discrimination of acceptable and contaminated heparin by chemometric analysis of proton nuclear ...

225 Pages·2011·2.52 MB·English

by  Amit Acharya

DISCRIMINATION OF ACCEPTABLE AND CONTAMINATED HEPARIN BY
CHEMOMETRIC ANALYSIS OF PROTON NUCLEAR MAGNETIC
RESONANCE SPECTRAL DATA

By
Qingda Zang

A Dissertation Submitted to
the University of Medicine and Dentistry of New Jersey – School of
Health Related Professions in partial fulfillment of the Requirements for
the Degree of Doctor of Philosophy

Department of Health Informatics

April, 2011
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ABSTRACT
DISCRIMINATION OF ACCEPTABLE AND CONTAMINATED HEPARIN BY
CHEMOMETRIC ANALYSIS OF PROTON NUCLEAR MAGNETIC
RESONANCE SPECTRAL DATA

Qingda Zang

Heparin is a highly effective anticoagulant that can contain varying
amounts of undesirable galactosamine impurities (mostly dermatan sulfate or
DS), the level of which indicates the purity of the drug substance. Currently,
the United States Pharmacopeia (USP) monograph for heparin purity dictates
that the weight percent of galactosamine in total hexosamine (%Gal) may not
exceed 1%. In 2007 and 2008, heparin contaminated with oversulfated
chondroitin sulfate (OSCS) was associated with adverse clinical effects, i.e., a
rapid and acute onset of a potentially fatal anaphylactoid-type reaction. In
order to develop efficient and reliable screening methods for detecting and
identifying contaminants in existing and future lots of heparin to ensure the
integrity of the global supply, chemometric techniques for heparin proton
nuclear magnetic resonance (1H NMR) spectral data were applied to establish
adequate multivariate statistical models for discrimination between pure
heparin samples and those deemed unacceptable based on their levels of DS
and/or OSCS.
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The whole research work consisted of two parts: (1) the development of
quantitative regression models to predict the %Gal in various heparin
samples from NMR spectral data. Multivariate analyses including multiple
linear regression (MLR), Ridge regression (RR), partial least squares
regression (PLSR), and support vector regression (SVR) were employed in
this investigation. To obtain stable and robust models with high predictive
ability, variables were selected by genetic algorithms (GA) and stepwise
methods; (2) differentiation of heparin samples from impurities and
contaminants by the different pattern recognition and classification
approaches, such as principal components analysis (PCA), partial least
squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), k-
nearest-neighbor (kNN), classification and regression tree (CART), artificial
neural networks (ANN) and support vector machine (SVM), as well as the
class modeling techniques soft-independent modeling of class analogy
(SIMCA) and unequal dispersed classes (UNEQ).
Overall, the results from this study demonstrate that NMR spectroscopy
coupled with multivariate chemometric techniques shows promise as a
valuable tool for evaluating the quality of heparin sodium active
pharmaceutical ingredients (APIs). These developed models may be useful in
monitoring purity of other complex pharmaceutical products from high
information content data.
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ACKNOWLEDGEMENTS
I would like to acknowledge my advisor Dr. Dinesh P. Mital for his inspiring
supervision and supportive attitudes. The completion of this dissertation could
not have been possible without his invaluable guidance and unending
patience.
I wish to express my gratitude to my co-advisor, Dr. William J. Welsh who
has given me the opportunity to be where I am today. I would like to thank
him for trusting me and letting me go my own way.
I want to express my sincere thanks to the faculty members at the
Department of Health Informatics, especially to Dr. Syed S. Haque, Dr.
Shankar Srinivasan, and Dr. Masayuki Shibata, for their expertise, training,
advice and assistance throughout my graduate study.
I am very grateful to Dr. Richard D. Wood at Snowdon, Inc. for his
stimulating discussion, timely encouragement and constructive suggestions.
I would also like to thank the staff at the US Food and Drug Administration
(FDA). They provided the analysis data and more importantly, the financial
support, which made the research work possible. The collaboration with them
has greatly broadened my perspectives and I have learned a great deal from
them. Special thanks to Dr. Lucinda F. Buhse, Dr. David A. Keire, Dr.
Christine M. V. Moore, Dr. Moheb Nasr, Dr. Ali Al-Hakim, and Dr. Michael L.
Trehy.

