Performance Evaluation of a Novel Biosourced Co-Processed Excipient in Direct Compression and Drug Release

This study exposes the potential usefulness of a new co-processed excipient, composed of alginic acid and microcrystalline cellulose (Cop AA-MCC), for the preparation of immediate drug release tablets by direct compression. Evaluation of the physical and mechanical properties as well as the disintegration behavior of Cop AA-MCC in comparison to commercial co-processed excipients (Cellactose®, Ludipress®, Prosolv® SMCC HD90 and Prosolv® ODT) and to the physical mixture of the native excipients (MCC and AA), was carried out. The obtained results illustrate the good performance of Cop AA-MCC in terms of powder flowability, tablet tensile strength, compressibility, and disintegration time. Although, this new co-processed excipient showed a slightly high lubricant sensitivity, which was explained by its more plastic than fragmentary deformation behavior, it presented a low lubricant requirement due to the remarkably low ejection force observed during compression. Compression speed and dwell time seemed not to affect significantly the tabletability of Cop AA-MCC. The study exposed evenly the performance of Cop AA-MCC compared to Prosolv® ODT, in terms of tabletability and dissolution rate of Melatonin. Cop AA-MCC presented comparable hardness, lower dilution potential, higher lubricant sensitivity, lower ejection force, and faster Melatonin’s release time than Prosolv® ODT. In summary, Cop AA-MCC exhibited interesting physical, mechanical, and biopharmaceutical properties, which demonstrate its concurrence to commercially available co-processed excipients. Furthermore, the simplicity of its composition and the scalability of its elaboration makes this multifunctional excipient highly recommended for direct compression.

Machine learning modelling of wet granulation scale-up using compressibility, compactibility and manufacturability parameters

The purpose of this extensive study is to use a quality by design (QbD) approach and multiple machine learning algorithms in facilitating wet granulation process scale-up. This study investigated the extent of influence of both formulation and process variables. Furthermore, measured responses covered compressibility, compactibility and manufacturability of a powder blend. Finally, the models developed on laboratory scale samples were tested on pilot and commercial scale runs. Tablet detachment and ejection work were calculated from force-displacement measurements. Significant numerical and categorical input variables were identified by using a stepwise regression model and their importance evaluated by using a boosted trees model. Pilot scale runs resulted in the highest tablet tensile strength and compaction work as well as the highest detachment and ejection work. Critical quality attributes (CQAs) that were the most successfully predicted were the compaction, decompaction, and net work, as well as the tablet height. The most important input variable influencing all CQAs was the compaction force. Application of the boosted regression trees model resulted in the lowest Root Mean Square Error (RMSE) values for all of the responses. This work demonstrates reliability of predictions of developed models that can be successfully used as a part of a QbD approach for wet granulation scale-up.