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Near-Infrared Spatially Resolved Spectroscopy for Tablet Quality Determination
RESEARCH ARTICLE – Pharmaceutics, Drug Delivery and Pharmaceutical Technology
Near-Infrared Spatially Resolved Spectroscopy for Tablet Quality Determination BENOˆIT IGNE,1 SAMEER TALWAR,2 HANZHOU FENG,2 JAMES K. DRENNEN,1,2 CARL A. ANDERSON1,2 1 2
Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania Graduate School of Pharmaceutical Sciences, Duquesne University, Pittsburgh, Pennsylvania 15282
Received 8 April 2015; revised 11 July 2015; accepted 4 August 2015 Published online 28 August 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.24618 ABSTRACT: Near-infrared (NIR) spectroscopy has become a well-established tool for the characterization of solid oral dosage forms manufacturing processes and finished products. In this work, the utility of a traditional single-point NIR measurement was compared with that of a spatially resolved spectroscopic (SRS) measurement for the determination of tablet assay. Experimental designs were used to create samples that allowed for calibration models to be developed and tested on both instruments. Samples possessing a poor distribution of ingredients (highly heterogeneous) were prepared by under-blending constituents prior to compaction to compare the analytical capabilities of the two NIR methods. The results indicate that SRS can provide spatial information that is usually obtainable only through imaging experiments for the determination of local heterogeneity and detection of abnormal tablets that would not be detected with single-point C 2015 Wiley spectroscopy, thus complementing traditional NIR measurement systems for in-line, and in real-time tablet analysis. Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:4074–4081, 2015 Keywords: near-infrared spectroscopy; partial least squares; multivariate analysis; chemometrics; content uniformity; diffuse reflectance; tableting; spatially resolved spectroscopy
INTRODUCTION As the pharmaceutical industry pushes toward continuous manufacturing for drug substance and drug products, process analytical technologies are increasingly relied upon to ensure product quality through real-time process understanding, monitoring, and control.1–3 In the past several decades, a significant amount of work has been carried out on the implementation of techniques that allow, in-line, and in realtime, the determination of intermediate and final product critical quality attributes.4–8 For instance, the content uniformity and end-points of blends can be determined by near-infrared (NIR) and Raman spectroscopy9–13 ; granule quality parameters such as uniformity and moisture content can be measured at any time during the granulation processes with NIR spectroscopy14–16 ; granule particle size can be assessed directly after milling by light diffraction, or focused beam reflectance measurements,17,18 and so on. A significant amount of work has also been carried out to characterize tablets.19–23 As an important drug delivery platform for a large majority of pharmaceutical products, tablets are of particular interest for realtime release testing. Traditional approaches to tablet testing rely on liquid chromatography methods that require sample preparation, solvents, and are often the bottleneck for batch release. Reflectance and transmittance NIR and Raman spectroscopy have been the surrogate techniques of choice to replace wet chemistry for release testing. These single-point spectroscopic techniques usually allow the determination of tablet assay in a few seconds and have shown to be as accurate as HPLC, the Correspondence to: Carl A. Anderson (Telephone: +1-412-396-1102; Fax: +1412-396-1608; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 104, 4074–4081 (2015)
C 2015 Wiley Periodicals, Inc. and the American Pharmacists Association
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reference method usually employed to generate the calibration model.24–27 Single-point instruments [one or several illuminations source(s) and one or several detector(s) whose outputs are combined into a single spectrum] provide into a single output a representation of the reflected or transmitted light by a sample. By nature, the properties of the signal are a function of the volume of interrogation. Thus, single-point instruments tend to average the signal from the whole transmitting or reflecting area and most, if not all, of the tablet is probed, HPLC outputs tend to correlate well with predictions. When the volume interrogated is limited and local heterogeneity occurs, correlation between assay and spectroscopic methods can be difficult. However, spatial information is not available from a singlepoint measurement. A tablet may be heterogeneous, but if the right amount of all the components is present and the whole tablet is interrogated by the spectroscopic method, the prediction of that tablet may be on target; however this tablet may not meet other quality attribute specifications. To gain spatial knowledge about the distribution of the active and excipient components in a tablet, hyperspectral chemical imaging has often been employed.28–31 Note that other techniques are available for local sampling of a tablet: laserinduced breakdown spectroscopy,32 mass spectrometry,33 and so on. Chemical imaging allows the determination of qualitative criteria that indicate if tablets possess an appropriate spatial distribution of its chemical constituents. Classification and curve resolution techniques can be helpful to associate the signal of a particular pixel with formulation components.34–37 Active pharmaceutical ingredient quantification is certainly possible but more complex by chemical imaging than by singlepoint spectroscopy. In single-point situations, one spectrum corresponds to one reference value. In imaging applications, a few hundreds or thousands of pixels can be available for a tablet and it is not possible to directly associate a reference value to
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RESEARCH ARTICLE – Pharmaceutics, Drug Delivery and Pharmaceutical Technology
Figure 1. The concept of spatially resolved spectroscopy (a) and the patented Indatech probe with collection channels in gray and light sources in yellow (b). Drawing not at scale.
each pixel (note that it is possible to assign reference values to each pixels using statistical methods). Consequently, quantitative models are difficult to build and, without careful experimentation, suffer from a lack of sensitivity. Spatially resolved spectroscopy (SRS) sits at the interface between hyperspectral imaging and single-point spectroscopy. Initially introduced for the determination of optical parameters (i.e., absorption and scattering coefficients), it offers the opportunity to provide spatial information while simplifying quantitative modeling capabilities.38–42 Spatially resolved spectroscopy uses one or several light sources and several collection channels. The distinct advantages of SRS reside in the location of the collection channels with respect to the sample illumination source(s). The spectra collected by the channels located in near proximity of the light source will exhibit strong scattering phenomena, whereas absorption is stronger as light travels farther in the sample. In one measurement, numerous spectra are collected with the same probe representing different degrees of light absorption and scattering. This data-cube can provide information about the sample local homogeneity, thus enabling real-time release testing capabilities for tablets in terms of assay and local homogeneity indicators and providing other measurement opportunities for ribbons, granules, and powder analysis. SRS can be seen as a simplified hyperspectral imaging system with collection fibers acting as discrete image pixels. Figure 1 summarizes the concept of SRS and sampling device used in this study. The capabilities of SRS for the determination of tablet assay were investigated. A traditional quantitative approach is presented to exploit the spectral information provided by SRS and results are contrasted with single point spectroscopy.
