Aaps 2009 Technical Poster (Usp Fda Buchi Irvine)
1
Conclusion Results Data 2009 AAPS Annual Meeting and Exposition Paper ID: XXXXXXXXXXX A Comparison of Near-Infrared (NIR) Feasibility Study Analyzing Pharmaceutical Drug Product Using Near-Infrared (NIR) Method Development Approaches Using a Drug Product on Different Spectrophotometers and Chemometric Software Algorithms It was found that using Savitsky-Golay, first derivative, 21 point smoothing, third order polynomial, pretreated spectra and either Principal Component Analysis (PCA) or Factorization model resulted in different models but possessing the same accuracy capabilities for predicting samples comprising similar validation sets. Each model correctly and accurately (100 %) predicated 160 validation samples using the Buchi model, and 148 validation samples using the Bruker and FOSS models. One validation sample set, a store-branded Ibuprofen (200 mg) Immediate Release Tablet, was correctly identified as not belonging to the sample represented in the calibration set by all three models. Based on these results and despite difference in instrument configuration number of spectral data points, PCA or Factorization algorithms, and validation modeling approach, exact and accurate spectroscopic results can be achieved using NIR spectroscopy for discriminate analysis. This study shows that the same NIR method spectral pretreatment parameters can be used, and that nearly the same multivariate models can be obtained, despite instrumental and software differences, to accurately predict the identity of pharmaceutical dosage forms. The instruments that were used included a Bruker Vector 22N FT-NIR spectrometer a Buchi NIRFlex Solids and FOSS XDS Rapid Content Analyzer. The Software used from each instrument, respectively, were OPUS TM 5.5, NIR Cal ®, NIRCal ® 5.2 and Vision TM 3.4. The Unscrambler ® 9.7, a stand-alone multivariate analysis and experimental design software package was used as referee software to assist in developing a common model. Assad J. Kazeminy,1 Saeed Hashemi,1 Roger L. Williams,2 Gary E. Ritchie,3 Ronald Rubinovitz,4 and *Sumit Sen,5 1. Irvine Pharmaceutical Services, Inc., 10 Vanderbilt, Irvine, CA 92618 2. United States Pharmacopeial Convention, 12601 Twinbrook Parkway Rockville, Maryland 20852 3. Former United States Pharmacopeial Convention, 12601 Twinbrook Parkway Rockville, Maryland 20852 4. Buchi Corporation, 19 Lukens Drive, New Castle, DE 19720 5. United States Food and Drug Administration, 19701 Fairchild, Irvine, CA 92612 *Corresponding Author: [email protected] Methods Abstract A study protocol was designed, using a common data set consisting of four formulations of Ibuprofen (200 mg): two branded and two store-branded Ibuprofen (200 mg) Immediate Release Tablets, involving three investigating parties, namely, the United States Food and Drug Administration (US FDA), the United State Pharmacopeia (USP), and Irvine Pharmaceutical Service, and three different NIR instruments. Each model consisted of 192 calibration samples and 64 test set samples developed for each NIR instrument. References Table 1 – Sources of Samples for Study Lot B rand -A dvil Brand -M otrin G eneric -C V S G eneric -R ite A id B 946681 P C A 189 6E E 0102 P 45032 2 x 200 = 400 tablets 3 x 100 = 300 tablets 2 x 250 = 500 tablets 1 x 500 = 500 tablets B 87154 LLA 103 6H E 0515 P 44389 2 x 200 = 400 tablets 3 x 100 = 300 tablets 4 x 100 = 400 tablets 1 x 500 = 500 tablets B 94669 P C A 112 7B E 0119 P 424477 3 x 100 = 300 tablets 3 x 100 = 300 tablets 1 x 750 = 750 tablets 1 x 250 = 250 tablets B 27624 P C A 226 7A E 0039 P 44686 4 