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FT-IR spectrophotometric analysis of dehydroepiandrosterone and its pharmaceutical formulation
IL FARMACO 60 (2005) 33–36 http://france.elsevier.com/direct/FARMAC/
Original article
FT-IR spectrophotometric analysis of dehydroepiandrosterone and its pharmaceutical formulation Andrei A. Bunaciu a, Hassan Y. Aboul-Enein b,*, S qerban Fleschin c a
b
ROMSPECTRA IMPEX SRL, Analytical Research Department, 25 Str. Maguricea, P.O. Box 52, Bucharest 3, Romania Pharmaceutical Analysis and Drug Development Laboratory, Biological and Medical Research (MBC 03-65), King Faisal Specialist Hospital and Research Centre, P.O. Box 3354, Riyadh 11211, Saudi Arabia c Department of Analytical Chemistry, Faculty of Chemistry, University of Bucharest, Sos. Panduri 90, Bucharest 5, Romania Accepted 26 August 2004 Available online 05 November 2004
Abstract A Fourier transform infrared (FT-IR) spectrometric method was developed for the rapid, direct measurement of dehydropeiandrosterone. Conventional KBr spectra and KBr + 2.0 mg microcrystalline cellulose (MCC) spectra were compared for best determination of active substance in drug formulation. Two chemometric approaches, partial least-squares (PLS) and principal component regression (PCR+) methods were used in data processing. The best results were obtained with PCR+ method. © 2004 Elsevier SAS. All rights reserved. Keywords: FT-IR analysis; Dehydroepiandrosterone; Drug analysis; Chemometric approaches
1. Introduction Infrared spectrometry (IR) provides a useful way for the identification of drugs [1–4]. However, the traditional techniques employed to obtain an IR spectra, such as alkali halides disks, mulls and thin films, are not all the time adequate for quantitative analysis, however the help of Fourier transform (FT-IR) permits continuous monitoring of the spectral baseline and simultaneous analysis of different components of the same sample [5,6]. Quantitative analysis of the component in pharmaceutical preparation by FT-IR spectrometry is based usually upon the Lambert–Beer law. The principal problems are the excipients present in the pharmaceutical preparations. A common problem associated with all IR methods is that there are no specific or unique wavelengths of absorption for any of the components. In this respect, the Fourier transform option is superior to the filter option of spectrophotometers because the determination is not based on a single wavelength that is not unique for the analyte of interest. Since the discovery of dehydroepiandrosterone in rat brain, several 17- and 20-oxosteroids, called ‘neurosteroids’, have * Corresponding author. Tel.: +966-1-442-78-59; fax: +966-1-442-78-58. E-mail address: [email protected] (H.Y. Aboul-Enein). 0014-827X/$ - see front matter © 2004 Elsevier SAS. All rights reserved. doi:10.1016/j.farmac.2004.08.009
been elucidated in mammalian brain [7]. In the last few years, major progress has been made towards the elucidation of the molecular mechanism of action of steroid hormones in the brain and anterior pituitary gland. Dehydroepiandrosterone (DHEA, 3b-hydroxy-5androsten-17-one, Fig. 1) is abundantly produced by adrenal steroids, whose serum concentration exceeds that of other adrenal steroids [8], and it is necessary for duplication and transcription of DNA [9]. DHEA has been shown to protect against heart disease and atherosclerosis. Clinical studies provide evidence that DHEA improves memory, mood, and EEG readings, and may play protective role against neuro-degenerative diseases [10]. DHEA was shown to prevent pharmacologically induced amnesia and mental impairment by benzodiazepine (Valium-like) drugs. Epidemiological studies show that low DHEA levels are associated with the risk of Alzheimer’s disease, and a new
Fig. 1. Structure of dehydroepiandrosterone (DHEA).
