Evaluation of Infrared-Reflection Absorption Spectroscopy

Technical Note pubs.acs.org/ac

Evaluation of Infrared-Reflection Absorption Spectroscopy Measurement and Locally Weighted Partial Least-Squares for Rapid Analysis of Residual Drug Substances in Cleaning Processes Hiroshi Nakagawa,*,†,‡ Takahiro Tajima,†,§ Manabu Kano,⊥ Sanghong Kim,⊥ Shinji Hasebe,⊥ Tatsuya Suzuki,‡ and Hiroaki Nakagami¶ †

The Japan Society of Pharmaceutical Machinery and Engineering, Miyoshi Bld 3F, 2-7-3, Tacho Kanda, Chiyoda-ku, Tokyo, Japan Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd., Kanagawa, Japan § Analytical & Measuring Instruments Division, Shimadzu Corporation, Kyoto, Japan ⊥ Department of Chemical Engineering, Kyoto University, Kyoto, Japan ¶ Pharmaceutical Manufacturers’ Association of Tokyo, Tokyo, Japan ‡

ABSTRACT: The usefulness of infrared-reflection absorption spectroscopy (IR-RAS) for the rapid measurement of residual drug substances without sampling was evaluated. In order to realize the highly accurate rapid measurement, locally weighted partial least-squares (LW-PLS) with a new weighting technique was developed. LW-PLS is an adaptive method that builds a calibration model on demand by using a database whenever prediction is required. By adding more weight to samples closer to a query, LW-PLS can achieve higher prediction accuracy than PLS. In this study, a new weighting technique is proposed to further improve the prediction accuracy of LW-PLS. The root-mean-square error of prediction (RMSEP) of the IR-RAS spectra analyzed by LW-PLS with the new weighting technique was compared with that analyzed by PLS and locally weighted regression (LWR). The RMSEP of LW-PLS with the proposed weighting technique was about 36% and 14% smaller than that of PLS and LWR, respectively, when ibuprofen was a residual drug substance. Similarly, LW-PLS with the weighting technique was about 39% and 24% better than PLS and LWR in RMSEP, respectively, when magnesium stearate was a residual excipient. The combination of IR-RAS and LW-PLS with the proposed weighting technique is a very useful rapid measurement technique of the residual drug substances.

F

accuracy because the accuracy may widely vary among inspectors.10 In UV, HPLC, and TOC methods, the amount of residual drug substances is measured by taking samples; the swab method21 and the rinse method22 are generally used for sampling. The swab method is highly recommended for equipment treating tablets and hard capsules whose drug substances are poorly soluble, because it takes samples directly off from the surface of the equipment. However, some issues remain in the swab method: (1) it is very difficult to completely recover the residual drug substances in equipment with sampling tools such as gauze, (2) the recovery rate depends on the skill of operators extracting drug substances from the sampling tools, and (3) it takes a long time to evaluate the results. Another major problem of the current cleaning process is the difficulty in its improvement. To adopt an improved cleaning procedure, cleaning validation is necessary for change controls.

or safety, the amount of residual drug substances in pharmaceutical manufacturing equipment is generally measured through a validated cleaning procedure. The issuance of the FDA’s “Guide to Inspection of Validation of Cleaning Processes”1 triggered the development of a validation method for cleaning procedures. The past research focused on cleaning validation programs in the research phase2 and production phase,3 the determination method of the cleaning level acceptance criteria for residual drug substances,4−8 and the maintenance program for cleaning validation.9 In cleaning validation, the key issue is how to measure the residual drug substances in equipment. Typical methods are visual inspection10 and laboratory analysis of samples taken from some selected parts of equipment with ultraviolet (UV), high-performance liquid chromatography (HPLC)11−14 or total organic carbon (TOC).15−18 Recently, liquid chromatography− mass spectrometry (LC-MS), which is a highly sensitive analytical method, has been applied to cleaning validation.19,20 These methods require validation of their detectability. In visual inspection, it is difficult to ensure constant measurement © 2012 American Chemical Society

