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Anal. Chem. 1987, 59,2045-2050
Factor Analysis and Kalman Filter Studies of Severely Overlapped Amino Acid Derivatives in Thin-Layer Chromatography Sarah C. Rutan* and Curtis B. Motley’ Department of Chemistry, Virginia Commonwealth University, Richmond, Virginia 23284-0001
The performance of several mathematical approaches for quantification of components in cases of severe chromatographic and spectroscopic overlap has been investigated. The problem under consideration was the separation of two amino acids, glycine and glutamine, on high-performance thin-layer chromatographic plates, followed by derivatiration with o-phthaidiaidehyde. The chromatographic characteristics of these two amlno acids are very similar, and the fluorescence responses of the two derivatives are very similar as well. Here, five approaches, based on two data analysis methods, factor analysis and Kaiman filtering, have been studied. Each approach was able to detect the presence of the two amino acids in a mixture chromatogram, and the resolved responses showed a peak-shaped morphology. The concentration estimates were within an order of magnitude of the correct values, with the results for the derivativeadaptive Kaiman filtering approach showing the smallest errors, 5.9% error for glycine and -6.4% for glutamine.
In the past several years, mathematical and statistical tools have been used increasingly to enhance the analysis of chemical data. In particular, several methods have been developed for the resolution of overlapped responses, which can arise in chromatographic and spectroscopic methods of analysis. In many cases, chromatographic methods can be coupled with multiwavelength spectroscopic detection approaches to enhance the analyst’s ability to distinguish a number of coeluting components. Two classes of curve resolution approaches have been employed those that require a model for the spectral or chromatographic characteristics and those that are “self-modeling”. The model-based curve resolution approaches are most useful for determining the relative contributions of each of the coeluting components to the overall spectroscopic response in cases where the identities of the contributing chemical species are known. These approaches include factor analysis methods ( l ) ,linear leastsquares methods (2,3),modified least-squares approaches (4, 5), and Kalman filtering techniques (6, 7). One major requirement for successful implementation of all of these techniques is that the responses due to each of the components must be linearly independent of one another. This requirement means that inaccurate concentration estimates may be obtained for compounds that cannot be resolved chromatographically and that give rise to very similar spectral responses. One area where severely overlapped chromatographic and spectroscopic responses may cause difficulties in obtaining accurate concentration estimates is when derivatization methods are employed in chromatography to increase the detection sensitivity. In particular, fluorogenic reagents are often used to form fluorescent molecules from nonfluorescent Present address: Department of Chemistry, Virginia Tech, Blacksburg, VA 24061.
analytes. The spectroscopic characteristics of these fluorescent derivatives are usually very similar, due to the fact that the energy levels in the fluorescent moiety are not substantially perturbed by the presence of the analyte substituent. While this characteristic of the fluorescent derivatives simplifies the selection of the detector conditions (Le., excitation and emission wavelengths), it also means that excellent chromatographic resolution is required to obtain accurate quantitative results. This is particularly important for thin-layer chromatographic (TLC) procedures, where only a relatively limited number of compounds can be separated with base line resolution. One type of analysis that may be prone to the difficulties described above is the determination of amino acids in physiological fluids. Most amino acids must be derivatized to allow detection at the concentration levels in these samples. A number of fluorogenic and colorimetric reagents have been developed to allow detection of amino acids subsequent to a chromatographic separation, including o-phthaldialdehyde (OPA), dansyl chloride, ninhydrin, and fluorescamine (8-10). Liquid chromatographic (LC) methods are frequently used for the quantitative determination of amino acids, and the resolution of these techniques usually permits accurate concentration estimates to be obtained. The major drawback to the LC methods is that the analyses must be run sequentially, with each analysis taking between 20 min and several hours, depending on the procedure that is employed. Methods based on TLC separation procedures are more often used for qualitative and semiquantitative analyses, with the advantage that many analyses can be run simultaneously, however, the likelihood is high that adjacent zones may be overlapped, especially for one-dimensional analyses. This means that TLC methods are not as frequently employed for the quantitative determination of amino acids. In this paper, the problem of severely overlapped amino acid derivatives that can arise in TLC methods is examined by using two of the model-based curve resolution approaches described above, factor analysis and Kalman filtering. In particular, one derivatizing agent for amino acids, OPA, has been investigated. The amino acids L-glutamine and L-glycine give rise to severely overlapped TLC zones in one common TLC developing solvent, and their corresponding OPA derivatives show very similar fluorescence spectral characteristics. Despite the high degree of similarity of the responses of these compounds, information can be obtained by using these mathematical methods. The results from five different data analysis approaches based on the two techniques described above are compared. The chemical system chosen for study here, while difficult, is real, and should given practicing chromatographers an indication of the range of applicability of the various mathematical methods.
