recommended, which can be generalized to the generalized linear (mixed effect) model. Thus numerous variable types occurring in pharmacology/toxicology can be adequately evaluated. Software is available through CRAN packages. The most significant limitation of this approach is for designs with very small sample sizes, often in pharmacology/toxicology.

Microplastics are emerging contaminants and their presence in water and soil ecosystems has recently drawn considerable attention because they pose a great threat to entire ecosystems. Recent researches have focused on the detection, occurrence, characterization, and toxicology of microplastics in marine and freshwater ecosystems; however, our understanding of the ecological effects of microplastics in soil ecosystems is still limited compared with that in aquatic ecosystems. Here, we have compiled literature, studying the sources, migration of microplastics in soil, negative impacts on soil health and function, trophic transfer in food chains, and the corresponding adverse effects on soil organisms in order to address the potential ecological and human health risks caused by microplastics in soil. This review aims to address gaps in knowledge, shed light on the ecological effects of microplastics in soil, and propose future studies on microplastic pollution and the resultant soil ecotoxicity. Furthermore, this review is focused on limiting microplastics in soil and establishing management and remediation measures to mitigate the risks posed by microplastic pollution.

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To claim similarity of multiple dose-response curves in interlaboratory studies in regulatory toxicology is a relevant issue during the assay validation process. Here we demonstrated the use of dose-by-laboratory interaction contrasts, particularly Williams-type by total mean contrasts. With the CRAN packages statint and multcomp in the open-source software R the estimation of adjusted p-values or compatible simultaneous confidence intervals is relatively easy. The interpretation in terms of global or partial equivalence, i.e. similarity, is challenging, because thresholds are not available a-priori. This approach is demonstrated by selected in-vitro Ames MPF assay data.

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.

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Ecological risk assessment requires large amounts of chemical effect data from laboratory experiments. Due to experimental effort and animal welfare concerns it is desired to extrapolate data from existing sources. To cover the required chemical effect data several data sources need to be integrated to enable their interoperability. In this paper we introduce the Toxicological Effect and Risk Assessment (TERA) knowledge graph, which aims at providing such integrated view, and the data preparation and steps followed to construct this knowledge graph. We also present the applications of TERA for chemical effect prediction and the potential applications within the Semantic Web community.