Received: September 15, 2011 Accepted: March 13, 2012 Published: March 13, 2012 3820

dx.doi.org/10.1021/ac202443a | Anal. Chem. 2012, 84, 3820−3826

Analytical Chemistry

Technical Note

data to the calibration set. In fact, it has been reported in the steel and the chemical industries that workload for the maintenance of calibration models was successfully reduced by employing just-intime (JIT) modeling similar to LW-PLS.39,40

It will require considerable time and manpower. As a result, an opportunity to improve the cleaning procedure is likely to be missed. In addition, it is difficult to deny the possibility of the variability of equipment cleanliness even when an established cleaning procedure is employed, because a large part of manufacturing equipment for solid drug products is used to produce diversified products and often cleaned manually. Therefore, it is preferable to evaluate the amount of residual drug substances after each cleaning. To perform such an evaluation and minimize the risk of cross contamination, it is crucial to develop a rapid measurement method that enables continuous monitoring of residual drug substances after each cleaning. If it is detected with a rapid measurement that the amount of residual drug substances is beyond the limit of acceptance criteria, the amount should be analyzed with a conventional method such as the swab method in more detail. Such a rapid measurement method will have the great advantage in mitigating the risk of the cross contamination and provide an opportunity to establish an efficient cleaning procedure; cleaning validation might possibly be unnecessary. In this way, rapid measurement methods of residual drug substances lead to paradigm shift of the cleaning process as process analytical technology (PAT)23 tools. As novel rapid measurement methods, ion mobility spectrometry (IMS)24,25 and infrared-reflection absorption spectroscopy (IR-RAS)26−29 have been investigated. IMS can directly measure residual drug substances recovered by the swab method, but sampling issues such as the recovery rate need to be solved. On the other hand, IR-RAS can measure residual drug substances without sampling, but it has not yet been evaluated using equipment such as a granulator and a blender. In addition, the prediction accuracy, which depends on a sample preparation method and a spectral analysis technique, needs to be improved so that this method becomes widely used. Regarding the spectral analysis technique, partial least-squares (PLS)30 has been applied to the calibration model development to estimate the amount of residual drug substances. However, the prediction accuracy of PLS deteriorates when the response of IR spectra to the amount of residual drug substances shows nonlinearity. In this study, to develop a rapid and accurate measurement method of residual drug substances without sampling, the usefulness of IR-RAS and locally weighted PLS (LW-PLS)31 is evaluated. LW-PLS is an extended version of the locally weighted regression (LWR) method32−37 in which the calibration model is not built in advance. The advantage of LW-PLS and LWR over PLS is the ability to cope with nonlinearity and clustering; thus, they can achieve higher prediction accuracy. Global nonlinear modeling methods such as artificial neural network are useful to solve the same issue but need a large number of samples to obtain reliable models. In LWR, some samples are selected from a calibration set on demand every time a calibration model is required. On the other hand, in LW-PLS, all samples stored in the calibration set are weighted on the basis of distance from a query and used to build a calibration model.31 In the present work, new weighting techniques for LW-PLS are investigated with the IRRAS spectra to further improve the prediction accuracy. Furthermore, workload of the life cycle management of the calibration model is a big issue to keep a high level performance continuously. In the process industry, for example, the result of a recent questionnaire survey has confirmed that the maintenance of models is the most important issue concerning softsensors in practice.38 The model maintenance workload can be reduced by the employment of LW-PLS because the calibration model is automatically updated by adding new measurement

