Project 2: Using user behavior data collected in the clinical lab to test hypotheses about advanced-generation ECIGs and generate population-level predictions regarding potential regulatory action

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。具体而言是 CSTP提出了FDA可以预测潜在法规影响的方法， 然后可以检验预测的效应是否发生在群体中。CSTP模型 评估潜在法规如何影响产品毒性（项目1）、用户行为（项目2），以及 产品成瘾/滥用责任（项目3）。在这方面，项目2将产生关于 先进一代ECIG对用户行为的影响，并将与项目1和3一起沿着， 人口水平预测：项目4将测试这些人口水平预测。 FDA法规旨在促进健康，但也可能产生有害的意外后果。为 例如，在欧盟（EU）中，将液体尼古丁限制在<20 mg/ml，可能会推动使用高功率 ECIG会释放更多的尼古丁和其他有毒物质。其他因素也可能产生意想不到的后果 作用，如限制ECIG尼古丁通量（单位时间内释放的尼古丁量）或减少风味 空房的这些和其他潜在的监管行动的后果可以使用结果来预测 来自临床实验室研究，其中与调节相关的变量被系统地和主观地操纵 效果，抽吸形貌（例如，测量抽吸持续时间、体积）、液体消耗和尼古丁递送。 结果可以告知关于设备/液体偏好、液体量 消费，双重ECIG/烟草香烟使用和依赖。因此，项目2的具体目标是使用 建立了临床实验室方法，以在独立研究中进行检查，每项研究均涉及68名独家ECIG用户 和68名非ECIG使用吸烟者，主观影响的程度，抽吸地形，液体消耗， 和尼古丁的输送受到三种潜在的监管措施的影响：（1）对尼古丁的限制，（2）限制 尼古丁通量，和（3）减少风味的可用性。项目2由上下文知识核心告知 确保独立变量反映真实世界的条件。结果将提供有关 尼古丁浓度、流量和香料可用性对ECIG用户行为的作用，同时告知 人口预测。项目4在人口一级审查这些预测。因此，该项目是 中心的一部分，具有影响分析的综合主题，该主题借鉴了团队的临床实验室专业知识， 提供可用于指导法规制定的FDA工具，以便在法规生效时 效果，预测人口水平现象的方法已经对其进行了测试，对其进行了改进，并生成了 表明其促进健康的效果最大化，意外后果最小化。

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. Specifically, the CSTP proposes methods with which FDA can generate predictions regarding a potential regulation’s effects, and then whether or not the predicted effects occur in the population can be tested. The CSTP’s model assesses how potential regulation might influence product toxicity (Project 1), user behavior (Project 2), and product addiction/abuse liability (Project 3). In this context, Project 2 will generate new data regarding the effects of advanced-generation ECIGs on user behavior and will contribute, along with Projects 1 and 3, to population-level predictions: Project 4 will test those population-level predictions. FDA regulations are intended to promote health, but also may have harmful unintended consequences. For example, limiting liquid nicotine to <20 mg/ml, as in the European Union (EU), could drive use of high power ECIGs that deliver more nicotine and other toxicants. Unintended consequences may also occur from other actions, like constraining ECIG nicotine flux (amount of emitted nicotine/unit time), or reducing flavor availability. The consequences of these and other potential regulatory actions may be predicted using results from clinical lab studies in which variables relevant to regulation are manipulated systematically and subjective effects, puff topography (e.g., puff duration, volume), liquid consumption, and nicotine delivery are measured. Results can inform testable population-level predictions regarding device/liquid preferences, amount of liquid consumed, dual ECIG/tobacco cigarette use, and dependence. Thus, Project 2 specific aims are to use established clinical lab methods to examine, in independent studies each involving 68 exclusive ECIG users and 68 non-ECIG using smokers, the extent to which subjective effects, puff topography, liquid consumption, and nicotine delivery are influenced by three potential regulatory actions: (1) limits on nicotine, (2) constraints on nicotine flux, and (3) reduction in flavor availability. Project 2 is informed by the Contextual Knowledge Core that ensures that independent variables reflect real-world conditions. Results will provide new data regarding the role of nicotine concentration, flux, and flavor availability on ECIG user behavior, while informing population-level predictions. Project 4 examines these predictions at the population level. Thus, this project is part of a center with an integrative theme of impact analysis that draws from the team’s clinical lab expertise to provide FDA tools that can be used to guide regulation development so that, by the time a regulation goes into effect, methods predictive of population-level phenomena have tested it, refined it, and generated data that show that its health-promoting effects are maximized and unintended consequences are minimized.