ProSensus (Canada)

Statistical methods were used to provide insight into a polymer complex system composed of lysozyme and alginate to quantify the effects of such parameters as pH, and ionic composition of the mixing solution on the properties of the complexes including composition, particle diameter, and zeta potential. Various crosslinkers (calcium, barium, iron[III], and bovine serum albumin), were used with lysozyme for complex formation to investigate the effect of crosslinker charge density on protein release kinetics, modelled using ktn . Multivariate statistical analysis showed that the kinetic parameters associated with the release were, not surprisingly highly dependent on the ionic strength of the release media, with higher ionic strength leading to faster release. The release parameter k was also shown to depend on the protein properties (size, ionic strength) while n was slightly, but not statistically dependent on the charge density of the crosslinker demonstrating that the nature of the crosslinker had minimal impact on drug release. The multivariate statistical has the potential to be used for optimization of the complexes and prediction of physical properties and degradation rates.

The Multiphase Latent Variable Model Predictive control (MLV-MPC) is developed based on the Principal component analysis (PCA) model. The proposed control methodology is capable of trajectory tracking as well as disturbance rejection. The model that is used in the course of MPC is a multiphase PCA model that is constructed based on the available data from the measurements on the process. Different data arrangements are studied and their effects on the performance of the control algorithm are evaluated.

We present a framework for the development of design and control spaces that simultaneously considers the raw material property space (Z), the critical to quality process variable space (X), and the critical quality attribute space (Y). The importance of jointly defining all of these spaces and simultaneously considering the eventual process feedforward–feedback control system is illustrated. It is shown that changes in any one of these spaces or in the control system will greatly affect the other spaces. Justification is provided for the use of multivariate principal component analysis and projection to latent structures methods to define more meaningful raw material design spaces and the use of statistical process control concepts to redefine control spaces.