Multivariate Chemometric Approach to Fiber-Optic Dissolution Testing

The use of fiber optics in in vitro dissolution testing opens up new possibilities for more powerful data ... Proton NMR: A New Tool for Understanding...
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Anal. Chem. 2006, 78, 5076-5085

Multivariate Chemometric Approach to Fiber-Optic Dissolution Testing Kent H. Wiberg* and Ulla-Karin Hultin

AstraZeneca R&D So¨derta¨lje, Analytical Development, SE-151 85 So¨derta¨lje, Sweden

The use of fiber optics in in vitro dissolution testing opens up new possibilities for more powerful data evaluation since an entire UV-Vis spectrum can be collected at each measuring point. This paper illustrates a multivariate chemometric approach to the solution of problems of interfering absorbance of excipients in in vitro dissolution testing. Two different chemometric approaches are tested: multivariate calibration using partial least squares (PLS) regression and curve resolution using multivariate curve resolution alternating least squares (MCR-ALS), generalized rank annihilation (GRAM), and parallel factor analysis (PARAFAC). Multivariate calibration (PLS) can, following the construction of a calibration model from a calibration sample set, give selective and accurate determinations of the active ingredient in dissolution testing despite the presence of interfering absorbance from excipients. Curve resolution (MCR-ALS, GRAM, or PARAFAC) can be applied to dissolution testing data in order to determine the dissolution rate profiles and spectra for the interfering excipients as well as for the active ingredient without any precalibration. The concept of the application of these chemometric methods to fiberoptic dissolution testing data is exemplified by analysis of glibenclamide tablets enclosed in hard gelatin capsules. The results show that, despite highly overlapping spectra and unresolved raw data, it is possible with PLS to obtain an accurate dissolution rate profile of glibenclamide. Applying curve resolution makes it possible to obtain accurate estimates of both dissolution rate profiles and spectra of both the gelatin capsule and the glibenclamide. The application of multivariate chemometric methods to fiber-optic dissolution testing brings a fresh scope for a deeper understanding of in vitro dissolution testing, solving the problem of interfering absorbance of excipients and making it possible to obtain dissolution rate profiles and spectra of these. Obtaining dissolution rate profiles of multiple active pharmaceutical ingredients in tablets consisting of several active compounds is another possibility. To investigate how solid drug formulations dissolve in humans without studying this in vivo, a model is generally created of dissolution behavior in the laboratory in vitro by means of * Corresponding author. Phone: +46 8 553 247 97. Fax: +46 8 553 259 84. E-mail: [email protected].

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dissolution testing. One of the rate-determining steps in the absorption of drugs is the dissolution rate in the gastrointestinal fluids, and through in vivo/in vitro correlation, dissolution testing can predict in vivo availability. Furthermore, dissolution testing is used in formulation development to investigate how different excipients interact with the active ingredient to influence the dissolution rate. Thus, in vitro dissolution is an important tool during pharmaceutical development and is also routinely used for quality control during production. One common and standardized way of performing dissolution testing is to use the equipment specified in the United States Pharmacopeia (USP).1 The most commonly used equipment, USP Apparatus 1 and 2, consist of a covered vessel made of glass, with a paddle or a basket on a shaft attached to a motor. The vessel is partially immersed in a thermostated water bath kept at 37 ( 0.5 °C. The most commonly used dissolution media are deionized water or some type of buffer. The solid drug formulations (e.g., tablets or capsules) are placed in the vessel either in the basket or on the bottom of the vessel, and analyses are made during the dissolution of the tablet at fixed intervals. The sampling in dissolution testing is traditionally done by extracting samples from the dissolution vessels. These samples are then evaluated using direct UV or HPLC. The latter is more time-consuming and is used when UV is used insufficiently due to low absorptivities or sample concentrations or in cases where a separation is needed because of interference from inactive ingredients and/or capsule shells. With both of these analytical techniques, external standards are used to determine the amount of substance dissolved. In recent years optic fibers have been used in dissolution testing, measuring the absorbance directly in the dissolution vessel with a fiber-optic probe.2-7 This approach is rapid and efficient, with less handling of the samples and consequently fewer sources of error. The use of UV fiber optics has become more and more widespread due to its simplification of the sampling process as the measurements take place in situ and thus no sample withdrawal is involved. In addition, fiber-optic measurements (1) United States Pharmacopeia (USP). 28 NF23 S1. (2) Josefson, M.; Johansson, E.; Torstensson, A. Anal. Chem. 1988, 60, 26662671. (3) Brown, C. W.; Lin, J. Appl. Spectrosc. 1993, 47, 615-618. (4) Cho, J. H.; Gemperline, P. J.; Salt, A.; Walker, D. S. Anal. Chem. 1995, 67, 2858-2863. (5) Bynum, K. C.; Kraft, E.; Pharm. Technol. 1999, 23, 42-44. (6) Aldridge, K.; Melvin, D. W.; Williams, B. A.; Bratin, K.; Kostek, L. J.; Sekulic, S. S. J. Pharm. Sci. 1995, 84, 909-914. (7) Nir, I.; Johnson, B. D.; Johansson, J.; Schatz, C. Pharm. Technol. Int. 2002, 14, 33-40. 10.1021/ac0602928 CCC: $33.50

