Wang Zhihui, Deisboeck Thomas S
Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, United States.
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Front Physiol. 2019 Feb 14;10:96. doi: 10.3389/fphys.2019.00096. eCollection 2019.
With the advent of personalized medicine, design and development of anti-cancer drugs that are specifically targeted to individual or sets of genes or proteins has been an active research area in both academia and industry. The underlying motivation for this approach is to interfere with several pathological crosstalk pathways in order to inhibit or at the very least control the proliferation of cancer cells. However, after initially conferring beneficial effects, if sub-lethal, these artificial perturbations in cell function pathways can inadvertently activate drug-induced up- and down-regulation of feedback loops, resulting in dynamic changes over time in the molecular network structure and potentially causing drug resistance as seen in clinics. Hence, the targets or their combined signatures should change in accordance with the evolution of the network (reflected by changes to the structure and/or functional output of the network) over the course of treatment. This suggests the need for a "dynamic targeting" strategy aimed at optimizing tumor control by interfering with different molecular targets, at varying stages. Understanding the dynamic changes of this complex network under various perturbed conditions due to drug treatment is extremely challenging under experimental conditions let alone in clinical settings. However, mathematical modeling can facilitate studying these effects at the network level and beyond, and also accelerate comparison of the impact of different dosage regimens and therapeutic modalities prior to sizeable investment in risky and expensive clinical trials. A dynamic targeting strategy based on the use of mathematical modeling can be a new, exciting research avenue in the discovery and development of therapeutic drugs.
随着个性化医疗的出现，针对单个或一组基因或蛋白质的抗癌药物的设计和开发一直是学术界和工业界的一个活跃研究领域。这种方法的潜在动机是干扰几种病理串扰途径，以抑制或至少控制癌细胞的增殖。然而，在最初产生有益效果后，如果是亚致死性的，细胞功能途径中的这些人为扰动可能会无意中激活药物诱导的反馈回路的上调和下调，导致分子网络结构随时间动态变化，并可能导致临床上出现的耐药性。因此，靶点或其组合特征应根据治疗过程中网络的演变（由网络结构和/或功能输出的变化反映）而变化。这表明需要一种“动态靶向”策略，旨在通过在不同阶段干扰不同的分子靶点来优化肿瘤控制。在实验条件下，更不用说在临床环境中，了解由于药物治疗在各种扰动条件下这个复杂网络的动态变化极具挑战性。然而，数学建模可以促进在网络层面及更广泛层面研究这些效应，还能在对风险大且昂贵的临床试验进行大规模投资之前，加速比较不同给药方案和治疗方式的影响。基于数学建模的动态靶向策略可能是治疗药物发现和开发中一条令人兴奋的新研究途径。
