Drug target optimization in chronic myeloid leukemia using innovative computational platform.
Drug target optimization in chronic myeloid leukemia using innovative computational platform.
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DOI：
10.1038/srep08190
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发表时间：
2015-02-03
影响因子：
4.6
通讯作者：
Fisher J
中科院分区：
文献类型：
--
作者：
Chuang R;Hall BA;Benque D;Cook B;Ishtiaq S;Piterman N;Taylor A;Vardi M;Koschmieder S;Gottgens B;Fisher J
慢性粒细胞白血病（CML）代表了更广泛的癌症领域的范例。尽管酪氨酸激酶抑制剂已经在CML中建立了靶向分子治疗，但患者通常面临由替代细胞途径的突变和/或激活引起的发展耐药性的风险。为了优化药物开发，人们需要系统地测试调节疾病的遗传网络中所有可能的药物靶点组合。BioModelAnalyzer（BMA）是一个用户友好的计算工具，允许我们做到这一点。我们使用BMA构建了一个CML网络模型，该模型由104个相互作用连接的54个节点组成，封装了从160个出版物中收集的实验数据。虽然以前的研究局限于单一途径或细胞过程，但我们的可执行模型使我们能够探测多个途径和细胞结果之间的动态相互作用，提出新的组合治疗靶点，并突出以前未探索的对白细胞介素-3的敏感性。
Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3.