Modeling strategies for pharmaceutical blend monitoring and end-point determination by near-infrared spectroscopy

Development of a Near-Infrared Spectroscopy (NIRS)-Based Characterization Approach for Inherent Powder Blend Heterogeneity in Direct Compression Formulations

With the advent of continuous direct compression (CDC) process, it becomes increasingly desirable to characterize inherent powder blend heterogeneity at a small batch scale for a robust and CDC-amenable formulation. To accomplish this goal, a near infrared spectroscopy (NIRS)-based characterization approach was developed and implemented on multiple direct compression (DC) blends in this study, with the intended purpose of complementing existing formulation development tools and enabling to build an early CMC data package for late-phased process analytical technology (PAT) method development. Three fumaric acid DC blends, designed to harbor varied degrees of inherent blend heterogeneity, were employed. Near infrared spectral data were collected on a kg-scale batch blender via both time- and angle-based triggering modes. The time-triggered data were used to investigate the blending heterogeneity with respect to rotation angles, while the angle-triggered data were used to provide blending variability characterization and compare against off-line HPLC-based results. The time-triggered data revealed that the greatest blend variability was observed between revolutions, while the blending variability within a single revolution stayed relatively low with respect to rotation angles. This confirmed earlier literature findings that the bottom layer of powder blends tends to move with the blender within each revolution, and the most intense powder mixing takes place across revolutions. This also indicates the use of blending speed and the number of co-adds are not able to increase sampling volume to improve signal-to-noise ratio under a tumble-bin blender as what were typically done in a feedframe application. The angle-triggered data showed that there is a consistent trend between NIRS and HPLC-based methods on characterizing blend heterogeneity across the blends at a given sample size. This study contributes to establishing NIRS as a potential characterization approach for inherent powder blend heterogeneity for early R&D. It also highlights the promise of continuous characterization of inherent powder blend heterogeneity from gram scale to mini-batch CDC scale.

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.

Development of Inline Near-Infrared Spectroscopy Method for Real-Time Monitoring of Blend Uniformity of Direct Compression and Granulation-Based Products at Commercial Scales

Blending is a critical intermediate unit operation for all solid oral formulations. For blend uniformity testing, API content in the blend must be quantified precisely. A detailed study was conducted to demonstrate the suitability of inline NIR (near-infrared) spectroscopy for blend uniformity testing of two solid oral formulations: existing direct compression (DC) product with a multistep blending process and granulation-based product with API granules. Both qualitative and quantitative methods were developed at a laboratory scale using statistical moving block standard deviation (MBSD) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. The qualitative MBSD method demonstrated that there was no need for multiple steps for the existing DC product. Hence, a simplified single-step process was developed for blending. Quantitative PLS models for blending processes of both the products were developed, validated, and successfully implemented at a commercial scale for the real-time release of blends. Results obtained from the validated model were in good agreement with the current method of sampling and chromatography.

SWiVIA - Sliding window variographic image analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral imaging

Controlling blending processes of solid material using advanced real-time sensing technologies tools is crucial to guarantee the quality attributes of manufactured products from diverse industries. The use of process analytical technology (PAT) tools based on chemical imaging systems are useful to assess heterogeneity information during mixing processes. Recently, a powerful procedure for heterogeneity assessment based on the combination of off-line acquired chemical images and variographic analysis has been proposed to provide specific heterogeneity indices related to global and distributional heterogeneity. This work proposes a novel PAT tool combining in situ chemical imaging and variogram-derived quantitative heterogeneity indices for the real-time monitoring of blending processes. The proposed method, so called sliding window variographic image analysis (SWiVIA), derives heterogeneity indices in real-time associated with a sliding image window that moves continuously until the full blending time interval is covered. The SWiVIA method is thoroughly assessed paying attention at the effect of relevant factors for continuous blending monitoring and heterogeneity description, such as the scale of scrutiny needed for heterogeneity definition or the blending period defined to set the sliding image window. SWiVIA is tested on blending runs of pharmaceutical and food products monitored with an in situ near-infrared chemical imaging system. The results obtained help to detect abnormal mixing phenomena and can be the basis to establish blending process control indicators in the future. SWiVIA is adapted to study blending behaviors of the bulk product or compound-specific blending evolutions.

