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.

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

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.

Combined UV/vis and micro-tomography investigation of acetaminophen dissolution from granules.

The X-ray micro-tomography (micro-CT) technique has been used to visualize the microstructure of granules produced by high shear wet granulation and the dynamic evolution of porosity during granule dissolution. Using acetaminophen (paracetamol) as the active pharmaceutical ingredient (API) and microcrystalline cellulose (Avicel PH-200) as an excipient, the porosity of the granules was systematically influenced by the granulation process parameters (binder/solids ratio, impeller speed and wet massing time). An increase of granule porosity from 7% to 10% and 18% lead to a decrease of the dissolution time t90 from 435 min to 98 min and 37 min, respectively. The combination of time-resolved micro-CT imaging with UV/vis detection of the quantity dissolved made it possible to evaluate the effective diffusion coefficient of the API through the granule structure, and thus establish a quantitative structure­property relationship for dissolution. A power-law dependence of the effective diffusivity on porosity (Archie's law) was found to hold.