ALCHEMISTIC POLYMERS FOR THE DELIVERY OF THERAPEUTIC AGENTS IN TREATMENT OF PEDIATRIC TRACHEOMALACIA
APPROVED BY SUPERVISORY COMMITTEE
Joseph Forbess, MD Chet Xu, PhD
Kytai T. Nguyen, PhD Romaine Johnson, MD, MPH Matthew Petroll, PhD
DEDICATION
I would like to thank the members of my Graduate Committee and especially my mentor
Dr. Joseph Forbess for their guidance and support completing this doctoral degree. Thank
you to my parents Linda and Jeff and my brother Brian for their love and support. I am
greatly appreciated to JBK for his love and support especially from long distance. I am
thankful to have the unconditional support and a reminder not to take life too seriously
from Milo, Orion, and Finn. Thank you to CT Stayton and DeeAnn Reeder for
motivating me and proving that nothing is truly impossible with some hard work and a little luck. A special thank you to other UT faculty members who played a key role in my
professional and personal development. I am grateful to those who have inspired my
dream to become a scientific expert at the highest academic level, these individuals include; Bill N., David A., Jane G., Charles D., Marie C., Christine V., and NDT. I would like to thank the other graduate students for their emotional support. I am appreciative of
the resources and support provided by the universities and their faculty to help me
complete this research.
ALCHEMISTIC POLYMERS FOR THE DELIVERY OF THERAPEUTIC AGENTS
IN TREATMENT OF PEDIATRIC TRACHEOMALACIA
by
AMY CLAIRE GOODFRIEND
DISSERTATION
Presented to the Faculty of the Graduate School of Biomedical Sciences
The University of Texas Southwestern Medical Center at Dallas
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
The University of Texas Southwestern Medical Center at Dallas
Dallas, Texas
MarchMay 2016, 2016
Copyright
by
AMY CLAIRE GOODFRIEND, 2016
All Rights Reserved
ALCHEMISTIC POLYMERS FOR THE DELIVERY OF THERAPEUTIC AGENTS
IN TREATMENT OF PEDIATRIC TRACHEOMALACIA
AMY CLAIRE GOODFRIEND, Ph.D.
The University of Texas Southwestern Medical Center at Dallas, 2016
Joseph M. Forbess, M.D.
Tracheomalacia is characterized by flaccidity of the airway whereby tracheal collapse occurs during respiration. Globally, approximately 1:21 children are affected by airway malacia whether it be acquired or from congenital origins. Of the available modalities of treatment, stenting has the greatest potential for success but remains controversial in pediatrics due to limitations in biocompatibility and internal reinforcement. There is a pressing need in the design of bioresorbable devices for the treatment of this disease. Ergo, this research shows the development of a MRI-visible multi-drug release composite coating that is to be applied to a bioresorbable stent. The
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coating combines novel polymers synthesized using non-traditional initiators such as contrast medium and therapeutic agents. The characterization of these polymers leads to the optimization of a coating platform. Using a factorial design, a library of drug delivery particles for the delivery of an anti-inflammatory agent was generated. The novel polymer containing the contrast agent was blended with preexisting polymers to formulate theranostic nanoparticles for a three month delivery of an anti-inflammatory agent. The optimized polymer platform is synthesized using a contrast medium and an antibiotic to inhibit bacterial infection up to two weeks. Thus the combination of the polymeric theranostic nanoparticles and the antibiotic release polymer platform were combined to generate a composite coating. Each individual component of the composite coating and the combination of components was analyzed for biocompatibility and therapeutic potential in-vitro. The local multi-drug delivery and imaging capabilities in this coating design in combination with a bioresorbable stent should result in a successful intervention specifically designed for pediatric tracheomalacia. This design should mitigate long-term risks associated with current permanent devices and provide necessary theranostic agents to facilitate healing and monitor progress via non-invasive imaging techniques.
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TABLE OF CONTENTS
CHAPTER ONE INTRODUCTION ...... 1
1.1 Background ...... 1
1.2 Bioresorbable Stents and Their Limitations ...... 6
1.3 Bioresorbable Polymers for Drug Delivery ...... 9
1.4 Polymeric Particles for Medical Applications ...... 11
1.5 Proposed Coating Design and Its Advantages ...... 16
1.6 Stent Coating Methods and Characterization ...... 20
1.7 Biocompatability of Polymeric Materials ...... 22
1.8 Specific Aims ...... 23
CHAPTER TWO METHODOLOGY ...... 34
2.1 Polymer Syntheses ...... 34
2.1.1 Poly(Fumaric Acid) ...... 34
2.1.2 Poly(Gadodiamide Fumaric Acid) ...... 37
2.1.3 Poly(Ciprofloxacin Fumaric Acid) ...... 39
2.1.4 Poly(Potassium Iodide Fumaric Acid) ...... 41
2.1.5 Poly(Gadodiamide Ciprofloxacin Fumaric Acid) ...... 43
2.2 Drug Delivery and Theranostic Particle Formulations ...... 45
2.2.1 PLGA Particles ...... 45
2.2.2 PLGA Particles via Novel Distillation Technique ...... 45
2.2.3 PLGA/PGFA Theranostic Nanoparticles ...... 47
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2.3 Fabrication of Poly(L-Lactide Acid) Fibers ...... 49
2.4 Fiber Coating Methods ...... 49
2.4.1 Non-Porous PLGA Dip Coating of PLLA Fibers ...... 49
2.4.2 Porous PLGA Dip Coating of PLLA Fibers ...... 49
2.5 Polymer and Particle Characterization...... 50
2.5.1 Fourier Transform Infrared Spectroscopy ...... 50
2.5.2 Proton Nuclear Magnetic Resonance ...... 50
2.5.3 Gel Permeation Chromatography and Refractice Index Detection...... 50
2.5.4 Differential Scanning Calorimetry ...... 51
2.5.5 Rheology ...... 51
2.5.6 Surface Morphology via Scanning Electron Microscopy ...... 53
2.5.7 Dynamic Light Scattering ...... 53
2.5.8 Mechanical Testing of Fibers ...... 53
2.5.9 Porosity Determination of Films ...... 54
2.6 Polymer and Particle Drug Release ...... 55
2.6.1 High Pressure Liquid Chromatography Detection of Dexamethasone
...... 55
2.6.2 High Pressure Liquid Chromatography Detection of Ciprofloxacin ... 55
2.6.3 Simultaneous Detection of Dexamethasone and Ciprofloxacin ...... 55
2.6.4 Drug Loading Efficiency ...... 56
2.6.5 Particle Drug Release ...... 58
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2.6.6 Polymer Drug Release ...... 58
2.6.7 Coated Fiber Drug Release ...... 58
2.7 Cell Culture ...... 59
2.7.1 Human Dermal Fibroblasts ...... 59
2.7.2 Tracheal Epithelial Cells ...... 59
2.7.3 RAW Mouse Macrophage Cells ...... 60
2.8 Biocompatibility Assays ...... 60
2.8.1 XTT Assay ...... 60
2.8.2 Alamar Blue Assay ...... 61
2.8.3 Live/Dead Fluorescent Staining ...... 63
2.9 In-Vitro Inflammation Assessment ...... 63
2.10 Microbial Culture ...... 64
2.10.1 Escherichia coli ...... 64
2.10.2 Klebsiella pneumoniae ...... 64
2.10.3 Moraxella catarrhilis ...... 64
2.10.4 Pseudomonas aeruginosa ...... 65
2.11 Kirby-Bauer Disk Diffusion Sensitivity Assay...... 66
CHAPTER THREE RESULTS ...... 69
3.1 Aim 1 Particle Formulation from a Factorial Design ...... 69
3.1.1 Development of Particle Formulation Technique ...... 69
3.1.2 Characterization of Particles Using a Factorial Design ...... 75
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3.2 Aim 2 Design of Multi-Drug Release Coating ...... 86
3.2.1 Material Characterization of PFA ...... 86
3.2.2 Material Characterization of PGFA ...... 90
3.2.3 Material Characterization of PCFA ...... 95
3.2.4 Material Characterization of PKIFA ...... 99
3.2.5 Material Characterization of PGCFA ...... 103
3.2.6 Mechanical Properties of Drawn PLLA Fiber ...... 108
3.2.7 Characterization of Dexamethasone Releasing PLGA Coatings ...... 109
3.3 Aim 3 Biocompatibility Studies of Coating Materials ...... 115
3.3.1 In-vitro Biocompatibility with Human Dermal Fibroblasts...... 115
3.3.2 In-vitro Biocompatibility with Human Tracheal Epithelial Cells...... 121
3.3.3 In-vitro Inflammation Assessment with Mouse Macrophages ...... 127
3.3.4 In-vitro Sensitivity Assessment of Airway Pathogens ...... 130
CHAPTER FOUR DISCUSSION ...... 133
4.1 Particle Formulastion From a Factorial Design ...... 133
4.1.1 Effects of Copolymer Ratio on Particle Characteristics ...... 133
4.1.2 Effects of Thermal Processing on Particle Characteristics ...... 134
4.1.3 Effects of PLGA/PGFA Blend on Particle Characteristics...... 136
4.1.4 Development and Future Prospects of Polymeric Theranostic
Nanoparticles ...... 138
4.2 Design of a Multi-Drug Coating for a Bioresorbable Stent ...... 139
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4.2.1 New Class of Radiopaque and MRI-Visible Polymers Utilizing
Contrast Medium Initiator Polymerization ...... 139
4.2.2 Radiopaque and MRI-Visible Polymer Applications in Medicine .... 141
4.2.3 Effects of Therapeutic Agents on Polymer Thermal and Rheological
Properties ...... 142
4.2.4 The Use of an Antibiotic as a Polymer Synthesis Initiator ...... 144
4.2.5 Degradation of PGFA and Drug Release Kinetics of PCFA and
PGCFA ...... 146
4.2.6 Current Stent Coatings and Coating Techniques ...... 148
4.2.7 Effects of Coatings on Stent Fiber Mechanical Properties ...... 150
4.2.8 Characterization and Drug Release of Coated Stent Fibers ...... 151
4.2.9 Bioresorbable Devices Offer Better Interventions in Pediatric Airways
...... 152
4.2.10 Coatings Can Improve Bioresorbable Stents for Airway Interventions
...... 155
4.3 Biocompatibility of Polymeric Particles and Coating Materials ...... 159
4.3.1 Current Biocompatibility Standards for Polymeric Materials in Medical
Applications ...... 159
4.3.2 Bioresorbable Polymers Demonstrate Superior Biocompatibility ..... 161
4.3.3 Advantages of Usings PTNPs as Part of a Composite Stent Coating
...... 164
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4.3.4 Dexamethasone-Loaded PTNPS Lower Inflammatory Cytokines in-
vitro ...... 165
4.3.5 Feasbility of Coating Bioresorbable Stents with PTNPs ...... 167
4.3.6 Bioresorbable Antimicrobial Polymers and Their Use in Medical
Applications ...... 170
4.3.7 Multifunctional Polymers and PTNPs as a Coating for Airway Stents ...
...... 171
CHAPTER FIVE CONCLUSIONS ...... 174
CHAPTER SIX FUTURE WORK ...... 187
APPENDIX A Nanoparticle Characterization Theories and Techniques ...... 192
APPENDIX B Polymer characterization: Differential Scanning Calorimetry Theory ......
...... 196
APPENDIX C Polymer Characterization: Rheology ...... 200
APPENDIX D Drug Release Theory and Mathematical Modeling ...... 207
APPENDIX E High Pressure Liquid Chromatography Standards ...... 212
APPENDIX F XTT Assay Standard Calibration Determination ...... 222
APPENDIX G Bacterial Sensitivity Assay Complete Analysis ...... 242
REFERENCES ...... 291
VITAE...... 312
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PRIOR PUBLICATIONS
Goodfriend AC, Welch TR, Nguyen KT, Johnson RF, Sebastian V, Reddy SV, Forbess J, Nugent A. Thermally processed polymeric microparticles for year-long delivery of dexamethasone. Materials Science and Engineering C. 58, 1 Jan 2016, pp 595-600 DOI: 10.1016/j.msec.2015.09.003
Goodfriend AC, Welch TR, Wang J, Nguyen KT, Johnson RF, Xu C, Reddy SV, Nugent A, Forbess JM. Design of a radiopaque drug delivery coating for bioresorbable stents. Proceedings of the 14th International Mechanical Engineering Congress & Exposition. American Society of Mechanical Engineers. Aug 2015.
Goodfriend AC, Welch TR, Nguyen KT, Wang J, Johnson RF, Reddy SV, Nugent A, Forbess JM. Poly(gadodiamide fumaric acid): A bioresorbable radiopaque and MRI- visible polymer for biomedical application. American Chemical Society Biomaterials Science & Engineering. 1(8), 22 June 2015 pp 677-684 DOI: 10.1021/acsbiomaterials.5b00091
Goodfriend AC, Barker G, Welch TR, Richard G, Reagel M, Reddy SV, Wang J, Nugent A, Forbess J. Novel Bioresorbable Stent Coating for Drug Release in Congenital Heart Disease Applications. Journal of Biomedical Materials Research Part A. 103(5), May 2015, pp 1761-1770 DOI: 10.1002/jbm.a.35313
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LIST OF FIGURES
Figure 1. Showing (A) H&E stain of trachea at a normal state,27 (B) histological section (H&E stain) of trachea indicating granulation of the wall,28 and (C) bronchoscopy of inflamed trachea state. 29 ...... 4
Figure 2. Showing common pediatric tracheal stents: (A) Montgomery® Sate T-Tube,33 (B) Novatech DumonTM stents,34 and (C) Palmaz® Genesis® stent.15 ...... 4
Figure 3. Hydrolytic degradation of PLGA.55 ...... 10
Figure 4. Synthesis scheme of Poly(Fumaric Acid)...... 36
Figure 5. Synthesis scheme of Poly(Gadodiamide Fumaric Acid)...... 38
Figure 6. Synthesis scheme of Poly(Ciprofloxacin Fumaric Acid)...... 40
Figure 7. Synthesis scheme of Poly(Potassium Iodide Fumaric Acid)...... 42
Figure 8. Synthesis scheme of Poly(Gadodiamide Ciprofloxacin Fumaric Acid)...... 44
Figure 9. Film porosity setup using Mettler-Toledo balance and density kit...... 54
Figure 10. Particle drug release apparatus...... 57
Figure 11. Particle drug release experimental setup...... 58
Figure 12. Polymer drug release setup...... 58
Figure 13. Coating fiber drug release setup...... 59
Figure 14. Example image depicting live and dead cell using Alamar Blue Assay...... 62
Figure 15. Countess cell counter...... 62
Figure 16. Countess® hemocytometer chambered slides...... 63
Figure 17. Sensitivity disk arrangement...... 67
Figure 18. PLGA nanoparticles formulated using (A) Method A and (B) Method B. .... 69
Figure 19. SEM image showing PLGA nanoparticles from Method B dip-coated onto PLLA fiber...... 71
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Figure 20. Dexamethasone release from Corbion Purac® 50:50 particles altering polymer to drug ratio. Data shown mean±SD, n=10...... 73
Figure 21. Dexamethasone release from Corbion Purac® 75:25 particles altering polymer to drug ratio. Data shown mean±SD, n=10...... 74
Figure 22. Cumulative dexamethasone release of (A) Purac PLGA 50:50 microparticle groups and (B) PLGA50:50/PPF nanoparticles. Data shown mean±SEM, n=10...... 77
Figure 23 Concentration of dexamethasone release of (A) Purac PLGA 50:50 microparticle groups in adult therapeutic window and (B) PLGA50:50/PPF nanoparticles in pediatric therapeutic window. Data shown mean±SEM, n=10...... 78
Figure 24. Cumulative dexamethasone release of (A) Purac PLGA 75:25 microparticle groups and (B) PLGA75:25/PPF nanoparticles. Data shown mean±SEM, n=10...... 79
Figure 25. Concentration of dexamethasone release of (A) Purac PLGA 75:25 microparticle groups in pediatric therapeutic window and (B) PLGA75:25/PPF nanoparticles in pediatric therapeutic window. Data shown mean±SEM, n=10...... 80
Figure 26. Cumulative dexamethasone release of (A) Evonik PLGA 50:50 microparticle groups and (B) PLGA50:50/PPF nanoparticles. Data shown mean±SEM, n=10...... 81
Figure 27. Concentration of dexamethasone release of (A) Evonik PLGA 50:50 microparticle groups in pediatric therapeutic window and (B) PLGA50:50/PPF nanoparticles in pediatric therapeutic window. Data shown mean±SEM, n=10...... 82
Figure 28. Cumulative dexamethasone release of (A) Evonik PLGA 75:25 microparticle groups and (B) PLGA75:25/PPF nanoparticles. Data shown mean±SEM, n=10...... 83
Figure 29. Concentration of dexamethasone release of (A) Evonik PLGA 75:25 microparticle groups in pediatric therapeutic window and (B) PLGA75:25/PPF nanoparticles in pediatric therapeutic window. Data shown mean±SEM, n=10...... 84
Figure 30. SEM of PLGA 50:50/PGFA microparticles at (A) 4500X and (B) 9000X. . 84
Figure 31. SEM of dexamethasone-loaded polymeric theranostic nanoparticles...... 85
Figure 32. Cumulative dexamethasone release of PTNPs. 97% of loaded drug is released in three months following a zero order release model. Data shown mean±SD, n=10. ... 86
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Figure 33. Determination of Gd relaxivity coefficients using T1 and T2 maps (top) generated from phantom using linear regression analysis (bottom) from six concentrations...... 87
Figure 34. 1H-NMR spectra of PFA...... 89
Figure 35. FTIR spectra of PFA...... 89
Figure 36. Typical DSC curve of PFA...... 90
Figure 37. Assessment of PFA viscosity using a broad torque range of 0.1 – 1000 μN·m...... 91
Figure 38. Assessment of PFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s...... 91
Figure 39. Assessment of PFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%...... 92
Figure 40. Assessment of PFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min...... 92
Figure 41. Degradation kinetics of PFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10)...... 93
Figure 42. 1H-NMR spectra of PGFA...... 94
Figure 43. FTIR spectra of PGFA...... 95
Figure 44. Typical DSC curve of PGFA...... 95
Figure 45. Assessment of PGFA viscosity using a broad torque range of 0.1 – 1000 μN·m...... 96
Figure 46. Assessment of PGFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s...... 96
Figure 47. Assessment of PGFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%...... 96
Figure 48. Assessment of PGFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min...... 97
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Figure 49. Degradation kinetics of PGFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10)...... 98
Figure 50. 1H-NMR spectra of PCFA...... 99
Figure 51. FTIR spectra of PCFA...... 100
Figure 52. Typical DSC curve of PCFA...... 100
Figure 53. Assessment of PCFA viscosity using a broad torque range of 0.1 – 1000 μN·m...... 101
Figure 54. Assessment of PCFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s...... 101
Figure 55. Assessment of PCFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%...... 101
Figure 56. Assessment of PCFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min...... 102
Figure 57. Degradation kinetics of PCFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10)...... 103
Figure 58. Cumulative ciprofloxacin release of PCFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10)...... 103
Figure 59. 1H-NMR spectra of PKIFA...... 104
Figure 60. FTIR spectra of PKIFA...... 105
Figure 61. Typical DSC curve of PKIFA...... 105
Figure 62. Assessment of PKIFA viscosity using a broad torque range of 0.1 – 1000 μN·m...... 106
Figure 63. Assessment of PKIFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s...... 106
Figure 64. Assessment of PKIFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%...... 106
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Figure 65. Assessment of PKIFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min...... 107
Figure 66. 1H-NMR spectra of PGCFA...... 108
Figure 67. FTIR spectra of PGCFA...... 109
Figure 68. Typical DSC curve of PGCFA...... 109
Figure 69. Assessment of PGCFA viscosity using a broad torque range of 0.1 – 1000 μN·m...... 110
Figure 70. Assessment of PGCFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s...... 110
Figure 71. Assessment of PGCFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%...... 110
Figure 72. Assessment of PGCFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min...... 111
Figure 73. Degradation kinetics of PGCFA in deionized water (pH 7.4) at 37°C. Raw data with computer nonlinear regression (n=10)...... 111
Figure 74. Cumulative ciprofloxacin release of PGCFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10)...... 112
Figure 75. Average stress strain curve of control annealed 180±0.01 μm PLLA fiber (Data shown mean±SEM, n=20)...... 112
Figure 76. Typical DSC curve of annealed PLLA fiber...... 114
Figure 77. FTIR readings of (A) PGLA with observed peaks at 2996, 1756, 1455, 1384 cm-1 and (B) Dexamethasone with a peak at 1660 cm-1 and (C) FTIR measurement of PGLA embedded with Dexamethasone. Dexamethasone peaks of 1661 cm-1 are detected...... 115
Figure 78. Showing the coated PLLA fiber surface at (A) 430x with no distinguishing features of a porous coating and (B) at 5000x showing the porous coating of the PLGA film...... 116
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Figure 79. Average stress-Strain curves for coated PLLA fibers. A slight weakening trend is observed with the coating of PLGA but not significantly different (Data shown mean±SEM, n=20 per group, p<0.05)...... 116
Figure 80. Displaying the cumulative drug release of dexamethasone on a Porous and Non-porous Coating of PLGA on PLLA Fibers. The porous coating showing a significantly faster release from 2-8 weeks (Data shown mean±SEM, n=10 per group, p<0.05)...... 118
Figure 81. Showing the morphological change of the PLGA surface from initial and 8 weeks during degradation 1000X...... 118
Figure 82. Biocompatibility assessment via XTT assay of PLLA, PLGA, and PGPF films indirectly contacted with human dermal fibroblasts. Data shown mean±SD, n=3 per group. ISO standard required minimum viability noted with dashed line at 80%...... 121
Figure 83. Biocompatibility assessment via XTT assay of PLLA, PLGA, and PGPF films directly contacted with human dermal fibroblasts. Data shown mean±SD, n=3 per group. ISO standard required minimum viability noted with dashed line at 80%...... 121
Figure 84. Fluorescent microscopy images of human dermal fibroblasts directly contacted with PLLA, PLGA, and PGPF using live/dead stain...... 123
Figure 85. Live (green) and dead (red) cell counts from fluorescent microscopy images of human dermal fibroblasts seeded on PLLA (left), PLGA (middle), and PGPF (right) films. Data shown mean±SD, n=4 per group...... 123
Figure 86. Viability of human dermal fibroblasts directly seeded on PLLA (black), PLGA, (blue), and PGPF (green) films. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%...... 124
Figure 87. Viability of human dermal fibroblasts in direct contact with PLGA/PGFA PTNPs via XTT assay. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%...... 125
Figure 88. Fluorescence microscopy images of human dermal fibroblasts directly contacted with varying concentrations of PTNPs using live/dead stain...... 125
Figure 89. Live (green) and dead (red) cell counts from fluorescence microscopy images of human dermal fibroblasts in direct contact with PLGA/PGFA PTNPs. Data shown mean±SD, n=4 per group...... 126
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Figure 90. Viability of human dermal fibroblasts directly contact with PLGA/PGFA PTNPs via fluorescence microscopy. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%...... 126
Figure 91. Biocompatibility assessment via XTT assay of PLLA (black) and PGPF (green) films directly contacted with tracheal epithelial cells. Data shown mean±SD, n=3 per group. ISO standard required minimum viability noted with dashed line at 80%. .. 128
Figure 92. Fluorescence microscopy images of tracheal epithelial cells directly contacted with PLLA and PGPF using live/dead stain...... 128
Figure 93. Live (green) and dead (red) cell counts from fluorescence microscopy images of tracheal epithelial cells seeded on PLLA (left) and PGPF (right) films. Data shown mean±SD, n=3 per group...... 129
Figure 94. Viability of tracheal epithelial cells directly seeded on PLLA (black) and PGPF (green) films. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%...... 129
Figure 95. Viability of tracheal epithelial cells in direct contact with PLGA/PGFA PTNPs via XTT assay. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%...... 130
Figure 96. Fluorescence microscopy images of tracheal epithelial cells directly contacted with varying concentrations of PTNPs using live/dead stain...... 131
Figure 97. Live (green) and dead (red) cell counts from fluorescence microscopy images of tracheal epithelial cells in direct contact with PLGA/PGFA PTNPs. Data shown mean±SD, n=4 per group...... 131
Figure 98. Viability of tracheal epithelial cells in direct contact with PLGA/PGFA PTNPs via fluorescent microscopy. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%...... 132
Figure 99. Biocompatibility assessment of coating formulations with tracheal epithelial cells via XTT assay. No material control shown as far left bar in each polymer concentration group with the polymer only control shown as horizontal striped bar in each group. Nanoparticle concentration increases from left to right in each group as indicated. Data shown mean±SD, n=3 per group. ISO standard required minimum viability noted with dashed line at 80%...... 133
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Figure 100. Viability of tracheal epithelial cells directly seeded on various PGPF with PTNP composite coating formulations. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%...... 134
Figure 101. The cell supernatant concentrations of TNF-α after LPS stimulation and 24 hr exposure to treatment with dexamethasone. Group A (control) received no LPS or treatment and group B received LPS stimulation only. Groups C-F were treated with PTNPs and groups G-J were treated with free dexamethasone in the media as described in Table 2. Dashed line indicates control cytokine concentration. Statistical significance (p<0.05) from control group is noted with asterisk. Data shown mean±SD, n=3 per group...... 136
Figure 102. The cell supernatant concentrations of IL-1β after LPS stimulation and 24 hr exposure to treatment with dexamethasone. Group A (control) received no LPS or treatment and group B received LPS stimulation only. Groups C-F were treated with PTNPs and groups G-J were treated with free dexamethasone in the media as described in Table 2. Dashed line indicates control cytokine concentration. Statistical significance (p<0.05) from control group is noted with asterisk. Data shown mean±SD, n=3 per group...... 137
Figure 103. Standard plate and curve for each bacteria strain. Data on standard curve shown as each replicate and linear regression equation with 95% confidence interval (n=3)...... 138
Figure 104. Biologically active concentrations (BACs) of ciprofloxacin for 14 days from sensitivity assays. (A) Escherichia coli BAC with minimum inhibitory concentration (MIC) of 2 ng/μl. (B) Klebsiella pneumoniae BAC with MIC 8 ng/μl. (C) Moraxella catarrhalis BAC with MIC 2 ng/μl. (D) Pseudomonas aeruginosa BAC with MIC of 30 ng/μl. Statistical significance of student’s T-test (p<0.05) noted with asterisk. Data shown mean±SEM, n=9 per group...... 139
Figure 105. Cytotoxicity scale according to ISO 10993-5 Tests for Cytotoxicity: In vitro methods...... 167
Figure 106. Showing the removal of the tracheal rings in a New Zealand White Rabbit with preservation of inner mucosal layer...... 193
