- Email: [email protected]
Screening of Heparin API by Near Infrared Reflectance and Raman Spectroscopy
Screening of Heparin API by Near Infrared Reflectance and Raman Spectroscopy JOHN A. SPENCER, JOHN F. KAUFFMAN, JOHN C. REEPMEYER, CONNIE M. GRYNIEWICZ, WEI YE, DUCKHEE Y. TOLER, LUCINDA F. BUHSE, BENJAMIN J. WESTENBERGER Food and Drug Administration, Center for Drug Evaluation and Research, Division of Pharmaceutical Analysis, 1114 Market St., St. Louis, Missouri 63101
Received 17 October 2008; revised 13 November 2008; accepted 14 November 2008 Published online 30 December 2008 in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/jps.21660
ABSTRACT: Near infrared (NIR) reflectance and laser Raman spectra for a set of 69 heparin powder samples obtained from several foreign and domestic suppliers were measured. Both the NIR and Raman spectra of individual heparin API powder samples were correlated with sample compositions determined from response corrected relative peak areas of the capillary electropherograms of the samples using a partial least squares (PLS) regression model. Twenty-eight sample spectra were used to develop PLS models for the three major sample components; heparin, oversulfated chondroitin sulfate (OSCS) and glycosaminoglycans (GAGs). The PLS models were then used to successfully predict the compositions of 41 additional heparin samples. The success of these rapid, nondestructive technologies to identify contamination of heparin with OSCS demonstrates the potential of spectroscopy and chemometrics for screening of processed raw materials. These technologies are meant for screening purposes and not meant to replace either of the methods (capillary electrophoresis and NMR) currently required by USP and FDA. ß 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:3540–3547, 2009
Keywords: near infrared; Raman; heparin; oversulfated chondroitin sulfate (OSCS); chemometrics; partial least squares (PLS)
INTRODUCTION There have been a number of reports of patients having adverse responses to heparin finished product solutions made from certain raw materials originating in China.1–3 Adverse events including severe allergic responses and even death4,5 have elicited a strong response from the FDA and manufacturers to remove tainted or suspect products from the market.6–8 Subsequent investigation into the cause of the adverse events identified several lots of heparin active pharmaceutical ingredient (API) that could be proCorrespondence to: John A. Spencer (Telephone: 314-5393859; Fax: 314-539-2113; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 98, 3540–3547 (2009) ß 2008 Wiley-Liss, Inc. and the American Pharmacists Association
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blematic. Both qualitative and quantitative analytical methods have been applied to these materials in an attempt to identify the cause of the adverse events. Capillary electrophoresis (CE) of the samples suggested that the suspect lots were contaminated. Subsequent isolation and analysis by nuclear magnetic resonance9 (NMR) and biological methods10 have identified oversulfated chondroitin sulfate (OSCS) as a contaminant and as the likely source of the adverse responses.11 Dermatan sulfate, a glycosaminoglycan (GAG) impurity, was also found in some lots of heparin. Now that OSCS and GAGs have been identified as potential contaminants, a rapid analytical method to screen for the presence of these materials in heparin is desirable. The work presented here employs multivariate calibration methods (chemometrics) to relate sample compositions
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determined from response-corrected peak areas of capillary electropherograms to subtle composition dependent variations in near infrared reflectance and Raman emission spectra of powdered heparin samples. These multivariate models can then be used to rapidly screen new lots of bulk heparin API for the presence of OSCS and GAG contaminants.
EXPERIMENTAL Reference samples of heparin sodium salt were Grade I-A from porcine intestinal mucosa (Sigma Chemical, St. Louis, MO, cat. no. H3393-500KU). Chondroitin sulfate A sodium salt was obtained from bovine trachea (Sigma Chemical, cat. no. C9819). Dermatan sulfate (chondroitin sulfate B sodium salt, minimum 90%, Sigma Chemical, cat. no. C3788) was obtained from Sigma Chemical. GAGs were obtained from Scientific Protein Laboratories (Waunakee, WI, product no. 809679). OSCS was prepared by the method of Maruyama et al.12 Sixty-nine separately collected samples of heparin API from several different Chinese and US sources were used in this study. These samples were primarily in the form of fine off-white powders, and represented 46 distinct lots of API. Samples from 28 different lots were selected as the training set for chemometric calibration of both NIR and Raman spectra. Thirty additional samples from 13 different lots comprised the validation test set for the NIR calibration model. Thirty-three additional samples from 18 different lots comprised the validation test set for the Raman calibration model. Most samples were measured using both NIR and Raman spectroscopy. Some samples were too fluorescent for Raman analysis, and other samples were available in quantities too small for analysis by NIR spectroscopy. All sample concentrations were determined by CE to provide reference concentrations for model development and assessment. CE13–16 was conducted on a Hewlett Packard 3D-CE instrument equipped with a diode array detector set at 200 nm with a 10 nm bandpass. Separations were performed in a fused silica capillary, internal diameter 50 mm, 64.5 cm-total length, 56 cm-effective length, with an electrolyte solution of 36 mM sodium phosphate buffer, pH 3.5, at 258C using reverse polarity at 30 kV. Samples at a concentration of approximately 10 mg/mL water were introduced by hydrodyDOI 10.1002/jps