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MATERIALS AND METHODS Materials and Solvents. Ibuprofen (Hamari Chemicals, Japan), which has poor water solubility, and magnesium stearate (Mallinckrodt, USA), which is a standard lubricant in the manufacturing of tablets, were used as a model drug substance and a model excipient, respectively. The solvent used for dissolving ibuprofen and magnesium stearate was methanol of HPLC grade (Wako Pure Chemical Industries, Japan). Lactose (DMV, Netherlands), mannitol (Roquette, France), cornstarch (Nisshin Seifun, Japan), and hydroxypropyl cellulose (Nippon Soda, Japan) were used in the blending experiment described in Verification Using the Small Scale Equipment and blended with ibuprofen and magnesium stearate. Sample Preparation and IR Measurement. A methanol solution of different concentrations of ibuprofen or magnesium stearate was dropped onto stainless (SUS316) test pieces (10 cm2), and the droplet was homogeneously spread with a rubber spatula. SUS316 is a typical material for pharmaceutical manufacturing equipment. Then, the measurements were taken with IR spectroscopy; infrared-reflection absorption spectroscopy (IR-RAS) was employed as a method of IR spectroscopy. In addition, powder samples of ibuprofen and magnesium stearate were measured by IR attenuated total reflection (IR-ATR) to confirm their peak position in the IR region. Evaluation of IR-RAS. Measurement Device. FT-IR spectrophotometer IR-Prestige 21 (Shimadzu, Japan) was used to measure filmy layer samples on SUS316 test pieces by IRRAS and several powder samples by IR-ATR. The range of the measured wavenumber was 4000−400 cm−1, the resolution was 4 cm−1, and the scan per measurement was 45 times. VeeMAXII (PIKE Technologies, USA) was used as a measurement unit for IR-RAS. The diameter of the irradiation spot of the unit was ø 10 mm, and the angle of incidence was set to 80°. DuraSampl IR-II (SensIR Technologies, USA) was used as a measurement unit for IR-ATR. Verification Using the Small Scale Equipment. IR-RAS for measuring a drug substance on the test pieces was developed,26 but its applicability to the manufacturing equipment has not been verified yet. In this study, the applicability of IR-RAS to the manufacturing equipment was investigated by using smallscale equipment. A small-scale V-blender (S-5; Tsutsui Scientific Instruments, Japan) was used to blend the mixture of components. The SUS316 test piece was attached on the inner surface of the lid of the V-blender, whose material is plastic. Since the application scope of IR-RAS is limited to the residual drug substances on equipment with the metal surface which reflects the light, the SUS316 test piece was attached on the lid in this study. From the practical point of view, there is no need to attach the SUS316 test piece in the commercial equipment, which is basically made of SUS316. After 30 min of blending, the mixed powder was exhausted from the V-blender, the lid was detached from the blender, and the amount of residual substances on the SUS316 test piece was measured by IR-RAS before and after water cleaning. Evaluation of Spectral Analysis Technique. LW-PLS was used to estimate the residual drug substances from the IRRAS spectra, and its prediction accuracy was compared with 3821



Technical Note

where T denotes the transpose of the matrix. In LW-PLS, similarity ωk between xk and xq is introduced to determine weights on samples in the calibration set.

that of LWR and PLS. LW-PLS, LWR, and PLS were programmed by Matlab (The MathWorks, US). To develop the calibration model with each spectral analysis technique, 69 samples with different ibuprofen concentrations (0.1−20 μg/cm2) and 85 samples with different magnesium stearate concentrations (0.08−4.0 μg/cm2) were prepared as calibration sets. In addition, 53 samples with different ibuprofen concentrations (0.5−20 μg/cm2) and 70 samples with different magnesium stearate concentrations (0.12−3.2 μg/cm2) were prepared as validation sets, and another 63 samples with different ibuprofen concentrations (0.2−20 μg/cm2) and 70 samples with different magnesium stearate concentrations (0.16−3.2 μg/cm2) were prepared as external validation sets. All samples were prepared by following the procedure described in Sample Preparation and IR Measurement and measured by IR-RAS. In general, detectability limit41 is used to evaluate the prediction accuracy and is very important for the analytical technique applied to the cleaning validation. As Faber has discussed,42 the detectability limit should be calculated rightly with samples close to the threshold. However, this study focuses more on investigating whether the prediction accuracy of IR-RAS measurement can be improved by using the proposed spectral analysis technique. Hence, the wider concentration range was used in this work to evaluate the prediction performance of each technique without respect to the detectability limit. In addition, the root-mean-square error of prediction (RMSEP) was used to evaluate the prediction accuracy of each spectral analysis technique as a substitute for the detectability limit in this study, because the detectability limit can be obtained as the product of the RMSEP and an appropriate t statistic according to the IUPAC recommendation43 of considering both false positive and false negative detection decisions. Parameters affecting the prediction accuracy are (1) the number of latent variables for all techniques, (2) the parameter defining the similarity for LW-PLS described in LW-PLS Algorithm, and (3) the number of samples to build a local model with LWR described in LWR Algorithm; these parameters were determined with the validation set so that the rootmean-square error of the model (RMSE) was minimized. The RMSEP was calculated with the external validation set, which was different from the calibration set and the validation set. In addition, the determination coefficient, the slope, the y-intercept, and the bias for each technique were evaluated with the external validation set. LW-PLS Algorithm. The database X (n × m matrix), which is the calibration set to build the model, consists of n samples. The kth sample xk in X has m variables which are absorbance at the wavenumbers selected to estimate the concentration of ibuprofen or magnesium stearate yk (μg/cm2). The query xq is the sample whose concentration needs to be estimated. X = [ x1 x2 ··· x n ]T