Figure 107. Malacic region in rabbit as indicated by arrows using (Top Left) 3D reconstruction of CT scan slices, (Top Right) X-ray, and (Bottom Left) post-study excision. (Bottom Right) Histological section of malacic region shows irregularity of tracheal layers and collapse of lumen...... 194
Figure 108. Successful implantation of metal and DH BDS stent were confirmed via bronchoscope. After one week the metal stent shows signs of inflammation while the bioresorbable stent does not...... 195
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Figure 109. Histological sections of metal and DH BDS stent in trachea. Tracheal epithelium is disrupted and fibrotic cells more prominent in metal stented specimen than DH BDS...... 196
Figure 110. Differential Scanning Calorimetry setup with a linear temperature scan rate.263 ...... 205
Figure 111. Typical Differential Scanning Calorimetry curve.263 ...... 206
Figure 112. Showing (A) Shear rate versus shear stress relationship and (B) apparent viscosity versus shear rate for Newtonian and Non-Newtonian fluids.265 ...... 210
Figure 113. Yield stress measurement of a cosmetic cream from a stress sweep experiment.153 ...... 211
Figure 114. Assessment of critical strain level of a water-based acrylic coating from a strain sweep experiment.153 ...... 212
Figure 115. Frequency sweep test on simulated rocket propellant material. At high strain amplitudes (blue) G’’>G’ and the material behaves more like a fluid and a low strains G’>G’’ and the material behaves more like a solid.153 ...... 213
Figure 116. Time-dependent “creep” test of cookie dough.153 ...... 213
Figure 117. Dexamethasone standard chromatograms via HPLC...... 221
Figure 118. Dexamethasone calibration curve. Data shown mean±SD and calibration curve with 95% confidence interval indicated by dotted line...... 222
Figure 119. Ciprofloxacin chromatograms from highly acidic mobile phase pH (A) 1.7, (B) 1.8, (C) 1.9...... 224
Figure 120. Ciprofloxacin chromatograms from moderately acidic mobile phase pH (A) 3.1 and (B) pH 5.05...... 224
Figure 121. Ciprofloxacin standard chromatograms via HPLC...... 226
Figure 122. Ciprofloxacin calibration curve. Data shown mean±SD and calibration curve with 95% confidence interval indicated by dotted line...... 227
Figure 123. Standard calibration curve for simultaneous release of ciprofloxacin (blue) and dexamethasone (black). Data shown mean±SD with ciprofloxacin nonlinear
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regression and dexamethasone linear regression including 95% confidence interval indicated by dotted line...... 229
Figure 124. Standard curves from 24 hour inoculation pre-optimization. Data shown mean±SD, n=3...... 250
Figure 125. Standard curves from 48 hour inoculation pre-optimization. Data shown mean±SD, n=3...... 250
Figure 126. Linear region of standard curve from 24 hour inoculation pre-optimization. Data shown with each replicate and linear regression model with 95% confidence band...... 251
Figure 127. Linear region of standard curve from 48 hour inoculation pre-optimization. Data shown with each replicate and linear regression model with 95% confidence band...... 252
Figure 128. E. coli standard ciprofloxacin standard...... 253
Figure 129. Linear region of Escherichia coli standard curve using average IZL. Data shown mean with linear regression linear and 95% confidence interval, n=3...... 254
Figure 130. Replicate one of Escherichia coli ciprofloxacin sensitivity via disk diffusion method...... 255
Figure 131. Replicate two of Escherichia coli ciprofloxacin sensitivity via disk diffusion method...... 256
Figure 132. Replicate three of Escherichia coli ciprofloxacin sensitivity via disk diffusion method...... 257
Figure 133. Power Law regression fit of Day 2 Escherichia coli inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 258
Figure 134. Escherichia coli ciprofloxacin BAC is not significantly different on Day 2 between PCFA and PGCFA...... 259
Figure 135. Power Law regression fit of Day 4 Escherichia coli inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 260
Figure 136. Escherichia coli ciprofloxacin BAC is not significantly different on Day 4 between PCFA and PGCFA...... 261
xxiii
Figure 137. Power Law regression fit of Day 7 Escherichia coli inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 262
Figure 138. Escherichia coli ciprofloxacin BAC is significantly different on Day 7 between PCFA and PGCFA...... 263
Figure 139. Power Law regression fit of Day 14 Escherichia coli inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 264
Figure 140. Escherichia. coli ciprofloxacin BAC is significantly different on Day 14 between PCFA and PGCFA...... 265
Figure 141. Klebsiella pneumoniae ciprofloxacin standard...... 266
Figure 142. Linear region of Klebsiella pneumoniae standard curve using average IZL. Data shown mean with linear regression linear and 95% confidence interval, n=3...... 267
Figure 143. Replicate one of Klebsiella pneumoniae ciprofloxacin sensitivity via disk diffusion method...... 268
Figure 144. Replicate two of Klebsiella pneumoniae ciprofloxacin sensitivity via disk diffusion method...... 269
Figure 145. Replicate three of Klebsiella pneumoniae ciprofloxacin sensitivity via disk diffusion method...... 270
Figure 146. Power Law regression fit of Day 2 Klebsiella pneumoniae inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 271
Figure 147. Klebsiella pneumoniae ciprofloxacin BAC is significantly different on Day 2 between PCFA and PGCFA...... 272
Figure 148. Power Law regression fit of Day 4 Klebsiella pneumoniae inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 273
Figure 149. Klebsiella pneumoniae ciprofloxacin BAC is significantly different on Day 4 between PCFA and PGCFA...... 274
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Figure 150. Power Law regression fit of Day 7 Klebsiella pneumoniae inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 275
Figure 151. Klebsiella pneumoniae ciprofloxacin BAC is significantly different on Day 7 between PCFA and PGCFA...... 276
Figure 152. Power Law regression fit of Day 14 Klebsiella pneumoniae inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 277
Figure 153. Klebsiella pneumoniae ciprofloxacin BAC is significantly different on Day 14 between PCFA and PGCFA...... 278
Figure 154. Pseudomonas aeruginosa ciprofloxacin standard...... 279
Figure 155. Linear region of Pseudomonas aeruginosa standard curve using average IZL. Data shown mean with linear regression linear and 95% confidence interval, n=3. .... 280
Figure 156. Replicate one of Pseudomonas aeruginosa ciprofloxacin sensitivity via disk diffusion method...... 281
Figure 157. Replicate two of Pseudomonas aeruginosa ciprofloxacin sensitivity via disk diffusion method...... 282
Figure 158. Replicate three of Pseudomonas aeruginosa ciprofloxacin sensitivity via disk diffusion method...... 283
Figure 159. Power Law regression fit of Day 2 Pseudomonas aeruginosa inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 284
Figure 160. Pseudomonas aeruginosa ciprofloxacin BAC is significantly different on Day 2 between PCFA and PGCFA...... 285
Figure 161. Power Law regression fit of Day 4 Pseudomonas aeruginosa inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 286
Figure 162. Pseudomonas aeruginosa ciprofloxacin BAC is significantly different on Day 4 between PCFA and PGCFA...... 287
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Figure 163. Power Law regression fit of Day 7 Pseudomonas aeruginosa inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 288
Figure 164. Pseudomonas aeruginosa ciprofloxacin BAC is significantly different on Day 14 between PCFA and PGCFA...... 289
Figure 165. Power Law regression fit of Day 14 Pseudomonas aeruginosa inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 290
Figure 166. Pseudomonas aeruginosa ciprofloxacin BAC is significantly different on Day 14 between PCFA and PGCFA...... 291
Figure 167. Moraxella catarrhalis ciprofloxacin standard...... 292
Figure 168. Linear region of Moraxella catarrhalis standard curve using average IZL. Data shown mean with linear regression linear and 95% confidence interval, n=3. .... 293
Figure 169. Replicate one of Moraxella catarrhalis ciprofloxacin sensitivity via disk diffusion method...... 294
Figure 170. Replicate two of Moraxella catarrhalis ciprofloxacin sensitivity via disk diffusion method...... 295
Figure 171. Replicate three of Moraxella catarrhalis ciprofloxacin sensitivity via disk diffusion method...... 296
Figure 172. Power Law regression fit of Day 2 Moraxella catarrhalis inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 297
Figure 173. Moraxella catarrhalis ciprofloxacin BAC is significantly different on Day 2 between PFA and PGCFA and PCFA and PGCFA. PFA and PCFA are not significantly different...... 298
Figure 174. Power Law regression fit of Day 4 Moraxella catarrhalis inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 299
Figure 175. Moraxella catarrhalis ciprofloxacin BAC is significantly different on Day 4 between PFA and PCFA and PGCFA. PCFA and PGCFA are not significantly different...... 300
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Figure 176. Power Law regression fit of Day 7 Moraxella catarrhalis inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 301
Figure 177. Moraxella catarrhalis ciprofloxacin BAC is significantly different on Day 7 between PFA and PCFA and PCFA and PGCFA. PFA and PGCFA are not significantly different...... 302
Figure 178. Power Law regression fit of Day 14 Moraxella catarrhalis inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3...... 303
Figure 179. Moraxella catarrhalis ciprofloxacin BAC is significantly different on Day 14 between PFA and PCFA and PCFA and PGCFA. PFA and PGCFA are not significantly different...... 304
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LIST OF TABLES
Table 1. PLGA particle group identification...... 45
Table 2. Particle experimental groups for novel distillation technique...... 46
Table 3. Particle experimental groups for novel PLGA/PGFA theranostic particles. .... 48
Table 4. Temperature sweeps for DSC samples...... 51
Table 5. Optimization parameters for XTT assay...... 61
Table 6. Concentration standards for bacterial sensitivity assay...... 66
Table 7. Known volume of degradation product solution pipetted for sensitivity assay. 68
Table 8. Morphological characteristics of resulting nanoparticles for modified solvent displacement technique. Data shown mean±SD, n= 100...... 70
Table 9. Particle characteristics from polymer drug ratio sensitivity study. Data shown mean±SD...... 72
Table 10. Coefficients of linear regression for Corbion Purac® 50:50 particles. Linear regression equation: y(x) = Ax+B. Correlation coefficient shown as r2...... 73
Table 11. Coefficients of linear regression for Corbion Purac® 75:25 particles. Linear regression equation: y(x) = Ax+B. Correlation coefficient shown as r2...... 75
Table 12. Characterization of Corbion Purac® PLGA 50:50 particles: control (A), distillation with 1 additional minute of heating (A2), distillation with 15 additional minutes of heating, and hybrid particles blended with PGFA (A4). Data shown mean±SD ...... 77
Table 13. Characterization of Corbion Purac® PLGA 75:25 particles: control (B), distillation with 1 additional minute of heating (B2), distillation with 15 additional minutes of heating (B3), and hybrid particles blended with PGFA (B4). Data shown mean±SD...... 79
Table 14. Characterization of Evonik Resomer® PLGA 50:50 particles: control (C), distillation with 1 additional minute of heating (C2), distillation with 15 additional minutes of heating (C3), and hybrid particles blended with PGFA (C4). Data shown mean±SD...... 81
xxviii
Table 15. Characterization of Evonik Resomer® PLGA 75:25 particles: control (D), distillation with 1 additional minute of heating (D2), distillation with 15 additional minutes of heating (D3), and hybrid particles blended with PGFA (D4). Data shown mean±SD...... 83
Table 16. Characterization of PLGA/PGFA nanoparticles. Data shown mean±SD...... 85
Table 17. Mechanical properties of annealed fibers (Data shown mean±SEM, n=20). 113
Table 18. Control fiber DSC results. Data shown mean±SD, n=20...... 113
Table 19. Film density, volume, and porosity measurements...... 115
Table 20 Mechanical properties of PLLA fiber and coated PLLA fiber (Data shown mean±SEM, n=20 per group,* indicates p<0.05)...... 117
Table 21 Fiber and coating diameter at time intervals (Data shown mean±SEM, n=5 per group, p<0.05)...... 119
Table 22. Inflammation assessment treatment groups and ELISA cytokine results. Data shown mean±SD, n=3 per group. Asterisk indicates statistically significant from control (p<0.05)...... 135
Table 23 Mathematical equations of the models used to characterize cumulative dexamethasone release...... 215
Table 24 Interpretation of diffusion exponent for drug release from polymeric matrices.269 ...... 217
Table 25. Dexamethasone calibration standards. Data shown with average results for each standard...... 220
Table 26 Ciprofloxacin calibration standards. Data shown with average results for each standard...... 225
Table 27. Ciprofloxacin calibration results from simultaneous detection. Data shown with average results for each standard...... 228
Table 28. Dexamethasone calibration results from simultaneous detection. Data shown with average results for each standard...... 229
Table 29. 96-well plate arrangement for XTT assay calibration. Values are cells per well...... 231
Table 30. Raw data absorbance reading at 450 nm...... 231
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Table 314. Raw data absorbance reading at 475 nm...... 231
Table 325. Raw data absorbance reading at 500 nm...... 232
Table 33. Raw data absorbance reading at 630 nm...... 232
Table 34. Raw data absorbance reading at 660 nm...... 232
Table 35. Raw data absorbance reading at 690 nm...... 232
Table 36. Raw data absorbance reading at 450 nm...... 233
Table 37. Raw data absorbance reading at 475 nm...... 233
Table 38.Raw data absorbance reading at 500 nm...... 233
Table 39. Raw data absorbance reading at 630 nm...... 233
Table 40. Raw data absorbance reading at 660 nm...... 234
Table 41. Raw data absorbance reading at 690 nm...... 234
Table 42. Raw data absorbance reading at 450 nm...... 234
Table 43. Raw data absorbance reading at 475 nm...... 234
Table 44.Raw data absorbance reading at 500 nm...... 235
Table 45. Raw data absorbance reading at 630 nm...... 235
Table 46. Raw data absorbance reading at 660 nm...... 235
Table 47. Raw data absorbance reading at 690 nm...... 235
Table 48. Calculated specific absorbance using raw data readings from 450 nm and 630 nm...... 236
Table 49. Calculated specific absorbance using raw data readings from 475 nm and 660 nm...... 236
Table 50. Calculated specific absorbance using raw data readings from 500 nm and 690 nm...... 237
xxx
Table 51. Calculated specific absorbance using raw data readings from 450 nm and 630 nm...... 237
Table 52. Calculated specific absorbance using raw data readings from 475 nm and 660 nm...... 238
Table 53. Calculated specific absorbance using raw data readings from 500 nm and 690 nm...... 238
Table 54. Calculated specific absorbance using raw data readings from 450 nm and 630 nm...... 239
Table 55. Calculated specific absorbance using raw data readings from 475 nm and 660 nm...... 239
Table 56. Calculated specific absorbance using raw data readings from 500 nm and 690 nm...... 240
Table 57. Raw data absorbance reading at 450 nm...... 240
Table 58. Raw data absorbance reading at 475 nm...... 240
Table 59. Raw data absorbance reading at 500 nm...... 241
Table 60. Raw data absorbance reading at 630 nm...... 241
Table 61. Raw data absorbance reading at 660 nm...... 241
Table 62. Raw data absorbance reading at 690 nm...... 241
Table 63. Raw data absorbance reading at 450 nm...... 242
Table 64. Raw data absorbance reading at 475 nm...... 242
Table 65.Raw data absorbance reading at 500 nm...... 242
Table 66. Raw data absorbance reading at 630 nm...... 243
Table 67. Raw data absorbance reading at 660 nm...... 243
Table 68. Raw data absorbance reading at 690 nm...... 243
Table 69. Raw data absorbance reading at 450 nm...... 244
Table 70. Raw data absorbance reading at 475 nm...... 244
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Table 71.Raw data absorbance reading at 500 nm...... 244
Table 72. Raw data absorbance reading at 630 nm...... 244
Table 73. Raw data absorbance reading at 660 nm...... 245
Table 74. Raw data absorbance reading at 690 nm...... 245
Table 75. Calculated specific absorbance using raw data readings from 450 nm and 630 nm...... 245
Table 76. Calculated specific absorbance using raw data readings from 475 nm and 660 nm...... 246
Table 77. Calculated specific absorbance using raw data readings from 500 nm and 690 nm...... 246
Table 78. Calculated specific absorbance using raw data readings from 450 nm and 630 nm...... 247
Table 79. Calculated specific absorbance using raw data readings from 475 nm and 660 nm...... 247
Table 80. Calculated specific absorbance using raw data readings from 500 nm and 690 nm...... 248
Table 81. Calculated specific absorbance using raw data readings from 450 nm and 630 nm...... 248
Table 82. Calculated specific absorbance using raw data readings from 475 nm and 660 nm...... 249
Table 83. Calculated specific absorbance using raw data readings from 500 nm and 690 nm...... 249
Table 84. Linear regression analysis of 24 hour inoculation pre-optimization...... 251
Table 85. Linear regression analysis of 48 hour inoculation pre-optimization...... 252
Table 86. Escherichia coli standard measurements...... 253
Table 87. Measured Escherichia coli IZL on Day 2 of polymer degradation...... 258
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Table 88. Calculated ciprofloxacin BAC of Escherichia coli on Day 2 of polymer degradation...... 259
Table 89. Measured Escherichia coli IZL on Day 4 of polymer degradation...... 260
Table 90. Calculated ciprofloxacin BAC of Escherichia coli on Day 4 of polymer degradation...... 261
Table 91. Measured Escherichia coli IZL on Day 7 of polymer degradation...... 262
Table 92. Calculated ciprofloxacin BAC of Escherichia coli on Day 7 of polymer degradation...... 263
Table 93. Measured Escherichia coli IZL on Day 14 of polymer degradation...... 264
Table 94. Calculated ciprofloxacin BAC of Escherichia coli on Day 14 of polymer degradation...... 265
Table 95. Klebsiella pneumoniae standard measurements...... 266
Table 96. Measured Klebsiella pneumoniae IZL on Day 2 of polymer degradation. ... 271
Table 97. Calculated ciprofloxacin BAC of Klebsiella pneumoniae on Day 2 of polymer degradation...... 272
Table 98. Measured Klebsiella pneumoniae IZL on Day 4 of polymer degradation. ... 273
Table 99. Calculated ciprofloxacin BAC of Klebsiella pneumoniae on Day 4 of polymer degradation...... 274
Table 100. Measured Klebsiella pneumoniae IZL on Day 7 of polymer degradation. . 275
Table 101. Calculated ciprofloxacin BAC of Klebsiella pneumoniae on Day 7 of polymer degradation...... 276
Table 102. Measured Klebsiella pneumoniae IZL on Day 14 of polymer degradation. 277
Table 103. Calculated ciprofloxacin BAC of Klebsiella pneumoniae on Day 14 of polymer degradation...... 278
Table 104. Pseudomonas aeruginosa standard measurements...... 279
Table 105. Measured Pseudomonas aeruginosa IZL on Day 2 of polymer degradation...... 284
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Table 106. Calculated ciprofloxacin BAC of Pseudomonas aeruginosa on Day 2 of polymer degradation...... 285
Table 107. Measured Pseudomonas aeruginosa IZL on Day 4 of polymer degradation...... 286
Table 108. Calculated ciprofloxacin BAC of Pseudomonas aeruginosa on Day 4 of polymer degradation...... 287
Table 109. Measured Pseudomonas aeruginosa IZL on Day 7 of polymer degradation...... 288
Table 110. Calculated ciprofloxacin BAC of Pseudomonas aeruginosa on Day 7 of polymer degradation...... 289
Table 111. Measured Pseudomonas aeruginosa IZL on Day 14 of polymer degradation...... 290
Table 112. Calculated ciprofloxacin BAC of Pseudomonas aeruginosa on Day 14 of polymer degradation...... 291
Table 113. Moraxella catarrhalis ciprofloxacin standard measurements...... 292
Table 114. Measured Moraxella catarrhalis IZL on Day 2 of polymer degradation. .. 297
Table 115. Power Law coefficients from regression fit (y(x) = AxB) of Day 2 Moraxella catarrhalis inhibition zone length dependent on volume...... 297
Table 116. Calculated ciprofloxacin BAC of Moraxella catarrhalis on Day 2 of polymer degradation...... 298
Table 117. Measured Moraxella catarrhalis IZL on Day 4 of polymer degradation. .. 299
Table 118. Power Law coefficients from regression fit (y(x) = AxB) of Day 4 Moraxella catarrhalis inhibition zone length dependent on volume...... 299
Table 119. Calculated ciprofloxacin BAC of Moraxella catarrhalis on Day 4 of polymer degradation...... 300
Table 120. Measured Moraxella catarrhalis IZL on Day 7 of polymer degradation. .. 301
Table 121. Power Law coefficients from regression fit (y(x) = AxB) of Day 7 Moraxella catarrhalis inhibition zone length dependent on volume...... 301
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Table 122. Calculated ciprofloxacin BAC of Moraxella catarrhalis on Day 7 of polymer degradation...... 302
Table 123. Measured Moraxella catarrhalis IZL on Day 14 of polymer degradation...... 303
Table 124. Power Law coefficients from regression fit (y(x) = AxB) of Day 7 Moraxella catarrhalis inhibition zone length dependent on volume...... 303
Table 125. Calculated ciprofloxacin BAC of Moraxella catarrhalis on Day 14 of polymer degradation...... 304
xxxv
LIST OF APPENDICES
A. NANOPARTICLE CHARACTERIZATION THEORIES AND TECHNIQUES
...... 192
B. POLYMER CHARACTERIZATION: DIFFERENTIAL SCANNING
CALORIMETRY THEORY ...... 196
C. POLYMER CHARACTERIZATION: RHEOLOGY ...... 200
D. DRUG RELEASE THEORY AND MATHEMATICAL MODELING ...... 207
E. HIGH PRESSURE LIQUID CHROMATOGRAPHY STANDARDS ...... 212
F. XTT ASSAY STANDARD CALIBRATION DETERMINATION ...... 222
G. BACTERIAL SENSITIVITY ASSAY COMPLETE ANALYSIS ...... 242
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LIST OF DEFINITIONS
1H-NMR – Proton Nuclear Magnetic Resonance Imaging
2E – 2-Butenedioic Acid
BAC – Biologically Active Concentration
BSA – Bovine Serum Albumin
BVS – Bioresorbable Vascular Scaffold
CDCL3 – Deuterated Chloroform
CT – Computed Tomography
Da – Daltons
DCM – Dichloromethane
DH BDS – Double Opposed Helical bioresorbable stent
DLE – Drug Loading Efficiency
DLS – Dynamic Light Scattering
ELISA – Enzyme Linked Immunosorbent Assay
EthD-1 – Ethidium Homodimer-1
FDA – Food and Drug Administration
FTIR – Fourier Transform Infrared Spectroscopy
Gd – Gadolinium
HA – Hyaluronic Acid
HCL – Hydrochloric Acid
HDF – Human Dermal Fibroblasts
HPLC – High Pressure Liquid Chromatography
HSA – Human Serum Clbumins
xxxvii
ISO- International Standards Organization kDa – Kilodalton kV – Kilovolts
LB – Luria Broth
LPS – Lipopolysaccharides mean±SD – Mean ± Standard Deviation mean±SEM – Mean ± Standard Error of the Mean
MIC – Minimum Inhibitory concentration
MMA – Methyl Methacrylate
Mn – Manganese
MNP – Magnetic Nanoparticles
MRI – Magnetic Resonance Imaging
Mw – Molecular Weight
NCCLS – National Committee of Clinical Laboratory Standars
NP-PLGA – Nonporous PLGA Coating
OCP – Office of Combination Products
PA – Polyamide
PCFA – Poly(Ciprofloxacin Fumaric Acid)
PCL – Poly(Caprolactone)
PDLLA – Poly-D-L-Lactide
PE – Polyethylene
PFA – Poly(Fumaric Acid)
PG – Propylene Glycol
xxxviii
PGA – Poly(Glycolic Acid)
PGCFA- Poly(Gadodiamide Ciprofloxacin Fumaric Acid)
PGFA – Poly(Gadodiamide Fumaric Acid)
PGPF – Poly(Gadodiamide Propylene Fumarate)
PHBV – Polyhydroxybutyrate-co-Hydroxyvalerate
PKIFA – Poly(Potassium Iodide Fumraic Acid)
PLA – Poly(Lactic Acid)
PLGA – Poly(Lactic-co-Glycolic Acid)
PLLA – Poly(L-Lactic Acid)
PMMA – Poly(Methyl Methacrylate)
PPF – Poly(Propylene Fumarate)
P-PLGA – Porous PLGA Coating ppm – Parts per Million
PS – Polystyrene
PTNP – Polymeric Theranostic Nanoparticle
RAW 264.7 - Mouse Macrophages
RI – Refractive Index
RPM – Revolutions per Minute
SEM – Scanning Electron Microscopy
SPIO – Super Paramagnetic Iron Oxide
TEC – Tracheal Epithelial Cells
TFA – Trifluoroacetic Acid
Tg – Glass Transition Temperature
xxxix
THF – Tetrahydrofuran
UV – Ultraviolet Light v/v – Volume to Volume
VEGF – Vascular Endothelial Growth Factor wt/vol% - Weight to Volume Percent wt/wt% - Weight to Weight Percent
XTT - 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanide
xl
CHAPTER ONE Introduction
1.1 BACKGROUND
Medical Definitions and Diagnosis
Tracheomalacia can be a life-threatening condition in pediatric patients, especially those treated in intensive care. Tracheomalacia is a pathological condition characterized by flaccidity of the supporting tracheal cartilage, widening of the posterior membranous wall, and a reduced anterior-posterior airway caliber. These characteristics lead to tracheal collapse when extraluminal pressure exceeds intraluminal pressure leading to airway obstruction. This disease state can be very difficult to manage due to the difficultly in weaning off mechanical ventilation and its association with other pulmonary issues.1 Patients born with tracheomalacia may have other congenital and acquired abnormalities such as heart defects, developmental delay, gastroesophageal reflux, and risk of recurrent pulmonary infections due to potential food inhalation and prior clearance of respiratory secretions.2-3
Once tracheomalacia is suspected, several imaging techniques are employed to ensure correct diagnosis. First non-invasive imaging is used, an X-ray of the chest and neck. If suspected collapse is observed a computed tomography (CT) scan may be obstained with inhalation and exhalation of the patient. Scans will look for collapse of the trachea during expiration; collapse during inhalation may not be observed. Pulmonary function test may then be ordered (depending on age of patient) to assess airflow and finally a bronchoscopy will be performed. During the bronchoscopy, a narrow tube with a camera on the end in inserted into the airway to observe physical characteristics of the
1 2 airway walls. It is preferable to have the patient spontaneously breathing, as this allows for better visualization of the dynamic airway collapse.
