Quantitative structure-Activity relationship (QSAR) analysis of the acute sublethal neurotoxicity of solvents

Cronin, Mark T.D.

doi:10.1016/0887-2333(95)00109-3

Cited by 23 publications

(10 citation statements)

References 19 publications

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“…Many investigators have used the 1-octanolawater partition coef®cient (log P) dependent QSAR as a basis for predicting the toxicity of non-polar narcotics to the guppy (Poecilia reticulata) [5], the fathead minnow (Pimephales promelas) [6] and the ciliate Tetrahymena pyriformis [7,8]. Hermens [9], Tichy and Schultz [8], Dearden et al [10] and Cronin [11] noted that certain alkenones and nitrobenzenes are (pro-)electrophiles and are therefore reactive chemicals. Schultz et al [12] suggested that phenols having pKa values of b 6.3 acted as polar narcotics, whilst phenols with pKa values b 6.3 acted as weak acid respiratory uncouplers.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure-Activity Relationships of Chemicals Acting by Non-polar Narcosis—Theoretical Considerations

Zhao¹,

Cronin²,

Dearden³

1998

Quant. Struct.-Act. Relat.

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Theoretical quantitative structure-activity relationships have been established based on narcotic mechanisms of action and toxicity data to the fathead minnow, Daphnia magna and Vibrio ®scheri. The results con®rmed that hydrophobicity is important in modelling narcosis. However, the results of the present investigation also con®rmed that molecular bulk may play an important role in toxicity to aqueous organisms, especially in the toxicity to organisms with lower lipid content, for example, V. ®scheri. The results are discussed in the light of other inter-species differences that may occur as a consequence of accumulation and clearance kinetics and the relative metabolism of xenobiotics by each organism. Because of the complex tissue structure of organisms, classi®cation of narcotic compounds using hydrophobicity parameters alone can sometimes lead to errors.Abbreviations: QSAR, quantitative structure-activity relationship; P, 1-octanolawater partition coef®cient; Vx, McGowan characteristic volume; Vi, Leahy's intrinsic volume; V M , molar volume; BCF, bioconcentration factor; EC 50 , concentration causing 50% inhibition of bioluminescence to Vibrio ®scheri; IC 50 , immobilisation concentration for 50% of Daphnia magna at 24 hours; LC 50 , lethal concentration for 50% of fathead minnow at 96 hours.

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Section: Introductionmentioning

confidence: 99%

Quantitative Structure-Activity Relationships of Chemicals Acting by Non-polar Narcosis—Theoretical Considerations

Zhao¹,

Cronin²,

Dearden³

1998

Quant. Struct.-Act. Relat.

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“…В литературе описаны различные нейротоксические эффекты и используемые для их изучения тесты, которые могут быть использованы для количественной оценки нейротоксичности соединений [9], включая величину ТД 50 (доза, вызывающая нейротоксический эффект у 50% животных) по тесту вращающегося стержня в исследованиях на мышах при внутрибрюшинном введении вещества [1]. В настоящей работе использованы литературные данные [6,7] по острой сублетальной токсичности (EC 30 , мкМ/л, ингаляционно) 47 органических растворителей по отношению к мышам и крысам. Оценку токсичности (по данным вышеуказанных авторов) проводили на основе установленного ранее нейротоксического эффекта, который проявляется в появлении электрически вызываемых судорог у животных в условиях эксперимента.…”

Section: материалы и методыunclassified

“…В литературе опубликовано большое количество QSAR моделей неспецифической токсичности, где в качестве механистического (то есть отражающего механизм действия) молекулярного дескриптора выступает коэффициент распределения вещества в системе н-октанол -вода (logP) [18,20]. Этот дескриптор был использован и при конструировании регрессионной модели для изучаемого ряда органических растворителей [7]. Ранее [21] нами было показано, что logP является сложным составным дескриптором, который может быть заменён на комбинацию двух других молекулярных характеристик: a и S(C a ).…”

Section: материалы и методыunclassified

“…В опубликованных источниках практически отсутствуют данные по острой нейротоксичности, которые по своему количественному и качественному составу соответствуют требованиям QSAR. Одним из немногих примеров является массив из 48 органических растворителей [6], на основе которого был создан ряд линейных регрессионных моделей [7,8]. Однако, этим разработкам присущи некоторые недостатки, связанные, главным образом, или с невысокими статистическими показателями качества полученных моделей, или с относительно большим числом неясно интерпретируемых параметров.…”

Section: Introductionunclassified

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QSAR Modeling of Acute Neurotoxicity of Some Organic Solvents with Respect to Rodents

Raevskaya

Yarkov

et al. 2018

BMCRM

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Using literature data analysis, the regression models of acute sublethal neurotoxicity of 47 organic solvents with respect to rats and mice have been developed. To construct the models, we used linear regression, random forest and support vector machines approaches. The linear regression equations were selected as the best models. They are designed on the basis of four molecular descriptors: polarizability, sum of positive atom charges, sum of proton acceptor descriptors and dipole moment. The developed models have good descriptive and predictive ability and clear physicochemical interpretation.

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“…Acute neurotoxicity data for some common solvents on both rats and mice were subjected to QSAR analysis by Cronin [19], using data taken from Frantik et al [20]. The structural parameters included in the study were chosen from a mechanistic point of view, so that they could be related to a putative mechanism of action.…”

Section: Qsars For Acute Mammalian Toxicitymentioning

confidence: 99%

A Mini Review of Mammalian Toxicity (Q)SAR Models

et al. 2008

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This paper is based on an in-depth review performed by the European Chemicals Bureau of the European Commissions Joint Research Centre in support of the development of technical guidance for the implementation of the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) legislation, and is one of a series of minireviews in this journal. Compared with the QSAR modelling of ecotoxicity, the modelling of mammalian toxicity is more complicated. This is reflected by the relatively small number of QSAR studies on mammalian toxicity published in the literature. The main problem is related to wide variations in the accuracy of the in vivo data, in the organisms used, and also in the complexity and poor understanding of the mechanisms involved, especially for long-term effects. Nevertheless, the efforts already made to develop QSAR models for mammalian toxicity demonstrate the usefulness of the approach not only for predictive purposes but also for a better understanding of the multiple mechanisms involved in the toxicity. However, there is a need to better characterise existing models in accordance with the OECD QSAR validation principles, to develop new models, and to investigate the applicabilities of individual models in the regulatory context, such as for the purposes of the REACH legislation.

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Quantitative structure-Activity relationship (QSAR) analysis of the acute sublethal neurotoxicity of solvents

Cited by 23 publications

References 19 publications

Quantitative Structure-Activity Relationships of Chemicals Acting by Non-polar Narcosis—Theoretical Considerations

Quantitative Structure-Activity Relationships of Chemicals Acting by Non-polar Narcosis—Theoretical Considerations

QSAR Modeling of Acute Neurotoxicity of Some Organic Solvents with Respect to Rodents

A Mini Review of Mammalian Toxicity (Q)SAR Models

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