Computational Toxicology Research Articles

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.

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.

Research trends of computational toxicology: a bibliometric analysis.

Computational toxicology utilizes computer models and simulations to predict the toxicity of chemicals. Bibliometric studies evaluate the impact of scientific research in a specific field. A bibliometric analysis of the computational methods used in toxicity assessment was conducted on the Web of Science between 1977 and 2024 February 12. Findings of this study showed that computational toxicology has evolved considerably over the years, moving towards more advanced computational methods, including machine learning, molecular docking, and deep learning. Artificial intelligence significantly enhances computational toxicology research by improving the accuracy and efficiency of toxicity predictions. Generally, the study highlighted a significant rise in research output in computational toxicology, with a growing interest in advanced methods and a notable focus on refining predictive models to optimize drug properties using tools like pkCSM for more precise predictions.

Non-targeted Screening and Toxicity Study of Safety Risk Substances in Facial Skincare Products: Molecular Networking and Computational Toxicology Strategy

Non-targeted Screening and Toxicity Study of Safety Risk Substances in Facial Skincare Products: Molecular Networking and Computational Toxicology Strategy

Adverse Outcome Pathways Mechanistically Describing Hepatotoxicity.

Adverse outcome pathways (AOPs) describe toxicological processes from a dynamic perspective by linking a molecular initiating event to a specific adverse outcome via a series of key events and key event relationships. In the field of computational toxicology, AOPs can potentially facilitate the design and development of in silico prediction models for hazard identification. Various AOPs have been introduced for several types of hepatotoxicity, such as steatosis, cholestasis, fibrosis, and liver cancer. This chapter provides an overview of AOPs on hepatotoxicity, including their development, assessment, and applications in toxicology.

Construction of an adverse outcome pathway for the cardiac toxicity of bisphenol a by using bioinformatics analysis

Bisphenol A (BPA), a common endocrine disruptor, has shown cardiovascular toxicity in several epidemiological studies, as well as in vivo and in vitro experimental studies. However, the related adverse outcome pathway (AOP) of BPA toxicity remains unraveled. This study aimed to develop an AOP for the cardiac toxicity of BPA through bioinformatics analysis. The interactions among BPA, genes, phenotypes, and cardiac toxicity were retrieved from several databases, including the Comparative Toxicogenomics Database, Computational Toxicology, DisGeNet, and MalaCards. The target genes and part of target phenotypes were obtained by Venn analysis and literature screening. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed for target genes by using the DAVID online analysis tool to obtain other target phenotypes. AOP hypotheses from BPA exposure to heart disease were established and evaluated comprehensively by a quantitative weight of evidence (QWOE) method. The target genes included ESR2, MAPK1, TGFB1, and ESR1, and the target phenotypes included heart contraction, cardiac muscle contraction, cellular Ca2+ homeostasis, cellular metabolic process, heart development, etc. Overall, the AOP of BPA cardiac toxicity was deduced to be as follows. Initially, BPA bound with ERα/β and then activated the MAPK, AKT, and IL-17 signaling pathways, leading to Ca2+ homeostasis disorder and increased inflammatory response. Subsequently, cardiac function was impaired, causing coronary heart disease, arrhythmia, cardiac dysplasia, and other heart diseases. According to the Bradford–Hill causal considerations, the score of AOP by QWOE was 69, demonstrating a moderate confidence and providing clues on cardiotoxicity-assessment procedure and further studies on BPA.

Per-and polyfluoroalkyl substances and disrupted sleep: mediating roles of proteins

BackgroundPer-and polyfluoroalkyl substances (PFAS) contamination may disrupt sleep through disrupted metabolic and immune functions. The study aims to investigate the association and potential mechanism between PFAS and sleep. MethodsWe included 136 young adults recruited between 2014-2018 and 76 were re-assessed between 2020-2022. Additional 8 participants only had complete data between 2020-2022. Plasma PFAS (PFOS, PFOA, PFHxS, PFHpS, PFPeS, PFNA, PFDA) were measured at both visits using liquid-chromatography high-resolution mass spectrometry. Plasma proteins were measured by Olink® Explore 384 Cardiometabolic and Inflammation Panel I. Sleep duration was self-reported at both visits along with follow-up sleep disturbance and sleep-related impairment using validated instruments. We utilized multiple linear regression to explore the association between individual PFAS (in tertile) and these sleep outcomes. PFAS associated with sleep outcomes were subjected to computational toxicology analysis using the Comparative Toxicogenomics Database and Toxicology in the 21st Century database to identify potential genetic links between them. Mediation analysis using proteomic data was then performed to confirm the findings from computational toxicology analysis. ResultsAt baseline, one tertile increase in PFDA was associated with 0.39 (95 % CI: 0.05, 0.73) hours of shorter nightly sleep duration, and, at follow-up, PFHxS and PFOA were associated with 0.39 (95 % CI: 0.05, 0.72) and 0.32 (95 % CI: 0.01, 0.63) hours shorter sleep duration, respectively. One tertile increase in PFOS exposure was associated with a 2.99-point increase in sleep disturbance scores (95 % CI: 0.67, 5.31) and a 3.35-point increase in sleep-related impairment scores (95 % CI: 0.51, 6.20). Computational toxicology and mediation analyses identified potential mediating roles for several proteins in the PFAS-sleep associations, including 11-beta-dehydrogenase isozyme 1 (HSD11B1), cathepsin B (CTSB) and several immune system-related proteins. ConclusionFuture large scale epidemiological and mechanistic studies should confirm our findings and test effect measure modification of the associations by age.