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calibration, whereas an inscribed design was the basis for the test set. Three replicate samples per level were manufactured from the same blend. In calibrations, three compression forces were used to generate flat faced compacts (2000, 3000, and 4000 pounds) for a total of 135 tablets (15 design points, three replicates, three compression forces). In test, only compacts at 3000 pounds were manufactured for a total of 45 samples (15 design points, three replicates). Table 1 presents the two designs. Materials for each design point were weighed and transferred into 40-mL plastic vials for the creation of samples used for the calibration and test sets. The materials were mixed for 30 min in a 5.5-L Bin-blender (L.B. Bohle, Ennigerloh, Germany) by placing them in a foam insert that was previously fit to match the dimensions of the Bin-blender. Subsequently, approximately 500 mg of powder was weighed from each blended sample and compressed into compacts on a Carver Automatic Tablet Press (Model 3887.1SD0A00; Wabash, Indiana) using a 13-mm die and flat-faced punches. In addition to calibration and test tablets, a set of compacts at the target composition—design O on Table 1—was created to test the ability of SRS to monitor manufacturing variability and drifts. This set is referred to as the homogeneity set. A total of 30 tablets were manufactured from a same blend with an equivalent mixing time as for the calibration and test samples (30 min). Twenty additional compacts underwent shorter blend times: five tablets with powder mixed for 5, 10, 15, and 20 min, respectively. Spectral Collection Single-point NIR reflectance measurements for both sides of samples were collected using a bench top scanning monochromator instrument (XDS Rapid Content Analyzer; Metrohm NIRSystems, Laurel, Maryland). Spectra were collected over the wavelength range of 400–2500 nm at 0.5-nm increments, averaging 32 co-adds per spectrum. R Spatially resolved spectra were collected with a Hy-Ternity system from Indatech (Clapiers, France). The system is composed of a NIR camera equipped with an InGaAs detector collecting data from 900 to 1700 nm at 5 nm increment and a R probe constituted of 12 channels and three light SAM-Spec sources. Figure 1b presents the design of the collection head. For each set of sample, a hypercube was collected for both sides: 12 channels, 256 wavelengths, 270/90/100 samples in calibration, test, and homogeneity respectively (samples scanned on both sides). An integration time of 0.29 ms was used to collect the data.
MATERIAL AND METHODS
Reference Testing
Sample Production
Acetaminophen reference values for all compacts were determined using HPLC (Waters Alliance 2790, Milford, Massachusetts), followed by UV detection (Waters 2487). A method, modified from USP Monograph 29 (APAP Tablets) was employed.43 The mobile phase was water:methanol:acetic acid (80:17:3) and the solid phase was a 15 cm by 4.6 mm, C18, column, with 3 :m packing. The detection wavelength was 243 nm. The error of the laboratory was estimated at 1.03%.
A formulation comprising of acetaminophen (APAP; Mallinckrodt Inc., Raleigh, North Carolina), lactose (monohydrate NF—product 316/Free flow; Foremost Farms USA, Rothschild, Wisconsin), microcrystalline cellulose (MCC; Avicel PH 200; FMC Biopolymer, Mechanicsburg, Pennsylvania), starch (potato starch; Penford Food Ingredient, Centennial, Colorado), and magnesium stearate (MgSt; Mallinckrodt, Hazelwood, Missouri) was selected. Two central composite designs were employed to generate calibration and test samples. A three-level (APAP, MCC, and lactose) circumscribed central composite design was used in DOI 10.1002/jps.24618
Modeling The data collected from SRS allows for a broad range of modeling approaches. It is possible to treat the data as a hypercube Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
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Table 1. Designs of Experiments Used to Generate Calibration and Test Sets Run ID
APAP (%, w/w)
MCC (%, w/w)
Lactose (%, w/w)
Starch (%, w/w)
MgSt (%, w/w)
Compression force (kp)
Calibration set
A B C D E F G H I J K L M N O*
23.21 23.21 23.21 23.21 31.40 31.40 31.40 31.40 20.44 34.21 27.30 27.30 27.30 27.30 27.30
30.21 30.21 40.87 40.87 30.21 30.21 40.87 40.87 35.53 35.53 26.60 44.53 35.53 35.53 35.53
17.57 23.77 17.57 23.77 17.57 23.77 17.57 23.77 20.67 20.67 20.67 20.67 15.48 25.91 20.67
28.51 22.31 17.85 11.65 20.32 14.12 9.66 3.46 22.86 9.09 24.93 7.00 21.19 10.76 16.00
0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50
2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4
Test set
A B C D E F G H I J K L M N O*
24.89 24.89 24.89 24.89 29.76 29.76 29.76 29.76 23.21 31.40 27.30 27.30 27.30 27.30 27.30
32.40 32.40 38.73 38.73 32.40 32.40 38.73 38.73 35.53 35.53 30.21 40.87 35.53 35.53 35.53
18.85 22.53 18.85 22.53 18.85 22.53 18.85 22.53 20.67 20.67 20.67 20.67 17.57 23.77 20.67
23.36 19.68 17.03 13.34 18.49 14.81 12.16 8.47 20.09 11.90 21.32 10.66 19.10 12.90 16.00
0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
*Designs centerpoint.