Tracheal Anatomy and Tracheomalacia Pathophysiology
The trachea is a short, flexible tube that serves as a conduit for air as well as assisting in conditioning of inspired air. The trachea extends from the larynx to the middle of the thorax where it then divides into two primary bronchi. The lumen of the trachea remains open due to the arrangement of ~20 hyaline cartilage C-shaped rings arranged in series. More specifically, the wall of the trachea is composed of four definable layers: adventitia, cartilaginous layer, submuscosa, and mucosa.4 Adventitia is composed of connective tissue that binds the trachea to adjacent structures. The cartilaginous layer contains the C-shape cartilage rings for structural support. Bundles of smooth muscle and fibroelastic tissue known as the trachealis muscle bridge the gaps between the cartilage rings providing flexibility and support. The submucosa is composed of slightly denser connective tissue aiding in the support of the inner most layer the mucosa. The mucosa is unique; composed of ciliated, pseudostratified epithelium and an elastic, fiber-rich lamina propia. Like much of the respiratory epithelium in other parts of the conducting airway, ciliated cells provide a sweeping motion in conjunction with mucous secreting goblet cells generating a “mucociliary escalator” protecting the lungs from inhaled small particulates.4
Tracheomalacia clinically is presented in three types: 1) congenital or intrinsic tracheal abnormalities, 2) extrinsic defects or anomalies, and 3) acquired.5 Type 1 tracheomalacia is present at birth and can be associated with a tracheoesophageal fistula or esophageal atresia. Tracheomalacia associated with extrinsic compression (such as
3 vascular ring deformities) is referred to a Type 2. Type 3 tracheomalacia acquired over time; it can occur with long-term intubation, chronic tracheal or inflammatory conditions, or other intra-airway irritation or inflammation.
Tracheomalacia Epidemiology and Treatment Options
The frequency of tracheomalacia still remains unclear. Globally, the prevalence of primary (congenital) airway malacia is 1:2,100 and secondary (acquired) airway malacia is 3:64.6 Most cases of tracheomalacia are associated with developmental defects and are therefore primarily seen in infants and young children. There are no known sex or race predilections known. Morbidity and mortality are considered low due to many cases correcting themselves overtime with development however outcomes are highly age dependent. Nonsurgical therapies are sufficient treatment for most cases of non- congenital tracheomalacia. These include air humidification, anti-inflammatory and/or antibacterial agents, and growth correction over time. More severe cases require surgical intervention.
Currently, the modalities of treatment for tracheomalacia include positive pressure ventilation, surgical resection of the affected segment, external splinting, tracheopexy, aortopexy, or stenting.7-11 Stenting has the greatest potential for successful treatment. Airway stents have been rather controversial in regards to their success at internal reinforcement and biocompatibility.12-14 As compared to a normal trachea (Figure
1A), tissue granulation of the tracheal wall (Figure 1B), fibrosis, high inflammatory response (Figure 1C), perforation, migration, and death by secondary surgery have been the most commonly reported complications with stenting.15-22 Other reported issues include over exuberant scar formation, accumulation of inflammatory cells surrounding
4 the implanted device, and high infection risk due to the device being in contact with inhaled air.23-26 A stent that provides structural support to enable transition out of intensive care that is biocompatible with the tracheal wall would be ideal.
Figure 1. Showing (A) H&E stain of trachea at a normal state,27 (B) histological section (H&E stain) of trachea indicating granulation of the wall,28 and (C) bronchoscopy of inflamed trachea state. 29
Tracheal Stenting and Complications
For years Montgomery® Safe T-Tubes, the Dumon stent, and some metallic stents such as the Palmaz stent have been used for tracheal stenting (Figure 2).30-32
Figure 2. Showing common pediatric tracheal stents: (A) Montgomery® Sate T-Tube,33 (B) Novatech DumonTM stents,34 and (C) Palmaz® Genesis® stent.15
The Montgomery® Safe T-Tube is composed of polyvinyl chloride (PVC) and is available radiopaque or clear.33 The diameter range for the T-Tube is 6-16 mm and 18 mm associated with a particular length of the long and short branch tube segments. They
5 are available in multiple lengths to meet patient dimensions. It is designed to maintain an adequate airway as well as provide support in the stenotic trachea after reconstitution or reconstruction. Their design incorporates a unique pattern of ridges and grooves along the extraluminal limb of the tube that allows a ring washer to be attached. Attaching the ring washer significantly reduces the possibility of accidental posterior displacement.35
Tapered ends help to minimize injury to the tracheal mucosa. Some clinicians have reported difficulty with insertion of the T-Tube and complications associated anesthetics.36
The Novatech DumonTM stents are made from implant-grade silicone suitable for long-term placement and feature a surface treatment to ensure the tube does not adhere to the trachel wall.34 The stent has a patented stud design to aid in preventing migration by natural fixing the stent between the cartilage rings. The ends of the stents are beveled to maximize airflow and prevent granulation tissue or crusting. They are available radiopaque or clear, and in a variety of shapes and sizes to accommodate patient dimensions. Some clinicians have reported tracheal wall damage and difficult removal of these stents after 3 months.37 A rigid bronchoscope or tracheostomy may be required for
Dumon stent removal.37
The Palmaz® Genesis® peripheral stent is a stainless steel balloon expandable stent that is laser cut. It is limited to expansion diameters of 5-8, 5-10, or 10-12 mm. The stent was initially designed for cardiovascular interventions. Clinicians have used this stent for the trachea due to is high radial strength. Several animal models including cat and dog were tested prior to clinical studies for pediatric use.15, 38 Difficulties with high inflammation and stent migration have been observed. For pediatric interventions, this
6 stent option proposes an even larger challenge in the fact that it is difficult to remove.
Clinicians have also been investigating using other self-expanding nitinol and balloon- expandable metal stents used in interventional cardiology as “off-label” options.39 These stent designs were not intended for pediatric use but designed and tested for adult diseases.17, 40-42 These stents are quite stiff and present delivery and retrieval difficulties for pediatric interventions.
1.2 BIORESORBABLE STENTS AND THEIR LIMITATIONS
Key Objectives for a Successful Drug-Eluting Stent
Three major criteria have been established to determine the overall success of a drug-eluting stent; deliverability, durability, and safety.43 First, a stent must be able to be delivered to the lesion site without loss or damage of the stent coating. The coating must have ample mechanical strength and resistance to avoid associated damage. Clinical practice, an increase in efficacy, safety, and success rate can be achieved when the stent is easy to deliver. Second, a stent must have a reliable manufacturing process and shelf life. This includes reproducibility of stent performance and maintenance of bioactivity of incorporated therapeutic agents. Having predictable durability can lead to minimization of late in-stent restenosis and thrombosis.44-45 Lastly, the stent must shorten the time needed for vessel healing and enhance regeneration of native cells safely. Healing and restoration of the vessel can occur more quickly and efficiently if the device has minimal negative interactions with the body. This includes safety of the delivery platform materials, their degradation products, and the controlled delivery of therapeutic agent(s).
Overall success of a bioresorbable drug-eluting stent requires that these three criteria be met at and after the implantation of the stent at the lesion site.
7
Bioresorbable Stents and Their Limitations
For cardiovascular applications, focus has shifted to the use of bioresorbable stents comprised of semicrystalline polymers. Efforts to make a stent composed of polymeric material started over two decades ago in response to the shortcomings of metallic stents. Most of the early polymeric stent designs failed due to the low molecular weight of the polymeric material comprising the stent. These low molecular weight polylactides were associated with an intense inflammatory neointimal response.46 Poly(L- lactic acid) (PLLA) offers a higher molecular weight to avoid this intense inflammatory response yet still provide enough mechanical strength and biodegradability to produce an effective stent. It is especially essential in pediatric stent applications to limit harsh inflammatory responses in growing patients. Metallic stents are permanent; a stent composed of PLLA might not only limit the negative inflammatory response associated with metallic stents but also provide a nonpermanent solution that is highly desirable for pediatric interventions.
The bioresorbable stent represents the next generation of stent design. Of the five commercially available resorbable stents, the bioabsorbable vascular scaffold (BVS)
(Abbott Vascular, Santa Clara, CA, USA) has demonstrated the most success in clinical trials in coronary arteries.47 This stent consists of a PLLA backbone coat with a PDLLA
(poly-D L-lactide) coating containing the antiproliferative agent everolimus. The mechanism of action of everolimus is to arrest the cell cycle at G1 phase ultimately inhibiting cell division and preventing cell hyperproliferation. In the ABSORB clinical trial, no instance of stent thrombosis was reported and only a small amount of intrastent neointimal growth were reported suggesting the success of everolimus in suppressing
8 neointimal formation.48 A major obstacle associated with bioabsorbable stents is maintaining integrity for a defined time frame at and after stent implantation, withstanding recoil and negative remodeling.43 Further, inflammation associated with the degradation of the stent can lead to neointimal formation and delayed healing. However, thus far a limited number of patients who have been treated with a bioresorbable stent have been reported to have stent thrombosis.43 The release of a therapeutic agent can enhance healing of the vessel lumen. Some of the available therapeutic agents have the ability to prevent or reduce restenosis and other perilous inflammatory response events.
This has been demonstrated in significantly reducing lumen loss associated with vessel recoil after balloon stenting.49
Proposed Stent Solution
At UT Southwestern, a novel stent, the double opposed helical bioresorbable stent (DH BDS), has been designed to manufacture larger diameters up to 16 mm diameter.50 In biocompatibility studies, the DH BDS design has minimal fibrin and platelet association.51 Some protein adhesion near weld joints have been observed, but fiber surface integrity remains intact with no potential impedance to blood flow.51 Most importantly, the double opposed-coil stent exhibited ample collapse pressure that would suffice for pediatric disease state models.50, 52 This stent design, combined with a coating that contains therapeutic agents, has the potential to be a superior bioresorbable intervention option for pediatric tracheomalacia.
Post-Surgical Tracheal Inflammation Timeline
In pediatric tracheomalacia, utilizing a coating on tracheal stents could help alleviate some of the common problems described after current stenting procedures.
9
These problems include excessive inflammation at the sight of implantation and aggressive bacterial infection. Drug-loaded coatings were introduced on cardiovascular stents to combat neointimal hyperplasia associated with bare metal stents.47 A coating would provide a more hospitable microenvironment and important therapeutic agents designed to promote epithelialization and mitigate the inflammatory response.
An important clinical problem facing surgeons of the trachea is overly exuberant scar formation leading to tracheal stenosis.26 The trachea undergoes three phases of healing after implantation of a stent. The first phase is the recovery of tracheal wall from destruction by the implantation. The second is the re-epithelization of the wall and epithelial coverage of the stent struts. In prior rabbit models, animals exhibited mucosal re-epithelialization and mucosal hypertrophy with much less squamous metaplasia at day
7.53 The abnormal squamous metaplastic surface exhibits diminished mucilliary function and is vulnerable to repetitive injury.26 By day 21, intact musoca was observed.53 The final phase is scar tissue formation.
During the early phases of healing, acute inflammatory cells such as lymphocytes and macrophages can be found around the stent struts. Also, because this device is in contact with inspired air, infection risk is increased due to airborne pathogen exposure.23-
25 In tracheomalacia, the acute onset of inflammation is estimated to be within one week after stent implantation with the chronic phase lasting up to three months, and some outlying cases lasting six months or more.
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1.3 BIORESORBABLE POLYMERS FOR DRUG DELIVERY
Poly(lactic-co-glycolic) acid
Poly(lactic-co-glycolic) acid (PLGA) is one of the most successfully used biodegradable copolymers for medical applications. This copolymer is Food and Drug
Administration (FDA) approved for use in human therapeutic devices due to its biodegradability and biocompatibility. PLGA is synthesized by random ring-opening co- polymerization of glycolic acid and lactic acid monomers via a catalyst. These monomers are linked via ester bonds in a linear chain resulting in an aliphatic polyester product.54
PLGA degrades by hydrolytic degradation leading to the two endogenous metabolite starting monomers, lactic acid and glycolic acid (Figure 3).
Figure 3. Hydrolytic degradation of PLGA.55
These metabolites provoke minimal systemic toxicity as these degradation products are easily metabolized in the body and used by cells in the Krebs cycle.56 PLGA is commercially available in a variety of molecular weights and monomer ratio compositions. PLGA is identified by its monomer composition; for example PLGA 50:50 is composed of 50% lactic acid and 50% glycolic acid. The degradation time PLGA is dependent upon this monomer ratio which can range from a few weeks to one year.57
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Poly(propylene fumarate)
Poly(propylene fumarate) (PPF) is a synthetic, unsaturated linear polyester which can be cross-linked through its fumarate double bonds and degraded by random hydrolytic scission of its ester groups.58 Its degradation products, propylene glycol and fumaric acid are biocompatible and readily removed from the body or used by cells in the citric acid cycle. Crosslinked PPF networks are FDA approved and impart high mechanical strength sufficient for use as bone replacement materials in orthopedic and dental applications.59-60 When an alternative synthesis is used and a low molecular weight polymer product is produced, a pre-polymer mixture of poly(fumaric acid) (PFA) and propylene glycol is formulated. PFA is a low viscosity liquid polyester. At low molecular weights, it has yet to be investigated for therapeutic agent delivery. This low molecular weight polymer will also degrade quickly, acting as an optimal platform to the one-week drug release component of the intended therapeutic window.
1.4 POLYMERIC PARTICLES FOR MEDICAL APPLICATIONS
Particle Formulation Methods
Formulation Techniques
To achieve long-lasting drug release, polymeric particles have been formulated.
Depending on the method of particle formulation, structural organization and inclusion of therapeutic agent differs. Most particle formulations fall into two major categories: polymerization reactions or preformed polymer manipulation. Polymerization reaction methods use a two-step process. In short, the first step is the creation of an emulsification solution and the second step is the formation of the particles by any means of crosslinking polymerization.61 Methods using preformed polymers tend to rely on a one-
12 step process, which does not necessarily require an emulsion. This method takes advantage of dispersing polymer chains during high agitation and removal of solvent utilizing various techniques, in order to formulate particles.62 The four most widely used techniques to formulate polymeric particles are emulsion diffusion, emulsion evaporation, salting out, and nanoprecipitation (also referred to as solvent diffusion or solvent displacement techniques).
Using an emulsion diffusion method, polymer is dissolved in an organic solvent
(phase) that must be partially miscible in water. Stirring with an aqueous solution that contains a surfactant emulsifies the organic phase. The diffusion of the organic solvent and the counter diffusion of water into the emulsion droplets induce the formation of polymeric particles.63 The advantages of this technique are that low toxicity organic solvents can be used and it requires low sheer stress for particle formation. No sonication or microfluidization is required. The disadvantages are that a large volume of water is necessary and size is highly sensitive to polymer concentration Also, only hydrophobic therapeutic agents can be captured in particles, and it is fairly time consuming.
When using an emulsion evaporation method, an emulsion of organic solution containing the polymer in an aqueous phase is generated followed by the evaporation of the organic solvent.64 Low toxicity organic solvents can be used in this technique as well.
Further, additives can be used to reduce particle diameter as well as both hydrophobic and hydrophilic therapeutic agents can be incorporated into the particles with minor technique modifications. The only major disadvantages of this method are that a high sheer stress is required for particle formation and final particle size is directly affected by the addition of therapeutic agents.
13
A salting out method dissolves polymer into a water miscible organic solvent
(such as acetone or tetrahydrofuran) and emulsifies it with strong mechanical shear stress in aqueous solution. The aqueous solution contains an emulsifier and a high concentration of salts, which are insoluble in the organic solvent. Unlike the emulsion diffusion method, particles are generated by the fast addition of pure water to the emulsion leading to migration of the organic solvent to the aqueous phase not by the diffusion of water.65 Particles and salts are then separated by centrifugation or cross-flow filtration. The advantages of this method are that the technique is quick and only normal stirring is required to generate particles. The main disadvantage to this method is that a purification step is required to remove the salting out agent. Other minor disadvantages are that only hydrophobic therapeutic agents can be incorporated into particles and the solvents used are not highly toxic but are highly volatile and potentially explosive.
In a nanoprecipitation method, polymer and therapeutic agents are dissolved in a polar, water miscible solvent. The solution is poured in a controlled fashion into the aqueous surfactant solution. Particles are then formed instantaneously by rapid solvent diffusion while mechanical stirring or sonication applies high sheer stress usually. The advantages of this method are, low toxicity solvents cane used and therapeutic agents both hydrophobic and hydrophilic can be incorporated. Furthermore, particle size can be controlled via adjustment of additive, polymer, or surfactant concentrations. The disadvantages of this method are that therapeutic agent must be highly soluble in polar solvent and drug loading efficiency can be lower for hydrophilic drugs. A solvent displacement method (more specific nanoprecipitation method) is a convenient, reproducible, fast and economic one-step manufacturing process for the preparation of
14 monodisperse, polymeric particles and therefore will be used.66 That being said, all particle formulation techniques still exhibit some pitfalls; difficulty scaling up, cost effectiveness, and toxicity concerns are issues with this technique.55
Qualities for Medical Applications
In the past twenty years a number of particle-based therapeutic and diagnostic agents have been developed for the treatment of cancer, diabetes, pain management, asthma, allergy, infections, and more.67-68 Micro- or nanoscale agents can provide more effective and possibly more convenient routes of administration, low systemic toxicity, increased product shelf-life, extended therapeutic time, and a reduction in medication costs.69 The goal for successful development of a particle-based system is to achieve the most efficient method in formulating particles of a particular size while minimizing product and therapeutic agent loss. Controlling the distribution of particles once introduced into the body still remains a challenge. Some research efforts have been put forth to target particles to particular organs or cell receptors by adding surface modifications, ligand receptors, or generating a specific surface charge.67 Current particle technology is challenged in a sense that all characterization must occur in-vitro, which does not reflect the complexity of its intended physiological environment. In-vivo particle studies largely remain a “black box” approach where pharmacokinetics and biodistribution are driven by a series of biological events that are not easily predicted or simulated in-vitro.70
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Novel Particle Formulation Techniques
Thermally Processing Particles via Thermal Relaxation
Some areas of particle formulation techniques remain unexplored. A simple method like thermal processing has been performed extensively on polymeric structures however has not been investigated in particle formulation. To extend the lifetime of microparticles for long-term delivery, one study in this proposal will investigate the use of distillation for solvent extraction as a method of thermally processing microparticles.
Significant changes in molecular mobility, morphology, physical, and mechanical properties can be detected in the polymer structure post-processing.71-73 Using heat for structural relaxation of materials has been applied to metallic nanoparticles to alter grain size and influence shape.74 The effect of annealing semi-crystalline polymers near its melting point is a simple but powerful technique. When semicrystalline polymer fibers are annealed crystallinity increases as a function of annealing temperature. With an increase in crystallinity comes an increase in degradation time.75 It seems plausible that using heat for solvent extraction on semicrystalline polymeric particles could alter polymer molecular characteristics that would increase degradation time and in-turn facilitate a longer drug release.
PFA/PLGA Blended Porous Particles
Another technique that can modify particle formulation is the use of polymer blends. As stated previously, a pre-polymer structure developed from novel low molecular weight PFA synthesis has yet to be investigated. Blending PFA with PLGA to generate hybrid particles can also be investigated. Studies have been reported using PPF and PLGA blended particles in which the molecular weight of the PPF is high.76-77 A
16 modified solvent displacement method of generating drug-loaded biodegradable hybrid nanoparticles is proposed. These particles should degrade quickly while providing a controlled release of therapeutic agent.
