Pharmaceutical Research Institute, Bristol-Myers Squibb, New Brunswick, New Jersey 08903

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1 A Process Analytical Technology Approach to Near-Infrared Process Control of Pharmaceutical Powder Blending: Part II: Qualitative Near-Infrared Models for Prediction of Blend Homogeneity ARWA S. EL-HAGRASY, 1 MIRIAM DELGADO-LOPEZ, 2 JAMES K. DRENNEN III 2 1 Process Analytical Technology Group, Pharmaceutical Development Center of Excellence, Pharmaceutical Research Institute, Bristol-Myers Squibb, New Brunswick, New Jersey Duquesne University Center for Pharmaceutical Technology, Pittsburgh, Pennsylvania Received 18 March 2005; revised 24 June 2005; accepted 14 July 2005 Published online in Wiley InterScience ( DOI /jps ABSTRACT: The successful implementation of near-infrared spectroscopy (NIRS) in process control of powder blending requires constructing an inclusive spectral database that reflects the anticipated voluntary or involuntary changes in processing conditions, thereby minimizing bias in prediction of blending behavior. In this study, experimental design was utilized as an efficient way of generating blend experiments conducted under varying processing conditions such as humidity, blender speed and component concentration. NIR spectral data, collected from different blending experiments, was used to build qualitative models for prediction of blend homogeneity. Two pattern recognition algorithms: Soft Independent Modeling of Class Analogies (SIMCA) and Principal Component Modified Bootstrap Error-adjusted Single-sample Technique (PC- MBEST) were evaluated for qualitative analysis of NIR blending data. Optimization of NIR models, for the two algorithms, was achieved by proper selection of spectral processing, and training set samples. The models developed were successful in predicting blend homogeneity of independent blend samples under different processing conditions. ß 2005 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 95: , 2006 Keywords: Process Analytical Technology (PAT); near-infrared spectroscopy; mixing; blend uniformity analysis; UV/VIS spectroscopy; multivariate analysis; principal component analysis; SIMCA; global model; nonparametric pattern recognition algorithms INTRODUCTION With the issuance of the Process Analytical Technology (PAT) draft guidance in September 2004, 1 the pharmaceutical industry has a focus on the implementation of new technologies for realtime process control of pharmaceutical unit operations. Such technologies will lead to a better understanding of the underlying relationships Correspondence to: James K. Drennen III (Telephone: ; Fax: ; drennen@duq.edu) Journal of Pharmaceutical Sciences, Vol. 95, (2006) ß 2005 Wiley-Liss, Inc. and the American Pharmacists Association among critical process and/or product variables. The ultimate goal of this initiative is to improve the quality of the final product while enhancing manufacturing efficiency. Manufacturing in a PAT environment will ultimately expedite the regulatory review process and result in enhanced efficiency and flexibility for the pharmaceutical industry. Owing to the technical limitations and difficulties of the current blend sampling technology, routine blend uniformity analysis (BUA) is considered unwarranted, provided adequate process validation and current Good Manufacturing Practice (cgmp) regulations are established. 2 This is 407

2 408 EL-HAGRASY, DELGADO-LOPEZ, AND DRENNEN partly based on the local segregation occurring within pharmaceutical mixtures and the erroneous results that can be generated by the use of a thief probe as a sampling tool. 3 8 Thief probes may not provide consistent and representative blend samples. Furthermore, BUA, using the current technology, can only ensure the adequacy of mixing of a blend at a single point in time. Blends can undergo segregation during subsequent handling processes such as discharging the blender into Figure 1. (A) PC-MBEST correlation coefficient time profiles of low humidity 3% SA blends using single model. (B) PC-MBEST correlation coefficient time profiles of high humidity 3% SA blends using single model.