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namic pressure at 50 mbar for 10 s. The capillary column was preconditioned at the beginning of each day by flushing with 1 M NaOH for 2 min and 0.1 M NaOH for 2 min. Prior to running each sample, the system was flushed with water for 2 min and then buffer solution for 2 min. Each sample solution was filtered through a 0.2 mm cellulose acetate centrifuge filter (Micro-Spin filter, Alltech Associates, Deerfield, IL). Run time was 15 min, and the heparin peak was normally seen at approximately 5.6 min. Electropherogram peak areas for OSCS, heparin and GAGs were determined by the tangent skim method. Relative areas were converted to relative concentrations (weight percents) for each major component using calibrated responsecorrected peak areas determined from pure component electropherograms. One, two, or three peaks were found in the heparin region and are reported in Table 1. The samples whose compositions are listed in Table 1 comprise the training set for chemometric calibration. Additional samples were analyzed by CE, NIR spectroscopy and Raman spectroscopy to test and validate the chemometric models, as described above. The near infrared reflectance was measured with a Thermo-Nicolet Antaris FT spectrometer. Powdered heparin API was transferred to 8 mm 40 mm standard clear glass flat-bottomed vials filled to a depth of 6–8 mm (180 mg). The vials were gently tapped against the bench to compact and level the powder before scanning through the bottom of the vials using an integrating sphere optical configuration. Spectra consisted of 6224 points over the range 4000– 10,000 cm1 collected at 2 cm1 resolution integrating 16 scans for both the background and the sample. NIR spectra of 28 samples from different heparin lots comprised the training set for model development. Thirty additional samples comprised the test set for validation of the NIR chemometric model. Forty-one NIR spectra of these 30 samples were measured, which included 11 replicate sample measurements. Raman spectra were collected with a Dimension-P1 spectrometer (Lambda Solutions, Inc., Waltham, MA) equipped with a fiber optically coupled microscope probe and a manually positioned x, y stage. Raman samples of the same materials as used in NIR were placed in the wells of a small-volume 96-well plate. Irradiation from above was used to avoid fluorescence. The 785 nm excitation was delivered to the sample through a microscope objective, and the focus was adjusted
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Table 1. Capillary Electrophoresis Results for the 28 Calibration Samples Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Wt.% OSCS
Wt.% Heparin
Wt.% GAGs
0.0 0.0 0.0 0.0 0.0 26.6 9.8 0.0 0.0 1.7 3.8 7.9 15.5 0.0 3.6 3.2 0.0 4.6 0.0 14.7 0.0 2.5 0.0 9.9 0.0 0.0 0.0 0.0
92.7 97.4 96.9 89.9 98.2 72.8 85.3 100.0 93.1 93.8 80.3 84.1 76.8 100.0 87.2 87.0 92.7 93.3 93.4 85.3 97.3 88.8 98.0 81.5 91.7 97.1 94.8 87.0
7.3 2.6 3.1 10.1 1.8 0.6 4.9 0.0 6.9 4.5 16.0 8.0 7.8 0.0 9.2 9.9 7.3 2.2 6.6 0.0 2.7 8.6 2.0 8.6 8.3 2.9 5.2 13.0
at each well to maximize the Raman signal before data collection. Spectra were measured over the Raman shift range 150–3100 cm1 at 5 cm1 resolution using 245 mW of laser power and 25 s total acquisition time. Raman spectra of 28 samples from separate heparin lots comprised the training set for chemometric model development. Thirty-three additional samples comprised the test set for validation of the Raman chemometric model. Thirty-eight Raman spectra of these 33 samples were measured, which included 5 replicate sample measurements. Partial least squares (PLS) calculations were performed using Pirouette 3.11 (Infometrix, Bothell, WA). The compositions measured with the CE reference method for the 28 samples listed in Table 1 were used as the dependent variables to generate the predictive models for both NIR and Raman spectral calibration models. A leave-oneout cross-validation strategy was used during model development to identify the best-fit model.
A variety of preprocessing and transform treatments were examined for both NIR and Raman spectral data prior to multivariate calibration. The optimum overall conditions for the NIR reflectance model were obtained by subtraction of a best-fit quadratic baseline, applying multiplicative scattering correction (MSC)17 and meancentering the spectral values for the entire measured range of 4000–10,000 cm1. The combination of quadratic baseline correction and MSC gave lower error values than either of the two separately. This suggests that particle size variation between samples is responsible for some of the NIR variance.17,18 The Raman spectra were preprocessed by subtracting the best-fit quartic baseline followed by truncation and vector normalization. The Raman spectra were truncated to include only the 145–1570 cm1 range for chemometric analysis. Following vector normalization, Raman spectra were mean-centered prior to chemometric analysis.
RESULTS CE electropherograms of heparin samples exhibited four distinct CE peak patterns. Typical electropherograms for samples in this study are shown in Figure 1. Three peaks are resolved at 5.2, 5.6, and 6.5 min retention time. The 5.6-min peak is assigned to heparin sulfate, and the 6.5-min peak has been assigned to a mixture of several closely related GAGs. The 5.2-min peak has been attributed to an OSCS compound that has been linked to the adverse effects of the heparin samples.10 The relative weight percent contributions of each of these species to the sample mass are presented in Table 1. Note that in many cases only one or two of the three possible peaks are observed. The relative weight percent values determined by CE provide the reference values that are the quantitative basis for the predictive chemometric model. The compositions reported in Table 1 assume that the three peaks in the 5- to 8-min range account for the entire active content of the sample. NIR Spectra of heparin and the two related solids are displayed in Figure 2. Absorption bands at 5200 and 6900 cm1 agree with earlier studies in solution.19 Heparin displays an irregular peak at 4730 cm1 and a shoulder at 6500 cm1 which distinguish it from dermatan sulfate (a GAG, also termed chondroitin B). OSCS has two small peaks in the region around 4730 cm1, another peak at
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Figure 1. Characteristic capillary electropherograms: (A) sample 8, single heparin peak, (B) sample 20, OSCS þ heparin, (C) sample 11, OSCS þ heparin þ GAGs, (D) sample 25, heparin þ GAGs.