(samples by channels by wavelengths) and apply multi-way quantitative or qualitative analysis.44,45 It is also possible to unfold the cube to employ more traditional methods. The latter option was chosen in this article for the purpose of comparing collection geometry; focusing on the properties of the data rather than the wealth of available modeling strategies. For tablet assay modeling, a spectrum corresponding to the average of the channels on either side of the light sources was employed: channels 1, 2, and 3 (31 and 32), 7 (71 and 72), 8, and 9 (see Fig. 2). Results were contrasted with single-point spectra obtained for each tablet. The spatial information provided by SRS was then utilized to provide an evaluation of the spatial
distribution of the active ingredient in homogeneous and underblended tablets. Partial least-squares regression was used to develop all quantitative models with the NIPALS algorithm.46 The choice of latent variables was determined by comparing the evolution of the root mean square error of calibration (RMSEC) and of cross-validation (RMSECV). All calculations were performed with MATLAB 2011a (The Mathworks, Natick, Massachusetts) equipped with the PLS Toolbox v. 6.2.1 (Eigenvector Research Inc., Wenatchee, Washington).
RESULTS AND DISCUSSION Spectral Investigation
Figure 2. Selected spectral channels. Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
The spectral output of a SRS measurement is information rich. With an integration time of 290 :s, a hypercube was created containing reflectance spectra of a particular sample at different locations. Figure 3 shows several of these outputs. Figure 3a displays the single-point spectra of two heterogeneous tablets (#4 and #20 with 5 and 20 min of blending time, respectively) and an homogeneous tablet (30 min blending). Although a clear difference exists between the spectra of homogeneous and heterogeneous tablets, spectra corresponding to the heterogeneous tablets provide an average difference which may correspond to both heterogeneity of low/high potency, without the ability to identify the root cause of the fault. As a DOI 10.1002/jps.24618
RESEARCH ARTICLE – Pharmaceutics, Drug Delivery and Pharmaceutical Technology
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Figure 3. Single point and spatially resolved spectroscopy outputs. (a) Average SRS output for heterogeneous and homogeneous tablets (5, 20, and 30 min blending, respectively); (b) single-point spectra of heterogeneous and homogeneous tablets (5, 20, and 30 min blending, respectively); (c) SRS surface plot of an heterogeneous tablet. Channels 1, 2, 3 and 7, 8, 9 are located on each side of the light source.
comparison, Figure 3b shows the average of individual spectra and their average for an homogeneous (30 min blending) and an heterogeneous tablet (5 min blending) using SRS. Individual SRS spectra for tablet 4 (5 min blending) are presented in Figure 3c. Figure 3c is a spatial representation of the spectra as a function of their collection channels. This particular plot corresponds to tablet #4 of the heterogeneous group (5 min blend time). It is possible to see the large discrepancy between the spectra located on either side of the light source due to differences in absorption and scattering, but also differences related to heterogeneity when comparing channels at equidistance from the light source. This is particular visible between channels 3 and 7. If the tablet was homogeneous, these data should be similar because the channels are located at the exact same distance on either side of the light source. However, the spectrum shows significant absorption level differences, indicating that the tablet is spatially heterogeneous. Differences between channels 1, 2, and 3 or 7, 8, and 9 are because of a combination of chemical and physical characteristics. These figures demonstrate that SRS possesses the same information as single-point spectroscopy while based on different scales of scrutiny, but also provides enhanced diagnostics capabilities that would approximate the information gained through chemical imaging. DOI 10.1002/jps.24618
Traditional Modeling Analysis Regression models were developed on comparable data collected from a single-point and an SRS spectrometer. Data collected from only one side of the tablet were used in both cases. The mean of all channels indicated as “selected” in top panel (red highlight) of Figure 2 was employed for SRS calibration. The spectrum of each tablet for single-point NIR was truncated to match the SRS instrument range. The spectral mean of the SRS data was considered to be most representative of a single point measurement where detectors average photons that have had both short and long paths into the samples. However, in single point measurements, it is not possible to observe these different light paths independently. Figure 4 presents the quantitative results. Random block cross-validation (five blocks) was used to determine the most appropriate spectral pretreatments and model complexity (number of latent variables). An optimization was independently conducted for each model. Standard Normal Variate47 followed by mean-centering was selected for the single-point model, whereas normalization to unit area also followed by mean-centering was employed for the SRS model. Performance of the single point and SRS instruments was compared. The root mean square error of prediction (RMSEP) Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
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Figure 4. Quantitative evaluation. (a) Single-point predicted versus reference plot for calibration (black circles) and test (red triangles) samples; (b) SRS predicted versus reference plot for calibration (black circles) and test (red triangles) samples; (c) single-point prediction residuals of homogeneous (blue dots) and heterogeneous (red dots) samples; (d) SRS prediction residuals of homogeneous (blue dots) and heterogeneous (red dots) samples. The dotted lines in figures c and d represent the ±5% limit. Two points representing prediction residuals above 25% were omitted to allow for the creation of comparable charts ((c) and (d)).
