Pattern array: in vivo mining for novel psychoactive drug discovery

模式阵列：用于新型精神活性药物发现的体内挖掘

基本信息

批准号：
8018156
负责人：
Gregory I Elmer
金额：
$ 28.81万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-01-01 至 2013-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8018156
关键词：
Adopted Adult Affect Algorithms Animal Model Animals Anti-Anxiety Agents Antidepressive Agents Antipsychotic Agents Area Behavior Behavioral Behavioral Assay Behavioral Genetics Bioinformatics Biological Biomedical Research Brain Budgets Chronic Classification Complex Data Data Set Databases Development Discrimination Dose Drug Industry Drug abuse Emotional Disturbance Exploratory Behavior Fingerprint Foundations Future Genetic Goals Grant Health Health Care Costs Human Illicit Drugs Impaired cognition In Vitro Individual Intervention Investments Isotonic Exercise Log-Linear Models Malignant Neoplasms Masks Measures Mental disorders Methods Mining Molecular Movement Names Nerve Degeneration New Drug Approvals Opioid Pathology Pattern Pharmaceutical Preparations Pharmacologic Substance Pharmacology Population Preclinical Drug Evaluation Property Psychopharmacology Psychotropic Drugs Quality of life Reporting Rest Screening Result Screening procedure Statistical Models Structure System Systems Biology Techniques Testing Therapeutic Time Treatment Efficacy Trees United States United States National Institutes of Health Work base data mining design detector drug development drug discovery drug of abuse experience improved in vivo innovation interest neuropathology novel practical application psychopharmacologic research and development response success therapeutic target

项目摘要

DESCRIPTION (provided by applicant): Over one-quarter of the adult population in the United States suffers from a diagnosable mental disorder in a given year and an astonishing 41% of 12th graders report some lifetime use of illicit drugs. Despite the societal and personal burden that psychiatric illness presents and the substantial investment in psychiatric drug discovery (albeit significantly less in drug abuse) there is a chronic shortfall in innovative psychiatric drugs. A primary stumbling block in psychiatric drug development is thought to be in the animal models used to screen drugs for treatment efficacy and the target-centric drug discovery approach. The focus on specific mechanistic interventions will prove unsatisfactory if the underlying pathology does not rest in a restricted biological entity but rather in a `system' response to the drug. Data mining techniques are increasingly used to discover predictive in vitro system profiles for a cancer or toxicological responses. Likewise, a system-based orientation to in vivo pharmacology has been suggested as a way to transform psychiatric drug discovery. The purpose of this application is to fully develop a novel in vivo data mining strategy for psychiatric drug research and development we have termed Pattern Array (PA). The behavioral context for PA is exploratory behavior. Extensive ethological, pharmacological and behavior genetics studies in our lab and others have shown that exploratory behavior is i) highly heritable, likely reflecting `hard-wired' brain systems, ii) amenable to mathematical description and high-throughput and iii) information-rich, generating ~105 relevant data points per animal. Our working hypothesis is that the effects of drugs on this `hard-wired' system are also amenable to algorithmic structuring and identification. The strategy we are proposing is certainly unconventional, however its foundation is well-grounded empirically and shown to work in preliminary studies. We propose to establish PA via three specific aims. First, we will develop a high quality database derived from five main therapeutic target areas: antipsychotics, antidepressants, anxiolytics, drugs of abuse and drug abuse therapeutics. Within each target area a range of subclasses and mechanisms are represented. Second, the strong core database and experience with new drug classes will provide the critical mass to enable us to boost the power, generality and reliability of PA through feature enhancements and statistical implementation. Third, we will utilize PA to mine potential behavioral "endpoints" (~100,000) and identifying those that best characterize a drug or drug class. These endpoints represent complex movement patterns, algorithmically defined as different combinations of several ethologically-relevant variables. The result of this last specific aim will be to provide a set of in vivo behavioral `predictors' for a broad range of compounds with psychoactive properties and provide a template for use in screening novel compounds. PA could then be used to screen novel pharmacotherapeutics for their similarity to proven therapeutics, thus providing a relatively rapid means to identify new molecular entities with unique therapeutic utility. PUBLIC HEALTH RELEVANCE: There is a significant decline in psychiatric drug development designed to treat the considerable portion of the US population that suffers from a diagnosable mental disorder or drug abuse. The purpose of this application is to fully develop an unconventional, novel in vivo data mining strategy for the behavioral effects of psychotherapeutic drugs termed Pattern Array (PA). The strategy outlined in this application could be used to screen novel compounds for their similarity to proven therapeutics, thus providing a relatively rapid means to identify new molecular entities with unique therapeutic utility.

描述（由申请人提供）：在给定的一年中，美国超过四分之一的成年人口患有可诊断的精神障碍，而12年级的12年级学生中有41％的人报告了对非法药物的终生使用。尽管社会疾病表现出了社会和个人负担，并且对精神病药物发现的大量投资（尽管在药物滥用方面显着少得多），但创新的精神药物仍存在长期的短缺。精神药物开发中的主要绊脚石被认为是用于筛查药物治疗功效和以目标为中心的药物发现方法的动物模型。如果潜在的病理不基于受限的生物学实体，而是对药物的“系统”反应，那么对特定机械干预措施的关注将不令人满意。数据挖掘技术越来越多地用于发现癌症或毒理学反应的预测性体外系统概况。同样，也提出了基于系统的体内药理学的定位，以改变精神病药物发现。该应用的目的是充分制定一种用于精神病药物研发的体内数据挖掘策略，我们称为模式阵列（PA）。 PA的行为环境是探索性行为。我们实验室和其他人的广泛的伦理，药理和行为遗传学研究表明，探索行为是i）高度可遗传的，可能反映了“硬连线”脑系统，ii）适合数学描述和高通量以及III），以及iii）信息丰富，产生了〜105个相关数据点。我们的工作假设是，药物对这种“硬连线”系统的影响也适合算法结构和识别。我们提出的策略肯定是非常规的，但是其基础在经验上是良好的，并且在初步研究中起着作用。我们建议通过三个特定目标建立PA。首先，我们将开发一个源自五个主要治疗目标领域的高质量数据库：抗精神病药，抗抑郁药，抗焦虑药，滥用药物和药物滥用疗法。在每个目标区域内，表示一系列子类和机制。其次，强大的核心数据库和新药物类别的经验将为我们提供临界质量，使我们能够通过功能增强和统计实施来提高PA的功率，一般性和可靠性。第三，我们将利用PA来挖掘潜在的行为“终点”（约100,000个），并确定那些最能表征药物或药物类别的行为。这些终点代表复杂的运动模式，从算法上定义为几个与伦理学相关的变量的不同组合。最后一个特定目的的结果是为具有精神活性特性的广泛化合物提供一组体内行为“预测因子”，并提供用于筛选新颖化合物的模板。然后，PA可用于筛选新型药物治疗药，以与经过验证的治疗剂相似，从而提供了一种相对较快的方法来识别具有独特治疗效用的新分子实体。公共卫生相关性：精神病药物开发的大幅下降，旨在治疗患有可诊断的精神障碍或药物滥用的美国人群中相当大的部分。该应用的目的是完全开发一种非常规的，新型的体内数据挖掘策略，以针对被称为模式阵列（PA）的心理治疗药物的行为效应。该应用程序中概述的策略可用于筛选新化合物，以与经过验证的治疗剂相似，从而提供了一种相对较快的方法来识别具有独特的治疗效用的新分子实体。