Figure 2. Comparison of the NIR reflectance of heparin (solid line) and the principal contaminants— OSCS (dashed line) and dermatan sulfate or GAGs (dotted line). All were measured through the bottom of 8 mm 40 mm clear glass vials. DOI 10.1002/jps
5800 cm1 and a third peak at 7000 cm1. The presence of OSCS as a contaminant in heparin is expected to shift the large heparin peak at 6900 cm1 to higher energy. The NIR spectra are so heavily overlapped that no distinguishing features between the ‘‘Good’’ and ‘‘Suspect’’ samples are apparent. The baseline-corrected, vector normalized Raman spectra of heparin, OSCS, chondroitin A and the GAG impurity dermatan sulfate are shown in Figure 3. The heparin spectra agree with earlier Raman studies of heparin in solid and solution.20–22 Small features at shifts above 1570 cm1 are nearly identical in all the components and provide justification for the truncation. Chemometric models that omitted spectral data above 1572 cm1 were found to produce more accurate predictive models. The strong peaks at 1040 and 1055 cm1 in the heparin spectrum have been assigned to the N-sulfate and 6-O-sulfate,
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Table 2. PLS Model Factors and Validation Errors for NIR and Raman Spectra of API Samples Wt.% OSCS Wt.% Heparin Wt.% GAGs NIR RMSECV Factors RMSEP Raman RMSECV Factors RMSEP Figure 3. Comparison of Raman spectra of heparin and the principal contaminants after quartic baseline correction, vector normalization and truncation to the range 146–1572 cm1. All Raman spectra were treated this way in developing the chemometric model. The spectra are offset upward in increments of 0.06 relative units for clarity.
respectively. The 2-O-sulfate peak at 1065 cm1 may broaden the strong peak on the high-energy side. The OSCS spectrum exhibits similar structure in the 1052 cm1 peak, with additional small peaks at 1014 and 986 cm1.23 The latter are not evident in the spectrum of chondroitin A, and may result from sulfation. The peak at 880 cm1 in heparin is considerably smaller and shifted in OSCS, and the spectral structure in the 1200 to 1450 cm1 region also differs. Both dermatan sulfate and chondroitin A exhibit some differences from heparin in these spectral regions. These spectral differences are expected to support multivariate correlation between Raman spectra and sample composition. As with the NIR, there are no clear-cut distinctions between the spectra of ‘‘Good’’ and ‘‘Suspect’’ samples in the Raman. Identical training sample sets were used to develop NIR and Raman PLS calibration models for each of the three major components (OSCS, heparin, and GAGs) of the heparin APIs. Thus the accuracy of the NIR and Raman calibration models can be directly compared. The quality of fit between the training set spectra and the reference compositions can be characterized by the root mean squared error of leave-one-outcross-validation (RMSECV), which was calculated for each of the three components of the heparin API samples in the training set. These values are reported in Table 2 along with the optimized number of PLS factors used in each model. The
1.2 2.8 2.5 6 6 6 41 test samples (11 replicates) 1.4 5.5 7.3 1.7 2.5 2.2 4 3 3 38 test samples (5 replicates) 1.5 3.3 3.2
The same 28 sample training set in Table 1 was used for both NIR and Raman models. NIR: Quadratic baseline adjustment, MSC, mean centered. Raman: quartic baseline adjustment, vector normalized, range truncated, mean centered.
RMSECV characterizes the error associated with the combination of the predictive error of the model and the sensitivity of the model to individual spectra. RMSECV values of 1.2% and 1.7% were obtained for NIR and Raman calibrations of OSCS, respectively. NIR and Raman models for Heparin and GAGs also had similar RMSECV values, all in the 2.2–2.8% range. The NIR and Raman PLS models were used to predict the sample compositions for a group of additional samples from a similar variety of sources as the training set. The root mean squared errors of prediction (RMSEP) values shown in Table 2 reflect the average error between the model prediction and the reference compositions. RMSEP values for the NIR and Raman models for OSCS were similar to the RMSECVs for these models, and indicate that both NIR and Raman calibrations are capable of determining the OSCS composition of samples with an uncertainty of about 1.5%. RMSEPs of the NIR models for heparin and GAGs were 2–3 times larger than the RMSECVs for these models, and RMSEPs of the Raman models for heparin and GAGs were about 50% larger than the RMSECV values. These results indicate the Raman heparin and GAGs models should provide more accurate composition estimates than the corresponding NIR model. Because OSCS has been correlated with heparin adverse events, a screening procedure to identify suspect heparin samples would focus on OSCS predictions. OSCS prediction plots for the NIR and Raman test sets are displayed in Figures 4 and 5, respectively. The plots suggest
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Figure 4. NIR PLS model test for OSCS. Forty-one sample test set including 11 replicate spectra. Solid diamond points are considered ‘‘Good’’; open square points, having more than 1% OSCS, are ‘‘Suspect.’’ The proximity of the replicate points demonstrates the reproducibility of the spectral measurement. [RMSEP ¼ 1.4 mg% (6 factors)].