© 2006 American Chemical Society Published on Web 05/28/2006

enable frequent sampling. As a consequence, an extensive amount of data is obtained, allowing for more powerful data evaluation since an entire UV-Vis spectrum can be collected at each measuring point. The principal aim of the dissolution testing of pharmaceutical tablets is to obtain accurate profiles of the dissolution rate of the active ingredient in the tablet. A pharmaceutical tablet, however, always consists of a number of other compounds (i.e., excipients), and very often these compounds can influence the determination of the active ingredient. This is especially the case when the determination of the amount of active substance dissolved is made without any separation since the excipients might absorb light at the same wavelength as the active ingredient, which can have a large influence on the dissolution profiles obtained. Furthermore, the dissolution of the excipients is an important part of the in vitro dissolution of the tablet, although the information about how the excipients behave is not detected by traditional in vitro dissolution testing. The use of fiber optics, however, together with powerful chemometric data evaluation can open up new possibilities for a deeper understanding of in vitro dissolution testing. Multivariate calibration using partial least squares (PLS) regression has previously been applied to multivariate dissolution testing data in order to correct for turbidity disturbances2 and to estimate the dissolution rate of simultaneously reacting compounds.8,9 This paper illustrates a multivariate chemometric approach to solving the problems of interfering absorbance of excipients in in vitro dissolution testing. It shows how the fiberoptic data can be used in multivariate calibration with PLS for determination of the dissolution rate profile of the active compound free from interfering absorbance. Using many spectral variables gives robust and accurate determinations of the active compound despite interference from excipients. It also shows how methods for curve resolution like multivariate curve resolution alternating least squares (MCR-ALS), generalized rank annihilation (GRAM), and parallel factor analysis (PARAFAC) can be applied to dissolution testing data in order to resolve dissolution rate profiles for excipients as well as for the active ingredient. The application of these chemometric techniques also gives estimates of the pure UV spectrum of the compounds, and with PARAFAC, estimates of the concentrations of the compounds in the tablets analyzed are also obtained. In this study, glibenclamide tablets (Glyburide, an antidiabetic drug) enclosed in hard gelatin capsules are used as a model system, illustrating interference with the dissolution rate profile of the active compound. Glibenclamide has a UV absorbance maximum at 229 nm, and the hard gelatin capsule absorbs UV light below about 250 nm, leading to a co-determination that results in overestimation of the amounts dissolved. The application of these chemometric methods to fiber-optic dissolution testing data opens up new possibilities for a deeper understanding of in vitro dissolution testing. Curve resolution applied to fiber-optic dissolution testing data to our knowledge has not previously been reported in the literature. The possibilities of applying these chemometric methods to fiber-optic dissolution testing data should be attractive in formulation development as (8) Otto, M. Analyst 1990, 115, 685-688. (9) Liu, X.-Z.; Liu, S.-S.; Wu, J.-F.; Fang, Z.-L. Anal. Chim Acta 1999, 392, 273281.

Figure 1. Illustration of a typical dissolution profile with the phases lag time, steady state, and decay indicated.

well as for the investigation of problems in everyday work on in vitro dissolution. THEORY Dissolution Testing. The evaluation of dissolution testing has conventionally taken place directly in a spectrophotometer or with prior separation using HPLC, the amounts dissolved being calculated against an external standard as a percentage of either the labeled amount or the observed amount. Figure 1 is an illustration of a typical dissolution profile with the characteristic phases of the process: lag time, steady state, and decay, of which lag time and the steady-state phases are the most important in detecting deviations, the steady-state phase being the linear part of the dissolution process. An analysis of in vitro dissolution testing of a solid pharmaceutical formulation (e.g., a tablet or capsule) can be conducted in different time scales, depending on the type of formulation analyzed. An instant release formulation should be dissolved within 2 h, and the analysis is normally performed during 1 h with a regulatory demand that a certain percentage of the tablets is to be dissolved within a specified time. There are a number of factors that influence in vitro dissolution, and they can roughly be divided into two categories: factors related to the formulation itself and factors related to the dissolution testing procedures. Solid-phase characteristics and polymorphism of the drug substance and excipients and particle size of the drug substance are examples of factors related to the formulation, whereas agitation intensity, temperature, flow pattern disturbances, sampling probes, and dosage form position are examples of factors related to the dissolution procedure. In addition, there are miscellaneous factors (e.g., adsorption and absorption) that do not belong to any given category.10 Multivariate Calibration. Multivariate calibration can be defined as the process of constructing a mathematical model that relates a property such as the content to the absorbances of a set of known reference samples at more than one wavelength. The calibration model is thus determined from a set of samples of known content, a calibration set. The regression method most often used for multivariate calibration is PLS and has been described in detail elsewhere11,12 together with the theories of multivariate calibration.13 The calibration model should preferably (10) Banakar, U. V. Pharmaceutical Dissolution Testing; Marcel Dekker: New York; 1992. (11) Geladi, P.; Kowalski, B. Anal.Chim. Acta 1986, 185, 1-17. (12) Wold, S.; M Sjo ¨stro ¨m, Eriksson, L. Chemom. Intell. Lab. Syst. 2001, 58, 109-130. (13) Martens, H.; Naes, T. Multivariate Calibration; Wiley: New York; 1989.