EXPERIMENTAL SECTION Chromatographic Method. Solutions of the amino acids, L-glycine and L-glutamine (Sigma Chemical Co.), were prepared in water at concentrations of 1.23 X low2and 1.19 X lo-’ M,
0003-2700/87/0359-2045$01.50/00 1987 Amerlcan Chemical Society
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ANALYTICAL CHEMISTRY, VOL. 59, NO. 17, SEPTEMBER 1, 1987
respectively. Sample "a of 500 nL were applied to a silica-gel high-performanceTLC plate (Whatman) by using a motor-driven TLC applicator (Degasa). A 500-nL aliquot of a mixture of the two amino acids was also applied to the TLC plate; the glycine and glutamine concentrations of this mixture were 6.15 X and 5.95 X M, respectively. Separation of the amino acids was accomplished by using a mobile phase of 1-butanol/acetone/acetic acid/water (7:7:2:4)(9). The measured Rf values for the two amino acids were found to be 0.33 for glycine and 0.35 for glutamine. The amino acids were reacted with the OPA reagent in situ according to a procedure described by Schiltz and co-workers (10). First, the plate is air-dried to allow the developing solvent to evaporate. The plate is then sprayed with a 10% solution of triethylamine and dried briefly. The OPA reagent that was employed w a a~ 0.05% (w/v) solution in methanol which contained 0.2% (v/v) 2-mercaptoethanol and 0.09% (w/v) Brij-35. Following the triethylamine spray, the plate was sprayed with the OPA reagent and then finally postsprayed with the triethylamine solution. The amino acid zones reached the maximum fluorescence intensity within 30 min, and the fluorescence intensities were stable for several hours. Fluorescence Detection Method. Spectral responses for each of the TLC zones were obtained by using a computer-controlled fluorescence spectrophotometer (Farrand MK-2) equipped with a TLC scanning device, which has been described previously (11). A Compupro 8/16-D computer running under the MS-DOS operating system was employed for data collection and data analysis. The program for collection of the fluorescence intensity data was written in the C programming language and has been described previously (11). In these experiments, a modified program was employed, which allowed both the TLC scanning motor and the excitation monochromator to be scanned under computer control. The wavelength for the emission monochromator was set manually. The effective band-pass for the excitation monochromator was 1 nm, and the band-pass for the emission monochromator was 10 nm. Chromatograms for each elution profiie were obtained with an excitation wavelength of 360 nm and an emission wavelength of 460 nm. Once the zone was located, a series of excitation spectra were obtained at 0.1-cm intervals across each amino acid zone. A total of 20 spectra for each pure component spot and each mixture spot were obtained. A series of background spectra were also measured by using the same protocol from a lane where no amino acid was present. For each of the above spectra, the excitation monochromator was scanned from 250 to 400 nm, and data were collected at 1-nm intervals. Data Analysis Methods. All data analysis programs were written in Pascal or the C programming language. The Kalman filter routines have been described previously (11);the data obtained here were analyzed by using the square-root Kalman filter for multicomponent analysis and the simplex-optimizedadaptive Kalman filter for multicomponent analysis. Both programs were written in Pascal. The factor analysis programs were written in C and employed the eigenanalysis routine and the matrix inversion routine available in the Wiley Scientific Subroutines package. In these studies, the covariance matrix was formed by multiplying the data matrix by its transpose, as opposed to the correlation matrix, to preserve the information available in spectral data sets of this type. These programs allowed for the determination of the major eigenvalues and eigenvectors. The number of major contributing factors was determined by a method described by Malinowski (12), which involves the reduced eigenvalues, &, defined as
xi = ( r - i + l)(cxi
-i
+ 1) for eigenvalues i = 1 ... c
(1)
where Xi is the ith eigenvalue, r is the number of wavelengths, and c is the number of spectra. The number of major components was determined by inspection of the reduced eigenvalues. Large values indicate major factors to be retained in the analysis. Small, equivalent values are produced by random errors and should be discarded. The number of components was confirmed by visual inspection of the spectral eigenvectors. The C programs allow a series of concentration profiles or spectra to be subjected to the target testing approach described by Malinowski ( I ) . In addition,
20. 0
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Relative 8.0 Irtensi ty (x
IC-')
4. 0
0.0 -4.
