Integrating computer prediction systems with in vitro methods towards a better understanding of toxicology

被引:18
作者
Barratt, MD [1 ]
机构
[1] Marlin Consultancy, Bedford MK43 7LW, England
关键词
structure-activity; quantitative structure activity relationships; biological variability;
D O I
10.1016/S0378-4274(98)00266-5
中图分类号
R99 [毒物学(毒理学)];
学科分类号
100405 ;
摘要
Structure Activity Relationships (SARs) or Quantitative Structure Activity Relationships (QSARs) form the basis of most computer prediction systems in toxicology. The underlying premise of SARs and QSARs is that the properties of a chemical are implicit in its molecular structure. For an SAR or QSAR to be valid and reliable, the dependent property for all of the chemicals covered by the relationship has to be elicited by a mechanism which is both common to the set of chemicals as well as relevant to that dependent property. Similar principles must also be applied to the development of in vitro alternatives to animal tests if those methods are to be reliable. A number of ways in which computer prediction systems and in vitro toxicology can complement each other in the development of alternatives to live animal experiments are described. (C) 1998 Elsevier Science Ireland Ltd. All rights reserved.
引用
收藏
页码:617 / 621
页数:5
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