Abstract
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.
Highlights
Quality by design (QbD) is a pharmaceutical design, development and research concept using a systematic, riskbased, holistic approach [1]
Tablets were made by varying concentrations of tribasic calcium phosphate (TCP) (Innophos Inc., Cranbury, New Jersey, USA) that was used as a filler and sodium starch glycolate (SSG) (DFE Pharma, Goch, Germany) that was used as a disintegrator
The difference between Wc and Wn increases with the increased applied compaction pressure, which points to an increase in the decompaction work (Fig. 3)
Summary
Quality by design (QbD) is a pharmaceutical design, development and research concept using a systematic, riskbased, holistic approach [1]. Wet granulation is a complex manufacturing process influenced by formulation variables (ingredient concentration, particle shape, particle size distribution, solubility, hygroscopic nature etc.), and process conditions (impeller speed, milling speed, screen size, mixing time, amount and rate of liquid addition [5], moisture of granules etc.). By developing a progressive design space with a QbD risk based approach, production of pharmaceutical products is robust enough to allow for scale-up adjustments of process parameters [9,10]. Studies utilizing the QbD approach and machine learning modeling for scale-up of wet granulation processes often use either process parameters or formulation factors [16]. We utilized and compared multiple machine learning techniques (regression, regularization, decision tree and ensemble algorithms) Both formulation and process parameters were used as input variables. This article provides an extensive example of how different machine learning techniques can be utilized to determine significant variables (both categorical and numerical) and the magnitude of their influence on tablet CQAs
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