Figure 5. Raman PLS model test for OSCS. Thirtyeight samples with five replicates. Solid diamond points are considered ‘‘Good’’; open square points are ‘‘Suspect’’. [RMSEP ¼ 1.5% (4 factors)]. DOI 10.1002/jps
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that a threshold value of 1% predicted OSCS can be used to eliminate suspicious heparin samples. When the NIR model is used, a 1% threshold resulted in 38 out of 41 samples correctly classified as being either good (15 samples at OSCS <1%) or suspect (26 samples >1% OSCS). One good sample was classified as suspect (1 false negative) and one suspect sample was classified as good (false positive). Prediction with the Raman model showed similar accuracy, with 36 out of 38 samples being correctly classified with one false positive and one false negative. The overall accuracy in classifying heparin samples as suspect or good using these spectroscopic/chemometric methods as screening tools can be expected to exceed 95%. Both NIR and Raman allow the elimination of over 60% of the heparin samples as suspicious. The remaining 40% would be subjected to additional analyses by CE, NMR or other separation methods to detect the presence of low levels of OSCS.
DISCUSSION NIR and Raman spectroscopy coupled with multivariate calibration are capable of distinguishing differences in the chemical composition of mixtures with virtually no sample preparation. Further, both methods are well suited for rapid screening procedures that are intended to evaluate a large number of samples quickly. NIR requires about 150–200 mg of sample, is rapid and nondestructive, and is insensitive to fluorescence. Conventional and micro Raman are also rapid and nondestructive. Micro Raman can measure a very small sample, but Raman spectroscopy is sensitive to fluorescence in the sample or the sample container. CE is the most precise of the three techniques examined here and, as such, must remain the reference method for developing chemometric models for new API sample sources. The precision of NIR and Raman are clearly shown to be comparable to each other in this study so the choice of method will depend on the size and state of the samples to be measured. The methods described in this work are appropriate for samples with chemical, physical, and optical properties that match the properties of the training set. The training set must represent a balanced cross-section of all the possible compositions and optical conditions of the larger general group to be monitored. New samples that differ
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significantly from the original set are likely to give spurious PLS model predictions. While the training set used here included samples from 28 heparin lots, application of the model to heparin lots not included in the training set did result in accurate predictions, with an average RMSEP of 1.5%.
CONCLUSIONS This work shows that both NIR and Raman spectroscopy can be used to identify solid heparin API samples containing more than 1% OSCS as a contaminant. Therefore either technology would provide a rapid means of screening raw heparin API material as a preliminary step to subsequent processing. Simple-to-operate hand-held NIR and Raman devices are available to assist the surveillance of heparin API. Both NIR and Raman should provide similar precision and accuracy for heparin purity determinations, so the choice of device can be largely based on sample properties. The spectroscopic methods described here provide compositional information on all three possible components (OSCS, heparin, and GAGs) of significance in the quality control of heparin, but are not meant to replace the methods currently required by FDA for detection of OSCS (CE and NMR).
ACKNOWLEDGMENTS Thanks to Dr. Ali Al Hakim and Dr. Moheb Nasr for helpful reviews of the manuscript. The work reported here reflects the current thinking and experience of the authors. This is not a policy document and should not be used in lieu of regulations, published FDA guidances or direct discussions with the agency.
REFERENCES 1. Anon. 2008. Acute allergic-type reactions among patients undergoing hemodialysis—Multiple states, 2007–2008. Morb Mortal Wkly Rep 57:124–125. 2. Nose Y. 2008. Hemodialysis patients’ deaths in the USA by contaminant suspected heparin originating from China. Artif Organs 32:425–426. 3. Tan J. 2008. Tainted heparin. Chem Eng News 86:6–6. 4. Anon. 2008. What you don’t know will hurt you. Nat Biotechnol 26:589–589.
5. Kuehn BM. 2008. Heparin deaths. JAMA 299:2379– 2379. 6. Jia H. 2008. Regulators scramble to tighten loopholes after heparin debacle. Nat Biotechnol 26:477– 478. 7. Laurencin CT, Nair L. 2008. The FDA and safety— Beyond the heparin crisis. Nat Biotechnol 26:621– 623. 8. Leiden JM. 2008. Canaries, coal mines and the drug supply. Nat Biotechnol 26:624–626. 9. Guerrini M, Beccati D, Shriver Z, Naggi A, Viswanathan K, Bisio A, Capila I, Lansing JC, Guglieri S, Fraser B, Al-Hakim A, Gunay NS, Zhang Z, Robinson L, Buhse L, Nasr M, Woodcock J, Langer R, Venkataraman G, Linhardt RJ, Casu B, Torri G, Sasisekharan R. 2008. Oversulfated chondroitin sulfate is a contaminant in heparin associated with adverse clinical events. Nat Biotechnol 26:669–675. 10. Kishimoto TK, Viswanathan K, Ganguly T, Elankumaran S, Smith S, Pelzer K, Lansing JC, Sriranganathan N, Zhao G, Galcheva-Gargova Z, Al-Hakim A, Bailey GS, Fraser B, Roy S, RogersCotrone T, Buhse L, Whary M, Fox J, Nasr M, Dal Pan GJ, Shriver Z, Langer RS, Venkataraman G, Austen KF, Woodcock J, Sasisekharan R. 2008. Contaminated heparin associated with adverse clinical events and activation of the contact system. N Engl J Med 358:2457–2467. 11. Amarasekara AS, Opoku G, Qiu X, Doctor V. 2007. Effect of oversulfation on the chemical and biological properties of chondroitin-6-sulfate. Carbohydr Polym 68:116–121. 12. Maruyama T, Toida T, Imanari T, Yu G, Linhardt RJ. 1998. Conformational changes and anticoagulant activity of chondroitin sulfate following its O-sulfonation. Carbohydr Res 306: 35–43. 13. Ampofo SA, Wang HM, Linhardt RJ. 1991. Disaccharide compositional analysis of heparin and heparan sulfate using capillary zone electrophoresis. Anal Biochem 199:249–255. 14. Gunay NS, Linhardt RJ. 2003. Capillary electrophoretic separation of heparin oligosaccharides under conditions amenable to mass spectrometric detection. J Chromatogr A 1014:225–233. 15. Patel RP, Narkowicz C, Hutchinson JP, Hilder EF, Jacobson GA. 2008. A simple capillary electrophoresis method for the rapid separation and determination of intact low molecular weight and unfractionated heparins. J Pharm Biomed Anal 46:30–35. 16. Volpi N, Maccari F, Linhardt RJ. 2008. Capillary electrophoresis of complex natural polysaccharides. Electrophoresis 29:3095–3106. 17. Geladi P, MacDougall D, Martens H. 1985. Linearization and scatter-correction for near-infrared reflectance spectra of meat. Appl Spectrosc 39: 491–500.
