Ames mutagenicity of 15 aryl, benzyl, and aliphatic ring N-nitrosamines.

The Ames mutagenicity test is an effective means of screening compounds for their carcinogenic potential. Here, we conducted Ames tests on 15 aryl, benzyl, and aliphatic ring N-nitrosamines. Then, by using two indicators of mutagenicity strength calculated from the Ames test results, namely, maximum specific activity (MSA; number of revertant colonies) and maximum fold increase (MFI; relative ratio of increased colonies), we examined the relationship between Ames mutagenicity strength and Carcinogenic Potency Categorization Approach (CPCA) potency category, which is a structure-activity-relationship-based prediction of the carcinogenic potency of nitrosamines.

Local QSAR based on quantum chemistry calculations for the stability of nitrenium ions to reduce false positive outcomes from standard QSAR systems for the mutagenicity of primary aromatic amines.

Background: Primary aromatic amines (PAAs) present significant challenges in the prediction of mutagenicity using current standard quantitative structure activity relationship (QSAR) systems, which are knowledge-based and statistics-based, because of their low positive prediction values (PPVs). Previous studies have suggested that PAAs are metabolized into genotoxic nitrenium ions. Moreover, ddE, a relative-energy based index derived from quantum chemistry calculations that measures the stability nitrenium ions, has been correlated with mutagenicity.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net).

Quantitative analyses of misclassification rates in the hazard classification of environmental chemicals: Evaluation of procedures for deriving predicted no-effect concentrations in the Chemical Substance Control Law in Japan.

The quality of chemical management depends more or less on practical procedures used to assess chemicals. This study quantitatively assessed the efficacy of a derivation procedure for calculating no-effect concentrations for screening assessment of environmental hazards under the Chemical Substance Control Law in Japan. We first evaluated the derivation procedure by applying a series of test ecotoxicity datasets to the procedure and calculating the resulting misclassification rates of the hazardous class of chemicals.

Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods.

There has been an increase in the use of non-animal approaches, such as in silico and/or in vitro methods, for assessing the risks of hazardous chemicals. A number of machine learning algorithms link molecular descriptors that interpret chemical structural properties with their biological activity. These computer-aided methods encounter several challenges, the most significant being the heterogeneity of datasets; more efficient and inclusive computational methods that are able to process large and heterogeneous chemical datasets are needed.

Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach.

Application of a kinetic energy partitioning scheme for ab initio molecular dynamics to reactions associated with ionization in water tetramers.

We analyze the short-time dynamics of 'cyclic' and 'branched' water tetramers after an ionization event, with the aid of a scheme that partitions the kinetic energy of a solute plus solvent system into separate solute and solvent (or bath) contributions, using instantaneous internal coordinates and atomic velocities. The analysis supports the partitioning of the tetrameric systems into two subsystems, a 'reactive trimer' and a 'solvent' molecule. The partitioned kinetic energy exhibits two features, a broad peak assigned to the interaction between the two sub-systems and a sharper peak arising from the proton transfer that occurs upon ionization.