Article pubs.acs.org/ac
How To Find a Needle (or Anything Else) in a Haystack: TwoDimensional Correlation Spectroscopy-Filtering with Iterative Random Sampling Applied to Pharmaceutical Heparin Timothy R. Rudd,*,†,‡ Eleonora Macchi,† Cristina Gardini,† Laura Muzi,† Marco Guerrini,†,§ Edwin A. Yates,‡,§ and Giangiacomo Torri†,§ †
Istituto di Ricerche Chimiche e Biochimiche “G. Ronzoni”, Via Giuseppe Colombo, 81 Milano, 20133 Italy Institute of Integrative Biology, University of Liverpool, P. O. Box 147, Liverpool, L69 3BX United Kingdom
‡
S Supporting Information *
ABSTRACT: Risks of contamination of the major clinical anticoagulant heparin can arise from deliberate adulteration with unnatural or natural polysaccharides, including heparin from other animal sources, other natural products, or artifacts of manufacture, and these can escape detection by conventional means. Currently, there is no generally applicable, objective test recommended by regulators that can detect these in pharmaceutical heparin, and this continues to leave heparin exposed to contamination risks. Two-dimensional correlation spectroscopic-filtering with iterative random sampling (2D-COS-firs) is reported. It employs a difference covariance matrix with iterative random sampling, and is capable of revealing contamination in pharmaceutical heparin to a high level of sensitivity irrespective of the nature of those features. The technique is suitable to any situation in which a comparison of a single entity to a family of heterogeneous entities, particularly natural products and biosimilars, needs to be made, and will find application in pharmaceutical monitoring, manufacturing quality control, materials science, biotechnology, and metabolomic investigations.
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particularly relevant to the detection of contaminants in pharmaceutical heparin, which suffered a serious contamination problem recently1,2 and which remains a threat. Heparin is a natural product derived from pig intestinal mucosa and major clinical anticoagulant, which exhibits variations in composition, even from batch to batch in a production run and is subject to risk of contamination or adulteration with unnatural agents or heparins from other animal sources. Contaminated pharmaceutical heparin containing the unnatural sulfated polysaccharide, oversulfated chondroitin sulfate (OSCS), and possibly other material1,2 came to notice in 2008. Nuclear magnetic resonance spectroscopy (NMR), which provides information dense spectra that are widely used to monitor the identity and purity of pharmaceuticals, proved its worth in identifying OSCS in these contaminated heparin samples. The major contaminant on that occasion was relatively
he challenge of detecting a set of features or signals from a single entity, among a complex data set belonging to many such entities, is common to many scientific and technological disciplines. Examples range from identifying defective or contaminated samples in industrial production processes and pharmaceutical quality control (especially of natural products and biosimilars), to signal or pattern recognition problems relevant in signal processing and computing applications. In the field of pharmaceutical quality control and monitoring, conventional taught or supervised chemometric methods are of little use in meeting this challenge because they rely on a priori knowledge of the features being sought. Solving this significant challenge requires appreciation of two aspects. First, the “definition” of the heterogeneous mixture must encompass the variable nature of its composition, and second, a way must be sought to reveal the common features among contributing components to establish the collective definition of that material, and hence provide the means for detecting alien features, that are not common to all of the contributing components. These challenges are © 2012 American Chemical Society
Received: May 24, 2012 Accepted: July 9, 2012 Published: July 10, 2012 6841
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easily detected because the 1H NMR spectrum of (OSCS) has a distinct peak at 2.16 ppm, which is not obscured by any natural heparin signals. This was, most likely, added for economic reasons because OSCS possesses significant anticoagulant activity, like heparin; unfortunately, it also provokes lifethreatening reactions in some people.2 Until this incident, NMR measurements of heparin had not been required by the pharmacopeia, and the contaminant, which reached heparin samples in clinical use, was suspected of having caused extensive illness and many deaths before it was identified and the appropriate steps to recall contaminated batches could be taken. Since the news broke of that case, a plethora of NMR analytical techniques have been developed with the aim of detecting OSCS in heparin, including DOSY,3,4 1H NMR and principal component analysis,5−7 high resolution 1H NOESY NMR,8 1H NMR with taught chemometric techniques,9−11 and 2D-COS-f with 1H NMR.12 At the time of writing (early 2012), however, there is still no generally applicable, objective test recommended by regulators which is capable of detecting all contaminants in pharmaceutical heparin, and this continues to leave heparin exposed to risks of contamination and adulteration. We have adopted a different approach, arguing that the definition of