Predictive Smoking Cessation Preclinical Battery

预测性戒烟临床前电池

• 批准号: 8455421
• 负责人: Daniela Brunner
• 金额: $ 82.92万
• 依托单位: PSYCHOGENICS, INC.
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8455421
• 关键词: Academia; Action Potentials; Acute; Affective; Agonist; Algorithms; Animals; Anti-Anxiety Agents; Antidepressive Agents; Antismoking; Anxiety; Area; Behavioral; Bioinformatics; Bupropion; Chronic; Clinic; Clinical; Clonidine; Cognition; Cognitive; Collaborations; Conditioned Stimulus; Cytosine; Data Base Management; Data Set; Databases; Decision Theory; Development; Discipline; Discrimination; Drug Addiction; Drug abuse; Economics; Emotional; Ensure; Exhibits; FDA approved; Fluoxetine; Future; GABA Agonists; Goals; Gold; Government; Health Care Costs; Impaired cognition; Impulsivity; Industry; Knowledge; Ligands; Machine Learning; Marketing; Mecamylamine; Mental Depression; Methods; Moclobemide; Naloxone; Neurobiology; Nicotine; Nicotine Dependence; Nicotine Withdrawal; Nicotinic Agonists; Nortriptyline; Output; Pattern; Pharmaceutical Preparations; Phase; Pre-Clinical Model; Preclinical Drug Evaluation; Preclinical Testing; Property; Relapse; Research; Rewards; Science; Screening procedure; Series; Smoker; Social Welfare; Stimulus; Stress; Testing; Therapeutic; Training; Ursidae Family; Withdrawal; addiction; base; behavior test; clinical efficacy; combat; computerized tools; cost; drug development; drug discovery; efficacy testing; improved; innovation; interest; member; nicotine abuse; novel; pre-clinical; programs; public-private partnership; receptor; research and development; smoking cessation; success; tool; trait; varenicline

DESCRIPTION (provided by applicant): Despite great advances in both the understanding of the neurobiology of addiction and the development and approval of smoking cessation therapies, a significant need remains for better smoking cessation aids. Our goal is aligned with NIDA's intent to bring the power of science to bear on drug abuse and addiction. We propose to use tools from a broad range of disciplines, and promise rapid and effective dissemination and use of the results of the proposed research to significantly improve treatment of nicotine abuse and addiction. The platform we propose to use will help identify, evaluate, and develop innovative medications to treat nicotine abuse and addiction. We propose to implement a research program through collaborations with academia, industry and government. Although there are two first-line (varenicline and bupropion) and two second-line (clonidine and notriptyline) approved medications for smoking cessation that significantly help to stop smoking, about 80% of smokers are unable to remain abstinent. As an explanation for such low success rate, it has been hypothesized that addiction develops in the presence of predisposing cognitive and affective states, which are unaffected by existing therapeutics but could be targeted by new smoking cessation aids for improved efficacy. One of the main reasons for the slow development of novel medications with improved efficacy is the lack of clearly translatable preclinical models of nicotine dependence that exhibit high degrees of predictive validity. Most preclinical tests are simply based on blocking nicotine-like effects but ignore other predisposing or underlying factors, either cognitive or emotional, that may trigger and maintain nicotine abuse. The availability of both approved medications and failed compounds gives us the opportunity to create a battery of nicotine dependence and CNS efficacy tests with enhanced predictive validity, potentially a key tool in enhancing future discovery and development efforts. During Phase I we will develop a test battery based on 1) consideration of multiple aspects underlying abuse (rewarding effects of acute and chronic nicotine, alleviation of withdrawal, relapse, anxiety, depression, cognitive dysfunction and impulsivity), 2) definition of a smoking cessation predictive score through a machine learning algorithm trained on a behavioral dataset generated with both effective and ineffective medications in our test battery and 3) minimization of animal and throughput costs. During Phase II the platform will grow to comprise a database of compounds and mechanisms of action of postulated smoking cessation potential, prioritized by their smoking cessation scores and predicted superiority in combating emotional and cognitive aspects of nicotine dependence. Finally, this platform (battery, database and computational tools) will be offered during Phase III as drug screening method to the members of a private public partnership, created to maintain, support, further develop and publicize the platform. The novelty of this project resides in the combination of economic principles and bioinformatics methods to take advantage of existing smoking cessation FDA-approved gold standards, the inclusion of cognitive and emotional state-relevant testing in the proposed preclinical battery, the creation of a knowledge database, and the management of the final platform by a private-public consortium to ensure maximal quality, value and access. We expect that the knowledge and tools generate by this project will stimulate further research and drug development both for smoking cessation and across other areas of drug abuse and discovery. PUBLIC HEALTH RELEVANCE: Predictive Smoking Cessation Preclinical Battery Despite great advances in both the understanding of the neurobiology of addiction and the development and approval of smoking cessation therapies, a significant need remains for better smoking cessation aids. Our goal is aligned with NIDA's intent to bring the power of science to bear on drug abuse and addiction. We propose to use tools from a broad range of disciplines, and promise rapid and effective dissemination and use of the results of the proposed research to significantly improve treatment of nicotine abuse and addiction. The platform we propose to use will help identify, evaluate, and develop innovative medications to treat nicotine abuse and addiction. We propose to implement a research program through collaborations with academia, industry and government. The existence of several medications for smoking cessation create a major opportunity for the creation of improved research tools for the discovery and development of novel, and more effective, smoking cessation medications. We propose to compare effective smoking cessation medications against ineffective medications in a comprehensive preclinical test battery to capture those features that best separate the two drug sets. We will use novel statistical and computational tools to determine which subset of faster and cheaper tests is necessary to distinguish these two classes to create an optimized predictive test battery. We will then characterize a series of compounds that are thought to be promising candidates for future anti- smoking medications using the novel screening battery. Using bioinformatics methods we will compare these promising compounds against the set of efficacious FDA-approved compounds and assign them a predictive score that represents the likelihood that such compounds will be effective in the clinic. We will further prioritize compounds that show additional positive features such as pro-cognitive or anxiolytic effects. If successful, this projet will have a dramatic impact on the cost and efficiency of the discovery and development of novel smoking cessation medications, ultimately saving millions of lives and millions of dollars in lost economic output and healthcare costs.

