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Brazil stands as the world's leading coffee producer, where the extensive use of pesticides is economically critical yet poses health and environmental risks due to their non-selective mechanisms of action. Specifically, triazole fungicides are widely used in agriculture to manage fungal diseases and are known to disrupt mammalian CYP450 and liver microsomal enzymes. This research establishes a framework for risk characterization of human exposure to triazole fungicides by internal-dose biomonitoring, biochemical marker measurements, and integration of high-throughput screening (HTS) data via computational toxicology workflows from the Integrated Chemical Environment (ICE). Volunteers from the southern region of Minas Gerais, Brazil, were divided into two groups: farmworkers and spouses occupationally and environmentally exposed to pesticides from rural areas (n = 140) and individuals from the urban area to serve as a comparison group (n = 50). Three triazole fungicides, cyproconazole, epoxiconazole, and triadimenol, were detected in the urine samples of both men and women in the rural group. Androstenedione and testosterone hormones were significantly reduced in the farmworker group (Mann-Whitney test, p < 0.0001). The data show a significant inverse association of testosterone with cholesterol, LDL, VLDL, triglycerides, and glucose and a direct association with HDL (Spearman's correlation, p < 0.05). In the ICE workflow, active in vitro HTS assays were identified for the three measured triazoles and three other active ingredients from the pesticide formulations. The curated HTS data confirm bioactivities predominantly related to steroid hormone metabolism, cellular stress processes, and CYP450 enzymes impacted by fungicide exposure at occupationally and environmentally relevant concentrations based on the in vitro to in vivo extrapolation models. These results characterize the potentially significant human health risk, particularly from the high frequency and intensity of exposure to epoxiconazole. This study showcases the critical role of biomonitoring and utility of computational tools in evaluating pesticide exposure and minimizing the risk.

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The environment faces increasing anthropogenic impacts, resulting in a rapid increase in environmental issues that undermine the natural capital essential for human wellbeing. These issues are complex and often influenced by various factors represented by data with different modalities. While machine learning (ML) provides data-driven tools for addressing the environmental issues, the current ML models in environmental science and engineering (ES&E) often neglect the utilization of multimodal data. With the advancement in deep learning, multimodal learning (MML) holds promise for comprehensive descriptions of the environmental issues by harnessing data from diverse modalities. This advancement has the potential to significantly elevate the accuracy and robustness of prediction models in ES&E studies, providing enhanced solutions for various environmental modeling tasks. This perspective summarizes MML methodologies and proposes potential applications of MML models in ES&E studies, including environmental quality assessment, prediction of chemical hazards, and optimization of pollution control techniques. Additionally, we discuss the challenges associated with implementing MML in ES&E and propose future research directions in this domain.

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Limonene, a key volatile chemical product (VCP) commonly found in personal care and cleaning agents, is emerging as a major indoor air pollutant. Recently, elevated levels of reactive chlorine species during bleach cleaning and disinfection have been reported to increase indoor oxidative capacity. However, incomplete knowledge of the indoor transformation of limonene, especially the missing chlorine chemistry, poses a barrier to evaluating the environmental implications associated with the concurrent use of cleaning agents and disinfectants. Here, we investigated the reaction mechanisms of chlorinated limonene peroxy radicals (Cl-lim-RO2•), key intermediates in determining the chlorine chemistry of limonene, and toxicity of transformation products (TPs) using quantum chemical calculations and toxicology modeling. The results indicate that Cl-lim-RO2• undergoes a concerted autoxidation process modulated by RO2• and alkoxy radicals (RO•), particularly emphasizing the importance of RO• isomerization. Following this generalized autoxidation mechanism, Cl-lim-RO2• can produce low-volatility precursors of secondary organic aerosols. Toxicological findings further indicate that the majority of TPs exhibit increased respiratory toxicity, mutagenicity, and eye/skin irritation compared to limonene, presenting an occupational hazard for indoor occupants. The proposed near-explicit reaction mechanism of chlorine-initiated limonene significantly enhances our current understanding of both RO2• and RO• chemistry while also highlighting the health risks associated with the concurrent use of cleaning agents and disinfectants.