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18. O’Neil AJ, Jee RD, Moffat AC. 2003. Measurement of the percentage volume particle size distribution of powdered microcrystalline cellulose using reflectance near-infrared spectroscopy. Analyst 128: 1326–1330. 19. Grant D, Long WF, Williamson FB. 1991. Heparinpolypeptide interaction. Near-i.r. spectroscopy in an anhydrous dispersant allows the involvement of polymer-associated water to be assessed. Biochem J 277:569–571. 20. Sushko NI, Firsov SP, Zhbankov RG, Tsarenkov VM, Marchewka M, Ratajczak C. 1994. Vibrational
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spectra of heparins. J Appl Spectrosc 61:704– 707. 21. Atha DH, Coxon B, Reipa V, Gaigalas AK. 1995. Physicochemical characterization of low molecular weight heparin. J Pharm Sci 84:360–364. 22. Atha DH, Gaigalas AK, Reipa V. 1996. Structural analysis of heparin by Raman spectroscopy. J Pharm Sci 85:52–56. 23. Cabassi F, Casu B, Perlin AS. 1978. Infrared absorption and Raman scattering of sulfate groups of heparin and related glycosaminoglycans in aqueous solution. Carbohydr Res 63:1–11.
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Received 17 October 2008; revised 13 November 2008; accepted 14 November 2008 Published online 30 December 2008 in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/jps.21660
ABSTRACT: Near infrared (NIR) reflectance and laser Raman spectra for a set of 69 heparin powder samples obtained from several foreign and domestic suppliers were measured. Both the NIR and Raman spectra of individual heparin API powder samples were correlated with sample compositions determined from response corrected relative peak areas of the capillary electropherograms of the samples using a partial least squares (PLS) regression model. Twenty-eight sample spectra were used to develop PLS models for the three major sample components; heparin, oversulfated chondroitin sulfate (OSCS) and glycosaminoglycans (GAGs). The PLS models were then used to successfully predict the compositions of 41 additional heparin samples. The success of these rapid, nondestructive technologies to identify contamination of heparin with OSCS demonstrates the potential of spectroscopy and chemometrics for screening of processed raw materials. These technologies are meant for screening purposes and not meant to replace either of the methods (capillary electrophoresis and NMR) currently required by USP and FDA. ß 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:3540–3547, 2009
Keywords: near infrared; Raman; heparin; oversulfated chondroitin sulfate (OSCS); chemometrics; partial least squares (PLS)
INTRODUCTION There have been a number of reports of patients having adverse responses to heparin finished product solutions made from certain raw materials originating in China.1–3 Adverse events including severe allergic responses and even death4,5 have elicited a strong response from the FDA and manufacturers to remove tainted or suspect products from the market.6–8 Subsequent investigation into the cause of the adverse events identified several lots of heparin active pharmaceutical ingredient (API) that could be proCorrespondence to: John A. Spencer (Telephone: 314-5393859; Fax: 314-539-2113; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 98, 3540–3547 (2009) ß 2008 Wiley-Liss, Inc. and the American Pharmacists Association
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blematic. Both qualitative and quantitative analytical methods have been applied to these materials in an attempt to identify the cause of the adverse events. Capillary electrophoresis (CE) of the samples suggested that the suspect lots were contaminated. Subsequent isolation and analysis by nuclear magnetic resonance9 (NMR) and biological methods10 have identified oversulfated chondroitin sulfate (OSCS) as a contaminant and as the likely source of the adverse responses.11 Dermatan sulfate, a glycosaminoglycan (GAG) impurity, was also found in some lots of heparin. Now that OSCS and GAGs have been identified as potential contaminants, a rapid analytical method to screen for the presence of these materials in heparin is desirable. The work presented here employs multivariate calibration methods (chemometrics) to relate sample compositions
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determined from response-corrected peak areas of capillary electropherograms to subtle composition dependent variations in near infrared reflectance and Raman emission spectra of powdered heparin samples. These multivariate models can then be used to rapidly screen new lots of bulk heparin API for the presence of OSCS and GAG contaminants.
