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.

Multi-layer Raman chemical mapping to investigate the effect of API particle size and blending shear rate on API domain sizes in pharmaceutical tablets.

While macromixing (gross uniformity) has received a lot of attention in pharmaceutical powder blending, micromixing (particularly, particle-level aggregation) has been significantly less studied. This study investigated the impact of active pharmaceutical ingredient (API) particle size (D50: 11, 28, and 70 µm) and blending shear rate (low and high) that was caused by tumbling blending (specifically, a V-blender) on micro-mixing. The effect on micro-mixing (API domain sizes) was assessed in direct compression tablets using high-resolution Raman chemical mapping. Analyses of multiple layers within tablets enabled a more reliable understanding of the variability in API domain sizes with respect to the independent variables. The relationship between API domain sizes and the manufactured tablets' content uniformity (CU) was also investigated using near-infrared transmission spectroscopy. Generally, at low shear, as the API particle size decreased, the frequency and size of API agglomerates increased, resulting in poor CU. However, in all cases, API domain sizes drastically reduced at high shear, resulting in an acceptable CU. The results of this work clearly demonstrated the utility of a multi-layer, multi-tablet, and high-resolution Raman chemical mapping as an off-line process analytical technology (PAT) system, to enable quality-by-design driven formulation and process development.

Sampling optimization for blend monitoring of a low dose formulation in a tablet press feed frame using spatially resolved near-infrared spectroscopy.

In-line measurements of low dose blends in the feed frame of a tablet press were performed for API concentration levels as low as 0.10% w/w. The proposed methodology utilizes the advanced sampling capabilities of a Spatially Resolved Near-Infrared (SR-NIR) probe to develop Partial Least-Squares calibration models. The fast acquisition speed of multipoint spectra allowed the evaluation of different numbers of co-adds and feed frame paddle speeds to establish the optimum conditions of data collection to predict low potency blends. The interaction of the feed frame paddles with the SR-NIR probe was captured with high resolution and allowed the implementation of a spectral data selection criterion to remove the effect of the paddles from the calibration and testing process. The method demonstrated accuracy and robustness when predicting drug concentrations across different feed frame paddle speeds.

Multivariate Spectroscopic Method Lifecycle Management as Part of the Quality Management System.

Multivariate model based spectroscopic methods require model maintenance through their lifecycle. A survey conducted by the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) in 2019 showed that regulatory reporting categories for the model related changes can be a hurdle for the routine use of these types of methods. This article introduces industry best practices on multivariate method and model lifecycle management within the Pharmaceutical Quality System. Case studies are provided to demonstrate how the Established Conditions and Post-Approval Change Management Protocol concepts may be leveraged to allow regulatory flexibility for change management and to encourage the use of these techniques for the development and commercialization of pharmaceutical products.

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).

Development of an In-Line Near-Infrared Method for Blend Content Uniformity Assessment in a Tablet Feed Frame.

Process analytical technology (PAT) has shown great potential for in-line tableting process monitoring. The study focuses on the development and validation of an in-line near-infrared (NIR) spectroscopic method for the determination of content uniformity of blends in a tablet feed frame. An in-line NIR method was developed after careful evaluation of the impact of potential experimental factors on the robustness and model accuracy and precision. The NIR method was validated according to the principles outlined in International Conference on Harmonization-Q2 for validation of analytical procedures and was demonstrated to be suitable for monitoring blend content for the formulation under evaluation. Reliable measurements of blend homogeneity rely on representative sampling. To reach the appropriate scale of scrutiny for a unit dose, the study assessed factors that influence the effective sample size measured by NIR. Spectral averaging, integration time, and feed frame paddle wheel speed were found to influence the effective sample size measured by the NIR probe. The effective sampling size was also estimated by comparing the distribution of predicted values with the reference values. The development of a robust, in-line PAT method was facilitated by thorough understanding of the sensitivity of PAT sensors to factors affecting pharmaceutical processes and products.

Analysis of unstable degradation impurities of a benzodiazepine and their quantification without isolation using multiple linear regression.