xk = [ xk ,1 xk ,2 ··· xk , m

⎛ d ⎞ ωk ≡ exp⎜ − k ⎟ ⎝ ϕ·σd ⎠

y = [ y1 y2 ··· yn ]T x q = [ xq ,1 xq ,2 ··· xq , m ]T

(5)

d = [ d1 d2 ··· dn ]T

(6)

n

1 n

d̅ =

∑ dk (7)

k=1

1 n−1

σd =

n

∑ (dk − d ̅ )2 (8)

k=1

where d denotes the distance vector consisting of dk, which is the distance between xk and xq, ϕ is the tuning parameter, d̅ is the mean of {dk}, and σd is the standard deviation of {dk}. The similarity ωk decreases in an exponential manner and approaches asymptotically to zero as the distance from the query increases. In addition, ωk decreases more slowly as the parameter ϕ is larger. In particular, LW-PLS is the same as PLS when ϕ = ∞ because ωk = 1 for all samples. In other words, LW-PLS includes PLS as a special case. In the past research,31 Euclidean distance m

∑ (xk , l − xq , l)2

dkED =

(9)

l=1

was used to calculate dk. Euclidean distance has been widely used to define the similarity because of its simplicity, but the prediction accuracy of LWR models can be further improved by modifying the distance.40 In this study, new weighting techniques indicated in eqs 10 and 11 were proposed and compared with the conventional technique of eq 9. m

dkMD =

∑ |λlMD|·(xk , l − xq , l)2 (10)

l=1

(MD = ibuprofen or magnesium stearate) m

dkPLS =

∑ |λlPLS|·(xk , l − xq , l)2 (11)

l=1

λlMD

where denotes the ATR spectrum of ibuprofen or magnesium stearate preprocessed by the method used in the calibration model development and λlPLS denotes the regression coefficient of the PLS calibration model. In addition, xk, xq, and yk were mean-centered in advance according to the following equations.

(1)

]T

(k = 1, 2, ···, n)

xl̃ =

(2)

(3)

(4) 3822

∑nk = 1 ωk xk , l ∑nk = 1 ωk

(l = 1, 2, ···, m) (12)

̃ ]T x̃ = [ x1̃ x2̃ ··· xm

(13)

x ̃k = x k − x ̃

(14)

X̃ = [ x1̃ x2̃ ··· x̃ n ]T

(15)

x̃ q = x q − x̃

(16) dx.doi.org/10.1021/ac202443a | Anal. Chem. 2012, 84, 3820−3826

Analytical Chemistry ỹ =

Technical Note

∑nk = 1 ωk yk

ỹ =

∑nk = 1 ωk

(17)

yk̃ = yk − y ̃ ỹ = [ y1̃ y2̃ ··· yñ ]T

(19)

qr =

(20)

|| tT r t r ||

(30)

RESULTS AND DISCUSSION Evaluation of IR-RAS. Figure 1 shows the result of comparing IR-ATR spectrum of ibuprofen powder and IR-RAS

X̃ T t r || tT r t r ||

ỹ T t r

■

(21)

(4) Calculate the rth loading vector pr for X̃ . pr =

(29)

was used in place of eq 23, because the similarity between xk and xq, which was introduced in LW-PLS, is left out of consideration, i.e., ωk = 1 (k = 1, 2, ···, n). The rest was calculated through the same procedure as LW-PLS. PLS Algorithm. In PLS, all samples were used to build a global calibration model different from LWR. The rest is the same as LWR. In addition, the regression coefficients of PLS model determined with the validation set were used as the weighting coefficients, λlPLS (l = 1, 2, ···, m), in eq 11.

(3) Calculate the rth latent variable tr.