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ß-caryophyllonic acid (BCA), as an important precursor of aqueous secondary organic aerosols (aqSOA), has adverse effects on the atmospheric environment and human health. However, the key atmospheric chemical reaction process in which BCA participates in the formation of aqueous secondary organic aerosols is still unclear. In this study, the reaction mechanism and kinetics of BCA with ·OH and O3 were investigated by quantum chemical calculations. The initiation reactions between BCA and ·OH include addition and H-abstraction reaction pathways, subsequent intermediates will also react with O2, ultimately undergo a cracking reaction to generate small molecular substances. The reaction of BCA with O3 can generate primary ozone oxides and the Criegee Intermediates oIM3, subsequent main reaction products include keto-BCA, as well as other small molecule aqSOA precursors. The entire reaction process increases the O/C ratio of aqSOA in the aqueous phase and generates products of small molecules such as 4-formylpropionic acid, which plays an important role in the formation of aqSOA. At 298K, the transformation rate constants of BCA initiated by ·OH and O3 are 1.47 × 1010 M-1 s-1 and 3.16 × 105 M-1 s-1, respectively, the atmospheric lifetimes of BCA reacting with ·OH range from 0.86 h-5.40 h, while the lifetimes of BCA reacting with O3 range from 0.44 h-10.04 years. This suggests that BCA primarily reacts with ·OH. However, under higher O3 concentrations, its ozonolysis becomes significant, promoting the formation of aqSOA. According to the risk assessment, the toxicity of most transformation products (TPs) gradually decreased, but the residual developmental toxicity could not be ignored. In this paper, the atmospheric liquid phase oxidation mechanisms of sesquiterpene unsaturated derived acid were studied from the microscopic level, which has guiding significance for the formation and transformation of aqSOA in atmosphere.

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Per- and polyfluoroalkyl substances (PFAS) are a diverse class of anthropogenic chemicals; many are persistent, bioaccumulative, and mobile in the environment. Worldwide, PFAS bioaccumulation causes serious adverse health impacts, yet the physiochemical determinants of bioaccumulation and toxicity for most PFAS are not well understood, largely due to experimental data deficiencies. As most PFAS are proteinophilic, protein binding is a critical parameter for predicting PFAS bioaccumulation and toxicity. Among these proteins, human serum albumin (HSA) is the predominant blood transport protein for many PFAS. We previously demonstrated the utility of an in vitro differential scanning fluorimetry assay for determining relative HSA binding affinities for 24 PFAS. Here, we report HSA affinities for 65 structurally diverse PFAS from 20 chemical classes. We leverage these experimental data, and chemical/molecular descriptors of PFAS, to build 7 machine learning classifier algorithms and 9 regression algorithms, and evaluate their performance to identify the best predictive binding models. Evaluation of model accuracy revealed that the top performing classifier model, logistic regression, had an AUROC statistic of 0.936. The top performing regression model, support vector regression, had an R2 of 0.854. These top performing models were then used to predict HSA-PFAS binding for chemicals in the EPAPFASINV list of 430 PFAS. These developed in vitro and in silico methodologies represent a high-throughput framework for predicting protein-PFAS binding based on empirical data, and generate directly comparable binding data of potential use in predictive modeling of PFAS bioaccumulation and other toxicokinetic endpoints.

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New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10-3 µM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R2 = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.

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Water quality criteria (WQC) serve as a scientific foundation for pollutant risk assessment and control in aquatic ecosystems. The development of regionally differentiated WQC tailored to specific regional characteristics has become an emerging trend. However, the current WQC is constrained by a lack of regional species toxicity data. To address these limitations, this study proposes the biological toxicity effect ratio (BER) method, which indirectly reflects the toxicity sensitivity of the overall aquatic ecosystem through the toxicity information on a limited number of species, enabling rapid WQC prediction. Using the established WQC in China and the USA as a case study, we combined mathematical derivation and data validation to evaluate the BER method. Among various species-taxon groups of freshwater organisms, planktonic crustaceans demonstrated the highest predictive accuracy. Our analysis further revealed that species toxicity sensitivity and regional variability jointly influence the prediction accuracy. Regardless of the evaluation indexes, planktonic crustaceans emerged as the most suitable species-taxon group for the BER method. Additionally, the BER method is particularly applicable to pollutants with conserved mechanisms across species. This study systematically explores the feasibility of using the BER method and offers new insights for deriving regionally differentiated WQC.

