Computational Toxicology最新文献

{"title":"O-QT assistant: a multi-agent AI system for streamlined chemical hazard assessment and read-across analysis using the OECD QSAR toolbox API","authors":"Ivo Djidrovski ,&nbsp;Raymond Pieters ,&nbsp;Juliette Legler ,&nbsp;Marc Teunis","doi":"10.1016/j.comtox.2025.100395","DOIUrl":"10.1016/j.comtox.2025.100395","url":null,"abstract":"<div><div>The OECD QSAR Toolbox is a vital resource in regulatory toxicology for assessing chemical hazards and filling data gaps using in silico methods, supporting the move away from animal testing. However, manually interpreting its complex outputs (physicochemical properties, profiling results, experimental data) and synthesizing this information into consistent, justified assessment reports represents a significant bottleneck requiring substantial expert effort. To address this challenge, we developed the O-QT assistant: the first open-source (Apache 2.0 licensed) pipeline employing a multi-agent Large Language Model (LLM) system, featuring distinct agents for interpreting properties, environmental fate, reactivity, metabolism, QSAR predictions, experimental data, and read-across strategies. The system offers both automated analysis and a guided mode allowing user customization of scope and methods. We demonstrate the O-QT Assistant’s workflow using 1,1-diethoxyheptane (CAS 688–82-4), a fragrance ingredient, as a detailed case study, supplemented by characterization across nine additional chemicals. Its LLM agents, operating under constraints derived from structured prompts and the retrieved data, synthesized these findings into a narrative report and a comprehensive JSON log. This approach, validated across multiple chemicals demonstrating high factual accuracy (&gt;99 %), enables full auditability of the AI interpretations against the source data.. The O-QT Assistant is freely available on GitHub at https://github.com/VHP4Safety/O-QT-OECD-QSAR-Toolbox-AI-assistant under the Apache 2.0 license. By automating key interpretation and reporting steps, the O-QT Assistant has the potential to significantly improve the efficiency and consistency of workflows involving OECD QSAR Toolbox data, promoting more standardized interpretations and potentially reducing variability in chemical safety assessments.</div></div><div><h3>Scientific Contribution</h3><div>An open-source multi-agent LLM assistant automating OECD QSAR Toolbox data interpretation and narrative report generation via its API for regulatory toxicology workflows.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100395"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finding optimal designs for estimating hormesis effect sizes","authors":"William Gertsch,&nbsp;Weng Kee Wong","doi":"10.1016/j.comtox.2026.100406","DOIUrl":"10.1016/j.comtox.2026.100406","url":null,"abstract":"<div><div>There is increasing interest in hormesis as the phenomenon gains growing recognition in toxicology, public health and medicine. This paper is the first to focus on constructing model-based optimal designs for estimating the expected amount of hormesis using measures based on the area-under-the-curve (AUC). These measures can be used regardless of the choice of dose–response model. We first review optimal design techniques and propose new designs for estimating the degree or amount of hormesis with maximum precision at minimal cost when the sample size is large or small. We use a variety of state-of-the-art algorithms , including nature-inspired metaheuristic algorithms to search for the optimal designs. While the latter are not new, they are very flexible, fast and do not need technical assumptions for them to work well. Consequently, they are general purpose optimization algorithms and can be used to optimize any one or more objective functions for different models. When the experiment has two or more objectives, possibly with unequal interest, we find Pareto-optimal designs that balance the tradeoffs among the objectives. Experimental data are used to motivate and illustrate our approaches.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100406"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanoparticle in vitro dosimetry via supervised machine learning","authors":"Linard David Hoessly ,&nbsp;Sandor Balog","doi":"10.1016/j.comtox.2026.100401","DOIUrl":"10.1016/j.comtox.2026.100401","url":null,"abstract":"<div><div>To advance the dosimetry of nanoparticles in the context of in vitro cell culture experiments (assays), we propose an inferential machine learning approach realized by supervising a deep neural network trained for function approximation as a substitute for nonparametric regression. This study explicitly addresses the limitations of current PDE-based models by introducing a supervised machine learning framework for parameter inference, ensuring predictive accuracy and interpretability for computational toxicology applications. The approach—exhaustively tested via Monte Carlo simulations—can quantitatively estimate fundamental parameters, such as the particle diffusion coefficient, particle settling velocity, and the probability of particle association with cells, directly from the temporal progression of dosimetry data. The results demonstrate that accurate analyses can be obtained through supervised machine learning, which has the capacity to define a key domain in the interpretation of in vitro assays dedicated to hazard and risk assessment of nanoparticles.