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以及戒烟疗法的开发和批准都有了很大的进步，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解决药物滥用和成瘾问题。我们建议使用来自广泛学科的工具，并承诺迅速有效地传播和使用拟议研究的结果，以显著改善尼古丁滥用和成瘾的治疗。我们计划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、产业界和政府合作实施一项研究计划。虽然有两种一线(varenicline和安非他酮)和两种二线(可乐定和诺替林)批准的戒烟药物可以显著帮助戒烟，但大约80%的吸烟者无法继续戒烟。作为这种低成功率的解释，人们假设成瘾是在易感的认知和情感状态下形成的，这些状态不受现有疗法的影响，但可以被新的戒烟辅助工具靶向，以提高疗效。提高疗效的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，这些模型显示出高度的预测有效性。大多数临床前测试只是基于阻止尼古丁样效应，而忽略了可能引发和维持尼古丁滥用的其他诱因或潜在因素，无论是认知因素还是情感因素。既有批准的药物，也有失败的化合物，这让我们有机会创建一系列尼古丁依赖和中枢神经系统疗效测试，并增强预测有效性，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，其基础是1)考虑滥用的多个方面(急性和慢性尼古丁的奖励效应，缓解戒烟、复发、焦虑、抑郁、认知功能障碍和冲动)，2)通过机器学习算法定义戒烟预测分数，该算法基于我们测试电池中有效和无效药物生成的行为数据集，以及3)最小化动物和吞吐成本。在第二阶段，该平台将成长为包括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预测的在对抗尼古丁依赖的情感和认知方面的优势来优先考虑。最后，这一平台(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为维护、支持、进一步开发和宣传该平台而建立的私营公共伙伴关系的成员。该项目的创新之处在于将经济学原理和生物信息学方法相结合，以利用FDA批准的现有戒烟黄金标准，在拟议的临床前电池中包括认知和情绪状态相关测试，创建知识数据库，以及由公私财团管理最终平台，以确保最大限度地提高质量、价值和获得机会。我们预计，该项目产生的知识和工具将促进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：预测性戒烟临床前小组尽管对成瘾的神经生物学的理解以及戒烟疗法的开发和批准都取得了很大进展，但对更好的戒烟辅助手段的需求仍然很大。我们的目标与NIDA的意图一致，即利用科学的力量来解决药物滥用和成瘾问题。我们建议使用来自广泛学科的工具，并承诺迅速有效地传播和使用拟议研究的结果，以显著改善尼古丁滥用和成瘾的治疗。我们计划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、产业界和政府合作实施一项研究计划。几种戒烟药物的存在为发现和开发新的、更有效的戒烟药物创造了一个重大的机会，创造了改进的研究工具。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和无效的药物，以捕捉最好地区分这两种药物的那些特征。我们将使用新的统计和计算工具来确定需要哪些更快和更便宜的测试子集来区分这两类测试，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的特征，这些化合物被认为是未来使用新型筛选电池进行禁烟药物治疗的有希望的候选药物。使用生物信息学方法，我们将把这些有希望的化合物与FDA批准的一组有效化合物进行比较，并为他们分配一个预测性分数，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出其他积极功能的化合物，如促进认知或缓解焦虑的作用。如果成功，该项目将对发现和开发新型戒烟药物的成本和效率产生巨大影响，最终挽救数百万人的生命和数百万美元 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.