Determination of figures of merit for near-infrared and raman spectrometry by net analyte signal analysis for a 4-component solid dosage system (original) (raw)
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Journal of Pharmaceutical Innovation, 2007
Near-infrared (NIR) spectroscopy is an important process analytical technology (PAT) tool for rapid characterization of pharmaceutical tablet quality. The time and expense required for calibration development has been a detriment to implementation of PAT sensors. While methods based on generalized least-squares and net analyte signal pure-component projection (PCP) have been demonstrated to be useful tools for efficient spectroscopic calibration, PCP methods are relatively difficult to deploy and maintain in industrial settings. Synthetic calibration based on augmentation of parallel-testing data with artificial interference spectra generated in silico is introduced as a method to achieve efficient NIR calibration using off-theshelf chemometric algorithms. A method for estimating a slope correction factor using parallel test data is shown. The results of this work demonstrate that, by using efficient calibration methods, accurate quantitative NIR calibration models for characterization of drug tablet quality can be created using only pure-component spectra and productionscale tablet samples.
Journal of pharmaceutical and biomedical analysis, 2015
Since near infrared spectroscopy (NIRS) was introduced to the pharmaceutical industry, efforts have been spent to leverage the power of chemometrics to extract out the best possible signal to correlate with the analyte of the interest. In contrast, only a few studies addressed the potential impact of instrument parameters, such as resolution and co-adds (i.e., the number of averaged replicate spectra), on the method performance of error statistics. In this study, a holistic approach was used to evaluate the effect of the instrument parameters of a FT-NIR spectrometer on the performance of a content uniformity method with respect to a list of figures of merit. The figures of merit included error statistics, signal-to-noise ratio (S/N), sensitivity, analytical sensitivity, effective resolution, selectivity, limit of detection (LOD), and noise. A Bruker MPA FT-NIR spectrometer was used for the investigation of an experimental design in terms of resolution (4 cm(-1) and 32 cm(-1)) and c...
International journal of pharmaceutics, 2005
This research note demonstrates the simultaneous quantitation of a pharmaceutical active ingredient and three excipients in a simulated powder blend containing acetaminophen, Prosolv and Crospovidone. An experimental design approach was used in generating a 5-level (%, w/w) calibration sample set that included 125 samples. The samples were prepared by weighing suitable amount of powders into separate 20-mL scintillation vials and were mixed manually. Partial least squares (PLS) regression was used in calibration model development. The models generated accurate results for quantitation of Crospovidone (at 5%, w/w) and magnesium stearate (at 0.5%, w/w). Further testing of the models demonstrated that the 2-level models were as effective as the 5-level ones, which reduced the calibration sample number to 50. The models had a small bias for quantitation of acetaminophen (at 30%, w/w) and Prosolv (at 64.5%, w/w) in the blend. The implication of the bias is discussed.
Journal of Pharmaceutical Sciences, 2009
Quantification analysis with near-infrared (NIR) spectroscopy typically requires utilizing chemometric techniques, such as partial least squares (PLS) method, to achieve the desired selectivity. This article points out a major limitation of these statistical-based calibration methods. The limitation is that the techniques suffer from the potential for chance correlation. In this article, the impact of chance correlation on the robustness of PLS model was illustrated via a pharmaceutical application with NIR to the content uniformity determination of tablets. The procedure involves evaluating the PLS models generated with two sets of calibration tablets incorporated with distinct degree of concentration correlation between the active pharmaceutical ingredient (API) and excipients. The selectivity and robustness of the two models were examined by using a series of data sets associated with placebo tablets and tablets incorporated with variations from excipient content, hardness and particle size. The result clearly revealed that the strong correlation observed in the PLS model created by the correlated design was not solely based on the API information, and there was an intrinsic difference in the variances described by the two calibration models. Diagnostic tools that enable the characterization of the chemical selectivity of the calibration model were also proposed for pharmaceutical quantitative analysis.