x 75 = 300 tablets 10 x 24 = 240 tablets 1 x 750 = 750 tablets 1 x 250 = 250 tablets B 91364 P B A 123 7C E 0268 P 41360 2 x 150 = 300 tablets 3 x 75 = 225 tablets 1 x 750 = 750 tablets 3 x 100 = 300 tablets B 73322 P B A 194 7A E 0699 P 42476 2 x 150 = 300 tablets 3 x 100 = 300 tablets 1 x 500 = 500 tablets 3 x 100 = 300 tablets B 33863 P A A 016 6LE 0478 P 42498 3 x 100 = 300 tablets 5 x 50 = 250 tablets 1 x 500 = 500 tablets 2 x 120 = 240 tablets B 91414 P B A 186 7A E 0270 P 44151 2 x 200 = 400 tablets 5 x 50 = 200 tablets 3 x 100 = 300 tablets 5 x 50 = 250 tablets B 98483 P E A 106 6G E 0118 P 44688 9 x 24 = 216 tablets 2 x 100 = 200 tablets 1 x 500 = 500 tablets 5 x 50 = 250 tablets B 91386 LLA 329 7B E 0606 P 42058 2 x 100 = 200 tablets 2 x 100 = 200 tablets 1 x 500 = 500 tablets 2 x 100 = 200 tablets 1 2 3 4 9 10 5 6 7 8 Laboratory / Instrument Experimental Samples Innovator Manufacturer Advil Innovator Manufacturer Motrin Generic Manufacturer CVS Generic Manufacturer Rite Aid Total United States Pharmacopeia/ FOSS Calibration Set 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 192 Test Set 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 64 Validation set 20 tabs x 2 lots = 40 14 tabs x 2 lots = 28 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 148 United States Pharmacopeia/ Bruker Calibration Set 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 192 Test Set 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 64 Validation set 20 tabs x 2 lots = 40 14 tabs x 2 lots = 28 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 148 FDA/ Irvine Pharmaceutical Services, Inc./ Buchi Calibration Set 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 192 Test Set 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 64 Validation set 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 160 Total 312 288 312 312 1224 Figures 1a., 1b., and 1c. – Unscrambler® 9.7 PCA Score Plots of Buchi (1a), FOSS (1b), and Bruker (1c) Calibration and Test 1 a 1b Innovator B Generic B Generic B Innovator A Innovator B Generic B Generic A Innovator B Figures 2a., 2b., and 2c. – Unscrambler® 9.7 PCA Score Plots of Buchi (2a), FOSS (2b), and Bruker (2c) Calibration and Test 2b Ab so rb an ce Lo g (1 /R ) Wavele ngth Ab so rb an ce Lo g (1 /R ) 2 a Table 2 – Study Design Figure 3., Expanded view of Buchi, FOSS, and Bruker Derivative Spectra of Calibration and Test Set c.b. Buchi b . c . a . d . 0.0010 0.0005 0 0.0005 0.0010 0.0015 0.0020 1400.61nm 1411.29nm1422.12nm 1433.13nm1444.31nm1455.66nm1467.19nm 1478.91nm1490.82nm Variables Line Plot c . b . a . d . c . b . d . a . 1c Innovator B Innovator B Generic A Generic B 1. P. de Peinder, M.J. Vredenbregt, T. Visser and D. de Kaste, “Detection Of Lipitor® counterfeits: A Comparison of NIR and Raman Spectroscopy in Combination with Chemometrics”, Journal of Pharmaceutical and Biomedical Analysis 47, 688 (2008). 2. J. Luypaerta, D.L. Massart and Y. Vander Heyden, “Near-infrared Spectroscopy Applications in Pharmaceutical Analysis”, Talanta 72, 865 (2007). 3. Y. Roggo, P. Chalusa, L. Maurera, C. Lema-Martineza, A. Edmonda and N. Jenta, “A Review of Near Infrared Spectroscopy and Chemometrics in Pharmaceutical Technologies”, J. Pharm. and Biomedical Analysis, 44, 683 (2007). 4. A. K., Deisingh, “Pharmaceutical Counterfeiting”, Analyst 130, 271 (2005). D. A. Burns and E. W. Ciurczak, Ed, “Near-Infrared Spectroscopy in Pharmaceutical Applications”, in Handbook of Near-Infrared Analysis”, 3rd Edn, (Practical Spectroscopy Series Volume 35), CRC Press, Boca Raton, London, New York, 585, (2008). 