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Aromatic sensitizers and related substances (SRCs), which are crucial in the paper industry for facilitating color-forming and color-developing chemical reactions, inadvertently contaminate effluents during paper recycling. Owing to their structural resemblance to endocrine-disrupting aromatic organic compounds, concerns have arisen about potential adverse effects on aquatic organisms. We focused on SRC effects via the aryl hydrocarbon receptor (AHR), employing molecular docking simulations and zebrafish (Danio rerio) embryo exposure assessments. Molecular docking revealed heightened binding affinities between certain SRCs in the paper recycling effluents and zebrafish Ahr2 and human AHR, which are pivotal components in the SRC toxicity mechanism. Fertilized zebrafish eggs were exposed to SRCs for up to 96 h post fertilization; among these substances, benzyl 2-naphthyl ether (BNE) caused morphological abnormalities, such as pericardial edema and shortened body length, at relatively low concentrations (1 µM) during embryogenesis. Gene expression of cytochrome P450 1A (cyp1a) and ahr2 was also significantly increased by BNE. Co-exposure to the AHR antagonist CH-223191 only partially mitigated BNE's phenotypic effects, despite the effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin being relatively well restored by CH-223191, indicating BNE's AHR-independent toxic mechanisms. Furthermore, some SRCs, including BNE, exhibited in silico binding affinity to the estrogen receptor and upregulation of cyp19a1b gene expression. Therefore, additional insights into the toxicity of SRCs and their mechanisms are essential. The present results provide important information on SRCs and other papermaking chemicals that could help minimize the environmental impact of the paper industry. Environ Toxicol Chem 2024;00:1-13. © 2024 SETAC.

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High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using eight concentrations and an exposure duration of 6 hours. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms-of-action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally-related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points-of-departure, predicting chemicals' molecular mechanism(s)-of-action (MeOA) and grouping chemicals with similar bioactivity profiles.

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In silico models for screening substances of healthy and ecological concern are essential for effective chemical management. However, current data-driven toxicity prediction models confront formidable challenges related to expressive capacity, data scarcity, and reliability issues. Thus, this study introduces TOX-BERT, a SMILES-based pretrained model for screening health and ecological toxicity. Results show that masked atom recovery pretraining and multi-task learning offer promising solutions to enhance model capacity and address data scarcity issues. Two novel application domain (AD) parameters, termed PCA-AD and LDS, were proposed to improve prediction reliability of TOX-BERT with accuracy surpassing 90 % and mean absolute error (MAE) below 0.52. TOX-BERT was applied to 18,905 IECSC chemicals, revealing distinct toxicity relationships that align with experimental studies such as those between cardiotoxicity and acute ecotoxicity. In addition to previous PBT screening, 156 potential high-risk chemicals for specific endpoint were identified covering 7 categories. Furthermore, a SMILES-based toxicity site detection approach was developed for structural toxicity analysis. These advancements carry profound implications to address challenges faced by current data-driven toxicity prediction models. TOX-BERT emerges as a valuable tool for more comprehensive, reliable, and applicable predictions of health and ecological toxicity in chemical risk assessment and management.

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From the past to the present, many chemicals have been used for the purpose of flame retardant. Due to PBDEs' (Polybrominated diphenyl ether) lipophilic and accumulative properties, some of them are banned from the market. As an alternative to these chemicals, OPFRs (organophosphorus flame retardants) have started to be used as flame retardants. In this article, acute toxicity profiles, mutagenicity, carcinogenicity, blood-brain barrier permeability, ecotoxicity and nutritional toxicity as also AHR, ER affinity and MMP, aromatase affinity, CYP2C9, CYP3A4 interaction of the of 16 different compounds of the OPFRs were investigated using a computational toxicology method; ProTox- 3.0. According to our results, eight compounds were found to be active in terms of carcinogenic effect, whereas two compounds were found to be active for mutagenicity. On the other hand, all compounds were found to be active in terms of blood-barrier permeability. Fourteen compounds and four compounds are found to have ecotoxic and nutritional toxic potency, respectively. Eight compounds were determined as active to AhR, and four chemicals were found to be active in Estrogen Receptor alpha. Eight chemicals were found to be active in terms of mitochondrial membrane potency. Lastly, three chemicals were found to be active in aromatase enzymes. In terms of CYP interaction potencies, eight compounds were found to be active in both CYP2C9 and CYP3A4. This research provided novel insights into the potential toxic effects of OPFRs. However, further studies are needed to evaluate their toxicity. Moreover, these findings lay the groundwork for in vitro and in vivo toxicity research.