Journal of Pharmaceutical Sciences, 2012
This article proposes a new sample selection strategy to simplify the traditional content uniformity (CU) test in early research and development (R&D) with improved statistical confidence. This strategy originated from the prescreening of a large amount of tablets by a near-infrared spectroscopy (NIRS)-based high-volume tablet tester to the selection of extreme tablets with highest, medium, and lowest content of active pharmaceutical ingredient (API) for further high-performance liquid chromatography (HPLC) test. The NIRS-based high-volume tablet tester was equipped with an internally developed and integrated automated bagging and labeling system, allowing the traceability of every individual tablet by its measured physical and chemical signatures. A qualitative NIR model was used to translate spectral information to a concentration-related metric, that is scores, which allowed the selection of those extreme tablets. This sample selection strategy of extreme tablets was shown to provide equivalent representation of CU in the process compared with the traditional CU test using a large number of random samples. Because it only requires reference tests on three extreme samples per stratified location, the time-and labor-saving nature of this strategy is advantageous for CU test in early R&D. The extreme sampling approach is also shown to outperform random sampling with respect to statistical confidence for representing the process variation. In addition, a chemometric approach, which utilizes only pure component raw materials to develop an NIRS model sensitive to API concentration, is discussed with the advantage that it does not require tablets at multiple API levels. Prospective applications of this sample selection strategy are also addressed.
Journal of Pharmaceutical Sciences, 2008
The content uniformity of low dose products is a major concern in the development of pharmaceutical formulations. Near infrared spectroscopy may be used to support the design and optimization of potent drug manufacturing processes through the analysis of blends and tablets in a relatively short time. A strategy for the selection of concentration ranges in the development of multivariate calibration is presented, evaluating the detection and quantitation limits of the obtained multivariate models. The strategy has been applied to the determination of an active principle in pharmaceutical tablets of low concentration (0-5%, w/w), using Fourier Transform Near Infrared (FT-NIR) transmission spectroscopy. The quantitation and detection limits decreased as the upper concentration level of the calibration models was reduced. The results obtained show that the selection of concentration ranges is a critical aspect during model design. The selection of wide concentration ranges with high levels is not recommended for the determination of analytes at minor levels (<1%, w/w), even when the concentration of interest is within the range of the model. ß
International Journal of Pharmaceutics, 2014
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.
Journal of Pharmaceutical Sciences, 2006
Near infrared spectroscopy (NIRS) is a nondestructive analytical technique that enables simultaneous measurements of chemical composition (viz. the content in active pharmaceutical ingredient, API) and various physical properties (viz. tablet hardness and dissolution profile) in pharmaceutical tablets. In this work, partial least squares (PLS) calibration models and discriminant partial least squares (DPLS) classification models were constructed by using calibration sets consisting of laboratory samples alone. The laboratory samples were mixtures of the API and excipients that were pressed into tablets. API content, tablet hardness, and dissolution measurements of intact tablets were made by using three different calibration models that are fast—results can be obtained within a few seconds—, simple and robust—they involve minimal analyst intervention—, and clean—they use no toxic reagent and produce no toxic waste. Based on the results, the proposed NIR method is an effective alternative to current reference methods for the intended purpose. The advantages provided by NIR spectroscopy in this context confirm its potential for inclusion in process analytical technologies in the pharmaceutical industry. © 2006 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 95:2137–2144, 2006
AAPS PharmSciTech, 2005
This article is the first of a series of articles detailing the development of near-infrared (NIR) methods for soliddosage 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.
Pharmaceutical Applications of Chemometric Techniques
ISRN Analytical Chemistry, 2013
Chemometrics involves application of various statistical methods for drawing vital information from various manufacturing-related processes. Multiway chemometric models like parallel factor analysis (PARAFAC), Tucker-3, N-partial least square (N-PLS), and bilinear models like principle component regression (PCR) and partial least squares (PLS) have been discussed in the paper. Chemometric approaches can be used to analyze the data obtained from various instruments including near infrared (NIR), attenuated total reflectance Fourier transform infrared (ATR-FTIR), high-performance liquid chromatography (HPLC), and terahertz pulse spectroscopy. The technique has been used in the quality assurance and quality control of pharmaceutical solid dosage forms. Moreover, application of chemometric methods in the evaluation of properties of pharmaceutical powders and tablet parametric tests has also been discussed in the review. It has been suggested as a useful method for the real-time in-proce...