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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