3 QUALITATIVE NEAR-INFRARED MODELS FOR PREDICTION OF BLEND HOMOGENEITY 409 drums, transporting drums on the manufacturing floor or dumping the contents of the drum into the equipment hopper. Spectroscopic techniques have evolved as more robust, consistent, rapid, and noninvasive blend monitoring techniques compared to traditional methods. Near-infrared spectroscopy (NIRS) is currently the leading method for monitoring of powder blend homogeneity. NIRS can also be implemented as an on-line monitoring tool during the different stages of pharmaceutical product manufacturing, leading to improved operational efficiency and reduced cycle time. NIRS has been used for blend monitoring from thieved powder samples, 9 in the blender, while discharging the blender, 14 and between the hopper and the tablet press. 15 The purpose of this study was to develop a PAT approach for monitoring and characterization of powder blend homogeneity using NIRS. In Part I, characterization of the powder blending process through experimental design was presented in a study of the effect of humidity, active ingredient concentration and blender speed on the end point of mixing and the physical properties of the mixture. 16 Evaluation of NIR spectral data demonstrated the sensitivity of NIRS to changes in physical and chemical properties of the mixtures due to variations in processing conditions. The importance of spectral preprocessing in minimizing spectral variations resulting from changes in processing conditions was also highlighted. The spectral preprocessing is a critical step in the development of a global model for prediction of blend homogeneity of future blend batches. This study will cover the qualitative analysis of spectral data arising from the D-optimal experimental design described in Part I. 16 Two pattern recognition algorithms will be examined: Soft Independent Modeling of Class Analogies (SIMCA) 17 and Principal Component-Modified Error-adjusted Single-sample Technique (PC- MBEST), 18 todeterminetheutilityofthesemethods in NIR process control of powder mixing operations conducted undervariable conditions. The D-optimal experimental design facilitated this goal by providing variations in processing conditions (humidity and blender speed) and active ingredient concentration of blend experiments. Assessment of optimal criteria to be used for selection of a global model will be performed as well. The focus of Part III in the series will be on the evaluation of quantitative algorithms for blend monitoring and characterization of powder mixing kinetics. 19 EXPERIMENTAL SECTION Materials Salicylic acid (Fisher Scientific, Pittsburgh, PA) and Fast-Flo lactose (Foremost, Baraboo, WI) were used as received. Methanol (Fisher Scientific) was OPTIMA grade. Powder Mixing and Data Acquisition A detailed description of D-optimal blend experiments and the procedure employed for NIR spectral data acquisition is presented in Part I of this series. 16 In brief, powder mixtures composed of salicylic acid (SA), at 3%, 7%, and 11% concentrations, and lactose were mixed in an 8 qt. V- Blender (Patterson-Kelly Co., Inc., East Stroudsburg, PA). Multiple optical ports (Edmund Scientific Company, Barrington, NJ) were mounted on the blender for collecting NIR spectra. Mixing was monitored for each blend, stopping periodically to collect NIR spectra using a fiber-optic probe (Foss NIRSystems, Silver Spring, MD) and remove powder samples for reference UV analysis using a sample thief (made in-house). A list of D-optimal blend experiments is summarized in Table 1. The collected NIR spectra were transformed using second derivative pretreatment. Preliminary data evaluation revealed that 2nd derivative was more effective than standard normal variate Table 1. Order D-Optimal Design for s Humidity Experimental Conditions Salicylic Acid Concentration Blender Speed a I 20% 3% 12.8 II 20% 11% 12.8 III 20% 3% 20.3 IV 20% 7% 12.8 V 20% 7% 20.3 VI 20% 11% 20.3 VII 20% 11% 12.8 VIII 60% 3% 20.3 IX 60% 11% 20.3 X 60% 7% 12.8 XI 60% 7% 20.3 XII 60% 7% 20.3 XIII 60% 11% 12.8 XIV 60% 3% 20.3 XV 60% 7% 12.8 XVI 60% 3% 12.8 a Blender speed measured in rpm.

4 410 EL-HAGRASY, DELGADO-LOPEZ, AND DRENNEN (SNV) or multiplicative scatter correction (MSC) in removing spectral differences related to process variation. All spectral manipulation and chemometric algorithms were performed using the following commercial software packages: Matlab (Mathworks, Inc., Natick, MA), Speakeasy Theta (Speakeasy Computing Corp., Chicago, IL), The Unscrambler (Camo, Inc., Oslo, Norway), and Vision (Foss NIRSystems). In the case of the Bootstrap algorithm, proprietary programs written in Speakeasy 1 were used for spectral analysis. Chemometric Algorithms The use of pattern recognition algorithms for the evaluation of blend homogeneity has been reported previously. 9,11,20 22 Two qualitative chemometric algorithms were studied to identify their utility in NIR process monitoring of blend experiments as described below. In each case, a training set was selected to represent a homogeneous mixture, based on the reference method of analysis. The selected training set was used for prediction of blending profiles of the same blend or different blends having the same SA concentration. Table 3. Percent Accepted Spectra by SIMCA of 7% SA Blends using Single Model IV V X XI XII XV Table 2. Percent Accepted Spectra by SIMCA of 3% SA Blends using Single Model I III VIII XIV XVI Table 4. Percent Accepted Spectra by SIMCA of 11% SA Blends using Single Model II VI VII IX XIII