pharmaceutical porcine intestinal mucosal heparin, which is perforce changeable, can only be defined at any particular moment using a library of bona f ide heparin samples. Potential contaminants can thereby be identified by comparison of a sample to this library.7,12 This approach is able to differentiate heparin that is contaminated with both glycosaminoglycan (GAG) and non-GAG polysaccharides, even though the latter can present NMR signals, which lie under those of heparin.7,12 The method described here provides for the sensitive detection of any contaminant, in an unbiased and objective manner among, as an example, a series of heparin samples and, crucially, does not depend on any a priori knowledge of the contaminant/s. It builds on earlier work proposing a method capable of forming a definition of a body of heterogeneous samples, all of which were considered bona f ide heparin samples as a library. This library contained a range of (1H NMR) spectral features and was followed by the proposal of a matrixbased approach allowing a test sample to be compared to that library.12 The approach is a form of two-dimensional correlation spectroscopy (2D-COS),13 termed two-dimensional correlation spectroscopic-filtering (2D-COS-f),12 the key step of which is the generation of a difference covariance matrix, which reveals the unknown features that are under investigation. Here, a powerful development of this basic approach is proposed which is highly adaptable and suitable to the problem of comparing biosimilars, whether for in-house monitoring of production, quality control, or regulatory analysis of products or biosimilars. It is likely that any new contaminant would have more subtle characteristics than OSCS, and would be more difficult to detect.12 Specifically, it might lack 1H NMR signals outside those of pharmaceutical heparin, which originates from pig intestinal mucosa, making it difficult to detect using conventional analytical means. One conceivable source of such material is heparin derived from animals other than pigs, principally, cows or sheep (FDA 2012 recommendations). Bovine heparin was used previously as a source of pharmaceutical heparin, until the bovine spongiform encephalopathy outbreak of the 1980s. Differences in substitution
pattern between samples of porcine and bovine heparin have been noted by Casu et al.14 using a mixture of 1H and 13C NMR, with the levels of 6-O-sulfation providing the principal distinction. Heparin from other animal sources would contain varied substitution patterns and epimerization levels (i.e., the ratio of α-L-iduronate to β-D-glucuronate), but these are also potentially variable in porcine heparin. Rudd et al.15 were able to differentiate porcine heparin from bovine heparin utilizing 13 C NMR and principal component analysis, while Ruiz-Calero et al.16 employed taught chemometric techniques, which depend on a priori knowledge of the contaminant, to distinguish samples of porcine heparin from porcine heparin containing ovine, bovine mucosal, and bovine lung heparin contaminants (with an error of ∼3% for bovine mucosa heparin and ∼9% for bovine lung porcine mucosa; the samples contained a minimum of ∼5% of each animal heparin). Another very sensitive means of detecting the animal origin of heparin is PCR, but this technique will only work on crude heparin, i.e., heparin that still contains the animal's DNA; once the heparin is purified this technique is redundant.17−20 The challenge is therefore to distinguish heparin samples exhibiting some, but not all, of the signals of pharmaceutical heparin or, exhibiting all of them, together with other, nonheparin signals, and to be able to do this when all of the signals potentially lie obscured by those of bona f ide heparin. Pharmaceutical heparin is a complex heterogeneous mixture of structures, and this fact alone could assist the concealment of alien components, either accidentally or deliberately, within the mixture. The ease with which foreign material can be hidden in the complex spectra of pharmaceutical heparin is illustrated in Figure 1. One of the heparin samples is pure, and the other two are mixed either with 20% (w/w) ovine or bovine heparin. Even at 20%, it is impossible to differentiate the samples by visual inspection. The problems for detection of unknown contaminants are 2-fold: the first is the extraction of alien features from a naturally heterogeneous mixture and the second is testing whether the extracted features are native varying features arising from natural heterogeneity, or unnatural signals. The first problem is solved by using 2D-COS-f,12 which allows the naturally occurring heterogeneous signals to be removed from the test spectrum as they correlate together, leaving the uncorrelated features exposed, which are inconsistent with the library of bona f ide heterogeneous samples. 1H NMR spectroscopy has emerged as the most detailed means of analyzing the structure of the complex structural nuances of heparin. It provides detailed information regarding the identity and environment of 1H nuclei throughout the structure,21 and this information is dispersed broadly, giving it great powers of discrimination and identification. The second quandary is solved here by an evolution of 2D-COS-f, termed twodimensional-correlation spectroscopy filtering with iterative random sampling (2D-COS-firs) (Scheme 1), which is presented here.
