Monitoring Granulation Drying Using Near-Infrared Spectroscopy

Transcription

1 Monitoring Granulation Drying Using Near-Infrared Spectroscopy A novel probe design that reduces the variation in powder-sample density also allows moisture and solvent levels to be accurately modelled and predicted. Near-infrared (NIR) spectroscopy is an analytical technique based on the absorption measured between the visible and mid-infrared electromagnetic spectral region. Strong fundamental absorption bands of moieties occur in the mid-ir region and frequently require mulls or dilutions to bring the absorbances within the detector s linear range. The overtone absorptions enable direct measurement without sample preparation because the absorption is relatively weak. Economic fibreoptic cables can be used to measure processes that are located far from the analyser by inserting probes into the processing equipment. For more than a decade, NIR spectroscopy and fibreoptic probes have been used for real-time process monitoring. 1,2 The turbulent flow of powder granules, however, poses a challenge for high-shear granulator moisture analysis monitoring. For example, the turbulence constantly and randomly changes the apparent sample density in front of the NIR probe window, making accurate analysis impossible. When the sample density change is brought within an acceptable narrow range, NIR analysis can be performed by various methods that account for density variance as well as moisture variance. This paper discusses a novel probe design to reduce the powdersample density variance so that moisture and solvent levels can be accurately modelled and predicted. Determining a formulation s moisture level is essential for subsequent process capability and product stability. Over-drying wastes energy, ties-up critical facilities, and can damage the formulation because of hydration changes in some active ingredients and excipients. 3,4 Typically, moisture content is monitored off-line with Karl Fischer volumetric titrimetry or at-line with thermogravimetric loss-on-drying (LOD) methodology. 5,6 Robert A. Mattes* is an instrumentation scientist at FOSS NIRSystems, Inc. USA. Rudolf Schroeder is a pharmaceutical scientist at L.B. Bohle, Germany. Vinny Dhopeshwarker is a principal scientist in the technical development group, Robert Kowal is a manager of technical operations and William Randolph is a senior director of technical operations at PSGA, A Division of Ortho- McNeil Pharmaceutical, USA. 41

2 Figure 1 Raw spectra of raw materials Figure 2 Second-derivative spectra of raw materials Figure 3 Raw spectra of drying samples Karl Fischer and LOD analyses usually require the operator to stop the process, use a sample thief to collect a sample, and then analyse the sample while the drying process continues. It is not possible to obtain real-time moisture level trends. In addition, Karl Fischer analysis is costly, time-consuming, and uses chemical reagents that need to be bought and properly disposed. LOD analysis is not moisture specific because all volatiles contribute to the thermogravimetric loss. In addition, monitoring the removal of granulation liquids such as is important for product purity. Typically, level is measured by taking a sample with a sample thief and sending it to the laboratory for high-performance liquid chromatography (HPLC) analysis. HPLC requires long elution times, uses solvents that must be disposed of properly, and requires calibration samples to be run frequently. NIR spectroscopy is a rapid, nondestructive technique that can provide real-time analysis without sample preparation and can store data automatically. NIR spectroscopy is well-suited for the process analytical technology (PAT) initiative proposed by the Food and Drug Administration The process can monitor low levels of residual moisture, alcohol, and other process constituents to yield better process control and quality management. Experimental A process instrument (FOSS NIRSystems, Silver Spring, MD, USA) was set up and connected to a fibreoptic probe with a specially designed tip, which was installed into a singlepot high-shear granulator (VMA 70, L.B. Bohle, Warminster, PA, USA). The probe (FOSS NIRSystems) has an angled face that diverts the powder sample in a quasilaminar flow over the measurement window. This design maintains a constant sample density at the window interface. A 24.0 kg charge of 91% lactose anhydrous and 9% was prepared and loaded into the granulator. The granulator blended the dry mixture to homogeneity for 10 min. Water, and yellow dye (FD&C #6) were sprayed into 42 ISubscribe online at FEBRUARY 2005 PHARMACEUTICAL TECHNOLOGY EUROPE

