QSTR modeling for predicting aquatic toxicity of pharmacological active compounds in multiple test species for regulatory purpose

Singh, Kunwar P.; Basant, Nikita

doi:10.1016/j.chemosphere.2014.10.025

Cited by 20 publications

(10 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…92 Pharmaceutical active compound toxicity prediction models based on inter-species correlation estimation have also been put forward. 93 In recent years, QSTR-based tools have emerged as sophisticated tools for predicting the carcinogenicity of diverse chemicals and for screening industrial chemicals and nanoparticles for their endpoint toxicities. Institutes such as the CSIR-CDRI and the CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, have been at the forefront in the development of various tools and software, which are briefly discussed below (also see http://bioinfo.cimap.res.in/): — Q-Lead is a software tool developed for predicting the biological activity of various untested natural molecules, and their derivatives/analogues, to provide sufficient molecular insight into relevant 2-D and 3-D chemical descriptors.…”

Section: Current Scientific Approaches Based On Animal Alternatives Imentioning

confidence: 99%

The Implementation of the Three Rs in Regulatory Toxicity and Biosafety Assessment: The Indian Perspective

Pant

2020

Altern Lab Anim

View full text Add to dashboard Cite

Animal models have long served as a basis for scientific experimentation, biomedical research, drug development and testing, disease modelling and toxicity studies, as they are widely thought to provide meaningful, human-relevant predictions. However, many of these systems are resource intensive and time-consuming, have low predictive value and are associated with great social and ethical dilemmas. Often drugs appear to be effective and safe in these classical animal models, but later prove to be ineffective and/or unsafe in clinical trials. These issues have paved the way for a paradigm shift from the use of in vivo approaches, toward the ‘science of alternatives’. This has fuelled several research and regulatory initiatives, including the ban on the testing of cosmetics on animals. The new paradigm has been shifted toward increasing the relevance of the models for human predictivity and translational efficacy, and this has resulted in the recent development of many new methodologies, from 3-D bio-organoids to bioengineered ‘human-on-a-chip’ models. These improvements have the potential to significantly advance medical research globally. This paper offers a stance on the existing strategies and practices that utilise alternatives to animals, and outlines progress on the incorporation of these models into basic and applied research and education, specifically in India. It also seeks to provide a strategic roadmap to streamline the future directions for the country’s policy changes and investments. This strategic roadmap could be a useful resource to guide research institutions, industries, regulatory agencies, contract research organisations and other stakeholders in transitioning toward modern approaches to safety and risk assessment that could replace or reduce the use of animals without compromising the safety of humans or the environment.

show abstract

Section: Current Scientific Approaches Based On Animal Alternatives Imentioning

confidence: 99%

The Implementation of the Three Rs in Regulatory Toxicity and Biosafety Assessment: The Indian Perspective

Pant

2020

Altern Lab Anim

View full text Add to dashboard Cite

show abstract

“…Recently, some authors argued that it is of necessity to develop the QSAR model of ecotoxicity specifically for pharmaceuticals as a substitute to ECOSAR, considering their special properties. As a result, some of QSAR models have been reported in literature [33][34][35][36]. In particular, Tugcu et al [34] developed a QSAR model (hereafter called the "Tugcu's model") specifically for pharmaceuticals to estimate the acute toxicity to fish using multiple molecular descriptors.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison between the Specific and Baseline Prediction Models of Ecotoxicity for Pharmaceuticals: Is a Specific QSAR Model Inevitable?

Liu

et al. 2021

Journal of Chemistry

View full text Add to dashboard Cite

Assessing the ecotoxicity of pharmaceuticals is of urgent need due to the recognition of their possible adverse effects on nontarget organisms in the aquatic environment. The reality of ecotoxicity data scarcity promotes the development and application of quantitative structure activity relationship (QSAR) models. In the present study, we aimed to clarify whether a QSAR model of ecotoxicity specifically for pharmaceuticals is needed considering that pharmaceuticals are a class of chemicals with complex structures, multiple functional groups, and reactive properties. To this end, we conducted a performance comparison of two previously developed and validated QSAR models specifically for pharmaceuticals with the commonly used narcosis toxicity prediction model, i.e., Ecological Structure Activity Relationship (ECOSAR), using a subset of pharmaceuticals produced in China that had not been included in the training datasets of QSAR models under consideration. A variety of statistical measures demonstrated that the pharmaceutical specific model outperformed ECOSAR, indicating the necessity of developing a specific QSAR model of ecotoxicity for the active pharmaceutical contaminants. ECOSAR, which was generally used to predict the baseline or the minimum toxicity of a compound, generally underestimated the ecotoxicity of the analyzed pharmaceuticals. This could possibly be because some pharmaceuticals can react through specific modes of action. Nonetheless, it should be noted that 95% prediction intervals spread over approximately four orders of magnitude for both tested QSAR models specifically for pharmaceuticals.

show abstract

“…9 Recently, a few QSARs have been developed for aquatic toxicity estimation of chemicals in multiple test species, and the majority of data were from freshwater organisms. [10][11][12][13][14][15] However, there are few studies on QSARs for toxicity assessment of chemicals in diverse crustacean species, especially for marine organisms. Basant et al established linear models to predict the toxicity of pesticides using the toxicity data of Daphnia magna, Americamysis bahia, Gammarus fasciatus and Penaeus duorarum.…”

Section: Introductionmentioning

confidence: 99%

In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning

et al. 2019

View full text Add to dashboard Cite

show abstract

QSTR modeling for predicting aquatic toxicity of pharmacological active compounds in multiple test species for regulatory purpose

Cited by 20 publications

References 53 publications

The Implementation of the Three Rs in Regulatory Toxicity and Biosafety Assessment: The Indian Perspective

The Implementation of the Three Rs in Regulatory Toxicity and Biosafety Assessment: The Indian Perspective

Performance Comparison between the Specific and Baseline Prediction Models of Ecotoxicity for Pharmaceuticals: Is a Specific QSAR Model Inevitable?

In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning

Contact Info

Product

Resources

About