Wikipedia on the CompTox Chemicals Dashboard: Connecting Resources to Enrich Public Chemical Data.

The online encyclopedia Wikipedia aggregates a large amount of data on chemistry, encompassing well over 20,000 individual Wikipedia pages and serves the general public as well as the chemistry community. Many other chemical databases and services utilize these data, and previous projects have focused on methods to index, search, and extract it for review and use. We present a comprehensive effort that combines bulk automated data extraction over tens of thousands of pages, semiautomated data extraction over hundreds of pages, and fine-grained manual extraction of individual lists and compounds of interest. We then correlate these data with the existing contents of the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database. This was performed with a number of intentions including ensuring as complete a mapping as possible between the Dashboard and Wikipedia so that relevant snippets of the article are loaded for the user to review. Conflicts between Dashboard content and Wikipedia in terms of, for example, identifiers such as chemical registry numbers, names, and InChIs and structure-based collisions such as SMILES were identified and used as the basis of curation of both DSSTox and Wikipedia. This work also allowed us to evaluate available data for sets of chemicals of interest to the Agency, such as synthetic cannabinoids, and expand the content in DSSTox as appropriate. This work also led to improved bidirectional linkage of the detailed chemistry and usage information from Wikipedia with expert-curated structure and identifier data from DSSTox for a new list of nearly 20,000 chemicals. All of this work ultimately enhances the data mappings that allow for the display of the introduction of the Wikipedia article in the community-accessible web-based EPA Comptox Chemicals Dashboard, enhancing the user experience for the thousands of users per day accessing the resource.

CAS Common Chemistry in 2021: Expanding Access to Trusted Chemical Information for the Scientific Community.

CAS Common Chemistry (https://commonchemistry.cas.org/) is an open web resource that provides access to reliable chemical substance information for the scientific community. Having served millions of visitors since its creation in 2009, the resource was extensively updated in 2021 with significant enhancements. The underlying dataset was expanded from 8000 to 500,000 chemical substances and includes additional associated information, such as basic properties and computer-readable chemical structure information. New use cases are supported with enhanced search capabilities and an integrated application programming interface. Reusable licensing of the content is provided through a Creative Commons Attribution-Non-Commercial (CC-BY-NC 4.0) license allowing other public resources to integrate the data into their systems. This paper provides an overview of the enhancements to data and functionality, discusses the benefits of the contribution to the chemistry community, and summarizes recent progress in leveraging this resource to strengthen other information sources.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation.

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

A proposed approach to defining per- and polyfluoroalkyl substances (PFAS) based on molecular structure and formula.

Various groups and researchers, including the authors of this work, have proposed different definitions of what constitutes per- and polyfluoroalkyl substances (PFAS). The different definitions are all based on a structural definition. Although a structural definition is reasonable, such an approach is difficult to execute if the intent is to narrow or refine the definition. This approach can also lead to inexplicable demarcations of what are and what are not PFAS. Our objective was to create a narrow, simple PFAS definition that allows interested groups to communicate with a common understanding and will also serve as a starting point to focus on substances that are of real environmental concern. Our studies have demonstrated that numerous highly fluorinated complex structures exist that make a structure-based definition difficult. We suggest that a definition based on fractional fluorination expressed as the percentage of fluorine of a molecular formula using atom counting offers advantages. Using a combination of a structure-based definition and a definition based on the fractional percentage of the molecular formula that is fluorine can provide a more inclusive and succinct definition. Thus, we propose a new definition based on four substructures along with any structures where the molecular formula is 30% fluorine by atom count. Integr Environ Assess Manag 2023;19:1333-1347. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Polanyi adsorption potential theory for estimating PFAS treatment with granular activated carbon.

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals that have gained interest because some PFAS have been shown to have negative health effects and prolonged environmental and biological persistence. Chemicals classified as PFAS have a wide range of chemical moieties that impart widely variable properties, leading to a range of water treatment process efficacies. The Polanyi Potential Adsorption Theory was used to estimate Freundlich isotherm parameters to predict the efficacy of granular activated carbon (GAC) treatment for 428 PFAS chemicals for which the vast majority had no previously published treatment data. This method accounts for the physical/chemical characteristics of the individual PFAS beyond molecular weight or chain length that have previously been employed. From a statistical analysis of available data and model results, many of the 428 PFAS were predicted to be effectively treatable by GAC. Although not directly applicable to full-scale design, the approach demonstrates a systematic method for predicting the effectiveness of GAC where isotherm or column data are not available. This then can be used to prioritize future research.

Assembly and Curation of Lists of Per- and Polyfluoroalkyl Substances (PFAS) to Support Environmental Science Research.

Per- and polyfluoroalkyl substances (PFAS) are a class of man-made chemicals of global concern for many health and regulatory agencies due to their widespread use and persistence in the environment (in soil, air, and water), bioaccumulation, and toxicity. This concern has catalyzed a need to aggregate data to support research efforts that can, in turn, inform regulatory and statutory actions. An ongoing challenge regarding PFAS has been the shifting definition of what qualifies a substance to be a member of the PFAS class. There is no single definition for a PFAS, but various attempts have been made to utilize substructural definitions that either encompass broad working scopes or satisfy narrower regulatory guidelines. Depending on the size and specificity of PFAS substructural filters applied to the U.S. Environmental Protection Agency (EPA) DSSTox database, currently exceeding 900,000 unique substances, PFAS substructure-defined space can span hundreds to tens of thousands of compounds. This manuscript reports on the curation of PFAS chemicals and assembly of lists that have been made publicly available to the community via the EPA's CompTox Chemicals Dashboard. Creation of these PFAS lists required the harvesting of data from EPA and online databases, peer-reviewed publications, and regulatory documents. These data have been extracted and manually curated, annotated with structures, and made available to the community in the form of lists defined by structure filters, as well as lists comprising non-structurable PFAS, such as polymers and complex mixtures. These lists, along with their associated linkages to predicted and measured data, are fueling PFAS research efforts within the EPA and are serving as a valuable resource to the international scientific community.