An Integrated Process Analytical Technology (PAT) Approach for Process Dynamics-Related Measurement Error Evaluation and Process Design Space Development of a Pharmaceutical Powder Blending Bed

NIR Spectroscopy as an Online PAT Tool for a Narrow Therapeutic Index Drug: Toward a Platform Approach Across Lab and Pilot Scales for Development of a Powder Blending Monitoring Method and Endpoint Determination

An online near-infrared (NIR) spectroscopy platform system for real-time powder blending monitoring and blend endpoint determination was tested for a phenytoin sodium formulation. The study utilized robust experimental design and multiple sensors to investigate multivariate data acquisition, model development, and model scale-up from lab to manufacturing. The impact of the selection of various blend endpoint algorithms on predicted blend endpoint (i.e., mixing time) was explored. Spectral data collected at two process scales using two NIR spectrometers was incorporated in a single (global) calibration model. Unique endpoints were obtained with different algorithms based on standard deviation, average, and distributions of concentration prediction for major components of the formulation. Control over phenytoin sodium's distribution was considered critical due to its narrow therapeutic index nature. It was found that algorithms sensitive to deviation from target concentration offered the simplest interpretation and consistent trends. In contrast, algorithms sensitive to global homogeneity of active and excipients yielded the longest mixing time to achieve blending endpoint. However, they were potentially more sensitive to subtle uniformity variations. Qualitative algorithms using principal component analysis (PCA) of spectral data yielded the prediction of shortest mixing time for blending endpoint. The hybrid approach of combining NIR data from different scales presents several advantages. It enables simplifying the chemometrics model building process and reduces the cost of model building compared to the approach of using data solely from commercial scale. Success of such a hybrid approach depends on the spectroscopic variability captured at different scales and their relative contributions in the final NIR model.

Method development and validation of a near-infrared spectroscopic method for in-line API quantification during fluidized bed granulation

Near-infrared spectroscopy (NIRS) is an excellent process analytical technology (PAT) tool for active pharmaceutical ingredient (API) quantification during fluidized granulation. Therefore, a portable near-infrared spectrometer combined with a new innovative method of extended iterative optimization technique (EIOT) was used to in-line monitor the API content uniformity during fluidized bed granulation. The principal component analysis (PCA) and partial least squares regression (PLSR) were also used to characterize and predict API concentration with changes from 75% to 125% of the label claim to prove the superiority of EIOT. The API content prediction accuracy of the EIOT method was verified through offline High Performance Liquid Chromatography (HPLC) measurement. Also, the spatial distribution of API in granules was visualized by Raman imaging technology. The results showed that the established NIRS method was suitable for the prediction of API content in fluidized bed granulation, which provides a new idea for the determination of API content during granulation.

Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

Raw Material Variability and Its Impact on the Online Adaptive Control of Cohesive Powder Blend Homogeneity Using NIR Spectroscopy

It is significant to analyze the blend homogeneity of cohesive powders during pharmaceutical manufacturing in order to provide the exact content of the active pharmaceutical ingredient (API) for each individual dose unit. In this paper, an online monitoring platform using an MEMS near infrared (NIR) sensor was designed to control the bin blending process of cohesive powders. The state of blend homogeneity was detected by an adaptive algorithm, which was calibration free. The online control procedures and algorithm’s parameters were fine-tuned through six pilot experiments and were successfully transferred to the industrial production. The reliability of homogeneity detection results was validated by 16 commercial scale experiments using 16 kinds of natural product powders (NPPs), respectively. Furthermore, 19 physical quality attributes of all NPPs and the excipient were fully characterized. The blending end time was used to denote the degree of difficulty of blending. The empirical relationships between variability of NPPs and the blending end time were captured by latent variable modeling. The critical material attributes (CMAs) affecting the blending process were identified as the particle shape and flowability descriptors of cohesive powders. Under the framework of quality by design (QbD) and process analytical technology (PAT), the online NIR spectroscopy together with the powder characterization facilitated a deeper understanding of the mixing process.

Chromatographic elution process design space development for the purification of saponins in Panax notoginseng extract using a probability-based approach

A Monte Carlo method was used to develop the design space of a chromatographic elution process for the purification of saponins in Panax notoginseng extract. During this process, saponin recovery ratios, saponin purity, and elution productivity are determined as process critical quality attributes, and ethanol concentration, elution rate, and elution volume are identified as critical process parameters. Quadratic equations between process critical quality attributes and critical process parameters were established using response surface methodology. Then probability-based design space was computed by calculating the prediction errors using Monte Carlo simulations. The influences of calculation parameters on computation results were investigated. The optimized calculation condition was as follows: calculation step length of 0.02, simulation times of 10 000, and a significance level value of 0.15 for adding or removing terms in a stepwise regression. Recommended normal operation region is located in ethanol concentration of 65.0-70.0%, elution rate of 1.7-2.0 bed volumes (BV)/h and elution volume of 3.0-3.6 BV. Verification experiments were carried out and the experimental values were in a good agreement with the predicted values. The application of present method is promising to develop a probability-based design space for other botanical drug manufacturing process.