EXPERIMENTAL Reference samples of heparin sodium salt were Grade I-A from porcine intestinal mucosa (Sigma Chemical, St. Louis, MO, cat. no. H3393-500KU). Chondroitin sulfate A sodium salt was obtained from bovine trachea (Sigma Chemical, cat. no. C9819). Dermatan sulfate (chondroitin sulfate B sodium salt, minimum 90%, Sigma Chemical, cat. no. C3788) was obtained from Sigma Chemical. GAGs were obtained from Scientific Protein Laboratories (Waunakee, WI, product no. 809679). OSCS was prepared by the method of Maruyama et al.12 Sixty-nine separately collected samples of heparin API from several different Chinese and US sources were used in this study. These samples were primarily in the form of fine off-white powders, and represented 46 distinct lots of API. Samples from 28 different lots were selected as the training set for chemometric calibration of both NIR and Raman spectra. Thirty additional samples from 13 different lots comprised the validation test set for the NIR calibration model. Thirty-three additional samples from 18 different lots comprised the validation test set for the Raman calibration model. Most samples were measured using both NIR and Raman spectroscopy. Some samples were too fluorescent for Raman analysis, and other samples were available in quantities too small for analysis by NIR spectroscopy. All sample concentrations were determined by CE to provide reference concentrations for model development and assessment. CE13–16 was conducted on a Hewlett Packard 3D-CE instrument equipped with a diode array detector set at 200 nm with a 10 nm bandpass. Separations were performed in a fused silica capillary, internal diameter 50 mm, 64.5 cm-total length, 56 cm-effective length, with an electrolyte solution of 36 mM sodium phosphate buffer, pH 3.5, at 258C using reverse polarity at 30 kV. Samples at a concentration of approximately 10 mg/mL water were introduced by hydrodyDOI 10.1002/jps
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namic pressure at 50 mbar for 10 s. The capillary column was preconditioned at the beginning of each day by flushing with 1 M NaOH for 2 min and 0.1 M NaOH for 2 min. Prior to running each sample, the system was flushed with water for 2 min and then buffer solution for 2 min. Each sample solution was filtered through a 0.2 mm cellulose acetate centrifuge filter (Micro-Spin filter, Alltech Associates, Deerfield, IL). Run time was 15 min, and the heparin peak was normally seen at approximately 5.6 min. Electropherogram peak areas for OSCS, heparin and GAGs were determined by the tangent skim method. Relative areas were converted to relative concentrations (weight percents) for each major component using calibrated responsecorrected peak areas determined from pure component electropherograms. One, two, or three peaks were found in the heparin region and are reported in Table 1. The samples whose compositions are listed in Table 1 comprise the training set for chemometric calibration. Additional samples were analyzed by CE, NIR spectroscopy and Raman spectroscopy to test and validate the chemometric models, as described above. The near infrared reflectance was measured with a Thermo-Nicolet Antaris FT spectrometer. Powdered heparin API was transferred to 8 mm 40 mm standard clear glass flat-bottomed vials filled to a depth of 6–8 mm (180 mg). The vials were gently tapped against the bench to compact and level the powder before scanning through the bottom of the vials using an integrating sphere optical configuration. Spectra consisted of 6224 points over the range 4000– 10,000 cm1 collected at 2 cm1 resolution integrating 16 scans for both the background and the sample. NIR spectra of 28 samples from different heparin lots comprised the training set for model development. Thirty additional samples comprised the test set for validation of the NIR chemometric model. Forty-one NIR spectra of these 30 samples were measured, which included 11 replicate sample measurements. Raman spectra were collected with a Dimension-P1 spectrometer (Lambda Solutions, Inc., Waltham, MA) equipped with a fiber optically coupled microscope probe and a manually positioned x, y stage. Raman samples of the same materials as used in NIR were placed in the wells of a small-volume 96-well plate. Irradiation from above was used to avoid fluorescence. The 785 nm excitation was delivered to the sample through a microscope objective, and the focus was adjusted
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Table 1. Capillary Electrophoresis Results for the 28 Calibration Samples Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Wt.% OSCS
Wt.% Heparin
Wt.% GAGs
0.0 0.0 0.0 0.0 0.0 26.6 9.8 0.0 0.0 1.7 3.8 7.9 15.5 0.0 3.6 3.2 0.0 4.6 0.0 14.7 0.0 2.5 0.0 9.9 0.0 0.0 0.0 0.0
92.7 97.4 96.9 89.9 98.2 72.8 85.3 100.0 93.1 93.8 80.3 84.1 76.8 100.0 87.2 87.0 92.7 93.3 93.4 85.3 97.3 88.8 98.0 81.5 91.7 97.1 94.8 87.0
7.3 2.6 3.1 10.1 1.8 0.6 4.9 0.0 6.9 4.5 16.0 8.0 7.8 0.0 9.2 9.9 7.3 2.2 6.6 0.0 2.7 8.6 2.0 8.6 8.3 2.9 5.2 13.0
at each well to maximize the Raman signal before data collection. Spectra were measured over the Raman shift range 150–3100 cm1 at 5 cm1 resolution using 245 mW of laser power and 25 s total acquisition time. Raman spectra of 28 samples from separate heparin lots comprised the training set for chemometric model development. Thirty-three additional samples comprised the test set for validation of the Raman chemometric model. Thirty-eight Raman spectra of these 33 samples were measured, which included 5 replicate sample measurements. Partial least squares (PLS) calculations were performed using Pirouette 3.11 (Infometrix, Bothell, WA). The compositions measured with the CE reference method for the 28 samples listed in Table 1 were used as the dependent variables to generate the predictive models for both NIR and Raman spectral calibration models. A leave-oneout cross-validation strategy was used during model development to identify the best-fit model.
