Predictive Smoking Cessation Preclinical Battery

描述（由申请人提供）：尽管对成瘾的神经生物学的理解和戒烟疗法的开发和批准取得了很大进展，但对更好的戒烟辅助工具的需求仍然很大。我们的目标与NIDA的意图一致，即利用科学的力量来应对药物滥用和成瘾。我们建议使用来自广泛学科的工具，并承诺快速有效地传播和使用所提议的研究结果，以显着改善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、评估和开发治疗尼古丁滥用和成瘾的创新药物。我们建议通过与学术界、工业界和政府合作来实施一项研究计划。虽然有两种一线（伐尼克兰和安非他酮）和两种二线（柯立定和诺替林）被批准的戒烟药物对戒烟有显著帮助，但大约80%的吸烟者无法保持戒烟。作为对如此低成功率的解释，有一种假设认为，成瘾是在易感的认知和情感状态下形成的，这些状态不受现有治疗方法的影响，但可以通过新的戒烟辅助工具来提高疗效。新药开发缓慢的主要原因之一是缺乏明确可翻译的尼古丁依赖的临床前模型，该模型具有高度的预测效度。大多数临床前试验仅仅是基于阻断尼古丁样效应，而忽略了可能引发和维持尼古丁滥用的其他易感因素或潜在因素，无论是认知因素还是情感因素。批准的药物和失败的化合物的可用性使我们有机会创建一系列尼古丁依赖和中枢神经系统功效测试，提高预测有效性，这可能是加强未来发现和开发工作的关键工具。在第一阶段，我们将开发一个测试电池，基于1)考虑滥用的多个方面（急性和慢性尼古丁的奖励效应，减轻戒断，复发，焦虑，抑郁，认知功能障碍和冲动）；2)通过机器学习算法来定义戒烟预测评分，该算法是在我们的测试电池中有效和无效药物生成的行为数据集上训练的；3)最小化动物和吞吐量成本。在第二阶段，该平台将发展成为一个包含假定戒烟潜力的化合物和作用机制的数据库，根据它们的戒烟分数和在对抗尼古丁依赖的情感和认知方面的预测优势进行优先排序。最后，该平台（电池、数据库和计算工具）将在III期期间作为药物筛选方法提供给公私合作伙伴关系的成员，旨在维护、支持、进一步开发和宣传该平台。该项目的新颖之处在于将经济原则和生物信息学方法结合起来，利用现有的fda批准的戒烟金标准，在拟议的临床前电池中纳入认知和情绪状态相关测试，创建知识数据库，以及由公私联盟管理最终平台，以确保最大的质量，价值和访问。我们期望这个项目产生的知识和工具将刺激戒烟和其他药物滥用和发现领域的进一步研究和药物开发。

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.