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be validated by the analysis of a set of new samples not included in the calibration (i.e., an external test set). The main advantages of using multivariate calibration are that it can give accurate and precise determinations despite interfering absorbance of other compounds. Furthermore, a multivariate calibration model can be used over time, which implies that no standard samples need to be prepared and analyzed together with the unknown samples. The main drawbacks are the difficulty of including all relevant variations in the calibration model (such as batch-to-batch variations and variations over time, etc.) and the relatively large calibration sample set needed, which has to be prepared and analyzed in order to establish the calibration model. Curve Resolution. Chemometric methods that attempt without any parameter estimate of any model function for peaks to obtain the shape of the pure profiles (i.e., chromatograms and spectra) of the compound in mixtures are generally referred to as curve resolution or self-modeling curve resolution methods. A large number of methods have been proposed for both two-way data and data of higher dimensionality. In high-performance liquid chromatography (HPLC) with diode array detection (DAD), a large number of methods have been proposed for the resolution of unresolved chromatographic peaks. One of the earliest and most frequently used methods is evolving factor analysis (EFA),14,15 which is based on principal component analysis (PCA).16,17 In EFA, multiple PCA decompositions are made on successively larger portions of an HPLC-DAD data file. The analysis starts by performing PCA on the first two spectra in the HPLC-DAD file, after which one spectrum at a time is added, followed by a new PCA decomposition (forward analysis). This is continued until eventually the entire HPLC-DAD data have been decomposed with PCA. The procedure is then repeated, although in reverse order (backward analysis), by starting at the end of the HPLC-DAD file with the last two spectra, performing PCA and adding one spectrum at a time, performing new PCA decompositions. By then plotting the eigenvalues (or the log of the eigenvalues) of the multiple PCA decompositions as a function of retention time, the number of compounds present and their retention-time windows in the chromatogram can be estimated. These EFA plots (log eigenvalue as a function of retention time) thus show the number of compounds in the unresolved chromatogram and the retention time interval where each compound elutes. The explanation is that as long as each spectrum added in the PCA decompositions comes from the same compound (the same UV spectrum), the data are sufficiently described by one principal component. When another UV spectrum appears (i.e., a new compound elutes), an additional principal component is needed. The theory of EFA has been described in detail elsewhere.14,15 The information obtained using EFA regarding the number of compounds and their retention-time windows is a good starting point for performing MCR-ALS,18,19 which is a method that can estimate the pure chromatographic peaks and pure spectra in an unresolved chromatogram. MCR-ALS requires an estimation of (14) Maeder, M.; Zuberbuehler, A. D. Anal. Chim. Acta 1986, 181, 287-291. (15) Maeder, M. Anal. Chem. 1987, 59, 527-530. (16) Wold, S.; Esbensen, K.; Geladi, P. Chemom. Intell. Lab. Syst. 1987, 2, 3752. (17) Jackson, J. E. A User’s Guide to Principal Components; John Wiley & Sons: New York, 1981. (18) Tauler, R. Chemom. Intell. Lab. Syst. 1995, 30, 133-146. (19) Tauler, R.; Kowalski, B. Anal. Chem. 1993, 65, 2040-2047.