34
A.A. Bunaciu et al. / IL FARMACO 60 (2005) 33–36
study provides some molecular mechanisms for how DHEA supplementation may help in part to prevent Alzheimer’s disease. In one study, DHEA inhibited chemically induced cancers in the colon, lung, breast, and skin. When DHEA was applied directly to the skin, it prevented chemically induced skin cancer. DHEA had this effect through inhibiting the binding of carcinogens to skin cells and by inhibiting the enzyme glucose-6-phosphate dehydrogenase (G6PDH). DHEA often declines 80–90% by age 70 or later. The determination of neurosteroids has been performed by gas chromatography–mass spectrometry or radioimmunoassay [11], but these methods have some problems in their simplicity and versatility. High performance liquid chromatography (HPLC) [12–15] was also used as promising modality as a convenient determination method. Recently, Liu et al. [16] described a method for analysis of neurosteroids in rat brain using liquid chromatography–mass spectrometry. Furthermore, Katayama et al. [17] reported a method for the simultaneous determination of 16 estrogens including DHEA using micellar capillary electrophoresis. The purpose of the present study is to use FT-IR spectrometry to investigate the possibility to quantify DHEA in pharmaceutical preparation which exist as capsule containing 25 mg of DHEA. Other excipients include: microcrystalline cellulose (MCC), cross-linked povidone, stearic acid, colloidal silica and gelatin. Also this work aimed to develop a chemometric procedure for the fast and accurate determination of DHEA in the commercial pharmaceutical formulations, using PCR+ and/or PLS approaches for calibration and quantification, reducing the sample pre-treatment and providing direct IR measurement.
2. Experimental 2.1. Apparatus Data acquisition was performed using a Digilab Excalibur FTS 3500 GX FT-IR spectrometer equipped with Merlin version 3.1 (Bio-Rad Laboratories Sadtler Division, USA). The commercial software used to generate analysis for the principal component analysis was PLSplus/IQ from Galactic Industries Corporation (USA). 2.2. Reagents and materials DHEA used for this study was provided by Sigma (St. Louis, MO, USA). DHEA as a dietary supplement tablet formulation (25 mg per tablet) was purchased from Nature’s Bounty (Bohemia, NY, USA). MCC pharmaceutical grade was supplied by SICOMED (Bucharest, Romania). 2.3. Analytical procedures FT-IR spectra were recorded with different resolutions. The spectra were scanned between 4000 and 400 cm–1, by
averaging 64 scans for each spectrum with a resolution of 4 cm–1 (data point resolution/interval 1 cm–1) and with a resolution of 8 cm–1 (data point resolution/interval 2 cm–1), respectively. In this way, we obtained two sets of spectra for each sample. The background spectra were obtained for each experimental condition. Conventional fused KBr disk spectra were recorded with a DTGS detector from samples prepared by compressing a standard substance (a calibration was made using only five standards 0.1, 0.25, 0.5, 1.0 and 1.5 mg, respectively). For the DHEA determination, the spectra were performed in two different ways: • 2.5 mg of drug sample in KBr disk while the background was obtained with KBr disk (DHEA–KBr); • 2.5 mg of drug sample in KBr disks while the background was obtained with 2.0 mg of MCC (the principal excipient of the drug formulation) (DHEA–MCC). 3. Results and discussion Determination of the major component in drugs with FT-IR spectrometry provides an enormous amount of spectroscopic information about the sample. Chemometric methods, such as principal component regression (PCR+, improved principal component regression) and partial leastsquares (PLS2, multicomponent partial least squares) analysis are commonly used to extract the specific information relevant to the analyte of interest from the full spectrum [1,18]. These two techniques yields more accurate calibration models compared with multiple linear regression (MLR) where a restricted set of absorption bands is used in the calibration [19]. The partial least squares (projection to latent structures, PLS) regression method was developed by Wold [20] in 1966. There is a substantial amount of literature devoted to the theoretical elucidation of properties of PLS algorithm. A good introduction to the method is given by Geladi and Kowalski [21]. PLS seeks to express the variance in the property information by correlating it with the spectral information (compare with PCR+, which, in the PCA stage—principal component analysis—only seeks to account for variation in the spectral data and then in the MLR stage correlates this with the property data). Because PLS1 treats all properties individually, if there is non-linearity difference from property to property, it would be expected that PLS1 would build better models than PLS2, which is trying to cater for the nonlinearities for all properties simultaneously [11]. However, if there is a significant amount of noise associated with property values, PLS2 may be expected to perform better than PLS1 because the noise in the properties form PLS2 will be averaged out when determining the PLS factors. Experimental parameters, such as resolutions (4 or 8 cm–1, respectively) and calibration methods, (PCR+, PLS1 or PLS2, respectively) were compared and recommendations on the best options for DHEA analysis were reported.
