Predictive Smoking Cessation Preclinical Battery

描述（由申请人提供）：尽管在对成瘾的神经生物学的理解以及戒烟疗法的开发和批准方面取得了很大进展，但仍然需要更好的戒烟辅助手段。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拟议研究的结果，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，工业界和政府合作实施研究计划。虽然有两种一线（伐尼克兰和安非他酮）和两种二线（可乐定和诺替林）批准的戒烟药物，显着帮助戒烟，约80%的吸烟者无法保持戒烟。作为成功率如此之低的一种解释，有人假设成瘾是在存在诱发性认知和情感状态的情况下发展的，这些状态不受现有疗法的影响，但可以通过新的戒烟辅助手段来提高疗效。具有改善疗效的新型药物开发缓慢的主要原因之一是缺乏表现出高度预测有效性的尼古丁依赖的可明确翻译的临床前模型。大多数临床前测试只是基于阻断尼古丁样作用，但忽视了其他可能引发和维持尼古丁滥用的诱发因素或潜在因素，无论是认知因素还是情感因素。获得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组具有增强预测有效性的尼古丁依赖和CNS功效测试，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，我们将根据1）对滥用行为背后的多个方面的考虑，（急性和慢性尼古丁的奖励作用，缓解戒断、复发、焦虑、抑郁、认知功能障碍和冲动），（二）通过在我们测试中使用有效和无效药物生成的行为数据集上训练的机器学习算法来定义戒烟预测评分电池和3）动物和生产成本的最小化。在第二阶段，该平台将发展成为一个化合物和假定戒烟潜力作用机制的数据库，根据其戒烟评分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）将在第三阶段作为药物筛选方法提供给公私伙伴关系的成员，该伙伴关系的创建是为了维护、支持、进一步发展和宣传该平台。该项目的新奇在于将经济学原理和生物信息学方法相结合，以利用现有的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，并由公私联合体管理最终平台，以确保最大的质量，价值和访问。我们希望，该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。 公共卫生相关性：尽管在成瘾的神经生物学的理解和戒烟疗法的开发和批准方面都取得了很大进展，但仍然需要更好的戒烟辅助工具。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拟议研究的结果，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，工业界和政府合作实施研究计划。几种戒烟药物的存在为创建改进的研究工具以发现和开发新颖且更有效的戒烟药物创造了重大机会。我们建议在一个全面的临床前测试组合中比较有效的戒烟药物与无效的药物，以捕获最能区分两种药物的特征。我们将使用新的统计和计算工具来确定哪一个更快，更便宜的测试子集是必要的，以区分这两类，以创建一个优化的预测测试电池。然后，我们将描述一系列的化合物，被认为是有前途的候选人，为未来的反吸烟药物使用新的筛选电池。使用生物信息学方法，我们将这些有前途的化合物与FDA批准的有效化合物进行比较，并为它们分配一个预测评分，该评分代表这些化合物在临床上有效的可能性。我们将进一步优先考虑显示额外积极特征的化合物，如促认知或抗焦虑作用。如果成功，该项目将对新型戒烟药物发现和开发的成本和效率产生巨大影响，最终挽救数百万人的生命并节省数百万美元 经济产出和医疗成本的损失。

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.