for the methods developed for the single point and SRS systems and tested using the test set was 1.27% (w/w) (Fig. 4a) and 1.71% (w/w) (Fig. 4b), respectively. The single-point instrument performed better than the SRS based solely on the independent test set. However, these results do not demonstrate the full utilization of the data collected by the SRS instrument. The two configurations do not interrogate the same volume of sample. The single-point measurement system used in this study has a spot size of 9.5 mm. Consequently, it illuminates most, if not all, the tablet surface and employs a series of eight detectors positioned under the sample that collect most of the diffusely reflected light. This system is specifically optimized for this type of measurement. The SRS probe interrogates the sample in a very different manner. It is designed to be sensitive to local chemical heterogeneity and physical properties differences (i.e., density or mean particle size) at the cost of reduced sampling area of the sample. The smaller area of the tablet sampled leads to an increase in the random noise and therefore an increase in RMSEP. It should be noted that this Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
increase in error is largely attributed to precision rather than accuracy. Spatially resolved spectroscopy for quantitative oral solid dosage forms provides not only determination of active pharmaceutical ingredient content and an indication of global tablet homogeneity, but also an indication of local heterogeneities without the requirement for multiple measurements per tablet. Figures 4c and 4d illustrate this point. The models were used to predict the homogeneity set and the prediction residuals were compared between the two instrumental configurations. Although the tablets corresponding to homogeneous blends (samples indicated in blue—30 samples) were well within the ±5% label claim, SRS predictions presented a higher variance (mean: 3.5% label claim; SD 1.26%) compared with single-point outputs (mean: 1.5% label claim; SD 0.59%) as expected because of the higher observed prediction error. Nonetheless, on heterogeneous samples, SRS results presented larger residuals (mean: 6.23% label claim; SD: 22.96%) than single-point spectroscopy (mean: 1.32% label claim; SD: 11.93%), indicating DOI 10.1002/jps.24618
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Figure 5. Distribution of the predicted values for the homogeneity set. Each boxplot represents the distribution of 10 predicted values per tablet according to the probe head design. The first 20 tablets were manufactured after reduced blending time (groups of five samples blended for 5, 10, 15, and 20 min). The next 30 samples correspond to tablets manufactured with the target blending time.
that the method is able to detect local heterogeneity. Note on Figure 4d that not all tablets appear on the graph, because being predicted outside of the displayed scale (±25%, w/w residual). This can be explained by the fact that non-homogeneous blends tend to have a poor distribution of various formulation components that can be probed by one or several fibers from the collection head, thus dramatically impacting the final mean spectrum from each individual fiber. Consequently, for a tablet to appear homogeneous, it must present the same spectral characteristics at all channels. This explains the sensitivity of SRS for the detection of heterogeneous samples.
Extracting Spatially Resolved Information In the preceding section, SRS was used as a single-point detector to demonstrate that similar work can be achieved. However, little is gained from using SRS combined with the traditional modeling approaches. Without expanding the present paper into n-way and other n-dimensional modeling techniques, the advantages that SRS provide can be demonstrated by treating predicted values from each channel as semi-independent values, thus enabling the intra and inter-tablet comparison of active distribution. This approach in presenting the results is closer to the treatment of chemical images where each pixel is a prediction, allowing the calculation of distribution statistics. Figure 5 presents, for the homogeneity set (30 homogeneous and 20 under-blended tablets), the distribution of the predicted values of each channel. The difference with the previous section is that instead of averaging the 10 predicted values per tablet, that distribution information is displayed. Using boxplots (a statistical display presenting mean, median, 25th and 75th quartiles, abnormal values and outliers), it is possible to understand the distribution of the predicted values for the active ingredient in each tablet. Thus, tablets manufactured after 30 min of blending presented a tight distribution around the label claim target (27.3%, w/w). Alternatively, tablets made after reduced blending time displayed wide distributions of predicted values and/or biased means. DOI 10.1002/jps.24618
Figure 5 illustrates the results for homogeneous tablets. While both techniques (SRS and single-point spectroscopy) accurately determine when a tablet is out of specification, only SRS can provide an assessment, for what would appear to be in specification tablets, of the distribution of active. Amongst the 30 “good” tablets, some presented very tight distributions in the active (i.e., tablets # 21, 29 38, 39, 48), whereas others, although displaying a predicted mean within the specification limits, had channels predicting higher or lower active values than the target. In this study, the tablets were assayed directly after measurements and no other quality attribute were investigated. The authors speculate that aggregates of active ingredient or excipients could have been responsible for the lower or higher predicted values. But for spatially resolved measurements such as the one SRS provides, such differences could not be seen, let alone diagnosed, and resolved.
CONCLUSIONS The ability of NIR SRS to evaluate tablet assay was investigated. Using a designed experiment and tablets made from blends at normal operating and under-blended conditions, calibration models were developed and compared with traditional single-point NIR measurement geometry. Values for the RMSEP of the two calibrations indicated that the single-point measurement spectrometer outperformed those of an SRS spectrometer. However, SRS outputs proved to be more sensitive to tablet heterogeneity than single-point measurements. Specifically, SRS was demonstrated to be sensitive to the degree of heterogeneity of ingredients within tablets using simple statistical tools such as boxplots. The distribution of the predicted values from each tablet could be further used to set specifications on the intra-tablet variability, a criterion usually available only through chemical imaging. More advanced mathematical treatments (n-way analysis), not considered in this study, may further enhance the sensitivity of the SRS to spatial distribution of ingredients and therefor the suitability of SRS for more sophisticated analyses. Nevertheless, SRS has the potential to Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
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provide real-time information about ingredient distribution in tablets.
ACKNOWLEDGMENTS The research team at the Duquesne Center for Pharmaceutical Technology would like to thank Indatech (Fabien Chauchard and Boris Larcheveque) and Metrohm NIRSystem (Denise Root) for lending the spectrometers.
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38. Farrell TJ, Patterson MS. 1992. A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo. Med Phys 19:879–888. 39. Shi Z, Anderson CA. 2010. Application of Monte Carlo simulationbased photon migration for enhanced understanding of near-infrared (NIR) diffuse reflectance. Part I: Depth of penetration in pharmaceutical materials. J Pharm Sci 99:2399–2412. 40. Shi Z, Anderson CA. 2010. Application of Monte Carlo simulationbased photon migration for enhanced understanding of near-infrared (NIR) diffuse reflectance. Part II: Photon radial diffusion in NIR chemical images. J Pharm Sci 99:4174–4182. 41. Kienle A, Lilge L, Patterson MS, Hibst R, Steiner R, Wilson BC. 1996. Spatially resolved absolute diffuse reflectance measurements for noninvasive determination of the optical scattering and absorption coefficients of biological tissue. Appl Opt 35:2304–2314. 42. Chauchard F, Roussel S. 2010. Spectroscopic probe and method for detecting an inhomogeneity Patent # WO 2010136732 A1.