Activated charge transfer by interfacial S−O “bridge” at MnIn2S4/WO3 for eliminating kanamycin: Z-scheme mechanism, pathways and toxicity assessment

In the realm of heterojunction photocatalysis, the efficiency of carrier separation and overall photocatalytic reactions are significantly influenced by the accompanying resistance at the interface as well as the pathway of charge transfer. Herein, a covalently linked Z-scheme MnIn2S4/WO3 heterojunction with interfacial S−O bonds was synthesized through simple calcination and hydrothermal strategies. This tight heterostructure exerted the collaborative impact of interfacial covalent bonding in a Z-scheme model, and its significant role in facilitating the separation of charge carriers was investigated. 19 wt% WO3 combined MnIn2S4 (MIS/WO-19) delivered a remarkable degradation efficiency of 97.4 % for kanamycin, which was 8.8 times higher than that of pure MnIn2S4. The formation of S−O linkages between MnIn2S4 and WO3 was confirmed through experimental analyses and theoretical modeling. The proposed Z-scheme charge transfer pathway was examined by integrating quenching experiments, ESR tests, and DFT computations. This distinctive S−O bond, acting as a specialized “bridge”, in combination with the Z-scheme pathway, efficiently promoted the migration and segregation of photo-induced charges, leading to a notable enhancement in the degradation of kanamycin. Furthermore, the decomposition pathways of kanamycin were investigated through a collaborative approach involving mass spectrometry and DFT simulations, and a toxicity analysis of the intermediates was conducted by the computational toxicology.

Perspectives on the Toxic Effects of Micro- and Nanoplastics on the Environment: A Bibliometric Analysis of the 2014 to 2023 Period.

Over the past decade, micro- and nanoplastics (MNPs) have garnered significant attention due to their frequent detection in and potential toxic effects on the environment and organisms, making them a serious threat to human health. To comprehensively understand the research on MNPs' toxicity, we employed the R language-based Bibliometrix toolkit (version 4.3.0), VOSviewer (version 1.6.11) and CiteSpace (version 6.3.R1) to perform statistical and visual analyses of 3541 articles pertaining to MNPs' toxicity between 2014 and 2023, which were retrieved from the Web of Science Core Collection (WOSCC) database. The analysis revealed that research related to MNPs' toxicity has experienced a rapid increase in recent years. China's particularly prominent influence in the field of MNPs' toxicity is evidenced by its academic exchanges and the establishment of a mature cooperation system with other countries (regions), such as the USA and Germany. Studies related to MNPs' toxicity are primarily published in leading journals, including the Science of the Total Environment, Environmental Pollution, and the Journal of Hazardous Materials. The Chinese Academy of Sciences was identified as the leading institution in terms of research on MNPs' toxicity, contributing 203 papers to the total number of studies published. Keyword co-occurrence and burst analyses indicated that the current research on MNPs' toxicity mainly focuses on the toxic effects of MNPs on aquatic organisms, the combined toxicity of MNPs and other contaminants, and the toxic effects and mechanisms of MNPs. Future research should integrate computational toxicology and toxicomics to enhance our understanding of MNPs' toxicity mechanisms and assess the potential health risks posed by atmospheric MNPs.

Investigating the relationship of co-exposure to multiple metals with chronic kidney disease: An integrated perspective from epidemiology and adverse outcome pathways

