Multivariate Analysis and Classification of the Chemical Quality of 7

Yara Gurgel Dall' Acqua , Luis Carlos Cunha Júnior , Viviani Nardini , Valquira Garcia Lopes , José Dalton da Cruz Pessoa , Gustavo Henrique de Alme...
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Anal. Chem. 2003, 75, 3460-3467

Multivariate Analysis and Classification of the Chemical Quality of 7-Aminocephalosporanic Acid Using Near-Infrared Reflectance Spectroscopy Max Andre*

Biochemie GmbH (Novartis Generics), 6250 Kundl, Austria

The capability of near-infrared (NIR) spectroscopy in comparison to conventional chemical testing to control the chemical quality of a pharmaceutical intermediate has been investigated. Multivariate projection methods including principal component analysis, partial leastsquares discriminant analysis and soft independent modeling of class analogy have been evaluated. 7-Aminocephalosporanic acid has been chosen as an example providing a large variation of quality due to its relative chemical instability. Three sets of production lots have been selected to study the extent of quality information extractable from NIR spectra. The first set of 91 lots covers a very broad range of chemical quality assessed by 8 parameters with a partially extented characterization by physical properties. The general congruence of spectral, chemical, and physical information has been investigated. The second set of 110 lots covers a very narrow range of chemical quality assessed by 11 parameters. With extended quality information, the intrinsic selectivity within the spectral data structure has been studied. The third set of 228 lots characterized by 8 parameters is a selection out of more than 1000 lots over a production period of two years. The ruggedness of the multivariate approach has been confirmed by a cross validation of the classification test. Near-infrared (NIR) spectroscopy is a frequently used analytical method within the pharmaceutical industry and in several other analytical fields.1 NIR spectroscopy has been introduced as a general analytical method in the official pharmacopoeias of Europe and the United States.2,3 Most applications of NIR spectroscopy in the pharmaceutical industry concern identification of raw materials and active drug substances as well as quantification of any ingredients by calibration methods. Recently, an application of NIR microscopy was reported for the investigation of pharmaceutical production problems.4 For quality control purposes, the combination of spectroscopy and chemometric methods is neces* E-mail: [email protected]. (1) Blanco, M.; Villarroya, I. Trends Anal. Chem. 2002, 21 (4), 240-250. (2) European Pharmacopoeia, 4th ed.; Council of Europe, 67075 Strasbourg Cedex, France, 2001; pp 55-56. (3) The United States Pharmacopeia; United States Pharmacopeial Convention, Inc., 12601 Twinbrook Parkway, Rockville, MD 20852, USP 25, Supplement 2, NF 20, 2002; pp 2903-2907. (4) Clarke, F. C.; Jamieson, M. J.; Clark, D. A.; Hammond, S. V.; Jee, R. D.; Moffat, A. C. Anal. Chem. 2001, 73 (10), 2213-2220.

