The Computational Pharmacology Core

计算药理学核心

基本信息

批准号：
10630358
负责人：
Ivet Bahar
金额：
$ 14.26万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-24 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10630358
关键词：
Acceleration Address Agonist Amlodipine Assessment tool Autophagocytosis Bioinformatics Calcium Signaling Calibration Cells Chemicals Combined Modality Therapy Computational Biology Computer Models Computer software Cyclodextrins Data Databases Development Discriminant Analysis Drug Interactions Enhancers Family Family member Funding Gene Expression Profile Genes Genetic Predisposition to Disease Glyburide Goals Hepatotoxicity In Vitro Investigation Kinetics Ligands Literature Liver Fibrosis Liver diseases Machine Learning Methods Modeling Mutagenesis Pathogenicity Pathway interactions Pharmaceutical Preparations Pharmacology Physics Prevention Process Prochlorperazine Protein Databases Proteins RNA Interference Recurrence Regulation Research Signal Pathway Signal Transduction Statistical Models System Systems Biology Testing Therapeutic Trypsin Validation Variant Vision alpha 1-Antitrypsin Deficiency analog candidate selection chemical group cheminformatics cohort design drug candidate drug modification exome sequencing experimental study high throughput screening in silico induced pluripotent stem cell insulin secretion insulin signaling machine learning algorithm machine learning classification mathematical analysis mathematical model member molecular modeling novel novel therapeutics pharmacologic pharmacophore predictive modeling programs proteostasis proteotoxicity public database response screening simulation small molecule small molecule libraries software development tool transcriptome sequencing variant detection

项目摘要

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.

核心 B：计算系统药理学项目概要计算系统药理学核心（核心 B）将提供计算生物学和对项目 1-3 的定量系统药理学 (QSP) 支持。核心将有助于 P01的研究目标主要通过三种方式：识别目标（蛋白质、途径或网络）涉及 ATZ 积累（具体目标 1）；的定量建模和分析确定与 ATZ 消除相关的蜂窝网络以及系统性询问这些网络为（多）药理学策略生成合理的假设（具体目标 2);以及第一学期发现的候选药物的完善（例如格列本脲及其类似物） G2、环糊精家族成员和选定的粘脂蛋白激动剂）以及新的鉴定可测试的候选药物（可重新利用的药物和新化合物）以协助设计和开发基于机制的 ATD 疗法（具体目标 3）。根据进展情况和第一学期做出的贡献，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）。定量模型构建与分析将支持项目 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) 候选药物的最佳修饰以增加其效力，以及特定的组合可能具有协同作用的疗法（例如丙氯拉嗪和氨氯地平）。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。