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METHODS H Nuclear Magnetic Resonance (NMR) Spectroscopy. Each heparin sample (20 mg) was dissolved in 0.6 mL of 0.15 mM TSP (trimethylsilyl-3-propionic acid) solution in deuterium oxide. TSP was added both for spectrum calibration and as a control of spectral resolution; measurements were accepted for analysis if the line width of the TSP signal was ≤1 Hz. Spectra were measured using a Bruker AVIII-600 instrument (Bruker, Karlsruhe, Germany) operating at 600.13 MHz, and
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Scheme 1. Schematic of the Mechanism behind 2D-COS-firs
memory, running Mac OS X version 10.7.3. The NMR spectra were processed using Topspin 3.1 (Bruker, Karlsruhe, Germany). The spectra were then imported into R (R version 2.15.0)22 employing the rNMR library (rNMR version 1.1.7 (2011−08−03)).23 Utilizing the internal functions of rNMR, 1 H NMR spectra could be batch imported quickly and easily. The spectra were then bucketed; every 10 points taken and averaged, the regions defined between 4.912−4.730 and 3.37− 3.345 ppm removed, water peak and solvent signal, respectively, they were then normalized for area, mean centered; Xij = xij − xavereagei and Pareto Scaled; Xij = (xij − xavereagei).√xsdi − 1. After the data were prepared in this manner, the 2D-COS spectrum (covariance matrix) was determined for the sample set; CM = 1/n − 1 × XXT. Two-dimension correlation spectroscopicfiltering was performed by determining the covariance matrix of the heparin library data set (library A) and subtracting that from the covariance matrix of the heparin library data set (library A) containing, in addition, the test sample [2D-COS-f spectra = 1/(nlib+testsample− 1)(Xlib+testsampleXlib+testsampleT)-1/ (nlib‑1)(XlibXlibT)]. The 2D-COS-f spectrum can be represented either as a 2D pseudo-TOSCY spectrum or, as the power spectrum, which is the diagonal of the 2D-COS-f spectrum matrix (the variance of the covariance matrix). 2D-COS-firs of NMR Spectra. To perform 2D-COS-firs, the above outlined actions are expanded first by the addition of a second library of bona f ide heparin samples, library B, which forms the basis for the determination of the natural variation within pharmaceutical porcine heparin. Both the filtering of the test sample (test sample filtered by library A) and the determination of the natural variation of heparin (sample from library B filtered by library A) follow the same route, as outlined in Scheme 1. In both steps, n − 1, where n is the number of compounds within library A, samples are randomly selected from library A to filter either a randomly selected sample from library B or the test sample. After the requisite number of iterations the filtered spectra determined after each iteration are averaged and any other statistics calculated. To determine the natural variation within pharmaceutical heparin/
Figure 1. Challenge of discriminating a porcine heparin spectrum from the spectra of porcine heparin adulterated with heparin from other species. 1H NMR spectra of porcine heparin (heparin A), porcine heparin adulterated with 20% (w/w) bovine heparin (heparin B), and porcine heparin adulterated with 20% (w/w) ovine heparin (heparin C). It is not possible to distinguish these spectra by visual inspection, and there are no obvious signals belonging to the contaminant which could be employed in taught analyses. See Supporting Information Figure 1 for spectra of pure porcine, bovine, and ovine mucosal heparin.