5. R. De Maesschalck, D. Jouan-Rimbaud and D.L. Massart, Tutorial – The Mahalanobis distance, Chemometrics and Intelligent Laboratory Systems 50, 1, (2000). 6. S H Scafi and C Pasquini, “Identification of Counterfeit Drugs Using Near-Infrared Spectroscopy”, Analyst 126, 2218 (2001). 7. J. Workman Jr. and J. Brown, “A New Standard Practice for Multivariate, Quantitative Infrared Analysis-Part I”, Spectroscopy 11(2), 48 (1996). 8. J. Workman Jr. and J. Brown, “A New Standard Practice for Multivariate, Quantitative Infrared Analysis-Part II”, Spectroscopy 11(9), 24 (1996). 9. A. Savitzky and M. J. E. Golay, "Smoothing and Differentiation of Data by Simplified Least Squares Procedures," Anal. Chem. 36, 1627 (1964). 10. Davies, A.M.C. and C. Miller, 1988, “Tentative Assignment of the 1440-nm Absorption Band in the Near- Infrared Spectrum of Crystalline Sucrose”, Appl. Spectros. 42 (4), 703-704). 2 c Table 3 – Data Pretreatment from 1000 nm – 2500 nm (Cluster) Table 4 – Data Pretreatment from 1400 nm – 1500 nm (Cluster) Spectra Treatment (1400 nm- 1500 nm) Innovator A Innovator B Generic A Generic B Untreated Spectra + _ _ _ Baseline Correction + + _ _ First Derivative + + + + Second Derivative Yes _ _ _ Baseline Corrected First Derivative + + + + Baseline Corrected Second Derivative + _ _ _ Spectra Treatment (1100 nm- 2500 nm) Innovator A Innovator B Generic A Generic B Untreated Spectra + + _ _ Baseline Correction + + + + First Derivative + _ + + Second Derivative + + _ _ Baseline Corrected First Derivative + + _ _ Baseline Corrected Second Derivative + _ + +
-
Upload
gary-ritchie -
Category
Technology
-
view
306 -
download
0
Transcript of Aaps 2009 Technical Poster (Usp Fda Buchi Irvine)
Conclusion
Results
Data
2009 AAPS Annual Meeting and Exposition Paper ID: XXXXXXXXXXX
A Comparison of Near-Infrared (NIR) Feasibility Study Analyzing Pharmaceutical Drug Product Using Near-Infrared (NIR) Method Development Approaches Using a Drug Product on Different Spectrophotometers and Chemometric Software Algorithms
It was found that using Savitsky-Golay, first derivative, 21 point smoothing, third order polynomial, pretreated spectra and either Principal Component Analysis (PCA) or Factorization model resulted in different models but possessing the same accuracy capabilities for predicting samples comprising similar validation sets. Each model correctly and accurately (100 %) predicated 160 validation samples using the Buchi model, and 148 validation samples using the Bruker and FOSS models. One validation sample set, a store-branded Ibuprofen (200 mg) Immediate Release Tablet, was correctly identified as not belonging to the sample represented in the calibration set by all three models. Based on these results and despite difference in instrument configuration number of spectral data points, PCA or Factorization algorithms, and validation modeling approach, exact and accurate spectroscopic results can be achieved using NIR spectroscopy for discriminate analysis.
This study shows that the same NIR method spectral pretreatment parameters can be used, and that nearly the same multivariate models can be obtained, despite instrumental and software differences, to accurately predict the identity of pharmaceutical dosage forms.
The instruments that were used included a Bruker Vector 22N FT-NIR spectrometer a Buchi NIRFlex Solids and FOSS XDS Rapid Content Analyzer. The Software used from each instrument, respectively, were OPUSTM 5.5, NIR Cal®, NIRCal® 5.2 and VisionTM 3.4. The Unscrambler® 9.7, a stand-alone multivariate analysis and experimental design software package was used as referee software to assist in developing a common model.