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the number of compounds present and an initial guess of either their retention-time windows or the pure spectra in order to iteratively estimate pure profiles. By utilizing the information from EFA together with constraints such as nonnegativity constraint, MCR-ALS can by means of a procedure of alternating and constrained least squares optimization iteratively estimate pure spectra and chromatograms. More constraints can also be used if present in order to improve the solution obtained. The results of applying MCR-ALS are dependent on the initial guess as well as the constraints used. If MCR-ALS is applied to the secondorder data of a fiber-optic dissolution run, it is possible to obtain estimates of pure dissolution rate profiles and spectra. GRAM is a multiway technique that simultaneously decomposes two matrixes of trilinear second-order data.20,21 GRAM is thus applied to two samples containing second-order data; usually one of these samples is a standard containing a known concentration of the analyte of interest, while the second sample contains unknown interferents as well as an unknown concentration of the analyte of interest. GRAM can be used for curve resolution of two samples containing second-order data if the number of compounds is known. One advantage is that it is a direct technique that does not require an initial estimation of the concentration or spectral profiles. If the second-order data of two fiber-optic dissolution runs are placed together, a three-way array is created to which GRAM can be applied to give estimates of pure dissolution rate profiles and spectra. It should be noted that with GRAM the number of samples is restricted to two, since this method can only decompose two matrixes of second-order data at the same time. PARAFAC22-24 is a chemometric decomposition method for three-way (or higher) arrays that can be seen as a generalization of bilinear PCA to higher-order arrays.24 In PARAFAC, however, each component is trilinear, in contrast to bilinear PCA. For a three-way array, therefore, three loading vectors are given for each PARAFAC component. An important characteristic of PARAFAC decomposition is that, under very mild assumptions, it is mathematically unique, and the solution obtained can often be directly interpretable and chemically meaningful. This means that PARAFAC can be applied for curve resolution to, for instance, unresolved HPLC-DAD data since it will provide estimates of pure chromatographic peaks, spectra, and, if a calibration sample with known content is available, the concentrations of the samples.25 PARAFAC like MCR-ALS also needs an estimation of the number of compounds present. The initial estimation of the loadings can be made in PARAFAC with trilinear decomposition.26 In fiber-optic dissolution testing, the absorbance of each sample is given as a function of wavelength and time. If the data of several fiber-optic dissolution runs are placed together, the resulting data matrix will be a three-way array, a cube of data. If PARAFAC is applied to this three-way fiber-optic dissolution testing data, it will give an estimate of the pure spectra, dissolution rate profiles, and concentration of the samples analyzed. (20) Sanchez, E.; Kowalski, B. R. J. Chemom. 1988, 2, 265-283. (21) Wilson, B. E.; Sanchez, E.; Kowalski, B. R. J. Chemom. 1989, 3, 493-498. (22) Carroll, J. D.; Chang, J. Psychometrica 1970, 35, 283-319. (23) Harshman, R. A. UCLA Work. Pap. Phonetics 1970, 16, 1-84. (24) Bro, R. Chemom. Intell. Lab. Syst 1997, 38, 149-171. (25) Wiberg, K.; Jacobsson, S. J. Anal. Chim. Acta 2004, 514, 203-209. (26) Sanchez, E.; Kowalski, B. R. J. Chemom. 1990, 4, 29-45.

The information gained from curve resolution is in some sense qualitatively deeper than the quantitative information obtained from multivariate calibration, since the former gives pure profiles that cannot be obtained through the use of PLS alone. The aims of these multivariate chemometric methods are different, however, since the object of using PLS in multivariate calibration is primarily to obtain accurate quantitative determinations of the compound(s) of concern, while the main goal of curve resolution is to give accurate estimates of the pure profiles. One important distinction, however, concerns the order of the data handled by these chemometric methods. While first-order multivariate calibration using PLS is generally capable of handling the absorbance from interfering compounds by means of the calibration model constructed from the precalibration (the firstorder advantage), chemometric techniques that utilize secondorder data (such as GRAM and PARAFAC) can be used to asses the concentration of an analyte in an unknown mixture (the second-order advantage27). EXPERIMENTAL SECTION Instrumentation. The equipment used for dissolution testing was a VanKel 7010 dissolution bath (Varian, Inc., Palo Alto, CA). Dissolution testing was carried out using the USP Apparatus 2 operated at a stirring speed of 75 rpm. The spectrophotometer was a Varian Cary 50 with a 12 channel multiplexer (Varian, Inc., Palo Alto, CA) equipped with UV fiber-optic probes (C Technologies, Bridgewater, NJ), 4 mm probe tips being used throughout. For data collection, the Cary WinUV software (Varian Inc., Palo Alto, CA) was used. For HPLC analysis, a Gynkotek UV/Vis detector UVD340U, HPLC pump P580HPG, and autosampler Gina 50 (Dionex Corporation, Sunnyvale, CA) were used. The HPLC evaluation was carried out with Chromeleon software (Dionex Corporation, Sunnyvale, CA). The HPLC column was an XTerra MS RP-18 (Waters, Milford, MA). Software. Multivariate calibration and predictions were performed using Simca-P 9.0 (Umetrics AB, Sweden), and the curve resolution (MCR-ALS, GRAM and PARAFAC) was done with Matlab 6.5 (Mathworks) using version 2.0 of the PLS toolbox.28 The MCR-ALS calculations were done using the MCR function in PLS toolbox 2.0 using nonnegativity constraints on both the dissolution rate profile and the spectra. The GRAM calculation was performed using the GRAM function in PLS toolbox 2.0. The PARAFAC decomposition was done with the PARAFAC function in PLS toolbox 2.0. The initial estimation of the loadings in this function is made with Trilinear decomposition (TLD) and the PARAFAC decomposition is carried out without any constraints. Reagents. The in vitro dissolution was carried out in 50 mM phosphate buffer pH 6.8 prepared from KH2PO4 (analytical grade) and NaOH. To the dissolution media was added 0.5% (w/w) of cetyltrimethylammonium bromide (CTAB), a cationic surfactant. The mobile phase was 50 mM pH 3 phosphate buffer and acetonitrile (50:50, v/v). For HPLC determination, 7.9 mg of Glyburide drug substance was prepared in 100 mL of acetonitrile. This solution was then further diluted from 4 to 50 mL with dissolution media. Commercially available tablets of glibenclamide (27) Smilde, A.; Bro, R.; Geladi, P. Multi-way Analysis; Wiley: Chichester, 2004 (28) Wise, B. M.; Gallagher, N. G. PLS_Toolbox 2.0; Eigenvector Research: 1998.