the samples were held at a temperature of 298 K during data acquisition. Water presaturation (power corresponding to 5 Hz) was applied during 12 s of relaxation time. Typically, 32 transients were acquired into 32 768 data points covering a spectral width of 10.800 Hz. Line broadening of 0.3 Hz was applied before Fourier transformation, and all spectral data sets were processed using TOPSPIN 3.1 (Bruker, Karlsruhe, Germany). 2D-COS-f of NMR Spectra. All analyses were performed on a MacBook Pro (Apple), 2.66 GHz Intel Core i7, 8 Gb 6843
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Figure 2. 2D-COS-f of a porcine intestinal mucosal heparin sample contaminated with 10% (w/w) bovine mucosal heparin. The 1H NMR spectrum of the test sample was tested against 57 verified porcine intestinal mucosal heparin 1H spectra. (A) Covariance matrix of the 57 verified porcine intestinal mucosal heparin spectra. (B) Covariance matrix of the 57 samples of verified porcine intestinal mucosal heparin spectra plus the test sample. (C) The difference between parts A and B. The one-dimensional spectrum is the diagonal of matrix C, the filtered test spectrum, which reveals the features within the test sample, which were not consistent with the 57 verified porcine intestinal heparin spectra. Major features have been tentatively assigned, and there are many overlapping signals. An overlay of the above spectra and a HSQC of bovine mucosal heparin can be found in Supporting Information. The amplitude of the filtered test spectrum has been normalized to the maximum amplitude of the covariance matrix of the 57 samples of verified porcine intestinal mucosal heparin.
also carried out for porcine mucosal heparin contaminated with 10% ovine mucosal heparin, Supporting Information Figure 5). The above example is a convincing demonstration of the power of 2D-COS-f. The approach has one pitfall, though; heparin itself is not homogeneous. Therefore, if 2D-COS-f were performed on a bona f ide porcine mucosal heparin sample that differed in any way from the library, features would still be seen; the filtered spectrum would not be flat, which poses the following question: What is the natural variation within porcine intestinal mucosal heparin? The new method presented here, 2D-COS-firs, contains two steps: first, the determination of the natural variation within the heterogeneous compound, and second, the filtering of the test sample, the procedure that allows the natural variation of the heterogeneous sample to be determined is iterative random sampling without replacement (Scheme 1). The first step of the analysis is to determine the natural variation within the heterogeneous system. This is performed using two libraries, A and B. Library A comprises a set of 1H NMR spectra, which constitutes the definition of the heterogeneous system, in this case pharmaceutical heparin, and consists of a set of bona f ide 1 H NMR heparin spectra. Library B, is also a set of bona f ide 1H NMR heparin spectra, but these are a smaller set distinct from library A. Library B is used to calculate the natural variation within the heterogeneous system to provide a reference against which any new features, or signals of interest, found within a test sample spectrum can be tested. The heparin samples contained within libraries A and B, which are employed in this example, all pass the European Pharmacopeia (EDQM) requirements. Library A is composed of 57 heparin samples from 11 manufacturers and also contains the sixth international
pass-or-fail critetia the modulus of the 95% confidence interval for the mean, ±|xavereage + 1.96 × (xsd/n1/2)|, was determined for the output of library A filtering single samples from library B. If any feature determined by the first step of filtering the test sample against library A is outside the pass-or-fail criteria it is determined to be alien to library A, which is the definition of the heterogeneous compound in question.