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43. United States Pharmacopeial Monograph 29, Acetaminophen Tablets. USP29-NF24. 44. Bro R. 1996. Multi-way calibration, Multilinear PLS. J Chemom 10:47–61. 45. Bro R. 1997. PARAFAC. Tutorial and applications. Chemom Intell Lab Syst 38:149–171. 46. Wold H. 1975. Path models with latent variables: The NIPALS approach. In Quantitative sociology: International perspectives on mathematical and statistical model building; Blalock HM, Aganbegian A, Borodkin FM, Boudon R, Capecchi V, editors. New York: Academic Press. pp. 307–357. 47. Barnes RJ, Dhanoa MS, Lister SJ. 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc 43:772–776. 48. Jackson JE, Mudholkar GS. 1979. Control procedures for residuals associated with principal components analysis. Technometrics 21:341– 349.
Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
Near-Infrared Spatially Resolved Spectroscopy for Tablet Quality Determination BENOˆIT IGNE,1 SAMEER TALWAR,2 HANZHOU FENG,2 JAMES K. DRENNEN,1,2 CARL A. ANDERSON1,2 1 2
Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania Graduate School of Pharmaceutical Sciences, Duquesne University, Pittsburgh, Pennsylvania 15282
Received 8 April 2015; revised 11 July 2015; accepted 4 August 2015 Published online 28 August 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.24618 ABSTRACT: Near-infrared (NIR) spectroscopy has become a well-established tool for the characterization of solid oral dosage forms manufacturing processes and finished products. In this work, the utility of a traditional single-point NIR measurement was compared with that of a spatially resolved spectroscopic (SRS) measurement for the determination of tablet assay. Experimental designs were used to create samples that allowed for calibration models to be developed and tested on both instruments. Samples possessing a poor distribution of ingredients (highly heterogeneous) were prepared by under-blending constituents prior to compaction to compare the analytical capabilities of the two NIR methods. The results indicate that SRS can provide spatial information that is usually obtainable only through imaging experiments for the determination of local heterogeneity and detection of abnormal tablets that would not be detected with single-point C 2015 Wiley spectroscopy, thus complementing traditional NIR measurement systems for in-line, and in real-time tablet analysis. Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:4074–4081, 2015 Keywords: near-infrared spectroscopy; partial least squares; multivariate analysis; chemometrics; content uniformity; diffuse reflectance; tableting; spatially resolved spectroscopy
INTRODUCTION As the pharmaceutical industry pushes toward continuous manufacturing for drug substance and drug products, process analytical technologies are increasingly relied upon to ensure product quality through real-time process understanding, monitoring, and control.1–3 In the past several decades, a significant amount of work has been carried out on the implementation of techniques that allow, in-line, and in realtime, the determination of intermediate and final product critical quality attributes.4–8 For instance, the content uniformity and end-points of blends can be determined by near-infrared (NIR) and Raman spectroscopy9–13 ; granule quality parameters such as uniformity and moisture content can be measured at any time during the granulation processes with NIR spectroscopy14–16 ; granule particle size can be assessed directly after milling by light diffraction, or focused beam reflectance measurements,17,18 and so on. A significant amount of work has also been carried out to characterize tablets.19–23 As an important drug delivery platform for a large majority of pharmaceutical products, tablets are of particular interest for realtime release testing. Traditional approaches to tablet testing rely on liquid chromatography methods that require sample preparation, solvents, and are often the bottleneck for batch release. Reflectance and transmittance NIR and Raman spectroscopy have been the surrogate techniques of choice to replace wet chemistry for release testing. These single-point spectroscopic techniques usually allow the determination of tablet assay in a few seconds and have shown to be as accurate as HPLC, the Correspondence to: Carl A. Anderson (Telephone: +1-412-396-1102; Fax: +1412-396-1608; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 104, 4074–4081 (2015)
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reference method usually employed to generate the calibration model.24–27 Single-point instruments [one or several illuminations source(s) and one or several detector(s) whose outputs are combined into a single spectrum] provide into a single output a representation of the reflected or transmitted light by a sample. By nature, the properties of the signal are a function of the volume of interrogation. Thus, single-point instruments tend to average the signal from the whole transmitting or reflecting area and most, if not all, of the tablet is probed, HPLC outputs tend to correlate well with predictions. When the volume interrogated is limited and local heterogeneity occurs, correlation between assay and spectroscopic methods can be difficult. However, spatial information is not available from a singlepoint measurement. A tablet may be heterogeneous, but if the right amount of all the components is present and the whole tablet is interrogated by the spectroscopic method, the prediction of that tablet may be on target; however this tablet may not meet other quality attribute specifications. To gain spatial knowledge about the distribution of the active and excipient components in a tablet, hyperspectral chemical imaging has often been employed.28–31 Note that other techniques are available for local sampling of a tablet: laserinduced breakdown spectroscopy,32 mass spectrometry,33 and so on. Chemical imaging allows the determination of qualitative criteria that indicate if tablets possess an appropriate spatial distribution of its chemical constituents. Classification and curve resolution techniques can be helpful to associate the signal of a particular pixel with formulation components.34–37 Active pharmaceutical ingredient quantification is certainly possible but more complex by chemical imaging than by singlepoint spectroscopy. In single-point situations, one spectrum corresponds to one reference value. In imaging applications, a few hundreds or thousands of pixels can be available for a tablet and it is not possible to directly associate a reference value to
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Figure 1. The concept of spatially resolved spectroscopy (a) and the patented Indatech probe with collection channels in gray and light sources in yellow (b). Drawing not at scale.