I t
0 240.
280.
320.
360.
400.
440.
480.
520
Excitotion Wovelength ( n m )
Figure 1. (A) Excitation spectrum of glycine-OPA derivative. (B) Excitation spectrum of glutamine-OPA derivative.
Table I. Amino Acid Concentration Errors
method target transformation-factor analysis (3 components) target transformation-factor analysis (5 components) background calibration-target transformation-factor analysis Kalman filter-curve resolution derivative-adaptive Kalman filter-curve resolution
concentration estimation errors, % glutglycine amine total 81
19
108
20
-44
108
88
5.9
-43 -6.4
-43 4.1
-10 -2.8 -0.5
a second C program allowed the background calibration target testing method, described by Gemperline (13),to be implemented. Models for the spectral responses for the glycine and glutamine derivatives were obtained by measuring a spectrum at the peak of each of the pure component responses and by subtracting the background response measured at the base of each chromatographic peak. The two spectra that were obtained with this procedure are shown in Figure 1. The approaches that were investigated here included two Kalman filtering approaches, one which utilizes the basic Kalman algorithm (6, 3,and the second which utilizes the adaptive modification with a derivative model (11, 14, 15). Three factor analysis based approaches were employed, two based on target transformation methods ( I , 16) and one that used the background calibration target transformation approach (13). For each of these data analysis approaches, estimated pure component chromatograms for the glycine and glutamine were obtained from the overlapped spectral-chromatographic data. The area of each of the chromatograms was obtained and used to calculate the concentration of the amino acid present in the mixture. The errors in the concentration estimates for glycine, glutamine, and total amino acid are given in Table I. RESULTS AND DISCUSSION In general, each of the five data analysis methods investigated gave similar results. Each method gave two roughly peak-shaped responses for the glycine and glutamine chromatograms, with the correct elution order predicted in each case. In some cases, a negative and/or sloping base line was observed, which is presumed to be caused by the difficulty of accurately removing the background responses in the development of the spectral models for glycine and glutamine. The total chromatogram, comprised of the sum of the extracted glycine and glutamine chromatograms, gave a single peak similar in shape to the measured chromatographic response of the mixture. The discussion given below is based on the results obtained for one set of experiments; similar
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ANALYTICAL CHEMISTRY, VOL. 59, NO. 17, SEPTEMBER 1, 1987
6. 0
.
4.0
..
2.0
.
Relative 3.0 Intensity
..
(x
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ti
Relative 4.0 I n t m s , ty (x lo-')
2.0
-2. C
..
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.
