Transcript networks and crowdsourcing to predict drug combinations in malaria par

转录网络和众包预测疟疾药物组合

• 批准号: 8911768
• 负责人: Michael T Ferdig
• 金额: $ 19万
• 依托单位: UNIVERSITY OF NOTRE DAME
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8911768
• 关键词: Artemisinins; Bioinformatics; Biological; Biological Assay; Cell physiology; Cells; Collaborations; Combined Modality Therapy; Communities; Complex; Computing Methodologies; Crowding; Data; Data Analyses; Data Set; Defect; Development; Drug Combinations; Drug Targeting; Drug resistance; Effectiveness; Engineering; Equilibrium; Failure; Genes; Genetic Crosses; Genetic Transcription; Genome; Genomics; Genotype; Growth; Health; Human; In Vitro; Individual; International; Knock-out; Knowledge; Laboratories; Link; Longevity; Malaria; Methods; Mining; Molecular; Multi-Drug Resistance; Normal Cell; Organism; Paper; Parasites; Pathway Analysis; Pathway interactions; Personal Satisfaction; Pharmaceutical Preparations; Phenotype; Plasmodium falciparum; Predisposition; Process; Publishing; Reporting; Research; Resistance; Resources; Science; Secondary to; Solutions; Source; Southeastern Asia; System; Systems Biology; Testing; Ticks; Time; Transcript; World Health Organization; analytical tool; artemisinine; base; biological systems; burden of illness; cellular targeting; clinically relevant; combinatorial; computerized tools; data mining; drug development; drug discovery; gene interaction; genetic approach; improved; innovation; mutant; novel; pathogen; resistant strain; response; treatment strategy

DESCRIPTION (provided by applicant): The global implementation of Artemisinin (ART)-based combination therapies (ACTs) has significantly reduced disease burden and is recognized by the World Health Organization as the first line treatment against the malaria parasite, Plasmodium falciparum, in all endemic regions. However, recent reports of ART resistance raise extreme alarms for the well-being of this last line of defense. With the looming prospect of the failure of traditional ACTs, the urgent challenge is to expand concepts and strategies to bolster the effectiveness and longevity of ART against multi-drug resistant malaria strains. Historically, this process of finding partners has been ad hoc, relying on a narrow existing array of compounds known to be individually effective against parasites. Rational and precise drug combinations are extraordinarily valuable for improving efficacy, minimizing off-target effects, decreasing the rate of resistance emergence in human pathogens. Traditional empirical approaches encounter significant challenges and new genomic/systems biology offers predictive power to optimally identify targets and focused empirical testing. We propose to formalize a conceptual framework that utilizes ART transcriptional responses of 30 genotypically diverse global isolates and integrates the resulting ART response networks with transcriptional responses to 30 diverse drugs across 3 isolates to predict optimal drug synergies for ART. i) The approach integrates genomic datasets with growth phenotypes from knockout lines and flux-balance analysis (FBA) to predict ART compromised gene interactions that potentially enhance susceptibility to secondary drugs. ii) The approach extends datasets generated in this proposal to the Dialogue for Reverse Engineering Assessments and Methods (DREAM), an established open innovation platform for systems biology, to engage an international community of data analysts in developing novel methods for predicting drug synergy at scale that is not attainable by conventional methods. The proposed project has the potential to advance the search for ART partner drugs while at the same time contributing to novel methods for linking genomic datasets to clinically relevant phenotypes.

描述（由申请人提供）：全球实施基于青蒿素（ART）的联合疗法（ACT）显著降低了疾病负担，并被世界卫生组织认定为所有流行地区抗疟疾寄生虫恶性疟原虫的一线治疗药物。然而，最近关于抗逆转录病毒疗法耐药性的报告为这最后一道防线的健康状况敲响了警钟。随着传统青蒿素综合疗法失败的前景日益迫近，迫切的挑战是扩大概念和战略，以加强抗逆转录病毒疗法对耐多药疟疾菌株的有效性和持久性。从历史上看，这种寻找伴侣的过程是临时性的，依赖于已知对寄生虫有效的一系列狭窄的现有化合物。合理、精确的药物组合对于提高疗效、减少脱靶效应、降低人类病原体的耐药率具有非常重要的价值。传统的经验方法遇到了重大挑战，新的基因组/系统生物学提供了预测能力，以最佳地识别目标和集中的经验测试。我们建议正式化一个概念框架，该框架利用30种基因型多样的全球分离株的ART转录应答，并将所得ART应答网络与3种分离株对30种不同药物的转录应答整合，以预测ART的最佳药物协同作用。i）该方法将基因组数据集与来自敲除株和通量平衡分析（FBA）的生长表型整合。预测ART损害的基因相互作用，可能会增加对二级药物的敏感性。该方法将本提案中生成的数据集扩展到逆向工程评估和方法对话（DREAM），这是一个已建立的系统生物学开放式创新平台，旨在吸引国际数据分析师社区开发新方法，用于预测传统方法无法实现的大规模药物协同作用。拟议的项目有可能推进ART合作药物的搜索，同时有助于将基因组数据集与临床相关表型联系起来的新方法。