The Computational Pharmacology Core

核心B：计算系统药理学 项目摘要 计算系统药理学核心(核心B)将提供计算生物学和 定量系统药理学(QSP)对项目1-3的支持。核心将有助于 P01在三个主要方面的研究目标：确定目标(蛋白质、途径或 网络)牵涉到ATZ积累(具体目标1)；对 被确认与ATZ消除有关的蜂窝网络，以及对 这些网络为(多)药理策略生成可信的假设(特定目的 2)；改进第一项中发现的候选药物(例如格列本脲及其类似物 G2、环糊精家族成员和选定的黏脂激动剂)和新的 可测试的候选药物(包括可重复使用的药物和新化合物)，以帮助设计和 开发以机制为基础的ATD疗法(具体目标3)。与进度保持一致， 在第一个任期内做出的贡献，核心将分析高吞吐量/内容(RNAi、EMS 化学诱变和小分子文库筛选)项目2和基因收集的数据 表达ATZ的iPS细胞的表达谱数据(项目3)。定量模型的构建和应用 分析将支持项目1团队评估(I)细胞信号和调节机制 主要决定细胞命运的反应是ATZ蓄积的蛋白毒性效应，与 关注自噬、蛋白稳定和钙信号通路；(Ii)病理生理效应 在队列外显子组测序研究中检测到的序列变异(与项目3协调)； (3)对候选药物进行最佳修改，以增加其效力和特定的组合 可能具有协同作用的治疗方法(如丙氯哌嗪和氨氯地平)。核心将使用 广泛的机器学习算法，以及生物信息学、化学信息学、QSP和 核心成员开发的分子建模方法、工具和软件以及相关 公开可用的数据库和软件。

Core B: Computational Systems Pharmacology Project Summary The Computational Systems Pharmacology Core (Core B) will provide computational biology and quantitative systems pharmacology (QSP) support to Projects 1-3. The Core will contribute to the research goals of the P01 in three major ways: identification of targets (proteins, pathways or networks) implicated in ATZ accumulation (Specific Aim 1); quantitative modeling and analysis of the cellular networks identified to be associated with ATZ elimination, and systematic interrogation of these networks to generate plausible hypotheses for (poly)pharmacological strategies (Specific Aim 2); and refinement of drug candidates discovered in the first term (e.g. glibenclamide and its analog G2, cyclodextrin family members, and selected agonists of mucolipins) and identification of new testable candidates (both repurposable drugs and new compounds) to assist in the design and development of mechanism-based ATD therapeutics (Specific Aim 3). In line with the progress and contributions made in the first term, the Core will analyze high-throughput/content (RNAi, EMS chemical mutagenesis, and small-molecule library screening) data collected by Project 2 and gene expression profile data of ATZ-expressing iPS cells (Project 3). Quantitative model construction and analyses will support Project 1 team to assess (i) the cell signaling and regulation mechanisms that predominantly determine the cell fate in response to the proteotoxic effect of ATZ accumulation, with focus on autophagic, proteostasis and calcium signaling pathways; (ii) the pathophysiological effects of sequence variants detected in cohort exome sequencing studies (in coordination with Project 3); (iii) optimal modifications of drug candidates to increase their potency, and particular combination therapies (e.g. prochlorperazine and amlodipine) that may act synergistically. The Core will use a broad range of machine learning algorithms, and bioinformatics, cheminformatics, QSP and molecular modeling methods, tools and software developed by the Core members as well as relevant databases and software publicly available.