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I1 I
1.2 1.2
1.6
2.3 O,stonce
2.4
2.6
3.2
3.6
4.0
(cm)
Figure 2. (A) Estimated chromatogram for the glutamine-OPA derivative, from target transformation-factor analysis procedure with a three-component mod&. (B) Estimated chromatogram for the glycine-OPA derivative.
results have been observed upon repetition of the experiments. The specific results obtained for each approach will be discussed below in more detail. Factor Analysis-Target Transformation. The method of factor analysis using target transformation, as described by Malinowski ( I ) , was used in the analysis of these data. Each pure component, background, and mixture data set, obtained as described in the Experimental Section, was subjected to the fist step in this procedure, principal components analysis (PCA), to determine the number of contributing components in the data sets. For the data described here, each background data set yielded three signif cant components, the pure component data sets yielded four significant components each for glutamine and glycine, and the mixture data sets yielded five significant components. These results indicated that the presence of the two amino acids present in the unresolved mixture could be detected, although this is only the case for the roughly one to one mixture described here. Due to the high degree of similarity of the glycine and glutamine spectral and chromatographic responses, PCA results for ratios in excess of five to one would probably indicate the presence of a single amino acid. The chemical significance of the three components, which are present due to the background response, is not known; however, factors such as the heterogeneity of the TLC plates, unreacted derivating reagent, and uneven spray application are considered to be important sources of this background variability. The variance inherent in the fluorescence measurement is on the order of 1 X lo4. (Units for all variances listed here are relative fluorescence intensity units squared.) A short circuit reproduction of the mixture data set employing the three largest eigenvectors was able to reconstruct the original data set with a variance of 2 X lod; a short circuit reproduction employing all five significant eigenvectors yielded a variance of 2 X lo4. The similarity of the spectral responses for the glycine and glutamine was examined by fitting the glycine spectra with a glutamine model spectrum, using the Kalman filter algorithm. The variance of fit in this case was 7 X lo4. These variances indicated that a target testing approach utilizing a three-component model might be employed successfully. This model consisted of an estimate of the background spectrum, generated by calculating the average of two background spectra, one obtained just prior to the leading edge of the chromatographic peak and one obtained immediately following the falling edge of the peak. The other two components of the model consisted of the estimated pure component spectra for the glycine and glutamine, after background subtraction, as described in the Experimental Section. The extracted glycine and glutamine chromatograms obtained for
1.6
2.0
2.4
2.6
3.2
3.6
4.0
Oistonce (cm)
Figure 3. Estimated total chromatogram, from target transformationfactor analysis procedure with a three-component model.
this approach are shown in Figure 2. The quality of these chromatograms is poor; however, some information can be obtained despite these difficulties. The glycine and glutamine chromatograms are skewed, and show negative, sloping base lines. These characteristics are most likely due to the difficulty in obtaining accurate spectral responses for the glycine and glutamine, as well as the fact that the model for the background contributions is incomplete. For each compound, however, a peak-shaped response is observed, with the correct elution order. The areas under these responses were calculated and used to obtain the concentration estimation errors listed in Table I. While these errors are relatively large, the magnitude of the concentrations is predicted correctly. The error in the total amino acid concentration is relatively small (4.1%); this concentration estimate was obtained by summing the extracted glycine and glutamine chromatograms and calculating the area of the resulting chromatogram, which is shown in Figure 3. When a data set for glycine or glutamine alone was subjected to the same data analysis procedure, the extracted component chromatograms either appeared as mirror images of one another (one positive peak, one negative peak), or the peak response predicted for the component that was not present was much smaller (- 10-fold) than the chromatogram predicted for the component that was present. From the variances given above, it is expected that a mixture with any more than a 4- to 5-fold excess of one component relative to the other would show evidence of only a single amino acid component. Since the PCA results indicated the presence of five important components in the mixture data set, a five component model was developed in an attempt to completely describe all sources of variation in the mixture data set. The estimated spectral responses for the glycine and glutamine were used as described above for the three component model. A three-component model for the background variations was developed by examination of six measured background responses: three obtained at the leading edge of the mixture chromatogram and three obtained from the trailing edge of the mixture chromatogram. These six spectra were subjected to the PCA procedure; three significant eigenvectors were observed. These three eigenvectors were used as the remaining three components for the target transformation model. The resulting chromatograms for glycine and glutamine estimated by using this five-component target transformation model are shown in Figure 4. As in the previous example, two peakshaped responses are obtained, with the correct elution order predicted. The fluctuations in the chromatograms are large; however, the large negative base lines are not observed in this case. The estimation errors for glutamine and glycine, as shown in Table I, are comparable to those observed for the three-component model; however, the total amino acid con-
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ANALYTICAL CHEMISTRY, VOL. 59, NO. 17, SEPTEMBER 1, 1987 c. j
75.’ c0 Relative 3 . 3 ;ntens: ty (x
10-11
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1 I
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.