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Accurate prediction of parameters related to the environmental exposure of chemicals is crucial for the sound management of chemicals. However, the lack of large data sets for training models may result in poor prediction accuracy and robustness. Herein, integrated transfer learning (TL) and multitask learning (MTL) was proposed for constructing a graph neural network (GNN) model (abbreviated as TL-MTL-GNN model) using n-octanol/water partition coefficients as a source domain. The TL-MTL-GNN model was trained to predict three bioaccumulation parameters based on enlarged data sets that cover 2496 compounds with at least one bioaccumulation parameter. Results show that the TL-MTL-GNN model outperformed single-task GNN models with and without the TL, as well as conventional machine learning models trained with molecular descriptors or fingerprints. Applicability domains were characterized by a state-of-the-art structure-activity landscape-based (abbreviated as ADSAL) methodology. The TL-MTL-GNN model coupled with the optimal ADSAL was employed to predict bioaccumulation parameters for around 60,000 chemicals, with more than 13,000 compounds identified as bioaccumulative chemicals. The high predictive accuracy and robustness of the TL-MTL-GNN model demonstrate the feasibility of integrating the TL and MTL strategy in modeling small-sized data sets. The strategy holds significant potential for addressing small data challenges in modeling environmental chemicals.

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A SEND toxicology data transformation, harmonization, and analysis platform were created to improve the identification of unique findings related to the intended target, species, and duration of dosing using data from multiple studies. The lack of a standardized digital format for data analysis had impeded large-scale analysis of in vivo toxicology studies. The CDISC SEND standard enables the analysis of data from multiple studies performed by different laboratories. This work describes methods to analyze data and automate cross-study analysis of toxicology studies. Cross-study analysis can be used to understand a single compound's toxicity profile across all studies performed and/or to evaluate on-target versus off-target toxicity for multiple compounds intended for the same pharmacological target. This work involved development of data harmonization/transformation strategies to enable cross-study analysis of both numerical and categorical SEND data. Four de-identified SEND datasets from the BioCelerate database were used for the analyses. Toxicity profiles for key organ systems were developed for liver, kidney, male reproductive tract, endocrine system, and hematopoietic system using SEND domains. A cross-study analysis dashboard with a built-in user-defined scoring system was created for custom analyses, including visualizations to evaluate data at the organ system level and drill down into individual animal data. This data analysis provides the tools for scientists to compare toxicity profiles across multiple studies using SEND. A cross-study analysis of 2 different compounds intended for the same pharmacological target is described and the analyses indicate potential on-target effects to liver, kidney, and hematopoietic systems.

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Environmental contaminants, such as polycyclic aromatic hydrocarbons (PAHs), have raised concerns regarding their potential endocrine-disrupting effects on aquatic organisms, including fish. In this study, molecular docking and molecular dynamics techniques were employed to evaluate the endocrine-disrupting potential of PAHs in zebrafish, as a model organism. A virtual screening with 72 PAHs revealed a correlation between the number of PAH aromatic rings and their binding affinity to proteins involved in endocrine regulation. Furthermore, PAHs with the highest binding affinities for each protein were identified: cyclopenta[cd]pyrene for AR (-9.7 kcal/mol), benzo(g)chrysene for ERα (-11.5 kcal/mol), dibenzo(a,e)pyrene for SHBG (-8.7 kcal/mol), dibenz(a,h)anthracene for StAR (-11.2 kcal/mol), and 2,3-benzofluorene for TRα (-9.8 kcal/mol). Molecular dynamics simulations confirmed the stability of the protein-ligand complexes formed by the PAHs with the highest binding affinities throughout the simulations. Additionally, the effectiveness of the protocol used in this study was demonstrated by the receiver operating characteristic curve (ROC) analysis, which effectively distinguished decoys from true ligands. Therefore, this research provides valuable insights into the endocrine-disrupting potential of PAHs in fish, highlighting the importance of assessing their impact on aquatic ecosystems.

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INTRODUCTION: This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance. AREAS COVERED: The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency. EXPERT OPINION: Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.

