title: Development of a design space and predictive statistical model for capsule filling of low-fill-weight inhalation products.
abstract: The objectives of this study were to develop a predictive statistical model for low-fill-weight capsule filling of inhalation products with dosator nozzles via the quality by design (QbD) approach and based on that to create refined models that include quadratic terms for significant parameters. Various controllable process parameters and uncontrolled material attributes of 12 powders were initially screened using a linear model with partial least square (PLS) regression to determine their effect on the critical quality attributes (CQA; fill weight and weight variability). After identifying critical material attributes (CMAs) and critical process parameters (CPPs) that influenced the CQA, model refinement was performed to study if interactions or quadratic terms influence the model. Based on the assessment of the effects of the CPPs and CMAs on fill weight and weight variability for low-fill-weight inhalation products, we developed an excellent linear predictive model for fill weight (R(2 )= 0.96, Q(2 )= 0.96 for powders with good flow properties and R(2 )= 0.94, Q(2 )= 0.93 for cohesive powders) and a model that provides a good approximation of the fill weight variability for each powder group. We validated the model, established a design space for the performance of different types of inhalation grade lactose on low-fill weight capsule filling and successfully used the CMAs and CPPs to predict fill weight of powders that were not included in the development set.
authors.affiliation: a Institute of Process and Particle Engineering, Graz University of Technology , Graz , Austria .|b Research Center Pharmaceutical Engineering , Graz , Austria .
authors.forename: E
authors.initials: E
authors.lastname: Faulhammer
authors.affiliation: b Research Center Pharmaceutical Engineering , Graz , Austria .
authors.forename: M
authors.initials: M
authors.lastname: Llusa
authors.affiliation: b Research Center Pharmaceutical Engineering , Graz , Austria .
authors.forename: P R
authors.initials: PR
authors.lastname: Wahl
authors.affiliation: b Research Center Pharmaceutical Engineering , Graz , Austria .
authors.forename: A
authors.initials: A
authors.lastname: Paudel
authors.affiliation: c GlaxoSmithKline (GSK), New Frontiers Science Park , Harlow, Essex , UK , and.
authors.forename: S
authors.initials: S
authors.lastname: Lawrence
authors.affiliation: d MG2, Pian di Macina di Pianoro , Bologna , Italy.
authors.forename: S
authors.initials: S
authors.lastname: Biserni
authors.affiliation: d MG2, Pian di Macina di Pianoro , Bologna , Italy.
authors.forename: V
authors.initials: V
authors.lastname: Calzolari
authors.affiliation: a Institute of Process and Particle Engineering, Graz University of Technology , Graz , Austria .|b Research Center Pharmaceutical Engineering , Graz , Austria .
authors.forename: J G
authors.initials: JG
authors.lastname: Khinast
mesh_terms: D000280:Administration, Inhalation; D002214:Capsules; D002626:Chemistry, Pharmaceutical / Q000379:methods*; D004339:Drug Compounding / Q000379:methods*; D005079:Excipients / Q000737:chemistry*; D007785:Lactose / Q000737:chemistry; D016018:Least-Squares Analysis; D015233:Models, Statistical; D011208:Powders; D013678:Technology, Pharmaceutical / Q000379:methods
chemical_list: D002214:Capsules; D005079:Excipients; D011208:Powders; D007785:Lactose
keywords: Design space; dosator nozzle capsule filling; low-dose inhalation products; multivariate data analysis; operating space; predictive statistical process model