each pixel (note that it is possible to assign reference values to each pixels using statistical methods). Consequently, quantitative models are difficult to build and, without careful experimentation, suffer from a lack of sensitivity. Spatially resolved spectroscopy (SRS) sits at the interface between hyperspectral imaging and single-point spectroscopy. Initially introduced for the determination of optical parameters (i.e., absorption and scattering coefficients), it offers the opportunity to provide spatial information while simplifying quantitative modeling capabilities.38–42 Spatially resolved spectroscopy uses one or several light sources and several collection channels. The distinct advantages of SRS reside in the location of the collection channels with respect to the sample illumination source(s). The spectra collected by the channels located in near proximity of the light source will exhibit strong scattering phenomena, whereas absorption is stronger as light travels farther in the sample. In one measurement, numerous spectra are collected with the same probe representing different degrees of light absorption and scattering. This data-cube can provide information about the sample local homogeneity, thus enabling real-time release testing capabilities for tablets in terms of assay and local homogeneity indicators and providing other measurement opportunities for ribbons, granules, and powder analysis. SRS can be seen as a simplified hyperspectral imaging system with collection fibers acting as discrete image pixels. Figure 1 summarizes the concept of SRS and sampling device used in this study. The capabilities of SRS for the determination of tablet assay were investigated. A traditional quantitative approach is presented to exploit the spectral information provided by SRS and results are contrasted with single point spectroscopy.
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calibration, whereas an inscribed design was the basis for the test set. Three replicate samples per level were manufactured from the same blend. In calibrations, three compression forces were used to generate flat faced compacts (2000, 3000, and 4000 pounds) for a total of 135 tablets (15 design points, three replicates, three compression forces). In test, only compacts at 3000 pounds were manufactured for a total of 45 samples (15 design points, three replicates). Table 1 presents the two designs. Materials for each design point were weighed and transferred into 40-mL plastic vials for the creation of samples used for the calibration and test sets. The materials were mixed for 30 min in a 5.5-L Bin-blender (L.B. Bohle, Ennigerloh, Germany) by placing them in a foam insert that was previously fit to match the dimensions of the Bin-blender. Subsequently, approximately 500 mg of powder was weighed from each blended sample and compressed into compacts on a Carver Automatic Tablet Press (Model 3887.1SD0A00; Wabash, Indiana) using a 13-mm die and flat-faced punches. In addition to calibration and test tablets, a set of compacts at the target composition—design O on Table 1—was created to test the ability of SRS to monitor manufacturing variability and drifts. This set is referred to as the homogeneity set. A total of 30 tablets were manufactured from a same blend with an equivalent mixing time as for the calibration and test samples (30 min). Twenty additional compacts underwent shorter blend times: five tablets with powder mixed for 5, 10, 15, and 20 min, respectively. Spectral Collection Single-point NIR reflectance measurements for both sides of samples were collected using a bench top scanning monochromator instrument (XDS Rapid Content Analyzer; Metrohm NIRSystems, Laurel, Maryland). Spectra were collected over the wavelength range of 400–2500 nm at 0.5-nm increments, averaging 32 co-adds per spectrum. R Spatially resolved spectra were collected with a Hy-Ternity system from Indatech (Clapiers, France). The system is composed of a NIR camera equipped with an InGaAs detector collecting data from 900 to 1700 nm at 5 nm increment and a R probe constituted of 12 channels and three light SAM-Spec sources. Figure 1b presents the design of the collection head. For each set of sample, a hypercube was collected for both sides: 12 channels, 256 wavelengths, 270/90/100 samples in calibration, test, and homogeneity respectively (samples scanned on both sides). An integration time of 0.29 ms was used to collect the data.
MATERIAL AND METHODS
Reference Testing
Sample Production
Acetaminophen reference values for all compacts were determined using HPLC (Waters Alliance 2790, Milford, Massachusetts), followed by UV detection (Waters 2487). A method, modified from USP Monograph 29 (APAP Tablets) was employed.43 The mobile phase was water:methanol:acetic acid (80:17:3) and the solid phase was a 15 cm by 4.6 mm, C18, column, with 3 :m packing. The detection wavelength was 243 nm. The error of the laboratory was estimated at 1.03%.
A formulation comprising of acetaminophen (APAP; Mallinckrodt Inc., Raleigh, North Carolina), lactose (monohydrate NF—product 316/Free flow; Foremost Farms USA, Rothschild, Wisconsin), microcrystalline cellulose (MCC; Avicel PH 200; FMC Biopolymer, Mechanicsburg, Pennsylvania), starch (potato starch; Penford Food Ingredient, Centennial, Colorado), and magnesium stearate (MgSt; Mallinckrodt, Hazelwood, Missouri) was selected. Two central composite designs were employed to generate calibration and test samples. A three-level (APAP, MCC, and lactose) circumscribed central composite design was used in DOI 10.1002/jps.24618
Modeling The data collected from SRS allows for a broad range of modeling approaches. It is possible to treat the data as a hypercube Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
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Table 1. Designs of Experiments Used to Generate Calibration and Test Sets Run ID
APAP (%, w/w)
MCC (%, w/w)
Lactose (%, w/w)
Starch (%, w/w)
MgSt (%, w/w)
Compression force (kp)
Calibration set
A B C D E F G H I J K L M N O*
23.21 23.21 23.21 23.21 31.40 31.40 31.40 31.40 20.44 34.21 27.30 27.30 27.30 27.30 27.30
30.21 30.21 40.87 40.87 30.21 30.21 40.87 40.87 35.53 35.53 26.60 44.53 35.53 35.53 35.53
17.57 23.77 17.57 23.77 17.57 23.77 17.57 23.77 20.67 20.67 20.67 20.67 15.48 25.91 20.67
28.51 22.31 17.85 11.65 20.32 14.12 9.66 3.46 22.86 9.09 24.93 7.00 21.19 10.76 16.00
0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50
2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4 2/3/4
Test set
A B C D E F G H I J K L M N O*
24.89 24.89 24.89 24.89 29.76 29.76 29.76 29.76 23.21 31.40 27.30 27.30 27.30 27.30 27.30
32.40 32.40 38.73 38.73 32.40 32.40 38.73 38.73 35.53 35.53 30.21 40.87 35.53 35.53 35.53
18.85 22.53 18.85 22.53 18.85 22.53 18.85 22.53 20.67 20.67 20.67 20.67 17.57 23.77 20.67
23.36 19.68 17.03 13.34 18.49 14.81 12.16 8.47 20.09 11.90 21.32 10.66 19.10 12.90 16.00
0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
*Designs centerpoint.
