Vera Julio, Curto Raul, Cascante Marta, Torres Néstor V
Systems Biology and Bioinformatics Group, University of Rostock, Albert Einstein Street 21, 18051, Germany.
Bioinformatics. 2007 Sep 1;23(17):2281-9. doi: 10.1093/bioinformatics/btm326. Epub 2007 Jun 22.
A very promising approach in drug discovery involves the integration of available biomedical data through mathematical modelling and data mining. We have developed a method called optimization program for drug discovery (OPDD) that allows new enzyme targets to be identified in enzymopathies through the integration of metabolic models and biomedical data in a mathematical optimization program. The method involves four steps: (i) collection of the necessary information about the metabolic system and disease; (ii) translation of the information into mathematical terms; (iii) computation of the optimization programs prioritizing the solutions that propose the inhibition of a reduced number of enzymes and (iv) application of additional biomedical criteria to select and classify the solutions. Each solution consists of a set of predicted values for metabolites, initial substrates and enzyme activities, which describe a biologically acceptable steady state of the system that shifts the pathologic state towards a healthy state.
The OPDD was used to detect target enzymes in an enzymopathy, the human hyperuricemia. An existing S-system model and bibliographic information about the disease were used. The method detected six single-target enzyme solutions involving dietary modification, one of them coinciding with the conventional clinical treatment using allopurinol. The OPDD detected a large number of possible solutions involving two enzyme targets. All except one contained one of the previously detected six enzyme targets. The purpose of this work was not to obtain solutions for direct clinical implementation but to illustrate how increasing levels of biomedical information can be integrated together with mathematical models in drug discovery.
Supplementary data are available at Bioinformatics online.
药物研发中一种非常有前景的方法是通过数学建模和数据挖掘整合现有的生物医学数据。我们开发了一种名为药物发现优化程序（OPDD）的方法，该方法通过在数学优化程序中整合代谢模型和生物医学数据，能够在酶病中识别新的酶靶点。该方法包括四个步骤：（i）收集有关代谢系统和疾病的必要信息；（ii）将信息转化为数学术语；（iii）计算优化程序，对提出抑制较少数量酶的解决方案进行优先级排序；（iv）应用额外的生物医学标准来选择和分类解决方案。每个解决方案都由一组代谢物、初始底物和酶活性的预测值组成，这些值描述了系统的一种生物学上可接受的稳态，该稳态将病理状态转变为健康状态。
OPDD被用于检测一种酶病——人类高尿酸血症中的靶酶。使用了现有的S系统模型和关于该疾病的文献信息。该方法检测到六种涉及饮食调整的单靶点酶解决方案，其中一种与使用别嘌呤醇的传统临床治疗方法一致。OPDD检测到大量涉及两个酶靶点的可能解决方案。除了一个之外，所有方案都包含之前检测到的六个酶靶点中的一个。这项工作的目的不是获得直接用于临床实施的解决方案，而是说明在药物发现中如何将越来越多的生物医学信息与数学模型整合在一起。
补充数据可在《生物信息学》在线获取。