5 QUALITATIVE NEAR-INFRARED MODELS FOR PREDICTION OF BLEND HOMOGENEITY 411 Figure 2. (A) PC-MBEST correlation coefficient time profiles of low humidity 7% SA blends using single model. (B) PC-MBEST correlation coefficient time profiles of high humidity 7% SA blends using single model.

6 412 EL-HAGRASY, DELGADO-LOPEZ, AND DRENNEN Soft Independent Modeling of Class Analogy (SIMCA) SIMCA is a pattern recognition technique where data clustering is based on principal component analysis(pca)ofeachclassinthespectraldataset. 17 Intheblendstudy,aPCmodel generatedforasingle or a combined training set was used to predict the blending profiles of blends of the same SA concentration. The probability of a test sample belonging to the training set is estimated by the F ratio of its residual variance (S 2 p ) and the total variance of the samples within the training class q (S 2 0 )accordingto the following equation F ¼ S2 p 1 ðn q NC 1Þ S 2 0 ð1þ where: N q ¼ number of spectra used in the training set for class q NC ¼ number of PCs used to model class q. The level of significance was set to 5%. The number of significant PC factors necessary to model the data was determined by crossvalidation. Principal Component-Modified Bootstrap Error-Adjusted Single-Sample Technique (PC-MBEST) This is a nonparametric pattern recognition algorithm, where an estimate of the population of training and test samples is obtained. 18 The next step in the PC-MBEST is to calculate cumulative distribution functions (CDFs): theoretical cumulative distribution function (TCDF) and empirical cumulative distribution function (ECDF), for the estimated population of the training set and test set, respectively. A plot of the ECDF versus the TCDF results in a quantile-quantile (QQ) plot. The more similar the spectra of training and test samples, the more linear the QQ plot will be. On the other hand, presence of breaks in the line indicates differences between the two clusters. A 98% confidence limit for the correlation coefficient of the training set QQ plot is calculated. Test sets with correlation coefficient greater than the Figure 3. (A) MBEST correlation coefficient-time profiles of low humidity 11% SA blends using single model.

7 QUALITATIVE NEAR-INFRARED MODELS FOR PREDICTION OF BLEND HOMOGENEITY 413 Table 5. Percent Accepted 11% SA Blends Spectra by SIMCA using Single Low Humidity Training Set II VI a VII a IX XIII A: Full spectrum Figure 3. (Continued ) (B) PC-MBEST correlation coefficient-time profiles of high humidity 11% SA blends using single model. confidence limit are considered similar to the training set. In this approach, PCA of the spectral dataset is performed prior to bootstrap analysis. Only the scores of significant factors are included in the bootstrap replication for estimation of the population of training and test sets. This approach significantly reduces the dimensionality of the spectral data into a defined number of factors. However, it is important to select an appropriate number of training samples that cover all the possible sample variations. The number of replicates used as an estimate of the true population of the training and test sets in all experiments was set to 1000 replicates. The confidence limit of the training set was taken as the median value of fifteen calculations. B: Water bands removed II VI a VII a IX XIII a Outliers removed.