(samples by channels by wavelengths) and apply multi-way quantitative or qualitative analysis.44,45 It is also possible to unfold the cube to employ more traditional methods. The latter option was chosen in this article for the purpose of comparing collection geometry; focusing on the properties of the data rather than the wealth of available modeling strategies. For tablet assay modeling, a spectrum corresponding to the average of the channels on either side of the light sources was employed: channels 1, 2, and 3 (31 and 32), 7 (71 and 72), 8, and 9 (see Fig. 2). Results were contrasted with single-point spectra obtained for each tablet. The spatial information provided by SRS was then utilized to provide an evaluation of the spatial
distribution of the active ingredient in homogeneous and underblended tablets. Partial least-squares regression was used to develop all quantitative models with the NIPALS algorithm.46 The choice of latent variables was determined by comparing the evolution of the root mean square error of calibration (RMSEC) and of cross-validation (RMSECV). All calculations were performed with MATLAB 2011a (The Mathworks, Natick, Massachusetts) equipped with the PLS Toolbox v. 6.2.1 (Eigenvector Research Inc., Wenatchee, Washington).
RESULTS AND DISCUSSION Spectral Investigation
Figure 2. Selected spectral channels. Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
The spectral output of a SRS measurement is information rich. With an integration time of 290 :s, a hypercube was created containing reflectance spectra of a particular sample at different locations. Figure 3 shows several of these outputs. Figure 3a displays the single-point spectra of two heterogeneous tablets (#4 and #20 with 5 and 20 min of blending time, respectively) and an homogeneous tablet (30 min blending). Although a clear difference exists between the spectra of homogeneous and heterogeneous tablets, spectra corresponding to the heterogeneous tablets provide an average difference which may correspond to both heterogeneity of low/high potency, without the ability to identify the root cause of the fault. As a DOI 10.1002/jps.24618
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Figure 3. Single point and spatially resolved spectroscopy outputs. (a) Average SRS output for heterogeneous and homogeneous tablets (5, 20, and 30 min blending, respectively); (b) single-point spectra of heterogeneous and homogeneous tablets (5, 20, and 30 min blending, respectively); (c) SRS surface plot of an heterogeneous tablet. Channels 1, 2, 3 and 7, 8, 9 are located on each side of the light source.
comparison, Figure 3b shows the average of individual spectra and their average for an homogeneous (30 min blending) and an heterogeneous tablet (5 min blending) using SRS. Individual SRS spectra for tablet 4 (5 min blending) are presented in Figure 3c. Figure 3c is a spatial representation of the spectra as a function of their collection channels. This particular plot corresponds to tablet #4 of the heterogeneous group (5 min blend time). It is possible to see the large discrepancy between the spectra located on either side of the light source due to differences in absorption and scattering, but also differences related to heterogeneity when comparing channels at equidistance from the light source. This is particular visible between channels 3 and 7. If the tablet was homogeneous, these data should be similar because the channels are located at the exact same distance on either side of the light source. However, the spectrum shows significant absorption level differences, indicating that the tablet is spatially heterogeneous. Differences between channels 1, 2, and 3 or 7, 8, and 9 are because of a combination of chemical and physical characteristics. These figures demonstrate that SRS possesses the same information as single-point spectroscopy while based on different scales of scrutiny, but also provides enhanced diagnostics capabilities that would approximate the information gained through chemical imaging. DOI 10.1002/jps.24618
Traditional Modeling Analysis Regression models were developed on comparable data collected from a single-point and an SRS spectrometer. Data collected from only one side of the tablet were used in both cases. The mean of all channels indicated as “selected” in top panel (red highlight) of Figure 2 was employed for SRS calibration. The spectrum of each tablet for single-point NIR was truncated to match the SRS instrument range. The spectral mean of the SRS data was considered to be most representative of a single point measurement where detectors average photons that have had both short and long paths into the samples. However, in single point measurements, it is not possible to observe these different light paths independently. Figure 4 presents the quantitative results. Random block cross-validation (five blocks) was used to determine the most appropriate spectral pretreatments and model complexity (number of latent variables). An optimization was independently conducted for each model. Standard Normal Variate47 followed by mean-centering was selected for the single-point model, whereas normalization to unit area also followed by mean-centering was employed for the SRS model. Performance of the single point and SRS instruments was compared. The root mean square error of prediction (RMSEP) Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
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Figure 4. Quantitative evaluation. (a) Single-point predicted versus reference plot for calibration (black circles) and test (red triangles) samples; (b) SRS predicted versus reference plot for calibration (black circles) and test (red triangles) samples; (c) single-point prediction residuals of homogeneous (blue dots) and heterogeneous (red dots) samples; (d) SRS prediction residuals of homogeneous (blue dots) and heterogeneous (red dots) samples. The dotted lines in figures c and d represent the ±5% limit. Two points representing prediction residuals above 25% were omitted to allow for the creation of comparable charts ((c) and (d)).