A variety of preprocessing and transform treatments were examined for both NIR and Raman spectral data prior to multivariate calibration. The optimum overall conditions for the NIR reflectance model were obtained by subtraction of a best-fit quadratic baseline, applying multiplicative scattering correction (MSC)17 and meancentering the spectral values for the entire measured range of 4000–10,000 cm1. The combination of quadratic baseline correction and MSC gave lower error values than either of the two separately. This suggests that particle size variation between samples is responsible for some of the NIR variance.17,18 The Raman spectra were preprocessed by subtracting the best-fit quartic baseline followed by truncation and vector normalization. The Raman spectra were truncated to include only the 145–1570 cm1 range for chemometric analysis. Following vector normalization, Raman spectra were mean-centered prior to chemometric analysis.
RESULTS CE electropherograms of heparin samples exhibited four distinct CE peak patterns. Typical electropherograms for samples in this study are shown in Figure 1. Three peaks are resolved at 5.2, 5.6, and 6.5 min retention time. The 5.6-min peak is assigned to heparin sulfate, and the 6.5-min peak has been assigned to a mixture of several closely related GAGs. The 5.2-min peak has been attributed to an OSCS compound that has been linked to the adverse effects of the heparin samples.10 The relative weight percent contributions of each of these species to the sample mass are presented in Table 1. Note that in many cases only one or two of the three possible peaks are observed. The relative weight percent values determined by CE provide the reference values that are the quantitative basis for the predictive chemometric model. The compositions reported in Table 1 assume that the three peaks in the 5- to 8-min range account for the entire active content of the sample. NIR Spectra of heparin and the two related solids are displayed in Figure 2. Absorption bands at 5200 and 6900 cm1 agree with earlier studies in solution.19 Heparin displays an irregular peak at 4730 cm1 and a shoulder at 6500 cm1 which distinguish it from dermatan sulfate (a GAG, also termed chondroitin B). OSCS has two small peaks in the region around 4730 cm1, another peak at
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Figure 1. Characteristic capillary electropherograms: (A) sample 8, single heparin peak, (B) sample 20, OSCS þ heparin, (C) sample 11, OSCS þ heparin þ GAGs, (D) sample 25, heparin þ GAGs.
Figure 2. Comparison of the NIR reflectance of heparin (solid line) and the principal contaminants— OSCS (dashed line) and dermatan sulfate or GAGs (dotted line). All were measured through the bottom of 8 mm 40 mm clear glass vials. DOI 10.1002/jps
5800 cm1 and a third peak at 7000 cm1. The presence of OSCS as a contaminant in heparin is expected to shift the large heparin peak at 6900 cm1 to higher energy. The NIR spectra are so heavily overlapped that no distinguishing features between the ‘‘Good’’ and ‘‘Suspect’’ samples are apparent. The baseline-corrected, vector normalized Raman spectra of heparin, OSCS, chondroitin A and the GAG impurity dermatan sulfate are shown in Figure 3. The heparin spectra agree with earlier Raman studies of heparin in solid and solution.20–22 Small features at shifts above 1570 cm1 are nearly identical in all the components and provide justification for the truncation. Chemometric models that omitted spectral data above 1572 cm1 were found to produce more accurate predictive models. The strong peaks at 1040 and 1055 cm1 in the heparin spectrum have been assigned to the N-sulfate and 6-O-sulfate,
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Table 2. PLS Model Factors and Validation Errors for NIR and Raman Spectra of API Samples Wt.% OSCS Wt.% Heparin Wt.% GAGs NIR RMSECV Factors RMSEP Raman RMSECV Factors RMSEP Figure 3. Comparison of Raman spectra of heparin and the principal contaminants after quartic baseline correction, vector normalization and truncation to the range 146–1572 cm1. All Raman spectra were treated this way in developing the chemometric model. The spectra are offset upward in increments of 0.06 relative units for clarity.
respectively. The 2-O-sulfate peak at 1065 cm1 may broaden the strong peak on the high-energy side. The OSCS spectrum exhibits similar structure in the 1052 cm1 peak, with additional small peaks at 1014 and 986 cm1.23 The latter are not evident in the spectrum of chondroitin A, and may result from sulfation. The peak at 880 cm1 in heparin is considerably smaller and shifted in OSCS, and the spectral structure in the 1200 to 1450 cm1 region also differs. Both dermatan sulfate and chondroitin A exhibit some differences from heparin in these spectral regions. These spectral differences are expected to support multivariate correlation between Raman spectra and sample composition. As with the NIR, there are no clear-cut distinctions between the spectra of ‘‘Good’’ and ‘‘Suspect’’ samples in the Raman. Identical training sample sets were used to develop NIR and Raman PLS calibration models for each of the three major components (OSCS, heparin, and GAGs) of the heparin APIs. Thus the accuracy of the NIR and Raman calibration models can be directly compared. The quality of fit between the training set spectra and the reference compositions can be characterized by the root mean squared error of leave-one-outcross-validation (RMSECV), which was calculated for each of the three components of the heparin API samples in the training set. These values are reported in Table 2 along with the optimized number of PLS factors used in each model. The
1.2 2.8 2.5 6 6 6 41 test samples (11 replicates) 1.4 5.5 7.3 1.7 2.5 2.2 4 3 3 38 test samples (5 replicates) 1.5 3.3 3.2
The same 28 sample training set in Table 1 was used for both NIR and Raman models. NIR: Quadratic baseline adjustment, MSC, mean centered. Raman: quartic baseline adjustment, vector normalized, range truncated, mean centered.