1.5 PROPOSED COATING DESIGN AND ITS ADVANTAGES
Composite Coating Design
Incorporation of hybrid tailored nanoparticles into low molecular weight PFA liquid coating may provide a fast drug delivery option as tracheal stent coating. Thus far, all therapeutic agent delivery coatings for tracheal devices have focused on the delivery of antiproliferative agents to prevent tracheal stenosis. None of these coatings address infection nor do they provide improvements in imaging of the device. In this work, the direct synthesis of a therapeutic agent and fluoroscopic/MRI contrast into the PFA polymer chain as a delivery vehicle will be investigated. This design will be completely bioresorbable and increase the contact area of the stent onto the tracheal wall. There are few cases in which nanoparticles and hydrogels have been combined as a biodegradable drug delivery system.78-82 However, hybrid nanoparticles incorporated in low molecular weight polyesters remains unexplored as a potential therapeutic coating.
Key Advantages of a Polyester Only System
There are three main advantages to this proposed system. First, this system can be a tailored drug delivery vehicle based upon polymer composition. The molecular weight of the synthesized polymers and the formulation technique used to make the particles will control the degradation time and subsequently the drug release. Second, using a system completely designed with polyesters avoids the irritation and dehydration of surrounding tissues associated with hydrogels due to their swelling behavior. This is a
17 key advantage for its intended for use in the trachea. The tracheal wall is a very moist environment due to the mucillatory escalator that is present to condition the air before it reaches the bronchi. Removing the moisture from the tissues would lead to uncontrollable inflammation and high infection risk in the surrounding tissues. Hydrogels that are not completely saturated could pose serious tissue damage in the trachea and surrounding blood vessels. Finally, this proposed composite coating allows for multiple drug delivery.
It is not far reaching to propose the use of low molecular weight polymer as a matrix carrier of hybrid nanoparticles, creating a composite coating. It is plausible to incorporate one drug into the “matrix” of the coating, and another or others into the hybrid nanoparticles. Using a combination of hybrid particles may be the long awaited answer for developing a drug delivery kinetic that can provide therapy for both the acute and chronic phases of inflammation and infection.
Therapeutic Agents for Composite Coating Design
In this design, there are two main components that can be used as drug delivery vehicles, the hybrid particles and the coating matrix. The particle formulation techniques in this proposal allow for the incorporation of both hydrophobic and hydrophilic therapeutic agents; making it easy to incorporate a variety therapeutic agents. The coating matrix material polymer can also incorporate therapeutic agents. The proposed linear polyester has the ability to co-polymerize with other polyesters and materials. This proposal will investigate the direct synthesis of a therapeutic agent and fluoroscopic/MRI contrast into the polymer chain as a delivery vehicle. Using direct incorporation in synthesis techniques, it would be possible to incorporate a therapeutic agent without using a solvent to facilitate the reaction thus eliminating potential toxicity concerns.
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For tracheomalacia, ciprofloxacin and dexamethasone are the most fitting candidates as deliverable therapeutic agents. Ciprofloxacin is a second-generation fluoroquinolone antibiotic that is used in a number of bacterial infections. It is hepatically metabolized and excreted in the renal system. Otolaryngologists commonly prescribe ciprofloxacin for respiratory infections with the most common bacterial strains:
Pseudomonas aeruginosa, Klebsiella pneumoniae, Moraxella catarrhalis, Haemophilus influenza, and Eschericia coli.83-87 It has been well established that ciprofloxacin penetrates well into airway tissues; therapies with intranasal inhalation or local airway delivery may therefore be more efficacious than an oral or IV administration.88 Using ciprofloxacin as part of the stent coating will mitigate infection risk while limiting systemic drug exposure with local delivery.
Dexamethasone is a glucocorticoid that is used for suppression of inflammation.
It is hepatically metabolized and excreted in urine. Dexamethasone is commonly used to combat airway inflammation and is also used for vascular inflammation control.89
Though effective, long-term systemic exposure to a corticosteroid such as dexamethasone can lead to side effects such as osteoporosis, dermal thinning, ophthalmological complications, and reduced growth velocity in children.90 Local delivery of dexamethasone, such as a coating on a stent, could alleviate local inflammation while limiting potential side effects from systemic exposure.
Currently, CIPRODEX® Otic is a FDA approved combination product, containing both of these compounds, used for inflammation and bacterial infection management for middle and outer ear infection in pediatrics.83, 91 There are also documented cases in which this product or both components of this product are used for
19 endoscopic airway management.92-95 Therefore a coating combining ciprofloxacin and dexamethasone is both appropriate and relevant for treatment of pediatric tracheomalacia.
Therapeutic Agents for Composite Coating Design
Current MRI contrast mediums rely on paramagnetic or superparamagnetic substances.96 These substances are attracted by an applied magnetic field and form an internally induced magnetic field in the direction of the applied magnetic field.97 In an
MRI scanner, a strong magnetic field followed by a radiofrequency pulse is applied causing a change in the net magnetization generated from protons (mostly from water). In time, relaxation mechanisms return the protons to their equilibrium magnetization and the change (the signal) is detected. Gadolinium (Gd) is the most commonly used compound for contrast enhancement followed by iodine, iron oxide and manganese (Mn).96 The heavy metal compounds (Gd and Mn) often are used in a chelate form in which the heavy metal is the central atom bound or in close proximity to a multiple bonded ligand.98 Iron oxide and other iron conjugations used as contrast agents are typically in the form of injectable nanoparticles or micelles.99
Biodegradable polymers lack one key desirable property that denser materials such as metal and ceramics possess - radiopacity. The radiographic visibility of conventional polymers used as medical implants or inserts is limited by their density.100
Incorporation of heavy elements and contrast medium into polymeric materials has been investigated for orthopedic and dental applications.101-105 Many limitations exist in these radiopacifying polymer formulations. Non-homogeneous distribution of radiopacifying agents and agent leaching can lead to potential toxic side effects. Also, cracking or failure of polymeric device at the interface between polymer and additive is common due to
20 moisture or bacteria penetration.104-105 Potassium iodide and a gadolinium chelate will be investigated in the novel polymer synthesis to provide a fluoroscopically and MRI-visible coating polymer and theranostic nanoparticles.
1.6 STENT COATING METHODS AND CHARACTERIZATION
Current Stent Coating Methods
The method of coating a medical device is nearly as important as the coating composition itself. The most common methods to apply stent coatings are spray coating, ultrasonic atomizing spray coating, inkjet coating, and dip coating.106
Spray coating applies microdroplets of the drug/polymer solution to the surface of the stent by means of a spray nozzle and pump that supplies coating material from a reservoir. Though this can be a successful method, spraying coating in some cases has proven to be inefficient and unreliable. It can produce defective coatings with damaged or uneven strut layers and a significant amount of coating material is lost during the spray process.
Ultrasonic atomizing spray coating utilizes a high frequency ultrasonic atomizing nozzle with a low-pressure gas stream to generate a narrow, soft spray beam to coat stent struts. This method has demonstrated significant improvement in coating uniformity compared to its predicesor spray coating however coating roughness and coating material loss remains an issue.
Inkjet printing is a new non-contact approach that enables processing of 1-100 pL droplets of liquid onto two-dimensional and three-dimension structures.107-108 This method requires dissolving or dispersing the material of interest in a liquid to generate an
“ink.” Drops are then ejected from a micrometer scale nozzle and either the “ink” is
21 heated to the boiling point of the liquid or vibration is applied via a piezoelectric transducer.109 Within the past few years, this printing technology has shown success in printing bioactive agents.110-111 This coating system, however, is costly and at present is not completely optimized.
Finally, a dip coating method limits the exposure of the fiber to solvent-dissolved coating mixtures and it easily produces porous coatings through incorporation of inert water-soluble agents. Another advantage to this coating method is the fiber can be dipped multiple times to generate a thicker coating, and hence higher drug content. Dip coating also reduces the loss of coating materials associated with spray methods. This method is also the most economical option, but comes with some disadvantages. Inconsistency in coating thickness from specimen to specimen is a common issue due to human error.
Robotic performance of the dipping process, though more expensive, could address this challenge.
Coated Fiber Characterization Considerations
The characteristics of the fibers that compose the stent play an important role in the function and strength properties. Modifications to these fibers (such as drawing and annealing) can improve the mechanical properties of the stent. Thermal processing of polymeric structures such as films, fibers, and biodegradable stents has been well documented.71-73 The effect of annealing semi-crystalline polymers near its melting point is a modest technique that can increase device or structure lifetime. Significant changes in molecular mobility, morphology, thermal, and mechanical properties can be detected after such modifications to these polymers.71-73
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Furthermore the addition of a coating onto fiber can alter the fiber thermal properties. Depending on the method of coating, the presence of residual solvent can facilitate reactions between the coating polymeric materials and the polymeric materials that compose the stent fiber. The goal is to minimize this solvent exposure to ensure these chemical reactions do not take place. Regardless, differential scanning calorimetry (DSC) can be used to determine if thermal properties of coated fibers differ significantly from control uncoated fibers.
A change in the mechanical properties of fibers is also of concern with the addition of a coating. The addition of a coating is intended to provide therapeutic agents for local drug delivery. For stents, various studies have investigated incorporation of drug via direct impregnation into fiber.112-116 This has proven to be difficult not only because many therapeutic agents cannot withstand the conditions necessary for processing, but the end product fiber demonstrated inferior mechanical properties.113-114 The same was also observed in fibers created with reservoirs for drug loading.115-117 As a result of these mechanical property shortcomings, studies began to investigate coatings consisting of therapeutic agents to be applied to the outside of the the fibers instead of direct incorporation. It is essential to ensure the addition of a coating does not alter fiber mechanical properties that would hinder overall stent performance and safety.
1.7 BIOCOMPATABILITY OF POLYMERIC MATERIALS
Polymeric Material Biocompatibility
The advancements of micro- and nano-technology have generated engineered particles that have the ability to interact with biological environments for the diagnosis and treatment of diseases. Particles that interact with cells and extracellular environments
23 can trigger a sequence of biological effects. These biological effects depend on the dynamic physiochemical characteristics of particles; which determine the biocompatibility and efficacy of intended therapy.118 As research continues, the underlying mechanisms of particle interactions with biologic environments will be unveiled. Understanding these different interactions and outcomes will allow for prediction of interactions between nanostructures and biological environments for future formulations and applications. At present, a standard of biocompatibility evaluation criteria is not firmly established for particles as drug delivery systems. That being said, a variety of general safety guidelines of nanoparticles for medical applications prepared by the FDA and ISO have been used consistently in research applications.
Biocompatibility by definition is the ability of a material to perform with an appropriate host response in a specific situation.119 In general, acceptable biocompatibility is achieved when a material interacts with a biological environment without inducing unacceptable immunogenic, thrombogenic, carcinogenic, or otherwise toxic responses. It is of paramount importantance to identify the anatomically relevant structures with which the particles will interact. Biocompatibility can vary dramatically between organ systems. For example, it is well known that PLGA nanoparticles provoke mild tissue reactions in most locations in the body, but strong acute inflammation results when the connective tissue surrounding nerves is exposed to these particles.120-122 As these materials are intended for use in the airway, biocompatibility assessment experimental design must be planned for appropriate airway tissue investigation.
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1.8 SPECIFIC AIMS
The overall goal is to design a multi-drug delivery coating to be applied to a stent for the treatment of pediatric tracheomalacia. There is a pressing need for the development of new tracheal devices, especially biodegradable options, for developing children. At UT Southwestern a coil based, Double Opposed Helical Biodegradable Stent
(DH BDS) has been designed with the potential to provide ample support in the case of tracheal collapse. Further improvements to minimize infection and inflammation are necessary seeing as endotracheal interventions are subject to airbone pathogens and interact with a complex mucosal environment. To achieve an optimal intervention, a coating that incorporates controlled delivery of an antibiotic and/or an anti-inflammatory agent will likely be advantageous.
Specific Aim 1: Determine optimal particle formulation for anti-inflammatory release using a factorial design.
1.10.1 Hypothesis 1.1: There will be no significant difference in particle characteristics in particles formulated using the same copolymer ratio from two different manufacturers. Particles formulated using PLGA 50:50 will have a larger effective hydrodynamic diameter, lower zeta potential, lower Tg, higher drug loading efficiency, and shorter drug release lifetime compared to particles formulated with PLGA 75:25.
Previous studies have established that variances in polymer viscosity can affect particle size distribution and drug loading.76 According to the manufacturers all of the
PLGA copolymers have a median inherent viscosity of 0.2 dL/g. One would expect to see differences using a 50:50 copolymer of PLGA compared to a 75:25 copolymer of PLGA,
25 but there should not be any difference using each copolymer from a different manufacturer. The increase ratio of lactic acid to glycolic acid in PLGA 75:25 compared to PLGA 50:50 will alter particle characteristics. Glycolic acid has a more mobile chemical structure than lactic acid, which allows 50:50 to accommodate binding of large molecules such as dexamethasone more efficiently than 75:25. The increased mobility of the polymer chains in PLGA 50:50 will also lead to a lower glass transition tempertature
(Tg) and zeta potential (ζ) and increase particle diameter compared to particles formulated with PLGA 75:25. Furthermore it will increase the total drug amount loaded and release its drug contents faster than a more stable particle such as one formulated with the 75:25 copolymer.
1.10.2 Hypothesis 1.2: Thermally processing particles with solvent removal via distillation will produce an increase in particle effective hydrodynamic diameter, Tg, zeta potential, drug loading efficiency, and drug release lifetime compared to particles with solvent removal via evaporation at room temperature.
Using thermal processing to structurally relax a polymer at or above Tg in thin films and other microparticle formulations has been investigated.123-125 In the solvent displacement technique, solvent is removed at 65°C which is the boiling point of tetrahydrofuran. This temperature is above the melting point of lactic acid (53°C) but below the melting point of glycolic acid (75-80°C).126-127 Therefore there is structural relaxation of lactic acid groups during distillation. The relaxation of lactic acid chains can lead to aggregation and fusion of particles that are in close proximity, increasing particle diameter. This phenomenon will be more apparent in particles formulated with PLGA
75:25 than PLGA 50:50 due to the increased lactic acid ratio. However, regardless of
26
copolymer ratio, all particles should exhibit an increase in size, Tg, zeta potential, and drug loading efficiency. Thermally processed particles will also considerably increase the therapeutic lifetime of the particles. As described previously, rapid solvent extraction during the formation of PLGA microparticles is analogous to thermal quenching and has been mathematically modeled.125, 128 When a microparticle is thermally quenched, particle density increases due to structural relaxation. The increase in density not only slows polymer degradation but limits the ability for dexamethasone to diffuse through the polymer matrix, slowing drug release.
1.10.3 Hypothesis 1.3: Blending PGFA with PLGA to formulate particles will decrease particle effective hydrodynamic diameter, Tg, zeta potential, and drug release lifetime. Blending will increase drug loading efficiency.
PGFA is lower in molecular weight than any of the PLGA copolymers and is a liquid at room temperature. Blending this with PLGA can result in polymer chains that are more flexible and less stable than PLGA alone. Using a liquid polymer will also decrease viscosity of solutions during synthesis. Therefore a decrease in size, Tg, stability, and drug release lifetime is expected.
The inclusion of gadodiamide will likely affect particle characteristics.
Gadodiamide is a large metal chelate with four available carboxylic acid side chains for bonding. It is a hydrophilic compound that when blended can affect the orientation of polymer chain side groups. The blending of PLGA and PGFA should increase drug loading efficiency. The inclusion of gadodiamide not only increases polymer chain mobility but also provides more binding sites for dexamethasone. When blended with
PLGA, more hydrophobic lactic acid and glycolic acid groups are made more mobile and
27 available for binding. Dexamethasone is also hydrophobic and will bind with available lactic acid and glycolic acid chains that are not available when formulation occurs without PGFA.
1.10.4 Hypothesis 1.4: Increasing the sonication time will generate particles of small effective hydrodynamic diameter without compromising other particle characteristics.
Sonication time has a direct relationship with particle size. The mean nanoparticle diameter decreases with an increase in sonication time until a threshold is reached and particle size plateaus. This relationship has been thoroughly reported in various studies using PLGA in the formulation of nanoparticles.129-131 Mean entrapment efficiency of therapeutic agent might alter with an increase in sonication time.
Therapeutic agent entrapment efficiency is also reduced different than drug loading; in entrapment the polymer traps drug at a later stage in particle formation. Drug loading
(used in this method) chemically binds the drug directly into the polymer chains before particle formation thus reducing therapeutic agent loss. Furthermore, using a blend of
PLGA with PGFA will overcome the associated loss due to geometric constraints and agent destruction. Gadodiamide can provide more binding sites for therapeutic agents that should enhance drug loading efficiency. Chemical properties of the particles are directly related to material composition and accordingly should not be affected by an increase in sonication time. The only parameter that should be affected by sonication time is particle effective diameter.
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Specific Aim 2: Design a multi-drug release coating for a bioresorbable stent.
1.10.5 Hypothesis 2.1: Poly(fumaric acid) is significantly different both chemically and in exhibited material properties compared to poly(propylene fumarate).
In the traditional synthesis of poly(propylene fumarate) (PPF) hydroquinone is used to promote the linear polymerization.132 Hydroquinone is not necessary in order to complete the reaction and was eliminated due to the excessive washing steps needed to remove it from the final product. The reaction is run with diethyl fumarate in excess of propylene glycol in a 1:3 ratio. It is possible that not all of the propylene glycol is consumed nor completely polymerized therefore may not be entirely eliminated from the final product. It is likely that the fumaric acid chains will polymerization and propylene glycol is consumed and reformed in this process. Fourier Transform Infrared
Spectroscopy (FTIR) and Proton Nuclear Magnetic Resonsance (1H-NMR) imaging will elucidate if there is a distinct difference between the final synthesis product (PFA) and
PPF.
1.10.6 Hypothesis 2.2: Using other therapeutic agents as initiators in poly(fumaric acid) synthesis will alter chemical and material properties, especially rheological behavior.
Traditionally chloride initiators are used to start polymerization reactions. In the synthesis of PPF, zinc chloride is used to facilitate the transesterification of diethyl fumarate and propylene glycol.60, 132-133 The free radicals produced in the separation of the zinc and the chloride ions initiate the polymerization. Similar materials that produce free radicals with the same caliber charge can also drive polymerization. In particular, therapeutic agents such as contrast mediums and drugs can be used as initiators due to
29 reactive hydroxyl or carboxylic acid side chains available. An initiator is different than a catalyst in that an initiator undergoes a chemical change during polymerization, while a catalyst does not. The chemical changes that occur in an initiator can make it available to be bound to the polymer chain. This can result in homogeneous distribution of therapeutic agent directly synthesized into the polymer final product.134 With the introduction of therapeutic agents into the polymer chains the chemical and material properties will likely be different from the parent polymer. It is expected that a significant change in rheological behavior of the polymers would be observed with therapeutic agents added due to the size and generally low mobility of therapeutic agents. Many of these therapeutic agents are distinctly hydrophobic or hydrophilic which can affect orientation of polymer chains as well as surface chemistry. Physiochemical characterization will show the distinctive differences in polymer properties resulting from the use of these non-traditional initiators in polymerization.
1.10.7 Hypothesis 2.3: Porous and non-porous PLGA coatings will not alter PLLA fiber mechanical properties. A porous PLGA coating will release dexamethasone faster than a non-porous coating.
A variety of therapeutic agent delivering coatings have been investigated on stent fibers for cardiovascular applications. For example, Zilberman and Kraitzer developed a method that added a coating containing paclitaxel to fiber.135 They observed a decrease in mechanical strength in these fibers with the addition of this coating. A reduction in fiber mechanical strength was also observed in Su et al. 136 with the addition of curcumin. On the contrary, Elsner et al. 137 developed a wound healing matrix with the aid of Bovine
Serum Albumin (BSA). Their prior wound healing matrices displayed mechanical failure
30 after three weeks but with the addition of BSA demonstrated improved maintenance of mechanical properties. In general, coatings have a significant role in abating the negative side effects of injury due to stent implantation; however, the method with which the therapeutic agent is incorporated with the device is crucial. Using a dip coating method dips fibers into a solvent-dissolved mixture for only a few seconds. This exposure time is unlikely sufficient to facilitate chemical reactions between polymers and solvents that could sufficiently alter fiber mechanical properties.
1.10.8 Hypothesis 2.4: The composite coating design will controllably deliver ciprofloxacin for at least one week and dexamethasone for at least three months fulfilling the optimal treatment for pediatric tracheomalacia.
Current stent technologies for pediatrics are limited; especially for airway applications.138 A multi-drug delivery coating applied to a bioresorbable stent offers a promising advance that can be applied to treatment of pediatric tracheomalacia. Two of the most common post-stent or post-surgical complications in these procedures is excessive inflammation and bacterial infection.1-2, 11, 40, 139
A composite coating with both poly(gadodiamide ciprofloxacin fumaric acid
(PGCFA) and polymeric theranostic nanoparticles (PTNPs) loaded with dexamethasone can optimally deliver these therapeutic agents via a stent for pediatric tracheomalacia.
Bench and in-vitro studies will demonstrate the biocompatibility and therapeutic potential of both components of the composite coating materials, in combination with one another to ensure therapeutic benefits.
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Specific Aim 3. Biocompatibility assessment of particle formulation, coating polymer, and composite coating.
1.10.9 Hypothesis 3.1: PGPF and PTNPs will comply with ISO 10993 and show a biocompatibility of 80% or higher with human dermal fibroblasts. PGPF and
PTNPs will be less cytotoxic than PLLA and PLGA.
Both PGPF and PTNPs are composed of medical grade polyester materials.
Many similar materials and materials included in both the formulation and synthesis are
FDA approved for clinical use in humans.140-141 PGPF has some minor concerns due to the potential increase in local acidity with increased concentration of fumaric acid.
However a similar reaction occurs with the breakdown of poly(lactic acid) (PLA). The pH of 100 mmol/L of lactic acid is 2.4 while fumaric acid is 2.03.142 At higher concentrations the polymer could provoke an acute inflammatory response that will abate as inflammatory cells and body processes remove the degradation products. In-vitro cell studies will determine the biocompatibility of PGPF and PTNPs with human dermal fibroblasts.
1.10.10 Hypothesis 3.2: PGPF, PTNPs, and a composite mixture of the two will comply with ISO 10993 and show a biocompatibility of 80% or higher with human tracheal epithelial cells. PGPF, PTNPs, and the composite mixture will be less cytotoxic than PLLA.
Tracheal epithelial cells will interact with the stent coating upon implantation of the stent. At present, many bioresorbable airway stents fail due to the incompatibility of the stent material with the tracheal epithelial cells.143-144 Although tracheal epithelial cells are more specialized and sensitive than human dermal fibroblasts, it is unlikely that an
32 extreme adverse reaction will occur when in contact with PGPF and PTNPs. As stated in section 1.10.10, similar materials and materials used in the synthesis or formulation of these polymers are FDA approved for clinical use.
1.10.11 Hypothesis 3.3: All four bacteria strains, Escherichia coli, Klebsiella pneumoniae, Moraxella catarrhalis, and Pseudomonas aeruginosa, will be susceptible to PCFA and PGCFA but not PFA. Inhibition zone length will mirror drug release kinetics; PGCFA will have large inhibition zones at early time points and PCFA will have consistent inhibition zone lengths throughout the experiment.
Escherichia coli, Klebsiella pneumoniae, Moraxella catarrhalis, and
Pseudomonas aeruginosa are all susceptible to ciprofloxacin. 84-86, 88, 145 The ciprofloxacin compound is directly synthesized into the polymer chain to create PCFA and PGCFA. In order to bind to fumaric acid groups, a single hydroxyl end group is available for binding.
The loss of the hydrogen ion on the ciprofloxacin and the hydroxyl group from the fumaric acid create the ester bond with a byproduct of water. Due to the steric hindrance of the larger molecular groups within the ciprofloxacin molecule, it is highly unlikely that binding occurs in any other region. Therefore the antibiotic functionality of the molecule should not be interfered with or destroyed during synthesis. All four bacterial strains therefore should be susceptible to both PCFA and PGCFA. The inhibition zone length formed around a sensitivity disk that contains degradation products from PCFA and
PGCFA will determine the degree of susceptibility for each strain. The inhibition zone lengths are dependent on the concentration of ciprofloxacin. The concentration of ciprofloxacin is dependent on the polymer drug release as determined by the polymer degradation kinetics. The inhibition zone lengths should correspond with the observed
33 drug release kinetics of PCFA and PGCFA. All four bacteria should not be susceptible to the control polymer, PFA, as it does not contain any ciprofloxacin or bactericidal compounds. It is possible that PFA and its degradation products may have some intrinsic antibacterial activity, and this will be confirmed or denied in the experimental design.
