A q-RASAR approach for oral and inhalational toxicity prediction of perfluorinated and polyfluorinated compounds (PFCs) using rodent toxicity data.

Sagnik Sarkar, Souvik Pore, Kunal Roy

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In one paragraph

Article in NAM journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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1 citing paper in PubMed.

A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction.RSC advances · 2026
Article

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5 · Who and what money

Authors and funding

3 authors.

Sagnik SarkarDrug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.

Souvik PoreDrug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.

Kunal RoyDrug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global demand for perfluorinated and polyfluorinated compounds (PFCs), including the subclass of per- and polyfluoroalkyl substances (PFASs), has grown due to their extreme stability and resistance to heat, enabling diverse industrial applications. However, their environmental persistence and potential health risks have earned some the title 'forever chemicals'. Regulations have been imposed to limit the use and release of PFCs into the environment. Computational studies offer a great alternative to animal studies, enabling broader, faster, and effective risk assessment while also being cost-effective. This study reports the development and validation of Quantitative Read-Across Structure-Activity Relationship (q-RASAR) models for the rodent toxicity of PFCs. This approach predicts toxicity for new compounds based on their structural resemblance to chemicals with known toxicity profiles. We employed a Partial Least Squares (PLS) regression algorithm to predict acute toxicity using oral pLD

Indexed as

Acute inhalation toxicityAcute oral toxicityPer- and polyfluorinated alkyl substances (PFASs)Perfluorinated and polyfluorinated compounds (PFCs)q-RASAR

Identifiers

PMID42369411

DOI10.1016/j.namjnl.2025.100061

PMCPMC13289267

What Socratic holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.