Gerber Susanne, Assmus Heike, Bakker Barbara, Klipp Edda
Max Planck Institute for Molecular Genetics, Computational Systems Biology, Berlin, Germany.
J Theor Biol. 2008 Jun 7;252(3):442-55. doi: 10.1016/j.jtbi.2007.09.027. Epub 2007 Sep 26.
Drug discovery usually focuses on candidate molecules that affect individual reactions with presumed essential functions in the cellular reaction network, especially in the development of diseases. Unfortunately, appropriately designed drugs often fail to show the expected biological effect, since the multitude of interactions in the biochemical reaction network buffers the individual changes or causes significant side effects. We address this problem through a computational approach, which considers the effect of drug application within a generalized biochemical pathway and by studying the effect of changes regarding the type and strength of inhibitors on the reduction of flux. This allows us to systematically search for the appropriate target and for type and concentration of the optimal inhibitor. We propose the flux selectivity as a measure for the discrimination of the effect on different pathways. Since the calculation of the flux selectivity is based on flux control coefficients that are calculated in the non-affected state, it is also a means for predicting the inhibitor efficacy. Furthermore, we will propose how to increase discriminative inhibition in the case of a parasitic disease by using multi-target drugs. This work is devoted to the memorial of our teacher Reinhart Heinrich, who made important contributions to the investigation of the regulation of metabolic networks, namely by introducing and applying the concept of metabolic control.
药物发现通常聚焦于那些影响细胞反应网络中具有假定基本功能的个体反应的候选分子,尤其是在疾病发展过程中。不幸的是,精心设计的药物往往未能展现出预期的生物学效应,因为生化反应网络中的众多相互作用会缓冲个体变化或引发显著的副作用。我们通过一种计算方法来解决这个问题,该方法考虑在广义生化途径中药物应用的效果,并通过研究抑制剂的类型和强度变化对通量降低的影响。这使我们能够系统地寻找合适的靶点以及最佳抑制剂的类型和浓度。我们提出通量选择性作为区分对不同途径影响的一种度量。由于通量选择性的计算基于在未受影响状态下计算的通量控制系数,它也是预测抑制剂功效的一种手段。此外,我们将提出如何通过使用多靶点药物在寄生虫病的情况下增加区分性抑制。这项工作是为了纪念我们的老师莱因哈特·海因里希,他对代谢网络调控的研究做出了重要贡献,即通过引入和应用代谢控制的概念。