̃ t r = Xw

k=1

In addition,

In LW-PLS, ŷq (μg/cm ), which is the estimated concentration corresponding to xq, is calculated as follows: (1) r = 1 and ŷq = 0. (2) Calculate the weight loading w. X̃ T ỹ || X̃ T ỹ ||

n

∑ yk

(18)

2

w=

1 n

(22)

(5) Calculate the rth regression coefficient qr. qr =

ỹ T Ωt r || tT r Ωt r ||

(23)

where Ω is the diagonal matrix whose elements are ωn. ⎡ ω1 0⎤ ⎢ ⎥ ω2 ⎢ ⎥ Ω≡⎢ ⎥ ⋱ ⎢ ⎥ ωn ⎦ ⎣0

(24)

Figure 1. Comparison between (a) IR-RAS spectrum (filmy layer: 5 μg/cm2) and (b) IR-ATR spectrum (powder) of ibuprofen. In both spectra, the absorption peak of the carbonyl group (>CO) was observed around 1700 cm−1 and the peaks of the methyl group (−CH3), the methylene group (−CH2−), and the hydroxyl group (−OH) of carboxylic acid were observed around 3000 cm−1.

(6) Calculate the rth latent variable tq,r for xq̃ .

tq , r = x̃ qw

(25)

(7) Calculate ŷq. yq̂ = yq̂ + tq , rqr

spectrum of the filmy layer of ibuprofen (5 μg/cm2). In both spectra, the absorption peak of the carbonyl group (>CO) was observed around 1700 cm−1, and the peaks of the methyl group (−CH3), the methylene group (−CH2−), and the hydroxyl group (−OH) of carboxylic acid were observed around 3000 cm−1. In particular, the absorption peak of the carbonyl group was specific to ibuprofen and was not observed in IR spectra of other ingredients. These observations confirmed that IR-RAS was able to detect ibuprofen on SUS316 test pieces. Figure 2 shows the result of evaluating the applicability of IRRAS to manufacturing equipment. Before cleaning, some peaks related to the ingredients remaining in the equipment were observed. The examination of IR-RAS and IR-ATR spectra confirmed that magnesium stearate preferentially remained in the equipment. After cleaning, ibuprofen was also detected in addition to magnesium stearate by the peak specific to the carbonyl group. These results suggested that IR-RAS was effective in detecting residual drug substances and residual excipients in manufacturing equipment. In particular, IR-RAS has the great

(26)

(8) If r is equivalent to the predetermined number of latent variables, then, finish the calculation. Otherwise, X̃ = X̃ − trprT, ỹ = ỹ − trqr, and r = r + 1; then, return to step 2. As the final step, the estimated value, ŷq, is calculated according to eq 27 with the finally obtained value ŷq by eq 26 and ỹ in eq 17.

yq̂ = yq̂ + y ̃

(27)

LWR Algorithm. In LWR, some samples are selected from X on the basis of the distance between xk and xq. A variety of methods to calculate the distance and build local models were proposed32−35 and evaluated with various spectral data sets by Centner et al.36 Their conclusion was that PLS with samples selected on the basis of Euclidean distance, i.e., eq 9, was simple and useful for LWR. In LWR, the following eqs 28 and 29 were used in place of eqs 12 and 17. xl̃ =

1 n

n

∑ xk , l k=1

(28) 3823



Technical Note

standardization was performed for magnesium stearate, and the result is shown in Table 2. Table 2. Model Construction Results with the Validation Set and Model Evaluation Results with the External Validation Set for Each Spectral Analysis Technique (Magnesium Stearate) spectral analysis technique sample numbers of the calibration set sample numbers of the validation set sample numbers of the external validation set spectra preprocessing wavenumber range the number of latent variables sample numbers to build the local calibration model in LWR tuning parameter (ϕ) in LW-PLS RMSE [μg/cm2] RMSEP [μg/cm2] determination coefficient (R2) slope y-intercept bias [μg/cm2]

Figure 2. IR-RAS spectra of the inner surface of the lid of a V-blender (a) before and (b) after water cleaning. (c) IR-ATR spectrum of magnesium stearate powder.