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RESULTS AND DISCUSSION The 2D-COS-f approach is shown in Figure 2. A sample of interest is tested against a library of 57 samples (library A), containing a representative set of pharmaceutical porcine intestinal mucosal heparin. The test sample contains 10% (w/ w) bovine mucosal heparin contaminant. The first step of conventional 2D-COS-f is to determine the covariance matrix of the reference data set (Figure 2A) and the covariance matrix of the reference data set with the test sample added (Figure 2B). These are then subtracted from each other, revealing those features of the test sample that are not consistent with the 57 reference spectra of library A (Figure 2C). The representation shown in Figure 2C (and Figure 2A,B) takes the form, in NMR parlance, of a pseudo-TOCSY spectrum, with the diagonal (power spectrum) being the variance of the covariance matrix. The power spectrum is also shown in Figure 2 (as a onedimensional spectrum). Features associated with bovine mucosal heparin can be observed clearly, for example, H-1 of 2-O-sulfated iduronic acid, located at ∼5.25 ppm, which is split due to 6-O-sulfation and de-6-O-sulfation (see Supporting Information Figure 4 for an overlay of a pure bovine mucosal heparin spectrum, the filtered spectrum of the test sample, and an HSQC spectrum of bovine heparin; the same procedure was 6844
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Figure 3. Effect of library A size on 2D-COS-firs. The size of library A, the definition of the heterogeneous polymer, varied from 10 to 57 spectra, with a step size of 1. At each step a test sample (heparin contaminated with 1% bovine mucosal or ovine mucosal heparin) was filtered with 100 iterations, with the mean spectrum and the standard deviation at each point along the spectrum being recorded. (A) The absolute response (area under the modulus of the power spectrum) at each step is plotted: gray, randomly filtered spectrum from library B (pass or fail criteria); black square, heparin contaminated with 1% ovine mucosal heparin; and black circle, heparin contaminated with 1% bovine heparin. (B) The standard deviation at a randomly chosen point (3.03 ppm). The iteration dependency of 2D-COS-firs: (C) The the absolute response (area under the modulus of the power spectrum) plotted against number of iterations for 2D-COS-firs of porcine intestinal mucosal heparin contaminated with 5% (small contamination) (black circle) and 20% (gross contamination) (gray circle) bovine mucosal hepain. (D) The standard deviation at a randomly chosen point (3.03 ppm) for the filtered spectra of heparin adulterated with 30% to 1% bovine heparin (gradient from black to light gray).
heparin standard and the EDQM and USP heparin standards, while library B contains 12 heparin samples from 3 different vendors; these manufacturers are also present in library A. Before a sample is added to library B the sample has to not only conform to the EDQM regulations, but also be consistent with library A; this is determined by performing principal component analysis of the new sample and library A. If the sample lies within the distribution of samples from library A, then the sample can be added to library B. The disaccharide composition of a proportion of libraries A and B, as determined by HSQC analysis,24 is in Supporting Information Tables 2 and 3, respectively. The third data set comprises a single 1H NMR spectrum of the test sample. A random selection of library A (the number of samples contained within library A minus one), chosen without replacement, is tested against one sample from library B, randomly chosen, and this process is repeated n times. From these comparisons, the 95% confidence levels can be
established for the variation within the heterogeneous system at all points throughout the 1H NMR spectra. These boundaries (both positive and negative, since correlations can be related to the appearance of new signals and decrease of heparin signals) are the pass or fail criteria for a sample being accepted as similar to those contained with library A. In the second step, the same process is performed with library A, but now, instead of the filtered sample being a representative of the natural variability within the heterogeneous compound of question, it is the test sample. Again, a fraction of the spectra of library A are chosen at random with replacement (the number of samples contained within library A minus one) to filter the test sample, and this is repeated n times and the average of these results found. If the features found within the filtered test spectrum are greater than the 95% confidence boundaries determined in the first step at any position in the spectrum then, in the case of pharmaceutical heparin, these signals are designated as having arisen from a unwanted compound. 6845
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Figure 4. 2D-COS-firs analysis of porcine heparin contaminated with 1% (w/w) bovine (top, blue spectrum) and ovine (bottom, red spectrum) heparin. The blue or red line represents the filtered test sample, containing the unnatural features, and the black, filled line is the natural variation of pharmaceutical porcine intestinal mucosal heparin. The amplitude of the filtered test spectrum is normalized to the maximum amplitude of the covariance matrix of the randomly selected verified porcine intestinal mucosal heparin, library A.