Table 1. Composition of Hard Gray and Swedish Orange Gelatin Capsules compound

amount (%)

Gray Hard Gelatin Capsule black iron oxide 0.3 titanium dioxide 2.7 gelatin qsp 100 Swedish Orange Gelatin Capsule red iron oxide 1.2 yellow iron oxide 1.2 titanium dioxide 1.0 gelatin qsp 100

Table 2. Composition of One 2.5 mg Glibenclamide Gelatin Capsule compound tableta

glibenclamide hard gelatin capsule, gray cellulose, microcrystalline magnesium stearate

amount 1 tablet 1 capsule 160 mg 0.4 mg

a According to the supplier, in addition to glibenclamide, glibenclamide tablets also contain lactose, croscarmellose sodium, and magnesium stearate.

Table 3. Composition of One 5 mg Glibenclamide Capsule compound tableta

glibenclamide hard gelatin capsule, Swedish orange cellulose, microcrystalline magnesium stearate

amount 2 tablets 1 capsule 192 mg 0.5 mg

a According to the supplier, in addition to glibenclamide, glibenclamide tablets also contain lactose, croscarmellose sodium, and magnesium stearate.

2.5 and 5 mg (APS Ltd, UK) and hard gelatin capsules (Capsugel, Morris Plains, NJ) were used for the dissolution testing throughout the study. The tablets, together with microcrystalline cellulose (filler) and magnesium stearate (lubricant), were filled into gray and orange hard gelatin capsules (the hard gelatin capsules differed in size, and the gray capsule held one 2.5 mg tablet, while the orange capsule held two tablets). The composition of the glibenclamide tablets and the hard gelatin capsules are shown in Tables 1-3, and the corresponding UV spectra are shown in Figure 2. It should be noted that the spectrum of the hard gelatin capsule shown in Figure 2 is not caused by absorbance due to magnesium stearate, cellulose, iron, or titanium oxide since these compounds have very low UV absorptivity. Rather it is caused by UV absorbance of gelatin. For the multivariate calibration, only 2.5 mg glibenclamide tablets enclosed in gray hard gelatin capsules were used, while for the curve resolution, both 2.5 and 5 mg glibenclamide tablets enclosed in both gray and orange hard gelatin capsules were analyzed. Procedures. In the multivariate calibration study the calibration model was constructed from a calibration set consisting of six separate dissolution runs with 2.5 mg glibenclamide tablets enclosed in gray hard gelatin capsules. During these runs fiberoptic sampling took place continuously, collecting a UV spectrum Analytical Chemistry, Vol. 78, No. 14, July 15, 2006

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Figure 2. UV spectra of glibenclamide (5 mg in 900 mL of dissolution medium) and one hard gelatin capsule (one hard gelatin capsule dissolved in 900 mL of dissolution medium). Solid line: glibenclamide. Dashed line: gelatin capsule. Table 4. Results of the HPLC Determination of the Glibenclamide Concentration at Eight Intervals during the Six Dissolution Runs Used in the Multivariate Calibration Set

3 min 6 min 9 min 12 min 15 min 30 min 45 min 60 min

run 1

run 2

run 3

run 4

run 5

run 6

1.3 30.9 49.8 58.3 76.9 89.0 94.7 95.5

10.5 29.7 54.8 56.0 69.0 91.9 94.8 98.2

1.9 47.5 65.4 88.4 82.5 91.8 94.9 94.4

23.8 34.5 59.4 71.0 75.9 87.9 92.7 96.4

1.5 16.8 65.0 70.0 74.4 93.6 97.2 99.3

1.5 24.8 67.7 68.9 75.4 91.7 93.2 95.5

every minute for 60 min. Samples for HPLC analysis were taken at 3, 6, 9, 12, 15, 30, 45, and 60 min and filtered immediately. The dissolved concentration of glibenclamide in these samples was determined using external standards in the HPLC analysis, and the results obtained are shown in Table 4. As can be seen, there is some variation in the dissolved amounts of glibenclamide, especially in the steady-state part of the analysis (5-15 min). This variation might seem large; however, for method assessment, an RSD of 20% is acceptable for time points up to 10 min and an RSD of 10% for later time points. The UV spectra at 3, 6, 9, 12, 15, 30, 45, and 60 min on the same runs were then used as X variables. The glibenclamide content of these samples (determined with HPLC) was used as Y variable. The calibration set thus consisted of 48 UV spectra and 131 variables (220-350 nm). The spectral variables above 350 nm were omitted in the calibration model since no absorbance is shown for either glibenclamide or the hard gelatin capsule at these wavelengths (Figure 2). The only pretreatment of the spectral data used was mean-centering. The calibration model was validated by internal validation (cross validation) and an external test set including the UV spectra at eight points in time of a new dissolution run of the same type of tablet (a 2.5 mg glibenclamide tablet enclosed in a gray hard gelatin capsule). At these time points a sample was also withdrawn for HPLC determination of the amount of dissolved glibenclamide using external standards. In the curve resolution study, the resulting data from the dissolution of the 2.5 mg tablet were supplemented with new 5080 Analytical Chemistry, Vol. 78, No. 14, July 15, 2006

