Multivariate evaluation of the effect of the particle size distribution of an Active Pharmaceutical Ingredient on the performance of a pharmaceutical drug product: a real-case study

• Published in: Chemometrics and intelligent laboratory systems Vol. 178; pp. 1 - 10
• Main Authors: Copelli, D., Cavecchi, A., Merusi, C., Leardi, R.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.07.2018
• Subjects: Active Pharmaceutical Ingredient; Aerodynamic particle Size Distribution; API; APSD; DPI; dry powder inhaler; MLR; Multiple linear regression; Multivariate analysis; Next Generation impactor; NGI; Particle size distribution; PCA; pMDI; pressurized metered dose inhaler; Principal Component analysis; Principal properties; Principal Property; PSD; PP; PSD; Next Generation impactor; pressurized metered dose inhaler; Principal Component analysis; Multiple linear regression; Multivariate analysis; dry powder inhaler; Principal Property; PCA; NGI; Particle size distribution; MLR; Aerodynamic particle Size Distribution; Principal properties; pMDI; API; Active Pharmaceutical Ingredient; DPI; APSD
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2018.04.013

In the pharmaceutical field, and in particular for inhalation drug products based on Dry Powder Inhaler, the Active Pharmaceutical Ingredient particle size distribution is one of the key parameters to drive the final drug product performance. In this paper the impact of the Active Pharmaceutical Ingredient particle size on the Aerodynamic Particle Size Distribution of the final drug product was evaluated by applying different multivariate approaches. By using both the commonly employed particle size distribution descriptors (D10, D50, D90 and SPAN) and the whole particle size distribution curves it has been demonstrated that the latter gives an information which is easier to understand and interpret. Finally, models estimating the effects of the Active Pharmaceutical Ingredient particle size distribution, device life and drug product dosage on the Aerodynamic Particle Size Distribution of the final drug product were also established. •The multivariate approach based on Principal Properties, has showed how to manage a huge data set to extract the information.•It was shown how to use the particle size distribution curve instead of particle size descriptors to extract more information from the data set.•The final characteristics of an Active Pharmaceutical Ingredient for Dry Powder Inhalers was found thanks to the multivariate approach proposed.