This manuscript presents a novel methodology for calculating the relative response factors (RRFs) of unstable degradation impurities of molibresib (1). The degradation impurities were observed by HPLC during stress testing and were accompanied by large mass balance deficits. However, the impurities could not be isolated for traditional RRF determination due to their instability. The RRFs of two degradation impurities were determined without isolation by multiple linear regression analysis of HPLC-UV data. The results permitted accurate quantification of the degradants. The benefits and drawbacks of the approach are discussed, including suggested validation acceptance criteria.

Influence of moisture variation on the performance of Raman spectroscopy in quantitative pharmaceutical analyses.

To develop a robust quantitative calibration model for spectroscopy, different sources of variability that are not directly related to the components of interest should be included in the calibration samples; this variability should be similar to that which is anticipated during validation and routine operation. Moisture content of pharmaceutical samples can vary as a function of supplier, storage conditions, geographic origin or seasonal variation. Additionally, some pharmaceutical operations (e. g., wet granulation) cause exposure of excipients and API to water. Although water is a weak Raman scatterer, moisture variability has an indirect effect on analytical model performance. Because many pharmaceutical components have intrinsic fluorescent characteristics (with broad spectral features), moisture variability may cause spectral artifacts in the form of baseline variation associated with fluorescence quenching. This work investigates the deleterious effects of water quenching on quantitative prediction accuracy of a multivariate calibration algorithm for Raman spectroscopy. To demonstrate this, a formulation composed of acetaminophen, lactose, microcrystalline cellulose, HPMC and magnesium stearate was used. Tablets were manufactured using laboratory scale equipment. A full-factorial design was used to vary acetaminophen (5 levels), and excipient ratios (3 levels) to generate tablets for calibration and testing. Tablet moisture variation was introduced by placing samples in different humidity chambers. Significant spectral effects arising from fluorescence were identified in the Raman spectra and due to moisture variation: the fluorescence related spectral variability caused substantial degradation of the prediction performance for API. The work demonstrated that accounting for moisture variation during method development reduced the prediction error of the multivariate prediction model.

Method Development and Validation of an Inline Process Analytical Technology Method for Blend Monitoring in the Tablet Feed Frame Using Raman Spectroscopy.

Inline process analytical technology sensors are the key elements to enable continuous manufacturing. They facilitate real-time monitoring of critical quality attributes of both intermediate materials and finished products. The aim of this study was to demonstrate method development and validation for inline and offline calibration strategies to determine the blend content during tablet compression via Raman spectroscopy. An inline principal component regression model was developed from Raman spectra collected in the feed frame. At the same time, an offline study was conducted over a small amount of the calibration blends using an in-house moving powder setup to simulate the environment of the feed frame. The model developed offline was able to predict the active ingredient content after a bias correction and used only a fraction of the material. The offline method can serve as a simple method to facilitate calibration development when the time and access to the press is limited. The study takes into consideration, the necessary components of method development and offers perspectives on the validation of an inline process analytics method. Method testing and validation was performed for the inline process analytical technology method. The established Raman method was demonstrated as suitable for the determination of bulk assay of the active ingredient in powders inside the feed frame for use during batch and continuous manufacturing processes.

Investigation of the Sensitivity of Transmission Raman Spectroscopy for Polymorph Detection in Pharmaceutical Tablets.

Polymorph detection is critical for ensuring pharmaceutical product quality in drug substances exhibiting polymorphism. Conventional analytical techniques such as X-ray powder diffraction and solid-state nuclear magnetic resonance are utilized primarily for characterizing the presence and identity of specific polymorphs in a sample. These techniques have encountered challenges in analyzing the constitution of polymorphs in the presence of other components commonly found in pharmaceutical dosage forms. Laborious sample preparation procedures are usually required to achieve satisfactory data interpretability. There is a need for alternative techniques capable of probing pharmaceutical dosage forms rapidly and nondestructively, which is dictated by the practical requirements of applications such as quality monitoring on production lines or when quantifying product shelf lifetime. The sensitivity of transmission Raman spectroscopy for detecting polymorphs in final tablet cores was investigated in this work. Carbamazepine was chosen as a model drug, polymorph form III is the commercial form, whereas form I is an undesired polymorph that requires effective detection. The concentration of form I in a direct compression tablet formulation containing 20% w/w of carbamazepine, 74.00% w/w of fillers (mannitol and microcrystalline cellulose), and 6% w/w of croscarmellose sodium, silicon dioxide, and magnesium stearate was estimated using transmission Raman spectroscopy. Quantitative models were generated and optimized using multivariate regression and data preprocessing. Prediction uncertainty was estimated for each validation sample by accounting for all the main variables contributing to the prediction. Multivariate detection limits were calculated based on statistical hypothesis testing. The transmission Raman spectroscopic model had an absolute prediction error of 0.241% w/w for the independent validation set. The method detection limit was estimated at 1.31% w/w. The results demonstrated that transmission Raman spectroscopy is a sensitive tool for polymorphs detection in pharmaceutical tablets.