analyses of 5 mg glibenclamide tablets enclosed in hard gelatin capsules in order to obtain three-way data for the GRAM and PARAFAC decomposition. The fiber-optic dissolution data of the analysis of the 2.5 and 5 mg tablets were imported into Matlab. The GRAM and PARAFAC analysis were made on three-way data sets consisting of 2 samples, 60 spectra (one spectrum per minute) and 181 spectral variables (220-400 nm). The MCR-ALS calculations were made on the same data but only on one sample (tablet strength) at a time (i.e., two-way data sets consisting of 60 spectra and 181 spectral variables). The functions for MCR-ALS, GRAM and PARAFAC in the PLS toolbox version 2.0 was used for the curve resolution. For all three methods, for curve resolution no pretreatment of the data was applied. In the models made, two components were used. In the MCR-ALS analysis the data consisted of a 60 × 181 matrix. The MCR-ALS analysis was made using nonnegativity constraints on both the dissolution rate profile and the spectra. As an initial guess, a separate matrix with estimations of when the gelatin capsule and glibenclamide existed in the dissolution vessel was applied (see further description below). In the GRAM and PARAFAC analysis, the data consisted of a 2 × 60 × 181 matrix. For both methods, the only input given was the number of components to be used in the model, no additional constraints being applied. The results of all three methods for curve resolution were evaluated with respect to shape of the dissolution rate profiles and resemblance between estimated spectra and pure spectra of gelatin capsule and glibenclamide. RESULTS AND DISCUSSION The raw data from dissolution testing of glibenclamide tablets enclosed in gelatin capsules are shown in Figure 3 with absorbance as a function of time (0-60 min) and wavelength (200400 nm). As can be seen, the absorbance is mainly in the low wavelength region at about 220-260 nm, where both glibenclamide and the gelatin capsule absorb (Figure 2). From a visual inspection of the raw data in Figure 3, there is no way possible to see the pure dissolution rate profile or UV spectrum of the two compounds since the UV absorbances of glibenclamide and the hard gelatin capsule are additive. It can also be seen in Figure 3 that different dissolution profiles are obtained at different wavelengths. By traditional dissolution testing using only one single wavelength, it is thus not possible to distinguish the dissolution profiles of the two compounds. Multivariate Calibration. The PLS calibration model constructed from the calibration samples (six dissolution runs using the UV spectrum at eight time points, Table 4) contained two PLS components that explained >96% of the variation in the data. The plausible reason that the model contained two PLS components is the variation seen in the HPLC-determined dissolved amounts of glibenclamide in the steady-state part of the dissolution profiles of the calibration set (Table 4). This variation also causes some uncertainty in the prediction accuracy (r2 of the linear calibration line being 0.95 and root-mean-square error of cross validation (RMSECV) being 6.6%). The results of the prediction of the concentration of dissolved glibenclamide at the eight time points in the external test set (3, 6, 9, 12, 15, 30, 45, and 60 min) using multivariate calibration with PLS are shown in Table 5, together with the results of the HPLC analysis. It should be noted that the sampling is not carried out at exactly the same place in the dissolution vessel and that, as the dissolution is in progress,

Figure 3. Raw data from dissolution testing of glibenclamide in a hard gelatin capsule (one orange hard gelatin capsule containing 5 mg of glibenclamide), absorbance as a function of time (0-60 min) and wavelength (200-400 nm). Table 5. Prediction Results (% Glibenclamide Dissolved) of the HPLC Determination and the Multivariate Determination Using PLS interval (min)

HPLC prediction (% dissolved)

PLS prediction (% dissolved)

difference (%)

3 6 9 12 15 30 45 60

4.0 34.3 53.2 61.0 69.8 85.9 89.6 91.2

8.1 32.1 53.6 72.7 79.8 88.4 92.3 91.6

4.1 2.2 0.8 11.7 10.0 2.5 2.7 2.2

differences between HPLC results and results obtained using the fiber-optic probe can be explained. Despite these differences, it can be seen that the two methods give fairly similar results. The largest differences between the two methods are around 12 and 15 min, where the prediction results differ by about 10%. The time points of about 12-15 min correspond to the end of the steadystate part of the dissolution of the glibenclamide tablets. The fact that the UV probe spectrum and HPLC analysis are not made on the same sample aliquots might explain the difference between the two methods at these time points. It can be concluded that the multivariate calibration using PLS is capable of giving an accurate dissolution rate profile despite interfering absorbance from the hard gelatin capsule. Curve Resolution. As described in the theory section, EFA is often a good starting point for obtaining information on the number of compounds present and the initial guess of their elution windows needed for the MCR-ALS analysis. In a pharmaceutical tablet dissolved in a dissolution vessel there is no chromatographic separation present, which implies that the EFA results will be more difficult to interpret as compared to when EFA is applied to HPLC-DAD data. In the samples used in this study, the hard gelatin capsules must, however, dissolve for a short time before the glibenclamide tablets dissolve, since the latter are enclosed in the gelatin capsules and cannot dissolve until they come in contact with the dissolution medium. The results of applying EFA analysis to the fiber-optic dissolution testing data for the dissolution of 5 mg glibenclamide enclosed

