Alternative animal models in predictive toxicology

Khabib, Muhammad Nur Hamizan; Sivasanku, Yogeethaa; Lee, Hong Boon; Kumar, Suresh; Kue, Chin Siang

doi:10.1016/j.tox.2021.153053

Cited by 35 publications

(21 citation statements)

References 134 publications

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“…This reflects a major paradigm shift towards rodent models and in vitro models. ( Khabib et al, 2022 , Kinter et al, 2021 ).…”

Section: Narrative Review Of Lung Surfactant As a Toxicological Modelmentioning

confidence: 99%

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Lung surfactant as a biophysical assay for inhalation toxicology

Liu

Sayes

2023

Current Research in Toxicology

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“…This reflects a major paradigm shift towards rodent models and in vitro models. ( Khabib et al, 2022 , Kinter et al, 2021 ).…”

Section: Narrative Review Of Lung Surfactant As a Toxicological Modelmentioning

confidence: 99%

“…Notable examples include zebrafish, roundworms, molluscs, and daphnia. ( Khabib et al, 2022 , Weber et al, 2011 , Ellenbroek and Youn, 2016 ).…”

Section: Narrative Review Of Lung Surfactant As a Toxicological Modelmentioning

confidence: 99%

Lung surfactant as a biophysical assay for inhalation toxicology

Liu

Sayes

2023

Current Research in Toxicology

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“…Но тестирование токсичности в значительной степени зависит от животных, особенно грызунов в рамках доклинических лабораторных испытаний веществ. Тем не менее, использование млекопитающих и количество животных, используемых в исследованиях, стало проблемой для институциональных комитетов по этике [5,6].…”

Section: результаты и обсуждениеunclassified

The problem of validation of toxicological models in pharmacy

Popova

2023

jour

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Animal toxicity testing is widely used in the chemical, pharmaceutical and research industries as a part of preclinical laboratory testing of substances to ensure that chemicals are safe for humans and animals. Under preclinical conditions, the safe and effective dose, acceptable dose, and exposure threshold of a substance can be established using mammals and rodents. There are enough materials on the introduction of biomodels in experiments. To summarize this knowledge, a polythematic abstract-bibliographic base was analyzed. The main attention was paid to such scientometric databases as: Web of Science Core Collection, Medline, PubMed, RSCI, as well as eLIBRARY.ru portal data for the last 10 years. An assessment of the statistical data and publication activity of the authors was made for the query modeling and ethics in veterinary medicine and medicine.The aim of the study was to analyze modern models and prospects for using new biomodels for experiments in pharmacology and toxicology.Having established protocols is important to ensure consistency between different studies within the same model, ensuring reproducibility and repeatability in experiments.Based on the analysis of literature sources over the past 10 years, it can be concluded that despite the large number of regulatory framework and transgenic laboratory animals with embedded human target genes in their genome, it is not possible to obtain an identity. Probably, the main bias in predictive toxicology will be made on neural networks and computer replacement of existing biomodels. Since, despite their relative accuracy, maintenance and feeding are costly, and the field of using animals in experiments is increasingly criticized and requires new approaches.

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“…Historically, the toxicity of new chemicals was determined through in vivo studies involving rodents and other mammals but due to ethical concerns and regulatory guidelines [1,2], there has been a shift toward the use of alternative methods [3][4][5]. In this context, the use of in silico toxicology, which is the application of computer technologies to detect relationships that connect chemical structures and toxicological activities, is extremely appealing.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm

Lepailleur

Schietgat²,

Cuissart³

et al. 2023

Molecular Informatics

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Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS. We present a comparison between our MCS‐based fingerprints and 12 well‐known chemical fingerprints when used as features in machine learning models. We provide an experimental evaluation and discuss the usefulness of the different methods on mutagenicity data. The features generated by the MCS method have a state‐of‐the‐art performance when predicting mutagenicity, while they are more interpretable than the traditional chemical fingerprints.

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Alternative animal models in predictive toxicology

Cited by 35 publications

References 134 publications

Lung surfactant as a biophysical assay for inhalation toxicology

Lung surfactant as a biophysical assay for inhalation toxicology

The problem of validation of toxicological models in pharmacy

Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm

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