3 Table 1 LOD samples and NIR predictions. MeOH H 2 O% LOD H 2 O% MeOH% Blend Sample LOD% NIR NIR NIR * * Indicates that the sample did not pass library qualification the mixture and blended. The yellow dye measured in the partsper-billion range and was not a source of interference with NIR measurements. NIR spectra were collected continuously during the blending and granulation operations. Samples for LOD analysis were withdrawn at specific intervals. Each NIR spectrum consisted of 32 co-added sample and reference scans in the nm NIR range. After the moisture and were added and blended, an agglomeration cycle was run for 6 min. The heated jacket was energized, and a vacuum was pulled to approximately 200 mbar to start the drying operation. The drying operation was uniform and gradual over a period of around 43 min with an agitator speed of 200 rpm and a nitrogen purge rate of 1.0 NM 3 /h at standard temperature and pressure. NIR spectra were collected continuously during the entire blending, granulation, and drying operation (approximately two complete scans of 32 co-added scans per minute) resulting in around 150 spectra per batch. Three test batches and three calibration batches were run. All spectral data collection and analyses were performed with Vision software (FOSS NIRSystems). Results and discussion Moisture analysis. Figure 1 shows spectra with no mathematical treatment (raw spectra) of the raw materials used in the granulation. Figure 2 illustrates the secondderivative spectra of the raw materials, which is commonly used in Figure 4 Second-derivative spectra in the analytical region NIR spectroscopy to remove the baseline offset caused by scattering and to enhance absorbance peaks. 11 The second-derivative spectral peaks appear inverted with respect to the raw spectra. 12 Figure 3 shows the samples raw spectra collected during the drying cycle. Water absorbs strongly around the nm and nm NIR range, as seen by the peaks in those regions. Figure 4 shows an enlarged view of the region used to model the samples moisture and content. Because of the second-derivative mathematical treatment, the moisture content increases the downward direction in this region. The LOD results taken for the raw materials and the gravimetric percent added during granulation were used as the initial value for modelling the moisture during the drying cycle. At least four degrees of freedom were modelled in this study: content, content, temperature variance and density variance. Figure 5 shows the NIR-predicted samples versus the values calculated from LOD values. The moisture levels with time were not precisely known because the LOD accounts for moisture and losses. For example, Table 1 shows that blend 4 initially had 1.22% moisture before wet granulation; 0.5% moisture was added during granulation resulting in approximately 0.53% after drying. This method was repeated for blends 5 and 6. Increasing H 2 O

