Online Monitoring of Pharmaceutical Materials Using Multiple NIR SensorsPart I: Blend Homogeneity (original) (raw)
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Journal of Pharmaceutical Innovation, 2013
Introduction The present article discusses the implementation of a semi-automated blend homogeneity control system by two near-infrared spectrometers. Methods A 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.
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.
Towards a 360º View of Blend Uniformity
Blend uniformity is an important challenge in pharmaceutical production. Blend uniformity is important in terms of the chemical distribution of the drug, and also in terms of the physical properties of a pharmaceutical formulation. New analytical methods are necessary to evaluate both the chemical and physical properties of pharmaceutical formulations. This article describes preliminary efforts to increase process knowledge for blending processes. The use of near infrared (NIR) spectroscopy is described to evaluate the effectiveness of blending processes for low dose formulations. This article also describes the use of in-line NIR spectroscopy where a spectrum is obtained for every revolution of a V-blender. In-line NIR spectroscopy permits real-time monitoring of blending processes, and provides information that is not available with current thief based sampling schemes.
European Journal of Pharmaceutics and Biopharmaceutics, 2013
The aim of this study was to develop a quantitative Near-Infrared (NIR) method which monitors the homogeneity of a pharmaceutical formulation coming out of a continuous blender. For this purpose, a NIR diode array spectrometer with fast data acquisition was selected. Additionally, the dynamic aspects of a continuous blending process were studied; the results showed a well-defined cluster for the steady state, and the paths for the start-up and emptying stages were clearly identified. The end point of the start-up phase was detected by moving block of standard deviation, relative standard deviation, and principal component analysis.
Pharmaceutical Blend uniformity Near-infrared Partial least-squares Off-line calibration
A multivariate calibration approach using near-infrared (NIR) spectroscopy for determining blend uniformity end-point of a pharmaceutical solid dosage form containing 29.4% (w/w) drug load with three major excipients (crospovidone, lactose, and microcrystalline cellulose) is presented. A set of 21 off-line, static calibration samples were used to develop a multivariate partial least-squares (PLS) calibration model for on-line predictions of the API content during the blending process. The concentrations of the API and the three major excipients were varied randomly to minimize correlations between the components. A micro-electrical-mechanical-system (MEMS) based NIR spectrometer was used for this study. To minimize spectral differences between the static and dynamic measurement modes, the acquired NIR spectra were preprocessed using standard normal variate (SNV) followed by second derivative Savitsky-Golay using 21 points. The performance of the off-line PLS calibration model were evaluated in real-time on 67 production scale (750 L bin size) blend experiments conducted over 3 years. The real-time API-NIR (%) predictions of all batches ranged from 93.7% to 104.8% with standard deviation ranging from 0.5% to 1.8%. These results showed the attainment of blend homogeneity and were confirmed with content uniformity by HPLC of respective manufactured tablets values ranging from 95.4% to 101.3% with standard deviation ranging from 0.5% to 2.1%. Furthermore, the performance of the PLS calibration model was evaluated against off-target batches manufactured with high and low amounts of water during the granulation phase of production. This approach affects the particle size and hence blending. All the off-target batches exhibited API-NIR (%) predictions of 94.6% to 103.5% with standard deviation ranging from 0.7% to 1.9%. Using off-target data, a systematic approach was developed to determine blend uniformity endpoint. This was confirmed with 3 production scale batches whereby the blend uniformity end-point was determined using the PLS calibration model. Subsequently, the uniformity was also ascertained with conventional thief sampling followed by HPLC analysis and content uniformity by HPLC of the manufactured tablets.
Powder Technology, 2013
In this paper, a multi-point fiber optic based NIR system was implemented to monitor API concentration at the discharge of a continuous mixer. The sample size being interrogated by NIR was determined by measuring the velocity of the powder on the chute, which allowed selection of the number of scans to be averaged. A methodology was developed that allows quantification of the error associated with the in-line measurements. Comparison of in-line and off-line measurements was made at equivalent sample sizes. Mathematical model fitting for the in-line as well as off-line data revealed that the contribution of the analytical method error was negligible. A baseline RSD of 0.02 in the in-line measurements persists because of the inherent non-uniform nature of powder material. These results are useful towards achieving Real-Time-Release (RTR) of continuously manufactured drug product.
International Journal of Pharmaceutics, 2009
The objective of this study was to develop an integrated multivariate approach to quantify the constituent concentrations of both drug and excipients of powder blends. A mixture design was created to include 26 powder formulations consisting of ibuprofen as the model drug and three excipients (HPMC, MCC, and Eudragit L100-55). The mixer was stopped at various time points to enable nearinfrared (NIR) scan of the powder mixture and sampling for UV assay. Partial least square (PLS), principal component regression (PCR), and multiple linear regression (MLR) models were established to link the formulation concentrations with the Savitzky-Golay 1st derivative NIR spectral data at various characteristic wavelengths of each component. PLS models based on the NIR data and UV data were calibrated and validated. They predicted the main components' concentrations well in the powder blends, although prediction errors were larger for minor components. As expected from the completerandom-mixture (CRM) model, the measurement uncertainties were higher for minor components in the powder formulations. The prediction performance differences between the NIR model and UV model were explained in the context of scale of scrutiny and model applicability. The importance of understanding excipient variability in powder blending and its implication for blending homogeneity assessment is highlighted.