Figure 4. Results of EFA analysis on dissolution data (two 2.5 mg glibenclamide tablets enclosed in an orange hard gelatin capsule), EFA plot (log eigenvalue against retention time). Solid lines: forward analysis. Dashed lines: backward analysis. The arrow points to an inflection point on the dissolution curve where glibenclamide starts dissolving.

in an orange hard gelatin capsule are shown in Figure 4 with log EV plots. Two PCs are used, the solid lines representing the forward analysis and the dashed lines the backward analysis. It should, however, be pointed out that the results of the backward analysis (dashed lines) are less informative when EFA is applied to fiber-optic dissolution testing data, compared to when applied to HPLC-DAD data, the explanation being that in dissolution testing data there is no separation present and hence no discrete elution windows such as the beginning and end of a chromatographic peak. It is rather a difference in the onset or start of the dissolution of the compounds in the tablet that is captured in EFA since the compounds, when they are dissolved, do not disappear or decrease in concentration in the dissolution vessel. As can be seen in Figure 4, the start of the dissolution of the two compounds is difficult to distinguish in the log EV plot. However, the arrow in Figure 4 points to an inflection point on the evolving profile of PC1 at about 3 min. This point is important since it indicates the point in time where glibenclamide starts dissolving. This is also indicated in the log EV plot of PC2 in the Analytical Chemistry, Vol. 78, No. 14, July 15, 2006

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Figure 5. Results of the application of MCR-ALS to the raw data from dissolution testing of two 2.5 mg glibenclamide tablets enclosed in an orange hard gelatin capsule. (a) Dissolution rate profile. (b) Spectral profile. Solid line: glibenclamide. Dashed line: gelatin capsule.

forward analysis, which also shows an inflection at the same time point. If this information is given as an initial guess to the MCR-ALS analysis (i.e., that the gelatin capsule starts dissolving immediately from 0 min, while glibenclamide starts dissolving only after 3 min), and it is combined with nonnegativity constraints on both the dissolution rate profile and the spectra, the results shown in Figure 5 are obtained. In Figure 5a, the estimated dissolution rate profiles with the relative amounts dissolved as a function of time are shown. In Figure 5b, the estimated spectra with the relative absorbance as a function of wavelength are shown. It should be noted that the scales of the relative amounts dissolved are not directly comparable due to the scale ambiguity of the estimated dissolution rate profiles. After about 15 min 90% of the gelatin capsule seems to be dissolved and after about 20 min 90% of the 5082

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glibenclamide. In Figure 5b the estimated spectra are shown and, as can be seen, they are quite reasonable estimates of the spectra of the two compounds obtained as compared with the actual spectra (Figure 2). It can be concluded that the results obtained with MCR-ALS on dissolution testing data of glibenclamide tablets enclosed in hard gelatin capsules are quite satisfactory. The MCRALS results of the analysis of orange and gray (not shown here) hard gelatin capsules gave similar results. In Figure 6 the corresponding results of GRAM analysis of the two samples are shown. In Figure 6a the estimated dissolution rate profiles are shown, and in Figure 6b the estimated spectra are shown. A comparison of the results of GRAM with the MCRALS results (Figure 5) shows that the dissolution profiles are very similar (though differing in the scales of the amounts dissolved) and the spectral profiles are almost identical. The GRAM-estimated

Figure 6. Results of the application of GRAM to the raw data from dissolution testing of two hard gelatin capsules (one gray and one orange capsule containing 2.5 and 5 mg glibenclamide, respectively). (a) Dissolution rate profile. (b) Spectral profile. Solid line: glibenclamide. Dashed line: gelatin capsule.

dissolution profile shows a negative dip for glibenclamide in the lag time. This is probably caused by the low concentration of the analytes (Figure 2) and the turbid environment. The MCR-ALS analysis was also performed with nonnegativity constraints. The GRAM-estimated dissolution profile shows that after about 15 min 90% of the gelatin capsule seems to be dissolved and after about 18 min 90% of the glibenclamide is dissolved. In Figure 7 the corresponding pure profiles obtained using PARAFAC decomposition on the data are shown. Figure 7a shows the estimated dissolution rate profiles, Figure 7b shows the spectra, and Figure 7c shows the estimated relative concentration of the two compounds in the two tablets (the latter profile is unique to the trilinear PARAFAC decomposition and it is not obtained

with MCR-ALS or GRAM). The dissolution profiles obtained with PARAFAC (Figure 7a) are quite similar to the ones obtained with MCR-ALS and GRAM, and after about 16 min, 90% of the gelatin capsule seems to be dissolved. After about 18 min, 90% of the glibenclamide is dissolved. Figure 7b shows the estimated spectra and, in contrast to the corresponding profile given by MCR-ALS and GRAM (Figures 5b and 6b), the glibenclamide spectrum appears to be sharper in the PARAFAC decomposition. Figure 7c shows the estimated relative concentrations of the two compounds in the two tablets analyzed. The solid line corresponds to the glibenclamide content, while the dashed line corresponds to the gelatin capsule. The content of glibenclamide and gelatin corresponds to the relative content values obtained from the PARAFAC Analytical Chemistry, Vol. 78, No. 14, July 15, 2006