A.A. Bunaciu et al. / IL FARMACO 60 (2005) 33–36
Fig. 2 presents the FT-IR spectra of a standard dehydropeiandrosterone, while Fig. 3 presents the FT-IR spectra of drug sample as mentioned above (DHEA–KBr) and (DHEA–MCC), respectively. It can be seen that there are no significant changes between the two spectra in the fingerprint region (under 2000 cm–1). The peaks in the DHEA–MCC spectra are a little more evident than in the DHEA–KBr one. The calibration procedure is based on either a modified form of principal component regression (PCR) or on a partial least squares (PLS) fit for one or more properties. The regression model for each property is refined by selecting only those factors considered to be of statistical significance in determining that property. In PCR and PLS2, the spectra are modelled by one set of factors and each property is modelled by relating the concentration values to those factors. In PLS1, the spectra are modelled by a different set of factors for each property and the concentration values are modelled by the respective fac-
35
tors. Hence, PLS1 contains n separate calibrations, where n is the number of properties in the method. The data interval was expanded and parts of spectra were eliminated to reduce the size of the data matrix required by the calibration modelling. The calibrations of this study were carried out with the use of the ‘expert’ option. Both PLS2 and PCR+ were carried out using a resolution of 4 cm–1 and a resolution of 8 cm–1. The first range used was 4000–400 cm–1 while the second range was 1700–700 cm–1. In both cases, no blanks were first selected, but after calibration was performed, the computer selects itself ranges of blanks due to the thresholds. The number of data points used for analysis is 4048 and 1319, respectively. The results are shown in Table 1, which are statistically similar. However, we suggest the use of the PCR+ method, because the peak to peak error value must be at least five times bigger than the RMS error value which is achieved by this technique. The results obtained by the proposed FT-IR method are comparable to one obtained by Aboul-Enein [14].
Fig. 2. FT-IR transmittance spectra of standard DHEA in KBr disk.
Fig. 3. FT-IR transmittance spectra of DHEA in KBr disk and DHEA–MCC disk. Table 1 Comparison of the dehydroepiandrosterone determination in DHEA pharmaceutical formulation using FT-IR chemometric approaches DHEA–KBr
RMS error Peak to peak error Total M-distance Content (mg/tablet)
Resolution 4 cm–1 PCR+ PLS 0.1536 0.1553 0.7425 0.7501 0.676 0.669 25.956 25.886
Resolution 8 cm–1 PCR+ PLS 0.1532 0.1563 0.7485 0.7521 0.653 0.660 25.826 25.986
DHEA–MCC Resolution 4 cm–1 PCR+ PLS 0.06302 0.06320 0.5082 0.5122 0.71 0.712 28.14 28.05
Resolution 8 cm–1 PCR+ PLS 0.06320 0.06320 0.5072 0.5102 0.712 0.712 27.85 28.00
PCR+, resolution of the data used in calibration using the PCR+ method; PLS, resolution of the data used in calibration using the PLS1/PLS2 method; RMS error, root mean square error; peak to peak error, the standard deviation resulted from peak to peak measurements; M-distance, Mahalanobis distance.
36
A.A. Bunaciu et al. / IL FARMACO 60 (2005) 33–36
4. Conclusions The proposed method indicates that FT-IR spectrometry is capable for the direct determination of DHEA in its pharmaceutical formulation. With the commercial software involving chemometric approaches, PCR+ and/or PLS, the method proposed is simple, precise and not time consuming.
[8]
[9]
[10]
[11]
Acknowledgements [12]
One of the author (H.Y.A.-E.) wish to thank the Administration of King Faisal Specialist Hospital and Research Centre for its support for the Pharmaceutical Analysis and Drug Development research program. Another author (A.A.B.) wishes to thank the Administration of ROMSPECTRA IMPEX SRL for the financial support.
[13]
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[3]
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USP XXII, in: (United States Pharmacopoeia, 22nd revision), Convention Inc, Rockville, MD, 1990, pp. 809. A.C. Moffat (Ed.), Clarke’s Isolation and Identification of Drugs, second ed, The Pharmaceutical Society of Great Britain, London, 1986. E.W. Ciurczak, J.K. Drennen III, in: Pharmaceutical and Medical Applications of Near-Infrared Spectroscopy, Marcel Dekker, Inc, New York, 2001, pp. 73–105. W.F. McClure, Analysis using Fourier transforms, in: D.A. Burns, E.W. Ciurczak (Eds.), Handbook of Near-Infrared Analysis, Marcel Dekker, Inc, New York, 1992, pp. 181–274. S. Garrigues, M. Gallignani, M. De la Guardia, Simultaneous determination of ortho-, meta- and para-xylene by flow injection Fourier transform infra-red spectroscopy, Analyst 117 (1992) 1849–1853. B.E. Miller, N.D. Danielson, J.E. Katon, Aqueous infra-red pharmaceutical analysis of two choline compounds by flow injection analysis using the CIRCLE [cylindrical internal reflectance] cell, Appl. Spectrosc. 42 (1988) 401–405. C. Mathur, V.V.K. Prasad, V.S. Raju, M. Welch, S. Lieberman, Steroids and their conjugates in the mammalian brain, Proc. Natl. Acad. Sci. USA 90 (1993) 85–88.
