AIMC Topic: Mutagenicity Tests

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Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

Regulatory toxicology and pharmacology : RTP
Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guidelin...

Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach.

Environmental and molecular mutagenesis
Several endpoints associated with cellular responses to DNA damage as well as overt cytotoxicity were multiplexed into a miniaturized, "add and read" type flow cytometric assay. Reagents included a detergent to liberate nuclei, RNase and propidium io...

Methodological considerations for using umu assay to assess photo-genotoxicity of engineered nanoparticles.

Mutation research. Genetic toxicology and environmental mutagenesis
In this study we investigated the feasibility of high-throughput (96-well plate) umu assay to test the genotoxic effect of TiO2 engineered nanoparticles (ENPs) under UV light (full spectrum) and visible light (455 nm). Exposure of TiO2 ENPs to up to ...

(Q)SAR assessments of potentially mutagenic impurities: a regulatory perspective on the utility of expert knowledge and data submission.

Regulatory toxicology and pharmacology : RTP
(Quantitative) structure activity relationship [(Q)SAR] modeling is the primary tool used to evaluate the mutagenic potential associated with drug impurities. General recommendations regarding the use of (Q)SAR in regulatory decision making have rece...

Development of Machine Learning-Based Models for Mutagenicity Predictions with Applications to Non-Sugar Sweeteners.

Molecular informatics
Artificial sweeteners, often known as non-sugar sweeteners (NSSs), have been utilized as food additives since World War II. However, there is also concern regarding the mutagenicity potential of NSSs. Every new chemical registration in the food and p...

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms.

The Journal of toxicological sciences
2,4-Dichlorophenoxyacetic acid (2,4-D) is one of the most widely used herbicides in the world, but its mutagenic and carcinogenic potential is still controversial. We simulated environmental exposure to 2,4-D, with the objective of evaluating the gen...

Investigating the Generalizability of the MultiFlow ® DNA Damage Assay and Several Companion Machine Learning Models With a Set of 103 Diverse Test Chemicals.

Toxicological sciences : an official journal of the Society of Toxicology
The in vitro MultiFlow DNA Damage assay multiplexes p53, γH2AX, phospho-histone H3, and polyploidization biomarkers into 1 flow cytometric analysis (Bryce, S. M., Bernacki, D. T., Bemis, J. C., and Dertinger, S. D. (2016). Genotoxic mode of action pr...