Peer-Reviewed Publications

      Respirable aerosol exposures of nicotine dry powder formulations to in vitro, ex vivo, and in vivo pre-clinical models demonstrate consistency of pharmacokinetic profiles

      Sciuscio, D.; Hoeng, J.; Peitsch, M. C.; Vanscheeuwijck, P.
      Published
      Sep 9, 2019
      DOI
      10.1080/08958378.2019.1662526
      PMID
      31496314
      Topic
      Summary

      Background: Nicotine, because of its volatility, has a complex dosimetry following inhalation as a vapor/aerosol mix. To better control the dosimetry, nicotine could be formulated with a suitable dry powder excipient for use in a clinical inhaler. Aim and Methods: The aim of this study was to investigate the pharmacokinetic PK profile of two dry powder formulations containing 2.5% or 5% nicotine using three experimental models associated to the PreciseInhaleTM aerosolization system: the in vitro DissolvIt dissolution system; the ex vivo isolated, ventilated, and perfused lung (IPL) of the rat; and the in vivo intratracheally intubated rat. Results and Discussion: Following exposure, both nicotine formulations had very rapid and similar dissolution and absorption kinetics in both the DissolvIt and IPL exposure models, with an initial half-time of absorption to the single-pass perfusate of 34 and 72 seconds, respectively. In the intratracheally intubated rat, following a rapid initial equilibration between the lungs and systemic compartments, nicotine had a systemic elimination half-time of 2.3-2.4 hours for both formulations. The rapid pulmonary PK of nicotine was likely close to the theoretical equilibration of a low-binding substance with a tissue-blood partition coefficient close to 1. Conclusions: The data generated with the three experimental models provided a comprehensive picture of the inhalation PK of the two nicotine formulations. In particular, the results showed a very rapid dissolution and absorption of the two nicotine formulations and these results could be highly useful for improving the design and calibration of physiologically based PK models to produce more robust predictions.