RMSECV characterizes the error associated with the combination of the predictive error of the model and the sensitivity of the model to individual spectra. RMSECV values of 1.2% and 1.7% were obtained for NIR and Raman calibrations of OSCS, respectively. NIR and Raman models for Heparin and GAGs also had similar RMSECV values, all in the 2.2–2.8% range. The NIR and Raman PLS models were used to predict the sample compositions for a group of additional samples from a similar variety of sources as the training set. The root mean squared errors of prediction (RMSEP) values shown in Table 2 reflect the average error between the model prediction and the reference compositions. RMSEP values for the NIR and Raman models for OSCS were similar to the RMSECVs for these models, and indicate that both NIR and Raman calibrations are capable of determining the OSCS composition of samples with an uncertainty of about 1.5%. RMSEPs of the NIR models for heparin and GAGs were 2–3 times larger than the RMSECVs for these models, and RMSEPs of the Raman models for heparin and GAGs were about 50% larger than the RMSECV values. These results indicate the Raman heparin and GAGs models should provide more accurate composition estimates than the corresponding NIR model. Because OSCS has been correlated with heparin adverse events, a screening procedure to identify suspect heparin samples would focus on OSCS predictions. OSCS prediction plots for the NIR and Raman test sets are displayed in Figures 4 and 5, respectively. The plots suggest
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Figure 4. NIR PLS model test for OSCS. Forty-one sample test set including 11 replicate spectra. Solid diamond points are considered ‘‘Good’’; open square points, having more than 1% OSCS, are ‘‘Suspect.’’ The proximity of the replicate points demonstrates the reproducibility of the spectral measurement. [RMSEP ¼ 1.4 mg% (6 factors)].
Figure 5. Raman PLS model test for OSCS. Thirtyeight samples with five replicates. Solid diamond points are considered ‘‘Good’’; open square points are ‘‘Suspect’’. [RMSEP ¼ 1.5% (4 factors)]. DOI 10.1002/jps
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that a threshold value of 1% predicted OSCS can be used to eliminate suspicious heparin samples. When the NIR model is used, a 1% threshold resulted in 38 out of 41 samples correctly classified as being either good (15 samples at OSCS <1%) or suspect (26 samples >1% OSCS). One good sample was classified as suspect (1 false negative) and one suspect sample was classified as good (false positive). Prediction with the Raman model showed similar accuracy, with 36 out of 38 samples being correctly classified with one false positive and one false negative. The overall accuracy in classifying heparin samples as suspect or good using these spectroscopic/chemometric methods as screening tools can be expected to exceed 95%. Both NIR and Raman allow the elimination of over 60% of the heparin samples as suspicious. The remaining 40% would be subjected to additional analyses by CE, NMR or other separation methods to detect the presence of low levels of OSCS.
DISCUSSION NIR and Raman spectroscopy coupled with multivariate calibration are capable of distinguishing differences in the chemical composition of mixtures with virtually no sample preparation. Further, both methods are well suited for rapid screening procedures that are intended to evaluate a large number of samples quickly. NIR requires about 150–200 mg of sample, is rapid and nondestructive, and is insensitive to fluorescence. Conventional and micro Raman are also rapid and nondestructive. Micro Raman can measure a very small sample, but Raman spectroscopy is sensitive to fluorescence in the sample or the sample container. CE is the most precise of the three techniques examined here and, as such, must remain the reference method for developing chemometric models for new API sample sources. The precision of NIR and Raman are clearly shown to be comparable to each other in this study so the choice of method will depend on the size and state of the samples to be measured. The methods described in this work are appropriate for samples with chemical, physical, and optical properties that match the properties of the training set. The training set must represent a balanced cross-section of all the possible compositions and optical conditions of the larger general group to be monitored. New samples that differ
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significantly from the original set are likely to give spurious PLS model predictions. While the training set used here included samples from 28 heparin lots, application of the model to heparin lots not included in the training set did result in accurate predictions, with an average RMSEP of 1.5%.
CONCLUSIONS This work shows that both NIR and Raman spectroscopy can be used to identify solid heparin API samples containing more than 1% OSCS as a contaminant. Therefore either technology would provide a rapid means of screening raw heparin API material as a preliminary step to subsequent processing. Simple-to-operate hand-held NIR and Raman devices are available to assist the surveillance of heparin API. Both NIR and Raman should provide similar precision and accuracy for heparin purity determinations, so the choice of device can be largely based on sample properties. The spectroscopic methods described here provide compositional information on all three possible components (OSCS, heparin, and GAGs) of significance in the quality control of heparin, but are not meant to replace the methods currently required by FDA for detection of OSCS (CE and NMR).
ACKNOWLEDGMENTS Thanks to Dr. Ali Al Hakim and Dr. Moheb Nasr for helpful reviews of the manuscript. The work reported here reflects the current thinking and experience of the authors. This is not a policy document and should not be used in lieu of regulations, published FDA guidances or direct discussions with the agency.
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