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1 1.2
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J 1.6
2.0
2.4
21stcclce
-4.0
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3.6
4.3
I
1.2
(cm)
Figure 4. (A) Estimated chromatogam for the glutamine-OPA derivative, from target transformation-factor analysis procedure with a five-component model. (E) Estimated chromatogram for the glycineOPA derivative.
centration from the area of the summed chromatogram is in error by -43%. It is presumed that this is due to the fact that the eigenvectors describing the background contribution do not adequately span the variations in the background occurring within the peak envelope. In addition, these eigenvectors may be more likely to form linear combinations, which are not completely linearly independent of the the glycine and glutamine spectral responses. The negative error for the total amino acid concentration, in contrast with the positive errors observed for the glycine and glutamine concentrations, is due to the uncertainty in the estimation of a linear base line for the area calculations. Background Calibration-Factor Analysis-Target Transformation. This method of factor analysis has been described by Gemperline and co-workers in a recent publication (13). These authors reported good results for the determination of component concentrations in the presence of a variable background contribution, provided that the background components were present in a calibration data set. The calibration data set, which includes contributions from the components of interest as well as the background responses, is decomposed by using the PCA procedure. The test vectors in this case are concentration vectors with correct concentrations of the components of interest contributing to each of the standard spectra. In these studies, this data set consisted of selected spectra from the pure component glycine and glutamine data sets, along with the six background spectra selected from the leading and trailing edges of the mixture Chromatogram, as described in the previous section. This data set was found to have significant contributions from five abstract components, as expected. The target transformation vectors were calculated by using the concentration test vectors, as described by Gemperline (13). The mixture data set was then projected onto the concentration vectors that described the calibration data set, and the transformation vectors resulting from this procedure were used to calculate the estimated chromatographic profiles for glycine and glutamine. These extracted chromatograms as shown in Figure 5. Provided that the background components that occur in the mixture data set are adequately represented by the background contributions in the calibration set, this approach should allow compensation for variable background contributions. As in the cases described above, two peak-shaped responses are observed; however, the glutamine contribution is overestimated while the glycine contribution is underestimated (see Table I). It is presumed that these errors are due to inadequate modeling of the background contributions under the mixture peak envelope and the difficulty in ob-
1.6
2.0
2.4
2.8
3.2
3.6
4.C
Distance (cm)
Figure 5. (A) Estimated chromatogram for the glutamine-OPA derivative, from background calibration-target transformation-factor analysis procedure. (E) Estimated chromatogram for the glycine-OPA derivative. I
1O.C
8. 0
Relative 4.3 :rtensi ty :x ,E-]) 2. 0
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1 1
i .-
..
.
