Quantitative chemical profiling of cellulose acetate excipient via 13C NMR spectroscopy in controlled release formulations.

Cellulose acetate (CA) is the main component of controlled-release (CR) coating of formulations such as osmotic-controlled release oral delivery system (OROS) and CR microspheres. Despite multiple applications, there are limited or no reports dealing with the characterization and quantification of CA in the formulated systems. Thus, the present investigation deals with the development of the Quantitative Carbon-13 Nuclear Magnetic Resonance (q13CNMR) spectroscopy method for the determination of CA amount in the CR microsphere formulations. The developed q13CNMR method was also verified using control CA samples from marketed OROS formulation. Thereafter, the concentration of CA in the microspheres was calculated. Furthermore, the impact of different concentrations of CA on the critical quality attributes such as the drug release profile from the formulation was investigated. The study demonstrated the CA coating levels to be inversely proportional to the extent and rate of release of API. The developed q13CNMR method was found to be accurate and precise and can be explored further to investigate the effect of different stability conditions on the degree of polymerization and degradation of CA resulting in altered quality of pharmaceutical products.

Computational Fluid Dynamics-Discrete Element Method Modeling of an Industrial-Scale Wurster Coater.

Large-scale fluid bed coating operations using Wurster coaters are common in the pharmaceutical industry. Experimental measurements of the coating thickness are usually analyzed for just few particles. To better predict the coating uniformity of the entire batch, computational techniques can be applied for process understanding of the key process parameters that influence the quality attributes. Recent advances in computational hardware, such as graphics processing unit, have enabled simulations of large industrial-scale systems. In this work, we perform coupled computational fluid dynamics-discrete element method simulations of a large-scale coater that model the actual particle sizes. The influence of process parameters, inlet air flow rate, atomizing air flow rate, bead size distribution, and Wurster gap height is studied. The focus of this study is to characterize the flow inside the coater; eventually, this information will be used to predict the coating uniformity of the beads. We report the residence time distribution of the beads inside the Wurster column, that is, the active coating zone, which serves as a proxy for the amount of coating received by the beads per pass. The residence time provides qualitative and quantitative measurements of the particle-coating uniformity. We find that inlet air flow rate has the largest impact on the flow behavior and, hence, the coating uniformity.

Multivariate wavelet texture analysis for pharmaceutical solid product characterization.

The application of multivariate wavelet texture analysis (MWTA) is presented and discussed as it is applied to three different types of pharmaceutical materials: (a) tablet cores, (b) wet granules and (c) controlled release tablets. The application of MWTA is initially proposed as a quantitative replacement to the human visual judgment of the textural appearance of the different materials. In all cases, the metrics obtained with MWTA agree with visual assessment on the progression of textural features such as erosion and surface roughness. This work further demonstrates that MWTA also represents a useful tool to increase the understanding of the manufacturing process, as it provides diagnostics to relate process parameters with textural features of the material that are difficult or costly to measure otherwise (such as granule size for wet material or surface appearance for a controlled release product). MWTA is also presented as a potential tool for real-time release for those cases where the textural features can be proven to provide accurate enough predictions of the final product performance; as shown here with the obtained prediction of dissolution from the controlled release tablet using the texture of the product as an input.