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。具体地说， CSTP提出了FDA可以对潜在法规的影响进行预测的方法， 然后，可以检验预测的影响是否在人群中发生。CSTP的模式 评估潜在的法规如何影响产品毒性(项目1)、用户行为(项目2)和 产品成瘾/滥用责任(项目3)。在这种情况下，项目2将生成关于 高级世代ECIG对用户行为的影响，并将与项目1和3一起为 人口水平预测：项目4将测试这些人口水平预测。 FDA的规定旨在促进健康，但也可能产生有害的意外后果。为 例如，将液态尼古丁限制在20毫克/毫升，就像在欧洲联盟(EU)一样，可能会推动高功率的使用 提供更多尼古丁和其他有毒物质的电动烟。意外的后果也可能发生在其他 行动，如限制ECIG尼古丁流量(每单位时间释放尼古丁的量)，或减少味道 可用性。可以使用结果来预测这些和其他潜在监管行动的后果 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 测量吸烟量、喷雾效果、喷雾形态(如喷烟持续时间、吸烟量)、液体消耗量和尼古丁输送。 结果可以通知关于设备/液体偏好、液体数量 消费、ECIG/烟草双重使用和依赖。因此，项目2的具体目标是使用 建立临床实验室方法进行检查，每项独立研究涉及68名专门使用ECIG的用户 和68名非ECIG吸烟者，主观影响的程度，烟尘地形，液体消耗量， 尼古丁的传递受到三个潜在的调控行动的影响：(1)限制尼古丁，(2)限制 对尼古丁通量的影响，以及(3)风味可利用性的降低。项目2由上下文知识核心提供信息 这确保了独立变量能够反映真实世界的状况。结果将提供关于以下方面的新数据 尼古丁浓度、流量和风味可用性对ECIG用户行为的影响，同时通知 人口水平的预测。项目4检查了人口层面的这些预测。因此，这个项目是 中心的一部分，以影响分析为综合主题，利用团队的临床实验室专业知识 提供FDA可用于指导法规制定的工具，以便在法规生效时 影响，预测人口水平现象的方法已经测试了它，改进了它，并产生了数据 表明其促进健康的作用最大化，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.