4 NIR-predicted (%) Methanol (%) Figure 5 Calibration set: NIR-predicted values versus those calculated from LOD data in Table 1. R 2 value is and SEC is %. Figure 7 Calibration set: NIR-predicted values versus those calculated from LOD in Table 1. R 2 value is and sec is %. NIR-predicted (%) Theoretical moisture (%) Figure 6 The moisture process trend chart for the granulation and drying operations for blend :09:36 5:16:48 5:24:00 5:31:12 5:38:24 5:45:36 5:52:48 6:00:00 6:07:12 6:14:24 6:21:36 Methanol Theoretical (%) These values and the linearly interpolated intermediate values were applied to the spectra acquired at corresponding times. A threefactor partial least squares (PLS) regression model was developed with the 18 data points in Table 1. The second derivative intensity and the standard normal variate (SNV) 6 for scatter correction over the nm range were used to develop the model with multiple correlation coefficient (R2) value of and a standard error of calibration (SEC) of %. The standard error of cross-validation was , which is not unreasonable for a small sample set. The spectra from the drying cycle used to develop the prediction model were used to develop a library to qualify samples for quantification. This library and the prediction model were then used in a routine analysis function to predict on the average of five consecutive samples. The results are plotted in Figure 6. Some initial samples were not predicted when the average spectrum did not meet library qualification because of high rpm and turbulence during the blending cycle. Although the true values are partly theoretical, the predictions are reasonable even through the granulation cycle (not modelled). Methanol analysis. Figure 4 shows the second-derivative spectra of blend 5 s analytical region as an example of the spectra used for predicting levels in the lactose anhydrous and blend. The NIR-predicted moisture values were subtracted from the LOD values. A model was made from the resulting 17 adjusted values (Table 1). According to the qualification library, one spectrum was an outlier and was not used in the model. As shown in Figure 7, a PLS regression model was developed using the second derivative intensity over the nm range and resulted in an R2 value of , and an SEC of %. The standard error of cross-validation was These samples are not well spaced because of the faster initial removal. The was removed in a quasi-linear function of time. The level, however, was nonlinear, thus leaving less data in the mid-level. To develop a robust model, more data must be taken initially. The use of HPLC as a reference method provides more accurate results. Figure 8 is the process trend chart from routine analysis for the granulation and drying operation. These spectra were analysed in routine analysis, averaging five spectra for each prediction, and tested against a qualification library. The level of some initial samples was not predicted by the routine analysis when the average spectrum did not meet library qualification because the granulator and turbulence had a high rpm during the blending cycle. The level reached a minimum and then increased slightly before reaching the final minimum. This effect was most likely caused by the removal of free, the fracturing of granules, and the release of additional interstitial. Again, although the true values are partly theoretical, the predictions are smooth and reasonable throughout the granulation cycle that was not modelled. Conclusion The in situ NIR spectroscopic method predicted and loss in samples of lactose anhydrous and during the blending, granulation, and drying operations. Although the accuracy is not known, the trended predictions appear smooth and reasonable from known initial and end-point data. The single-pot granulator trial indicates that an NIR process instrument with the novel angled probe can measure a moving sample in turbulent flow when it is placed in the 44 ISubscribe online at FEBRUARY 2005 PHARMACEUTICAL TECHNOLOGY EUROPE

5 sample medium in a manner that provides sufficiently constant sample density. Proper probe placement and design in process equipment is essential for successful implementation of in situ measurements. With HPLC reference data for the and Karl Fischer reference data for the moisture, the accuracy and precision of the model predictions should be improved. It should be possible to model particle size, blend uniformity and temperature with reasonable precision if proper reference data are provided. Acknowledgments The authors wish to thank Karl Norris and Katherine Bakeev for their review and valued comments on this manuscript. References 1. J.B. Callis, D.L. Illman and B.K. Kowalski, Anal. Chem. 59(9), 624A 635A (1987). 2. K.A. Bakeev, Spectroscopy 19(1), (2004). 3. S.M. Maggard, D.E. Root and M. Duell, J. Process Anal. Chem. 7(1) (2002). Figure 8 The process trend chart for the granulation and drying operations for blend 4. Methanol (%) :09:36 5:16:48 5:24:00 5:31:12 5:38:24 5:45:36 5:52:48 6:00:00 6:07:12 6:14:24 6:21:36 Time 4. R.M. Leasure and M.K. Gangwer, Am. Pharm. Rev. 5(1), (2002). 5. E.W. Ciurczak and J.K. Drennen III, Pharmaceutical and Medical Applications of Near-Infrared Spectroscopy (Marcel Dekker, New York, NY, 2002). 6. M.B. Hicks et al., J. Pharm. Sci. 92(3), (2003). 7. M.L. Balboni, Pharm. Technol. 27(10), (2003). 8. FDA Draft Guidance PAT A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance, August H. Forcinio, Spectroscopy 18(9) (2003). 10. R.C. Lyon et al., Am. Pharm. Rev. 6(3), (2003). 11. T.C. O Haver and T. Begley, Anal. Chem. 53(12), (1981). 12. M. and J. Workman, Spectroscopy 18(4), (2003). This article was first published in Pharmaceutical Technology s Process Analytical Supplement, September