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J.E. Nestler, J.N. Clore, W.G. Blackard, Metabolism and actions of dehydroepiandrosterone in humans, J. Steroid Biochem. Mol. Biol. 40 (1991) 599–605. M.A. Lane, D.K. Ingram, S.S. Ball, G.S. Roth, Dehydroepiandrosterone sulfate: a biomarker of primate aging slowed by calorie restriction, J. Clin. Endocrinol. Metab. 82 (1997) 2093–2096. S. Legrain, L. Girard, Pharmacology and therapeutic effects of dehydroepiandrosterone in older subjects, Drugs and Aging 20 (2003) 949–967. Y. Akwa, R.F. Morfin, P. Robel, E.E. Baulier, Neurosteroid metabolism 7-alpha hydroxylation of dehydroepiandrosterone and pregnenolone by rat brain microsomes, Biochem. J. 288 (1992) 959–964. K. Shimada, K. Mitamura, S. Ishitoya, K. Hirakata, High performance liquid chromatographic separation of sensitive fluorescent derivatives of bile acids with cyclodextrin containing mobile phase, J. Liquid Chromatogr. 16 (1993) 3965–3976. K. Shimada, Y. Satoh, S. Nishimura, Studies on neurosteroids. I. Retention behaviour of derivatized 17-oxosteroids using high performance liquid chromatography, J. Liquid Chromatogr. 18 (1995) 713– 723. H.Y. Aboul-Enein, High performance liquid chromatographic analysis of dehydroepiandrosterone and its pharmaceutical tablet formulation, Biomed. Chromatogr. 18 (2004) 6–9. K. Mitamura, T. Nakagawa, K. Shimada, M. Namiki, E. Koh, A. Mizokami, S. Honma, Identification of dehydroepiandrosterone metabolites formed from human prostate homogenate using liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry, J. Chromatogr. A 961 (2002) 97–105. S. Liu, J. Sjovall, W.J. Griffith, Neurosteroids in rat brain: extraction, isolation, and analysis by nanoscale liquid chromatography–electrospray mass spectrometry, Anal. Chem. 75 (2003) 5835–5846. M. Katayama, Y. Matsuda, K. Shimokawa, S. Kaneko, Simultaneous determination of 16 estrogens, dehydroepiandrosterone and their glucuronide and sulfate conjugates in serum using sodium cholate micelle capillary electrophoresis, Biomed. Chromatogr. 17 (2003) 263–267. J. Haleblian, W. McCrone, Pharmaceutical applications of polymorphism, J. Pharm. Sci. 58 (1969) 911–929. H.J. Luinge, E. Hop, E.T.G. Lutz, J.A. Van Hemert, E.A.M. de Jong, Determination of the fat, protein and lactose content of milk using Fourier-transform infra-red spectrometry, Anal. Chim. Acta 284 (1993) 419–433. H. Wold, in: F.N. David (Ed.), Research Papers in Statistics, Wiley, New York, 1966. P. Geladi, B.R. Kowalski, Partial least-squares regression: a tutorial, Anal. Chim. Acta 185 (1986) 1–17.