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I would like to extend my gratitude to Dr. Dmitriy Chekmarev at the
Department of Pharmacology for spending his time in reviewing this
dissertation and valuable comments and feed-back.
Finally, I wish to thank my colleagues at Dr. Welsh‟s group, Dr. Ni Ai, Dr.
Vladyslav Kholodovych, Dr. Eric Kaipeen Yang and Dr. Oyenike Olabisi for
their consistent enthusiasm and reliable willingness to help, and friendly and
pleasant environment.

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TABLE OF CONTENTS

ABSTRACT ..................................................................................................... iii
ACKNOWLEDGEMENTS ............................................................................... v
LIST OF TABLES ............................................................................................ix
LIST OF FIGURES ..........................................................................................xi
Chapter I. INTRODUCTION ............................................................................ 1
1.1 Statement of the Problem ...................................................................... 1
1.2 Background of the Problem ................................................................... 4
1.3 Objectives of the Research .................................................................... 7
1.4 Research Hypotheses ........................................................................... 9
1.5 Results and Significance of the Research ........................................... 11
Chapter II. LITERATURE REVIEW ............................................................... 16
2.1 The Structure, Preparation and Medical Use of Heparin ..................... 17
2.1.1 Structures of Glycosaminoglycans (GAGs) ................................... 17
2.1.2 Preparation of Heparin .................................................................. 21
2.1.3 Medical Use of Heparin ................................................................. 22
2.2 Heparin Crisis ...................................................................................... 24
2.2.1 Adverse Events ............................................................................. 25
2.2.2 Contaminant Identification ............................................................. 26
2.2.3 USP Monograph for Heparin Quality ............................................. 32
2.3 Chemometrics and its Application in Heparin Field ............................. 33
2.3.1 Variable Selection ......................................................................... 34
2.3.2 Multivariate Regression Analysis .................................................. 39
2.3.3 Chemometric Pattern Recognition ................................................ 46
2.3.4 Application of Chemometrics in Heparin Field .............................. 67
Chapter III. DATA AND METHODS .............................................................. 72
3.1 Heparin Samples ................................................................................. 72
3.1.1 Pure, Impure and Contaminated Heparin APIs for Classification .. 72
3.1.2 Heparin API Samples for %Gal Determination .............................. 73
3.1.3 Blends of Heparin Spiked with other GAGs .................................. 74
3.2 Proton NMR Spectra............................................................................ 75
3.3 Data Processing .................................................................................. 77
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3.4 Computational Programs ..................................................................... 79
3.5 Performance Validation ....................................................................... 80
Chapter IV. RESULTS AND DISCUSSION ................................................... 82
4.1 Multivariate Regression Analysis for Predicting %Gal ......................... 82
4.1.1 Variable Selection ......................................................................... 82
4.1.2 Multiple Linear Regression Analysis ............................................. 90
4.1.3 Ridge Regression Analysis ........................................................... 97
4.1.4 Partial Least Squares Regression Analysis ................................ 101
4.1.5 Support Vector Regression Analysis ........................................... 105
4.2 Classification of Pure and Contaminated Heparin Samples .............. 108
4.2.1 Principal Components Analysis ................................................... 110
4.2.2 Partial Least Squares Discriminant Analysis ............................... 115
4.2.3 Linear Discriminant Analysis ....................................................... 119
4.2.4 k-Nearest-Neighbor ..................................................................... 123
4.2.5 Classification and Regression Tree ............................................. 128
4.2.6 Artificial Neural Networks ............................................................ 133
4.2.7 Support Vector Machine .............................................................. 137
4.2.8 Analysis of Misclassifications ...................................................... 141
4.2.9 Classification Analysis of Heparin Spiked with other GAGs ........ 145
4.3 Class Modeling for Discriminating Heparin Samples ......................... 149
4.3.1 SIMCA Analysis .......................................................................... 149
4.3.2 UNEQ Analysis ........................................................................... 165
Chapter V. SUMMARY AND CONCLUSIONS ............................................ 173
5.1 Multivariate Regression for Predicting %Gal ..................................... 173
5.2 Classification for Pure and Contaminated Heparin Samples ............. 175
5.3 Class Modeling Using SIMCA and UNEQ ......................................... 180
Chapter VI. FUTURE DIRECTION FOR RESEARCH ................................ 184
References .................................................................................................. 188
Appendix A: Abbreviations .......................................................................... 204
Appendix B: Index ....................................................................................... 207