Assad J. Kazeminy,1 Saeed Hashemi,1 Roger L. Williams,2 Gary E. Ritchie,3 Ronald Rubinovitz,4 and *Sumit Sen,51. Irvine Pharmaceutical Services, Inc., 10 Vanderbilt, Irvine, CA 92618 2. United States Pharmacopeial Convention, 12601 Twinbrook Parkway Rockville, Maryland 208523. Former United States Pharmacopeial Convention, 12601 Twinbrook Parkway Rockville, Maryland 208524. Buchi Corporation, 19 Lukens Drive, New Castle, DE 197205. United States Food and Drug Administration, 19701 Fairchild, Irvine, CA 92612 *Corresponding Author: [email protected]
Methods
AbstractA study protocol was designed, using a common data set consisting of four formulations of Ibuprofen (200 mg): two branded and two store-branded Ibuprofen (200 mg) Immediate Release Tablets, involving three investigating parties, namely, the United States Food and Drug Administration (US FDA), the United State Pharmacopeia (USP), and Irvine Pharmaceutical Service, and three different NIR instruments. Each model consisted of 192 calibration samples and 64 test set samples developed for each NIR instrument.
References
Table 1 – Sources of Samples for StudyLot Brand - Advil Brand - Motrin Generic - CVS Generic - Rite Aid
B946681 PCA189 6EE0102 P450322 x 200 = 400 tablets 3 x 100 = 300 tablets 2 x 250 = 500 tablets 1 x 500 = 500 tablets
B87154 LLA103 6HE0515 P443892 x 200 = 400 tablets 3 x 100 = 300 tablets 4 x 100 = 400 tablets 1 x 500 = 500 tablets
B94669 PCA112 7BE0119 P4244773 x 100 = 300 tablets 3 x 100 = 300 tablets 1 x 750 = 750 tablets 1 x 250 = 250 tablets
B27624 PCA226 7AE0039 P446864 x 75 = 300 tablets 10 x 24 = 240 tablets 1 x 750 = 750 tablets 1 x 250 = 250 tablets
B91364 PBA123 7CE0268 P413602 x 150 = 300 tablets 3 x 75 = 225 tablets 1 x 750 = 750 tablets 3 x 100 = 300 tablets
B73322 PBA194 7AE0699 P424762 x 150 = 300 tablets 3 x 100 = 300 tablets 1 x 500 = 500 tablets 3 x 100 = 300 tablets
B33863 PAA016 6LE0478 P424983 x 100 = 300 tablets 5 x 50 = 250 tablets 1 x 500 = 500 tablets 2 x 120 = 240 tablets
B91414 PBA186 7AE0270 P441512 x 200 = 400 tablets 5 x 50 = 200 tablets 3 x 100 = 300 tablets 5 x 50 = 250 tablets
B98483 PEA106 6GE0118 P446889 x 24 = 216 tablets 2 x 100 = 200 tablets 1 x 500 = 500 tablets 5 x 50 = 250 tablets
B91386 LLA329 7BE0606 P420582 x 100 = 200 tablets 2 x 100 = 200 tablets 1 x 500 = 500 tablets 2 x 100 = 200 tablets
1
2
3
4
9
10
5
6
7
8
Laboratory / Instrument
Experimental Samples
Innovator Manufacturer
Advil
Innovator Manufacturer
Motrin
Generic Manufacturer CVS
Generic Manufacturer Rite Aid
Total
United States Pharmacopeia/
FOSS
Calibration Set 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 192
Test Set 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 64
Validation set 20 tabs x 2 lots = 40 14 tabs x 2 lots = 28 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 148
United States Pharmacopeia/
Bruker
Calibration Set 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 192
Test Set 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 64
Validation set 20 tabs x 2 lots = 40 14 tabs x 2 lots = 28 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 148
FDA/Irvine Pharmaceutical
Services, Inc./Buchi
Calibration Set 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 6 tabs x 8 lots = 48 192
Test Set 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 2 tabs x 8 lots = 16 64
Validation set 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 20 tabs x 2 lots = 40 160
Total 312 288 312 312 1224
Figures 1a., 1b., and 1c. – Unscrambler® 9.7 PCA Score Plots of Buchi (1a), FOSS (1b), and Bruker (1c) Calibration and Test
1a 1b
Innovator B
Generic B
Generic B
Innovator A
Innovator B
Generic BGeneric A
Innovator B
Figures 2a., 2b., and 2c. – Unscrambler® 9.7 PCA Score Plots of Buchi (2a), FOSS (2b), and Bruker (2c) Calibration and Test
2b
Ab
sor
ba
nce
L
og (1
/R
)
Wavelength
Ab
so rb an ce
Lo
g (1/
R)
2a
Table 2 – Study Design
Figure 3., Expanded view of Buchi, FOSS, and Bruker Derivative Spectra of Calibration and Test Set c.b. Buchi
b.
c.
a.d.