Original article
FT-IR spectrophotometric analysis of dehydroepiandrosterone and its pharmaceutical formulation Andrei A. Bunaciu a, Hassan Y. Aboul-Enein b,*, S qerban Fleschin c a
b
ROMSPECTRA IMPEX SRL, Analytical Research Department, 25 Str. Maguricea, P.O. Box 52, Bucharest 3, Romania Pharmaceutical Analysis and Drug Development Laboratory, Biological and Medical Research (MBC 03-65), King Faisal Specialist Hospital and Research Centre, P.O. Box 3354, Riyadh 11211, Saudi Arabia c Department of Analytical Chemistry, Faculty of Chemistry, University of Bucharest, Sos. Panduri 90, Bucharest 5, Romania Accepted 26 August 2004 Available online 05 November 2004
Abstract A Fourier transform infrared (FT-IR) spectrometric method was developed for the rapid, direct measurement of dehydropeiandrosterone. Conventional KBr spectra and KBr + 2.0 mg microcrystalline cellulose (MCC) spectra were compared for best determination of active substance in drug formulation. Two chemometric approaches, partial least-squares (PLS) and principal component regression (PCR+) methods were used in data processing. The best results were obtained with PCR+ method. © 2004 Elsevier SAS. All rights reserved. Keywords: FT-IR analysis; Dehydroepiandrosterone; Drug analysis; Chemometric approaches
1. Introduction Infrared spectrometry (IR) provides a useful way for the identification of drugs [1–4]. However, the traditional techniques employed to obtain an IR spectra, such as alkali halides disks, mulls and thin films, are not all the time adequate for quantitative analysis, however the help of Fourier transform (FT-IR) permits continuous monitoring of the spectral baseline and simultaneous analysis of different components of the same sample [5,6]. Quantitative analysis of the component in pharmaceutical preparation by FT-IR spectrometry is based usually upon the Lambert–Beer law. The principal problems are the excipients present in the pharmaceutical preparations. A common problem associated with all IR methods is that there are no specific or unique wavelengths of absorption for any of the components. In this respect, the Fourier transform option is superior to the filter option of spectrophotometers because the determination is not based on a single wavelength that is not unique for the analyte of interest. Since the discovery of dehydroepiandrosterone in rat brain, several 17- and 20-oxosteroids, called ‘neurosteroids’, have * Corresponding author. Tel.: +966-1-442-78-59; fax: +966-1-442-78-58. E-mail address: [email protected] (H.Y. Aboul-Enein). 0014-827X/$ - see front matter © 2004 Elsevier SAS. All rights reserved. doi:10.1016/j.farmac.2004.08.009
been elucidated in mammalian brain [7]. In the last few years, major progress has been made towards the elucidation of the molecular mechanism of action of steroid hormones in the brain and anterior pituitary gland. Dehydroepiandrosterone (DHEA, 3b-hydroxy-5androsten-17-one, Fig. 1) is abundantly produced by adrenal steroids, whose serum concentration exceeds that of other adrenal steroids [8], and it is necessary for duplication and transcription of DNA [9]. DHEA has been shown to protect against heart disease and atherosclerosis. Clinical studies provide evidence that DHEA improves memory, mood, and EEG readings, and may play protective role against neuro-degenerative diseases [10]. DHEA was shown to prevent pharmacologically induced amnesia and mental impairment by benzodiazepine (Valium-like) drugs. Epidemiological studies show that low DHEA levels are associated with the risk of Alzheimer’s disease, and a new
Fig. 1. Structure of dehydroepiandrosterone (DHEA).
34
A.A. Bunaciu et al. / IL FARMACO 60 (2005) 33–36
study provides some molecular mechanisms for how DHEA supplementation may help in part to prevent Alzheimer’s disease. In one study, DHEA inhibited chemically induced cancers in the colon, lung, breast, and skin. When DHEA was applied directly to the skin, it prevented chemically induced skin cancer. DHEA had this effect through inhibiting the binding of carcinogens to skin cells and by inhibiting the enzyme glucose-6-phosphate dehydrogenase (G6PDH). DHEA often declines 80–90% by age 70 or later. The determination of neurosteroids has been performed by gas chromatography–mass spectrometry or radioimmunoassay [11], but these methods have some problems in their simplicity and versatility. High performance liquid chromatography (HPLC) [12–15] was also used as promising modality as a convenient determination method. Recently, Liu et al. [16] described a method for analysis of neurosteroids in rat brain using liquid chromatography–mass spectrometry. Furthermore, Katayama et al. [17] reported a method for the simultaneous determination of 16 estrogens including DHEA using micellar capillary electrophoresis. The purpose of the present study is to use FT-IR spectrometry to investigate the possibility to quantify DHEA in pharmaceutical preparation which exist as capsule containing 25 mg of DHEA. Other excipients include: microcrystalline cellulose (MCC), cross-linked povidone, stearic acid, colloidal silica and gelatin. Also this work aimed to develop a chemometric procedure for the fast and accurate determination of DHEA in the commercial pharmaceutical formulations, using PCR+ and/or PLS approaches for calibration and quantification, reducing the sample pre-treatment and providing direct IR measurement.