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taining accurate glycine and glutamine spectral models, as described in the previous section. Kalman Filter Curve Resolution. The Kalman filter, a recursive, linear least-squares algorithm was also investigated as an approach for the resolution of the overlapped glycine and glutamine spectral responses. In this case, a three component model was employed that was equivalent to the model employed for the factor analysis-target transformation approach described above. One of the required input parameters for the Kalman filter is an estimate of the variance in the measured response. While the estimated variance for the instrumentation employed here is approximately 1 x lo4, the variance used to obtain Kalman filter estimates for the concentrations of glycine and glutamine was 5 X lo-’, which is consistent with the errors expected with this particular model, as discussed above. This variance was used in an attempt to prevent “overfitting” caused by the inaccuracies in modeling the glycine and glutamine spectra and the background contribution. Every excitation spectrum comprising the mixture data set was fit by using the Kalman filter; in each case, the initial guesses for the relative concentrations of background, glycine, and glutamine were 1.0,0.5, and 0.5, respectively. The resulting chromatograms estimated from this procedure are shown in Figure 6. The errors in the concentrations of the amino acids obtained from these chromatograms are shown in Table I. As in the previous examples, a set of roughly peak shaped responses is observed, and the concentrations are predicted within an order of magnitude of the true values. The
ANALYTICAL CHEMISTRY, VOL. 59, NO. 17, SEPTEMBER 1, 1987 48.0
.
40. 0
..
1.2
1.6
2.3
2.4
2.E
3.2
3.6
4.0
O1stonce (cm)
Figure 7. (A) Estimated chromatogram for the glutamine-0PA derivative, from derivative-adaptive Kaiman filter procedure with a threecomponent model. (B) Estimated chromatogram for the glycine-OPA derivative.
error in the total amino acid concentration is relatively small a t -2.8%. Derivative-Adaptive Kalman Filter Curve Resolution. Recently, a method has been proposed that allows for the compensation of modeling errors that may affect multicomponent analyses (14, 15). The adaptive Kalman filter is a modification of the Kalman filter that allows accurate parameter estimates to be obtained when some types of model errors are present. In addition, the use of a first derivative model has been shown to work well in conjunction with the adaptive Kalman filter for correction for some types of background variabilities (11). A similar approach has been investigated here to determine whether this method is able to compensate for the difficulties in modeling the glycine and glutamine spectra and the background contributions. The model employed in this case was the same as for the Kalman filter investigations, except that the first derivative was calculated for each of the three model spectra, and for each of the spectra comprising the mixture data set. A Savitzky4olay seven-point first derivative smooth was used to calculate these derivative spectra (17,18). The variance in these derivative spectra was estimated to be 5 X this was the value that was employed in the Kalman filter algorithm. The resulting chromatograms for glycine and glutamine are shown in Figure 7. As before, peak-shaped responses were observed. In this case, no negative base line values are obtained, since this algorithm restricts the concentration contributions from taking negative values. This restriction can be employed in this case, since the algorithm is able to compensate for model errors that could cause negative estimates to be obtained. The errors in the estimated concentrations for glycine and glutamine are much smaller than those observed for the other methods described above (see Table I). There were, however, some difficulties in using this approach for the analysis of these data. The simplex-optimized adaptive Kalman filter was employed here, and there was evidence that there was more than one optimum on the response surface. Repeating the fit with different initial guesses can, in some cases, alleviate this difficulty. In this case, the error in the total amino acid concentration is small, indicating that this approach was successful in compensating for the difficulty in modeling the background contributions. CONCLUSION For most of the methods examined here, the results that were obtained were similar. This is most likely due to the fact that identical, or closely related models were employed, and that all of the methods described here are based in some way
2049
