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Physicochemical properties of chemicals affect their exposure, toxicokinetics/fate and hazard, and for nanomaterials, the variation of these properties results in a wide variety of materials with potentially different risks. To limit the amount of testing for risk assessment, the information gathering process for nanomaterials needs to be efficient. At the same time, sufficient information to assess the safety of human health and the environment should be available for each nanomaterial. Grouping and read-across approaches can be utilised to meet these goals. This article presents different possible applications of grouping and read-across for nanomaterials within the broader perspective of the MARINA Risk Assessment Strategy (RAS), as developed in the EU FP7 project MARINA. Firstly, nanomaterials can be grouped based on limited variation in physicochemical properties to subsequently design an efficient testing strategy that covers the entire group. Secondly, knowledge about exposure, toxicokinetics/fate or hazard, for example via properties such as dissolution rate, aspect ratio, chemical (non-)activity, can be used to organise similar materials in generic groups to frame issues that need further attention, or potentially to read-across. Thirdly, when data related to specific endpoints is required, read-across can be considered, using data from a source material for the target nanomaterial. Read-across could be based on a scientifically sound justification that exposure, distribution to the target (fate/toxicokinetics) and hazard of the target material are similar to, or less than, the source material. These grouping and read-across approaches pave the way for better use of available information on nanomaterials and are flexible enough to allow future adaptations related to scientific developments.
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http://dx.doi.org/10.3390/ijerph121013415 | DOI Listing |
Toxicol Lett
August 2025
College of Pharmacy, Natural Products Research Institute, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826, Republic of Korea. Electronic address:
The identification of environmental obesogens has become increasingly urgent amid rising rates of metabolic disorders linked to chemical exposures. Here, we employed a cluster-based read-across framework that integrates structural descriptors with high-throughput screening (HTS) data from the ToxCast database to systematically identify potential obesogens. A total of 8971 chemicals were represented in a 2,217-dimensional structure-activity matrix, combining 1905 chemical fingerprints and 312 bioactivity endpoints, which were reduced and clustered into 135 distinct chemical groups.
View Article and Find Full Text PDFMol Divers
August 2025
Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
The terminal half-life ( ) is a crucial pharmacokinetic parameter for estimating the dose regimen and duration of action of a drug. Previously, few research papers have been published on the pharmacokinetic parameters that correlate with the chemical structure of pharmaceuticals, but these are time-consuming and costly. The main goal of the current study is to generate a quantitative read-across structure-activity relationship (q-RASAR) for terminal half-life estimation of diverse pharmaceuticals.
View Article and Find Full Text PDFJ Hazard Mater
September 2025
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India. Electronic address:
The accumulation of organic pollutants in the environment has significantly impacted the lives of flora and fauna, resulting in disruptions in the biological ecosystem. Carcinogenicity has been one of the most alarming adverse effects exhibited by these chemicals, affecting millions worldwide. In this study, we developed simple Quantitative Structure-Activity Relationship (QSAR) models to predict the Oral Slope Factor (OSF) and Inhalation Slope Factor (ISF) of organic pollutants, identifying and prioritizing their carcinogenicity risks.
View Article and Find Full Text PDFSci Total Environ
September 2025
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India. Electronic address:
Per- and polyfluoroalkyl substances (PFASs) contamination poses an environmental concern due to their ability to bioaccumulate in aquatic species and adversely impact human health. Experimental bioconcentration factor (log BCF) data of freshwater fish (Teleostei taxonomic class) for representative PFASs were used to develop the quantitative structure-property relationship (QSPR) and machine learning (ML)-based quantitative Read-Across Structure-Property Relationship (q-RASPR) models. We utilized various ML algorithms to effectively consider both linear and non-linear relationships.
View Article and Find Full Text PDFRegul Toxicol Pharmacol
November 2025
Michabo Health Science Limited, Union House, 111 New Union Street, Coventry, CV1 2NT, U.K.; Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K.. Electronic address:
By grouping structurally similar chemicals, toxicity endpoints from data-rich substances can be read across to data-poor substances, supporting environmental and human health risk assessment without animal testing. However, structural similarity alone is insufficient, and additional supporting data can strengthen a grouping justification. This study aimed to demonstrate how multi-omics bioactivity data can increase confidence in a grouping hypothesis, where the bioactivity profiles can reflect a chemical's mode(s) of action.
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