-0.0010
-0.0005
0
0.0005
0.0010
0.0015
0.0020
1400.61nm1411.29nm1422.12nm1433.13nm1444.31nm1455.66nm1467.19nm1478.91nm1490.82nm
Variables
Line Plot
c.
b.a.
d.c.
b.
d.
a.
1c
Innovator B
Innovator B
Generic A Generic B
1. P. de Peinder, M.J. Vredenbregt, T. Visser and D. de Kaste, “Detection Of Lipitor® counterfeits: A Comparison of NIR and Raman Spectroscopy in Combination with Chemometrics”, Journal of Pharmaceutical and Biomedical Analysis 47, 688 (2008).2. J. Luypaerta, D.L. Massart and Y. Vander Heyden, “Near-infrared Spectroscopy Applications in Pharmaceutical Analysis”, Talanta 72, 865 (2007). 3. Y. Roggo, P. Chalusa, L. Maurera, C. Lema-Martineza, A. Edmonda and N. Jenta, “A Review of Near Infrared Spectroscopy and Chemometrics in Pharmaceutical Technologies”, J. Pharm. and Biomedical Analysis, 44, 683 (2007).4. A. K., Deisingh, “Pharmaceutical Counterfeiting”, Analyst 130, 271 (2005).D. A. Burns and E. W. Ciurczak, Ed, “Near-Infrared Spectroscopy in Pharmaceutical Applications”, in Handbook of Near-Infrared Analysis”, 3rd Edn, (Practical Spectroscopy Series Volume 35), CRC Press, Boca Raton, London, New York, 585, (2008).5. R. De Maesschalck, D. Jouan-Rimbaud and D.L. Massart, Tutorial – The Mahalanobis distance, Chemometrics and Intelligent Laboratory Systems 50, 1, (2000). 6. S H Scafi and C Pasquini, “Identification of Counterfeit Drugs Using Near-Infrared Spectroscopy”, Analyst 126, 2218 (2001).7. J. Workman Jr. and J. Brown, “A New Standard Practice for Multivariate, Quantitative Infrared Analysis-Part I”, Spectroscopy 11(2), 48 (1996).8. J. Workman Jr. and J. Brown, “A New Standard Practice for Multivariate, Quantitative Infrared Analysis-Part II”, Spectroscopy 11(9), 24 (1996).9. A. Savitzky and M. J. E. Golay, "Smoothing and Differentiation of Data by Simplified Least Squares Procedures," Anal. Chem. 36, 1627 (1964).10. Davies, A.M.C. and C. Miller, 1988, “Tentative Assignment of the 1440-nm Absorption Band in the Near-Infrared Spectrum of Crystalline Sucrose”, Appl. Spectros. 42 (4), 703-704).
2c
Table 3 – Data Pretreatment from 1000 nm – 2500 nm (Cluster)
Table 4 – Data Pretreatment from 1400 nm – 1500 nm (Cluster)
Spectra Treatment (1400 nm- 1500 nm)
Innovator A Innovator B Generic A Generic B
Untreated Spectra + _ _ _
Baseline Correction
+ + _ _
First Derivative + + + +
Second Derivative Yes _ _ _
Baseline Corrected First Derivative
+ + + +
Baseline Corrected Second Derivative
+ _ _ _
Spectra Treatment (1100 nm- 2500 nm)
Innovator A Innovator B Generic A Generic B
Untreated Spectra
+ + _ _
Baseline Correction
+ + + +
First Derivative + _ + +
Second Derivative + + _ _
Baseline Corrected First Derivative
+ + _ _
Baseline Corrected Second Derivative
+ _ + +