Effect of Sampling Frequency for Real-Time Tablet Coating Monitoring Using Near Infrared Spectroscopy.

While the sampling of pharmaceutical products typically follows well-defined protocols, the parameterization of spectroscopic methods and their associated sampling frequency is not standard. Whereas, for blending, the sampling frequency is limited by the nature of the process, in other processes, such as tablet film coating, practitioners must determine the best approach to collecting spectral data. The present article studied how sampling practices affected the interpretation of the results provided by a near-infrared spectroscopy method for the monitoring of tablet moisture and coating weight gain during a pan-coating experiment. Several coating runs were monitored with different sampling frequencies (with or without co-adds (also known as sub-samples)) and with spectral averaging corresponding to processing cycles (1 to 15 pan rotations). Beyond integrating the sensor into the equipment, the present work demonstrated that it is necessary to have a good sense of the underlying phenomena that have the potential to affect the quality of the signal. The effects of co-adds and averaging was significant with respect to the quality of the spectral data. However, the type of output obtained from a sampling method dictated the type of information that one can gain on the dynamics of a process. Thus, different sampling frequencies may be needed at different stages of process development.

Method development and validation for pharmaceutical tablets analysis using transmission Raman spectroscopy.

The objective of the study is to demonstrate the development and validation of a transmission Raman spectroscopic method using the ICH-Q2 Guidance as a template. Specifically, Raman spectroscopy was used to determine niacinamide content in tablet cores. A 3-level, 2-factor full factorial design was utilized to generate a partial least-squares model for active pharmaceutical ingredient quantification. Validation of the transmission Raman model was focused on figures of merit from three independent batches manufactured at pilot scale. The resultant model statistics were evaluated along with the linearity, accuracy, precision and robustness assessments. Method specificity was demonstrated by accurate determination of niacinamide in the presence of niacin (an expected related substance). The method was demonstrated as fit for purpose and had the desirable characteristics of very short analysis times (∼2.5s per tablet). The resulting method was used for routine content uniformity analysis of single dosage units in a stability study.

Near-Infrared Spatially Resolved Spectroscopy for Tablet Quality Determination.

Near-infrared (NIR) spectroscopy has become a well-established tool for the characterization of solid oral dosage forms manufacturing processes and finished products. In this work, the utility of a traditional single-point NIR measurement was compared with that of a spatially resolved spectroscopic (SRS) measurement for the determination of tablet assay. Experimental designs were used to create samples that allowed for calibration models to be developed and tested on both instruments. Samples possessing a poor distribution of ingredients (highly heterogeneous) were prepared by under-blending constituents prior to compaction to compare the analytical capabilities of the two NIR methods. The results indicate that SRS can provide spatial information that is usually obtainable only through imaging experiments for the determination of local heterogeneity and detection of abnormal tablets that would not be detected with single-point spectroscopy, thus complementing traditional NIR measurement systems for in-line, and in real-time tablet analysis.

Designed blending for near infrared calibration.

Spectroscopic methods are increasingly used for monitoring pharmaceutical manufacturing unit operations that involve powder handling and processing. With that regard, chemometric models are required to interpret the obtained spectra. There are many ways to prepare artificial powder blend samples used in a chemometric model for predicting the chemical content. Basically, an infinite number of possible concentration levels exist in terms of the individual components. In our study, design of experiments for ternary mixtures was used to establish a suitable number of blend compositions that represents the entire mixture region of interest for a three component blend. Various experimental designs and their effect on the predictive power of a chemometric model for near infrared spectra were investigated. It was determined that a particular choice of experimental design could change the predictive power of a model, even with the same number of calibration experiments.

Improving near-infrared prediction model robustness with support vector machine regression: a pharmaceutical tablet assay example.

