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毒性模型预测能力的验证是否已经成为一项成熟的任务?

Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

机构信息

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy.

出版信息

Curr Top Med Chem. 2019;19(29):2643-2657. doi: 10.2174/1568026619666191105111817.

Abstract

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.

摘要

不同种类的生物活性是由复杂的生化相互作用定义的,这些相互作用不仅被称为“数学函数”,而且还与一些额外的情况有关,如物理化学条件、物质与生物体、器官、细胞之间的能量和信息效应的相互作用。这些情况导致生化终点的预测非常复杂,因为对于准确的记录和分析,相应现象的所有“细节”实际上都无法获得。研究人员没有可能通过直接实验进行和分析定义毒理学或治疗吸引力效应的所有可能的生化相互作用方式。因此,需要做出妥协,即开发上述现象的预测模型。然而,评估这些模型的预测潜力仍然是一个仅部分解决的任务。这篇迷你评论介绍了用于上述任务的尝试的集合,提出了两个特殊的统计指标,它们可以作为模型预测潜力的度量。这些指标是:(i)相关性理想指数;和(ii)相关性矛盾指数。

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