描述（由申请人提供）：尽管了解成瘾的神经生物学以及对戒烟疗法的发展和批准都取得了长足的进步，但仍然需要更需要戒烟剂。我们的目标与NIDA的意图保持一致，以使科学的力量承担滥用毒品和成瘾。我们建议使用来自广泛学科的工具，并保证快速有效的传播和对拟议研究结果的使用，以显着改善对尼古丁滥用和成瘾的治疗。我们建议使用的平台将有助于识别，评估和开发创新的药物来治疗尼古丁滥用和成瘾。我们建议通过与学术界，工业和政府合作实施研究计划。尽管有两条第一线（Varenicline和Bupropion）和两条二线（可乐定和Notriptyline）批准了戒烟的药物，可大大有助于停止吸烟，但约80％的吸烟者无法戒烟。作为对如此低成功率的解释，已经假设成瘾在存在易感性和情感状态的情况下发展出来，这些状态不受现有疗法的影响，但可以通过新的戒烟辅助工具来实现，以提高功效。新颖的药物发展效率提高的新型药物发展缓慢的主要原因之一是缺乏尼古丁依赖性的明显可翻译的临床前模型，这些模型表现出很高的预测有效性。大多数临床前测试只是基于阻断尼古丁的效果，但忽略了其他诱发或情感上的诱发或潜在因素，这些因素可能会触发和维持尼古丁滥用。批准的药物和失败化合物的可用性使我们有机会创建一系列尼古丁依赖和CNS功效测试，并具有增强的预测有效性，这可能是增强未来发现和开发工作的关键工具。在第一阶段期间，我们将根据1）考虑滥用滥用的多个方面的考虑（急性和慢性尼古丁的奖励效果，减轻戒断，复发，焦虑，焦虑，抑郁，抑郁，抑郁症，抑郁症和冲动性的奖励），2）2）定义通过对机器学习的启动型和3）的定义。动物和吞吐量成本。在第二阶段期间，该平台将增长，包括一个化合物的数据库和假定的戒烟潜力的作用机制，该数据库通过其吸烟戒烟得分优先考虑，并预测了对尼古丁依赖的情绪和认知方面的优势。最后，该平台（电池，数据库和计算工具）将在第三阶段作为药物筛查方法提供给私人公共合作伙伴的成员，这些方法是为了维护，支持，支持，开发和宣传平台。 The novelty of this project resides in the combination of economic principles and bioinformatics methods to take advantage of existing smoking cessation FDA-approved gold standards, the inclusion of cognitive and emotional state-relevant testing in the proposed preclinical battery, the creation of a knowledge database, and the management of the final platform by a private-public consortium to ensure maximal quality, value and access.我们预计，该项目产生的知识和工具将刺激戒烟以及在其他药物滥用和发现的其他领域的进一步研究和药物开发。 公共卫生相关性：尽管了解成瘾的神经生物学以及对戒烟疗法的发展和认可，但预测性戒烟临床前电池仍取得了长足的进步，但仍然需要更好的戒烟辅助工具。我们的目标与NIDA的意图保持一致，以使科学的力量承担滥用毒品和成瘾。我们建议使用来自广泛学科的工具，并保证快速有效的传播和对拟议研究结果的使用，以显着改善对尼古丁滥用和成瘾的治疗。我们建议使用的平台将有助于识别，评估和开发创新的药物来治疗尼古丁滥用和成瘾。我们建议通过与学术界，工业和政府合作实施研究计划。几种用于戒烟的药物的存在为创造改进的研究和开发新颖，更有效的戒烟药物创造的研究工具创造了主要机会。我们建议在全面的临床前测试电池中比较有效的戒烟药物与无效的药物进行比较，以捕获那些最好将两种药物分开的功能。我们将使用新颖的统计和计算工具来确定哪个更快，更便宜的测试子集以区分这两个类以创建优化的预测测试电池。然后，我们将描述一系列化合物，这些化合物被认为是使用新型筛选电池的未来抗吸烟药物的有前途的候选者。使用生物信息学方法，我们将将这些有希望的化合物与有效FDA批准的化合物进行比较，并为它们分配了预测分数，代表了这种化合物在诊所有效的可能性。我们将进一步对化合物进行优先排序，这些化合物显示出其他积极特征，例如培养或抗焦虑作用。如果成功的话，这种Projet将对新型戒烟药物的发现和开发的成本和效率产生巨大影响，最终挽救了数百万的生命和数百万美元 失去经济产出和医疗费用。