Influence of in line monitored fluid bed granulation process parameters on the stability of Ethinylestradiol

Comparing Integrated Continuous Process "LaVortex®" and Conventional Batch Processes for the Pharmaceutical Manufacturing of Acetaminophen Oral Dosage Formulations: Challenges and Pharmaceutical Properties of the Granular and Tableted Products

LaVortex® was developed as a novel free-flow continuous granulation/drying (CGD) system. In this study, we compared the advantages and disadvantages of granules prepared by continuous and batchwise manufacturing systems. Granules containing 30 % acetaminophen were manufactured under various operating conditions using CGD system, with comparison granules manufactured using conventional batch systems that involve a combination of fluid bed granulation (FG), agitation granulation (AG), continuous drying, fluid bed drying, and/or shelf drying, after which the pharmaceutical properties of each type of manufactured granule were evaluated. Cumulative particle-size distributions were determined by sieving, powder flowabilities were determined by angle of repose measurements, and scanning electron microscopy was employed to examine granule morphologies. The CGD system produced fine-to-large spherical or ellipsoidal granules that exhibited excellent powder fluidities and tabletabilities that are almost identical to those of AG granules. Moreover, the CGD granules exhibited better powder flowability than the FG granules. The addition of water promoted CGD-granule growth and improved significantly powder flowability, and did a little in tabletability. Small spherical granules with good fluidity suitable for fine-particle-coating core materials, or large granules with excellent fluidity and tabletability, were prepared by adjusting the values of the elemental parameters of the CGD process.

Implementation of Dynamic and Static Moisture Control in Fluidized Bed Granulation

The application of process analysis and control is essential to enhance process understanding and ensure output material quality. The present study focuses on the stability of the feedback control system for a fluidized bed granulation process. Two strategies of dynamic moisture control (DMC) and static moisture control (SMC) were established based on the in-line moisture value obtained from the near-infrared sensor and control algorithm. The performance of these strategies on quality consistency control was examined using process moisture similarity analysis and principal component analysis. The stable moisture control performance and low batch-to-batch variability indicated that the DMC method was significantly better than other granulation methods. In addition, the investigation of robustness further showed that the implemented DMC method was able to produce predetermined target moisture values by varying process parameters. This study provides an advanced and simple control method for fluidized bed granulation quality assurance.

An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation

Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters MAE, MAPE, RMSE, and Adj. R2 were 0.0596, 1.5819, 0.0844, and 0.99485, respectively.

Moisture Monitoring in Fluid-Bed Granulation by Multi-Resonance Microwave Sensor: Applicability on Crystal-Water Containing Donepezil Granules

Multi-resonance microwave sensors have recently been introduced for moisture monitoring of pharmaceutical particulates up to > 20% residual moisture. The extended measuring range compared to previous systems as well as the microwave moisture values independent of other physical attributes make them promising process analytical technology (PAT) tools for various pharmaceutical production processes. However, so far, research focused on measurements on raw materials or drug-free model granulates and has neither evaluated the applicability for materials with crystal water containing excipients nor for active ingredients. In this study, possible influence of crystal water was evaluated using lactose monohydrate and donepezil hydrochloride, an active pharmaceutical ingredient (API) against dementia. The study clearly showed that the contained hydrate does not cause interferences and is not monitored by the applied frequencies. Material-related limits measuring lactose monohydrate were only observed above typical granulation moistures and could be explained using raw resonance curves. Furthermore, the inclusion of donepezil hydrochloride into the monitored formulations and varied process parameters demonstrated the versatility of the microwave resonance sensor system. Inlet air temperature, spraying rate, and air flow were varied according to a 2³ full factorial experimental design. A predictive model (R² = 0.9699, RMSEC = 0.33%) could be established using samples produced with different process parameter settings adjusted according to the corner points of the full factorial design and validated on the center point granulation processes (RMSEV = 0.38%). Thereby, performance on actual formulations and conditions faced during process development could be thoroughly assessed, and hence, another key requirement for applicability in formulation development could be met.

Real-time process monitoring in a semi-continuous fluid-bed dryer - microwave resonance technology versus near-infrared spectroscopy

The trend towards continuous manufacturing in the pharmaceutical industry is associated with an increasing demand for advanced control strategies. It is a mandatory requirement to obtain reliable real-time information on critical quality attributes (CQA) during every process step as the decision on diversion of material needs to be performed fast and automatically. Where possible, production equipment should provide redundant systems for in-process control (IPC) measurements to ensure continuous process monitoring even if one of the systems is not available. In this paper, two methods for real-time monitoring of granule moisture in a semi-continuous fluid-bed drying unit are compared. While near-infrared (NIR) spectroscopy has already proven to be a suitable process analytical technology (PAT) tool for moisture measurements in fluid-bed applications, microwave resonance technology (MRT) showed difficulties to monitor moistures above 8% until recently. The results indicate, that the newly developed MRT sensor operating at four resonances is capable to compete with NIR spectroscopy. While NIR spectra were preprocessed by mean centering and first derivative before application of partial least squares (PLS) regression to build predictive models (RMSEP = 0.20%), microwave moisture values of two resonances sufficed to build a statistically close multiple linear regression (MLR) model (RMSEP = 0.07%) for moisture prediction. Thereby, it could be verified that moisture monitoring by MRT sensor systems could be a valuable alternative to NIR spectroscopy or could be used as a redundant system providing great ease of application.