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sary. Several applications have been thoroughly reviewed in the literature.1,5-7 A main disadvantage of calibration methods such as partial least-squares regression is the necessity of a continuous recalibration even in the case of very minor changes of production steps. Since the quality of pharmaceutical substances is usually specified by a limit range of certain parameters, semiquantitative classification methods have been evaluated as applied in different other fields to determine the compliance with a definite quality profile.8,9 For the investigation presented here, multivariate analyses including principal component analysis (PCA), partial least-squares discriminant analysis (PLSDA), and soft independent modeling of class analogy (SIMCA) have been used to study and classify the quality of 7-aminocephalosporanic acid (7-ACA) [95768-6]. The chemical structures of 7-ACA and its related substances are shown in Figure S1 of the Supporting Information. METHODOLOGY Chemical Methods and Data Sets. Up to 11 chemical parameters were selected to assess the chemical quality of three sets of lots as listed in Table 1 (see also Supporting Information). The UV absorbance at 425 nm was measured after stressing the product at 60 °C for 4 days. Water was analyzed by Karl Fischer titration, methanol, dichloromethane, and N,N-dimethylaniline were analyzed by static headspace gas chromatography. The content of 7-ACA byproducts and degradation products have been determined by reversed-phase high-performance liquid chromatography. All methods used have been validated according to the current guideline of the International Conference on Harmonization (ICH/Q2A).10 The basic statistical data of the three sets of chemical data described by the values for mean, minimum, maximum, and standard deviation are listed in Table 1. Near-Infrared Spectroscopy. Near-infrared spectra of data set 1 were recorded in the reflectance mode by a Bran+Luebbe InfraAlyzer 500, a scanning grid spectrometer (http://www. (5) Broad, N. W.; Jee, R. D.; Moffat, A. C.; Eaves, M. J.; Mann, W. C.; Dziki, W. Analyst 2000, 125 (11), 2054-2058. (6) Sanchez, M. S.; Bertran, E.; Sarabia, L. A.; Ortiz, M. C.; Blanco, M.; Coello, J. Chemom. Intell. Lab. Syst. 2000, 53 (1-2), 69-80. (7) Rantanen, J.; Rasanen, E.; Antikainen, O.; Mannermaa, J.-P.; Yliruusi, J. Chemom. Intell. Lab. Syst. 2001, 56 (1), 51-58. (8) Gemperline, P. J.; Webber, L. D.; Cox, F. O. Anal. Chem. 1989, 61 (2), 138-44. (9) Svensson, O.; Josefsson, M.; Langkilde, F. W. Appl. Spectrosc. 1997, 51 (12), 1826-1835. (10) http://www.mcclurenet.com/ICHquality.html (Q2A-Guideline on Validation of Analytical Procedures: Methodology). 10.1021/ac026393x CCC: $25.00

© 2003 American Chemical Society Published on Web 06/03/2003

Table 1. Chemical Parameters and Results Tested on Three Data Sets of Lots of 7-Aminocephalosporanic Acid

a Acceptance criteria according specification and dimension of values of each parameter; parameter E is for informative purposes. b Abbreviation used in graphical presentations. c For selection and ranking of lots deacetoxy-7-ACA, deacetyl-7-ACA and deacetyl-7-ACA-lactone are summarized to a single parameter K.

bran-luebbe.de), in the wavelength range between 1100 and 2500 nm in 2-nm steps (701 data points). Samples were prepared in a quartz glass cup. Spectra of data set 2 and data set 3 were recorded in the reflectance mode through the bottom of a glass vial by a Bruker Vector 22N Fourier transform spectrometer (http:// www.bruker.de) in the same spectral range between 4000 and 9090 cm-1 but with a spectral resolution of 2 cm-1 applying 128 scans (5282 data points). Software. PCA and PLS calculations were performed by SIMCA P 10+ (Umetrics AB, Umea, http://www.umetrics.com); Kubelka-Munk transformation and derivation of the spectra were calculated by GRAMS 32 V5.21 (Galactic Industries, http:// www.galactic.com) or UNSCRAMBLER 7.01 (Camo ASA, http:// www.camo.no). RESULTS AND DISCUSSION The basic objective of this study was to investigate step by step the extent of quality information obtainable from NIR spectroscopy in comparison to conventional chemical analysis. A promising approach was a retrospective investigation of selected sets of lots covering the chemical quality of production over several years ensuring a maximum variation of variables and a reliable estimation of the ruggedness of the results. Data Set 1. Data set 1 is a selection of 91 lots divided into two groups, a first scale-up trial to optimize the chemical process and a second trial to establish a rework procedure for lots out of specification. A scale-up situation offers the rare opportunity to investigate the relationship within a data structure with a large variation of the variables of each quality parameter. Multivariate projection methods have been applied to reveal and visualize a common relationship within the data structure of both chemical data and spectroscopic data. Principal Component Analysis. PCA was introduced by Pearson11 in 1901. Currently, PCA is a widely used method for data reduction since personal computers are available for processing of large data sets. The basic concept of the method can be described geometrically as a reduction of multidimensional data (11) Jolliffe, I. T. Principal Component Analysis; Springer Series in Statistics; Springer-Verlag: New York, 1986; pp 5-7.

sets by projecting the data of n dimensions onto a subspace of a few dimensions. The mathematical background is decomposing the original variable matrix X into the product of the score matrix T and the transposed loading matrix P plus a residual matrix E.12,13