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100401"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A weighted-likelihood framework for class imbalance in Bayesian prediction models","authors":"Stanley E. Lazic","doi":"10.1016/j.comtox.2026.100400","DOIUrl":"10.1016/j.comtox.2026.100400","url":null,"abstract":"<div><div>Class imbalance is a pervasive problem in predictive toxicology, where the number of non-toxic compounds often exceeds the number of toxic ones. Models trained on such data often perform well on the majority class but poorly on the minority class, which is most relevant for safety assessment. We propose a simple and general Bayesian framework that addresses class imbalance by modifying the likelihood function. Each observation’s likelihood is raised to a power inversely proportional to its class proportion, with the weights normalised to preserve the overall information content. This weighted-likelihood (or power-likelihood) approach embeds cost-sensitive learning directly into Bayesian updating. The method is demonstrated using simulated binary data and an ordered logistic model for drug-induced liver injury (DILI). Weighting alters parameter estimates and decision boundaries, improving balanced accuracy and sensitivity for the minority (toxic) class. The approach can be implemented with minimal changes in standard probabilistic programming languages such as <span>Stan</span>, <span>PyMC</span>, and <span>Turing.jl</span>. This framework provides an easily extensible foundation for developing Bayesian prediction models that better reflect the asymmetric costs of safety-critical decisions.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100400"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular dynamics insights into antibiotic–microplastic interactions: mechanisms, environmental risks, and predictive perspectives","authors":"K. Shashikala ,&nbsp;V. Sudheesh ,&nbsp;Deepa Janardanan ,&nbsp;Suja P Devipriya","doi":"10.1016/j.comtox.2026.100402","DOIUrl":"10.1016/j.comtox.2026.100402","url":null,"abstract":"<div><div>Microplastics (MPs) have evolved from being viewed as inert pollutants to dynamic vectors that alter the environmental behaviour of antibiotics, intensifying their persistence, transport, and ecotoxicological impact. Despite a surge of experimental and computational studies, inconsistencies in methodology, polymer selection, and environmental realism continue to obscure the mechanistic understanding of antibiotic-MP interactions. This critical review re-evaluates the current evidence, contrasting adsorption kinetics, isotherm models, and desorption dynamics reported across different microplastic-antibiotic systems. We examine how polymer composition, environmental ageing, and biofilm colonisation jointly modulate the strength and nature of antibiotic adsorption, while also addressing inconsistencies in reported adsorption behaviours that stem from overly simplified laboratory conditions. Molecular dynamics (MD) and quantum–mechanical (DFT) simulations have provided unprecedented atomistic insights into these interactions; yet, their predictive potential remains underexploited due to inconsistent parameterisation, limited simulation time scales, and weak integration with environmental data. By synthesising empirical observations with simulation results, this review identifies dominant interaction pathways, including hydrophobic, electrostatic, hydrogen bonding, and π–π stacking, and examines how these mechanisms are modulated by environmental variables such as pH, salinity, and natural organic matter. We further assess the emerging role of machine-learning-accelerated MD, hybrid QM/MM approaches, and multiscale digital-twin frameworks that aim to bridge molecular-scale processes with ecosystem-level behaviour. Finally, this review proposes a unified framework for standardising simulation protocols, integrating MD-derived energetics into environmental fate and transport models, and translating atomistic insights into regulatory and risk-assessment contexts. Collectively, these critical perspectives reposition MD simulations not merely as interpretive tools but as predictive engines essential for managing the intertwined challenges of microplastic pollution and antimicrobial resistance.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100402"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federation of toxicological data resources for in silico new approach methodologies (NAMs)","authors":"Nicoleta Spînu ,&nbsp;Dimitris Stripelis ,&nbsp;Mark T.D. Cronin ,&nbsp;Gregory L. Warren ,&nbsp;Andrew P. Worth","doi":"10.1016/j.comtox.2026.100404","DOIUrl":"10.1016/j.comtox.2026.100404","url":null,"abstract":"<div><div>Next Generation Risk Assessment (NGRA) promotes animal-free, exposure-informed, and hypothesis-driven approaches to chemical safety assessment. <em>In silico</em> tools, such as quantitative structure–activity relationship (QSAR) models, are valuable new approach methodologies (NAMs) for use in NGRA. However, the practical implementation of <em>in silico</em> NAMs remains limited by challenges in data availability, heterogeneity, and regulatory acceptance. In this study, federated learning is introduced to advance chemical safety assessment while leveraging proprietary data domains. Federated learning is a decentralised machine learning approach where multiple organisations, devices or servers collaboratively train a model while keeping their data locally, sharing only model updates to preserve confidentiality and privacy. Three use cases were simulated with the Flower open-source federated learning framework, namely (i) federated analytics for dermal permeability (log Kp) screening; (ii) federated convolutional neural networks (CNNs) for mutagenicity prediction from SMILES strings, and (iii) federated eXtreme Gradient Boosting (XGBoost) models for predicting skin sensitisation potential using molecular fingerprints and descriptors. The results show that federated learning approaches can yield predictive performance comparable to centralised models while mitigating concerns over the visibility of, and access to, commercially sensitive data. Open challenges related to data curation, interpretability, and model governance, as well as future directions, are discussed. This work demonstrates that federated learning can facilitate secure collaboration across organisations, enhance the utility of distributed chemical datasets, and accelerate the adoption of <em>in silico</em> NAMs.