8 414 EL-HAGRASY, DELGADO-LOPEZ, AND DRENNEN RESULTS AND DISCUSSION Local Model for Prediction of Blending Profile and Optimum Mixing A single model generated from NIR spectra of the first UV-determined end point in each blend is used to predict the blending profile of the same blend. SIMCA and PC-MBEST were utilized as pattern recognition techniques to test the unknown blend samples for homogeneity as described below. SIMCA SIMCA is a powerful tool for classification of multivariate datasets. PC models are generated for each class, followed by comparing the characteristics of each class to the training set. An F-statistic of the variance ratio of the testsample and the training set is used to assign the test sample to a particular group. A non significant F ratio indicates that the test sample belongs to the training set while a significant F ratio indicates that the test sample is different from the training set. Table 6. Percent Accepted Spectra by SIMCA of 11% SA Blends using Global Model* II VI VII IX XIII *End point of experiments II, IX, and XIII. Table 7. Percent Accepted Spectra by SIMCA of 3% SA Blends using Global Model I III VIII XIV XVI *End point of Experiments I, III, VIII, and XVI. The percent of NIR spectra accepted at each time point, using a single training set, is summarized in Tables 2 4 for 3%, 7%, and 11% SA blends, respectively. For each blend, its training set represents spectra from a homogeneous mixture as determined by UV analysis. The time of homogeneity selected for each training set is always the first time the blend is deemed homogeneous by the reference method. The shaded cells represent well-mixed blends according to UV analysis. The Roman numeral in the header of each column indicates the experiment number in the D-optimal design shown in Table 1. It is clear that SIMCA predictions improve as the potency of the blend increases from 3% to 7% to 11% SA concentrations. Yet, it should be noted that the UV-determined mixing profile for experiment III in Table 2 is unstable as visualized from the discontinuity in shaded cells. This could be attributed to the development of static charge, at low humidity, that hinders proper mixing and/or sampling of powder. However, SIMCA calculations coincide with the reference method results in the majority of time points for 7% and 11% SA blends.

9 QUALITATIVE NEAR-INFRARED MODELS FOR PREDICTION OF BLEND HOMOGENEITY 415 PC-MBEST In this method, PCA is used for data compression prior to conduction of bootstrap analysis. The resulting spectral matrix will contain only information embedded in the factors selected during PC transformation step. The rationale for discarding remaining factors is that they describe mostly spectral noise. The correlation coefficient-time profiles for 3%, 7%, and 11% SA blends (low and high humidity) are depicted in Figures 1 3 (A and B), respectively. The Roman numeral on each graph indicates the experiment number in the D-optimal design shown in Table 1. The shaded boxes in each figure highlight the time points at which the PC- MBEST-predicted results coincide with the UVdetermined homogeneity period. The predicted blend homogeneity by PC- MBEST agrees with UV data in most of the cases. The optimum results are obtained with 11% SA blends that contain the highest content of SA, the minor component. This could be visualized from the continuity of the shaded boxes over time, indicating more agreement with the reference method. Predictions for 7% SA blends are acceptable except for experiment number XII in Figure 2B, where the blending profile looks unstable and points of agreement with UV data are few and scattered. As the percent SA in the mixture decreases to 3% SA, the prediction capability of the model deteriorates, especially for high humidity blends. Global Model for NIR-Blend Process Control A global model, composed of selected well-mixed NIR blend spectra of different processing conditions, is used to estimate the optimum mixing time for all other blends of the same SA Table 8. Percent Accepted Spectra by SIMCA of 7% SA Blends using Global Model* IV V X XI XII XV *End point of experiments IV, V, X, and XI. Figure 4. (A) PC-MBEST correlation coefficient time profiles of low humidity 3% SA blends using global model. End points of experiment I, III, VIII, and XVI.

10 416 EL-HAGRASY, DELGADO-LOPEZ, AND DRENNEN Figure 4. (Continued ) (B) PC-MBEST correlation coefficient time profiles of high humidity 3% SA blends using global model. End points of experiment I, III, VIII, and XVI. concentration. SIMCA and PC-MBEST are utilized as pattern recognition techniques to test the unknown blend samples for homogeneity as described below. Optimization of Global Model Since 11% SA blends displayed the best prediction results in the single model approach, data from these blends were used for optimization of combined training set selection criteria. Table 5A and B summarize the results of SIMCA classification of 2nd derivative-pretreated spectra of 11% blend experiments using full spectral range ( nm) and after removal of water bands ( and nm), respectively. The training set consists of NIR spectra collected at the first UV-determined end point of experiment II in the D-optimal design. The outliers referred to in the Table s footnote indicate NIR spectra from specific windows that consistently showed the highest intensity for SA or lactose bands throughout the blending process of an experiment, indicating powder sticking to that specific window. This phenomenon was only observed for low humidity blends and is probably due to development of static charge during mixing under such dry conditions. This was verified by scanning the optical ports after the blender was discharged. As seen from Table 5A, the mixing profile based on % accepted NIR spectra of low humidity blends correlated well with reference data. However, using the same training set of experiment II to predict the blending profile of high humidity blend experiments resulted in 0% accepted spectra at all time points. This is likely due to significant differences between spectra of low and high humidity blends, even after spectral pretreatment. Removal of water band regions from the NIR spectra did not improve the prediction capability of the model as seen in Table 5B, which indicates that the effect of humid conditions during blending is not restricted to regions of the water bands. A similar trend is observed when using a training set from a high humidity 11% SA blend to classify NIR spectra of all other 11% blends (data not shown).