on linear least-squares principles. The largest difficulties seemed to be due to the problems involved in developing an accurate and complete model for the glycine, glutamine, and background contributions, rather than the high degree of similarity of the spectral and chromatographic responses. If more accurate model information could be obtained, than it is likely that the performance of these methods would be greatly improved, especially the five-component target transformation approach and Gemperline's background calibration approach. The only method employed here that can potentially compensate for incomplete and inaccurate models is the derivativeadaptive Kalman filtering approach, and this method appeared to yield the best results. Another technique recently described in the literature can also correct for unknown background contributions. Burns,Callis, and Christian have described a rank annihilation method based on incomplete information that would be amenable to the type of chromatographic-spectroscopic data sets described here (19). That method presumes, however, that the known pure component responses can be modeled accurately, while the derivative-adaptive Kalman filtering approach employed here may be able to compensate for some errors in modeling these pure component responses. Since there appeared to be some difficulty in obtaining accurate models for the components contributing the the overlapped spectral-chromatographic data, a self-modeling type algorithm might be considered in the solution of this problem (20-23); however, most of these approaches restrict the number of components to two or three or are based on the assumption that each component shows a peak-shaped response. In addition, these techniques do not take advantage of the available model information. While it is clear that the results described in this paper are not reliable enough to have direct analytical utility, it is encouraging to note that some information can be obtained for a difficult problem, for which traditional dogma would assert a solution is impossible. The combined difficulties that arise due to the use of the thin-layer chromatographic separation system in conjunction with a postseparation derivatization step-namely the variability of the background response and the difficulty in developing a reliable model for the spectra of the pure components-do not prevent some useful results from being obtained. In addition, there are very few instances in the literature of direct comparisons of mathematical curve resolution approaches; here a comparison has been made for a most demanding problem. It appears from these results that the derivativeadaptive Kalman filtering approach offers some advantages over the other approaches investigated here; however, a more complete study would be required to confirm this observation. ACKNOWLEDGMENT The authors thank P. J. Gemperline for making available a preprint of his work on the background calibration factor analysis method. LITERATURE CITED (1) Malinowski, Edmund R.; Howery, Darryl G. Factor Analysis in Chemistry; Wiley: New York, 1980. (2) Meites, Louis CRC Crit. Rev. Anal. Chem. 1979, 8 , 1-53. (3) Rossi, David T.; Desllets, David J.; Pardue, Harry L. Anal. Chim. Acta 1984, 161, 191-199.
(4) Leggett, D. J. Anal. Chem. 1977, 4 9 , 276-281. (5) Jochurn, Peter; Schrott, Erich, L. Anal. Chim. Acta 1984, 157,
211-226. (6) Brown, Steven D. Anal. Chim. Acta lB86, 181, 1-26. (7) Rutan, Sarah C. J . Chemometrics 1987, 1 , 7-18. (8) Scott, Ronald M. Ciinicai Analysis by Thin-Layer Chromatography Techniques;Ann Arbor: London, 1969;Chapter 4. (9) Berry, Helen K. I n Quantitative Thin-Layer Chromatography;Touchstone, Joseph, Ed.; Why: New York, 1973;Chapter 7. (10) Schiltz, E.; Schnacknerz, K. D.; Gracy, R . W. Anal. Biochem. 1977, 79, 33-41. (11) Gerow, David D.; Rutan, Sarah C. Anal. Chim. Acta 1966, 184, 53-64.
Anal. Chem. 1987, 59, 2050-2054
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12) Malinowski, Edmund R. J . Chemmetrics 1887, 1 , 33-40. 13) Gemperllne, Paul J.; Boyette, Stacey E.: Tyndall. Kimberly Appl. Spectrosc. 1887, 41, 454-459. 14) Rutan. Sarah C.; Brown, Steven D.. Anal. Chim. Acta 1984, 160, 99-1 19. (15) Rutan, Sarah C.: Brown, Steven D. Anal. Chim. Acta 1985, 167, 39-50. (16) Malinowski. Edmund R.: McCue, M. Anal. Chem. 1977, 49, 284-287. (17) Savitzky, Abraham: Golay, Marcel J. E. Anal. Chem. 1984, 3 6 , 1627-1639. (18) Steinier, Jean; Terrnonla, Yves; Deltour, Jules Anal. Chem. 1972, 4 4 , 1906- 1909. (19) Burns. David H.;Callis, James B.; Christian, Gary D. Anal. Chem. 1988, 58, 2805-2811. (20) Gemperline, Paul J. J. Chem. I n f . Comput. Sci. 1884, 2 4 , 206-212.
(21) Lawton, Willlam H.; Sylvestre, Edward A. Technometrics 1871, 73, 61 - .7-833. . - - -. (22) Vandeglnste, Bernard G. M.; Derks, Wilber?; Kateman, Gerrit Anal. Chim. Acta 1885, 173, 253-264. (23) Borgen, Odd S.; Kowalski, Bruce R. Anal. Chim. Acta 1985, 774, 1-26.
