Center for the Study of Tobacco Products

项目摘要。烟草制品研究中心（CSTP）开发了一个模型， 使用电子香烟（ECIG）作为范例评估新型烟草产品，所述电子香烟加热液体， 通常含有尼古丁，形成使用者吸入的气雾剂。现在，CSTP利用其方法和 ECIG专业知识从产品评估转向影响分析的综合主题。FDA烟草 监管必须保护公众健康，尽管监管也可能产生意想不到的后果， 伤害例如，限制ECIG液体尼古丁含量，如在欧盟，可以推动使用更高的 电子烟雾化器会使更多的液体雾化，导致使用者吸入更多的尼古丁和其他有毒物质。其他 潜在的ECIG法规涉及限制单位时间内尼古丁的排放量（尼古丁“通量”）， 影响产品毒性和滥用倾向，或减少可能导致用户混合自己的香料的可用性 调味液体如果FDA可以预测潜在法规的影响，那么在法规出台之前的改进就是 所发布的信息可能会最大限度地发挥其促进健康的作用，并尽量减少意外后果。方法存在于 评估一项法规实施后的效果，但很少有数据驱动的模型能事先预测其影响。任何 这种模型必须从几个领域中提取，以评估潜在法规可能如何改变产品 毒性、用户行为和成瘾/滥用倾向。此外，模型必须证明 关于潜在监管影响的预测描述了实际的人口水平结果。CSTP解决了这个问题 此应用程序的问题：项目1-3将测试假设，并生成关于 三个潜在的ECIG法规（即，限制尼古丁，限制通量，减少风味可用性）;项目4将测试 这些预测在人口水平上。CSTP的愿景是提供工具来指导法规的制定， 当法规生效时，经过验证的方法已经对其进行了测试，完善了它，并生成了数据 这表明其促进健康的效果最大化，意外后果最小化。 该团队（Drs. Bensenberg，Breland，Shihadeh，Cobb，巴恩斯，Cohen，Fagan）将完成4个集成 项目旨在：预测产品毒性，预测用户行为，预测滥用责任，并评估潜在的 监管影响。上下文知识核心使用混合方法来确保项目1-4反映真实的 世界一个职业提升核心进行烟草监管科学课程，支持前和后， 博士生和试点项目。行政核心支持财务/管理流程， 生物统计需求，接收外部咨询委员会的输入，并与FDA、NIH和其他机构进行沟通。 总的来说，CSTP的影响分析综合主题借鉴了该团队在烟草制品毒性方面的专业知识， 用户行为和滥用责任。我们寻求向FDA提供关于高级 生成ECIG并测试对一些潜在法规的预测，同时开发一个模型， 可以用来塑造，完善和预测未来许多潜在的监管行动的影响。

Project Summary. The Center for the Study of Tobacco Products (CSTP) has developed a model for evaluating novel tobacco products using, as exemplars, electronic cigarettes (ECIGs) that heat a liquid that often contains nicotine, forming an aerosol that users inhale. Now, CSTP leverages its methodological and ECIG expertise to pivot from product evaluation to an integrative theme of impact analysis. FDA tobacco regulation must protect public health, though regulations may also have unintended consequences that cause harm. For example, limiting ECIG liquid nicotine content, as in the European Union, can drive use of higher power ECIGs that aerosolize more liquid, leading users to inhale more nicotine and other toxicants. Other potential ECIG regulations involve constraining amount of nicotine emitted/unit time (nicotine “flux”) that may influence product toxicity and abuse liability, or reducing flavor availability that may lead users to mix their own flavored liquids. If FDA could predict a potential regulation’s effects, then refinements before the regulation is issued might maximize its health-promoting effects and minimize unintended consequences. Methods exist for assessing a regulation’s effects once it is in place, but few data-driven models predict impact beforehand. Any such model must draw from several domains to assess how a potential regulation might change product toxicity, user behavior, and addiction/abuse liability. Also, the model must demonstrate the extent to which predictions about potential regulatory effects describe actual population-level outcomes. CSTP addresses this issue with this application: Projects 1-3 will test hypotheses and generate predictions regarding the impact of three potential ECIG regulations (i.e., limit nicotine, constrain flux, reduce flavor availability); Project 4 will test these predictions at the population level. CSTP’s vision is to provide tools to guide regulation development so that, by the time a regulation goes into effect, validated methods have tested it, refined it, and generated data showing that its health-promoting effects are maximized and unintended consequences minimized. The team (Drs. Eissenberg, Breland, Shihadeh, Cobb, Barnes, Cohen, Fagan) will complete 4 integrated projects that aim to: predict product toxicity, predict user behavior, predict abuse liability, and assess potential regulation impact. A Contextual Knowledge Core uses mixed methods to ensure Projects 1-4 reflect the real world. A Career Enhancement Core conducts a tobacco regulatory science course, supports pre- and post- doctoral appointees, and pilot projects. An Administrative Core supports financial/managerial processes, biostatistical needs, receives input from an External Advisory Board, and interfaces with FDA, NIH, and others. Overall, CSTP’s integrative theme of impact analysis draws on the team’s expertise in tobacco product toxicity, user behavior, and abuse liability. We seek to provide FDA with hypothesis-driven data regarding advanced generation ECIGs and test predictions about some potential regulations now, while developing a model that can be used to shape, refine, and predict the effects of many potential regulatory actions in the future.