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100404"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of machine learning-based multi-task quantitative structure–activity relationship models for predicting toxicities in six human organ systems","authors":"Pei-Yu Wu ,&nbsp;Wei-Chun Chou ,&nbsp;Venkata N. Kamineni ,&nbsp;Chi-Yun Chen ,&nbsp;Jui-Hua Hsieh ,&nbsp;Chris D. Vulpe ,&nbsp;Zhoumeng Lin","doi":"10.1016/j.comtox.2025.100399","DOIUrl":"10.1016/j.comtox.2025.100399","url":null,"abstract":"<div><div>Traditional toxicity assessment relies heavily on animal testing, particularly for chemicals lacking toxicity data. This study developed machine learning (ML)-driven quantitative structure–activity relationship (QSAR) models to predict human organ-specific toxicities, including cardiotoxicity, developmental toxicity, hepatotoxicity, neurotoxicity, nephrotoxicity, and reproductive toxicity. We collected in vivo data for 2,389 chemicals and Tox21 high-throughput screening data for 1,746 chemicals, resulting in 1,743 chemicals with matched datasets. Eighty-eight ML-based QSAR models were developed using three feature scenarios: (1) Tox21 data alone, (2) molecular descriptors alone, and (3) combined features. Five descriptor types and four ML algorithms (random forests, decision trees, support vector machines, and deep neural network [DNN]) were applied, with and without chi-square-based feature selection. Performance was evaluated using nested cross-validation and five metrics (recall, precision, balanced accuracy, F1 score, and ROC-AUC). DNN models in Scenario 2 performed best for developmental and neurotoxicity, while those in Scenario 3 outperformed others for the remaining toxicities. ROC-AUC values approached 0.8 across endpoints, and models without feature selection generally performed better. SHAP and contribution maps enhanced interpretability, highlighting key structural features of toxicity. This study demonstrates the potential of ML-assisted QSAR models for accurate multi-organ toxicity prediction, supporting drug development and chemical risk assessment.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100399"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modernizing pharmacopoeias with artificial intelligence","authors":"Pawan Kumar ,&nbsp;Saurabh Sahoo ,&nbsp;Gaurav Pratap Singh Jadaun ,&nbsp;Rajeev Singh Raghuvanshi","doi":"10.1016/j.comtox.2026.100407","DOIUrl":"10.1016/j.comtox.2026.100407","url":null,"abstract":"<div><div>Pharmacopoeias are official compendia that provide legally binding specifications for drug substances, products, and excipients, ensuring quality, safety, and efficacy in pharmaceutical manufacturing. They define standards for identity, purity, potency, impurity limits, assays, storage, and labeling. Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in pharmaceuticals and regulatory sciences, offering opportunities to modernize compendial processes. Their integration into pharmacopoeial science can accelerate monograph development, streamline revisions, enhance quality control, and support global harmonization. AI-driven technologies such as automated data processing, predictive analytics, and natural language processing can reduce revision timelines, improve transparency, and strengthen stakeholder engagement. These innovations support evidence-based regulatory governance by enabling co-production among regulators, manufacturers, and public health stakeholders. This contemporary review examines current and potential applications of AI and ML in compendial science, including monograph drafting, public comment processing, compliance analytics, and harmonization across pharmacopoeias, providing a regulatory- and policy-oriented perspective that prioritizes governance and implementation considerations. It also presents a pilot study on AI-assisted life cycle management of Indian Pharmacopoeia monographs. The pilot study findings indicated substantial reductions in initial drafting and review turnaround times, enhanced internal consistency across monograph sections, and more efficient categorization of stakeholder feedback during public consultation. Key challenges related to data availability, regulatory compliance, and system integration are discussed, alongside future directions for embedding AI into pharmacopoeial systems, improving accountability, efficiency, and access to quality-assured medicines.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100407"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “A computational framework for modeling VX skin penetration and RSDL-based neutralization” [Comput. Toxicol. 36 (2025) 100393]","authors":"Laurent Simon","doi":"10.1016/j.comtox.2025.100396","DOIUrl":"10.1016/j.comtox.2025.100396","url":null,"abstract":"","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100396"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling metabolism: Evolution of toxicodynamic and toxicokinetic considerations. Adding a new kinetics layer","authors":"A. Chapkanov ,&nbsp;H. Ivanova ,&nbsp;G. Poryazova ,&nbsp;I. Todorova ,&nbsp;T.W. Schultz ,&nbsp;O.G. Mekenyan","doi":"10.1016/j.comtox.2025.100394","DOIUrl":"10.1016/j.comtox.2025.100394","url":null,"abstract":"<div><div>Modern metabolic simulation encompasses five key attributes that align with a typical data matrix, enabling accurate predictions of metabolism. These attributes are 1) the structural features of the parent molecule (S), 2) the metabolic transformations, both individual and grouped standard types (T), 3) the probability that a specific reaction will occur (P), especially if a particular structural fragment is present nearby, 4) reaction rate (R) (such as the depletion rate of a parent structure), and 5) the quantity of reaction products generated at a given time (Q). The thermodynamically informed phase of metabolism includes STP. Here, the previously described kinetic phase is expanded to include the R and Q attributes. Specifically, a proof-of-concept is described that shows how 2D, 3D, or local parameters can be aligned through regression analysis with hydroxylation and hydrolysis to explicitly simulate metabolic kinetics. In this approach, the amount of metabolite formed depends on the substrate reaction rate via chemical half-lives.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100394"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}