Carl A. Anderson

Process characterization of powder blending by near-infrared spectroscopy: Blend end-points and beyond

The purpose of this paper is to utilize near-infrared (NIR) spectroscopy to characterize powder blending in-line. A multivariate model-based approach was used to determine end-point and variability at the end-point of blending processes. Two monitoring positions for NIR spectrometers were evaluated; one was located on the top of the Bin-blender and the other was on the rotation axis. A ternary powder mixture including acetaminophen (APAP, fine and coarse powder), lactose (LAC) and microcrystalline cellulose (MCC, Avicel 101 and 200) was used as a test system. A Plackett-Burman design of experiments (DOE) for different blending parameters and compositions was utilized to compare the robustness of end-point determination between the multivariate model-based algorithm and reference algorithms. The end-point determination algorithm, including root mean square from nominal value (RMSNV) and two-tailed Students t-test, was developed based on PLS predicted concentrations of all three constituents. Mean and standard deviation of RMSNV after end-point were used to characterize blending variability at the end-point. The blending end-point and variability of two sensors were also compared. The multivariate model-based algorithm proved to be more robust on end-point determination compared to the reference algorithms. Blending behavior at the two sensor locations demonstrated a significant difference in terms of end-point and blending variability, indicating the advantage to employ process monitoring via NIR spectroscopy on more than one location on the Bin-blender.

Process analytical technology case study, part III: Calibration monitoring and transfer

This is the third of a series of articles detailing the development of near-infrared spectroscopy methods for solid dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to develop a system for continuous calibration monitoring and formulate an appropriate strategy for calibration transfer. Indcators of high-flux noise (noise factor level) and wave-length uncertainty were developed. These measurements, in combination with Hotelling’s T2 and Q residual, are used to continuously monitor instrument performance and model relevance. Four calibration transfer techniques were compared. Three established techniques, finite impulse response filtering, generalized least squares weighting, and piecewise direct standardization were evaluated. A fourth technique, baseline subtraction, was the most effective for calibration transfer. Using as few as 15 transfer samples, predictive capability of the analytical method was maintained across multiple instruments and major instrument maintenance.

Process analytical technology case study part I: Feasibility studies for quantitative near-infrared method development

This article is the first of a series of articles detailing the development of near-infrared (NIR) methods for solid-dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to qualify the capabilities of instrumentation and sample handling systems, evaluate the potential effect of one source of a process signature on calibration development, and compare the utility of reflection and transmission data collection methods. A database of 572 production-scale sample spectra was used to evaluate the interbatch spectral variability of samples produced under routine manufacturing conditions. A second database of 540 spectra from samples produced under various compression conditions was analyzed to determine the feasibility of pooling spectral data acquired from samples produced at diverse scales. Instrument qualification tests were performed, and appropriate limits for instrument performance were established. To evaluate the repeatability of the sample positioning system, multiple measurements of a single tablet were collected. With the application of appropriate spectral preprocessing techniques, sample repositioning error was found to be insignificant with respect to NIR analyses of product quality attributes. Sample shielding was demonstrated to be unnecessary for transmission analyses. A process signature was identified in the reflection data. Additional tests demonstrated that the process signature was largely orthogonal to spectral variation because of hardness. Principal component analysis of the compression sample set data demonstrated the potential for quantitative model development. For the data sets studied, reflection analysis was demonstrated to be more robust than transmission analysis.