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Figure 7. Results of the application of PARAFAC to the raw data from dissolution testing of two hard gelatin capsules (one gray and one orange capsule containing 2.5 and 5 mg glibenclamide, respectively). (a) PARAFAC loading 1 (dissolution rate profile). (b) PARAFAC loading 2 (spectral profile). (c) PARAFAC loading 3 (concentration profile). Solid line: glibenclamide. Dashed line: gelatin capsule.

decomposition. As can be seen, the glibenclamide content clearly differs between the two capsules, while the gelatin content seems to be more similar. The three methods used for curve resolution seem to give fairly similar results. The estimated dissolution profiles were very similar, and the estimated spectra in all three cases displayed a large resemblance with the pure spectra of gelatin capsule and 5084

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glibenclamide (Figure 2). However, the PARAFAC decomposition gave a more distinct spectrum of glibenclamide. The explained variance (sum of squares) for the three methods for curve resolution can be seen in Table 6. As can be seen, all three curve resolution models explained almost all of the variation in the data. Figure 8 presents the dissolution profiles obtained with HPLC determination, PLS determination, and the profile estimated with PARAFAC at the eight time points: 3, 6, 9, 12, 15, 30, 45, and 60 min. As previously described, the PLS predictions and PARAFAC estimates are made on the same data, while the HPLC determination is made on another sample. As can be seen, the PLSpredicted and PARAFAC-estimated dissolution profiles are very similar, with only small differences at 3, 12, and 15 min. Both the PLS-predicted and the PARAFAC-estimated dissolution rate profiles are quite similar to the HPLC-determined dissolution rate profile, which shows that both chemometric approaches give accurate dissolution rate profiles. This study illustrates new possibilities of multivariate chemometric methods handling the absorbance of interfering compounds in in vitro dissolution testing. Two often-used standard methods for this problem are baseline subtraction and second derivative spectra. It is not very likely that baseline correction would be an option in the example shown in this study due to the similarities between the spectra of the two compounds as well as the short period of time between the onsets of their dissolution. Second derivative spectra, might be an alternative, although the results of some initial calculations using second derivative filtered data from this study gave results that indicated that this approach seems to be less feasible compared to the chemometric methods proposed. The spectra of the samples used in the PLS study (calibration and test set, 53 samples, 220400 nm) were first converted to second derivative spectra. Thereafter plots were made of the second derivative “absorbance values” of these samples (at a single wavelength a time) together with the actual glibenclamide concentrations (after applying a scaling factor to the concentration values). The results showed that for all wavelengths there is no clear correlation in these plots between the glibenclamide concentration and the second derivative absorbance values, which speaks against the possibility of second derivative spectra being able to handle the interference from the gelatin capsule. Linear regression calculations were also applied, combining the second derivative filtered data and the glibenclamide concentrations. The results of these univariate models were poor, however, giving very weak correlations between glibenclamide concentration and second derivative absorbance values. These calculations indicate that second derivative spectra are probably not able to handle the interference of the gelatin capsule, which, in turn illustrates the strength of the chemometric methods proposed. The advantages of the chemometric methods compared to the existing methods lie primarily in their ability to handle, both quantitatively and qualitatively, the absorbance of interfering compounds even if the spectrum of the interferent is very similar to the main analyte. The key feature is the capability of the multivariate data analysis methods to capture the majority of the information available in the fiber-optic data. The use of the proposed chemometric methods on fiber-optic dissolution testing data opens up new possibilities since it makes it possible to obtain simultaneously dissolution curves for more than one active pharmaceutical ingredient in tablets containing

Figure 8. Comparison of dissolution profiles obtained with HPLC determination, PLS prediction, and the profile estimated with PARAFAC at eight intervals: 3, 6, 9, 12, 15, 30, 45, and 60 min. Solid line: HPLC. Dashed line: PLS. Dotted line: PARAFAC.

Table 6. Explained Variance for the Three-Curve Resolution Models MCR-ALS explained variance (%) a

99.82

GRAM 94.97,a 99.29b

PARAFAC 99.72

The 2.5 mg tablet data matrix. b The 5 mg tablet data matrix.

several active compounds. It also makes it possible to study the dissolution behavior of excipients. CONCLUSION A multivariate chemometric approach utilizing fiber-optic dissolution testing data has been presented, making it possible to deal with interfering absorbance of other compounds than the

active ingredient and also to obtain dissolution rate profiles of excipients or other active ingredients (if present). Multivariate calibration utilizing whole UV spectra makes it possible to obtain accurate dissolution rate profiles despite overlapping spectra of excipients. Applying curve resolution to fiber-optic dissolution testing data gives estimates of pure dissolution rate profiles of the UV-absorbing excipients in the tablet as well as their pure spectra. The application of multivariate chemometric techniques to fiber-optic dissolution testing data opens up possibilities for a deeper understanding of in vitro dissolution testing.

Received for review February 16, 2006. Accepted April 19, 2006. AC0602928

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