X ) T‚PT + E In a first step, chemical data and NIR spectral data have been analyzed by PCA. The PCA score plots of the projected chemical data, pure spectral data, and spectral data after data pretreatment projected onto the plane of the first two principal components are shown in Figure S2 (Supporting Information). To remove the dominating effects of variables with a larger magnitude within a data set, all data have been autoscaled.12 Qualitative and quantitative chemometric methods applied on NIR data are essentially influenced by the selected spectral data pretreatment. The influence of the following methods of data pretreatment on the PCA results have been studied including Kubelka-Munk (KM) transformation14 for separating the scattered and the absorbed fraction of the diffuse reflected light, first and second derivative, signal normal variate (SNV) transformation15 and multiplicative scatter correction (MSC),16 orthogonal signal correction (OSC),17 and wavelet compression methods18,19 for reducing particle size effects20,21 as well as redundant spectral information. Also com(12) Massart, D. L.; Vandeginste, B. G. M.; Buydens, L. M. C.; De Jong, S.; Lewi, P. J.; Smeyers-Verbeke, J. Handbook of Chemometrics and Qualimetrics: Part A; Elsevier Science: Amsterdam, 1997; pp 519-556; p 52. (13) Eriksson, L.; Johansson, E.; Kettaneh-Wold, N.; Wold, S. Introduction to Multi- and Megavariate Data Analysis using Projection Methods (PCA&PLS); Umetrics AB.; Umeå, 1999; p 87; http://www.umetrics.com/. (14) Osborne, B. G.; Fearn, T.; Hindle, P. H. Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, 2nd ed.; Longman Scientific and Technical: New York, 1993; pp 45-47. (15) Barnes, R. J.; Dhanoa, M. S.; Lister, S. J. Appl. Spectrosc. 1989, 43 (5), 772-777. (16) Martens, H.; Stark, E. J. Pharm. Biomed. Anal. 1991, 9 (8), 625-35. (17) Wold, S.; Antti, H.; Lindgren, F.; Ohman, J. Chemom. Intell. Lab. Syst. 1998, 44 (1, 2), 175-185. (18) Alsberg, B. K.; Woodward, A. M.; Kell, D. B. Chemom. Intell. Lab. Syst. 1997, 37 (2), 215-239. (19) Trygg, J.; Wold, S. Chemom. Intell. Lab. Syst. 1998, 42 (1, 2), 209-220. (20) Aucott, L. S.; Garthwaite, P. H.; Buckland, S. T. Analyst 1988, 113 (12), 1849-1854.

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Figure 1. PLSDA score plots of data set 1. (A) Chemical data, 8 parameters autoscaled, 75 lots of scale-up process (1) and 16 lots of rework process (2). (B) NIR spectra with data pretreatment: Kubelka-Munk transformation, second derivative, autoscaled.

bination of first and second derivatives, KM, SNV, MSC, OSC, and wavelet compression methods have been tested thoroughly. The combination of KM transformation and the second derivative of the spectra has turned out to be the best data pretreatment for optimum separation of the both groups. The loss of the variance explained by the two first components is due to the increase of noise in the data caused by the derivation of spectra.22 Nevertheless, derivation reduces particle size effects and increases the chemical information by reducing the dominating effects of wavelength ranges with high absorbances. The PCA of the pure spectral data as well as of the pretreated data is very important information about the basic data structure regarding a potential capability of separation of objects. As PCA is fitting a subspace with respect to the optimized maximium variance of the data structure, multivariate discriminant analysis has been applied for an improved separation of the groups. Partial Least-Squares Discriminant Analysis. PLS methods23,13 differ from PCA methods as the X variables (e.g., spectral data) and the corresponding Y variables (response data of each object) are projected simultanously on a subspace with respect to a maximum covariance between X and Y data for the final purpose to predict Y from X. The mathematical background is decomposing the original variable matrix X into the product of the score matrix T and the transposed loading matrix P plus a residual matrix E and the response matrix Y into the product of (21) Bull, Ch. R. Analyst 1991, 116 (8), 781-786. (22) Talsky, G. Derivative Spectrophotometry, Low and Higher Order; VCH: Weinheim: 1994; pp 31-33. (23) Gerlach, R. W.; Kowalski, B. R.; Wold, H. O. A. Anal. Chim. Acta 1979, 112 (4), 417-421.

