Defining the critical material attributes of lactose monohydrate in carrier based dry powder inhaler formulations using artificial neural networks.

The study aimed to establish a function-based relationship between the physical and bulk properties of pre-blended mixtures of fine and coarse lactose grades with the in vitro performance of an adhesive active pharmaceutical ingredient (API). Different grades of micronised and milled lactose (Lactohale (LH) LH300, LH230, LH210 and Sorbolac 400) were pre-blended with coarse grades of lactose (LH100, LH206 and Respitose SV010) at concentrations of 2.5, 5, 10 and 20 wt.%. The bulk and rheological properties and particle size distributions were characterised. The pre-blends were formulated with micronised budesonide and in vitro performance in a Cyclohaler device tested using a next-generation impactor (NGI) at 90 l/min. Correlations between the lactose properties and in vitro performance were established using linear regression and artificial neural network (ANN) analyses. The addition of milled and micronised lactose fines with the coarse lactose had a significant influence on physical and rheological properties of the bulk lactose. Formulations of the different pre-blends with budesonide directly influenced in vitro performance attributes including fine particle fraction, mass median aerodynamic diameter and pre-separator deposition. While linear regression suggested a number of physical and bulk properties may influence in vitro performance, ANN analysis suggested the critical parameters in describing in vitro deposition patterns were the relative concentrations of lactose fines % < 4.5 μm and % < 15 μm. These data suggest that, for an adhesive API, the proportion of fine particles below % < 4.5 μm and % < 15 μm could be used in rational dry powder inhaler formulation design.