Discovery of a low order drug-cell response surface for applications in personalized medicine
Discovery of a low order drug-cell response surface for applications in personalized medicine
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发现用于个性化医疗的低阶药物细胞响应表面
DOI：
10.1088/1478-3975/11/6/065003
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发表时间：
2014-12-01
期刊：
影响因子：
2
通讯作者：
Ho, Chih-Ming
中科院分区：
文献类型：
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作者：
Ding, Xianting;Liu, Wenjia;Ho, Chih-Ming
细胞是一个复杂的系统，涉及许多组件，往往可以在一个非线性的动态方式相互作用。因此，细胞水平的疾病可能涉及多种细胞成分和途径。由于一些药物或药物组合可能对这些多个途径起协同作用，因此它们可能比相应的单一靶向药物更有效。针对特定患者中的给定疾病优化药物混合物是特别具有挑战性的，这是由于在开始时选择药物混合物组分的困难以及要应用的这些药物的所有重要剂量。对于m种药物的n种浓度，原则上必须检测nm组合。由于这可能导致对每个患者进行昂贵且耗时的调查，我们开发了反馈系统控制（FSC）技术，该技术可以从数百万种可能的组合中快速选择最佳药物剂量组合。通过在代表不同疾病状态的许多实验系统中测试这种FSC技术，我们发现细胞对多种药物的反应由低阶的、相当平滑的药物混合物输入/药物效应输出多维表面很好地描述。这样做的主要结果是，可以在数量少得令人惊讶的测试中找到最佳药物组合，并且简化了从体外到体内的转化。这表明在不久的将来个性化的最佳药物混合物的可能性。这种出乎意料的简单输入-输出关系也可能为处理癌症治疗中的人类多样性问题提供简单的解决方案。
The cell is a complex system involving numerous components, which may often interact in a non-linear dynamic manner. Diseases at the cellular level are thus likely to involve multiple cellular constituents and pathways. As some drugs, or drug combinations, may act synergistically on these multiple pathways, they might be more effective than the respective single target agents. Optimizing a drug mixture for a given disease in a particular patient is particularly challenging due to both the difficulty in the selection of the drug mixture components to start out with, and the all-important doses of these drugs to be applied. For n concentrations of m drugs, in principle, nm combinations will have to be tested. As this may lead to a costly and time-consuming investigation for each individual patient, we have developed a Feedback System Control (FSC) technique which can rapidly select the optimal drug–dose combination from the often millions of possible combinations. By testing this FSC technique in a number of experimental systems representing different disease states, we found that the response of cells to multiple drugs is well described by a low order, rather smooth, drug-mixture-input/drug-effect-output multidimensional surface. The main consequences of this are that optimal drug combinations can be found in a surprisingly small number of tests, and that translation from in vitro to in vivo is simplified. This points to the possibility of personalized optimal drug mixtures in the near future. This unexpectedly simple input–output relationship may also lead to a simple solution for handling the issue of human diversity in cancer therapeutics.