Process analytical technology case study: Part II. Development and validation of quantitative near-infrared calibrations in support of a process analytical technology application for real-time release

This article is the second of a series of articles detailing the development of near-infrared (NIR) methods for solid dosage-form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to demonstrate a method for developing and validating NIR models for the analysis of active pharmaceutical ingredient (API) content and hardness of a solid dosage form. Robustness and cross-validation testing were used to optimize the API content and hardness models. For the API content calibration, the optimal model was determined as multiplicative scatter correction with Savitsky-Golay first-derivative preprocessing followed by partial least-squares (PLS) regression including 4 latent variables. API content calibration achieved root mean squared error (RMSE) and root mean square error of cross validation (RMSECV) of 1.48 and 1.80 mg, respectively. PLS regression and baseline-fit calibration models were compared for the prediction of tablet hardness. Based on robustness testing, PLS regression was selected for the final hardness model, with RMSE and RMSECV of 8.1 and 8.8 N, respectively. Validation testing indicated that API content and hardness of production-scale tablets is predicted with root mean square error of prediction of 1.04 mg and 8.5 N, respectively. Explicit robustness testing for high-flux noise and wavelength uncertainty demonstrated the robustness of the API concentration calibration model with respect to normal instrument operating conditions.

A Near-Infrared Spectroscopic Investigation of Relative Density and Crushing Strength in Four-Component Compacts

Near-infrared spectroscopy (NIRS) is commonly employed for the analysis of chemical and physical attributes of intact pharmaceutical compacts. Specifically, NIRS has proven useful in the nondestructive measurement of tablet hardness or crushing strength. Near-infrared (NIR) reflectance and transmittance spectra were acquired for 174 13-mm compacts, which were produced according to a four-constituent mixture design (29 points) composed of anhydrous theophylline, lactose monohydrate, microcrystalline cellulose, and soluble starch. Six compacts were produced for each design point by compacting at multiple pressures. Physical testing and regression analyses were used to model the effect of variation in relative density (and crushing strength) on NIR spectra. Chemometric analyses demonstrated that the overall spectral variance was strongly influenced by anhydrous theophylline as a result of the experimental design and the components spectroscopic signature. The calibration for crushing strength was more linear than the relative density model, although accuracy was poorer in comparison to the density model due to imprecision of the reference measurements. Based on the consideration of reflectance and transmittance measurements, a revised rationalization for NIR sensitivity to compact hardness is presented.

Determination of figures of merit for near-infrared and raman spectrometry by net analyte signal analysis for a 4-component solid dosage system

Process analytical technology has elevated the role of sensors in pharmaceutical manufacturing. Often the ideal technology must be selected from many suitable candidates based on limited data. Net analyte signal (NAS) theory provides an effective platform for method characterization based on multivariate figures of merit (FOM). The objective of this work was to demonstrate that these tools can be used to characterize the performance of 2 dissimilar analyzers based on different underlying spectroscopic principles for the analysis of pharmaceutical compacts. A fully balanced, 4-constituent mixture design composed of anhydrous theophylline, lactose monohydrate, microcrystalline cellulose, and starch was generated; it consisted of 29 design points. Six 13-mm tablets were produced from each mixture at 5 compaction levels and were analyzed by near-infrared and Raman spectroscopy. Partial least squares regression and NAS analyses were performed for each component, which allowed for the computation of FOM. Based on the calibration error statistics, both instruments were capable of accurately modeling all constituents. The results of this work indicate that these statistical tools are a suitable platform for comparing dissimilar analyzers and illustrate the complexity of technology selection.

Pharmaceutical Applications of Separation of Absorption and Scattering in Near-Infrared Spectroscopy (NIRS)

The number of near-infrared (NIR) spectroscopic applications in the pharmaceutical sciences has grown significantly in the last decade. Despite its widespread application, the fundamental interaction between NIR radiation and pharmaceutical materials is often not mechanistically well understood. Separation of absorption and scattering in near-infrared spectroscopy (NIRS) is intended to extract absorption and scattering spectra (i.e., absorption and reduced scattering coefficients) from reflectance/transmittance NIR measurements. The purpose of the separation is twofold: (1) to enhance the understanding of the individual roles played by absorption and scattering in NIRS and (2) to apply the separated absorption and scattering spectra for practical spectroscopic analyses. This review paper surveys the multiple techniques reported to date on the separation of NIR absorption and scattering within pharmaceutical applications, focusing on the instrumentations, mathematical approaches used to separate absorption and scattering and related pharmaceutical applications. This literature review is expected to enhance the understanding and thereby the utility of NIRS in pharmaceutical science. Further, the measurement and subsequent understanding of the separation of absorption and scattering is expected to increase not only the number of NIRS applications, but also their robustness.