2. Experimental 2.1. Apparatus Data acquisition was performed using a Digilab Excalibur FTS 3500 GX FT-IR spectrometer equipped with Merlin version 3.1 (Bio-Rad Laboratories Sadtler Division, USA). The commercial software used to generate analysis for the principal component analysis was PLSplus/IQ from Galactic Industries Corporation (USA). 2.2. Reagents and materials DHEA used for this study was provided by Sigma (St. Louis, MO, USA). DHEA as a dietary supplement tablet formulation (25 mg per tablet) was purchased from Nature’s Bounty (Bohemia, NY, USA). MCC pharmaceutical grade was supplied by SICOMED (Bucharest, Romania). 2.3. Analytical procedures FT-IR spectra were recorded with different resolutions. The spectra were scanned between 4000 and 400 cm–1, by
averaging 64 scans for each spectrum with a resolution of 4 cm–1 (data point resolution/interval 1 cm–1) and with a resolution of 8 cm–1 (data point resolution/interval 2 cm–1), respectively. In this way, we obtained two sets of spectra for each sample. The background spectra were obtained for each experimental condition. Conventional fused KBr disk spectra were recorded with a DTGS detector from samples prepared by compressing a standard substance (a calibration was made using only five standards 0.1, 0.25, 0.5, 1.0 and 1.5 mg, respectively). For the DHEA determination, the spectra were performed in two different ways: • 2.5 mg of drug sample in KBr disk while the background was obtained with KBr disk (DHEA–KBr); • 2.5 mg of drug sample in KBr disks while the background was obtained with 2.0 mg of MCC (the principal excipient of the drug formulation) (DHEA–MCC). 3. Results and discussion Determination of the major component in drugs with FT-IR spectrometry provides an enormous amount of spectroscopic information about the sample. Chemometric methods, such as principal component regression (PCR+, improved principal component regression) and partial leastsquares (PLS2, multicomponent partial least squares) analysis are commonly used to extract the specific information relevant to the analyte of interest from the full spectrum [1,18]. These two techniques yields more accurate calibration models compared with multiple linear regression (MLR) where a restricted set of absorption bands is used in the calibration [19]. The partial least squares (projection to latent structures, PLS) regression method was developed by Wold [20] in 1966. There is a substantial amount of literature devoted to the theoretical elucidation of properties of PLS algorithm. A good introduction to the method is given by Geladi and Kowalski [21]. PLS seeks to express the variance in the property information by correlating it with the spectral information (compare with PCR+, which, in the PCA stage—principal component analysis—only seeks to account for variation in the spectral data and then in the MLR stage correlates this with the property data). Because PLS1 treats all properties individually, if there is non-linearity difference from property to property, it would be expected that PLS1 would build better models than PLS2, which is trying to cater for the nonlinearities for all properties simultaneously [11]. However, if there is a significant amount of noise associated with property values, PLS2 may be expected to perform better than PLS1 because the noise in the properties form PLS2 will be averaged out when determining the PLS factors. Experimental parameters, such as resolutions (4 or 8 cm–1, respectively) and calibration methods, (PCR+, PLS1 or PLS2, respectively) were compared and recommendations on the best options for DHEA analysis were reported.
A.A. Bunaciu et al. / IL FARMACO 60 (2005) 33–36
Fig. 2 presents the FT-IR spectra of a standard dehydropeiandrosterone, while Fig. 3 presents the FT-IR spectra of drug sample as mentioned above (DHEA–KBr) and (DHEA–MCC), respectively. It can be seen that there are no significant changes between the two spectra in the fingerprint region (under 2000 cm–1). The peaks in the DHEA–MCC spectra are a little more evident than in the DHEA–KBr one. The calibration procedure is based on either a modified form of principal component regression (PCR) or on a partial least squares (PLS) fit for one or more properties. The regression model for each property is refined by selecting only those factors considered to be of statistical significance in determining that property. In PCR and PLS2, the spectra are modelled by one set of factors and each property is modelled by relating the concentration values to those factors. In PLS1, the spectra are modelled by a different set of factors for each property and the concentration values are modelled by the respective fac-
35
tors. Hence, PLS1 contains n separate calibrations, where n is the number of properties in the method. The data interval was expanded and parts of spectra were eliminated to reduce the size of the data matrix required by the calibration modelling. The calibrations of this study were carried out with the use of the ‘expert’ option. Both PLS2 and PCR+ were carried out using a resolution of 4 cm–1 and a resolution of 8 cm–1. The first range used was 4000–400 cm–1 while the second range was 1700–700 cm–1. In both cases, no blanks were first selected, but after calibration was performed, the computer selects itself ranges of blanks due to the thresholds. The number of data points used for analysis is 4048 and 1319, respectively. The results are shown in Table 1, which are statistically similar. However, we suggest the use of the PCR+ method, because the peak to peak error value must be at least five times bigger than the RMS error value which is achieved by this technique. The results obtained by the proposed FT-IR method are comparable to one obtained by Aboul-Enein [14].