Two key parameters for 2D-COS-firs are the number of samples contained within library A and the number of iterations used for filtering, and these parameters are explored in Figure 3. The size of library A was varied from 10 to 57 samples, 100 iteration filtering was used to filter samples adulterated with 1% bovine and ovine heparin and reference heparin samples from library B. The absolute response (area under the modulus of the power spectrum) for the filtering of all three was virtually stable at a library A size of 57; for all sizes of library A the absolute response of heparin adulterated with 1% bovine or ovine was greater than twice that of reference heparin from library B. As well as the absolute response becoming stable as the size of library A increases, so does the standard deviation at all points along the filtered spectrum. An illustration is shown in Figure 3B, and the standard deviation for all three samples (1% bovine, ovine and reference heparin) at a random point is plotted [3.03 ppm]. Second, to investigate the effect of the number of iterations upon the resultant filtered spectrum, heparin contaminated with bovine (1−30%) was iterated between 10 to 8000 times, the absolute response was stable after 500 iterations, and an illustration of this is shown in Figure 3C for bovine heparin contaminated with 5% and 20% bovine heparin. Even though the absolute response was stable by 500 scans, it took three times as many iterations for the standard deviation at specific points along the filtered spectrum to become stable, Figure 3D (standard deviation at 3.03 ppm for heparin contaminated with 1−30% bovine heparin). The use of 2D-COS-firs is illustrated here in an investigation of pharmaceutical porcine heparin contaminated with ovine and bovine heparin, representing contaminants that have many similar features to porcine heparin and only subtle differences. This represents a challenging but realistic test for any technique seeking to monitor pharmaceutical heparin. It is important to appreciate that this analysis does not depend on any prior knowledge concerning these contaminants. As can be seen in
Figure 4, by using 2D-COS-firs, it is possible to uncover the spectral features of bovine and ovine heparin from porcine heparin samples contaminated with only 1% bovine or ovine heparin. The uncovered alien features (red and blue spectra) are clearly above the black spectra, which define the natural variation within porcine mucosal heparin. As stated above, the absolute response (area under the modulus of the power spectrum) for both the contaminated samples in Figure 4 is greater than twice that of the absolute response of the natural variation within porcine heparin. The composition analysis of the bovine, ovine, and an example porcine heparin can be found in Supporting Information, as determined by analysis of their HSQC spectra24 (Supporting Information Table 1). The starkest differences between ovine, porcine, and bovine heparin relate to the amount of de-6-O-sulfation; both ovine and bovine heparin have >20% more de-6-O-sulfation than porcine heparin.
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CONCLUSIONS 2D-COS-firs, utilizing two libraries of verified heparins, can detect the features of 1% (w/w) ovine or bovine heparin contained within porcine intestinal mucosal heparin (Figure 4). This represents a substantial improvement on the previous NMR/chemometric methods proposed for the detection of contaminants within heparin. This improvement is due to two key factors: first, the contaminant is not required to be known a priori, and second, the very high sensitivity of the approach. In effect, this approach is capable of differentiating any signals from those of the heparin library as long as the contaminant contains some anomaly, which is not common to all the samples in the heparin library, even if this anomaly is accompanied by signals otherwise indistinguishable from those of accepted heparin, or consists of the absence of some signals from accepted heparin. In the examples reported here, 2D-COS-firs is capable of identifying compounds that are 6846