1.10.12 Hypothesis 3.4: After 24 hr exposure, PTNPs loaded with dexamethasone will effectively lower inflammatory cytokine concentrations in mouse macrophages comparable to free dexamethasone.
Dexamethasone is a steroid medication widely used for anti-inflammatory and immunosuppressant effects.83, 146-147 Of the many relevant cytokines, TNF-α and IL-1β are significant inflammatory markers monitored after stent deployment. Other polymeric nanoparticle systems have demonstrated a reduction in both of these cytokines when particles encapsulate or load dexamethasone.148-150 A number of those nanoparticle systems utilized PLGA as the dexamethasone vehicle as well. Therefore the PTNPs composed of PLGA blended with PGFA should provide ample dexamethasone release to lower TNF-α and IL-1β in-vitro using a mouse macrophage model.
CHAPTER TWO Methodology
2.1 POLYMER SYNTHESES
2.1.1 Poly(Fumaric Acid)
The poly(fumaric acid) (PFA) synthesis protocol is derived from Kasper et al.
2009 132 and He et al 2001.151 The synthesis scheme is shown in Figure 4. Diethyl fumarate (1 mol) and propylene glycol (3 mol) were combined in a 250 mL 3-neck flask under nitrogen and heated to 180 °C with stirring (220 rpm) under a nitrogen gas purge.
Zinc chloride (3.0 x 10-3 mol) was then added and allowed to dissolve, and heating was continued at 180 °C. The reaction was allowed to continue until 90% of theoretical yield of ethanol (24.5 mL) was collected in the receiving flask. Nitrogen gas purge was then stopped and the system was placed under reduced pressure (1 mmHg). The reaction is terminated when the desired molecular weight of product is obtained (600-1500 Da). The mixture was then cooled to room temperature then dissolved in 100 mL of dichloromethane (DCM) to perform purification washes. The polymer solution was transferred into a 1 L separatory funnel positioned above a 250 mL beaker. 200 mL of
5% (vol/vol) HCL solution was added to the funnel. The funnel was capped, inverted, and stopcock was opened to vent gas. This process was repeated incorporating a brief shaking period before venting. The intensity of shaking was increased until no noticeable gas was relieved from the system. The clear aqueous phase and cloudy amber polymer solution were allowed to separate and the cloudy amber phase was collected and the aqueous phase discarded. The HCL wash procedure was repeated twice using deionized water in place of 5% HCL solution. With each subsequent wash the aqueous phase
34 35 appeared turbid. The wash procedure was then repeated twice with 26% sodium chloride solution with the final solution appearing clear amber in color. The washed collected polymer solution was placed in a covered beaker and was then stirred at 100 rpm with 1 g of sodium sulfate for 30 minutes. Stirring was then stopped and the solution was vacuum filtered with a Buchner funnel and filter paper. The polymer solution was then transferred to a clean beaker and theDCM was evaporated with stirring at 60 rpm on a hot plate heated to 80 °C overnight. The final purified amber polymer solution was transferred to an air tight storage vessel and stored at 4 °C.
36
Figure 4. Synthesis scheme of Poly(Fumaric Acid).
37
2.1.2 Poly(Gadodiamide Fumaric Acid)
Poly(Gadodiamide Fumaric Acid) (PGFA) was synthesized using the same method as PFA, however, gadodiamide anhydrous was substituted as the initiator of the reaction. The synthesis scheme is shown in Figure 5. Omniscan® (gadodiamide in aqueous solution) was dehydrated until only white crystalline powder remains. An equimolar amount of gadodiamide anhydrous (3.0 x 10-3 mol) was used in place of zinc chloride.
38
Figure 5. Synthesis scheme of Poly(Gadodiamide Fumaric Acid).
39
2.1.3 Poly(Ciprofloxacin Fumaric Acid)
Poly(Ciprofloxacin Fumaric Acid) (PCFA) was synthesized using the same method as PFA, however, powdered ciprofloxacin was substituted as the initiator of the reaction. The synthesis scheme is shown in Figure 6. An equimolar amount of ciprofloxacin (3.0 x 10-3 mol) was used in place of zinc chloride.
40
Figure 6. Synthesis scheme of Poly(Ciprofloxacin Fumaric Acid)
41
2.1.4 Poly(Potassium Iodide Fumaric Acid)
Poly(Potassium Iodide Fumaric Acid) (PKIFA) was synthesized using the same method as PFA, however, powdered potassium iodide was substituted as the initiator of the reaction. The synthesis scheme is shown in Figure 7. An equimolar amount of potassium iodide was used in place of zinc chloride.
42
Figure 7. Synthesis scheme of Poly(Potassium Iodide Fumaric Acid).
43
2.1.5 Poly(Gadodiamide Ciprofloxacin Fumaric Acid)
Poly(Ciprofloxacin Fumaric Acid) (PGCFA) was synthesized using the same method as PFA, however, anhydrous gadodiamide and powdered ciprofloxacin was substituted as the initiator of the reaction. The synthesis scheme is shown in Figure 8. An equimolar amount of gadodiamide and ciprofloxacin were used.
Figure 8. Synthesis scheme of Poly(Gadodiamide Ciprofloxacin Fumaric Acid).
44
2.2 NANOPARTICLE FORMULATIONS
2.2.1 PLGA Particles
1 g of PLGA (see Table 1for groups) was added to 5 mL of tetrahydrofuran
(THF) and vortexed until dissolved. Dexamethasone (12.5% w/w) was then dissolved into the solution. 5 mL of 0.35% F127 solution was added to the PGLA solution and vortexed briefly sonicated (Misonix Sonicator, S-4000, Newtown, CT) for 30 min.
Solvent was removed by evaporation at room temperature and the particles were washed by centrifugation with distilled water at 1500 RPM for 5 min. three times. After a final wash, particles were re-suspended in 10 mL distilled water and stored at -20°C.
Table 1. PLGA particle group identification.
Experimental Group ID Polymer Molecular Weight A Corbion Purac PLGA 50:50 153 kDa B Corbion Purac PLGA 75:25 76 kDa C Evonik PLGA 50:50 54 kDa D Evonik PLGA 75:25 114 kDa
2.2.2 PLGA Particles via Novel Distillation Technique
Following the same method above for PLGA particles (section 2.2.1) although removing the solvent via distillation using a simple organic glassware set developed two additional formulations. Table 2 below shows the groups. Solvent removal via evaporation at room temperature is the control. One distillation group (groups with 2) removed solvent via distillation at the solvent boiling point (tetrahydrofuran, BP: 66°C) and was held at this temperature for one additional minute after solvent was removed. A second distillation group (groups with 3) removed solvent by distillation and was held at this temperature for fifteen additional minutes after solvent was removed. The same washing and storage procedures were followed as PLGA control particle method.
45
Table 2. Particle experimental groups for novel distillation technique.
Polymer Type Solvent Removal Technique Purac Purac Evonik Evonik Distillation Distillation Group Evaporation 50:50 75:25 50:50 75:25 (1 min) (15 min) A X X
A2 X X
A3 X X
B X X
B2 X X
B3 X X
C X X
C2 X X
C3 X X
D X X
D2 X X
D3 X X
46
2.2.3 PLGA/PGFA Theranostic Nanoparticles
Following the same method of PLGA particles (section 2.2.1) an additional 1 g of PGFA was dissolved in the PLGA solution (Table 3). Particle theory and mathematical characterization can be found in Appendix A.
47
Table 3. Particle experimental groups for novel PLGA/PGFA theranostic particles.
Polymer Type Additive
Group Purac 50:50 Purac 75:25 Evonik 50:50 Evonik 75:25 None PGFA
A X X
A4 X X
B X X
B4 X X
C X X
C4 X X
D X X
D4 X X
48
2.3 FABRICATION OF POLY(L-LACTIDE ACID) FIBERS
PLLA resins (PL-32, 565 kDa, PURAC Switzerland) were weighed, and poured into a single screw extruder (ATR Plasti-Corder, Winext Software, C.W. Brabender,
Hackensack, NJ) and melt-extruded at 180-182°C to form Ø 0.35±0.10 mm fiber. Next, the fiber was drawn at 55°C to a final diameter of 0.18 ± 0.01 mm. Fiber was then annealed by heating to 62°C. Fibers were stored in a desiccator at room temperature.
2.4 FIBER COATING METHODS
2.4.1 Non-Porous PLGA Dip Coating of PLLA Fibers
PLGA (17 kDa) (Corbion Purac, Netherlands) with a glass transition temperature
(Tg) of 42.0±0.9°C was used. A solution of PLGA in THF (15 wt/vol%) was formulated at room temperature. Dexamethasone was dissolved into the PLGA solution (40 wt/wt%, dexamethasone/PLGA) until homogeneous. Annealed PLLA fibers were then dipped into the solution, removed, and dried at room temperature.
2.4.2 Porous PLGA Dip Coating of PLLA Fibers
Following the same method as the non-porous PLGA dip coating method
(section 2.4.1) PLLA fibers were dipped with a sucrose containing solution. A sucrose in water (5 wt/vol%) solution was formulated and added to the PLGA/dexamethasone suspension at a ratio of 95:5 (v/v). The suspension was then sonicated at 60 Hz for 30 minutes. PLLA fibers were then dipped, removed, and dried at room temperature for 48 hrs. Fibers were then submerged into deionized water for 30 min, dried, and stored in desiccator.
49 50
2.5 POLYMER AND PARTICLE CHARACTERIZATION
2.5.1 Fourier Transform Infrared Spectroscopy
A NaCl crystal was cleaned with DCM and used as a background control for the initialization of the spectrophotometer (Perkin Elmer Spectrum 1000 FT-IR). Polymer samples were dissolved in 1-2 mL of DCM in separate vials. After dissolving, drops were placed on the NaCl crystal via pipette and dried. Samples were run on the FTIR scanning from 500 to 4000 cm-1.
2.5.2 Proton Nuclear Magnetic Resonance
Polymer samples were dissolved in deuterated chloroform (CDCl3) and analyzed using the Varian Unity Inova 500 MHz 1H-NMR. The acquisition used 128 scans and data were recorded from 0 to 14 ppm.
2.5.3 Gel Permeation Chromatography and Refractive Index Detection
Molecular weight is always reported as an average of each polymer run triplicate unless otherwise specified. Polymer samples were dissolved in THF and analyzed using the Ultimate 3000 High Pressure Liquid Chromatography system (Thermo Scientific
Dionex, Chicago, IL) equipped with ultraviolet (UV) diode array detector and integrated refractive index detection system (Viscotek VE 3580 RI Detector, Malvern,
Worcestershire, UK). The I-OLIGO (Viscotek, 10 μm, 7.8x30 cm) column was used for detection at 35°C with a mobile phase of 100% THF. The flow rate was 1.0 mL/min with an injection volume of 30 μL. For polymer detection using the UV diode array detector, the UV wavelength was set at 250 nm. Molecular weight was always reported as an average of a triplicate polymer run unless otherwise specified.
51
2.5.4 Differential Scanning Calorimetry
A known volume of polymer, particles, therapeutic agent, etc. was measured into a TZero aluminum pan. If the compound was in solution, pans were placed in a desiccator for 48 hrs before proceeding. Pans were then sealed and analyzed via a Q20
Differential Scanning Calorimeter (TA Instruments, New Castle, DE). Samples followed an equilibration followed by a controlled temperature increase ramp (10°C/min). Once temperature maximum was achieved the sample was held isothermal at the specified temperature for one minute. The sample was then cooled using the controlled temperature decrease ramp (10°C/min). Exact heating and cooling temperatures can be found in Table
4. Heating curves were analyzed using TA Universal Analysis Software (TA Instruments,
New Castle, DE). Theoretical considerations and calculations can be found in Appendix
B.
Table 4. Temperature sweeps for DSC samples.
Equilibration Max/Isothermal Final Temp Number of Sample Temp (°C) Temp (°C) (°C) Sweep Cycles PLGA 10 100 10 2 PLLA 10 190 10 2 PFA and its -50 100 -50 2 derivatives PLGA Particles 10 70 10 2 PLGA/PGFA -30 70 -30 2 Particles Dexamethasone 10 285 10 1 Ciprofloxacin 10 275 10 1 Potassium 10 300 10 1 Iodide
52
2.5.5 Rheology
Fluid behavior of polymers and coating composite was assessed via AR G2 rheometer (TA Instruments, New Castle, DE) and analyzed with TA Universal Software
Analysis (TA Instruments, New Castle, DE). More description on rheology theory can be found in Appendix C. Each sample was examined with a stress sweep, strain sweep, frequency sweep, and time dependence assessment. Each experimental procedure was performed at 10, 25, 27, and 50°C to determine temperature dependence. A gap size of
1000 μm was used and data was collected with 10 points per decade. All samples were conditioned for five minutes at the respective testing temperature prior to testing.
Stress Sweep
A broad stress (τ) range of 0.1 – 10,000 uN·m was used with a constant frequency (ω) of 1 rad/s. Viscosity versus shear rate was recorded and analyzed using linear regression. Yield stress, if present, was determined.
Strain Sweep
A broad strain (γ) range of 0.1 - 30% was used with a constant frequency (ω) of 1 rad/s. Modulus G’, G’’ versus strain % (γ) was analyzed using linear regression to determine material’s linearity. The presence or absence of yield stress was verified at the point in which the material behavior changes from linear to nonlinear.
Frequency Sweep
A frequency (ω) sweep with a range of 0.1 – 100 rad/s was used. A constant stress (τ) that falls within the sample’s linear viscoelastic region determined from the stress sweep. G’ was analyzed over the frequency range. The material was considered
53 solid-like when G’ behaves independent of frequency and the material was considered liquid-like when G’ is frequency dependent. Any samples containing an additive used G’ to determine the stability of the additive particulates in the polymer.
Time Dependence Assessment
The sample was held at a constant low strain (γ) 0.1%, constant frequency (ω) 1 rad/s and constant temperature, respectively. The sample was run at these constant conditions for 5 min to determine time-dependent nature of the sample. Using the curves generated from viscosity versus time, zero shear viscosity (ƞ0) and equilibrium compliance (Jeo) were determined.
2.5.6 Surface Morphology via Scanning Electron Microscopy
Surface features of particles and coated fibers were examined utilizing a
Scanning Electron Microscope (Zeiss-LEO 1530), operating at 1-10 kV. Samples were mounted onto a metal stub by carbon adhesion film. Mounted samples were sputter coated with gold/palladium in an Anatech Hummer VI coating machine operating for 120 seconds at 70 Ȧ.
2.5.7 Dynamic Light Scattering
Particle effective hydrodynamic diameter and zeta potential were determined via
Zeta PALS Dynamic Light Scattering apparatus (Brookhaven Instruments Corporation,
Novato, CA, USA). For theory and calculations see Appendix A.
2.5.8 Mechanical Testing of Fibers
Drawn, annealed PLLA fiber with and without coatings were tested on an
INSTRON 5565 Tensiometer with a 10N load cell and pneumatic fiber grips. An initial length 25.4 mm was used with a pull rate of 5 mm/min. Data was used to determine
54
Young’s Modulus (E), Yield Stress (Sy), Yield Strain (εy), Ultimate Tensile Stress (UTS), and Fail Strain (εf).
2.5.9 Porosity Determination of Films
Film porosity was assessed utilizing a density measurement kit assembled in an analytical balance (Metler Toledo XP 205) (Figure 9).152 Films are weighted in air and ethanol. Using the following formulas, density, volume, and porosity can be calculated:
Density: ρ =
Volume: V = α
Porosity (%) =
Where: ρ = density of sample A = weight of sample in air B = weight of sample in ethanol = density of ethanol = air density (0.0012 g/cm3) α = balance correction factor (0.99985)
Figure 9. Film porosity setup using Mettler-Toledo balance and density kit.
55
2.6 POLYMER AND PARTICLE DRUG RELEASE
2.6.1 High Pressure Liquid Chromatography Detection of Dexamethasone
To generate a calibration curve, a standard of dexamethasone in methanol (1 mg/mL) was prepared. Serially decreasing injection volumes (8.0 µL to 0.0001 µL) were analyzed using the Ultimate 3000 HPLC system (Thermo Scientific Dionex, Chicago, IL) with an Acclaim C30 column (Thermo Fischer Scientific, 3 μm, 3.0x 150 mm). The mobile phase was 68% water/acetonitrile (60/40% v/v), 30% THF, and 2% methanol.
The column oven was heated to 35°C at a flow rate of 0.5 mL/min with an injection volume (for unknown samples) of 25 μL unless otherwise specified. The UV-diode array detector was set at 240 nm for dexamethasone detection. Drug release theorectical considerations can be found in Appendix D and dexamethasone standard curve generated from this method can be found in Appendix E.
2.6.2 High Pressure Liquid Chromatography Detection of Ciprofloxacin
A similar HPLC setup as section 2.6.1 was used to detect ciprofloxacin. Due to the pH sensitivity of ciprofloxacin, trifluoroacetic acid (TFA) was added to the water/acetonitrile mobile phase component (0.1% v/v) such that the final pH of the mobile phase was 5.05. The ciprofloxacin standard was dissolved in 1 mg/mL of methanol with 440 μL 0.1% TFA. The UV-diode array detector was set at 275 nm for ciprofloxacin detection. The ciprofloxacin standard curve generated from this method can be found in Appendix E.
56
2.6.3 Simultaneous Detection of Dexamethasone and Ciprofloxacin
The HPLC setup from sections 2.6.1 and 2.6.2 was used for simultaneous detection of dexamethasone and ciprofloxacin. All conditions remained the same and the mobile phase for ciprofloxacin detection was used. The column oven temperature was increased to 50°C and two diode array channels were used. UV channel 1 was set to 240 nm to detect dexamethasone and UV channel 2 was set to 275 to detect ciprofloxacin. A standard curve was generated with serial dilution injection from 3.0 µL to 0.1 µL. The standard curve for simultaneous detection of dexamethasone and ciprofloxacin generated using this method can be found in Appendix E.
2.6.4 Drug Loading Efficiency
Polymer or particles were dissolved until clear in 1 mL of THF. Solutions were analyzed via HPLC set up for the detection of dexamethasone (section 2.6.1), ciprofloxacin (section 2.6.2), or simultaneous detection of dexamethasone and ciprofloxacin (section 2.6.3). Drug loading efficiency (DLE) was determined by the following equation:
A1 = Measured amount of drug A0 = Initial amount of drug
2.6.5 Particle Drug Release
0.5 mL of drug-loaded particles suspended in distilled water (pH 7.38) was pipetted into 0.5 mL MINI Dialysis Device (Slide-A-Lyzer 10K MWCO, Thermo
Scientific USA) (Figure 10).
57
Figure 10. Particle drug release apparatus.
The dialysis device was inserted into 2 mL tube filled with distilled water. The fully assembled dialysis apparatus was placed on a shaker (120 rpm) in 37°C incubator (n=10 per group) and sealed with parafilm (Figure 11). 1 mL of the distilled water solution was removed 2, 4, 7 days then weekly until end of release experiment (no longer exhibiting measurable drug release). Any remaining water solution was decanted, fresh distilled water was added and the apparatus was resealed with parafilm. The removed solutions were analyzed for drug concentration using the method described above for HPLC analysis.
58
Figure 11. Particle drug release experimental setup.
2.6.6 Polymer Drug Release
Following a similar set as for drug release (section 2.6.5), polymer drug release was completed without the use of a dialyzer. Polymer (liquid at room temperature) was measured directly into the 2 mL vial. The polymer sank to the bottom of the vial due to its density. The aqueous solution above the polymer was used for testing (Figure 12).
Figure 12. Polymer drug release setup.
59
2.6.7 Coated Fiber Drug Release
Same method for drug release is followed as the particle drug release (section
2.6.6). A 25.4 mm coated fiber was measured directly placed into the 2 mL vial (Figure
13). The aqueous solution surrounding the fiber was used for testing on the HPLC.
Figure 13. Coating fiber drug release setup.
2.7 CELL CULTURE
2.7.1 Human Dermal Fibroblasts
Normal, primary adult human dermal fibroblasts (ATCC® PCS-201-012TM) were cultured and maintained as previously described and per the manufacturer’s protocol with fibroblast growth kit – low serum (ATCC® PCS-201-041). One bottle of Fibroblast
Basal Medium (ATCC PCS-201-030) was supplemented with 5 ng/mL rh FGF b, 7.5 mM L-glutamine, 50 ug/mL ascorbic acid, 1 ug/mL hydrocortisone hemisuccinate, 5 ug/mL rh insulin, and 2% fetal bovine serum. Media was also supplemented with 10 ug/mL gentamicin, 50 ng/mL amphotericin, 10 units/mL penicillin and 33 uM phenol red. Cells were plated and grown to ≥80% confluency prior to assessment.
60
2.7.2 Tracheal Epithelial Cells
Normal, human primary bronchial/tracheal epithelial cells (ATTC® PCS-300-
010TM) were cultured and maintained as previously described and per manufacturer’s protocol with brochial/tracheal epithelial cell growth kit (ATTC® PCS-300-040). One bottle of Airway Epithelial Cell Basal Medium (ATCC PCS-300-030) was supplemented with 500 mg/mL human serum albumins (HSA), 0.6 mM linoleic acid, 0.6 mg/mL lecithin, 6 mM L-glutamine, 0.4% extract P, 1.0 mM epinephrine, 5 mg/mL transferrin,
10 nM T3, 5 mg/mL hydrocortisone, 5 ng/mL rh EGF, 5 mg/mL rh insulin, 10 μg/mL gentamicin, 50 ng/mL amphotericin B, 10 units/mL penicillin, 10 μg/mL streptomycin, and 33 μM phenol red. Cells were plated and grown to 70-80% confluency prior to assessment.
2.7.3 RAW Mouse Macrophage Cells
RAW 264.7 mouse macrophage cells (ATCC® TIB-71) were cultured and maintained as previously described and per manufacturer’s protocol with Dulbecco’s
Modified Eagle’s Medium (ATCC 30-2002) with 10 % fetal bovine serum. Cells had fresh medium replaced or added every 2-3 days and subcultured with a subcultivation ratio of 1:3-1:6.
2.8 BIOCOMPATIBILITY ASSAYS
2.8.1 XTT Assay
This colorimetric assay is based on the conversion of water-soluble XXT (2,3- bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanide) reagent from a colorless substance to an orange formazan product by actively respiring cells. Pre-
61 optimization assays were first performed to determine optimal conditions for maximum absorbance values (Table 5).
Table 5. Optimization parameters for XTT assay.
Condition Parameter Optimization Cell Inoculation Time 48 hours Cell Density 1x104 – 1x105 cells/well Reaction Time 5 hours Specific Absorbance Filter 475 nm Non-Specific Absorbance Filter 660 nm
Pre-optimization standards, calculations, and standard curves for XTT assay can be found in Appendix F. Following previously described methods and manufacturer’s protocol, cell culture is completed on 96-well, black walled, clear flat bottom plates (Corning,
USA). Plates are analyzed using a Synergy HT Plate Reader (BioTek, Winooki, VT,
USA) where specific absorbance is calculated as follows:
All standards and samples were run in triplicate. Pre-optimization standards can be found in Appendix E.
2.8.2 Alamar Blue Assay
This assay is based on the permeability of trypan blue through dead cells membranes and the dye’s impermeability through viable cell membranes (Figure 14).
62
Figure 14. Example image depicting live and dead cell using Alamar Blue Assay.
Alamar Blue Assay was performed using the Countess® Cell Counter (Invitrogen,
Carlsbad, CA, USA) (Figure 15).
Figure 15. Countess cell counter.
10 μL of cell suspension and 10 μL of trypan blue were pipetted into the test chamber of the Countess® hemocytometer glass slide (Figure 16).
63
Figure 16. Countess® hemocytometer chambered slides.
Slides are analyzed using microscopy inside the Countess® machine and mathematical analysis is performed. Live cell count, dead cell count, cell viability, cell size, and cell size distribution are reported.
