(712g) Component Mixing In Bicomponent Aggregation: Classification of Kernels

Wet granulation is a size enlargement process in which a multicomponent mixture that contains an active compound, an excipient, and a binder, is lead to produce an agglomerated powder until desired properties are met with respect to mechanical strength of granules, flow characteristics and uniformity of composition. Over the past 15 years, there has been considerable effort towards the development of simulation tools for granulation that make use of increasingly sophisticated models to describe granule interactions and their coupling to the population balance equation (PBE). Virtually all of the previous studies consider granulation of a single component (univariate PBE). Yet more often that not, granulation is a multicomponent process. In pharmaceutical granulation, for example, an active pharmaceutical ingredient (API) is co-granulated with an inert excipient, in preparation for subsequent tablet formation. The purpose of granulation in this case is not only to increase the size of the granules, but also to improve mixing of components. Thus, in addition to changes in the size distribution, one needs to know the compositional distribution of components among granules of different sizes. Ideally, all granules should have the same composition that is equal to the overall ratio of the bulk amounts loaded in the unit. In reality, actual composition varies with granule size and time.

In this work, we present theoretical and experimental results of bicomponent granulation. We formulate a population balance approach to describe a bicomponent population of particles that contain an ingredient of interest ("solute") mixed with an inert compound ("excipient"). We classify granulation kernels into three categories: (i) kernels such that dissimilar components have higher rate of agglomeration compared to similar components; (ii) kernels such that dissimilar components have lower rate of agglomeration; and (iii) kernels that make no distinction between the components. Case (iii), which we refer to as "ideal" aggregation, leads to blending of components into a distribution that is Gaussian in the mass fraction of the solute. The mean composition of granules approaches quickly the desired composition and the variance of the distribution decreases inversely with granule size. Kernels of type (i) lead to quick blending of components. Kernels of the type (ii) inhibit blending and lead to a population of granules that is relatively segregated. Eventually, however, these kernels also produce Gaussian distributions but at a much slower rate compared to kernels (i) and (iii). The results of these simulations are compared with experimental granulation studies using mixtures of two excipients. We find that depending on the surface properties of the excipient, these systems exhibit behavior similar to that of kernels of type (ii) and (iii).