PFAS-Biomolecule Interactions: Case Study Using Asclepios Nodes and Automated Workflows in KNIME for Drug Discovery and Toxicology.

The Asclepios suite of KNIME nodes represents an innovative solution for conducting cheminformatics and computational chemistry tasks, specifically tailored for applications in drug discovery and computational toxicology. This suite has been developed using open-source and publicly accessible software. In this chapter, we introduce and explore the Asclepios suite through the lens of a case study. This case study revolves around investigating the interactions between per- and polyfluorinated alkyl substances (PFAS) and biomolecules, such as nuclear receptors. The objective is to characterize the potential toxicity of PFAS and gain insights into their chemical mode of action at the molecular level. The Asclepios KNIME nodes have been designed as versatile tools capable of addressing a wide range of computational toxicology challenges. Furthermore, they can be adapted and customized to accomodate the specific needs of individual users, spanning various domains such as nanoinformatics, biomedical research, and other related applications. This chapter provides an in-depth examination of the technical underpinnings and foundations of these tools. It is accompanied by a practical case study that demonstrates the utilization of Asclepios nodes in a computational toxicology investigation. This showcases the extendable functionalities that can be applied in diverse computational chemistry contexts. By the end of this chapter, we aim for readers to have a comprehensive understanding of the effectiveness of the Asclepios node functions. These functions hold significant potential for enhancing a wide spectrum of cheminformatics applications.

Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals.

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically.

Predictive, integrative, and regulatory aspects of AI-driven computational toxicology - Highlights of the German Pharm-Tox Summit (GPTS) 2024.

The 9th German Pharm-Tox Summit (GPTS) and the 90th Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) took place in Munich from March 13-15, 2024. The event brought together over 700 participants from around the world to discuss cutting-edge developments in the fields of pharmacology and toxicology as well as scientific innovations and novel insights. A key focus of the conference was on the rapidly increasing role of computational toxicology, artificial intelligence (AI), and machine learning (ML) into the field, marking a shift away from traditional methods and allowing the reduction of animal testing as primary tool for toxicological risk assessment. Tools such as Toxometris.ai showcased the potential of AI-based risk assessments for predicting carcinogenicity, offering more ethical and efficient alternatives. Additionally, computer-driven models like computer-aided pattern analysis (C@PA) for drug toxicity prediction were presented, emphasizing the growing role of chem- and bioinformatic applications in computational sciences. Throughout the summit, there was a strong focus on the need for regulatory innovation to support the adoption of these advanced technologies and ensure the safety and sustainability of chemical substances and drugs.

The digital evolution in toxicology: pioneering computational education for emerging challenges.

The educational landscape of toxicology is increasingly integrating computational methodologies due to ethical concerns about animal testing and advancements in biotechnological and data analysis tools. This paper examines the evolution and significance of the Toxicology in the 21st century (Tox21) initiative and its impact on computational toxicology education. It contrasts computational toxicology with traditional methods, highlighting the limitations of conventional approaches and the new perspectives offered by computational techniques. The study emphasizes the importance of incorporating computational toxicology into curricula, including case studies that demonstrate how this integration enhances students' problem-solving abilities, real-time data analysis skills, and innovation capabilities. Furthermore, it outlines effective teaching content and methods, including software tools, online resources, and academic literature. The paper also addresses the challenges and limitations faced in this educational shift and explores prospects for advancing computational toxicology education. By documenting these developments, the study aims to clarify the current advancements in toxicology education and the preparedness of students to address global chemical safety challenges with innovative solutions.

Development of a Physiologically Based Pharmacokinetic Model for Flunixin in Cattle and Swine Following Dermal Exposure.

Flunixin meglumine is a nonsteroidal anti-inflammatory drug (NSAID). Banamine® Transdermal is a pour-on formulation of flunixin approved for pain control in beef and dairy cattle, but not for calves and some classes of dairy cattle or swine. Violative flunixin residues in edible tissues in cattle and swine have been reported and are usually attributed to non-compliant drug use or failure to observe an appropriate withdrawal time. This project aimed to develop a physiologically based pharmacokinetic (PBPK) model for flunixin in cattle and swine to predict withdrawal intervals (WDI) after exposures to different therapeutic regimens of Banamine® Transdermal. Due to the lack of comprehensive skin physiological data in cattle, the model was initially developed for swine and then adapted for cattle. Monte Carlo simulation was employed for population variability analysis. The model predicted WDIs were rounded to 1 and 2 days for liver and muscle in cattle, respectively, under FDA tolerance levels, while under EU maximum residue limits (MRLs), the WDIs were rounded to 1, 3, 2, and 2 days for liver, kidney, muscle, and fat, respectively, following a labeled single transdermal 3.3 mg/kg dose in cattle. The model was converted into a user-friendly interactive PBPK (iPBPK) interface. This study reports the first transdermal absorption model for drugs in cattle. This iPBPK model provides a scientifically based tool for the prediction of WDIs in cattle and swine administered with flunixin in an extra-label manner, especially by the transdermal route.

An in vitro and machine learning framework for quantifying serum albumin binding of per- and polyfluoroalkyl substances.

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.

A novel approach to triazole fungicides risk characterization: Bridging human biomonitoring and computational toxicology.

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.

Liquid phase transformation mechanism of ß-caryophyllonic acid initiated by hydroxyl radicals and ozone in atmosphere.

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.

Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies.

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.

A Near-Explicit Reaction Mechanism of Chlorine-Initiated Limonene: Implications for Health Risks Associated with the Concurrent Use of Cleaning Agents and Disinfectants.

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.

Assessment of the Aryl Hydrocarbon Receptor-Mediated Effects of Aromatic Sensitizers in Paper Recycling Effluent Employing Zebrafish Embryos and in Silico Docking.

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;43:2176-2188. © 2024 SETAC.

High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells.

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.

Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping.

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 1,751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. 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' MeOA and grouping chemicals with similar bioactivity profiles.

Application of the Biological Toxicity Effect Ratio (BER) Method for Advancing Water Quality Criteria Derivation.

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.

The investigation of the toxicity of organophosphorus flame retardants (OPFRs) by using in silico toxicity prediction platform ProTox- 3.0.

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.

Integrated Transfer Learning and Multitask Learning Strategies to Construct Graph Neural Network Models for Predicting Bioaccumulation Parameters of Chemicals.

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.

Multi-task pretrained language model with novel application domains enables more comprehensive health and ecological toxicity prediction.

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.

Structure-based modeling to assess binding and endocrine disrupting potential of polycyclic aromatic hydrocarbons in Daniorerio.

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.

Cross study analyses of SEND data: toxicity profile classification.

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.