High-shear granulation: An investigation into the granule consolidation and layering mechanism

Multi-dimensional population balance model development using a breakage mode probability kernel for prediction of multiple granule attributes

Milling affects not only particle size distributions but also other important granule quality attributes, such as API content and porosity, which can have a significant impact on the quality of the final drug form. The ability to understand and predict the effects of milling conditions on these attributes is crucial. A hybrid population balance model (PBM) was developed to model the Comil, which was validated using experimental results with an R2 of above 0.9. This presented model is dependent on the process conditions, material properties and equipment geometry, such as the classification screen size. In order to incorporate the effects of different quality attributes in the model physics, the dimensionality of the PBM was increased to account for changes in API content and porosity, which also produced predictions for these attributes in the results. Additionally, a breakage mode probability kernel was used to introduce dynamic breakage modes by predicting the probability of attrition and impact mode, which are dependent on the process conditions and feed properties at each timestep.

Using a material database and data fusion method to accelerate the process model development of high shear wet granulation

High shear wet granulation (HSWG) has been wildly used in manufacturing of oral solid dosage (OSD) forms, and process modeling is vital to understanding and controlling this complex process. In this paper, data fusion and multivariate modeling technique were applied to develop a formulation-process-quality model for HSWG process. The HSWG experimental data from both literature and the authors' laboratory were fused into a single and formatted representation. A material database and material matching method were used to compensate the incomplete physical characterization of literature formulation materials, and dimensionless parameters were utilized to reconstruct process variables at different granulator scales. The exploratory study on input materials properties by principal component analysis (PCA) revealed that the formulation data collected from different articles generated a formulation library which was full of diversity. In prediction of the median granule size, the partial least squares (PLS) regression models derived from literature data only and a combination of literature data and laboratory data were compared. The results demonstrated that incorporating a small number of laboratory data into the multivariate calibration model could help significantly reduce the prediction error, especially at low level of liquid to solid ratio. The proposed data fusion methodology was beneficial to scientific development of HSWG formulation and process, with potential advantages of saving both experimental time and cost.

Wet granulation end point prediction using dimensionless numbers in a mixer torque rheometer: Relationship between capillary and Weber numbers and the optimal wet mass consistency

The optimal wet mass consistency during wet granulation is often determined using the hand squeezing test. In this study, torque values recorded inside the wet mass were measured using a mixer torque rheometer (MTR) via multiple additions of liquid. The main objective of this work was to predict the optimal wet mass consistency of pharmaceutical powders using the modified capillary (Ca^∗) and Weber (We^∗) dimensionless numbers. The results show that the optimal wet mass consistency versus Ca^∗ (or We^∗) can be fitted with a power-law function, whereas the improved capillary number Ca^' proposed in this work gives different relationships and behaviors depending on the spreadability and wettability of the blend. The wettability was obtained by measuring the contact angle between the liquids and the pharmaceutical powders. The surface free energy and the polar and dispersive parts of a liquid's surface energy were obtained from Young's equation and the Owens-Wendt-Rabel-Kaelble (OWRK) model. This study demonstrated the importance of the interfacial energy σ_b-s and the pore radius, R_pore in the establishment of a dimensionless number, Ca^∗, that can satisfactorily predict with an R² of 0.80, the optimal wet mass consistency of pharmaceutical powders measured by the MTR.

Agglomeration of wet particles in dense granular flows

In order to get insight into the wet agglomeration process, we numerically investigate the growth of a single granule inside a dense flow of an initially homogeneous distribution of wet and dry particles. The simulations are performed by means of the discrete element method and the binding liquid is assumed to be transported by the wet particles, which interact via capillary and viscous force laws. The granule size is found to be an exponential function of time, reflecting the conservation of the amount of liquid and the decrease of the number of available wet particles inside the flow during agglomeration. We analyze this behavior in terms of the accretion and erosion rates of wet particles for a range of different values of material parameters such as mean particle size, size polydispersity, friction coefficient and liquid viscosity. In particular, we propose a phase diagram of the granule growth as a function of the mean primary particle diameter and particle size span, which separates the parametric domain in which the granule grows from the domain in which the granule does not survive.