Laboratory of Mathematical Chemistry, University, Bourgas, Bulgaria.
SAR QSAR Environ Res. 2011 Oct;22(7-8):699-718. doi: 10.1080/1062936X.2011.623323. Epub 2011 Oct 14.
Information regarding the metabolism of xenobiotic chemicals plays a central role in regulatory risk assessments. In regulatory programmes where metabolism studies are required, the studies of metabolic pathways are often incomplete and the identification of activated metabolites and important degradation products are limited by analytical methods. Because so many more new chemicals are being produced than can be assessed for potential hazards, setting assessment priorities among the thousands of untested chemicals requires methods for predictive hazard identification which can be derived directly from chemical structure and their likely metabolites. In a series of papers we are sharing our experience in the computerized management of metabolic data and the development of simulators of metabolism for predicting the environmental fate and (eco)toxicity of chemicals. The first paper of the series presents a knowledge-based formalism for the computer simulation of non-intermediary metabolism for untested chemicals, with an emphasis on qualitative and quantitative aspects of modelling metabolism.
有关外源化学物质代谢的信息在外源化学物质风险评估中起着核心作用。在需要进行代谢研究的监管计划中，代谢途径的研究往往并不完全，并且由于分析方法的限制，激活代谢物和重要降解产物的鉴定也受到限制。由于生产的新化学物质比可以评估潜在危害的化学物质多得多，因此需要有预测性危险识别方法，这些方法可以直接从化学结构及其可能的代谢物中得出，以便在数千种未经测试的化学物质中确定评估优先级。在一系列论文中，我们分享了我们在代谢数据计算机管理以及用于预测化学物质环境命运和（生态）毒性的代谢模拟物开发方面的经验。该系列的第一篇论文提出了一种基于知识的形式化方法，用于对未经测试的化学物质进行非中介代谢的计算机模拟，重点是代谢建模的定性和定量方面。
