Computational Toxicology最新文献

The use of in silico modeling tools for predictive toxicology has potential to improve force health protection in the military by helping to efficiently evaluate the risk of adverse health effects from operational exposures. Thus, a systematic review was performed to understand if existing quantitative structure–activity relationship (QSAR) models for tissue-specific toxicity were potentially adaptable for use in risk assessments of military-relevant exposures. Within this systematic review, we assessed 563 peer-reviewed publications in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines and Organization for Economic Co-operation and Development (OECD) 2023 quantitative structural-activity relationship Assessment Framework. From these publications, we further evaluated 129 existing models that utilize QSAR and tissue-specific data for predicting toxicity in the following tissues: liver (i.e., hepatotoxicity), heart (i.e., cardiotoxicity), lung (i.e., respiratory toxicity), the central nervous system (neurotoxicity), and kidney (i.e., nephrotoxicity). The methodology, performance, and accessibility of these models and analysis code were thoroughly documented and then assessed to determine advancements and inadequacies for occupational and military application. While ∼ 58 % of the 129 tissue-specific QSAR approaches followed at least 3 OECD guidelines, there were only 8 tissue-specific models that satisfied all screening criteria. The most common criteria not satisfied was mechanistic interpretation of the model (i.e., OECD criteria number five). Furthermore, while the greatest number of publications and models were available for the liver, many of them were for pharmaceutical applications. Moreover, there were limited available models for heart and kidney for any application. In conclusion, our findings underscore the necessity for additional and updated tissue-specific QSAR models to predict various organ-specific targets while addressing military specific needs. Furthermore, increased publication of model workflows or user-friendly applications are crucial to enhancing model accessibility. In this systematic review, we provide an overview of the databases, resources, and future strategies to advance tissue-specific QSAR model development.

In silico systems can reduce the need for (animal) testing, increase human safety and support critical decisions. They are increasingly being cited in regulatory guidance documents and are forming a key element of New Approach Methodologies (NAMs). Performance is being improved through a combination of new methodologies, increased understanding of mechanistic toxicology and access to experimental data from new assays. Trust and acceptance of in silico methodologies requires them to be accurate and transparent while also providing an explanation and confidence-assessment for each prediction. This paper summarises the state-of-art of in silico models and provides an action plan for further advances in this field.

Per- and Polyfluoroalkyl substances (PFAS) are a class of manufactured chemicals that are in widespread use and many present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterized for their hazard profiles, the vast majority have not been extensively studied. Herein, a chemical category approach was developed and applied to PFAS that could be readily characterized by a chemical structure. The PFAS definition as described in the Toxic Substances Control Act (TSCA) section 8(a)(7) rule was applied to the Distributed Structure-Searchable Toxicity (DSSTox) database to retrieve an initial list of 13,054 PFAS. Plausible degradation products from the 563 PFAS on the non-confidential TSCA Inventory were simulated using the Catalogic expert system, and the unique predicted PFAS degradants (2484) that conformed to the same PFAS definition were added to the list resulting in a set of 15,538 PFAS. Each PFAS was then assigned into a primary category using Organisation for Economic Co-operation and Development (OECD) structure-based classifications. The primary categories were subdivided into secondary categories based on a chain length threshold (>=7 vs < 7). Secondary categories were subcategorized using chemical fingerprints to achieve a balance between total number of structural categories vs. level of structural similarity within a category based on the Jaccard index. A set of 128 terminal structural categories were derived from which a subset of representative candidates could be proposed for potential data collection, considering the sparsity of relevant toxicity data within each category, presence on environmental monitoring lists, and the ability to identify plausible manufacturers/importers. Refinements to the approach taking into consideration ways in which the categories could be updated by mechanistic data and physicochemical property information are also described. This categorization approach may be used to form the basis of identifying candidates for data collection with related applications in QSAR development, read-across and hazard assessment.