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Algorithm for drug discovery

Algorithm for drug discovery
药物发现算法
批准号:
7178239
负责人:
Gregory I Elmer
金额:
$7.43万
依托单位国家:
美国
项目类别:
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-09-30 至 2008-08-31

项目摘要

项目成果

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中文摘要
翻译
描述(由申请人提供):我们对吸毒行为背后的神经机制的理解已经取得了重大进展,但在开发药物治疗干预措施方面还没有实现类似的进展,特别是在精神运动性兴奋剂滥用方面。迫切需要用于治疗干预药物滥用的药物。本申请的目的是描述一种我们认为是一种发现药物精神药理特性的新方法,并将其应用于新型治疗剂的研究。我们的方法使用一个自动化的程序来分析开放领域中的探索行为。我们正在开发的被称为模式阵列的算法,能够在单个会话中筛选非常大量的潜在行为终点(>100,000),并识别最能表征特定药物的行为模式。这种模式的星座可以作为已知和新的精神活性化合物的高度特定的精神药理‘指纹’。这项建议的目的是探索模式阵列的能力,以表征具有高度特异性的精神活性药物。该提案必须在筛选多种药物的发展需要和对单一滥用药物类别进行重点系统检查的愿望之间取得平衡。考虑到这一点,我们将开发适当的方法和工作数据库,通过调查几个滥用药物类别的行为‘特征’,然后将我们的注意力转向可卡因疗法。这项提案的目标将通过两个具体目标来实现。具体目标1将使用来自不同类别的药物和具有不同作用机制的类别内的药物创建一个精神药理学概况数据库。具体目标2将确定潜在可卡因疗法的算法派生的“模式分布”。此外,这一目标还将研究拮抗剂与可卡因联合给药时的‘模式特征’。正在进行临床试验的可卡因疗法以及D3拮抗剂和苯并托品类似物等新疗法将被研究。这些实验的结果将服务于两个目标。首先,它将细化算法中的数学和编程要求,并测试模式阵列中的优势和劣势。其次,它将建立一个用于数据挖掘的核心数据库,并为筛选具有潜在临床疗效的新化合物提供模板。
英文摘要
DESCRIPTION (provided by applicant): Significant advances in our understanding of the neural mechanisms that underlie drug-taking behavior have been made, however similar advances in developing pharmacotherapeutic interventions have yet to be realized, especially in psychomotor stimulant abuse. Medications for treatment intervention in drug abuse are sorely needed. The purpose of this application is to describe what we believe to be a novel approach to discovering the psychopharmacological properties of a drug and to apply it to the study of novel therapeutic agents. Our approach uses an automated procedure for analyzing exploratory behavior in the open field. The algorithm we are developing, termed Pattern Arrays, is capable of screening a very large number of potential behavioral endpoints (>100,000) within a single session and identifying those patterns of behavior that best characterize the particular drug. A constellation of such patterns could serve as a highly specific psychopharmacological 'fingerprint' for known and novel psychoactive compounds. The objective of this proposal is to explore the capacity of the Pattern Array to characterize psychoactive drugs with a high degree of specificity. The proposal necessarily balances a developmental need to screen a diverse set of drugs with the desire to conduct a focused systematic examination of a single abused drug class. With this in mind, we will develop appropriate methodologies and a working database by investigating the behavioral 'signatures' of several abused drug classes, and then turn our attention to cocaine therapeutics. The objectives of this proposal will be realized in two specific aims. Specific Aim 1 will create a database of psychopharmacological profiles using drugs from different classes and drugs within a class that have different mechanisms of action. Specific Aim 2 will determine the algorithmically derived 'pattern profile' of potential cocaine therapeutics. In addition this aim will investigate the 'pattern profile' when the antagonist is given in combination with cocaine. Cocaine therapeutics that are in clinical trials as well as novel therapeutics such as D3 antagonists and benzotropine analogues will be investigated. The results of these experiments will serve two goals. First, it will refine the mathematical and programming requirements in the algorithm and test strengths and weakness in the Pattern Array. Second, it will establish a core database for data mining and provide a template for use in screening novel compounds with potential clinical efficacy.
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