advantage in detectability of residual excipients such as magnesium stearate, which are not detected by conventional methods such as HPLC, UV, and LC-MS. The detectability of residual excipients is crucial because they cause an unexpected interaction with substances contained in other drug products. In addition, IR-RAS does not require sampling while the other technique does. As a result, the accuracy of IR-RAS measurement depends mainly on the spectral analysis technique.29 Hence, it is important to employ the optimal spectral analysis technique for this measurement. Evaluation of Spectral Analysis Technique. To compare the estimation performance of several spectral analysis techniques, the preprocessing of IR-RAS spectra, which removes the baseline shift and emphasizes the absorption peak, and the wavenumber range, which has the characteristic absorption peaks derived from ibuprofen, were standardized as shown in Table 1. The similar

sample numbers of the calibration set sample numbers of the validation set sample numbers of the external validation set spectra preprocessing wavenumber range the number of latent variables sample numbers to build the local calibration model in LWR tuning parameter (ϕ) in LW-PLS RMSE [μg/cm2] RMSEP [μg/cm2] determination coefficient (R2) slope y-intercept bias [μg/cm2]

LWPLSIbp

LWPLSPLS

LWPLS

LWR

PLS

69 53 63

12

secondary differentiation 1750−1550 cm−1, 3500−2200 cm−1 12 8 9 8 32

0.7

1.7

1.5

1.03 1.12 0.954

1.34 1.40 0.938

1.33 1.38 0.938

1.30 1.30 0.943

1.79 1.76 0.922

1.004 −0.045 −0.028

1.054 −0.189 0.045

1.040 0.013 0.184

1.033 0.042 0.184

1.104 −0.005 0.443

LWPLSPLS

LW-PLS

LWR

PLS

85 70 70 secondary differentiation

1

1

3487−907 cm−1 1 2

3

3

0.05

0.07

0.7

0.390 0.215 0.955

0.385 0.331 0.908

0.485 0.319 0.890

0.351 0.284 0.908

0.478 0.354 0.869

1.018 −0.078 −0.060

1.080 −0.126 −0.042

0.924 0.007 −0.073

0.878 −1.563 −0.004

0.975 −1.689 −0.028

The model construction results with the validation set, and the evaluation results of the constructed models with the external validation set were also shown in Tables 1 and 2. These results have shown that RMSE calculated with the validation set and RMSEP calculated with the external validation set were almost the same for each technique; thus, the performance of each technique is not affected by overfitting. The RMSEP of LW-PLS with weight coefficients of IR-ATR second differential spectrum of ibuprofen (LW-PLSIbp) and that of IR-ATR second differential spectrum of magnesium stearate (LW-PLSMg‑St) were the best of all techniques. The RMSEP of LW-PLSIbp was about 36% and 14% smaller than that of PLS and LWR, respectively; the RMSEP of LW-PLSMg‑St was about 39% and 24% smaller than that of PLS and LWR, respectively. As an example, Figure 3 shows the results of the external validation using LW-PLSIbp, LWR, and PLS. These results have confirmed that JIT modeling such as LW-PLS and LWR is useful for spectral analysis of IR-RAS and the proposed weighting technique, introduced in LW-PLS, is useful in improving the prediction accuracy. Figure 4 is the plot of the similarities between samples in the calibration set and queries in the external validation set for LW-PLSIbp. The mean plot confirms that most samples have significant similarities and they are used to building a local model in LW-PLS. Although the similarities for a query distant from samples in the calibration set are very small, many samples are used for local modeling because the relative difference of the similarities among samples is important. It is crucial to build a calibration model that can estimate the intended drug substances under coexistence with several

Table 1. Model Construction Results with the Validation Set and Model Evaluation Results with the External Validation Set for Each Spectral Analysis Technique (Ibuprofen) spectral analysis technique

LWPLSMg-St

3824



Technical Note

Figure 4. Similarities of samples in the calibration set (X) for queries in the external validation set (xq) in LW-PLSIbp. The leftmost points indicate the similarities of the closest samples in the calibration set for each xq: (●) shows the mean of similarities for all queries, (■) shows an example of the similarity for a query close to the calibration set, and (▲) shows an example of the similarity for a query distant from the calibration set.

magnesium stearate spectrum as weighting coefficients. In general, however, the best prediction performance is not always achieved by using a spectrum of pure component. For example, in the IR-RAS application for residual drug substances, if a peak of an excipient overlaps a peak of a residual drug substance, regression coefficients or loadings of the main latent variable of PLS would be useful as the weight instead of the purecomponent spectrum of the residual drug substance to improve the prediction accuracy of LW-PLS.

