A formulation development strategy for multivariate kinetic responses

The purpose of this paper was to evaluate a multivariate strategy for handling time-dependent kinetic data during formulation development. Dissolution profiles were evaluated by the Weibull equation, multiple linear regression (MLR), principal component analysis (PCA), alone and in combination. In addition a soft independent modeling of class analogy (SIMCA) was performed. Employing a typical kinetic model for solid formulations (here Weibull) showed difficulties with the model adaptation, resulting in increased model standard deviation and thereby failure in identifying significant variables. In general, the selection of a kinetic model is crucial for finding the significant formulation variables. Describing the dissolution profile based on MLR models of individual time points described the dissolution rates as a function of formulation variables with good precision. Establishing prediction models made it easy to evaluate effects on the entire dissolution profile. The use of PCA/MLR (PCR) reduced the influence of noise from single measurements in a kinetic profile, since they develop statistical parameters representing the profile without being dependent on a physicochemically-modeled profile. The use of PCA reduced the eight time-point variables to two latent variables (principal components), simplifying the classification of formulations and new samples as well as avoiding unwanted effects of model non-linearities between the factors and responses (model error). The group membership of new samples was demonstrated by SIMCA.