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Surface interactions in propellant driven metered dose inhaler product design
Hickey, A. J., & Holt, J. (2014). Surface interactions in propellant driven metered dose inhaler product design. In H. Ohshima, & K. Makino (Eds.), Colloid and Interface Science in Pharmaceutical Research and Development (pp. 79-102). Elsevier, Ltd.. https://doi.org/10.1016/B978-0-444-62614-1.00004-1
Metered dose inhalers have been employed to deliver drugs for the treatment of asthma for over 50 years. In that period the requirement to transition from chlorofluorocarbon propellants, implicated in atmospheric ozone depletion, to hydrofluorocarbon propellants resulted in a significant increase in research and development activity to reformulate old drugs and prepare new products. This enhanced effort has occurred in parallel to regulatory emphasis on quality by design as a means of ensuring the quality and performance of the product through control of material properties and manufacturing processes. Few studies have been performed to systematically vary these properties and processes within the range of normal product variability to evaluate their influence on product quality and performance. Drugs may be employed with significant differences in their physico-chemical properties notably from each of the major therapeutic categories (β2-adrenergic agonists, anticholinergics and glucocorticosteroids); their particle size may vary through manipulation of conventional micronization conditions, and surfactant and co-solvent concentrations are varied to achieve product optimization. A range of analytical methods can be employed to establish product quality and performance. Controlling key manufacturing variables and measuring a range of quality and performance attributes will result in a significant 'weight of evidence' to support the accuracy and reproducibility of the dose delivered and the manner in which it is delivered with respect to important measures for biological effect, namely aerodynamic particle-size distribution and where relevant dissolution. Any predictive mathematical model should be based on fundamental physico-chemical properties and device-dependent empirical factors.