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CONCLUSIONS First, the usefulness of IR-RAS was evaluated to develop a rapid measurement method of the drug substances remaining in manufacturing equipment after cleaning. Next, the usefulness of LW-PLS as a spectral analysis technique for the IR-RAS spectra was evaluated to improve the prediction accuracy of IR-RAS. The results confirmed that the residual drug substances and the excipient were detected simultaneously and quickly with IRRAS. It was also found that the prediction accuracy of residual drug substances with IR-RAS was improved by using LW-PLS with the proposed weighting technique. In this study, RMSEP of LW-PLS with weight coefficients of IR-ATR second differential spectrum of ibuprofen was 36% and 14% smaller than that of PLS and LWR, respectively; similarly, that of magnesium stearate was improved by 39% and 24%, respectively. This improvement is crucial for the rapid measurement of residual drug substances, which requires high accuracy. From these results, we concluded that the combination of IR-RAS and LW-PLS with the proposed weighting technique realized sensitive and quick measurement of the residual drug substances. In the future, LWPLS with the proposed weighting technique will be applied to the analysis of NIR spectra which are widely used for monitoring critical quality attributes in manufacturing processes of drug products.

Figure 3. (A) Correlation between charged values of ibuprofen on SUS 316 test pieces and estimated concentrations using LW-PLSIbp for IR-RAS spectra. (B) Correlation between charged values of ibuprofen on SUS 316 test pieces and estimated concentrations using LWR for IR-RAS spectra. (C) Correlation between charged values of ibuprofen on SUS 316 test pieces and estimated concentrations using PLS for IRRAS spectra.

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components in actual manufacturing equipment, because the drug substances and other components remain simultaneously as shown in Figure 2. JIT modeling such as LW-PLS is more appropriate to this situation than conventional methods such as PLS and ANN, because it can cope with various situations by using a suitable weighting technique, which is useful for estimating the concentration of a query with great accuracy. In this study, RMSEP was minimized by using the ibuprofen or

AUTHOR INFORMATION

Corresponding Author

*Address: Daiichi Sankyo Co., Ltd., Formulation Technology Research Laboratories, Pharmaceutical Technology Division, 1-12-1, Shinomiya, Hiratsuka, Kanagawa 254-0014, Japan. Tel.: +81-463-31-6954. Fax: +81-463-31-6475. E-mail: nakagawa. [email protected]. 3825



Technical Note

Notes

(33) Naes, T.; Isaksson, T.; Kowalski, B. R. Anal. Chem. 1990, 62, 664−673. (34) Naes, T.; Isaksson, T. Appl. Spectrosc. 1992, 46, 34−43. (35) Wang, Z.; Isaksson, T.; Kowalski, B. R. Anal. Chem. 1994, 66, 249−260. (36) Centner, V.; Massart, D. L. Anal. Chem. 1998, 70, 4206−4211. (37) Despagne, F.; Massart, D. Anal. Chem. 2000, 72, 1657−1665. (38) Kano, M.; Ogawa, M. J. Process Control 2010, 20, 969−982. (39) Fujiwara, K.; Kano, M.; Hasebe, S.; Takinami, A. AIChE J. 2009, 55, 1754−1765. (40) Shigemori, H.; Kawamura, S.; Kano, M.; Hasebe, S. J. Process Control 2010, 21, 293−301. (41) Currie, L. A. Anal. Chem. 1968, 40, 586−593. (42) Faber, N. M. Accredit. Qual. Assur. 2008, 13, 277−278. (43) Olivieri, A. C.; Faber, N. M.; Ferré, J.; Boqué, R.; Kalivas, J. H; Mark, H. Pure Appl. Chem. 2006, 78, 633−661.

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study is the result of research conducted in the PAT committee of the Japan Society of Pharmaceutical Machinery and Engineering. We are grateful to the committee members for their comments, which helped us promote our research. In addition, this work was partially supported by Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (C) 21560793.

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