Fig. 2. FT-IR transmittance spectra of standard DHEA in KBr disk.
Fig. 3. FT-IR transmittance spectra of DHEA in KBr disk and DHEA–MCC disk. Table 1 Comparison of the dehydroepiandrosterone determination in DHEA pharmaceutical formulation using FT-IR chemometric approaches DHEA–KBr
RMS error Peak to peak error Total M-distance Content (mg/tablet)
Resolution 4 cm–1 PCR+ PLS 0.1536 0.1553 0.7425 0.7501 0.676 0.669 25.956 25.886
Resolution 8 cm–1 PCR+ PLS 0.1532 0.1563 0.7485 0.7521 0.653 0.660 25.826 25.986
DHEA–MCC Resolution 4 cm–1 PCR+ PLS 0.06302 0.06320 0.5082 0.5122 0.71 0.712 28.14 28.05
Resolution 8 cm–1 PCR+ PLS 0.06320 0.06320 0.5072 0.5102 0.712 0.712 27.85 28.00
PCR+, resolution of the data used in calibration using the PCR+ method; PLS, resolution of the data used in calibration using the PLS1/PLS2 method; RMS error, root mean square error; peak to peak error, the standard deviation resulted from peak to peak measurements; M-distance, Mahalanobis distance.
36
A.A. Bunaciu et al. / IL FARMACO 60 (2005) 33–36
4. Conclusions The proposed method indicates that FT-IR spectrometry is capable for the direct determination of DHEA in its pharmaceutical formulation. With the commercial software involving chemometric approaches, PCR+ and/or PLS, the method proposed is simple, precise and not time consuming.
[8]
[9]
[10]
[11]
Acknowledgements [12]
One of the author (H.Y.A.-E.) wish to thank the Administration of King Faisal Specialist Hospital and Research Centre for its support for the Pharmaceutical Analysis and Drug Development research program. Another author (A.A.B.) wishes to thank the Administration of ROMSPECTRA IMPEX SRL for the financial support.
[13]
References
[15]
[1] [2]
[3]
[4]
[5]
[6]
[7]
USP XXII, in: (United States Pharmacopoeia, 22nd revision), Convention Inc, Rockville, MD, 1990, pp. 809. A.C. Moffat (Ed.), Clarke’s Isolation and Identification of Drugs, second ed, The Pharmaceutical Society of Great Britain, London, 1986. E.W. Ciurczak, J.K. Drennen III, in: Pharmaceutical and Medical Applications of Near-Infrared Spectroscopy, Marcel Dekker, Inc, New York, 2001, pp. 73–105. W.F. McClure, Analysis using Fourier transforms, in: D.A. Burns, E.W. Ciurczak (Eds.), Handbook of Near-Infrared Analysis, Marcel Dekker, Inc, New York, 1992, pp. 181–274. S. Garrigues, M. Gallignani, M. De la Guardia, Simultaneous determination of ortho-, meta- and para-xylene by flow injection Fourier transform infra-red spectroscopy, Analyst 117 (1992) 1849–1853. B.E. Miller, N.D. Danielson, J.E. Katon, Aqueous infra-red pharmaceutical analysis of two choline compounds by flow injection analysis using the CIRCLE [cylindrical internal reflectance] cell, Appl. Spectrosc. 42 (1988) 401–405. C. Mathur, V.V.K. Prasad, V.S. Raju, M. Welch, S. Lieberman, Steroids and their conjugates in the mammalian brain, Proc. Natl. Acad. Sci. USA 90 (1993) 85–88.
[14]
[16]
[17]
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