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Biological Evaluations support Endangered Species Act (ESA) consultation with the US Fish and Wildlife Service and National Marine Fisheries Service by federal action agencies, such as the USEPA, regarding impacts of federal activities on threatened or endangered species. However, they are often time-consuming and challenging to conduct. The identification of pollutant benchmarks or guidance to protect taxa for states and tribes when USEPA has not yet developed criteria recommendations is also of importance to ensure a streamlined approach to Clean Water Act program implementation. Due to substantial workloads, tight regulatory timelines, and the often-protracted length of ESA consultations, there is a need to streamline the development of biological evaluation toxicity assessments for determining the impact of chemical pollutants on ESA-listed species. Moreover, there is limited availability of species-specific toxicity data for many contaminants, further complicating the consultation process. New approach methodologies are being increasingly used in toxicology and chemical safety assessment to rapidly and cost-effectively provide data that can fill gaps in hazard and/or exposure characterization. Here, we present the development of an automated computational pipeline-RASRTox (Rapidly Acquire, Score, and Rank Toxicological data)-to rapidly extract and categorize ecological toxicity benchmark values from curated data sources (ECOTOX, ToxCast) and well-established quantitative structure-activity relationships (TEST, ECOSAR). As a proof of concept, points-of-departure (PODs) generated in RASRTox for 13 chemicals were compared against benchmark values derived using traditional methods-toxicity reference values (TRVs) and water quality criteria (WQC). The RASRTox PODs were generally within an order of magnitude of corresponding TRVs, though less concordant compared with WQC. The greatest utility of RASRTox, however, lies in its ability to quickly and systematically identify critical studies that may serve as a basis for screening value derivation by toxicologists as part of an ecological hazard assessment. As such, the strategy described in this case study can potentially be adapted for other risk assessment contexts and stakeholder needs. Integr Environ Assess Manag 2024;00:1-15. © 2024 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

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New approach methodologies (NAMs) can address information gaps on potential neurotoxicity or developmental neurotoxicity hazard for data-poor chemicals. Two assays have been previously developed using microelectrode arrays (MEA), a technology which measures neural activity. The MEA acute network function assay (AcN) uses dissociated rat cortical cells cultured at postnatal day 0 and evaluates network activity during a 40-minute chemical exposure on day in vitro (DIV)13 or 15. In contrast, the MEA network formation assay (NFA) uses a developmental exposure paradigm spanning DIV0 through DIV12. Measures of network activity over time at DIV5, 7, 9, and 12 in the NFA are reduced to an estimated area under the curve to facilitate concentration-response evaluation. Here, we evaluated the hypothesis that chemicals with effects in the AcN also perturb the NFA by examining quantitative and qualitative concordance between assays. Out of 243 chemicals screened in both assays, we observed 70.3% concordance between the AcN and NFA after eliminating activity inferred to be cytotoxic (selective activity), with the majority of discordance explained by chemicals that altered selective activity in the AcN but not NFA. The NFA detected more active chemicals when evaluating activity associated with cytotoxicity. Median potency values were lower in the NFA compared to the AcN, but within-chemical potency values were not uniformly lower in the NFA than the AcN. Lastly, the AcN and NFA captured unique bioactivity fingerprints; the AcN was more informative for identifying chemicals with a shared mode of action, while the NFA provided information relevant to developmental exposure. Taken together, this analysis provides a rationale for using both approaches for chemical evaluation with consideration of the context of use, such as screening/ prioritization, hazard identification, or to address questions regarding biological mechanism or function.

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To understand and describe neurotoxicity mechanistically, we must first understand the processes and responses that occur within neuronal cell systems after the administration of a chemical. The dataset we present is a collection of experimental results from the literature that comprises various neurotoxic endpoints in human-derived in vitro models, allowing for easy data analysis. Currently available and free databases such as the EPA's ToxCast, which focuses on forecasting toxic health risks, are created by collecting reports on cytotoxicity testing and creating mathematical fits that could help predict the effects of a given chemical on various types of cells. We, in contrast, provide a smaller, raw, and heterogeneous dataset created solely of results on human-derived cell models that not only summarises the cytotoxic effects of certain substances but also creates a possibility for analysing the significance of the experimental set-up for the prediction of outcome.

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In this study, proportional response addition (Prop-RA), a model for predicting response from chemical mixture exposure, is demonstrated and evaluated by statistically analyzing data on all possible binary combinations of the four regulated trihalomethanes (THMs). These THMs were the subject of a multipurpose toxicology study specifically designed to evaluate Prop-RA. The experimental design used a set of doses common to all components and mixtures, providing hepatotoxicity data on the four single THMs and the binary combinations. In Prop-RA, the contribution of each component to mixture toxicity is proportional to its fraction in the mixture based on its response at the total mixture dose. The primary analysis consisted of 160 evaluations. Statistically significant departures from the Prop-RA prediction were found for seven evaluations, with three predications that were greater than and four that were less than the predicted response; interaction magnitudes (n-fold difference in response vs. prediction) ranged from 1.3 to 1.4 for the former and 2.6 to 3.8 for the latter. These predictions support the idea that Prop-RA works best with chemicals where the effective dose ranges overlap. Prop-RA does not assume the similarity of toxic action or independence, but it can be applied to a mixture of components that affect the same organ/system, with perhaps unknown toxic modes of action.