Center for the Study of Tobacco Products

项目摘要。烟草产品研究中心(CSTP)开发了一个模型，用于 使用加热液体的电子烟(ECIG)作为样本来评估新型烟草产品 通常含有尼古丁，形成一种气雾剂，使用者可以吸入。现在，CSTP利用其方法和 ECig的专业知识将从产品评估转向影响分析的综合主题。FDA烟草公司 监管必须保护公众健康，尽管监管也可能造成意想不到的后果 伤害。例如，限制ECIG液体尼古丁含量，就像在欧盟一样，可以推动使用更高的 为ECIG提供动力，使其雾化更多的液体，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法规涉及限制尼古丁排放量/单位时间(尼古丁“流量”)，这可能 影响产品毒性和滥用责任，或减少风味供应，可能导致用户混合他们自己的 有味道的液体。如果FDA可以预测一项潜在法规的影响，那么在法规出台之前的改进是 发布可能会最大限度地发挥其促进健康的作用，并最大限度地减少意外后果。现有的方法用于 一旦监管到位，就会评估其影响，但很少有数据驱动的模型事先预测影响。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/滥用责任。此外，模型还必须证明在多大程度上 对潜在监管影响的预测描述了人口层面的实际结果。CSTP解决了这一问题 此应用程序的问题：项目1-3将测试假设并生成关于以下影响的预测 三个潜在的ECIG法规(即，限制尼古丁、限制流量、减少香料可获得性)；项目4将测试 这些在人口层面上的预测。CSTP的愿景是提供工具来指导监管发展，从而 在法规生效时，经过验证的方法已经对其进行了测试、改进并生成了数据 表明其促进健康的效果是最大化的，意外后果是最小的。 团队(Eissenberg博士、Bland博士、Shihadeh博士、Cobb博士、Barnes博士、Cohen博士、Fagan博士)将完成4个集成 旨在：预测产品毒性、预测用户行为、预测滥用责任和评估潜力的项目 监管影响。背景知识核心使用混合方法来确保项目1-4反映真实 世界。一家职业提升核心机构开设了一门烟草监管科学课程，支持 博士任命者和试点项目。行政核心支持财务/管理流程， 生物统计学需要，接受外部咨询委员会的意见，并与FDA、NIH和其他机构接口。 总体而言，CSTP的影响分析综合主题借鉴了该小组在烟草产品毒性方面的专业知识， 用户行为和滥用责任。我们试图向FDA提供关于先进技术的假设驱动的数据 生成ECIG并测试对一些潜在法规的预测，同时开发一个模型 可用于塑造、改进和预测未来许多潜在监管行动的影响。

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.