A database framework for rapid screening of structure-function relationships in PFAS chemistry

Su, An; Rajan, Krishna

doi:10.1038/s41597-021-00798-x

Cited by 34 publications

(20 citation statements)

References 26 publications

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“…A database framework to facilitate structural category assignment of PFAS referred to as "PFAS-Map" developed by Su and Rajan (2021) was used to profile the PFAS into nominal OECD 56 chemical structure categories. 57 Further details of how these have been used to profile other PFAS lists on the Dashboard and how these related to the PFAS tested are discussed in Patlewicz. 60 Su and Rajan (Su and Rajan 2021) implemented PFAS-Map as an open-source tool (https://github.…”

Section: ■ Methodsmentioning

confidence: 99%

Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity

Carstens

Freudenrich

Wallace

et al. 2023

Chem. Res. Toxicol.

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Per- and polyfluoroalkyl substances (PFAS) are a diverse set of commercial chemicals widely detected in humans and the environment. However, only a limited number of PFAS are associated with epidemiological or experimental data for hazard identification. To provide developmental neurotoxicity (DNT) hazard information, the work herein employed DNT new approach methods (NAMs) to generate in vitro screening data for a set of 160 PFAS. The DNT NAMs battery was comprised of the microelectrode array neuronal network formation assay (NFA) and high-content imaging (HCI) assays to evaluate proliferation, apoptosis, and neurite outgrowth. The majority of PFAS (118/160) were inactive or equivocal in the DNT NAMs, leaving 42 active PFAS that decreased measures of neural network connectivity and neurite length. Analytical quality control indicated 43/118 inactive PFAS samples and 10/42 active PFAS samples were degraded; as such, careful interpretation is required as some negatives may have been due to loss of the parent PFAS, and some actives may have resulted from a mixture of parent and/or degradants of PFAS. PFAS containing a perfluorinated carbon (C) chain length ≥8, a high C:fluorine ratio, or a carboxylic acid moiety were more likely to be bioactive in the DNT NAMs. Of the PFAS positives in DNT NAMs, 85% were also active in other EPA ToxCast assays, whereas 79% of PFAS inactives in the DNT NAMs were active in other assays. These data demonstrate that a subset of PFAS perturb neurodevelopmental processes in vitro and suggest focusing future studies of DNT on PFAS with certain structural feature descriptors.

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Section: ■ Methodsmentioning

confidence: 99%

Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity

Carstens

Freudenrich

Wallace

et al. 2023

Chem. Res. Toxicol.

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“…Most applications of unsupervised ML have been used to automatically categorize data into separate groups or “clusters” that have similar characteristics. In the context of environmental studies, there have been recent unsupervised ML applications, such as t -distributed stochastic neighbor embedding (t-SNE) or k -means clustering, on categorizing the carbon–fluorine bond dissociation energies of per- and polyfluoroalkyl substances (PFAS) to understand bond dissociation energies. , These algorithms allow the visualization of high-dimensional data as two-dimensional “clusters” where data points grouped within a cluster share similar characteristics with each other. It is important to reiterate that these clusters were automatically chosen by these unsupervised ML algorithms, without human intervention.…”

Section: Current Status Of ML Applications In Esementioning

confidence: 99%

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

Zhong

Zhang

Bagheri

et al. 2021

Environ. Sci. Technol.

249

254

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The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

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“…In practice, however, the approach was limited to relatively few PFAS categories and fully fluorinated C n F 2n+1 terminal chains, and the subsequent application of ClassyFire met with only limited success largely due to lack of PFAS-specific knowledge. A second published approach, PFAS-Map, took a hybrid, semiempirical approach using modern machine-learning methods . The authors assigned a subset of 7,866 PFAS structures contained in a 2020 version of the DSSTox PFASSTRUCT file () to expert-based categories consistent with the Buck et al or OECD approaches, where possible.…”

Section: Introductionmentioning

confidence: 99%

A New CSRML Structure-Based Fingerprint Method for Profiling and Categorizing Per- and Polyfluoroalkyl Substances (PFAS)

Richard

Lougee

Adams

et al. 2023

Chem. Res. Toxicol.

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The term PFAS encompasses diverse per- and polyfluorinated alkyl (and increasingly aromatic) chemicals spanning industrial processes, commercial uses, environmental occurrence, and potential concerns. With increased chemical curation, currently exceeding 14,000 structures in the PFASSTRUCTV5 inventory on EPA’s CompTox Chemicals Dashboard, has come increased motivation to profile, categorize, and analyze the PFAS structure space using modern cheminformatics approaches. Making use of the publicly available ToxPrint chemotypes and ChemoTyper application, we have developed a new PFAS-specific fingerprint set consisting of 129 TxP_PFAS chemotypes coded in CSRML, a chemical-based XML-query language. These are split into two groups, the first containing 56 mostly bond-type ToxPrints modified to incorporate attachment to either a CF group or F atom to enforce proximity to the fluorinated portion of the chemical. This focus resulted in a dramatic reduction in TxP_PFAS chemotype counts relative to the corresponding ToxPrint counts (averaging 54%). The remaining TxP_PFAS chemotypes consist of various lengths and types of fluorinated chains, rings, and bonding patterns covering indications of branching, alternate halogenation, and fluorotelomers. Both groups of chemotypes are well represented across the PFASSTRUCT inventory. Using the ChemoTyper application, we show how the TxP_PFAS chemotypes can be visualized, filtered, and used to profile the PFASSTRUCT inventory, as well as to construct chemically intuitive, structure-based PFAS categories. Lastly, we used a selection of expert-based PFAS categories from the OECD Global PFAS list to evaluate a small set of analogous structure-based TxP_PFAS categories. TxP_PFAS chemotypes were able to recapitulate the expert-based PFAS category concepts based on clearly defined structure rules that can be computationally implemented and reproducibly applied to process large PFAS inventories without need to consult an expert. The TxP_PFAS chemotypes have the potential to support computational modeling, harmonize PFAS structure-based categories, facilitate communication, and allow for more efficient and chemically informed exploration of PFAS chemicals moving forward.

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A database framework for rapid screening of structure-function relationships in PFAS chemistry

Cited by 34 publications

References 26 publications

Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity

Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

A New CSRML Structure-Based Fingerprint Method for Profiling and Categorizing Per- and Polyfluoroalkyl Substances (PFAS)

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