Systematic studies on the associations between co-exposure to multiple metals and chronic kidney disease (CKD), as well as the underlying mechanisms, remain insufficient. This study aimed to provide a comprehensive perspective on the risk of CKD induced by multiple metal co-exposures through the integration of occupational epidemiology and adverse outcome pathway (AOP). The study participants included 401 male mine workers whose blood metal, β2-microglobulin (β2-MG), and cystatin C (Cys-C) levels were measured. Generalized linear models (GLMs), quantile g-computation models (qgcomp), least absolute shrinkage and selection operator (LASSO), and bayesian kernel machine regression (BKMR) were utilized to identify critical nephrotoxic metals. The mean concentrations of lead, cadmium, mercury, arsenic, and manganese were 191.93, 3.92, 4.66, 3.11, 11.35, and 16.33 µg/L, respectively. GLM, LASSO, qgcomp, and BKMR models consistently identified lead, cadmium, mercury, and arsenic as the primary contributors to kidney toxicity. Based on our epidemiological analysis, we used a computational toxicology method to construct a chemical-genetic-phenotype-disease network (CGPDN) from the Comparative Toxicogenomics Database (CTD), DisGeNET, and GeneCard databases, and further linked key events (KEs) related to kidney toxicity from the AOP-Wiki and PubMed databases. Finally, an AOP framework of multiple metals was constructed by integrating the common molecular initiating events (reactive oxygen species) and KEs (MAPK signaling pathway, oxidative stress, mitochondrial dysfunction, DNA damage, inflammation, hypertension, cell death, and kidney toxicity). This is the first AOP network to elucidate the internal association between multiple metal co-exposures and CKD, providing a crucial basis for the risk assessment of multiple metal co-exposures.

Toxicity prediction and risk assessment of per- and polyfluoroalkyl substances for threatened and endangered fishes

Per- and polyfluoroalkyl substances (PFASs) are severely polluted in aquatic environments and can harm aquatic organisms. Due to the limitation of conducting toxicity experiments directly on threatened and endangered (T&E) species, their toxicity data is scarce, hindering accurate risk assessments. The development of computational toxicology makes it possible to assess the risk of pollutants to T&E fishes. This study innovatively combined machine learning models, including random forest (RF), artificial neural network (ANN), and XGBoost, and the QSAR-ICE model to predict chronic developmental toxicity data of PFASs to T&E fishes. Among these, the XGBoost model exhibited superior performance, with R2 of 0.95 and 0.81 for the training and testing sets, respectively. Internal and external validation further confirmed that the XGBoost model is robust and reliable. Subsequently, it was used to predict chronic developmental toxicity data for seven priority PFASs to T&E fishes in the Yangtze River. Acipenseridae fishes (e.g., Acipenser dabryanus and Acipenser sinensis) showed high sensitivity to PFASs, possibly due to their unique lifestyle and physiological characteristics. Based on these data, the predicted no-effect concentration (PNEC) of individual PFASs was calculated, and the risk for T&E fishes in the Yangtze River was assessed. The results indicated that the risk of PFASs to T&E fishes is low (3.85 × 10−9∼8.20 × 10−4), with perfluorohexanoic acid (PFHxA) and perfluorooctanoic acid (PFOA) as the high-risk pollutants. The risk in the middle and lower reaches of the river is higher than in the upper reaches. This study provides a new approach for obtaining chronic toxicity data and conducting risk assessments for T&E species, advancing the protection of T&E species worldwide.

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.

Progress and prospects of safety assessment methods of medicinal and food homologous substances

Food and medicinal substances(FAMS) with both edible and medicinal uses have a long history in China, being an important part of China's excellent traditional culture and traditional Chinese medicine(TCM). The legal management of FAMS is in the form of catalogue, which has a history of nearly 40 years. More than 100 substances have been included in the China's FAMS catalogue. According to the Regulation of Food and Medicinal Substances Catalogue, safety assessment is a basis for substances to be included in the catalogue. The safety assessment of FAMS should follow the principles and requirements of food safety risk assessment. However, FAMS is a complex mixture, and the nature and data adequacy of the assessed substance should be comprehensively considered. Different eva-luation models and methods should be selected according to the principle of case analysis. With the development of next-generation technologies such as big data, artificial intelligence, high-throughput and high-content in vitro testing, and computational toxicology and the trend of increasing edible substances with medicinal effects applying for the inclusion in the China's FAMS catalogue, the methods of risk assessment are applied in the management of FAMS, novel food products, and local characteristic food products, playing a scientific role. This paper systematically reviews the methods, challenges, and prospects of safety assessment of FAMS.

Knowledge-based machine learning for predicting and understanding the androgen receptor (AR)-mediated reproductive toxicity in zebrafish

Traditional methods for identifying endocrine-disrupting chemicals (EDCs) that activate androgen receptors (AR) are costly, time-consuming, and low-throughput. This study developed a knowledge-based deep neural network model (AR-DNN) to predict AR-mediated adverse outcomes on female zebrafish fertility. This model started with chemical fingerprints as the input layer and was implemented through a five-layer virtual AR-induced adverse outcome pathway (AOP). Results indicated that the AR-DNN effectively and accurately screens new reproductive toxicants (AUC = 0.94, accuracy = 0.85), providing potential toxicity pathways. Furthermore, 1477 and 2448 chemicals that could lead to infertility were identified in the plastic additives list (PLASTICMAP, n = 7112) and the Inventory of Existing Chemical Substances in China (IECSC, n = 17741), respectively. Colourants containing steroid-like structures are the major active plastic additives that might lower female zebrafish fertility through AR binding, DNA binding, and transcriptional activation. While active IECSC chemicals primarily have the same fragments, such as benzonitrile, nitrobenzene, and quinolone. The predicted toxicity pathways were consistent with existing fish evidence, demonstrating the model’s applicability. This knowledge-based approach offers a promising computational toxicology strategy for predicting and characterising the endocrine-disrupting effects and toxic mechanisms of organic chemicals, potentially leading to more efficient and cost-effective screening of EDCs.