2.8.3 Live/Dead Fluorescent Staining
A two color fluorescence cell viability assay was used to image and discriminate live cells (green) and dead cells (red) (Live/Dead Viability/Cytotoxicity Kit, Life
Technologies, Carlsbad, CA, USA). The green fluorescent component, calcein AM is enzymatically covered to calcein by intracellular esterase activity of live cells. The red fluorescent component, ethidium homodimer-1 (EthD-1), can only permeate through damage cell membranes. EthD-1 has a high binding affinity for DNA and red fluorescence is emitted when bound. Images were taken using the EVOS® FL Auto with
Onstage Incubator (Life Technologies, Grand Island, NY, USA) equipped with FITC and
Texas Red® filters. Nine randomly selected images were scanned and analyzed per well.
Live/Dead staining pre-assay optimization can be found in Appendix F.
64
2.9 IN-VITRO INFLAMMATION ASSESSMENT
The concentration of TNF-alpha and IL-1B in culture supernatants of RAW264.7 cells after LPS stimulation were analyzed with commercially available ELISA kits according to manufacturer’s protocol. Briefly, RAW264.7 cells were seeded in a 24-well plate with 0.5 mL growth medium and allowed to attach for 24 hrs. 1 μg/mL of LPS was added to each appropriate well and incubated for an additional 24 hrs. The cells were given the appropriate treatment (free dexamethasone in media, dexamethasone loaded
NPs in media, or no treatment) and incubated for a final 24 hrs and supernatants from control, and LPS-stimulated cells were collected and analyzed for chemokine levels via
ELISA kits. Samples were diluted 1:10 and absorbance read at 450 nm using a microplate reader (Synergy HT, Biotek, Winooski, VT, USA) with samples and standard run in triplicate.
2.10 MICROBIAL CULTURE
2.10.1 Escherichia coli
Escherichia coli (Carolina Biological Supply Company #124300) were received freeze-dried and were reconstituted with Luria broth (LB) at room temperature. Tubes with broth and cells were incubated on a shaker plate operating at 2000 rpm at 37°C overnight. Tubes were then removed, the cap was loosened, and the culture tube was held upright in a test tube rack. To initiate a fresh culture, a sterile inoculating loop was dipped into the culture tube and streaked onto an agar plate. Plates were streaked in three directions pulling from the end of each previous streak in order to decrease the concentration of bacteria each subsequent direction (to form individual colonies for
65 experimentation). Streaked plates were then inverted in an incubator for 37°C overnight before use.
2.10.2 Klebsiella pneumoniae
Klebsiella pneumoniae (Carolina Biological Supply Company #155095A) were received freeze-dried and were reconstituted with LB at room temperature. Tubes with broth and cells were incubated on a shaker plate operating at 2000 rpm at 37°C overnight.
Tubes were then removed, the cap was loosened, and the culture tube was held upright in a test tube rack. To initiate a fresh culture, a sterile inoculating loop was dipped into the culture tube and streaked onto an agar plate. Plates were streaked in three directions pulling from the end of each previous streak. Streaked plates were inverted in an incubator for 37°C overnight.
2.10.3 Moraxella catarrhalis
Moraxella catarrhalis (Carolina Biological Supply Company #154928) were received freeze-dried and were reconstituted with Brain-Heart Infusion at room temperature. Tubes with broth and cells were incubated on a shaker plate operating at
2000 rpm at 37°C overnight. Tubes were then removed, the cap was loosened, and the culture tube was held upright in a test tube rack. To initiate a fresh culture, a sterile inoculating loop was dipped into the culture tube and streaked onto an agar plate. Plates were streaked in three directions pulling from the end of each previous streak. Streaked plates were then inverted in an incubator for 37°C overnight.
2.10.4 Pseudomonas aeruginosa
Pseudomonas aeruginosa (Carolina Biological Supply Company #155205A) were received freeze-dried and were reconstituted with LB at room temperature. Tubes
66 with broth and cells were incubated on a shaker plate operating at 2000 rpm at 37°C overnight. Tubes were then removed, the cap was loosened, and the culture tube was held upright in a test tube rack. To initiate a fresh culture, a sterile inoculating loop was dipped into the culture tube and streaked onto an agar plate. Plates were streaked in three directions pulling from the end of each previous streak. Streaked plates were then inverted in an incubator for 37°C overnight.
2.11 KIRBY-BAUER DISK DIFFUSION SENSITIVITY ASSAY
Standards
The standard procedure from the National Committee of Clinical Laboratory
Standards (NCCLS) was performed on each bacterial strain using unsupplemented
Mueller-Hinton agar (Sigma Aldrich, USA). First, a standard plate was developed for each bacterial strain using literature minimum inhibitory concentration (MIC) values. A stock solution of ciprofloxacin in deionized water was made (10 mg/μL). The stock solution was then diluted to generate at least four standard solutions of known concentrations for each bacterial strain standard plate in relation to MIC value (Table 6).
Table 6. Concentration standards for bacterial sensitivity assay.
Bacterial Concentration Standards (ng/μL) Strain 1 2 3 4 5 6 7 E. coli 0 1.25 2.5 5 10 20 40 K. pneumoniae 0 1 10 15 20 M. catarrhalis 0 5 10 20 40 80 P. aeruginosa 0 50 150 300 600 1000
25 μL of stock solution was pipetted onto a blank sterile sensitivity disk (Carolina
Biological Supply Company, Burlington, North Carolina, USA) and drier for one hour. A
67 single isolated bacteria colony was vortexed in 1 mL Luria Broth (LB). A sterile cotton swab was dipped into LB and dried surface of the agar plate was inoculated. Dried disks were spaced evenly apart from one another (Figure 17) and gently pressed with forceps onto the agar to ensure adherence.
Figure 17. Sensitivity disk arrangement.
Plates were inverted and incubated for approximately 24 hours at 37°C. Incubated plates were then imaged using a BioRad ChemidocTM MP Imaging System (BioRad, Hercules,
CA, USA). Inhibition zone length was determined using ImageJ (Rasband, W.S., ImageJ,
U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/,
1997-2015) measuring from edge of sensitivity disk to inhibition zone edge (n=3 per disk). Linear regression analysis was performed to determine the biologically active concentration of ciprofloxacin (BAC) using GraphPad Prism 6 (GraphPad Software, La
Jolla, California, USA). Standard plates and corresponding linear regression for all bacterial strains can be found in Appendix G.
Experimental Plates
Solutions from degrading polymers were used in experimental sensitivity disks to simulate in-vitro degradation and release. Poly(Fumaric Acid) (PFA), Poly(Ciprofloxacin
68
Fumaric Acid) (PCFA) and Poly(Gadodiamide Ciprofloxacin Fumaric Acid) (PGCFA) were used with PFA serving as the control. 0.5 mL of pre-polymer was placed in a 2 mL tube with 1 mL of deionized water. Samples were placed in a shaker oven operating at
120 rpm at 37°C. Deionized water containing degradation products were removed after 2,
4, 7, and 14 days. Ciprofloxacin concentration was determined in each solution using
HPLC and solutions were frozen until needed.
Each degradation time point had a separate plate. A known volume of water solution was pipetted on blank sterile sensitivity disk dependent on bacterial strain (Table
7). These volumes were chosen based on the hypothesis ciprofloxacin concentration and known minimum inhibitory concentrations (MIC) of ciprofloxacin for each bacteria strain. If volume was less than 20 μL, additional deionized water was pipetted onto disk to equal 20 μL to ensure even distribution on sensitivity disk.
Table 7. Known volume of degradation product solution pipetted for sensitivity assay.
Bacterial Strain Volume of Pipetted Solution (μL) 1 2 3 4 5 E. coli 0 1 2 5 10 K. pneumoniae 0 1 10 15 20 M. catarrhalis 0 1 2 5 10 P. aeruginosa 0 15 20 30 35
Using the linear regression equation determined from the standard plate BAC was determined. BAC was compared from PCFA and PGCFA at each time point using a two- tailed student t-test with the hypothesis that:
H0:
HA:
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CHAPTER THREE Results
3.1 AIM 1 PARTICLE FORMULATION FROM A FACTORIAL DESIGN FOR
ANTI-INFLAMMATORY RELEASE ON A STENT
3.1.1 Development of Particle Formulation Technique
A modified solvent displacement technique was investigated for particle formulation (Figure 18). Using this modified technique with a surfactant concentration of
0.25% and sonication for 20 min (Method A) resulted in particles with an average diameter of 550±30 nm. Modifying the technique a second time with a surfactant concentration of 0.35% and a sonication time of 30 min (Method B) resulted in particles with an average diameter of 130±10 nm (Table 8).
Figure 18. PLGA nanoparticles formulated using (A) Method A and (B) Method B.
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Table 8. Morphological characteristics of resulting nanoparticles for modified solvent displacement technique. Data shown mean±SD, n= 100.
Average Hydraulic Diameter Area Diameter (µm) (µm2) (nm) Method A (0.25%, 20 min) 0.55±0.3 8.3±3.2 1.92±0.7 Method B (0.35%, 30 min) 0.13±0.1 0.17±0.1 0.1±0.02
The nanoparticles formulated from this basic technique were investigated using
SEM and ImageJ software only. These two techniques are proof of concept that PLGA nanoparticles can be formed with a simple one-step solvent displacement technique. It also shows that surfactant concentration and sonication time are critical factors in determining nanoparticle size distribution.
Nanoparticles were dipped coated onto annealed drawn PLLA fibers to determine if they would associate with the fiber surface without the addition of a coating. Without any manipulation to the fiber or coating addition some nanoparticles were successfully dip coated onto the PLLA fiber (Figure 19). A better method of coating is necessary for a more uniform distribution and satisfactory coverage of particles onto the fiber surface.
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Figure 19. SEM image showing PLGA nanoparticles from Method B dip-coated onto PLLA fiber.
Method B produced particles of a small diameter and size distribution that is more appropriate for the intended application. All particle synthesis will use a surfactant concentration of 0.35% and a sonication time of 30 min unless otherwise specified.
Due to the availability and prior knowledge known of Corbion Purac® PLGA, a sensitivity study investigating the optimal ratio of polymer to drug was investigated. Two copolymer ratios (50:50 and 75:25) were used with the following ratios of dexamethasone to PLGA in formulation: 8:1, 4:1, 2:1, and 1:1. Effective hydrodynamic diameter, zeta potential, drug loading efficiency, Tg, and drug release lifetime were reported (Table 9).
Effective diameter, drug loading efficiency, and drug release lifetime increased as the ratio of polymer to drug approaches 1:1. There is no significant change in Tg. The magnitude of zeta potential increases as the ratio of polymer to drug approaches 1:1, indicating higher stability.
Table 9. Particle characteristics from polymer drug ratio sensitivity study. Data shown mean±SD.
Effective Diameter Zeta Potential Drug Loading Efficiency T Drug Release Polymer: Drug g (μm) (mV) (%) (°C) Lifetime Purac 50:50 8:1 32.3±4.4 -24.3±2.0 60.0±2.3 50.0±2.3 14 months (98%) 4:1 4.4±1.1 -30.0±0.9 17.0±0.2 48.6±0.3 3 months (97%) 2:1 1.6±0.2 -36.1±1.4 22.7±0.3 46.9±0.1 3 months (99%) 1:1 1.3±0.2 -36.9±1.3 19.1±0.2 46.1±0.3 3 months (98%) Purac 75:25 8:1 9.2±1.5 -9.4±0.6 76.4±2.4 49.8±1.0 8 months (50%) 4:1 1.8±0.6 -21.8±2.6 35.8±2.4 49.6±0.4 3 months (13%) 2:1 1.6±0.7 -35.8±2.6 37.6±1.6 51.6±0.5 3 months (12%) 1:1 1.2±0.4 -36.3±0.9 40.5±0.4 49.9±0.2 3 months (11%)
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Looking at the dexamethasone release profiles, there is a threshold in which the ratio of polymer to drug affects the release kinetics. The release from the 8:1 ratio of
PLGA to Dexamethasone was significantly slower than all the other ratios (Figure 20).
Figure 20. Dexamethasone release from Corbion Purac® 50:50 particles altering polymer to drug ratio. Data shown mean±SD, n=10.
All particle groups formulated with Corbion Purac® 50:50 exhibited a zero order release kinetic as the best fit (Table 10).
Table 10. Coefficients of linear regression for Corbion Purac® 50:50 particles. Linear regression equation: y(x) = Ax+B. Correlation coefficient shown as r2.
A B r2
8:1 0.15 1.39 0.95 4:1 1.06 8.78 0.97 2:1 1.09 6.24 0.98 1:1 1.09 7.75 0.98
73 74
Dexamethasone release from ratios of 4:1 or less was much quicker than the ratio of 8:1 as indicated by the slope of the linear regression equations. This may elucidate a threshold point in which the ratio of PLGA to dexamethasone is affecting drug release.
With a ratio of 8:1 or higher, dexamethasone release kinetics are likely both polymer degradation and diffusion controlled. Ratios of 4:1 or less are likely solely dependent on diffusion for dexamethasone release.
The same trends for effective hydrodynamic diameter, zeta potential, drug loading efficiency, and Tg are observed in Corbion Purac® 75:25 as Corbion Purac®
50:50. Dexamethasone release from particles formulated with Corbion Purac® 75:25 was significantly slower than observed with Corbion Purac® 50:50 (Figure 21).
Figure 21. Dexamethasone release from Corbion Purac® 75:25 particles altering polymer to drug ratio. Data shown mean±SD, n=10.
All groups formulated with Corbion Purac® 75:25 also exhibit a zero order release as the best fit (Table 11).
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Table 11. Coefficients of linear regression for Corbion Purac® 75:25 particles. Linear regression equation: y(x) = Ax+B. Correlation coefficient shown as r2.
A B r2
8:1 0.24 0.62 0.96 4:1 0.22 0.69 0.96 2:1 0.22 0.99 0.95 1:1 0.20 0.90 0.97
The dexamethasone release kinetics were nearly equivalent for all ratios of polymer to drug using this copolymer. Due to the higher ratio of lactic acid to glycolic acid in the polymer formulation, the release of dexamethasone was both degradation and diffusion controlled regardless of polymer to drug ratio. In using Corbion Purac® 75:25 there was no observed advantage in drug release using a lower ratio of polymer to drug within this reported range.
3.1.2 Characterization of Particles Using a Factorial Design
Following the methods described in section 2.2 a factorial design was used to screen for the optimal particle formulation for dexamethasone delivery on a stent. PLGA
50:50 and 75:25 copolymers were obtained from two manufacturers (Corbion Purac® and Evonik Resomer®). Three novel technique modifications (distillation 1 min,
76 distillation 15 min, blending with PGFA) were compared to one control technique
(evaporation).
Particle Formulations with Corbion Purac® PLGA 50:50
Both distillation and polymer blending modifications had significant impact on particle characteristics with formulations from Corbion Purac® PLGA 50:50 (Table 12).
Distillation techniques increased particle effective diameter, drug loading efficiency, and
Tg. Distillation did not have a clear impact on zeta potential (A2&A3). The blending of
PGFA with PLGA decreased particle size from a micron scale to a nanometer scale (A4).
The blending decreased zeta potential, and Tg. Polymer blending did not affect drug loading efficiency.
Corbion Purac® PLGA 50:50 control group (A) released dexamethasone with a
98% cumulative release in 14 months (Figure 22A). In the same time frame, both distilled groups released 75-80% of total dexamethasone. Blending with PGFA reduced the drug release lifetime to 2 weeks with a 99% release of loaded dexamethasone (Figure 22B).
Control and distillation group release concentrations fit in the appropriate therapeutic dosage windows for dexamethasone treatment for adult and some pediatric airway intervention (Figure 23A). The blended group did not provide ample drug concentration for adult or pediatric dosage (Figure 23B).
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Table 12. Characterization of Corbion Purac® PLGA 50:50 particles: control (A), distillation with 1 additional minute of heating (A2), distillation with 15 additional minutes of heating, and hybrid particles blended with PGFA (A4). Data shown mean±SD.
Experimental Effective Zeta Potential Drug Loading Tg Release Lifetime Groups Diameter (µm) (-mV) Efficiency (%) (°C) A 32.3±4.4 24.3±2.0 60.0±2.3 50.0±2.3 14 months (98%) A2 54.8±8.3* 20.78±2.9 82.0±9.6* 90.0±1.2* 14 months (80%) A3 69.9±10.2* 25.7±2.9 87.3±1.3* 193.5±3.3* 14 months (75%) A4 0.75±0.05* 12.5±3.5* 57.2±0.8 26.1±0.3 2 weeks (99%) * Asterisk indicates statistical significance compared to the control (p<0.05).
Figure 22. Cumulative dexamethasone release of (A) Purac PLGA 50:50 microparticle groups and (B) PLGA50:50/PPF nanoparticles. Data shown mean±SEM, n=10.
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Figure 23 Concentration of dexamethasone release of (A) Purac PLGA 50:50 microparticle groups in adult therapeutic window and (B) PLGA50:50/PPF nanoparticles in pediatric therapeutic window. Data shown mean±SEM, n=10.
Particle Formulations with Corbion Purac® PLGA 75:25
Both distillation and polymer blending modifications had significant impact of particle characteristics with formulations from Corbion Purac® PLGA 75:25 (Table 13).
Distillation techniques increased particle effective diameter, and Tg. Distillation did not have a clear impact on zeta potential or drug loading efficiency (B2 &B3). The blending of PFA with PLGA decreased particle size to a nanometer scale (B4). The blending decreased zeta potential, and Tg. Polymer blending increased drug loading efficiency.
Corbion Purac® PLGA 75:25 control group (B) released dexamethasone with a
40% cumulative release in 7 months (Figure 24A). In the same time frame, both distilled groups released 40% of total dexamethasone, not different from the control. Blending with PFA altered the drug release lifetime to 6 months with a 92% release of loaded dexamethasone (Figure 24B). Control, distillation, and blended groups did not provide ample drug concentration for adult or pediatric dosage (Figure 25A&B).
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Table 13. Characterization of Corbion Purac® PLGA 75:25 particles: control (B), distillation with 1 additional minute of heating (B2), distillation with 15 additional minutes of heating (B3), and hybrid particles blended with PGFA (B4). Data shown mean±SD.
Experimental Effective Zeta Potential Drug Loading Tg Release Lifetime Groups Diameter (µm) (-mV) Efficiency (%) (°C) B 3.3±0.4 9.4±0.6 76.4±2.4 49.8±1.0 7 months (40%) B2 7.4±1.6 10.6±1.0 44.4±1.3 50.1±0.7 7 month (40%) B3 9.2±1.3 11.1±1.0 68.1±1.0 52.6±0.1 7 months (40%) B4 0.48±0.04 4.3±1.3 97.6±0.7 34.7±1.1 6 months (92%) * Asterisk indicates statistical significance compared to the control (p<0.05).
Figure 24. Cumulative dexamethasone release of (A) Purac PLGA 75:25 microparticle groups and (B) PLGA75:25/PPF nanoparticles. Data shown mean±SEM, n=10.
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Figure 25. Concentration of dexamethasone release of (A) Purac PLGA 75:25 microparticle groups in pediatric therapeutic window and (B) PLGA75:25/PPF nanoparticles in pediatric therapeutic window. Data shown mean±SEM, n=10.
Particle Formulations with Evonik Resomer® PLGA 50:50
Both distillation and polymer blending modifications had significant impact on particle characteristics with formulations from Evonik Resomer® PLGA 50:50 (Table
14). Distillation techniques increased particle effective diameter, drug loading efficiency and Tg. Distillation decreased zeta potential (C2&C3). The blending of PFA with PLGA decreased particle size to a nanometer scale (C4). The blending decreased zeta potential and Tg. Polymer blending increased drug loading efficiency.
Evonik Resomer® PLGA 50:50 control group (C) released dexamethasone with a 12% cumulative release in 3 months (Figure 26A). In the same time frame, both distilled groups released 7% of total dexamethasone. Blending with PFA did not alter the drug release lifetime. The blended release showed at 3 months a 12% release of loaded dexamethasone (Figure 26B). Control, distillation, and blended groups did not provide ample drug concentration for adult or pediatric dosage (Figure 27A&B).
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Table 14. Characterization of Evonik Resomer® PLGA 50:50 particles: control (C), distillation with 1 additional minute of heating (C2), distillation with 15 additional minutes of heating (C3), and hybrid particles blended with PGFA (C4). Data shown mean±SD.
Experimental Effective Zeta Potential Drug Loading Tg Release Lifetime Groups Diameter (µm) (-mV) Efficiency (%) (°C) C 2.6±0.4 50.1±1.1 78.0±1.6 45.6±1.2 3 months (12%) C2 10.5±1.4 11.5±1.8 88.7±3.3 108.4±5.2 3 months (7%) C3 11.7±1.5 19.5±1.5 99.0±0.4 115.1±4.0 3 months (7%) C4 0.78±0.03 4.3±1.3 97.4±1.2 24.6±2.8 3 months (12 %) * Asterisk indicates statistical significance compared to the control (p<0.05).
Figure 26. Cumulative dexamethasone release of (A) Evonik PLGA 50:50 microparticle groups and (B) PLGA50:50/PPF nanoparticles. Data shown mean±SEM, n=10.
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Figure 27. Concentration of dexamethasone release of (A) Evonik PLGA 50:50 microparticle groups in pediatric therapeutic window and (B) PLGA50:50/PPF nanoparticles in pediatric therapeutic window. Data shown mean±SEM, n=10.
Particle Formulations with Evonik Resomer® PLGA 75:25
Both distillation and polymer blending modifications had significant impact on particle characteristics with formulations from Evonik Resomer® PLGA 75:25 (Table
15). Distillation techniques increased particle effective diameter, drug loading efficiency in one group, and Tg (D2&D3). Distillation decreased zeta potential. The blending of
PFA with PLGA decreased particle size to a nanometer scale (D4). The blending decreased zeta potential and Tg. Polymer blending increased drug loading efficiency.
Evonik Resomer® PLGA 50:50 control group (D) released dexamethasone with a 18% cumulative release in 7 months (Figure 28A). In the same time frame, both distilled groups released 11-14% of total dexamethasone. Blending with PFA did alter the drug release lifetime. The blended release showed at 8 months a 50% release of loaded dexamethasone (Figure 28B). Control and distillation groups release concentrations that fit in the appropriate therapeutic dosage windows for dexamethasone treatment for adult
83 and some pediatric airway intervention The blended group did not provide ample drug concentration for adult or pediatric dosage (Figure 29A&B).
Table 15. Characterization of Evonik Resomer® PLGA 75:25 particles: control (D), distillation with 1 additional minute of heating (D2), distillation with 15 additional minutes of heating (D3), and hybrid particles blended with PGFA (D4). Data shown mean±SD.
Experimental Effective Zeta Potential Drug Loading Tg Release Lifetime Groups Diameter (µm) (-mV) Efficiency (%) (°C) D 54.9±7.5 20.8±3.0 65.2±1.5 50.0±0.3 7 months (18%) D2 62.5±8.6* 1.0±1.0* 76.6±3.2* 54.8±0.1 7 months (14%) D3 102.6±11.8* 1.4±1.0* 69.1±3.2 55.2±0.1 7 months (11%) D4 0.69±0.04* 7.8±1.1* 81.8±3.7* 43.5±1.1 8 months (50%) * Asterisk indicates statistical significance compared to the control (p<0.05).
Figure 28. Cumulative dexamethasone release of (A) Evonik PLGA 75:25 microparticle groups and (B) PLGA75:25/PPF nanoparticles. Data shown mean±SEM, n=10.
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Figure 29. Concentration of dexamethasone release of (A) Evonik PLGA 75:25 microparticle groups in pediatric therapeutic window and (B) PLGA75:25/PPF nanoparticles in pediatric therapeutic window. Data shown mean±SEM, n=10.
Optimal Theranostic Nanoparticles
The blending formulation with Corbion Purac® 50:50 and Evonik Resomer® 75:25 copolymers demonstrated better characteristics for their use as dexamethasone delivery carriers on a stent. Both copolymers were further investigated with PGFA for theranostic purposes. Following prior methods large porous microparticles were formed (Figure 30).
Figure 30. SEM of PLGA 50:50/PGFA microparticles at (A) 4500X and (B) 9000X.
