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The agglomeration tendency of dry pharmaceutical mixtures containing various concentrations of Xylitab®100 (Xylitol), calcium carbonate precipitated (CCP) and magnesium stearate (MgSt) was evaluated statistically as a function of mixing time. A Ro-Tap tester was employed to mix the three pharmaceutical components, and the agglomerates formed were measured with respect to their weight and size. An experimental design was devised and applied to structure and then statistically analyze the results. Xylitab was found not to be influential in the formation of agglomerates, but aided in deagglomeration when mixed with other components. CCP and MgSt formed agglomerates over time and showed positive interactions favouring agglomeration. The agglomerates started to fracture when they reached a critical size, at which stage the particles’ attraction forces (cohesion forces) were weaker than both gravity and inertia. It has been shown and quantitatively demonstrated that the mixing time and ingredient concentrations of a three-component pharmaceutical mixture can affect agglomeration tendency.

Segregation phenomena are of importance in nearly all processes involving dry granular and powder mixtures. The extent of segregation directly influences the eventual rejection of a considerable percentage of the final product in the majority of pharmaceutical processes; among these are those mixtures destined for powder compression processing for the production of tablets. Although the parameters influencing segregation are relatively well-known qualitatively, there are, so far, no widely accepted quantitative prediction tools that permit process improvement and optimization of production as a function of the mixture's composition and the particulars of individual processes (e.g., geometry of the vessels). Thus, within present practice, only general design considerations and the technical expertise of engineers and operators are relied upon to optimize these processes on a case-by-case basis. It is in these circumstances that a study of the tendency towards segregation in free flowing granular materials was conducted, using a simple tool previously developed for the study of the behavior of continuous chemical reactors with classical fluid flows. The measurement of average residence times and their variance is used to calculate the deviation of chemical reactors from the ideal behavior of a perfectly mixed vessel or a plug flow pattern. In this work, these measurements are adapted to evaluate the tendency of a granular mixture to segregate. The method consists of introducing a pulse perturbation (of another material) to the established regular flow of a single granular material or a granular mixture and to then calculate the response of the system in terms of the concentration of the pulsed material at the process outlet. The average granular particle residence time and its standard deviation are then related to the segregation tendency.

Process analytical technologies (PAT) enable process insight, process control and real-time testing. Light-induced fluorescence (LIF) spectroscopy is especially well suited for low-concentration ingredients as, in many cases, it is the most sensitive probe of the in-line PAT toolbox. This study is aimed at verifying the applicability of a multivariate LIF analyzer to monitor granulated powder blends in industrial settings. Its targets are to: (1) evaluate the critical parameters of powders to obtain robust, precise and accurate concentration predictions and (2) assess technology performance for in-line monitoring of blending operations. Varying dye properties, moisture levels and particle sizes have been shown to have the most significant impact on fluorescence emission. Reliable quantitative models can be obtained by controlling and/or mitigating these factors.

Non-aerated powder flows are frequently encountered in downstream pharmaceutical processes. Such flows occur at the entrance of powder compression units, and their characteristics are of great interest because any powder agglomeration or segregation can be detrimental to the quality of the final solid oral dosage form. This work was aimed at developing a process analytical technology (PAT) method, based on near-infrared spectroscopy (NIR) for the in-line powder flow characterization of pharmaceutical formulations. An Ibuprofen drug formulation was selected for study. A bench-scale hopper system was assembled to monitor powder flow behaviour. An in-line commercial NIR Axsun spectrometer and probe were chosen to collect in-line spectral data on dense, multicomponent, non-aerated powder flow prior to compression. Spectra were collected on flowing mannitol and pharmaceutical product blends. A specially designed, non-contact sampling interface allowed the collection of representative process powder flow spectra without affecting blend uniformity. A partial least squares chemometric model was developed for laboratory-prepared samples, to quantitatively determine the flowing powder's active pharmaceutical ingredient (API) level. Static sample spectra and flowing pure mannitol spectra proved to have a high degree of reproducibility. The model's standard error of calibration was 2.95% of the API level with a R2 of 0.991. Flowing blend powder spectra and API estimates showed variations consistent with those seen in model samples. The average values for flowing pharmaceutical blends were close to the API concentration, indicating that the proposed procedure was statistically acceptable. The model is considered very promising, and some improvements would lead to its final acceptance at production scale as a PAT tool.

Under different charging conditions, particles can be either attracted or repulsed by each other, causing powder agglomeration or segregation. Such behavior can be detrimental in many processes aimed at achieving particulate mixture homogeneity. Consequently, the effects of electrostatic charges on mixing kinetics must be well understood to insure a high level of process control, product quality, and reproducibility. In Part 1, an electrostatic charger is used to evaluate the ability of the studied particles to develop and retain an induced charge at the surface for a fixed period of time. Part 2 assesses the natural electrostatic charge developed by powders sliding across a stainless steel, plastic, or Tyvek chute. In Part 3, 2 binary systems were formed according to an experimental design under different charging conditions, and their behavior was quantified by measuring the degree of agglomeration attained. This work has shown that each raw material develops a different charge according to its physico-chemical properties and the type of contact surface. Electrostatic charges influence the creation of agglomerates under certain conditions. The presence of electrostatic charges must be accounted for in any effort to maximize mixing efficiency.

The measurement of average residence times and their variance, used to calculate the deviation of chemical reactors from the ideal behaviour of a perfectly-mixed vessel, or a plug flow pattern, has already been proposed in the literature to evaluate the segregation tendency of granular mixtures. The method consists of introducing pulse perturbation (of another material) to the established regular flow of a single granular material or a granular mixture and to assess the response of the system in terms of pulsed material concentration at the process outlet. The particles' average residence time and its standard deviation are then related to segregation tendency. Results from the application of this new method are useful when compared to those obtained from a reference mixture to be chosen according to a particular formulation development or process understanding need. This work applies the proposed method for various mixtures, both cohesive and non-cohesive, and derives phenomenological mathematical models expressing segregation tendency as a function of the parameters shown to be critical (i.e. statistically significant) to component segregation. The models were shown to be statistically and experimentally robust in the case of non-cohesive to slightly cohesive mixtures. Although the mathematical models are phenomenological, the findings allow for deriving mechanistic explanations on segregation tendency.