for the methods developed for the single point and SRS systems and tested using the test set was 1.27% (w/w) (Fig. 4a) and 1.71% (w/w) (Fig. 4b), respectively. The single-point instrument performed better than the SRS based solely on the independent test set. However, these results do not demonstrate the full utilization of the data collected by the SRS instrument. The two configurations do not interrogate the same volume of sample. The single-point measurement system used in this study has a spot size of 9.5 mm. Consequently, it illuminates most, if not all, the tablet surface and employs a series of eight detectors positioned under the sample that collect most of the diffusely reflected light. This system is specifically optimized for this type of measurement. The SRS probe interrogates the sample in a very different manner. It is designed to be sensitive to local chemical heterogeneity and physical properties differences (i.e., density or mean particle size) at the cost of reduced sampling area of the sample. The smaller area of the tablet sampled leads to an increase in the random noise and therefore an increase in RMSEP. It should be noted that this Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
increase in error is largely attributed to precision rather than accuracy. Spatially resolved spectroscopy for quantitative oral solid dosage forms provides not only determination of active pharmaceutical ingredient content and an indication of global tablet homogeneity, but also an indication of local heterogeneities without the requirement for multiple measurements per tablet. Figures 4c and 4d illustrate this point. The models were used to predict the homogeneity set and the prediction residuals were compared between the two instrumental configurations. Although the tablets corresponding to homogeneous blends (samples indicated in blue—30 samples) were well within the ±5% label claim, SRS predictions presented a higher variance (mean: 3.5% label claim; SD 1.26%) compared with single-point outputs (mean: 1.5% label claim; SD 0.59%) as expected because of the higher observed prediction error. Nonetheless, on heterogeneous samples, SRS results presented larger residuals (mean: 6.23% label claim; SD: 22.96%) than single-point spectroscopy (mean: 1.32% label claim; SD: 11.93%), indicating DOI 10.1002/jps.24618
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Figure 5. Distribution of the predicted values for the homogeneity set. Each boxplot represents the distribution of 10 predicted values per tablet according to the probe head design. The first 20 tablets were manufactured after reduced blending time (groups of five samples blended for 5, 10, 15, and 20 min). The next 30 samples correspond to tablets manufactured with the target blending time.
that the method is able to detect local heterogeneity. Note on Figure 4d that not all tablets appear on the graph, because being predicted outside of the displayed scale (±25%, w/w residual). This can be explained by the fact that non-homogeneous blends tend to have a poor distribution of various formulation components that can be probed by one or several fibers from the collection head, thus dramatically impacting the final mean spectrum from each individual fiber. Consequently, for a tablet to appear homogeneous, it must present the same spectral characteristics at all channels. This explains the sensitivity of SRS for the detection of heterogeneous samples.
Extracting Spatially Resolved Information In the preceding section, SRS was used as a single-point detector to demonstrate that similar work can be achieved. However, little is gained from using SRS combined with the traditional modeling approaches. Without expanding the present paper into n-way and other n-dimensional modeling techniques, the advantages that SRS provide can be demonstrated by treating predicted values from each channel as semi-independent values, thus enabling the intra and inter-tablet comparison of active distribution. This approach in presenting the results is closer to the treatment of chemical images where each pixel is a prediction, allowing the calculation of distribution statistics. Figure 5 presents, for the homogeneity set (30 homogeneous and 20 under-blended tablets), the distribution of the predicted values of each channel. The difference with the previous section is that instead of averaging the 10 predicted values per tablet, that distribution information is displayed. Using boxplots (a statistical display presenting mean, median, 25th and 75th quartiles, abnormal values and outliers), it is possible to understand the distribution of the predicted values for the active ingredient in each tablet. Thus, tablets manufactured after 30 min of blending presented a tight distribution around the label claim target (27.3%, w/w). Alternatively, tablets made after reduced blending time displayed wide distributions of predicted values and/or biased means. DOI 10.1002/jps.24618
Figure 5 illustrates the results for homogeneous tablets. While both techniques (SRS and single-point spectroscopy) accurately determine when a tablet is out of specification, only SRS can provide an assessment, for what would appear to be in specification tablets, of the distribution of active. Amongst the 30 “good” tablets, some presented very tight distributions in the active (i.e., tablets # 21, 29 38, 39, 48), whereas others, although displaying a predicted mean within the specification limits, had channels predicting higher or lower active values than the target. In this study, the tablets were assayed directly after measurements and no other quality attribute were investigated. The authors speculate that aggregates of active ingredient or excipients could have been responsible for the lower or higher predicted values. But for spatially resolved measurements such as the one SRS provides, such differences could not be seen, let alone diagnosed, and resolved.
CONCLUSIONS The ability of NIR SRS to evaluate tablet assay was investigated. Using a designed experiment and tablets made from blends at normal operating and under-blended conditions, calibration models were developed and compared with traditional single-point NIR measurement geometry. Values for the RMSEP of the two calibrations indicated that the single-point measurement spectrometer outperformed those of an SRS spectrometer. However, SRS outputs proved to be more sensitive to tablet heterogeneity than single-point measurements. Specifically, SRS was demonstrated to be sensitive to the degree of heterogeneity of ingredients within tablets using simple statistical tools such as boxplots. The distribution of the predicted values from each tablet could be further used to set specifications on the intra-tablet variability, a criterion usually available only through chemical imaging. More advanced mathematical treatments (n-way analysis), not considered in this study, may further enhance the sensitivity of the SRS to spatial distribution of ingredients and therefor the suitability of SRS for more sophisticated analyses. Nevertheless, SRS has the potential to Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015
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provide real-time information about ingredient distribution in tablets.
ACKNOWLEDGMENTS The research team at the Duquesne Center for Pharmaceutical Technology would like to thank Indatech (Fabien Chauchard and Boris Larcheveque) and Metrohm NIRSystem (Denise Root) for lending the spectrometers.
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Igne et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:4074–4081, 2015