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Sonication time was then increased to 45 min to produce particles of acceptable size. The increase in sonication time produced nanoparticles with satisfactory drug delivery characteristics (Table 16).
Table 16. Characterization of PLGA/PGFA nanoparticles. Data shown mean±SD.
Effective Zeta Potential Drug Loading Tg Polymer Diameter (nm) (-mV) Efficiency (%) (°C) Purac® 50:50 758±46 -12.5±3.5 57.2±0.9 26.1±0.3 Resomer ®75:25 250±50 -4.3±1.3 97.6±0.7 34.7±1.1
Due to the small effective hydrodynamic diameter and high drug loading efficiency, Corbion Purac® 75:25 blended with PGFA was further investigated for theranostic capabilities. SEM imaging showed that formulation resulted in a wide size distribution (Figure 31). A centrifuge filter was use to obtain particles of approximately
250 nm. Polymeric theranostic nanoparticles (PTNPs) exhibited a three month release of
97% of loaded dexamethasone follow a zero order release model (Figure 32).
Figure 31. SEM of dexamethasone-loaded polymeric theranostic nanoparticles.
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Figure 32. Cumulative dexamethasone release of PTNPs. 97% of loaded drug is released in three months following a zero order release model. Data shown mean±SD, n=10.
With the inclusion of contrast medium initiated polymerization polymer, PGFA,
PTNPs were examined for use in MRI imaging via a phantom study. Using a phantom of six concentrations of PTNPs suspended in water, linear regression analysis was performed to determine gadolinium relaxivity coefficients (Figure 33). PTNPs exhibited a T1 relaxivity coefficient of 4.85 and a T2 relaxivity coefficient of -1.33.
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Figure 33. Determination of Gd relaxivity coefficients using T1 and T2 maps (top) generated from phantom using linear regression analysis (bottom) from six concentrations.
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3.2 AIM 2 DESIGN A MULTI-DRUG RELEASE COATING FOR A
BIORESORBABLE STENT.
3.2.1 Material Characterization of Poly(fumaric acid) (PFA)
The PFA synthesis showed chemical structure different than prior described
PPF.60, 132-133 The polymer product from PFA synthesis was also a viscous liquid, unlike that previously described which a was solid. This is confirmed via FTIR with a large hydroxyl group peak at 3448 cm-1 (Figure 34). Further examination of the 1H-NMR spectra reveals the presence of propylene glycol (PG), thus the synthesized polymer product is poly(fumaric acid) (PFA) with associated PG. PFA and PG are discernable from PPF with all noted peaks associated with PG and a significant hydroxyl peak that are not present in PPF literature (Figure 35 labels 1-4) . In the synthesis methoxyethane is lost as a waste product along with ethanol in the first step of the reaction. Methoxyethane in the reaction environment loses two protons and condenses to form denatured propylene alcohol. Hence the actual waste products of the reaction are: ethanol, denatured propylene alcohol, and PG. It is indiscernible if 2-butenedioic acid (2E) or fumaric acid or a combination thereof is polymerized because they are stereoisomers. Due to the starting reactants it is most probable that fumaric acid is polymerized. The molecular weight of the synthesized PFA was 612 Da. DSC shows a Tg of -34.83±2.6°C (Figure 36).
89
Figure 34. 1H-NMR spectra of PFA.
Figure 35. FTIR spectra of PFA.
90
Figure 36. Typical DSC curve of PFA.
PFA exhibited temperature dependent behavior for all rheological experiments.
PFA viscosity showed nonlinear behavior within a shear rate range of 0.1-1000 μN·m
(Figure 37). The viscosity of this material was dependent on shear rate and was not a constant coefficient, thus PFA is a Non-Newtonian fluid. PFA did not exhibit a yield stress and displays shear thinning (viscosity decreased with increased stress) within this range (Figure 38A). The behavior of storage modulus (G’) and loss modulus (G”) for
PFA (Figure 38B&C) were independent of strain rate, which classifies this material as a pseudoplastic. G’ describes the elastic properties and G” the viscous properties of the system. At 37°C, the value of G” was always greater than G’. Therefore the viscous properties dominated the elastic properties i.e. the material behaved more like a viscous fluid than an elastic solid. When a load is applied, energy is lost (G”) in the form of heat.
The amount of stored energy (G’) cannot compensate for the amount of energy lost (G”)
91 therefore plastic deformation occurs.153 This behavior was further confirmed in strain and frequency sweeps (Figure 39 & Figure 40). There was also no noticeable decay in viscosity or compliance over time for each given temperature (Figure 40).
Figure 37. Assessment of PFA viscosity using a broad torque range of 0.1 – 1000 μN·m.
Figure 38. Assessment of PFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s.
92
Figure 39. Assessment of PFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%.
Figure 40. Assessment of PFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min.
The degradation of PFA was linear in aqueous environment at 37°C (Figure 41).
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Figure 41. Degradation kinetics of PFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10).
3.2.2 Material Characterization of Poly(gadodiamide fumaric acid) (PGFA)
1H-NMR was used to confirm structure, finding only mild peak shifts of signature peaks in PGFA compared to PFA due to the presence of gadolinium (Figure 42). Compared to the PFA spectra, PGFA has signature peaks at 736 cm-1 indicating the presence of gadolinium and a dual peak around 3500 cm-1 indicating the presence of amide and hydroxyl bonds (Figure 43). DSC showed a Tg of -38.13±2.2°C (Figure 44).
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Figure 42. 1H-NMR spectra of PGFA.
95
Figure 43. FTIR spectra of PGFA.
Figure 44. Typical DSC curve of PGFA.
PGFA viscosity showed nonlinear behavior within a shear rate range of 0.1-1000
μN·m (Figure 45). PGFA also showed similar Non-Newtonian pseudoplastic fluid behavior in strain, frequency, and time sweeps similar that observed for PFA (Figure 46-
96
Figure 48). PGFA exhibited temperature dependent rheological behavior, however temperature had less of an impact than observed for PFA.
Figure 45. Assessment of PGFA viscosity using a broad torque range of 0.1 – 1000 μN·m.
Figure 46. Assessment of PGFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s.
Figure 47. Assessment of PGFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%.
97
Figure 48. Assessment of PGFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min.
Normalizing the data, PGFA degraded at a lower rate than PFA in aqueous environment at 37°C (Figure 49). The inclusion of gadodiamide in the synthesis reaction resulted in a polymer with significantly higher molecular weight. Using equivalent molar ratios of reactants, there was a difference in degradation kinetics between polymers. The presence of gadodiamide in the polymer chain may hinder hydrolytic degradation slowing overall degradation kinetics.
98
Figure 49. Degradation kinetics of PGFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10).
3.2.3 Material Characterization of Poly(ciprofloxacin fumaric acid) (PCFA)
The PCFA 1H-NMR spectrum was indistinguishable from PFA spectrum due to the similar hydrogens present in both polymer structures (Figure 50). Incorporation of ciprofloxacin was therefore confirmed via FTIR with the signature peak at 1077 cm-1 in
PCFA spectra, indicative of a carbon-fluorine bond (Figure 51). The Mw of the synthesized PCFA was 1360 Da. PCFA is also a low viscosity liquid at room temperature with a Tg of -46.9±0.9°C (Figure 52).
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Figure 50. 1H-NMR spectra of PCFA.
100
Figure 51. FTIR spectra of PCFA.
Figure 52. Typical DSC curve of PCFA.
PCFA showed Non-Newtonian pseudoplastic behavior for all rheological testing including stress, strain, frequency, and time-dependent sweeps (Figure 53-Figure 56).
PCFA showed temperature dependent rheological behavior similar to PGFA.
101
Figure 53. Assessment of PCFA viscosity using a broad torque range of 0.1 – 1000 μN·m.
Figure 54. Assessment of PCFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s.
Figure 55. Assessment of PCFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%.
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Figure 56. Assessment of PCFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min.
The degradation of PCFA was linear (Figure 57). PCFA degraded faster than
PFA. The addition of ciprofloxacin was equimolar to zinc chloride, thus the inclusion of ciprofloxacin significantly increased polymer Mw compared to control (PFA). It is likely that the addition of ciprofloxacin altered the rate of hydrolytic degradation of the polymer chain. Cumulative release of ciprofloxacin from PCFA followed the same mathematical model that mirrored the degradation with a zero order release being the best fit (Figure
58).
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Figure 57. Degradation kinetics of PCFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10).
Figure 58. Cumulative ciprofloxacin release of PCFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10).
3.2.4 Material Characterization of Poly(potassium iodide fumaric acid) (PKIFA)
The PKIFA 1H-NMR spectrum was indistinguishable compared to PFA spectrum due to the similar hydrogens present in both polymer structures (Figure 59).
Therefore incorporation of potassium iodide was confirmed via FTIR with the signature
104 peak split at 3569 cm-1 in PKIFA spectrum, indicative of a carbon-iodine bond (Figure
60). The Mw of the synthesized PCFA was 1200 Da. PCFA was also a low viscosity liquid at room temperature with a Tg of -47.9±1.3°C (Figure 61).
Figure 59. 1H-NMR spectra of PKIFA.
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Figure 60. FTIR spectra of PKIFA.
Figure 61. Typical DSC curve of PKIFA.
PKIFA showed Non-Newtonian pseudoplastic behavior for all rheological testing including stress, strain, frequency, and time-dependent sweeps (Figure 62-Figure 65).
PKIFA showed temperature dependent rheological behavior similar to PGFA and PCFA.
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Figure 62. Assessment of PKIFA viscosity using a broad torque range of 0.1 – 1000 μN·m.
Figure 63. Assessment of PKIFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s.
Figure 64. Assessment of PKIFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%.
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Figure 65. Assessment of PKIFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min.
3.2.5 Material Characterization of Poly(gadodiamide ciprofloxacin fumaric acid)
(PGCFA)
The PGCFA 1H-NMR spectrum was indistinguishable compared to PFA spectrum due to the similar hydrogens present in both polymer structures (Figure 66).
Incorporation of gadodiamide and ciprofloxacin was therefore confirmed via FTIR
(Figure 67). PGCFA spectrum indicated a carbon-fluorine bond indicative of
-1 -1 ciprofloxacin at 1050 cm and a signature gadolinium peak at 776 cm . The Mw of the synthesized PCFA was 1376 Da. PCFA was also a low viscosity liquid at room temperature with a Tg of -43.2±01.4°C (Figure 68).
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Figure 66. 1H-NMR spectra of PGCFA.
109
Figure 67. FTIR spectra of PGCFA.
Figure 68. Typical DSC curve of PGCFA.
PGCFA showed Non-Newtonian pseudoplastic behavior for all rheological testing including stress, strain, frequency, and time-dependent sweeps (Figure 69-Figure
72). PGCFA showed temperature dependent rheological behavior.
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Figure 69. Assessment of PGCFA viscosity using a broad torque range of 0.1 – 1000 μN·m.
Figure 70. Assessment of PGCFA (A) storage modulus (G’), (B) loss modulus (G”), and (C) viscosity using a frequency range of 0.1 – 100 rad/s.
Figure 71. Assessment of PGCFA (A) storage modulus and (B) loss modulus using a strain range of 0.1 – 30%.
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Figure 72. Assessment of PGCFA viscosity and compliance using a constant strain of 0.1% and constant frequency of 1 rad/s for 5 min.
The degradation of PGCFA was nonlinear (Figure 73). PGCFA degraded faster than both PCFA unexpectedly and PFA. It is likely that the addition of gadodiamide and ciprofloxacin altered the rate of hydrolytic degradation of the polymer chain. Cumulative release of ciprofloxacin from PGCFA followed the same mathematical model that mirrored the degradation with a second order release being the best fit (Figure 74).
Figure 73. Degradation kinetics of PGCFA in deionized water (pH 7.4) at 37°C. Raw data with computer nonlinear regression (n=10).
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Figure 74. Cumulative ciprofloxacin release of PGCFA in deionized water (pH 7.4) at 37°C. Raw data with computer linear regression (n=10).
3.2.6 Mechanical Properties of Drawn PLLA Fiber
Average stress-strain curve of the annealed 180 μm PLLA fiber is presented in
Figure 75 and the mechanical properties in Table 17.
Figure 75. Average stress strain curve of control annealed 180±0.01 μm PLLA fiber (Data shown mean±SEM, n=20).
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Table 17. Mechanical properties of annealed fibers (Data shown mean±SEM, n=20).
Experiment UTS f E (MPa) Sy (MPa) y (mm/mm) Group (MPa) (mm/mm) Control 2700±31 155±4 0.08±0.002 481±13 1.01±0.03
DSC was used to evaluate thermal properties of drawn, annealed PLLA fibers.
Briefly, sample and reference were equilibrated at 10°C, ramped to 225°C at 10°C/min, held isothermal at 225°C for one minute and ramped down to 10°C at 50°C/min. Control fiber results can be found in Table 18. A typical DSC curve is shown in Figure 76. These results are to be compared to coated fibers in the future for the assessment of structural integrity with the addition of a coating.
Table 18. Control fiber DSC results. Data shown mean±SD, n=20.
Enthalpy Heat Enthalpy of Experiment Tg Tc Tm of Capacity Crystallization Group (°C) (°C) (°C) Melting (J/g·°C) (J/g) (J/g) Control 62.4±0.4 106.7±0.7 178.0±0.1 0.6±0.1 31.8±6.0 49.1±9.9
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Figure 76. Typical DSC curve of annealed PLLA fiber.
3.2.7 Characterization of Dexamethasone Releasing PLGA Coatings
Two coating methods were developed to improve biocompatibility of PLLA stents by targeting the inflammatory response with incorporated dexamethasone. Both coatings were PLGA but one was made porous with the addition of sucrose while the other remained nonporous. It was hypothesized that the porous and nonporous stent coatings would weaken the mechanical properties of PLLA fiber (Ø0.10±0.01 μm) and the cumulative drug release would be faster for porous coating than the nonporous coating.
The glass transition temperature of the PLGA was 42.0±0.9ºC. The PLGA and dexamethasone were visualized separately to provide control spectras (Figure 77A&B).
The presence of dexamethasone was confirmed in the coating mixture with the signature peak at 1663 (C=O) cm-1 (Figure 77C). Average film porosity was calculated as
92.2±1.01% from volume and density measurements (Table 19) with an average pore size of 0.4±0.2µm. SEM of the porous coated fiber showed no distinguishing features or
115 artifacts created on the surface by the dip coat method (Figure 78A). Further magnification verified a coating of high porosity with random pore distribution (Figure
78B).
Figure 77. FTIR readings of (A) PGLA with observed peaks at 2996, 1756, 1455, 1384 cm-1 and (B) Dexamethasone with a peak at 1660 cm-1 and (C) FTIR measurement of PGLA embedded with Dexamethasone. Dexamethasone peaks of 1661 cm-1 are detected.
Table 19. Film density, volume, and porosity measurements.
Sample Density (g/cm3) Volume (cm3) Porosity (%) 1 1.156 0.360 92.1 2 1.132 0.293 90.2 3 1.164 0.325 92.8 4 1.172 0.233 93.4 5 1.151 0.379 91.7 6 1.167 0.317 93.0 7 1.174 0.313 93.6 8 1.152 0.347 91.8 9 1.150 0.378 91.6 10 1.149 0.387 91.6 Average 1.157±0.013 0.333±0.047 92.2±1.0
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Figure 78. Showing the coated PLLA fiber surface at (A) 430x with no distinguishing features of a porous coating and (B) at 5000x showing the porous coating of the PLGA film.
Average stress-strain curves of the coated and control fiber are presented in Figure
79. The porous coating resulted a reduction in fiber mechanical properties compared to the nonporous coating method (Table 20). Regardless, neither coating generated an elastic modulus and mechanical properties that were significantly different than the control.
Figure 79. Average stress-Strain curves for coated PLLA fibers. A slight weakening trend is observed with the coating of PLGA but not significantly different (Data shown mean±SEM, n=20 per group, p<0.05).
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Table 20 Mechanical properties of PLLA fiber and coated PLLA fiber (Data shown mean±SEM, n=20 per group,* indicates p<0.05).
Experiment E (MPa) Sy (MPa) y (mm/mm) UTS (MPa) f (mm/mm) Group PL-32 7183±190 180±4 4.2±0.1 353±11 49±3.1 NP-PLGA 6559±291 148±5* 3.5±0.1* 332±13 49±0.03 P-PLGA 6503±198 137±5* 3.9±0.1 287±16* 42±0.04
Cumulative release profiles indicated the porous coating had a significantly greater release of dexamethasone during weeks 2-8 compared to the nonporous coating
(Figure 80). The degradation of the coatings was captured at different time points (Figure
81). Fiber diameter changes were quantitatively assessed (Table 21). A significant reduction in coating thickness from initial to week 8 was observed in porous coated fibers but not non-porous coated fibers. In Figure 81, micro-fracturing was noted in the porous fibers and some fracturing was observed in the non-porous fibers at regional locations.
However, the surface coating was primarily uniform. The concentration of drug per fiber was calculated as NP-PLGA of 0.17±0.06 mg/ml, and P-PLGA of 0.18±0.07 mg/ml.
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Figure 80. Displaying the cumulative drug release of dexamethasone on a Porous and Non-porous Coating of PLGA on PLLA Fibers. The porous coating showing a significantly faster release from 2-8 weeks (Data shown mean±SEM, n=10 per group, p<0.05).
Figure 81. Showing the morphological change of the PLGA surface from initial and 8 weeks during degradation 1000X.
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Table 21 Fiber and coating diameter at time intervals (Data shown mean±SEM, n=5 per group, p<0.05).
Fiber Diameter Initial Coating Week 8 Coating
(μm) (μm) (μm) Non-porous 100.0 ± 0.1 132.7 ± 3.6 123.0 ± 3.2 Porous 96.3 ± 1.0 129.1 ± 1.2 97.8 ± 0.7*
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3.3 AIM 3 BIOCOMPATIBILITY STUDIES OF PARTICLE FORMULATION
AND COMPOSITE COATING DETERMINED FROM AIM 1 AND AIM 2.
3.3.1 In-vitro Biocompatibility with Human Dermal Fibroblasts
Polymer Films
Indirect contact of human dermal fibroblasts showed minimal cytotoxic effects
(Figure 82). Regardless of concentration, the viability of human dermal fibroblasts indirectly contacted with PGPF was not significantly different from the control. Both
PLLA and PLGA exhibited reduced viability at 1 mg/mm3 or higher. PLLA and PLGA exhibited a concentration dependent response however PGPF did not in the indirect contact XTT assay. Similar results were observed in human dermal fibroblasts in direct contact at concentrations 2 mg/mm3 or lower (Figure 83). At 5 mg/mm3 or higher PGPF was very cytotoxic to human dermal fibroblasts. A concentration dependent response was observed for all polymers in the direct contract XTT assay. The drop in viability associated with PGPF is likely attributed to an increase in pH associated with the breakdown of fumaric acid chains. It is also more relevant to use a concentration of 1 mg/mm3 for a stent coating. The higher concentration of material was used to find a threshold that human dermal fibroblasts could tolerate.
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Figure 82. Biocompatibility assessment via XTT assay of PLLA, PLGA, and PGPF films indirectly contacted with human dermal fibroblasts. Data shown mean±SD, n=3 per group. ISO standard required minimum viability noted with dashed line at 80%.
Figure 83. Biocompatibility assessment via XTT assay of PLLA, PLGA, and PGPF films directly contacted with human dermal fibroblasts. Data shown mean±SD, n=3 per group. ISO standard required minimum viability noted with dashed line at 80%.
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Fluorescent microscopy images of human dermal fibroblasts in direct contact with polymer films utilizing live/dead stain are shown in Figure 84. Noticeable cell spreading was observed at low concentrations of polymer films with a decrease in spreading and cell count as concentrations increased. Fluorescent image cell counts are quantified in Figure 85. Human dermal fibroblasts in contact with PLLA showed a decrease in live cells and an increase in dead cells as the concentration of polymer film increases. A similar result was observed with PLGA with an unexpected significant increase of cells at 1 mg/mm3. It is possible at this lower concentration a hyperproliferative response was provoked resulting in a cell count spike or this could be a statistical artifact. Live cell counts for human dermal fibroblasts in contact with PGPF were not concentration dependent; they remained consistent for all concentrations.
However, the dead cell counts were positively correlated to concentration, the number of dead cells increased as concentration increased. Cell viability was calculated using the ratio of live cell count to total cell count and the percentages are reported in Figure 86.
No cytotoxic effect was observed for all concentrations of PLGA and for all concentrations of PGPF except 10 mg/mm3. Cell viability fell below the required 80% in cell groups in contact with 2 mg/mm3 of PLLA or greater. The results from the quantified fluorescent images validated the observed concentration dependent XTT results.
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Figure 84. Fluorescent microscopy images of human dermal fibroblasts directly contacted with PLLA, PLGA, and PGPF using live/dead stain.
Figure 85. Live (green) and dead (red) cell counts from fluorescent microscopy images of human dermal fibroblasts seeded on PLLA (left), PLGA (middle), and PGPF (right) films. Data shown mean±SD, n=4 per group.
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Figure 86. Viability of human dermal fibroblasts directly seeded on PLLA (black), PLGA, (blue), and PGPF (green) films. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%.
PTNPs
Human dermal fibroblasts were also subjected to direct contact with various concentrations of PLGA/PGFA theranostic nanoparticles. The XTT assay showed no significant cytotoxic effects in the concentraton range of 0.00 – 1.00 mg/mL (Figure 87).
There was a slight decrease in human dermal fibroblast viability at 1.00 mg/mL.
However, it remained well above the required 80% established as the cytotoxic threshold.
Fluorescence microscopy images using live/dead stain showed very few dead cells in all images (Figure 88). There was a trivial decrease in cell density at 1.00 mg/mL.
Quantification of live and dead cell counts is shown in Figure 89. Similar to the polymer film results, a concentration dependent trend was observed with both live and dead cell counts for all PTNP concentrations. Nonetheless, calculated cell viability was validated, with all groups being well above 80% viable (Figure 90).
125
Figure 87. Viability of human dermal fibroblasts in direct contact with PLGA/PGFA PTNPs via XTT assay. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%.
Figure 88. Fluorescence microscopy images of human dermal fibroblasts directly contacted with varying concentrations of PTNPs using live/dead stain.
126
Figure 89. Live (green) and dead (red) cell counts from fluorescence microscopy images of human dermal fibroblasts in direct contact with PLGA/PGFA PTNPs. Data shown mean±SD, n=4 per group.
Figure 90. Viability of human dermal fibroblasts directly contact with PLGA/PGFA PTNPs via fluorescence microscopy. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%.
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3.3.2 In-vitro Biocompatibility with Tracheal Epithelial Cells
Polymer Films
Tracheal epithelial cells were cultured in direct contact with PLLA to simulate a bare bioresorbable stent and PGPF to simulate a coating. The XTT assay showed a concentration dependent decrease in cell viability for both PLLA and PGPF (Figure 91).
Cell viability fell below the required 80% for PLLA at concentrations 2 mg/mm3 or higher and 5 mg/mm3 or higher for PGFA. Fluorescence microscopy images of tracheal epithelial cells in direct contact with the polymer films utilizing live/dead stain are shown in Figure 92. The quantification of the images mirrored the XTT assay results showed the concentration effect in both live and dead cell counts for both polymers (Figure 93).
Calculated viability of tracheal epithelial cells from the cell counts indicated that PLLA was considered cytotoxic to tracheal epithelial cells at all concentrations (Figure 94).
PGPF concentrations 2 mg/mm3 or lower were not cytotoxic to tracheal epithelial cells but any concentration greater was (Figure 94).
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Figure 91. Biocompatibility assessment via XTT assay of PLLA (black) and PGPF (green) films directly contacted with tracheal epithelial cells. Data shown mean±SD, n=3 per group. ISO standard required minimum viability noted with dashed line at 80%.
Figure 92. Fluorescence microscopy images of tracheal epithelial cells directly contacted with PLLA and PGPF using live/dead stain.
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Figure 93. Live (green) and dead (red) cell counts from fluorescence microscopy images of tracheal epithelial cells seeded on PLLA (left) and PGPF (right) films. Data shown mean±SD, n=3 per group.
Figure 94. Viability of tracheal epithelial cells directly seeded on PLLA (black) and PGPF (green) films. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%.
