Characterization and optimization of orodispersible mosapride film formulations.

Orodispersible film (ODF) technology offers new possibilities for drug delivery by providing the advantages of oral delivery coupled with the enhanced onset of action and convenience to special patient categories such as pediatrics and geriatrics. In this study, mosapride (MOS) was formulated in an ODF preparation that can be used for treatment of patients who suffer from gastrointestinal disorders, especially difficulty in swallowing due to gastroesophageal reflux disease. Poloxamer 188 was used to solubilize MOS to allow its incorporation into the film matrix. The films were prepared by solvent-casting method using different polymer ratios of maltodextrin and hydroxypropyl methylcellulose and plasticizer levels of glycerol and propylene glycol. A D-optimal design was utilized to study the effect of polymer ratio, plasticizer type, and level on film mechanical properties, disintegration time, and dissolution rate. Statistical analysis of the experimental design showed that the increase of maltodextrin fraction and plasticizer level conferred optimum attributes to the prepared films in terms of film elasticity, film disintegration time, and MOS release rate. The ODF formulations were further tested for moisture sorption capacity, with formulations containing a higher ratio of maltodextrin and percent plasticizer showing more moisture uptake. The optimum film composition was also tested in vivo for film palatability and disintegration time. An optimized mosapride orodispersible film formulation was achieved that could be of benefit to patients suffering from gastrointestinal disorders.

A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending. Part III: Quantitative near-infrared calibration for prediction of blend homogeneity and characterization of powder mixing kinetics.

The Process Analytical Technology (PAT) initiative, undertaken by the Food and Drug Administration (FDA), paves the way for improvement of drug manufacturing through real-time measurements that allow better process understanding. This study is the third and final Part in a series of studies that represent an integrated approach for real-time blend uniformity assessment using near-infrared (NIR) technology. In this study, the development of a quantitative NIR model for prediction of blending end point is presented. Process signature was built into NIR calibration models by using blend samples that were collected from actual blend experiments under different processing conditions. Evaluation of various calibration algorithms including principal component regression (PCR), partial least squares (PLS), and multi-term linear regression (MLR) was performed. It was found that linear regression, using a single wavelength, yielded optimum calibration and prediction results. The blending profiles predicted by the NIR quantitative model correlated well to those determined by the UV reference analytical method. Characterization of intra-shell versus inter-shell powder mixing kinetics and its implication in sensor positioning was also performed and will be discussed.

A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending: Part II: Qualitative near-infrared models for prediction of blend homogeneity.

The successful implementation of near-infrared spectroscopy (NIRS) in process control of powder blending requires constructing an inclusive spectral database that reflects the anticipated voluntary or involuntary changes in processing conditions, thereby minimizing bias in prediction of blending behavior. In this study, experimental design was utilized as an efficient way of generating blend experiments conducted under varying processing conditions such as humidity, blender speed and component concentration. NIR spectral data, collected from different blending experiments, was used to build qualitative models for prediction of blend homogeneity. Two pattern recognition algorithms: Soft Independent Modeling of Class Analogies (SIMCA) and Principal Component Modified Bootstrap Error-adjusted Single-sample Technique (PC-MBEST) were evaluated for qualitative analysis of NIR blending data. Optimization of NIR models, for the two algorithms, was achieved by proper selection of spectral processing, and training set samples. The models developed were successful in predicting blend homogeneity of independent blend samples under different processing conditions.

A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending. Part I: D-optimal design for characterization of powder mixing and preliminary spectral data evaluation.

Experimental design, multivariate data acquisition, and analysis in addition to real time monitoring and control through process analyzers, represent an integrated approach for implementation of Process Analytical Technology (PAT) in the pharmaceutical industry. This study, which is the first in a series of three parts, uses an experimental design approach to identify critical factors affecting powder blending. Powder mixtures composed of salicylic acid and lactose were mixed in an 8 qt. V-blender. D-optimal design was employed to characterize the blending process, by studying the effect of humidity, component concentration, and blender speed on mixing end point. Additionally, changes in particle size and density of powder mixtures were examined. A near-infrared (NIR) fiber-optic probe was used to monitor mixing, through multiple optical ports on the blender. Humidity, component concentration, and blender speed were shown to have a significant impact on the blending process. Furthermore, humidity and concentration had a significant effect on particle size and density of powder mixtures. NIRS was sensitive to changes in physicochemical properties of the mixtures, resulting from process variables. Proper selection of NIR spectral preprocessing is of ultimate importance for successful implementation of this technology in the monitoring and control of powder blending and is discussed.

Evaluation of risk and benefit in the implementation of near-infrared spectroscopy for monitoring of lubricant mixing.

On-line near-infrared spectroscopy (NIRS) was used to monitor lubricant blending to ensure the quality of the final dosage form. A quantitative multivariate NIR model was developed using different lubricant concentration levels. Real-time model predictions correlated well with the expected lubricant concentration during blending, which allowed determination of blend quality. The significance of sensor location on the blender at different fill levels was evaluated. The capability of this application was further assessed by real-time study of blending dynamics under varying process conditions and raw material attributes. The response of the developed NIR method to sudden spikes in analyte concentration, changes in raw material attributes, and perturbations to standard mixing procedures was evaluated. This study allows an understanding of risk factors associated with the implemented technology, and its ability to accurately monitor the process events. Furthermore, it highlights the importance of proper selection of processing conditions and raw material attributes to improve process robustness.