Monte Carlo Simulation of Pesticide Toxicity for Rainbow Trout (Oncorhynchus mykiss) Using New Criteria of Predictive Potential DOI Creative Commons
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati

и другие.

Journal of Xenobiotics, Год журнала: 2025, Номер 15(3), С. 82 - 82

Опубликована: Июнь 1, 2025

Background: The toxicity of pesticides for fish in general and Rainbow Trout (Oncorhynchus mykiss) particular is an important ecological indicator required by regulations, it implies the use a large number fish. animals needed would be even higher to evaluate metabolites pesticide impurities. Considering ethical issues, costs, necessary resources, silico models often proposed. Aim study: We explore advanced Monte Carlo methods obtain improved results testing acute toxicity. Several versions stochastic simulation Trout, carried out using CORAL software, were studied. set substances was split into four subsets: active training, passive calibration, validation. Modeling repeated five times enable better statistical evaluation. To improve predictive potential models, index ideality correlation (IIC), intensity (CII), coefficient conformism prediction (CCCP) applied. Main novelty: most suitable observed case CCCP-based optimization SMILES-based descriptors, achieving R2 0.88 on validation set, all random splits, demonstrating consistent robust modeling performance. relationship information systems related QSAR new ideas discussed, assigning key role fundamental concepts like mass energy. study mentioned criteria during conducted computer experiments showed that though they are aimed at improving potential, their values do not correlate, except CII CCCP. This means that, general, impact considered has different nature, least mykiss). applicability domain model specific pesticides; software identifies outliers looking rare molecular fragments.

Язык: Английский

A tiered next-generation risk assessment framework integrating toxicokinetics and NAM-based toxicodynamics: “proof of concept” case study using pyrethroids DOI Creative Commons
Ana Fernandez-Agudo, José Tarazona

Archives of Toxicology, Год журнала: 2025, Номер unknown

Опубликована: Май 7, 2025

Язык: Английский

Процитировано

0
Monte Carlo Simulation of Pesticide Toxicity for Rainbow Trout (Oncorhynchus mykiss) Using New Criteria of Predictive Potential DOI Creative Commons
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati

и другие.

Journal of Xenobiotics, Год журнала: 2025, Номер 15(3), С. 82 - 82

Опубликована: Июнь 1, 2025

Background: The toxicity of pesticides for fish in general and Rainbow Trout (Oncorhynchus mykiss) particular is an important ecological indicator required by regulations, it implies the use a large number fish. animals needed would be even higher to evaluate metabolites pesticide impurities. Considering ethical issues, costs, necessary resources, silico models often proposed. Aim study: We explore advanced Monte Carlo methods obtain improved results testing acute toxicity. Several versions stochastic simulation Trout, carried out using CORAL software, were studied. set substances was split into four subsets: active training, passive calibration, validation. Modeling repeated five times enable better statistical evaluation. To improve predictive potential models, index ideality correlation (IIC), intensity (CII), coefficient conformism prediction (CCCP) applied. Main novelty: most suitable observed case CCCP-based optimization SMILES-based descriptors, achieving R2 0.88 on validation set, all random splits, demonstrating consistent robust modeling performance. relationship information systems related QSAR new ideas discussed, assigning key role fundamental concepts like mass energy. study mentioned criteria during conducted computer experiments showed that though they are aimed at improving potential, their values do not correlate, except CII CCCP. This means that, general, impact considered has different nature, least mykiss). applicability domain model specific pesticides; software identifies outliers looking rare molecular fragments.

Язык: Английский

Процитировано

0