Design of Heterogeneity Indices for Blending Quality Assessment Based on Hyperspectral Images and Variographic Analysis

Heterogeneity characterization is crucial to define the quality of end products and to describe the evolution of processes that involve blending of compounds. The heterogeneity concept describes both the diversity of physicochemical characteristics of sample fragments (constitutional heterogeneity) and the diversity of spatial distribution of the materials/compounds in the sample (distributional heterogeneity, DH). Hyperspectral images (HSIs) are unique analytical measurements that provide physicochemical and spatial information on samples and, hence, are ideal to perform heterogeneity studies. This work proposes a new methodology combining HSI and variographic analysis to obtain a good qualitative and quantitative description of global heterogeneity (GH) and DH for samples and blending processes. An initial step of image unmixing provides a set of pure distribution maps of the blending constituents as a function of time that allows a qualitative visualization of the heterogeneity variation along the blending process. These maps are used as seeding information for a subsequent variographic analysis that furnishes the newly designed quantitative global heterogeneity index (GHI) and distributional uniformity index (DUI), related to GH and DH indices, respectively. GHI and DUI indices can be described at a sample level and per component within the sample. GHI and DUI curves of blending processes are easily interpretable and adaptable for blending monitoring and control and provide invaluable information to understand the sources of the abnormal blending behavior.

Characterization of NIR interfaces for the feeding and in-line monitoring of a continuous granulation process

This work describes the characterization of three NIR interfaces intended to monitor a continuous granulation process. Two interfaces (i.e. a barrel interface and a rotating paddle interface) were evaluated to monitor the API concentration at the entrance of the granulator, and a third interface (i.e. an outlet interface), was evaluated to examine the quality of the resulting outlet granules. The barrel interface provided an assessment of the API concentration during the feeding process by scanning the material conveyed by the screws of the loss-in-weight feeder. The rotating paddle interface analyzed discrete amounts of powder upon exiting the feeder via the accumulation of material on the paddles. Partial Least Squares (PLS) calibration models were developed using the same powder blends for the two inlet interfaces and using the outlet granules for the outlet interface. Five independent batches were used to evaluate the prediction performance of each inlet calibration model. The outlet interface produced the lowest error of prediction due to the homogeneity of the granules. The barrel interface produced lower errors of prediction than the rotating paddle interface. However, powder density affected only the barrel interface, producing deviations in the predicted values. Therefore, powder density is a factor that should be considered in the calibration sample design for spectroscopic measurements when using this type of interface. A variographic analysis demonstrated that the continuous 1-dimensional motion in the barrel and outlet interfaces produced representative measurements of each batch during calibration and test experiments, generating a low minimum practical error (MPE).

A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing

The Quality-by-Design (QbD) guidance issued by the US Food and Drug Administration (FDA) has catalyzed the modernization of pharmaceutical manufacturing practices including the adoption of continuous manufacturing. Active process control was highlighted recently as a means to improve the QbD implementation. This advance has since been evolving into the concept of Quality-by-Control (QbC). In this study, the concept of QbC is discussed, including a definition of QbC, a review of the recent developments towards the QbC, and a perspective on the challenges of QbC implementation in continuous manufacturing. The QbC concept is demonstrated using a rotary tablet press, integrated into a pilot scale continuous direct compaction process. The results conclusively showed that active process control, based on product and process knowledge and advanced model-based techniques, including data reconciliation, model predictive control (MPC), and risk analysis, is indispensable to comprehensive QbC implementation, and ensures robustness and efficiency.