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LIST OF TABLES
Table 1. Summary Statistics of %Gal Measured from HPLC ........................... 74

Table 2. Variable IDs and their Corresponding Chemical Shifts ...................... 79

Table 3. The Stepwise Variable Selection Procedure for Dataset A ............... 85

Table 4. The Stepwise Variable Selection Procedure for Dataset B ............... 86

Table 5. Parameters for the Genetic Algorithms ................................................ 87

Table 6. The Variables (ppm) Selected by Genetic Algorithms ....................... 89

Table 7. Model Parameters of Multiple Linear Regression (MLR) ................... 92

Table 8. Model Parameters of Ridge Regression (RR) ................................... 100

Table 9. Model Parameters of Partial Least Squares Regression (PLSR) .. 104

Table 10. Model Parameters for Support Vector Regression with RBF Kernel
................................................................................................................................... 107

Table 11. Number and Type of Misclassifications (Errors) by PLS-DA
Classification ........................................................................................................... 118

Table 12. Wilks‟ Lambda ( ) & F-to-enter (F) of Variables (V) for Various
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Models ...................................................................................................................... 120

Table 13. Performance of LDA Classification Models under Different
Variables .................................................................................................................. 121

Table 14. Performance of kNN Classification Models for Original Data ....... 124

Table 15. Performance of PCA-kNN Classification Models under Different
PCs ........................................................................................................................... 125

Table 16. Model Parameters and Classification Rates for CART .................. 130

Table 17. Model Parameters and Classification Rates for ANN .................... 137

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Table 18. Model Parameters and Classification Rates for SVM .................... 141

Table 19. Classification Matrices for the Heparin vs DS Model in the 1.95-
5.70 ppm Region .................................................................................................... 143

Table 20. Classification Matrices for the Heparin vs [DS + OSCS] Model in
the 1.95-5.70 ppm Region .................................................................................... 144

Table 21. Classification Matrices for the Heparin vs DS vs OSCS Model in the
1.95-5.70 ppm Region ........................................................................................... 144

Table 22. Compositions of the Series of Blends of Heparin Spiked with other
GAGs and Test Results for Classification from SVM, CART and ANN in the
1.95-5.70 ppm Region ........................................................................................... 148

Table 23. Sensitivity and Specificity from SIMCA Modeling for Heparin, DS,
and OSCS ............................................................................................................... 151

Table 24. Classification Matrices and Success Rates from SIMCA Class
Modeling for Heparin, DS and OSCS ................................................................. 157

Table 25. Discriminant Powers (DP) of Variables (V) for Various Models ... 161

Table 26. The Compositions of the Series of Blends of Heparin Spiked with
other GAGs and Test Results from Class Modeling ......................................... 164

Table 27. Wilks Lambda (λ) and F-to-enter (F) Values of Variables (V) ....... 167

Table 28. Sensitivity and Specificity from UNEQ Class Modeling for Heparin,
DS and OSCS ......................................................................................................... 169

Table 29. Classification Matrices from UNEQ Class Modeling for Heparin, DS
and OSCS ............................................................................................................... 172

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