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TSRC, Tob. Sci. Res. Conf., 2016, 70, abstr. 34

Nicotine delivery from e-cigarettes Part 2: data and learnings from two pharmacokinetic studies

FEARON I.M.(1); ELDRIDGE A.(1); GALE N.(1); SHEPPERD C.J.(1); McEWAN M.(1); CAMACHO O.M.(1); MAVROPOULOU E.(1); NIDES M.(2); McADAM K.(1); PROCTOR C.(1)
(1) British American Tobacco (Investments) Limited, Southampton, UK; (2) Los Angeles Clinical Trials, Burbank, CA, USA

Nicotine pharmacokinetic (PK) studies are an important tool in developing our understanding of actual nicotine delivery into the body from electronic cigarettes and other nicotine-containing products. Furthermore, data from such studies may potentially be required as part of a regulatory package, particularly as one aspect of the abuse liability assessment of a novel product. This presentation is Part 2 of 2 and will describe the findings from nicotine PK studies, designs of which were outlined in the first presentation.

Compliance with abstention requirements and other inclusion/exclusion criteria was high in both studies. 22 of 24 subjects completed Study 1 while all enrolled 18 subjects completed Study 2. In Study 1, blood nicotine Cmax was, on average, 5-fold greater for the cigarette compared to the e cigarette. In contrast, in Study 2 peak blood nicotine levels were similar for the cigarette and the e-cigarette. While some differences were observed between the different products, the variability of data in Study 2 precluded the detection of statistically-significant effects. Study 2 also showed large inter-subject differences in Cmax values, for example ranging from 40 ng/ml.

Our data show a high level of variability when subjects from different populations and with different smoking histories use similar products. Puffing schedule (standardised vs ad libitum) may also contribute to this variability. These data may support a need for standardisation of protocols fore cigarette clinical research, in orderto facilitate comparisons between products in different studies. However, study design needs to take into account study objectives and cohorts, real-world usage patterns, and which comparisons need to be made between one product and another.