Suppr超能文献

基于结构的生物降解估算方法综述。

A review of structure-based biodegradation estimation methods.

作者信息

Raymond J W, Rogers T N, Shonnard D R, Kline A A

机构信息

Department of Biophysics, University of Michigan, 930 N. University, Ann Arbor, Michigan, MI 48109-1055, USA.

出版信息

J Hazard Mater. 2001 Jun 29;84(2-3):189-215. doi: 10.1016/s0304-3894(01)00207-2.

Abstract

Biodegradation, being the principal abatement process in the environment, is the most important parameter influencing the toxicity, persistence, and ultimate fate in aquatic and terrestrial ecosystems. Biodegradation of an organic chemical in natural systems may be classified as primary (alteration of molecular integrity), ultimate (complete mineralization; i.e. conversion to inorganic compounds and/or normal metabolic processes), or acceptable (toxicity ameliorated). Most of the biodegradation correlations presented in the literature focus on the characterization of primary or ultimate, aerobic degradation. The US Environmental Protection Agency (USEPA) is charged with determining the risks associated with the thousands of chemicals employed in commerce, an effort that is being facilitated through much research aimed at reliable structure-activity relationships (SAR) to predict biodegradation of chemicals in natural systems. To this end, models are needed to understand the mechanisms of biodegradation, to classify chemicals according to relative biodegradability, and to develop reliable biodegradation estimation methods for new chemicals. Frequently, published correlations associating molecular structure to biodegradation will attempt to quantify the degradability of a limited set of homologous chemicals. These correlations have been dubbed quantitative structure biodegradability relationships (QSBRs). More scarce and valuable to researchers are those models that predict the biodegradability of compounds possessing a wide variety of chemical structures. The latter may use any of several techniques and molecular descriptors to correlate biodegradability: QSBRs, pattern recognition, discriminant analysis, and principle component analysis (PCA), to name several. Generally, models either predict the propensity of a chemical to biodegrade using Boolean-type logic (i.e. whether a chemical will "readily biodegrade" or not), or else they quantify the degree of biodegradation by providing information such as rate constants. Such quantitative predictions of biodegradability come in a diversity of parameters, including half-lives, various biodegradation rates and rates constants, theoretical oxygen demand (ThOD), biological oxygen demand (BOD), and others. In this paper, after describing the advantages and disadvantages of the various biodegradation estimation methods found in the literature, the best models are compared to conclude which provide the greatest utility for determining the biodegradability of chemicals with widely varying structures. The group contribution technique presented by Boethling et al. [Environmen. Sci. Technol. 28 (1994) 459] appears to be the most advantageous for use in broad screening for tendency to biodegrade. The model is simple to use, calculating a probability of biodegrading ranging from 0 (none) to 1 (certain), and has proven to be accurate for a wide range of chemical structures, as established by the large, high-quality data set (BIODEG evaluated biodegradation database, Syracuse Research Corporation, Merrill Lane, Syracuse, NY 13210) used to develop this correlation. The authors, therefore, recommend the method of Boethling et al. [Environ. Sci. Technol. 28 (1994) 459] for the initial screening of chemicals to aid in determining whether additional information is necessary to establish relative biodegradability. For readers with applications requiring more quantitative results, such as biodegradation rate constants, enough model details are presented in this paper to allow the reader to pick a suitable correlation, although the reader is cautioned to consult the original, primary reference for the complete method description, equations, and limitations.

摘要

生物降解作为环境中主要的消减过程,是影响水生和陆地生态系统中毒性、持久性及最终归宿的最重要参数。天然系统中有机化学品的生物降解可分为初级降解(分子完整性改变)、最终降解(完全矿化,即转化为无机化合物和/或正常代谢过程)或可接受降解(毒性改善)。文献中呈现的大多数生物降解相关性研究都集中在初级或最终好氧降解的表征上。美国环境保护局(USEPA)负责确定与商业中使用的数千种化学品相关的风险,通过大量旨在建立可靠结构-活性关系(SAR)以预测天然系统中化学品生物降解的研究,这一工作得到了推动。为此,需要模型来理解生物降解机制,根据相对生物降解性对化学品进行分类,并为新化学品开发可靠的生物降解估算方法。通常,已发表的将分子结构与生物降解相关联的相关性研究试图量化一组有限的同系物化学品的降解能力。这些相关性被称为定量结构生物降解性关系(QSBRs)。对研究人员来说更稀缺且更有价值的是那些能够预测具有多种化学结构的化合物生物降解性的模型。后者可使用几种技术和分子描述符中的任何一种来关联生物降解性:QSBRs、模式识别、判别分析和主成分分析(PCA)等等。一般来说,模型要么使用布尔型逻辑预测化学品生物降解的倾向(即一种化学品是否会“容易生物降解”),要么通过提供诸如速率常数等信息来量化生物降解程度。这种生物降解性的定量预测有多种参数,包括半衰期、各种生物降解速率和速率常数、理论需氧量(ThOD)、生化需氧量(BOD)等。在本文中,在描述了文献中发现的各种生物降解估算方法的优缺点之后,对最佳模型进行了比较,以确定哪些模型在确定结构差异很大的化学品的生物降解性方面具有最大效用。Boethling等人[《环境科学与技术》28 (1994) 459]提出的基团贡献技术似乎在广泛筛选生物降解倾向方面最具优势。该模型使用简单,计算出的生物降解概率范围从0(无)到1(确定),并且正如用于建立这种相关性的大型高质量数据集(锡拉丘兹研究公司的BIODEG评估生物降解数据库,纽约州锡拉丘兹市梅里尔巷13210)所证实的那样,已证明对广泛的化学结构都准确。因此,作者推荐Boethling等人[《环境科学与技术》28 (1994) 459]的方法用于化学品的初步筛选,以帮助确定是否需要更多信息来确定相对生物降解性。对于需要更定量结果(如生物降解速率常数)的应用的读者,本文提供了足够的模型细节,以便读者选择合适的相关性,不过提醒读者要查阅原始的主要参考文献以获取完整的方法描述、方程和局限性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验