11 QUALITATIVE NEAR-INFRARED MODELS FOR PREDICTION OF BLEND HOMOGENEITY 417 In contrast to the results in Table 5, including spectra of homogeneous low and high humidity blends in the training set significantly improves the prediction as seen in Table 6. The predictions obtained using the combined training set were even superior to those obtained using the blend s own training set as seen by comparing results in Table 4. The combined training set constitutes a global model that could be used for homogeneity prediction of future blend samples of the same concentration. This advantage gained with the global model can be attributed to the global model accounting for more sample variability as opposed to the single model approach. Hence, to ensure accurate prediction and reliable process control, a robust global model must account for possible future process variations expected to affect the unit operation under study. Based on the optimization study, similar criteria were used in building a global model for 3% and 7% SA blends using SIMCA. The use of a global model in PC-MBEST was also investigated for 3%, 7%, and 11% blends as described below. SIMCA Based on the optimization study described above, a global model constructed from blends subjected to different processing conditions was used for the prediction of blend homogeneity for all blends of the same concentration. Tables 7 and 8 summarize the results of SIMCA classification of 3% and 7% SA blends, respectively. In both cases, the full NIR spectral range was used. The predictions for 3% SA blends (Table 7) correlate with the results of UV analysis to a greater extent than was observed in Table 2 (single model approach SIMCA classification results for 7% SA blends (Table 8)) also show improvement compared to the results of the single model approach. However, poor predictions are observed for experiment XI, where only one time point is 100% accepted by the model. Nonetheless, classification data from its replicate experiment, XII, coincides better with the reference method homogeneity results. Reasons for rejection of most time points in experiment XI are unknown. Considering the previous phenomena, it appears that the inclusion of additional datasets in the model (from more blend experiments) enhances the sensitivity of end point detection. This is because the global model accounts for spectral variation that is inadequately modeled using the single model approach. Other possibilities for the poor performance of SIMCA and PC-MBEST in the single model approach for certain blends will be addressed in a future publication. PC-MBEST Prediction profiles of 3% SA blends are illustrated in Figure 4A and B for low and high humidity blends, respectively. Improvement in prediction profiles can be observed when comparing the correlation coefficient time plots in Figures 1A and B and 4A and B obtained with single versus global models, respectively. Particularly, high humidity blends, which previously displayed poor prediction profiles, coincide with UV results to a greater extent. Variation of sample attributes in Figure 5. (A) PC-MBEST correlation coefficient time profiles of low humidity 7% SA blends using global model. End points of experiment IV, V, X, and XI.

12 418 EL-HAGRASY, DELGADO-LOPEZ, AND DRENNEN Figure 5. (Continued) (B) PC-MBEST correlation coefficient time profiles of high humidity 7% SA blends using global model. End points of experiment IV, V, X, and XI. Figure 6. Spectral cluster plot of individual training sets constructing the global model of 7% SA blends.

13 QUALITATIVE NEAR-INFRARED MODELS FOR PREDICTION OF BLEND HOMOGENEITY 419 the training set of the global model renders it more capable of predicting blend homogeneity, under different conditions, than in the single model approach. Reasonable prediction profiles are obtained for most 7% SA blends as seen in Figure 5A and B for low and high humidity conditions, respectively. Similar to 3% SA blends, use of a global model improves the prediction for some of the poorly predicted blends relative to single model approach. This could be seen from the prediction results of experiment XII (compare from Fig. 2B, plot XII). However, the prediction profile of experiment IV is less accurate than all others. Only 30% of posthomogeneity points agree with UV data. The bootstrap clusters of NIR spectra constituting the combined training set for 7% SA blends are depicted in Figure 6. Using 1000 replicates, it is clear that spectral clusters of experiments V, X, and XI are overlapping and displaced away from that of experiment IV. Although all four spectral clusters construct the training set, the properties of the three overlapping clusters outweigh the influence of spectra from experiment IV. The distinction of the spectral cluster of experiment IV from the others could be attributed to a difference in the properties of the former. This particular experiment required the longest time (24 min) to achieve homogeneity compared to all other experiments in the D-optimal design. In such a case, more spectra from blends like this one would need to be included in the training set in order to be well represented in the model. As seen before with other algorithms, 11% SA blends display the best prediction profiles (Fig. 7A and B) for low and high humidity conditions, respectively. The relatively higher % active in these blends makes them less sensitive to changes in blend processing conditions compared to 3% or 7% blends. Consequently, the prediction capability of the 11% SA model is superior to that of the other two concentrations. Evaluation of Pattern Recognition Techniques in Qualitative Powder Blend Analysis The limitation in the prediction capability, observed with SIMCA and PC-MBEST techniques in the single model approach for low concentration blends, was resolved when using a global model. The use of multiple batches in constructing the training set was shown to improve the robustness of the model for these techniques. The resulting global model encompassed the Figure 7. (A) PC-MBEST correlation coefficient time profiles of low humidity 11% SA blends using global model. End points of experiment II, IX, and XIII.

14 420 EL-HAGRASY, DELGADO-LOPEZ, AND DRENNEN Figure 7. (Continued) (B) PC-MBEST correlation coefficient time profiles of high humidity 11% SA blends using global model. End points of experiment II, IX, and XIII. possible spectral variations as a result of the process, thereby providing accurate prediction of blend homogeneity. The need for more batches at low blend concentrations was further confirmed when training sets from a minimum of four different blends were required to obtain acceptable results for 3% and 7% SA blends, as opposed to only three blends in case of 11% blends. The mixing end point predicted by NIRS does not always agree with the UV-determined end point. Nonetheless, a common homogeneity period is generally observed between NIRS and UV data. Inconsistency in predictions of the first mixing end point between NIRS and UV could be attributed to l differences in the sample size and sampling location between the two methods. Furthermore, NIR spectral similarity depends on the physical and chemical properties of all ingredients in a sample. In contrast, UV analysis results reflect solely the chemical content of active ingredient(s) in the sample. Such discrepancies could also be the result of sampling error with the thief probe, reflected in the results of the UV analysis. In addition, pattern recognition algorithms rely on probability to define the boundary of the training set. The level of significance used determines how conservative the model will be. The use of a higher significance level indicates a more conservative model with a higher probability of rejecting good samples and vice versa. The decision on the significance level to be used will depend primarily on how critical the process is. Verification of the true mixing end point can be accomplished by analyzing the final product after mixing for different periods of time. While the correlation of blend uniformity with end product quality was not possible with this study, NIRS and imaging have been used to assess the quality of a pharmaceutical blend by evaluating the final dosage form. 23 Five grades of experimental tablets, ranging in quality from well-mixed to unmixed tablets, were obtained by varying the mixing time of the powder blend in each case. Quality of the different tablet grades was readily differentiated by NIRS and imaging. Dissimilarity in the first end point predicted by NIRS and UV should not hinder the utility of the NIRS technique in process quality control. In an industrial setting, termination of the blending process is not likely to take place at the first detection of homogeneity. Some limited degree of continuous homogeneity detection would be needed to ensure a stable blending profile and a final product of high quality. CONCLUSIONS Qualitative pattern recognition algorithms including SIMCA and PC-MBEST were evaluated to determine the utility of these different approaches in controlling the powder blending process. In the single model approach, SIMCA and PC-MBEST provided acceptable results in most cases. The limitation in prediction capability observed for SIMCA and PC-MBEST, for low blend concentrations, was resolved when using the global model approach. The global approach

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