RECEIVED for review December 23,1986. Accepted April 22, 1987. This research was supported by the Grants-In-Aid Program for Faculty of Virginia Commonwealth University, the Virginia Commonwealth University Biomedical GrantIn-Aid Program, and the Jeffress Trust.
Silica-Based Size Exclusion Chromatography To Characterize the Decapeptide Nafarelin in a Controlled-Release Pharmaceutical Formulation Richard A. Kenley,*' Karen J. Hamme, Maryann 0. Lee, and John Tom Syntex Research, 3401 Hillview, Palo Alto, California 94304
This report descrlbes a hlgh-performance llquid chromatography (HPLC) method that simultaneously detemdnes peptide concentration and polymer molecular welght dlstrlbutlon in a controlled-release pharmaceutical forquiatlon. The peptlde Is nafarelin (a decapeptlde analogue of lutelnlrlng hormonereleaslng hormone) and the polymer Is poiy(lact1de-cogiycoilde), PLGA, a biodegradable polyester. The method uses a TSK 3000SW column with aqueous acetonttrlle mobile phase to separate the peptlde from Its hydrolytic decomposltion products and from PLGA and to fractionate the PLGA on the basis of molecular weight. Increasing mobile-phase ionic strength and decreasing organic fractlon decrease nafarelln retention, Indicating that the peptlde retains via Coulombic Interactions wlth dissociated sllanols on the packlng. PLGA retention Is essentialty lnvarlanl with mobtie phase h l c strength and organic fractlon, Indicating PLGA retention by slze exclusion.
Modern column packing materials have greatly advanced the chromatographic analysis of peptides, proteins, and synthetic macromolecules. Among the important recent advances in stationary-phase technology are controlled-porosity silicas that have been derivatized with hydrophilic organic polymers. Such materials permit size exclusion chromatography of polymers, peptides, and proteins without excessive solute losses via strong interactions with silanols on the solid support surface. The TSK SW-type columns represent a widely used and well-characterized example of silica-based materials designed primarily for purification and analysis of biopolymers. The literature includes many examples of specific applications that use the TSK SW-type columns (1-8) and several reviews are available as well (9-12). The literature reveals that the TSK SW columns feature a proprietary, hydroxylated polyether bonded phase. UnPresent
address:
Travenol Laboratories, 6301 Lincoln Ave.,
Morton Grove, IL 60053.
derivatized silanols contribute to a net negative charge on the solid support surface a t near-neutral pH. As with other examples of organically modified silicas, the TSK SW-type packing materials contribute to retention on the dual bases of ideal and nonideal behavior. Retention solely on the basis of size exclusion represents the ideal component of separation. Nonideal (sorption) retention mechanisms include Coulombic interactions of ionic solutes with dissociated silanols and hydrophobic interactions of nonpolar solutes with the organic bonded phase. Although the literature dedicates considerable discussion to ion exchange and hydrophobic interactions in TSK SW chromatography, the emphasis clearly has been on selecting mobile phase components that reduce multiple retention mechanisms. Less well represented, by far, are literature examples that deliberately manipulate mobile-phase composition to effect separations on the basis of multiple retention mechanisms. This report describes our efforts to exploit the sorption retention mechanisms intrinsic to the TSK SW columns to simultaneously quantitate drug potency and polymer molecular weight distributions in a controlled-release pharmaceutical formulation. Specifically, nafarelin (see structure) is a decapeptide luI
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:
0 1 1
O i l
0
I l l
I l l
I1
~
I
I
I
(CH,COO-)
0
H
I1
I l l
H N-C-CHrN
O
-
CG
/I
0 -
-
C
II
I
-fll-!l-C-C!l
1
i
I
teinizing hormone-releasing hormone (LHRH) analogue in-
0003-2700/87/0359-2050$01.50/0 0 1987 American Chemical Society
~
~
