Project 1: Using toxicity testing data 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1将生成关于 高世代ECIG排放的毒物，并有助于关于种群水平的假设 监管影响(项目2和项目3)：项目4将检验这些人口水平的假设。 FDA的法规旨在促进健康，但也可能产生意想不到的后果。例如, 将ECIG液体的尼古丁含量限制在20毫克/毫升，就像在欧盟(EU)一样，可以推动更高功率的使用 电子烟会雾化更多的液体/烟雾，导致使用者吸入更多的尼古丁和其他有毒物质。非故意的 其他行动也可能产生后果，如限制ECIG尼古丁流量(排放量 尼古丁/单位时间)，或降低风味可获得性。这些和其他潜在监管措施的后果 可以使用气溶胶研究方法来预测行动，这些方法允许对关键参数进行严格控制，例如 如设备功率、液体成分和喷雾地形(例如，喷雾体积/持续时间)。结果衡量标准 包括产生气雾剂量、数学预测的尼古丁输送、实际尼古丁产量以及 气雾剂毒物轮廓。结果可以为关于设备/液体的可测试总体水平假设提供信息 偏好和双重ECIG/烟草卷烟使用。因此，项目1的具体目标是使用已建立的气雾剂 考察ECIG排放如何受到三种潜在监管行动影响的研究方法：(1) 尼古丁的限制，(2)尼古丁流量的限制，以及(3)风味可获得性的减少。项目1由以下人员通知 背景知识核心，将完善Aim 2设备/液体选择，并提供Aim 3 DIY方法。 项目1提供了关于尼古丁浓度、流量和ECIG上液体可用性的作用的新数据 毒性，同时告知关于潜在监管行动的后果的预测。项目4 在人口层面上检验这些预测。因此，项目1是影响的综合主题的一部分 分析并利用团队的气溶胶研究专业知识提供FDA工具，可用于指导 规章制度的发展，当一项规章制度生效时，预测人口水平的方法 现象已经对它进行了测试，改进了它，并产生了数据，表明它的健康促进作用是 最大化和意想不到的后果被最小化。

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 1 will generate new data regarding the toxicants emitted by advanced-generation ECIGs and contribute to hypotheses about population-level regulatory impact (with Projects 2 and 3): Project 4 will test those population-level hypotheses. FDA regulations are designed to promote health, but may also have unintended consequences. For example, limiting ECIG liquids to <20 mg/ml nicotine, as in the European Union (EU), can drive use of higher power ECIGs that aerosolize more liquid/puff, leading users to inhale more nicotine and other toxicants. Unintended consequences may also occur from other action, 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 aerosol research methods that allow rigorous control of key parameters, such as device power, liquid constituents, and puff topography (e.g., puff volume/duration). Outcome measures include amount of aerosol produced, mathematically-predicted nicotine delivery, actual nicotine yield, and aerosol toxicant profile. Results can inform testable population-level hypotheses regarding device/liquid preferences and dual ECIG/tobacco cigarette use. Thus, Project 1 specific aims are to use established aerosol research methods to examine how ECIG emissions are influenced by three potential regulatory actions: (1) limits on nicotine, (2) constraints on nicotine flux, and (3) reduction in flavor availability. Project 1 is informed by the Contextual Knowledge Core that will refine Aim 2 device/liquid choices and provide Aim 3 DIY methods. Project 1 provides new data regarding the role of nicotine concentration, flux, and liquid availability on ECIG toxicity, while informing predictions regarding the consequences of potential regulatory action. Project 4 examines these predictions at the population level. Thus, Project 1 is part of an integrative theme of impact analysis and draws on the team’s aerosol research 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.