A “Large-N” Content Uniformity Process Analytical Technology (PAT) Method for Phenytoin Sodium Tablets

Abstract

Accurate assessment of tablet content uniformity is critical for narrow therapeutic index drugs such as phenytoin sodium. This work presents a near-infrared (NIR)-based analytical method for rapid prediction of content uniformity based on a large number of phenytoin sodium formulation tablets. Calibration tablets were generated through an integrated experimental design by varying formulation and process parameters, and scale of manufacturing. A partial least squares model for individual tablet content was developed based on tablet NIR spectra. The tablet content was obtained from a modified United States Pharmacopeia phenytoin sodium high-performance liquid chromatography assay method. The partial least squares model with 4 latent variables explained 92% of the composition variability and yielded a root mean square error of prediction of 0.48% w/w. The resultant NIR model successfully assayed the composition of tablets manufactured at the pilot scale. For one such batch, bootstrapping was applied to calculate the confidence intervals on the mean, acceptance value, and relative SD for different sample sizes, n = 10, 30, and 100. As the bootstrap sample size increased, the confidence interval on the mean, acceptance value, and relative SD became narrower and symmetric. Such a 'large N' NIR-based process analytical technology method can increase reliability of quality assessments in solid dosage manufacturing.

Non-Destructive Analysis of Chlorpheniramine Maleate Tablets and Granules by Chemometrics-Assisted Attenuated Total Reflectance Infrared Spectroscopy

“…Several models in Table 2 were found to be acceptable using these criteria, such as models 5, 8, 9, 10, 11, and 13. Model 13 was selected as the most suitable model for tablets because it has a high R 2 model, R 2 Pearson, and almost the lowest bias. The bias of model 13 is almost three times less than model 11.…”

“…Applications for non-destructive analysis based on chemometrics-assisted spectroscopic methods such as near-infrared (NIR) spectroscopy, Raman spectroscopy, Terahertz spectroscopy, Fourier transform infrared spectroscopy (FTIR) for process analytical technology (PAT), and quality control in pharmaceutical manufacturing are increasing [2][3][4][5][6][7][8]. In addition, trend quality guidelines for regulation use chemometrics-assisted spectroscopic methods in drug quality control [9].…”

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts

“…Similarly, physical features of the samples (e.g. particle size, shape, and surface areas), as well as experimental variables, can alter NIR spectra, masking chemical information relating to the analyte(s) of interest [1,2,[21][22][23][24][25][26]. Various data preprocessing approaches are implemented to overcome the effect of artefacts on NIR data.…”

Fiberoptic-Coupled Spectrofluorometer with Array Detection as a Process Analytical Chemistry Tool for Continuous Flow Monitoring of Fluoroquinolone Antibiotics

“…e Process Analytical Technology (PAT) [10,11] was defined in 2004 by the Food and Drug Administration (FDA) to support innovation in the industry quality control. PAT has many applications in the pharmaceutical and antibiotics manufacturing [12][13][14][15][16][17][18][19][20][21][22][23][24][25], chemical [26,27], petrochemical [28], and food industries [29][30][31][32][33]. In addition, there are many recent progress in realtime monitoring of cultivations in bioreactors and cell culture process [34][35][36][37][38][39][40][41], fermentation [42,43] and biological process [44], and electrochemical [45,46] and protein purification [47].…”