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the score matrix U and the transposed loading matrix C plus a residual matrix F

X ) T‚PT + E Y ) U‚CT+ F with the inner relation,U ) T + H, H is a residual matrix.13 PLSDA24 is a PLS application for the optimum separation of classes using dummy variables 0 and 1 as variables of the Y matrix. Each class is assigned by 0 or 1 and a regression of Y onto X is calculated. For a two-dimensional subspace, the PLSDA operation can be described as the positioning of a plane in the X space ensuring an optimum separation of assigned classes. The two-class approach is identical to classical linear discriminant analysis.25 The PLSDA score plot of chemical data and spectroscopic data projected onto the plane of the first two PLS factors is shown in Figure 1. A considerable improvement of the separation is observed for the projected NIR spectra in comparison to the PCA plot (see Figure S2C, Supporting Information). The degree of separation of the lots of both processes is similar to that for the chemical data. In a second step, data set 1 has been investigated regarding quality aspects based on a definite specification of the corresponding eight parameters. A total of 67 lots meet a definite specification (24) Ståhle, L.; Wold, S. J. Chemom. 1987, 1 (3), 185-196. (25) Indahl, U. G.; Sahni, N. S.; Kirkhus, B.; Naes, T. Chemom. Intell. Lab. Syst. 1999, 49 (1), 19-31.

Figure 2. PLSDA score plots of data set 1. (A) Chemical data, 8 parameters autoscaled, 67 lots within a definite specification (green), 24 lots out of specification (red). Selection of the 10 best and 10 worst lots of data set 1. Both subsets contain the best and worst lot with respect to the parameters A, K, P, S, U, and W. For the selection of lots. parameter K as the sum of three known byproducts and degradation products was used (see footnote c in Table 1). Lots marked as L are near the limit within specification. (B) NIR spectra with data pretreatment: KubelkaMunk transformation, second derivative, autoscaled.

whereas 24 lots are out of the specification with respect to one of the 8 chemical parameters. In both groups, all members of the scale-up and the rework trials are included. The PLSDA score plots of the projected chemical data and spectral data onto the first two PLS factors along with a detailed analysis is available in the Supporting Information (Figure S3). In a third step, data set 1 has been investigated in more detail regarding the ranking of quality of lots by PLSDA. Ranking has been applied by sorting all lots regarding highest assay and lowest sum of all byproducts and degradation products in order to select 2 subsets of the 10 best and the 10 worst lots. Each of these subsets contains the corresponding best and worst lots with respect to each parameter. Parameters A, K, P, S, U, and W are assigned as mentioned in Table 1. For a better transparency within the plot, values of three known byproducts and degradation products of each lot have been summarized assigned as parameter K (see footnote c in Table 1), but all eight parameters were used for PLSDA calculations. Additionally, lots within the specification but close to the specification limit (assigned as L) have been selected to study the consistency with the ranked best and worst lots. For byproducts and degradation products, the distance selected for lots close to the limit was 10% (0.1% absolute) of the corresponding specified limit or the next nearest neighbor to the limit. For the assay, a distance of 0.3% absolute to the limit has been set as a criterion for selection. The PLSDA score plot of the chemical data projected onto the first two PLS factors is shown in Figure 2A. The lots within

