A computational approach to analyze the mechanism of action of the kinase inhibitor bafetinib.
A computational approach to analyze the mechanism of action of the kinase inhibitor bafetinib.
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DOI：
10.1371/journal.pcbi.1001001
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发表时间：
2010-11-18
影响因子：
4.3
通讯作者：
Colinge J
中科院分区：
文献类型：
--
作者：
Burkard TR;Rix U;Breitwieser FP;Superti-Furga G;Colinge J
预测药物在人体细胞中的作用是生物医学研究中的一个主要挑战。此外，人们对发现已批准药物的新应用和确定潜在的副作用有浓厚的兴趣。我们提出了一种计算策略，以预测机制，风险和潜在的新领域的药物治疗的基础上，通过化学蛋白质组学获得的目标配置文件。构建共享一种生物学功能的功能性蛋白质-蛋白质相互作用网络，并就功能破坏对它们与药物的串扰进行评分。我们将此方法应用于第二代BCR-ABL抑制剂bafetinib的靶向特征，该药物正在开发中，用于治疗伊马替尼耐药的慢性粒细胞白血病。除了众所周知的对细胞凋亡的作用外，我们提出了肺癌和表达IGF 1 R的原始细胞危象的潜在治疗方法。蛋白质相互作用的数据正在迅速积累，虽然不完善和不完整，但它们提供了对人类细胞中蛋白质复杂相互作用的有价值的全局描述。与此同时，现代蛋白质组学技术使得以无偏的方式测量药物的蛋白质靶点成为可能。这些数据揭示了多个靶点，这与以前流行的药物具有单一或非常有限数量的靶点的范式形成了鲜明对比。在新的可用系统级数据和关于药物相互作用的更精确和完整的信息的背景下，很自然地尝试确定药物对人类细胞施加的全局扰动，以识别潜在的副作用和其他适应症。我们提出了一种计算方法，旨在作出这样的预测，并将其应用于bafetinib，最近开发的白血病药物。我们表明，获得了对其他癌症或耐药病例的额外应用以及可能的副作用的有意义的预测，这些预测并不直接用现有算法来确定。我们的方法具有很强的潜力，可适用于其他药物。
Prediction of drug action in human cells is a major challenge in biomedical research. Additionally, there is strong interest in finding new applications for approved drugs and identifying potential side effects. We present a computational strategy to predict mechanisms, risks and potential new domains of drug treatment on the basis of target profiles acquired through chemical proteomics. Functional protein-protein interaction networks that share one biological function are constructed and their crosstalk with the drug is scored regarding function disruption. We apply this procedure to the target profile of the second-generation BCR-ABL inhibitor bafetinib which is in development for the treatment of imatinib-resistant chronic myeloid leukemia. Beside the well known effect on apoptosis, we propose potential treatment of lung cancer and IGF1R expressing blast crisis. Protein interaction data are accumulating rapidly and, although imperfect and incomplete, they provide a valuable global description of the complex interplay of proteins in a human cell. In parallel, modern proteomics technologies make it possible to measure in an unbiased manner the protein targets of a drug. Such data reveal multiple targets in a view that contrasts with a previously prevalent paradigm that drugs had single – or a very limited number of – targets. In this context of newly available systems level data and more precise and complete information about drug interactions, it is natural to try to determine the global perturbation exerted by a drug on a human cell to identify potential side effects and additional indications. We present a computational method that aims at making such predictions and apply it to bafetinib, a recently developed leukemia drug. We show that meaningful predictions of additional applications to other cancers or resistant cases and likely side effects are obtained that are not straightforward to determine with existing algorithms. Our method has a strong potential to be applicable to other drugs.
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作者：
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通讯作者：
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