Prediction of environmental toxicity and fate using quantitative structure-activity relationships (QSARs)

Abstract

Atualmente, muito pouco se sabe sobre a toxidez de mais de 100.000 substâncias químicas que são liberadas no meio-ambiente. O custo necessário para a obtenção dessas informações é elevado não apenas em termos financeiros, mas também no consumo de tempo e de animais. Por isso, muitas indústrias e agências governamentais regulatórias estão focalizando sua atenção na predição da toxidez e suas causas através de relações entre a estrutura química e a atividade (QSARs). Este artigo examina o uso de QSARs neste contexto. Como, em geral, as QSARs são dependentes de mecanismos específicos, a primeira etapa é classificar a substância tóxica em uma das quatro classes de toxidez: narcose apolar, polar, reatividade inespecífica e ação específica (por exemplo, atividades anticolinesterase). Uma QSAR apropriada pode, então, ser selecionada para predizer a toxidez de uma dada substância química. Também existem sistemas inteligentes para a predição da toxidez. As predições de bioconcentrações, absorções do solo e biodegrabilidade também podem ser realizadas. A predição por QSARs e sistemas inteligentes de propriedades físico-químicas, tais como o coeficiente de partição, solubilidade aquosa, pontos de fusão e ebulição, pressão de vapor e constante de lei de Henry, pode ser prontamente obtida. Little or nothing is known about the toxicity of most of the >100,000 chemicals released into the environment. The cost of obtaining such information experimentally would be enormous in terms of money, time and animals. Companies and regulatory agencies are therefore turning to the prediction of environmental toxicity and fate through the use of quantitative structure-activity relationships (QSARs). This paper examines the use of QSARs in this context. Generally, QSARs are mechanismspecific, so that the first step is to classify a toxicant into one of four broad classes of toxicity: nonpolar narcosis, polar narcosis, unselective reactivity and specific action (e.g. anticholinesterase activity). An appropriate QSAR can then be selected in order to predict the toxicity of a given chemical. There are also some expert systems available for toxicity prediction. QSAR predictions of bioconcentration, soil sorption and biodegradability can also be made; again, expert systems are available for such prediction. QSAR and expert system prediction of physico-chemical properties such as partition coefficient, aqueous solubility, melting and boiling point, vapour pressure and Henry's law constant can readily be made.

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