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(8) Bigler, P.; Brenneisen, R. J. Pharm. Biomed. Anal. 2009, 49 (4), 1060−4. (9) Zang, Q.; Keire, D. A.; Wood, R. D.; Buhse, L. F.; Moore, C. M.; Nasr, M.; Al-Hakim, A.; Trehy, M. L.; Welsh, W. J. Anal. Chem. 2011, 83 (3), 1030−9. (10) Zang, Q.; Keire, D. A.; Wood, R. D.; Buhse, L. F.; Moore, C. M.; Nasr, M.; Al-Hakim, A.; Trehy, M. L.; Welsh, W. J. J. Pharm. Biomed. Anal. 2011, 54 (5), 1020−9. (11) Zang, Q.; Keire, D. A.; Buhse, L. F.; Wood, R. D.; Mital, D. P.; Haque, S.; Srinivasan, S.; Moore, C. M.; Nasr, M.; Al-Hakim, A.; Trehy, M. L.; Welsh, W. J. Anal. Bioanal. Chem. 2011, 401 (3), 939− 55. (12) Rudd, T. R.; Gaudesi, D.; Lima, M. A.; Skidmore, M. A.; Mulloy, B.; Torri, G.; Nader, H. B.; Guerrini, M.; Yates, E. A. Analyst 2011, 136 (7), 1390−8. (13) Noda, I.; Dowrey, A. E.; Marcott, C.; Story, G. M.; Ozaki, Y. Appl. Spectrosc. 2000, 54 (7), 236A−248A. (14) Casu, B.; Guerrini, M.; Naggi, A.; Torri, G.; De-Ambrosi, L.; Boveri, G.; Gonella, S.; Cedro, A.; Ferro, L.; Lanzarotti, E.; Paterno, M.; Attolini, M.; Valle, M. G. Arzneimittelforschung 1996, 46 (5), 472− 7. (15) Rudd, T. R.; Skidmore, M. A.; Guimond, S. E.; Cosentino, C.; Torri, G.; Fernig, D. G.; Lauder, R. M.; Guerrini, M.; Yates, E. A. Glycobiology 2009, 19 (1), 52−67. (16) Ruiz-Calero, V.; Saurina, J.; Galceran, M. T.; Hernandez-Cassou, S.; Puignou, L. Anal. Bioanal. Chem. 2002, 373 (4−5), 259−65. (17) Huang, Q.; Xu, T.; Wang, G. Y.; Huang, J. F.; Xia, H.; Yin, R.; Tang, A.; Fu, W. L. Anal. Bioanal. Chem. 2012, 402 (4), 1625−34. (18) Concannon, S. P.; Wimberley, P. B.; Workman, W. E. Anal. Bioanal. Chem. 2011, 399 (2), 757−62. (19) Houiste, C.; Auguste, C.; Macrez, C.; Dereux, S.; Derouet, A.; Anger, P. Clin. Appl. Thromb./Hemostasis 2009, 15 (1), 50−8. (20) Huang, Q.; Yao, C. Y.; Chen, B.; Wang, F.; Huang, J. F.; Zhang, X.; Fu, W. L. Mol. Cell. Probes 2006, 20 (3−4), 250−8. (21) Yates, E. A.; Santini, F.; Guerrini, M.; Naggi, A.; Torri, G.; Casu, B. Carbohydr. Res. 1996, 294, 15−27. (22) R Development Core Team. R: A Language and Environment for Statistical Computing, R version 2.13.1 (2011−07−08); R Foundation for Statistical Computing: Vienna, Austria, 2008. (23) Lewis, I. A.; Schommer, S. C.; Markley, J. L. Magn. Reson. Chem. 2009, 47 (Suppl 1), S123−6. (24) Guerrini, M.; Naggi, A.; Guglieri, S.; Santarsiero, R.; Torri, G. Anal. Biochem. 2005, 337 (1), 35−47.
contaminated with material that is made of the same building blocks as the heterogeneous compound being investigated (Supporting Information Table 1), extracting sequence effects from the test spectra. Not only is this technique relevant to the monitoring of heterogeneous pharmaceutical products, but it can also be applied to any heterogeneous mixture of compounds, for example, food products, diagnostic medical, and many other technical situations.
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ASSOCIATED CONTENT
S Supporting Information *
Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Author Contributions §
These authors contributed equally to this manuscript.
Notes
The authors declare the following competing financial interest(s): Istituto di Ricerche Chimiche e Biochimiche “G. Ronzoni” is a nonprofit organization that performs analysis and research on glycosaminoglycans, including pharmaceutical heparin. Dr. Timothy R. Rudd is a director of Anglo-Italian Chemometrics Ltd.
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ACKNOWLEDGMENTS The authors would like to thank Drs. Davide Gaudesi and Marcelo A. da Lima for their enlightening discussions. The authors are also particularly indebted to Professor Benito Casu. The authors gratefully acknowledge funding from The Wellcome Trust, The Royal Society (International Joint Project to E.A.Y. and M.G.), BBSRC, and Finlombardia SPA “Fondo per la promozione di Accordi Istituzionali” (M.G. and G.T.).
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