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）支持项目1-3。核心将为 P01的研究目标在三个主要方面：目标的识别（蛋白质，途径或 网络）涉及ATZ积累（具体目标1）;定量建模和分析 被确定与ATZ消除相关的蜂窝网络，以及对ATZ的系统询问 这些网络为（多）药理学策略（特定目的）生成合理的假设 2）;以及对第一个学期发现的候选药物（例如格列本脲及其类似物）进行改良 G2、环糊精家族成员和选定的粘脂激动剂）和新的 可测试的候选药物（可重复使用的药物和新化合物），以帮助设计和 开发基于机制的ATD疗法（具体目标3）。根据进展情况， 在第一学期的贡献，核心将分析高通量/内容（RNAi，EMS 化学诱变和小分子库筛选）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）。定量模型的建立和 分析将支持项目1团队评估（i）细胞信号传导和调节机制， 主要决定响应ATZ积累的蛋白毒性作用的细胞命运， 关注自噬、蛋白质稳态和钙信号通路;（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）; (iii)对候选药物进行最佳修饰以增加其效力， 可能协同作用的治疗（例如，丙氯拉嗪和氯吡格雷）。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的 公开的数据库和软件。

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.