specification (green) and lots out of specification (red) are separated according to the position of all lots already shown in Figure S3A (Supporting Information). Within each group (green, red), the ranked 10 best and the 10 worst lots with respect to the parameters A, K, P, S, U, and W and the remaining lots 7-10 are assigned. The group of lots within specification (green) exhibit a dense subgroup for the lots A, K, P, U, W, 7, 8, 9, and 10 separated from the lots close to the limit (L). The best lot regarding S, a member of the rework process, is totally separated from this subgroup. As already discussed, the reason for this is a selective reduction of the concentration of the acid scavenger N,N-dimethylaniline (S) by the rework process without improving the quality of the remaining parameters with the result that this lot is close to the limit (L) with respect to two byproducts and degradation products. In the group of lots out of specification (red), the 10 worst lots are distributed over a large area due to the high variance of the parameters. The lots close to the specification limit (L) are positioned between lots within specification and lots out of specification with a marginal overlapping. The PLSDA score plot of the corresponding NIR spectra projected onto the first two PLS factors is shown in Figure 2B. A high agreement with the plot of the chemical data regarding the relative position is observed. The 10 best and the 10 worst lots are positioned in a manner similar to that in the chemical data plot with a clear separation from the lots close to the limit. All lots close to the limit are positioned between the groups of lots within specification and lots out of specification. The difference Analytical Chemistry, Vol. 75, No. 14, July 15, 2003

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Figure 3. PLSDA score plots of data set 2. (A) Chemical data, 11 parameters paretoscaled, 98 lots within a definite specification (green), 12 lots out of specification (red). Lots circled has been selected to study the mutual position in both plots. (B) NIR spectra with data pretreatment: first derivative, signal normal variate transformation, paretoscaled. Two subsets in extreme position has been selected for testing statistical differences.

of the position in both plots for the worst lot A (lot 74) has been already discussed (see Figure S3, Supporting Information). Data Set 2. Data set 2 is a subset of data set 3 containing 110 lots (Table 1). The quality of these lots was controlled by 11 chemical tests. In addition to the eight parameters measured on data set 1, the residual solvents methanol and dichloromethane and the UV absorbance at 425 nm after stressing the material have been tested. A total of 98 lots meet a definite specification whereas 12 lots are out of specification. The basic difference to data set 1 is an improvement of the entire quality and a very narrow variation of the quality (see Table 1 and Supporting Information).The goal of the investigation of data set 2 was a comprehensive testing of the capability of NIR spectroscopy under more stringent conditions but with the benefit of additional information about the chemical quality. In a first trial, the entire chemical data and NIR spectral data has been analyzed by PLSDA. The PLSDA score plot of the chemical data onto the first two PLS factors (Figure 3A) shows the separation of lots within specification (green) and lots out of specification (red). All lots out of specification do not meet the limit for N,N-dimethylaniline (S). The lots 1 and 91 are within specification with a 130% and 210% higher content of N,Ndimethylaniline (S), respectively, than the average of the lots within specification. The lots 4 and 110 are the best and worst members of the lots within specification for parameter E. The value for the UV absorbance at 425 nm (E) is 40% lower for lot 4 and 70% higher for lot 110 than the average of the lots within specification but are of medium quality with respect to all other parameters. As test E is a highly sensitive test for the judgment of the overall quality of 7-ACA, the scaling of the chemical data has been optimized to reveal the relative quality information in 3464

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the plot for parameter E. Paretoscaling with a base weight computed as 1/(square root of the standard deviation of a variable) enables an optimum representation of the differing quality of lots 4 and 110. Lot 84 is the lot of the lowest quality within data set 2. The PLSDA score plot of the corresponding paretoscaled NIR spectra projected onto the first two PLS factors is shown in Figure 3B. First derivation combined with signal normal variate transformation proved to be the optimum data pretreatment for these spectra recorded by a different spectrometer as used for data set 1. The extent of the total variance explained by the first two PLSDA factors is 31%. Signal normal variate transformation without first derivation results in a very similar pattern with a small overlapping of lots within and out of specification with a variance explained of 89%. A very high coincident relative position is given for all lots out of specification (red). Lot 84 with the lowest quality is separated in the same manner as in the chemical plot. Lots 1 and 91 occupy a very similar position. A surprising result is observed for lot 110, which is a significant outlier in both plots. In the same way, lot 4 occupies a separated position in both plots. A detailed investigation of the influence of scaling of the NIR data on the relative position of 110 confirmed that the separation observed is not an accidental result. The corresponding autoscaled NIR data result in a practically identical relative distribution of all lots as in the case of paretoscaling. This result is an indication that the quality information obtained by the measurement of the UV absorbance after stress is already obtainable by NIR spectroscopy of the original unstressed lot. A plausible explanation of these facts is the high sensitivity of 7-ACA to the exposure to air. It is known from experience that lots of 7-ACA exposed to air are correlating with lots of high values of absorbance at 425 nm after

