I-Corps: Combination targeted drug design for personalized cancer therapy

I-Corps：用于个性化癌症治疗的组合靶向药物设计

• 批准号: 1445177
• 负责人: Ranadip Pal
• 金额: $ 5万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1445177&HistoricalAwards=false
• 关键词: Corps; Combination; targeted; drug; design

An estimated 1.6 million people in the U.S. developed some form of cancer last year and this number is predicted to increase in coming years. Standard treatment approaches for some forms of cancer (such as breast or prostrate) provide high chances of survival, whereas for other types of cancer such as pancreas or brain cancer, survivals rates are significantly lower. One significant issue with the current approaches is that a cancer patient is treated based on their cohort and not based on their personalized genetic makeup. This often leads to ineffective treatments and poor outcomes for patients at all levels of risk. The proposed technology is an optimized algorithm and software to discover potential multi-target protein combinations that can produce highly effective combination drug treatments. The proposed approach is novel in integrating drug screen and genomic characterization data for predicting effective drug combinations.The proposed process can produce truly personalized therapeutic options with a small set of input data. Specifically, the team is looking at multi-target protein combinations that can produce highly effective combination drug treatments, with a current focus on protein kinases. Protein kinases been shown to be effective in multiple types of cancer, and have led to a treatment for CML, a type of leukemia. The framework allows the team to refine and focus predictions as more data becomes available through parallel tests that can be performed, such as exome sequencing, RNA sequencing and siRNA knockdown experiments. Most existing personalized approaches are primarily focused on applying drugs to target specific mutations based on what happens in other similar models. This team's approach intends to improve on this by specifically looking for multi-target combinations and multi-drug therapeutics to overcome many of the obstacles faced in the expansion of targeted therapies to new cancer types.

据估计，去年美国有160万人患上了某种形式的癌症，预计这一数字在未来几年还会增加。某些形式的癌症（如乳腺癌或前列腺癌）的标准治疗方法提供了很高的生存机会，而对于其他类型的癌症，如胰腺癌或脑癌，生存率明显较低。当前方法的一个重要问题是，癌症患者是基于他们的队列而不是基于他们的个性化基因组成来治疗的。这通常会导致无效的治疗和所有风险水平的患者的不良结局。所提出的技术是一种优化的算法和软件，用于发现潜在的多靶点蛋白质组合，从而产生高效的组合药物治疗。 该方法在整合药物筛选和基因组特征数据以预测有效药物组合方面是新颖的，所提出的方法可以用少量的输入数据产生真正个性化的治疗方案。具体来说，该团队正在研究可以产生高效组合药物治疗的多靶点蛋白质组合，目前的重点是蛋白激酶。蛋白激酶已被证明对多种类型的癌症有效，并已导致对CML（一种白血病）的治疗。该框架允许团队通过可以进行的平行测试（如外显子组测序，RNA测序和siRNA敲除实验）来改进和集中预测。大多数现有的个性化方法主要集中在根据其他类似模型中发生的情况应用药物靶向特定突变。该团队的方法旨在通过专门寻找多靶点组合和多药物疗法来改善这一点，以克服将靶向疗法扩展到新癌症类型所面临的许多障碍。