Novel computational approaches to predict drug response and combination effects

预测药物反应和组合效应的新计算方法

• 批准号: 10594584
• 负责人: Yang Xie
• 金额: $ 41万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594584
• 关键词: Address; Algorithms; Artificial Intelligence; Biological; Biological Assay; Characteristics; Chemicals; Collaborations; Communities; Complex; Computer Models; Computing Methodologies; Consumption; Data; Disease; Drug Combinations; Drug Modelings; Drug Side Effects; Drug usage; Effectiveness; Genetic; Genomics; Goals; Human Cell Line; Methodology; Molecular; Molecular Profiling; Patients; Pattern; Pharmaceutical Preparations; Pharmacogenomics; Positioning Attribute; Research; Therapeutic; Time; computer framework; deep learning; drug action; drug mechanism; drug response prediction; epigenomics; high dimensionality; improved; individual patient; insight; interest; novel; precision medicine; predictive modeling; response; user-friendly; web portal

Summary Tailoring the most desired therapy to each individual patient is the primary goal of precision medicine. A reliable and robust predictive model of drug effectiveness based on patients' unique genomic background is the key. For decades, communities have been trying to establish the relationship between molecular characteristics and drug response in complex diseases. Over the last decade, a large amount of genomic and epigenomic data together with pharmacogenomics data and response to perturbations data has been generated for many human cell lines through collaborations in the research community. These projects have led to significant therapeutic discoveries and have provided unprecedented opportunities to predict drug response using molecular fingerprints. However, even with great interest and effort in developing computational methods for predicting drug response, the prediction accuracies are at best only moderate. A related but distinct question is to understand the mechanisms of action (MOA) of drugs. Understanding drug MOAs enables characterization of drug side effects and identification of old drugs for new uses (i.e. drug repositioning). The traditional experimental assays to identify MOAs of drugs are expensive and time- consuming. There are three key questions to be addressed in the study. 1. Can novel computational approaches largely improve prediction accuracy of response to single drugs using comprehensive genomic and chemical information? 2. Can computational approaches provide a systematic way to mine genomics and drug response data to generate biological insights into the mechanisms of actions of various drugs? 3. Is it possible to develop an interpretable and accurate computation model to predict drug combination effects using pharmacogenomics data? Inherent features make it very challenging to predict drug response accurately: High-dimensionality of input data, the complex relationship between input features and response data; and heterogeneous drug/compound response patterns across different genetic lineages. Recently, artificial intelligence (AI) has been making remarkable strides in various applications owing to the rapid progress of “deep learning. In Aim 1 of this study, we will develop novel AI-based approaches to address the computational challenges of improving the prediction accuracy of drug response. In Aim 2 of the study, we will develop a novel computation framework to study of MOA of drugs. In Aim 3, we will develop an interpretable deep-learning based computational framework to predict drug combination effects. In addition, we will develop a user-friendly web portal as an integrated research platform to share the methodology, algorithms and data generated from this proposed study to the research community.

摘要 为每个患者量身定做最理想的治疗方案是精准医疗的首要目标。一个 基于患者独特基因组背景的可靠和稳健的药物疗效预测模型 钥匙。几十年来，社区一直试图建立分子之间的关系 复杂疾病的特点和药物反应。在过去的十年里，大量的基因组和 表观基因组学数据与药物基因组学数据和对扰动的反应数据一起被 通过研究界的合作为许多人类细胞系产生。这些项目有 导致了重大的治疗发现，并为预测药物提供了前所未有的机会 使用分子指纹进行响应。然而，即使有极大的兴趣和努力开发 对于预测药物反应的计算方法，预测精度充其量只能是中等的。一个 相关但不同的问题是了解药物的作用机制(MOA)。了解药物 MOAS能够确定药物副作用的特征，并确定用于新用途的旧药物(即药物 重新定位)。传统的鉴定药物MOA的实验分析方法昂贵且耗时长。 在消费。这项研究有三个关键问题需要解决。1.能否进行新颖的计算 利用综合基因组预测单一药物疗效的方法 和化学信息？2.计算方法能否提供一种系统的方法来挖掘基因组学和 药物反应数据，以产生对各种药物作用机制的生物学见解？3.它是 有可能开发一种可解释的和准确的计算模型来预测药物组合效应 药物基因组学数据？固有的特征使得准确预测药物反应非常具有挑战性： 输入数据的高维度、输入特征和响应数据之间的复杂关系；以及 跨不同遗传谱系的不同药物/化合物反应模式。最近，人工的 由于人工智能(AI)的快速发展，它在各种应用中都取得了显著的进展 “深度学习。在这项研究的目标1中，我们将开发基于人工智能的新方法来解决 提高药物反应预测准确性的计算挑战。在研究的目标2中，我们将 开发一种新的计算框架来研究药物的MOA。在目标3中，我们将开发一个可解释的 基于深度学习的药物联合效应预测计算框架。此外，我们还将发展 一个用户友好的门户网站，作为一个综合研究平台，以共享方法、算法和数据 从这项拟议的研究中产生的数据提供给研究界。