Functional regression framework with applications to drug response prediction

功能回归框架及其在药物反应预测中的应用

• 批准号: 9247487
• 负责人: Ranadip Pal
• 金额: $ 21.39万
• 依托单位: TEXAS TECH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9247487
• 关键词: Area Under Curve; Bayesian Modeling; Cell Line; Cells; Characteristics; Chemicals; Databases; Disease; Dose; Drug Combinations; Drug Targeting; Genetic; Goals; Hybrids; Joints; Machine Learning; Modeling; Pharmaceutical Preparations; Phosphotransferases; Techniques; base; computer framework; design; drug sensitivity; genetic profiling; improved; improved outcome; individual patient; innovation; novel; personalized medicine; precision medicine; predicting response; response

Drug sensitivity prediction for individual patients is a significant challenge for precision medicine. Current modeling approaches consider prediction of a single feature of the drug response curve such as Area Under the Curve or IC50. However, the single feature summary of the dose response curve does not provide the entire drug sensitivity profile as some features vary systematically with cell lines while others with drugs. The overall goal of this proposal is to design a mathematical and computational framework for dose response curve prediction based on target response curves and genetic characterizations. For individual patients, the problem is formulated as functional prediction from functional predictors. The functional predictors refer to the dose response of specific targets inhibited by the drug that can be obtained from chemical databases and drug kinase activity studies. To achieve our goal, we propose three specific aims: In Aim 1, we will design a Bayesian framework for estimating the significant drivers of a disease along with the design of a model for single and combination drug response prediction. In Aim 2, we will enhance the model by incorporating non-functional predictors in the form of genetic characterizations and develop a joint model to predict dose response curves with both genetic characteristics as well as target response curves as inputs. We propose to develop an efficient way of searching such extremely high dimensional predictor space. In Aim 3, we will develop a hybrid prediction mechanism that combines inferentially motivated model-based techniques and computationally efficient machine learning techniques to improve predictions as well as obtain significant predictors simultaneously. The proposed framework is appropriate for modeling and analyzing cellular response to single or combination of perturbation agents and the successful completion of the aims will allow us to design personalized therapy along with increased understanding of functional response of cells to external perturbations.

个体患者的药物敏感性预测是精准医疗的重大挑战。目前的建模方法考虑预测药物反应曲线的单一特征，如曲线下面积或IC50。然而，剂量反应曲线的单一特征总结并不能提供整个药物敏感性曲线，因为一些特征随细胞系而系统性变化，而另一些特征则随药物而变化。该建议的总体目标是设计一个数学和计算框架，用于基于靶响应曲线和遗传表征的剂量响应曲线预测。对于个体患者，问题被公式化为从功能预测因子的功能预测。功能预测因子是指可从化学数据库和药物激酶活性研究中获得的药物抑制的特定靶点的剂量反应。为了实现我们的目标，我们提出了三个具体目标：在目标1中，我们将设计一个贝叶斯框架来估计疾病的重要驱动因素沿着设计一个模型来预测单一和联合药物的反应。在目标2中，我们将通过以遗传特征的形式纳入非功能性预测因子来增强模型，并开发一种联合模型，以遗传特征和靶响应曲线作为输入来预测剂量响应曲线。我们建议开发一种有效的方法来搜索这种极高维的预测空间。在目标3中，我们将开发一种混合预测机制，该机制结合了基于模型的非随机激励技术和计算效率高的机器学习技术，以改善预测并同时获得重要的预测因子。所提出的框架是适当的建模和分析细胞的反应，以单一或组合的扰动剂和目标的成功完成将使我们能够设计个性化的治疗沿着增加了解细胞对外部扰动的功能反应。