Changes in raw materials and process wear and tear can have significant effects on the prediction error of near-infrared calibration models. When the variability that is present during routine manufacturing is not included in the calibration, test, and validation sets, the long-term performance and robustness of the model will be limited. Nonlinearity is a major source of interference. In near-infrared spectroscopy, nonlinearity can arise from light path-length differences that can come from differences in particle size or density. The usefulness of support vector machine (SVM) regression to handle nonlinearity and improve the robustness of calibration models in scenarios where the calibration set did not include all the variability present in test was evaluated. Compared to partial least squares (PLS) regression, SVM regression was less affected by physical (particle size) and chemical (moisture) differences. The linearity of the SVM predicted values was also improved. Nevertheless, although visualization and interpretation tools have been developed to enhance the usability of SVM-based methods, work is yet to be done to provide chemometricians in the pharmaceutical industry with a regression method that can supplement PLS-based methods.

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

The implementation of a blend monitoring and control method based on a process analytical technology such as near infrared spectroscopy requires the selection and optimization of numerous criteria that will affect the monitoring outputs and expected blend end-point. Using a five component formulation, the present article contrasts the modeling strategies and end-point determination of a traditional quantitative method based on the prediction of the blend parameters employing partial least-squares regression with a qualitative strategy based on principal component analysis and Hotelling's T(2) and residual distance to the model, called Prototype. The possibility to monitor and control blend homogeneity with multivariate curve resolution was also assessed. The implementation of the above methods in the presence of designed experiments (with variation of the amount of active ingredient and excipients) and with normal operating condition samples (nominal concentrations of the active ingredient and excipients) was tested. The impact of criteria used to stop the blends (related to precision and/or accuracy) was assessed. Results demonstrated that while all methods showed similarities in their outputs, some approaches were preferred for decision making. The selectivity of regression based methods was also contrasted with the capacity of qualitative methods to determine the homogeneity of the entire formulation.

Effects and detection of raw material variability on the performance of near-infrared calibration models for pharmaceutical products.

The impact of raw material variability on the prediction ability of a near-infrared calibration model was studied. Calibrations, developed from a quaternary mixture design comprising theophylline anhydrous, lactose monohydrate, microcrystalline cellulose, and soluble starch, were challenged by intentional variation of raw material properties. A design with two theophylline physical forms, three lactose particle sizes, and two starch manufacturers was created to test model robustness. Further challenges to the models were accomplished through environmental conditions. Along with full-spectrum partial least squares (PLS) modeling, variable selection by dynamic backward PLS and genetic algorithms was utilized in an effort to mitigate the effects of raw material variability. In addition to evaluating models based on their prediction statistics, prediction residuals were analyzed by analyses of variance and model diagnostics (Hotelling's T(2) and Q residuals). Full-spectrum models were significantly affected by lactose particle size. Models developed by selecting variables gave lower prediction errors and proved to be a good approach to limit the effect of changing raw material characteristics. Hotelling's T(2) and Q residuals provided valuable information that was not detectable when studying only prediction trends. Diagnostic statistics were demonstrated to be critical in the appropriate interpretation of the prediction of quality parameters.

Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications.

Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).

Radial tensile strength prediction of relaxing and relaxed compacts by near-infrared chemical imaging.

The prediction of radial tensile strength (RTS) of relaxing solid dosage forms by near-infrared hyperspectral chemical imaging was studied. Compacts consisting of starch, lactose, and a mixture of four components were created at different compression forces to develop density models. Predicted density distribution parameters were subsequently used to estimate RTS. Chemical images were collected shortly after compression, repeated every 30 min for 2h, and a final image was collected after 2 weeks. A two step process, involving first the prediction of compact density at each pixel (using a partial least squares model) and second the relationship between compact density distributions and RTS was implemented. Among the parameters with a significant relationship with RTS, the median of the distribution of density predictions in an image was identified as a robust parameter. Coefficients of determinations for this prediction ranged from 0.96 to 0.99 were obtained with a maximum error in validation of 0.10 MPa for the four-component formulation compacts. The prediction of RTS of fully relaxed compacts from spectral data collected on relaxing compacts was demonstrated. These results demonstrate the potential to use near-infrared chemical imaging in real-time to predict RTS values of fully relaxed compacts.