Figure 4. Zoomed PLSDA score plots of data set 2. (A) Chemical data, 11 parameters autoscaled, 30 lots within a definite specification (colored), 12 lots out of specification (O). Individual colored lots are members of the five best with respect to each of the parameters A, B, D, E, M, S, and W. For the selection of lots, parameter B as the sum of the known and unknown byproducts and degradation products K, P, and U was used. (B) NIR spectra with data pretreatment: first derivative, signal normal variate transformation, paretoscaled. (C) Position of the lots of subset (+) defined by a definite quality of the chemical data.

stress (test E). Obviously, the underlying chemical alteration by air is detected by test E only after stressing the material whereas NIR spectroscopy sensitive to the chemical and possible physical alteration is capable of detecting the alteration after exposure to air without stressing the material. In the NIR data plot, the lots within specification cover a larger area than in the chemical data plot due to a larger variation of the NIR data. Two subsets with a dense accumulation of members are located at the extreme opposite position. The chemical values of the corresponding lots of subset 1 and subset 2 have been analyzed regarding a statistical difference of the means of each parameter. Applying an unpaired t-test, a significant statistical difference at the 95% confidence level is given for the four parameters B, D, S, and W. Parameter B is the sum of all known and unknown byproducts and degradation products K, P, and U. Based on these results, a selective distribution of quality within the PLSDA plot can be assumed even for a narrow range of quality. In a second trial, the distribution of quality defined by 11 parameters over the plot area of the lots within specification of chemical data and NIR data has been analyzed by PLSDA. For a

better transperancy of the plotted objects, the 11 parameters have been condensed to the 7 parameters A, B, D, E, M, S, and W by summarizing K, P, and U to the parameter B as the sum of all known and unknown byproducts and degradation products, but all parameters have been used separately for PLSDA calculations. By ranking the data, six subsets have been generated, with each set containing the five best lots of the corresponding parameter. The zoomed PLSDA score plot of the autoscaled chemical data onto the first two PLS factors is shown in Figure 4A. As it was the intention of this trial to reveal the internal distribution of quality, autoscaling of the data has been chosen in order to suppress the dominance of variables with high values ensuring a balanced importance of each parameter. The five best lots of all six parameters are distributed as separated clusters with partial overlapping. The zoomed PLSDA score plot of the corresponding paretoscaled NIR spectra onto the first two PLS factors is shown in Figure 4B. A surprisingly high agreement with the plot of the chemical data due to the relative position of the cluster of each parameter is given. The clusters of the parameters E, W, and M Analytical Chemistry, Vol. 75, No. 14, July 15, 2003

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are separated to a higher extent than in the chemical data plot. This is due to a high sensitivity of NIR spectroscopy to methanol (M), water (W), and unknown alterations measured by the absorbance at 425 nm after stress (E). In both plots, the parameters A, B, D, and S occupy a common cluster but are more tightly grouped in the NIR data plot. This result is a confirmation of a higher correlation between the parameters assay (A) and the sum of the known and unknown byproducts and degradation products (B) within the NIR data than in the chemical data due to the larger variance of some chemical tests. A high selectivity and sensitivity of NIR spectroscopy for N,N-dimethylaniline (S) is obvious as all lots are correctly separated in the NIR data plot covering a total concentration range of 200-1000 ppm. The sensitivity for N,N-dimethylaniline has been estimated by comparing all lots of subset 1 and subset 2 (Figure 3B) with an identical quality except for N,N-dimethylaniline. The difference of the mean values of these selected lots is 600 ppm covering concentration ranges of 400-600 ppm for subset 1 and 1000-1350 ppm for subset 2. This sensitivity has been confirmed by PLS calibration of N,N-dimethylaniline of data set 3 with a calculated root-meansquare error of prediction of 300 ppm. In a third trial, the congruence of quality information has been tested on a larger subset defined by a certain chemical quality. The chemical data of data set 2 have been filtered for a selected quality by selecting all lots meeting a definite limit for each of the six parameters, e.g., A >95.4%, B 0.3%, E