Simultaneous Determination of Several Fiber Contents in Blended Fabrics by Near-Infrared Spectroscopy and Multivariate Calibration

The qualitative and quantitative determination of the components of textile fibers takes an important position in quality control. A fast and nondestructive method of simultaneously analyzing four fiber components in blended fabrics was studied by near-infrared (NIR) spectroscopy combined with multivariate calibration. Two sample sets including 39 and 25 samples were designed by simplex mixture lattice design methods and used for experiment. Four components include wool, polyester, polyacrylonitrile, and nylon and their mixture is one of the most popular formulas of textiles. Uninformative variable elimination-partial least squares (UVEPLS) and the full-spectrum partial least squares (PLS) were used as the tool. On the test set, the mean standard error of prediction (SEP) and the mean ratio of the standard deviation of the response variable and SEP (RPD) of the full-spectrum PLS model and UVEPLS model were 0.38, 0.32 and 7.6, 8.3, respectively. This result reveals that the UVEPLS can construct local models with acceptable and better performance than the full-spectrum PLS. It indicates that this method is valuable for nondestructive analysis in the field of wool content detection since it can avoid time-consuming, costly, and laborious wet chemical analysis.

Fast discrimination of the geographical origins of notoginseng by near-infrared spectroscopy and chemometrics

Notoginseng is a type of highly valued Traditional Chinese medicine (TCM) due to its hemostatic and cardiovascular functions. Notoginseng of Yunnan in China usually commands a premium price and is often the subject of fraudulent practices. The feasibility of combining near-infrared (NIR) spectroscopy with chemometrics was investigated to discriminate notoginseng of different geographical origins. A total of 250 samples of four different provinces in China were collected and divided equally into the training and test sets. Principal component analysis (PCA) was used for observing possible trend of grouping. Two chemometric algorithms including partial least squares-discriminant analysis (PLSDA) and soft independent modeling of class analogy (SIMCA) were used to construct the discriminant models. Standard normal variate (SNV) and first derivative were used for pre-processing spectra. On the independent test set, the PLSDA model outperforms the SIMCA model. When combining both pre-processing methods, the constructed PLSDA model achieved 100% sensitivity and 100% specificity on both the training set and the test set. It indicates that SNV+first derivative pre-processing and PLSDA algorithm can serve as the potential tool of fast discriminating the geographical origins of notoginseng.

Using 3D Printing for Rapid Prototyping of Characterization Tools for Investigating Powder Blend Behavior

There is an increasing need to provide more detailed insight into the behavior of particulate systems. The current powder characterization tools are developed empirically and in many cases, modification of existing equipment is difficult. More flexible tools are needed to provide understanding of complex powder behavior, such as mixing process and segregation phenomenon. An approach based on the fast prototyping of new powder handling geometries and interfacing solutions for process analytical tools is reported. This study utilized 3D printing for rapid prototyping of customized geometries; overall goal was to assess mixing process of powder blends at small-scale with a combination of spectroscopic and mechanical monitoring. As part of the segregation evaluation studies, the flowability of three different paracetamol/filler-blends at different ratios was investigated, inter alia to define the percolation thresholds. Blends with a paracetamol wt% above the percolation threshold were subsequently investigated in relation to their segregation behavior. Rapid prototyping using 3D printing allowed designing two funnels with tailored flow behavior (funnel flow) of model formulations, which could be monitored with an in-line near-infrared (NIR) spectrometer. Calculating the root mean square (RMS) of the scores of the two first principal components of the NIR spectra visualized spectral variation as a function of process time. In a same setup, mechanical properties (basic flow energy) of the powder blend were monitored during blending. Rapid prototyping allowed for fast modification of powder testing geometries and easy interfacing with process analytical tools, opening new possibilities for more detailed powder characterization.

Detecting Blending End-Point Using Mean Squares Successive Difference Test and Near-Infrared Spectroscopy

An algorithm based on mean squares successive difference test applied to near-infrared and principal component analysis scores was developed to monitor and determine the blending profile and to assess the end-point in the statistical stabile phase. Model formulations consisting of an active compound (acetylsalicylic acid), together with microcrystalline cellulose and two grades of calcium carbonate with dramatically different particle shapes, were prepared. The formulation comprising angular-shaped calcium carbonate reached blending end-point slower when compared with the formulation comprising equant-shaped calcium carbonate. Utilizing the ring shear test, this distinction in end-point could be related to the difference in flowability of the formulations. On the basis of the two model formulations, a design of experiments was conducted to characterize the blending process by studying the effect of CaCO3 grades and fill level of the bin on blending end-point. Calcium carbonate grades, fill level, and their interaction were shown to have a significant impact on the blending process.