Coupling observational study with computational toxicology to elucidate the relationships and potential mechanisms of per-and polyfluoroalkyl substances and sleep

Coupling observational study with computational toxicology to elucidate the relationships and potential mechanisms of per-and polyfluoroalkyl substances and sleep

Identifying the toxic mechanisms of emerging electronic contaminations liquid crystal monomers and the construction of a priority control list for graded control

Liquid crystal monomers (LCMs) are identified as emerging organic contaminations with largely unexplored health impacts. To elucidate their toxic mechanisms, support the establishment of environmental discharge and management standards, and promote effective LCMs control, this study constructs a database covering 20,545 potential targets of 1431 LCMs, highlighting 9 key toxic target proteins that disrupt the nervous system and metabolic functions. GO and KEGG pathway analysis suggests LCMs severely affect nervous system, linked to neurodegenerative diseases and mental health disorders, with toxicity variations driven by electronegativity and structural complexity of LCM terminal groups. To achieve tiered control of LCMs, construct toxicity risk control lists for 9 key toxic target proteins, suitable for the graded control of LCMs, management recommendations are provided based on toxicity levels. These lists were validated for reliability and offer reliable toxicity predictions for LCMs. SHAP analysis points to electronic properties, molecular shape, and structural characteristics of LCMs as primary health impact factors. As the first study integrating machine learning with computational toxicology to outline LCMs health impacts, it aims to enhance public understanding of LCM toxicity risks and support the development of environmental standards, effective management of LCM production and emissions, and reduction of public exposure risks.

Urolithin A attenuates hexavalent chromium-induced small intestinal injury by modulating PP2A/Hippo/YAP1 pathway

Hexavalent chromium (Cr(VI)) exposure has been linked with gastrointestinal toxicity, whereas the molecular pathways and key targets remain elusive. Computational toxicology analysis predicted the correlation between protein phosphatase 2A (PP2A) and genes regarding Cr(VI)-induced intestinal injury. Here, we generated a mouse model with intestinal epithelium-specific knock out of Ppp2r1a (encoding PP2A Aα subunit) to investigate the mechanisms underlying Cr(VI)-induced small intestinal toxicity. Heterozygous (HE) mice and matched WT littermates were administrated with Cr(VI) at 0, 5, 20, and 80mg/l for 28 successive days. Cr(VI) treatment led to crypt hyperplasia, epithelial cell apoptosis, and intestinal barrier dysfunction, accompanied by the decline of goblet cell counts and Occludin expression in WT mice. Notably, these effects were aggravated in HE mice, indicating that PP2A Aα deficiency conferred mice with susceptibility to Cr(VI)-induced intestinal injury. The combination of data analysis and biological experiments revealed Cr(VI) exposure could decrease YAP1 phosphorylation at Ser127 but increase protein expression and activity, together with elevated transcriptional coactivator with PDZ-binding motif protein driving epithelial crypt cells proliferation following damage, suggesting the involvement of Hippo/YAP1 signaling pathway in Cr(VI)-induced intestinal toxicity. Nevertheless, the enhanced phosphorylation of YAP1 in HE mice resulted in proliferation/repair defects in intestinal epithelium, thereby exacerbating Cr(VI)-induced gut barrier dysfunction. Notably, by molecular docking and further studies, we identified urolithin A, a microbial metabolite, attenuated Cr(VI)-induced disruption of intestinal barrier function, partly by modulating YAP1 expression and activity. Our findings reveal the novel molecular pathways participated in Cr(VI)-caused small intestinal injury and urolithin A could potentially protect against environmental hazards-induced intestinal diseases.

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.

Computational assessment of the toxicological profiles of various chemicals to which humans are exposed. A review

Abstract This study contains a brief description of the common computational methods used in the prediction of the toxicological effects of chemical substances, and a synthetic review of the literature on the results of computational studies on the prediction of the toxicological effects of substances to which humans are frequently exposed: food additives, food contaminants, cosmetic ingredients, drug-related compounds and pesticides. The advantages and limitations of using current computational toxicology in assessing the toxicity of chemicals are also discussed.

From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: Dual computational toxicological approaches for prioritization

The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles.