In-Line Multipoint Near-Infrared Spectroscopy for ... - ACS Publications

Jan 28, 2013 - evaluated multipoint NIR spectroscopy for in-line moisture content ... dryer shelf was possible with the multipoint NIR system in use...
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In-Line Multipoint Near-Infrared Spectroscopy for Moisture Content Quantification during Freeze-Drying Ari Kauppinen,*,† Maunu Toiviainen,‡ Ossi Korhonen,† Jaakko Aaltonen,§ Kristiina Jar̈ vinen,† Janne Paaso,∥ Mikko Juuti,‡ and Jarkko Ketolainen† †

School of Pharmacy, Promis Centre, Faculty of Health Sciences, University of Eastern Finland, P.O. Box 1627, FI-70211 Kuopio, Finland ‡ Optical Measurement Technologies, VTT Technical Research Centre of Finland, P.O. Box 1199, FI-70211 Kuopio, Finland § Division of Pharmaceutical Technology, Faculty of Pharmacy, University of Helsinki, P.O. Box 56, FI-00014 Helsinki, Finland ∥ Optical Measurement Technologies, VTT Technical Research Centre of Finland, P.O. Box 1100, FI-90571 Oulu, Finland S Supporting Information *

ABSTRACT: During the past decade, near-infrared (NIR) spectroscopy has been applied for in-line moisture content quantification during a freeze-drying process. However, NIR has been used as a single-vial technique and thus is not representative of the entire batch. This has been considered as one of the main barriers for NIR spectroscopy becoming widely used in process analytical technology (PAT) for freeze-drying. Clearly it would be essential to monitor samples that reliably represent the whole batch. The present study evaluated multipoint NIR spectroscopy for in-line moisture content quantification during a freeze-drying process. Aqueous sucrose solutions were used as model formulations. NIR data was calibrated to predict the moisture content using partial least-squares (PLS) regression with Karl Fischer titration being used as a reference method. PLS calibrations resulted in root-mean-square error of prediction (RMSEP) values lower than 0.13%. Three noncontact, diffuse reflectance NIR probe heads were positioned on the freeze-dryer shelf to measure the moisture content in a noninvasive manner, through the side of the glass vials. The results showed that the detection of unequal sublimation rates within a freezedryer shelf was possible with the multipoint NIR system in use. Furthermore, in-line moisture content quantification was reliable especially toward the end of the process. These findings indicate that the use of multipoint NIR spectroscopy can achieve representative quantification of moisture content and hence a drying end point determination to a desired residual moisture level.

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describing the correlation between residual moisture and storage stability of the API.4,8,9 It has been reported that a high level of residual moisture can cause structural collapse in a freeze-dried product upon storage.2 In addition, water could be bound in freeze-dried product as a hydrate form. Ambient storage conditions can initiate the release of hydrate water via excipient conversion into the anhydrous forms.10 These kinds of undesired events can cause degradation of API, eventually leading to a loss of its biological activity.11−13 As a summary, accurate quantification of the residual moisture of a freeze-dried product is very important for many reasons. In an ideal situation, drying would be homogeneous within the whole sample shelf of the freeze-dryer. In reality, this is not the case. Various effects, such as heterogeneous nucleation of ice and uneven heat transfer within the drying chamber, lead to variations in the residual moisture of the products even within

reeze-drying has an established place as one of the main downstream processes in the manufacturing of biopharmaceuticals. According to the U.S. Food and Drug Administration (FDA), 19% of approved therapeutic biological applications were freeze-dried in 2011.1 Freeze-drying is a batch process in which a solvent, commonly water, is removed from the formulation by sublimation and desorption under low temperature and low pressure. A typical freeze-dried product contains residual moisture at less than 1% (w/v)2,3 yet some active pharmaceutical ingredients (API) demand an intermediate level of residual moisture above 1% in order to achieve sufficient storage stability.4−7 Residual moisture is a major critical quality attribute (CQA) in a freeze-dried product. The moisture content of a product exerts many effects on different manufacturing stages. First, the glass transition temperature (Tg) of the formulation during secondary drying is influenced by the moisture content.2 Thus the moisture content restricts the process conditions (e.g., heating rate and temperature); that is, if it is excessive then undesired collapse or melt-back may occur. Second, there are a large number of published studies © 2013 American Chemical Society

Received: November 23, 2012 Accepted: January 27, 2013 Published: January 28, 2013 2377

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stability studies.32,33 Perhaps more importantly, it has been shown that the accuracy of the moisture content quantification with NIR is equivalent to Karl Fischer titration technique.27,34 Recent studies have demonstrated that NIR spectroscopy is also applicable for in-line monitoring of the freeze-drying process.25,35,36 However, in all of the reviewed studies, NIR spectroscopy has been used as a single-vial technique. Therefore, the measured sample does not represent the entire batch. This limitation has been considered as one of the major barriers to the acceptance of NIR spectroscopy as a freezedrying PAT tool in the future, at both the laboratory and production scales.18,37 The purpose of this study was to evaluate the feasibility of using multipoint NIR spectroscopy for in-line moisture content quantification during a freeze-drying process. As far as we are aware, there are no publications where an in-line multipoint NIR system has been applied to the analysis of a freeze-drying process. The major benefit of the multipoint measurement system is that it achieves continuous observation of individual samples at the different locations on the freeze-dryer shelf. Therefore, it enables monitoring vials in those areas on the freeze-dryer shelf that display the largest variations during sublimation of the product.

the same batch. Heterogeneous nucleation of ice induces vialto-vial variation in the degree of supercooling. As a consequence, the size of ice crystals differs between vials and subsequently leads to an unequal sublimation rate during primary drying.3,14 Uneven heat transfer within the drying chamber is known to lead to temperature differences in the vials that are located in different sections on the freeze-dryer shelf. The reason for this so-called “edge effect” is the greater heat radiation transfer from the warmer surroundings (e.g., door and walls of the freeze-dryer) to those vials located in the corners and edges compared to the situation with vials in the center of the shelf.15 The outermost vials on the shelf absorb most of this additional heat radiation and therefore run at a higher temperature during the freeze-drying process than the central vials. This temperature difference causes variation in the drying rate of vials, e.g., those on the edges dry faster than those in the central section.16 The higher temperatures in the edge vials can lead to structural collapse and, hence, to higher residual moisture compared to products without collapse.17 Both heterogeneous nucleation and the edge effect can lead to vialto-vial variability in the sublimation rate and subsequently to variances in the residual moisture of the end-products within the same batch. Therefore it is important to monitor the vials that exhibit the largest variation inside a freeze-dryer.18 FDA’s approved methods for residual moisture analysis of dried biological products are Karl Fischer titration, the gravimetric method based loss on drying, thermogravimetry and gas chromatography with a water evaluation analyzer.19 The Karl Fischer titration is often considered as a standard method for residual moisture quantification of freeze-dried products. However, all of these techniques are destructive and, relatively slow off-line methods. From the point of view of process analytical technology (PAT), none of these attributes are suitable for a good PAT tool. Since FDA’s PAT initiative,20 a large and growing body of literature has described methods for in-line moisture content analysis during the freeze-drying process. Methods such as manometric temperature measurement (MTM),21,22 tunable diode laser absorption spectroscopy (TDLAS),23,24 and nearinfrared (NIR) spectroscopy25 have been applied to monitor the moisture content during freeze-drying process. Both MTM and TDLAS are batch monitoring techniques and therefore promising PAT tools. These methods provide representative information about the freeze-drying process from the batch as a whole. This is a strength, but on the other hand also a major weakness, due to the loss of spatial and individual vial information. Furthermore, neither MTM nor TDLAS is able to recognize differences in drying phenomena between the edge or center vials. In contrast, NIR spectroscopy is a method that provides information from individual samples. NIR spectroscopy is also a nondestructive, noncontact, robust and fast method. The use of NIR spectroscopy in the moisture content quantification of freeze-dried products has been extensively documented in the literature. Kamat et al. were the first to report on the use of noninvasive NIR spectroscopy in the quantification of residual moisture in a freeze-dried product.26 The robustness of partial least-squares (PLS) regression has been demonstrated in moisture content prediction based on NIR spectroscopy with formulations containing varying compositions.27,28 A number of studies have demonstrated that NIR can be used to differentiate surface water from latticebound hydrate water.29−31 NIR has also been applied for moisture content quantification of freeze-dried products during

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MATERIALS AND METHODS Materials. Sucrose (≥99.5%) was purchased from SigmaAldrich (USA). Aqueous sucrose solutions were prepared at three different concentrations of 146, 365, and 584 mM (5, 12.5, and 20% w/v) in filtered (0.22 μm filter), deionized MilliQ water (Millipore, USA). Washed and autoclaved 2 mL clear glass tubing injection vials (Schott AG, Germany) were used for freeze-drying. Bromobutyl freeze-drying stoppers and aluminum seals (West Pharmaceutical Services Inc., USA) were used to seal the freeze-dried end-products. Freeze-Drying. A laboratory-scale freeze-dryer, LyoStar II (SP Scientific Inc., USA) was used for freeze-drying. The end point of primary drying was determined with a comparative pressure measurement using a capacitance manometer and a Pirani gauge. Ice Sublimation Experiments and Statistical Analysis. Ice sublimation experiments were done prior to process monitoring with NIR spectroscopy. The purpose of these experiments was to assess the largest variation of the sublimation rate within the shelf for the optimal NIR probe head placement. One full shelf of vials (589 items) was filled with 1 mL of Milli-Q water and loaded onto the bottom shelf of the freeze-dryer with a steel guardrail surrounding the vial array. All vials were weighed individually before and after addition of water using a calibrated PB-3002S DeltaRange precision balance (Mettler Toledo GmbH, Switzerland). Primary drying was performed at 0 °C and 200 mTorr for approximately 2.5 h. Detailed freeze-drying process parameters are presented in Figure S-1a (Supporting Information). The vials were weighed again after freeze-drying. The mass loss of the water was calculated as the mass difference and normalized to drying time. The ice sublimation experiment was repeated three times. Averaged ice sublimation rates were statistically analyzed using two-sample t test and one-way analysis of variance (ANOVA)38 with a multiple comparison of means39 as a follow-up test. Statistical analysis was applied for two purposes: (1) two-sample t test was used to evaluate maximum variation in the sublimation rate within the vial array and (2) ANOVA with the multiple comparison test was applied to determine the 2378

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were performed once while the experiment with 365 mM sucrose was performed in triplicate, in order to evaluate the reproducibility of the system. Near-Infrared Moisture Calibration Sample Preparation and Karl Fischer Titration. In order to calibrate NIR data for the real moisture content, sample sets containing different amounts of residual moisture were prepared separately for each sucrose concentration. Sample vials were loaded onto the same freeze-dryer shelf with the NIR probe heads. A conservative primary drying temperature of −36 °C was used to prevent collapse of the sample during sampling. Otherwise the freeze-drying conditions were the same as described in Figure S-1b (Supporting Information). Vials were extracted from the process during primary and secondary drying at 18 sampling points using a sample extractor door on the freeze-dryer (SP Scientific Inc., USA). At each sampling point, the sample vials were positioned in the line of sight of the NIR probes using the selector arm of the sampling door. The NIR signals were simultaneously collected from each channel at a rate of 1 Hz over a 40-s period. The measured samples were extracted from the process, stoppered under vacuum and sealed with aluminum seals. Karl Fischer titration was used as a reference method for determination of residual moisture. Freeze-dried samples were reconstituted with 1000 μL of dry methanol of which 200 μL was analyzed using a coulometric Karl Fischer titrator C30 (Mettler Toledo GmbH, Switzerland). The moisture content of each sample was measured in triplicate and their average value was used in the multivariate analysis. Replied measurements resulted in mean standard deviation of 0.03%. Near-Infrared Spectroscopic Data Analysis. NIR spectroscopic data was analyzed by principal component analysis (PCA) and partial least-squares (PLS) regression. The application of PCA and PLS for spectroscopic data analysis has been reviewed elsewhere.40,41 PCA was used to remove outliers from the calibration samples and to observe the major sources that cause variation in the data. PLS was applied for inline prediction of moisture content during freeze-drying process. The NIR spectral region was narrowed to 1300− 2160 nm and Whittaker smoothing42 was applied as a standard procedure to compensate missing data caused by the dead pixels in the HgCdTe (MCT) matrix detector of the SWIR camera. A data matrix containing NIR spectra and corresponding reference moisture values were subjected to multivariate analysis using SIMCA-P+ 12.0.1 (Umetrics AB, Sweden). Spectral data were centered and preprocessed using standard normal variate (SNV) transformation prior to multivariate analysis. In the PLS analysis, calibration samples were randomized into the training and test sets at ratios of 2/3 and 1/3, respectively. The data of the end-products from the multipoint NIR monitored processes were added to the test sets. The goodness of established PLS model was evaluated with root-mean-square error of cross-validation (RMSECV) and root-mean-square error of prediction (RMSEP) values. The model was cross-validated using 7-fold method in which training set was divided into seven subgroups that were selected so that each of them contained similar residual moisture values. The RMSECV was obtained by a repeated protocol in which the model is trained with 6 subgroups to predict the values of kept out subgroup. The external validation of the model was conducted using randomized test set. The RMSEP was determined using test set which contained calibration samples

number of outer vial rows that absorb most of the heat radiation. For the first purpose, two groups were established, namely edge and center vials. The outermost vial row was selected to represent the edge vials (n = 96) and the rectangular area of vials (8 × 12) in the middle of the array was designated as the center vials (n = 96). For the second purpose, sublimation rates of the five outermost vial rows were compared. MATLAB 7.12.0.635 software R2011a (The Matworks Inc., USA) was used for statistical analysis. Multipoint Near-Infrared Spectroscopy − In-line Moisture Content Quantification during the FreezeDrying Process. A dispersive multipoint NIR spectrometer was used for process monitoring. The NIR instrument consisted of a short-wavelength infrared (SWIR) hyperspectral camera operating in the wavelength region of 970−2500 nm (Specim Oy, Finland). The SWIR camera was connected to a multichannel fiber-optic input module (VTT, Finland), a multichannel fiber-optic light source containing a 65 W halogen lamp (VTT, Finland) and three fiber-optic noncontact diffusion reflectance probe heads (VTT, Finland). NIR probe heads were instrumented inside the freeze-dryer using tailor-made optic fibers (Oplatek Group Oy, Finland) and fiber-optic feedthrough (Vacom GmbH, Germany) on top of the freeze-dryer. Fiber-optic feedthrough and NIR probe heads were connected with three illumination and three collection fibers with core diameters 600 and 400 μm, respectively. Photographs of the implementation of the multipoint NIR system are presented in Figure S-2 (Supporting Information). NIR probe heads were placed on the diagonal of the bottom shelf to measure vials in the corner, center and middle as illustrated in Figure 1. The

Figure 1. NIR probe head placement on the vial tray and measured vials.

NIR illumination spot of each probe (ø 2 mm) was adjusted to measure through the side of the vial as close to the bottom of the sample as possible. NIR spectra were collected continuously during the process at a rate of 1 Hz with an integration time of 15 ms. In the analysis, the data were averaged in blocks of 60 consecutive spectra to achieve enhanced signal-to-noise ratio. A total of 450 vials were filled with 1 mL of sucrose solution, semistoppered, and loaded onto the bottom shelf of the freezedryer with a steel guardrail surrounding the vial tray. The primary and secondary drying stages were conducted at −23 and +30 °C, respectively. The primary drying time was 28−38 h, depending on the concentration of sucrose. Both primary and secondary drying stages were carried out under a chamber pressure of 55 mTorr. Detailed freeze-drying process parameters are presented in Figure S-1b (Supporting Information). Experiments with 146 and 584 mM sucrose 2379

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with varying residual moisture values that were not used in the training and validation of the model.

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RESULTS AND DISCUSSION Ice Sublimation Experiments − Statistical Analysis of the Edge Effect. Ice sublimation experiments were carried out in order to assess the optimal placement of the NIR probe heads. Therefore the objective of these experiments was to determine the regions on the freeze-dryer shelf that display the largest variation in the sublimation rate. Figure 2 presents an

Figure 3. (a) Box-plot of sublimation rates on the edge and in the center regions (n = 96). Difference between groups was statistically highly significant (p < 0.001). (b) Box-plot of sublimation rates of the five outermost vial rows (n = 64). Sublimation rates of first two vial rows were significantly different compared to the other vial rows. The red line inside the box is the median value of the group. Tops and bottoms of the boxes are the 25th and 75th percentiles of the group. Dashed lines represent the minimum and maximum values within groups. Outliers are marked with red + sign.

Figure 2. Sublimation rate of the pure ice in the different locations of the freeze-dryer shelf. Values are the average of three experiments.

rows was statistically highly significant. Further, a statistically highly significant difference in sublimation rate was also observed between the second row and the rest of the rows. In contrast, the sublimation rate differences between third, fourth and fifth vial rows were not statistically significant (p > 0.1). Thus, inner vial rows could not be statistically distinguished from each other by means of the sublimation rate. Therefore it can be stated that the two outermost vial rows represent the edge vials which absorb most of the atypical heat radiation. These findings from the statistical analysis are in good agreement with those of Rambhatla et al. which showed that atypical heat radiation caused higher sublimation rate from the vials located at the edges of an array.15 Principal Component Analysis of the Calibration Samples. PCA was done in order remove outliers from the calibration sets and to observe major sources responsible for the variation in the data. It was found that samples with residual moisture of over 3% were outliers and thus excluded from the analysis. PCA was first generated using first two principal components, which explained 94.6 and 2.8% of the total variance in the data, respectively. The score plot of PC1 and PC2 revealed clustering of data along the PC2 axis according to the sucrose concentration as shown in Figure 4a. Although NIR spectra were measured using three different probes, no clustering was observed according to the probe number (data not shown). This finding indicated that there were no

overview of the results of ice sublimation experiments. The largest vial-to-vial variation in the sublimation rate was found to exist between the corner and center vials with the values of 0.25 and 0.14 g/h, respectively. A two-sample t test was performed for statistical determination of the maximum variation within the vial array. Sublimation rates of the outermost edge vial row (n = 96) were compared to the center vials (n = 96). Figure 3a presents a box-plot of mean sublimation rates at the edge and center locations. As shown, mean sublimation rates at the edge and center locations were 0.21 and 0.16 g/h, respectively. The result of t test revealed that the sublimation rate difference between the edge and center vials was statistically highly significant (p < 0.001). Figure 3a also reveals that the sublimation rate varied more in the edge vials than in the center vials. The narrow distribution of sublimation rates in the center location is evidence of more uniform heat and mass transfer. One-way ANOVA with multiple comparison of means as a follow-up test was used to statistically determine the number of vial rows that are affected by the edge effect. Sublimation rates of the five outermost vial rows were compared as presented in Figure 3b. ANOVA revealed that differences between the row means were highly significant (p < 0.001). Detailed comparison of row means with the follow-up test indicated that the sublimation rate difference between first row and the rest of the 2380

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Partial Least Squares Regression Moisture Calibration Model. First, PLS was applied to construct a general model to predict the moisture content of all of the studied sucrose concentrations. For that purpose, a two-component PLS model was established using outlier-removed calibration samples of all sucrose concentrations. However, the correlation plot between observed and predicted values exhibited poor linearity as presented in Figure S-3a (Supporting Information). An applicable PLS model for all sucrose concentrations could only be obtained by adding approximately 10 components (data not shown). However, applying so many components led to obvious overfitting, which should be avoided if possible. This finding indicates that the varying solid content of formulation is the limiting factor in the establishment of a general PLS model for the quantification of moisture content by NIR. The present finding is consistent with those of earlier studies, which showed that NIR moisture calibration is dependent on the concentration of excipient34 and API.45 The further development of the quantification method for moisture content focused on constructing individual PLS models for each sucrose concentration. It was found that two PLS components were sufficient to establish valid PLS models for moisture content quantification. In all PLS models, the first component explained the majority (approximately 97%) of the total variance in the data. Although the second component explained only a minor percentage (approximately 1%) of the total variance, it was found that it made a significant contribution to the model. Exclusion of second component led to poorer linearity and higher RMSECV and RMSEP values (data not shown). Thus, the second component was included in all models. All established models had a slope of 1.00 and the y-intercept was essentially at 0%. The characteristic values of the established PLS models are summarized in Table S-1 (Supporting Information). Figure 5 presents the correlation plot between the observed and predicted moisture contents for PLS model of 365 mM

Figure 4. Principal component analysis of SNV-corrected NIR spectra. (a) PCA score plot of PC1 and PC2. Scores are colored according to sucrose concentration. (b) PCA loading plot of the two first principal components. PC1 and PC2 explained 94.6% and 2.8% of total variance in data, respectively.

systematical differences between probes and the spectra measured using different channels were comparable. In the interpretation of the principal components, the loadings were inspected (Figure 4b). The loadings of PC1 had high weights at the wavelength region 1900−2050 nm. This result could be explained by the fact that water has a strong NIR absorption peak at 1940 nm, which is a combination of the OH stretching band and the OH bending band.43 Therefore it can be stated that PC1 originated from the intensity changes in the NIR water absorption peak. The loadings of PC2 had high weights at NIR water absorption peak wavelengths but also at both ends of the NIR spectrum. As scores of different sucrose concentrations formed clusters mainly within PC2, it can thus be postulated that PC2 described spectral differences between different sucrose concentrations. The explanation for the presence of the water band in the loadings of PC2 might relate to increased degree of hydrogen bonding which affects the NIR absorption of water as sugar concentration increases.44 PC3 and PC4 were also inspected as they explained 1.3 and 0.7% of the total variance in the data, respectively. The loadings of PC3 and PC4 showed similar features and described mainly changes in the shape and location of the NIR water absorption peak (data not shown). To summarize the results of the PC analysis, the main sources leading to variation in the calibration data set were the intensity changes in the NIR water absorption peak, the spectral differences between different sucrose concentrations and the changes in the shape and location of the NIR water absorption peak.

Figure 5. Correlation plot between Karl Fischer results and NIR prediction for two-component PLS model of 365 mM sucrose.

sucrose samples. The model resulted in low RMSECV and RMSEP values, 0.136 and 0.068%, respectively. The corresponding correlation plots for PLS models of 146 and 584 mM sucrose are presented in Figures S-3b and S-3c (Supporting Information), respectively. 2381

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The loadings of PLS components were investigated in order to reveal their physical origin. Figure 6 shows the loadings for

Figure 6. Loading plot for PLS model of 365 mM sucrose. The first PLS component explained 97.4% of total variance in data and it mainly described the changes in the intensity of NIR water absorption band at 1900−2050 nm. The second PLS component explained 1.6% of total variance and it described changes in the shape and location of the NIR water absorption peak but also intensity changes at both ends of the NIR spectrum.

the PLS model with 365 mM sucrose. From the graph, one can observe that the loadings of the first component have high weights in the region of the NIR water absorption peak at 1900−2050 nm. Thus, the first component described differences in the moisture content of the samples. As the first PLS component explained 97.4% of total variance, this confirmed that the moisture content was the main contributor in the model. The second component explained 1.6% of total variance and it was found to describe changes in the shape and location of the NIR water absorption peak as well as reflecting intensity changes at both ends of the NIR spectrum. In order to interpret the second PLS component, SNV-corrected NIR spectra of the calibration samples were inspected. It was observed that the water absorption peak maximum shifted from 1964 to 1958 nm and the peak became narrower as the measurement temperature increased and the intensity of the peak decreased (Figure S-4, Supporting Information). The possible explanation for this phenomenon was thought to be temperature associated changes in the strength of hydrogen bonds between water molecules. As the temperature increases, hydrogen bonding of water molecules becomes weaker which allows the covalent OH bonds of water to vibrate more freely. As a consequence, the vibration peaks shift to lower wavelengths and become narrower.46 Other sucrose concentrations (146 and 584 mM) resulted in similar PLS components (Figure S-5, Supporting Information). In-Line Moisture Content Quantification during the Freeze-Drying Process by Multipoint Near-Infrared Spectroscopy. The main purpose of the current study was to evaluate the feasibility of using multipoint NIR spectroscopy for in-line monitoring of the moisture content inside of vials during the drying phases in a freeze-drying process. Based on the results from the ice sublimation experiments, NIR probe heads were positioned diagonally on the freeze-dryer shelf at three locations (in the center, corner and middle) as illustrated in Figure 1. The established PLS moisture calibration models were used to the multipoint NIR process data in order to predict moisture content during the freeze-drying processes as presented in Figure 7.

Figure 7. Predicted moisture content profiles of in-line multipoint NIR experiments with (a) 146, (b) 365, and (c) 584 mM sucrose. Lines are colored according to NIR probe location: red, corner; green, middle; blue, center. The upper limit of the prediction range of the PLS model is identified with a dashed horizontal line labeled as a “PLS max”.

From the graphs in Figure 7, it can be noted that the predicted moisture content was outside of the prediction range of the PLS model at the beginning of primary drying. The imprecise prediction can be explained by the fact that the NIR probe was positioned so that it caused saturation of NIR signal at the start of the drying; the NIR signal was measured at a height of 1−3 mm while the height of icy sample in the vials was initially 7.2 mm. In addition, as the product contains both liquid and frozen water during the freeze-drying process, one calibration model is inadequate to cover the whole process due to substantially different NIR spectrum of water in its liquid and solid phases. As drying proceeded, the height of ice decreased and sublimation front came into the vicinity of the NIR measurement spot. This was observed as a decrease in the predicted moisture content and it occurred first in the corner vial and a couple of hours later in the middle and center vials. In order to confirm that the decrease was related to ice sublimation, the NIR ice absorption peak (1492 nm) area was plotted as a function of process time. This absorption is a first overtone of the OH stretching band.43 The time points of the decrease in the ice peak area agreed well with the decrease in 2382

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Analytical Chemistry PLS predicted moisture content (Figure S-6, Supporting Information). When one moves further along the drying curve, sublimation reached the upper limit of the prediction range of PLS model and the prediction of the moisture content became reliable. For all batches studied, this occurred in the first half of primary drying. The predicted moisture content reached a plateau, which is in agreement with the primary drying end point determined using the comparative pressure measurement of the capacitance manometer and the Pirani gauge (data not shown). This finding indicates that the multipoint NIR system could also be used for primary drying end point detection. Moreover, differences in the moisture content between measured vials had essentially leveled off toward the end of primary drying. However, some differences in moisture content remained between the measured vials but no consistent pattern was observed which could be attributed to vial location. Differences in the moisture content at the end of primary drying are more likely to originate from the unequal ice crystal sizes in the samples due to the heterogeneous nucleation during the freezing step.14 In the next process phase, i.e., secondary drying, an increased temperature induced desorption of water, which could be seen as a decrease in the predicted moisture content. The observed decrease in predicted moisture content accorded well with the data from comparative pressure measurement (data not shown). The average residual moistures of 146, 365, and 584 mM sucrose end-products were 0.12 ± 0.00%, 0.35 ± 0.07%, and 0.72 ± 0.13%, respectively. Replicate experiments with 365 mM sucrose resulted in very similar moisture content profiles, evidence of the good reproducibility of the system (Figure S-7, Supporting Information). The most striking result to emerge from the data is that unequal ice sublimation rates within different parts of the freeze-dryer shelf could be detected with this multipoint NIR setup. The sublimation of ice was fastest in the corner vial in all monitored experiments. Further, rate of sublimation of ice in the middle and center vials was very similar. This finding is in agreement with the results from one-way ANOVA, which showed that the sublimation rates within the inner vial rows could not be statistically distinguished. In addition, the prediction of moisture content displayed very reliable results, especially toward the end of the process. Limitations of the Multipoint NIR Measurement SetUp. There are not simply advantages obtained with the multipoint system, there are also a few challenges to overcome. First, the NIR probe heads interfere with the hexagonal array of vials, altering the heat radiation directed to the vial under analysis. This increased heat radiation could cause the measurement vial to run at a higher temperature than the other vials on the tray. Second, the NIR probe heads used here are physically relatively large and somewhat difficult to install on the vial tray, partly due to their sensitive optic fibers. In its present form, the measurement setup is not yet suitable for incorporation into production scale freeze-dryers, especially if an automated vial loading system is being used. Lastly, as with any other analytical method, the multipoint NIR method for moisture content quantification needs to be validated. In particular, one needs to clarify how many probe heads are sufficient to achieve an accurate determination of the moisture content.

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CONCLUSIONS

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ASSOCIATED CONTENT

Article

This study evaluated the feasibility of using multipoint NIR spectroscopy for in-line moisture content quantification during the freeze-drying process. Although the establishment of the general PLS model for all sucrose concentrations was unsuccessful, individual PLS calibration of NIR data for each concentration resulted in RMSECV and RMSEP values lower than 0.23 and 0.13%, respectively. The results demonstrated that unequal sublimation rates within the freeze-dryer shelf could be detected with this dispersive multipoint NIR spectrometer. As the monitored vials represented those with the greatest variation in the sublimation rate, the multipoint feature offers a representative platform for moisture content quantification of an entire batch without losing sample-specific information. These results indicate that an in-line multipoint NIR measurement system combined with appropriate data processing tools could be used to determine the end point of the process, i.e., the desired residual moisture of the product. Therefore, this methodology offers the potential to be applied to monitoring the freeze-drying process of products in which the residual moisture content is a major critical quality attribute. The current measurement setup could be beneficial for researchers developing new freeze-drying processes or formulations, since the setup combines the types of information gathered by single-vial techniques (e.g., single-point spectroscopy) and batch techniques (e.g., MTM and TDLAS). In addition, freeze-drying equipment manufacturers could use the multipoint information to pinpoint and avoid possible “weak” spots inside the drying chamber, which may lead to undesired vial-to-vial variability within a single batch undergoing freezedrying. Besides the advantages, implementation of multipoint NIR system has challenges arising mainly from the physical dimensions of the probe heads and optic fibers (e.g., altered heat transfer of the vial under analysis, complicated loading of the vials). These limitations are merely technical, and it should not be impossible to develop an “intelligent” shelf system, with in-built analytical probes and optical fibers.

S Supporting Information *

Additional information as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: ari.kauppinen@uef.fi. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was financially supported by the Finnish Funding Agency for Technology and Innovation (Tekes, Project No. 40313/09 FERMET), FinPharma Doctoral Program, and the PROMIS Centre consortium (supported by the Finnish Funding Agency for Technology and Innovation (Tekes), Regional Council of Pohjois-Savo, North Savo Centre for Economic Development, Transport and the Environment and participating industrial partners). The authors acknowledge Oplatek Group Oy and Specim Oy for technical support and cooperation on multipoint fiber-optic sensors. 2383

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dx.doi.org/10.1021/ac303403p | Anal. Chem. 2013, 85, 2377−2384