Development of a design space and predictive statistical model for capsule filling of low-fill-weight inhalation products.

The objectives of this study were to develop a predictive statistical model for low-fill-weight capsule filling of inhalation products with dosator nozzles via the quality by design (QbD) approach and based on that to create refined models that include quadratic terms for significant parameters. Various controllable process parameters and uncontrolled material attributes of 12 powders were initially screened using a linear model with partial least square (PLS) regression to determine their effect on the critical quality attributes (CQA; fill weight and weight variability). After identifying critical material attributes (CMAs) and critical process parameters (CPPs) that influenced the CQA, model refinement was performed to study if interactions or quadratic terms influence the model. Based on the assessment of the effects of the CPPs and CMAs on fill weight and weight variability for low-fill-weight inhalation products, we developed an excellent linear predictive model for fill weight (R(2 )= 0.96, Q(2 )= 0.96 for powders with good flow properties and R(2 )= 0.94, Q(2 )= 0.93 for cohesive powders) and a model that provides a good approximation of the fill weight variability for each powder group. We validated the model, established a design space for the performance of different types of inhalation grade lactose on low-fill weight capsule filling and successfully used the CMAs and CPPs to predict fill weight of powders that were not included in the development set.

How to measure coating thickness of tablets: Method comparison of optical coherence tomography, near-infrared spectroscopy and weight-, height- and diameter gain.

Film coating of pharmaceutical dosage forms, such as tablets and pellets, can be used to tailor the drug release profile. With that regard, a uniform coating thickness of a single tablet (intra-tablet), all tablets (inter-tablet) and subsequent batches (inter-batch) is crucial. We present a method comparison between in-line (optical coherence tomography and near-infrared spectroscopy) and well-established off-line (height-, weight- and diameter-gain) approaches to determining the coating thickness of tablets. We used single tablets drawn during a commercial coating process. Comparing the low intra- and high inter-tablet coating variability indicated that the tablets had a broad distribution of spray zone passes but at a random tablet orientation. Even at the end of the coating process at a mean coating thickness of about 70 µm, the inter-tablet standard deviation was about 9 µm or 13% relative standard deviation. Determining correlations between the methods identified the factors that contribute to the measurement uncertainty and bias for each method. Ultimately, we aimed to establish that in-line methods match or even surpass the conventional off-line reference methods in terms of accuracy and precision of coating thickness measurement.

Continuous monitoring of API content, API distribution and crushing strength after tableting via near-infrared chemical imaging.

Near-infrared chemical imaging (NIR-CI) with high-speed cameras based on the push-broom acquisition principle is a rapidly-evolving and can be used for a variety of purposes, from classification (and sorting) of products to mapping spatial distribution of materials. The present study examined if NIR-CI is suitable for tablet manufacturing. To that end, the tablets were introduced into the CI system via a flat belt conveyor. A formulation, which consisted of 4wt.%-6wt.% caffeine, 5wt.% crospovidone as a disintegrant, 88wt.%-90wt.% lactose as a filler and 1wt.% magnesium stearate as a lubricator, was tableted at compression forces ranging from 5kN to 30kN. The intra- and inter-tablet homogeneity of caffeine and the tablet's hardness were analyzed via NIR-CI. For the homogeneity evaluation, two methods were applied: standard deviation (SD) and distributional homogeneity index (DHI). The results showed that the SD of caffeine in a single tablet increased with an increase in the caffeine content. This was attributed to natural variations in a binary mixture of caffeine and excipients. Overall, the chosen NIR-CI setup has strong potential to be transferred to the production scale to monitor all tablets in a production stream.