An efficient method-development strategy for quantitative chemical imaging using terahertz pulse spectroscopy

The purpose of our research was to investigate efficient procedures for generating multivariate prediction vectors for quantitative chemical analysis of solid dosage forms using terahertz pulse imaging (TPI) reflection spectroscopy. A set of calibration development and validation tablet samples was created following a ternary mixture of anhydrous theophylline, lactose monohydrate, and microcrystalline cellulose (MCC). Spectral images of one side of each tablet were acquired over the range of 8 cm−1 to 60 cm−1. Calibration models were generated by partial least-squares (PLS) type II regression of the TPI spectra and by generating a pure-component projection (PCP) basis set using net analyte signal (NAS) processing. Following generation of the calibration vectors, the performance of both methods at predicting the concentration of theophylline, lactose, and MCC was compared using the validation spectra and by generating chemical images from samples with known composition patterns. Sensitivity was observed for the PLS calibration over the range of all constituents for both the calibration and the validation datasets; however, some of the calibration statistics indicate that PLS overfits the spectra. Multicomponent prediction images verified the spatial and composition fidelity of the system. The NAS-PCP calibration procedure yielded accurate linear predictions of theophylline and lactose, whereas the results for MCC prediction were poor. The poor sensitivity for MCC is assumed to be related to the relative lack of phonon absorption bands, which concurs with the characterization of MCC as being semi-crystalline. The results of this study demonstrate the use of TPI reflection spectroscopy and efficient NAS-PCP for the quantitative analysis of crystalline pharmaceutical materials.

Efficient spectroscopic calibration using net analyte signal and pure component projection methods

In the wake of FDAs finalisation of the process analytical technology guidance to industry, the application of near infrared (NIR) spectroscopy for quality analysis in pharmaceutical manufacturing has continued to grow. The required level of variation needed to develop a NIR method often exceeds that observed in a well-controlled pharmaceutical production process. This insufficiency can be addressed by developing non-production samples to introduce range, but at high cost in labour and complexity. The recently-introduced pure-component projection (PCP) method utilises the information in the spectral characteristics of pure sample constituents to reduce NIR spectra to a univariate signal, thereby mitigating the need for non-production samples. The PCP method is compared to net analyte signal (NAS) processing and PLS regression calibration when relatively little calibration data are available. The predictive performance of all algorithms was observed to be similar, although NAS and PCP have advantages in selecting the optimal number of latent variables for calibration. PCP holds a definite advantage as the only algorithm capable of producing a sensitive, linear regression coefficient vector without chemical reference data or non-production samples.

Online Monitoring of Pharmaceutical Materials Using Multiple NIR Sensors—Part I: Blend Homogeneity

IntroductionThe present article discusses the implementation of a semi-automated blend homogeneity control system by two near-infrared spectrometers.MethodsA statistic was introduced to combine blend trends output by individual instruments based on the root mean squared error from the nominal value calculation. The necessity to monitor homogeneity at more than one location of a V-blender is highlighted and the impact of sensor and model differences on blend trends was evaluated. Using two different formulations, classical least-squares based models were developed to monitor blending. Calibration transfer between the two sensors was demonstrated as a useful approach when more than one sensor is used. Several classical transfer methods were implemented (optical, post-regression correction, and orthogonalization based) to balance the two sensors.Results and ConclusionResults showed that the use of only one calibration model, transferred to all units monitoring the process was highly beneficial to achieving consistent results. Specifically, standardization methods targeting instrument differences were demonstrated to be the most successful. However, results showed that the optimization of a given transfer method was formulation-dependent.