130
PTNPs
Tracheal epithelial cells were also directly contacted with various concentrations of PLGA/PGFA theranostic nanoparticles. The XTT assay showed no significant cytotoxic effects in the concentraton range of 0.00 – 1.00 mg/mL (Figure 95). There was a decrease in human tracheal epithelial cell viability at 1.00 mg/mL however it was above the required 80% and considered not cytotoxic. Fluorescence microscopy images using live/dead stain showed minimal cell death in all images (Figure 96). There was a minor concentration dependent decrease in cell density. Quantification of live and dead cell counts is shown inFigure 97. Similar to human dermal fibroblast results, a concentration dependent trend was observed with both live and dead cell counts for all PTNP concentrations. Nonetheless, calculated cell viability was validated with all groups being well above 80% viable (Figure 98).
Figure 95. Viability of tracheal epithelial cells in direct contact with PLGA/PGFA PTNPs via XTT assay. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%.
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Figure 96. Fluorescence microscopy images of tracheal epithelial cells directly contacted with varying concentrations of PTNPs using live/dead stain.
Figure 97. Live (green) and dead (red) cell counts from fluorescence microscopy images of tracheal epithelial cells in direct contact with PLGA/PGFA PTNPs. Data shown mean±SD, n=4 per group.
132
Figure 98. Viability of tracheal epithelial cells in direct contact with PLGA/PGFA PTNPs via fluorescent microscopy. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%.
Coatings
Tracheal epithelial cells were also plated in direct contact with the composite coating materials. Subjecting the tracheal epithelial cells to both materials was used to observe if a compound effect on cell viability would be observed when cells were in direct contact with PGPF and PTNPs simultaneously. The XTT assay results of the composite coating formulations are shown in Figure 99. Coating formulations utilizing 2 mg/mm3 of PGPF or less and 0.75 mg/mL of PTNPs or less were not cytotoxic to tracheal epithelial cells. Using polymer concentrations from the prior experiement (1,2 5,
10 mg/mL) in combination with PTNPs fell below the required 80% viability. The concentration dependent behavior of both PGPF and PTNP is illustrated in Figure 100.
The optimal coating formulation that was biocompatible with tracheal epithelial cells was
1-2 mg/mm3 PGPF and 0.25-0.75 mg/mL PTNPs.
133
Figure 99. Biocompatibility assessment of coating formulations with tracheal epithelial cells via XTT assay. No material control shown as far left bar in each polymer concentration group with the polymer only control shown as horizontal striped bar in each group. Nanoparticle concentration increases from left to right in each group as indicated. Data shown mean±SD, n=3 per group. ISO standard required minimum viability noted with dashed line at 80%.
134
Figure 100. Viability of tracheal epithelial cells directly seeded on various PGPF with PTNP composite coating formulations. Data shown mean±SD, n=4 per group. ISO standard required minimum viability noted with dashed line at 80%.
3.3.3 In-vitro Inflammation Assessment with Mouse Macrophages
It is essential that the PTNPs controllably deliver dexamethasone to abate inflammation immediately upon particle deployment. To investigate the anti- inflammatory effect of dexamethasone loaded PTNPs after 24 h exposure; TNF-α and IL-
1β concentrations were measured in RAW 264.7 cells. PTNP concentrations were compared to free dexamethasone in media, which simulates systemic delivery. The experimental design including the expected concentrations of dexamethasone released from PTNP groups based on prior drug release studies is shown in Table 22.
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Table 22. Inflammation assessment treatment groups and ELISA cytokine results. Data shown mean±SD, n=3 per group. Asterisk indicates statistically significant from control (p<0.05).
Group Treatment Cytokine Concentration PTNPs Free DEX DEX Concentration TNF-α IL-1β
(mg/mL) (mg/mL) (mg/mL) (pg/mL) (pg/mL) A - - 0.00 35±1 369±52 B - - 0.00 104±6* 1018±61* C 0.25 0.07 66±3* 903±4* D 0.50 0.15 43±6 707±7* E 0.75 0.23 42±13 352±2 F 1.00 0.31 33±9 256±2 G 0.10 0.10 78±3* 1009±7* H 0.50 0.50 66±3* 759±7* I 1.00 1.00 50±3 491±2 J 2.00 2.00 35±6 489±4
Compared to the control (no treatment or LPS) a significant increase in TNF-α and IL-
1β was observed with LPS stimulation (Figure 101 & Figure 102 Group B). TNF-α concentrations of mouse macrophages exposed to PTNPs at a concentration of 0.50-1.00 mg/mL were not significantly different from the control (Figure 101 Group D-F). A concentration of 0.25 mg/mL significantly lowered TNF-α concentration compared to
LPS stimulated but not comparable to the control (Figure 101 Group C). Groups with free dexamethasone in media at a concentration of 1 and 2 mg/mL also lowered TNF-α concentrations to control levels (Figure 101 Groups I&J). Free dexamethasone concentrations of 0.10-0.50 mg/mL also lowered TNF-α but not comparable to the control. Similar results were observed with IL-1β ELISA with the exception that a PTNP concentration of at least 0.75 mg/mL was required to reduce IL-1β concentration to control levels (Figure 102). The ELISAs demonstrated that a concentration of 0.23 mg/mL of dexamethasone or a PTNP concentration of 0.75 mg/mL was required to maintain TNF-α and IL-1β at control levels in the presence of stimuli.
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Figure 101. The cell supernatant concentrations of TNF-α after LPS stimulation and 24 hr exposure to treatment with dexamethasone. Group A (control) received no LPS or treatment and group B received LPS stimulation only. Groups C-F were treated with PTNPs and groups G-J were treated with free dexamethasone in the media as described in Table 2. Dashed line indicates control cytokine concentration. Statistical significance (p<0.05) from control group is noted with asterisk. Data shown mean±SD, n=3 per group.
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Figure 102. The cell supernatant concentrations of IL-1β after LPS stimulation and 24 hr exposure to treatment with dexamethasone. Group A (control) received no LPS or treatment and group B received LPS stimulation only. Groups C-F were treated with PTNPs and groups G-J were treated with free dexamethasone in the media as described in Table 2. Dashed line indicates control cytokine concentration. Statistical significance (p<0.05) from control group is noted with asterisk. Data shown mean±SD, n=3 per group.
3.3.4 In-vitro Sensitivity Assessment of Airway Pathogens
All four bacteria strains were susceptible to ciprofloxacin. Standard curves demonstrated that E. coli was most susceptible followed by M. catarrhalis, K. pneumoniae, and P. aeruginosa (Figure 103). The standard results were consistent with current bacterial susceptibility literature.154 The standard curve for P. aeruginosa was color inverted due to the blue-green pigment produced by the bacteria. All experimental plates with degradation extracts from PCFA and PGCFA showed the formations of growth inhibition zones (See Appendix I). All bacteria except M. catarrhalis showed no inhibition zone formation with PFA degradation extracts (Appendix I). M. catarrhalis, unlike the other strains, is gram-negative. The susceptibility of M. catarrhalis is increased to PFA due to the difference in membrane permeability comparaed to gram-
138 positive strains. The formation of inhibition zones indicates that synthesis conditions and subsequent degradation did not denature the antibacterial properties of ciprofloxacin.
Calculated BAC reflects the active concentration of ciprofloxacin at the outer edge of the growth inhibition zone. BAC values in comparison to known MIC are shown in Figure
104.
Figure 103. Standard plate and curve for each bacteria strain. Data on standard curve shown as each replicate and linear regression equation with 95% confidence interval (n=3).
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Figure 104. Biologically active concentrations (BACs) of ciprofloxacin for 14 days from sensitivity assays. (A) Escherichia coli BAC with minimum inhibitory concentration (MIC) of 2 ng/μl. (B) Klebsiella pneumoniae BAC with MIC 8 ng/μl. (C) Moraxella catarrhalis BAC with MIC 2 ng/μl. (D) Pseudomonas aeruginosa BAC with MIC of 30 ng/μl. Statistical significance of student’s T-test (p<0.05) noted with asterisk. Data shown mean±SEM, n=9 per group.
All measured BAC values from PCFA were within the known MIC range for the full testing period. For all bacteria except P. aeruginosa, BAC values from PGCFA were above the MIC until day 4 then fell below the lower limit. The plates were a validation of the cumulative ciprofloxacin release profile. PGCFA released ciprofloxacin at a faster rate in a shorter period of time compared to PCFA. Plates with PGCFA degradation extracts exhibited large inhibition zones at day 2 and 4 and tapered by day 7. PCFA showed a relatively consistent inhibition zone length for each respective volume throughout the two weeks with slight increases in some samples at day 7 and 14.
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CHAPTER FOUR Discussion
4.1 PARTICLE FORMULATION FROM A FACTORAL DESIGN
4.1.1 Effects of Copolymer Ratio on Particle Characteristics
Microparticles formulated with PLGA 75:25 are larger in effective diameter than the PLGA 50:50 subject to the same processing conditions. This difference in diameter is likely due to the ratio of lactic acid to glycolic acid. Glycolic acid is more mobile in nature than lactic acid facilitating more side chain interactions resulting in tighter molecular packing. There is no significant difference in zeta potential between control
PLGA 50:50 and 75:25 groups. PLGA 50:50 formulation groups can be categorized as moderately stable with zeta potential values approximately -21 to -26 mV. The solvent removed via evaporation in 75:25 is also within this range.
Differences observed in drug loading efficiency values are likely attributable to the chemistry produced by the copolymer ratios. PLGA 50:50 contains a higher ratio of glycolic acid to lactic acid than PLGA 75:25. Glycolic acid is more mobile than lactic acid. Therefore PLGA 50:50 has a more mobile structure than 75:25, which allows for dexamethasone to bind to the backbone chain more efficiently. The addition of heat increases the relaxation of the backbone chain further increasing the binding efficiency.
The higher ratio of lactic acid in PLGA 75:25 is responsible for the steric hindrance blocking dexamethasone from binding. Drug release from PLGA follows diffusion- controlled release governed by the copolymer composition. To release, drug molecules need to diffuse through the polymer matrix. The diffusion coefficient depends on the size of the drug molecules, pore size of the polymer matrix, drug hydrophobicity, and
141 degradation rate of the polymer matrix.141 In an aqueous environment, PLGA degrades by random hydrolytic chain scission of its ester linkages. Glycolic acid contains more of these ester linkages, causing it to degrade more rapidly than lactic acid which has an increased number of carbon-carbon linkages.155 Lactic acid groups also are more hydrophobic due to a methyl side group, thus they degrade more slowly than glycolic acid groups in water.156 Microparticles formulated with PLGA 50:50 release dexamethasone significantly faster than PLGA 75:25. These results are in agreement with prior findings investigating lactic acid and glycolic acid ratio and particle drug release.157
Copolymer ratio has an important role in specific heat capacity and Tg. Specific heat capacity is the amount of heat energy required to change the temperature of a substance. DSC curves reveal that these copolymer ratios have different specific heat capacities. In agreement with prior work, the specific heat capacity of PLGA 75:25 microparticles is greater than PLGA 50:50.158 PLGA 75:25 has the ability to store more energy than 50:50 and requires more energy to transition. The incorporation of dexamethasone into PLGA 75:25 does not alter its capacity for energy storage. This is not the case for the PLGA 50:50 copolymer ratio, hence the Tg shift.
4.1.2 Effects of Thermal Processing on Particle Characteristics
This novel distillation technique removed THF at its boiling point of 65°C. This temperature is above the melting point of lactic acid (53°C) but below the melting point of glycolic acid (75-80°C).126-127 Therefore, there is structural relaxation of lactic acid groups during distillation. Structural relaxation of polymers at or above Tg in thin films and other microparticle formulations has been reported.123-125 In the presence of solvent
142 being removed, the lactic acid relaxation can lead to aggregation and fusion of particles that are in close proximity. This phenomenon is further accelerated due to the increased hydrophobicity of the lactic acid groups.
Within each copolymer ratio, dexamethasone releases significantly faster from microparticles with solvent removed by evaporation compared to distillation. According to the manufacturers without any manipulation or processing the PLGA 50:50 should degrade by three months and PLGA 75:25 by six months.159 Solvent removal via distillation considerably increases the lifetime of the polymer product as a microparticle.
As described previously, rapid solvent extraction during the formation of PLGA microparticles is analogous to thermal quenching and has been mathematically modeled.125, 128 When a microparticle is thermally quenched, particle density increases due to structural relaxation. The increase in density not only slows polymer degradation but limits the ability for dexamethasone to diffuse through the polymer matrix, slowing drug release.
Processing technique is the most influential factor in particle Tg. An increase in
Tg is observed in 50:50 as a function of time in which solvent removal via distillation occurs, with a similar but weaker trend in 75:25. Solvent extraction by distillation leads to molecular events similar to annealing. An increase in crystallinity and degradation time has been shown in annealed polyester fibers as a function of annealing temperature.75 PLGA quenching via solvent removal is analogous to thermal quenching of pure amorphous polymer from a molten state.125 It is reasonable that an increase in crystallinity or molecular stability by quenching occurs in particles formulated from this method. Polymer crystallinity by X-Ray diffraction was not determined in this study but
143 may provide further insight for future work regarding microparticle degradation kinetics.
One major limitation of this research is that heat sensitive therapeutic agents (such as biomolecules) can be destroyed during the distillation step of this technique. Therefore this technique is limited to microparticles formulation with therapeutic agents that can resist heat up to 65°C and retain their bioactivity.
4.1.3 Effects of PLGA/PGFA Blend on Particle Characteristics
These PTNPs blend commercially available drug delivery polyester (PLGA) with an MRI-visible polyester, PGFA, synthesized from a new technique known as contrast medium initiated polymerization.134 Polymer-based systems have been widely used as therapeutic agent carriers or bioimaging agents. However, very few polymeric systems have been successful in combining both of these functionalities and the majority of these systems are not biodegradable. The goal of this strategy is to utilize both the therapeutic component and the imaging component of PTNPs to improve bioresorbable stents for the airway applications. Current bioresorbable stent designs lack either of these components.
It is critical, especially for pediatric applications, to have a design that can degrade overtime in the body to eliminate removal procedures and allow for growth. The degree of control over the characteristics of PTNPs developed using the PLGA/PGFA blend permits the formation of particles capable of long-term controlled drug release and imaging without compromising bioresorbability or biocompatibility.
According to the manufacturers, each of the four PLGA copolymers have a median inherent viscosity of 0.2 g/dL. Using polymers of the same inherent viscosity eliminates any variation that could affect particle characteristics using a solvent displacement technique. Other studies have demonstrated that differences in polymer
144 viscosity can affect particle size distribution and drug loading or entrapment.76 All formulation parameters were kept constant including polymer concentration, drug concentration, sonication power, sonication time, solvent amount, surfactant concentration, post-formation washing, and storage procedures.
PGFA is lower in molecular weight (~1000 Da) than any of the PLGA copolymers (at least 10 kDa). Thus blending with PLGA results in polymer chains that are more flexible and less stable than PLGA alone. Less stable polymer chains on the molecular level result in less stable particle geometry. The inclusion of contrast medium gadodiamide likely has effects on particle stability as well. Gadodiamide is a large metal chelate that establishes a network between linear chains of fumaric acid. It is a hydrophilic compound that, when blended, can affect the orientation of polymer chain side groups due to its size and hydrophilicity. In the presence of aqueous surfactant, glycolic acid and lactic acid chains are oriented towards the particle core to be shielded from water interaction. With more chain flexibility and hydrophilicity, PGFA is drawn towards the outer portion of the particle during formation leading to a change in surface charge or zeta potential.
The blending of PLGA and PGFA increased drug loading efficiency for all polymer groups. The inclusion of gadodiamide not only increases polymer chain mobility but also provides more binding sites for dexamethasone. Gadodiamide has four carboxylic acid side chains capable of being hydrolyzed and make bonds. During synthesis, the four carboxylic acid chains produce a mobile polymer network with the gadodiamide and the linear chains of fumaric acid. When blended with PLGA, more hydrophobic lactic acid and glycolic acid groups are made more mobile and available for
145 binding. Dexamethasone is also hydrophobic and will bind with available lactic acid and glycolic acid chains that are not available when formulation occurs without PGFA.
4.1.4 Development and Future Prospects of Polymeric Theranostic Nanoparticles
At present, MRI is a leading non-invasive imaging technique for clinical diagnoses, characterization, and treatment monitoring in the body. There is a great need in the development of theranostic systems in nanoparticle technology to compliment MRI imaging. A theranostic system is a dual-function system that can act as both a diagnostic device and drug delivery vehicle.160 At this time there are numerous research groups working in nanoparticles research, however the development of polymer based theranostic systems has not been achieved.160 Here we show the formulation and characterization of polymeric theranostic nanoparticles (PTNPs). PTNPs have many potential applications, one being stent coatings. Coating with PTNPs can render a medical device MRI-visible while also acting as a therapeutic agent carrier. Beyond coatings, PTNPs have the potential to be used as an injection or in combination with other medical devices and technologies. The advantage of PTNPs over other MRI-visible particle technologies is that the system is biodegradable. Currently, other particles being investigated are composed of potentially hazardous or non-degradable materials such as super paramagnetic iron oxide (SPIO), precious metals, silica, and naked contrast agents.161 Several SPIO and Gd-chelate based particle agents have been FDA approved but safety concerns have been raised with regard to the toxicity, biodistribution, and body clearance of these particles.161 Unlike these agents, PTNPs will degrade over time into non-toxic products that can be used in various cellular processes or cleared by the body,
146 and the concentration of gadolinium used if 100-1000 times less than current clinically used dosages.
4.2 DESIGN OF A MULTI-DRUG COATING FOR A BIORESORBABLE STENT
4.2.1 New Class of Radiopaque and MRI-Visible Polymers Utilizing Contrast
Medium Initiator Polymerization
Current radiopaque polymer systems can be categorized into three classes: heterogeneous polymer blends with radiopacifiers, radiopaque polymer-salt complexes, and polymers composed of radiopaque monomers. A direct covalent bond between the radiopacifying agent and the polymer is not formed in the first two classes. Without a direct bond these systems generate non-homogeneous mixtures susceptible to heavy element leaching, imaging artifacts, and material failure.162 Early development of dental and orthopedic applications used gold gauze, lead foil, and fine metal wires inserted into poly(methyl methacrylate) (PMMA).105 The inconsistency of material mixing resulted in imaging artifacts and material failure. Thus sifting fine grains of metals into the mixture was tried to improve the homogeneity of the system. Bowen and Cleek, in 1972, examined blending powdered glasses with a high content of barium, lead or bismuth forming a polymer slurry prior to polymerization.163 Although their work was promising, the inhomogeneity of these polymer slurries led to failures at the interface of the polymer and additive.104-105
Subsequent investigations sought to improve polymer homogeneity by modifying monomers with radiopacifying agents prior to polymerization. Early approaches relied on the addition of halogen groups such as iodine and bromine.105 Other approaches relied on
147 the polymerization of heavy metal (i.e. bismuth, tin, lead) containing monomers.103
Although this improved the radiopacity of polymers, oxidation of the heavy metals led to inflammation and foreign body response upon implantation.164-165 Using a heavy metal in the polymer is plausible for generating a homogeneous distribution of radiopacifying agents. However, modifying the monomers prior to polymerization can significantly alter polymer system properties (i.e. viscosity, mechanical strength, glass transition temperature).
In order to preserve polymer system properties and the homogeneity of the system, using a contrast medium containing a heavy metal as a polymerization initiator is a viable option. By definition an initiator starts a chemical reaction by undergoing a chemical change to provide free radicals. In our synthesis, gadodiamide can provide free radicals from four available carboxylic acid groups. The generation of free radicals initiates transesterification and renders gadodiamide able to bind to the polymer chain.
Gadodiamide has four available binding sites; thus possibly forming it to a network between linear chains of fumaric acid. Covalent bonds lock gadodiamide between linear polymer chains preventing leaching as well as preserving polymer structure of PFA.
Our results confirm the structure of PGFA. Signature bonds associated with fumaric acid and PG are detected via 1H-NMR and FTIR with the addition of associated gadodiamide signature bonds. It is possible that polymer chains polymerize in a strictly linear fashion with gadodiamide. PGFA degrades linearly and there are not significant differences in rheological behavior from PFA. However, due to the configuration of gadodiamide bonds, it is more logical that a network of linear chains is formed. This novel synthesis method has created a new generation of polymers visible via MRI and
148 fluoroscopic techniques. Contrast medium initiated polymerization offers a solution for homogeneous distribution of heavy elements and preservation of polymer properties.
4.2.2 Radiopaque and MRI-Visible Polymer Applications in Medicine
Clinicians continue to widen the ever-expanding scope of MRI techniques that can be used for clinical diagnoses, characterization, monitoring, and treatment of various illnesses. X-ray and other fluoroscopic techniques dominated in prior years. Radiopaque polymers were therefore developed to improve the clinical utility of non-metal medical devices. Extensive research has been devoted to these radiopaque polymers, particularly idio-polymer compounds for medical applications.100 With the shift from fluoroscopic imaging to MRI, there is a need for MRI-visible materials. Limited published research is available in regards to MRI-visible devices; some examples include catheters, the REVA stent, and radiation dosimetry gels.166-168 This research has sought to meet this need within the realm of available core technology, that is, bioresorbable polymers.
The unique composition of PGFA allows a multitude of potential applications including coatings, injections, nanoparticles, and device design. Synthesizing PGFA at a higher Mw increasing the Tg could result in an in-situ crosslinked, visible polymer as shown in prior studies with PPF.59-60, 133, 169 This research describes the development and
MRI visualization of PGFA/PLGA nanoparticles that have theranostic potential. The additional incorporation of therapeutic agents (such as dexamethasone) will render these particles theranostic. If controlled therapeutic agent delivery can be achieved, this system can provide a drug delivery vehicle visible on both MRI and X-ray images.
There have been many concerns raised regarding the biodistribution and clearance of contrast mediums and nanoparticles.161 This system without a therapeutic
149 agent can serve as a research tool for biodistribution and clearance research. A polymeric theranostic system can overcome the disadvantages associated with conventional MRI contrast mediums. Advanced MRI imaging techniques are leading to the discovery and development of new therapeutic agents and therapies in humans and animals. Combining the benefits of diagnostics with the ability to treat a disease has defined a new field of research known as theranostics.
4.2.3 Effects of Therapeutic Agents on Polymer Thermal and Rheological Properties
Understanding of the rheological behavior of a polymer is useful in the evaluation of its suitability in processing environments and applications. In the construction of polymeric medical devices, thermally processing of polymer (such as injection molding, extrusion, annealing, etc.) is essential. Liquid polymers also can be used as injectable materials. Rheological properties, especially shear viscosity (η), have important effects on thermal and other processes.170 Rheological behavior of amorphous and semi-crystalline polymers is assessed in one of three ways: melt, shear, or extensional (acoustic) rheology. Liquid polymers (like PGFA and PFA) are tested on a shear rheometer without the addition of a solvent. Solid polymers are typically examined via melt rheometer but can also be examined via shear rheometer if dissolved in a solvent.
All synthesized polymers from this research exhibit Non-Newtonian pseudoplastic system behavior. These polymers do not exhibit a yield stress and polymer viscosity decreased with increased stress (shear thinning). Storage (G’) and loss moduli
(G”) indicated in all polymers behave more like a viscous fluid than an elastic solid.
Many conventionally used polymers for biomedical devices are Non-Newtonian
150 pseudoplastic systems. Polycaprolactone (PCL), polyhydroxybutyrate-co- hydroxyvalerate (PHBV), polystyrene (PS), polyethylene (PE), polyamide (PA), chitosan, PMMA, and PLA are all Non-Newtonian pseudoplastics.170-174 The majority of these biomedical polymers also have higher G” values than G’ meaning they behave more like a viscous fluid than an elastic solid. PCL and PLA show slightly more elastic solid behavior than the other polymers.170-171 The behavior of chitosan in solution is very similar to PGFA; however, it is highly concentration dependent.174 The behavior of G’ and G” in PE and PA are frequency dependent unlike PGFA.173 All of these polymers except for chitosan in solution are solid at 37°C. Chitosan did exhibit similar rheological properties as a liquid polymer such as PGFA. The values for viscosity, storage and loss moduli for the solid structure polymers are much greater than the liquid polymers. Thus thermal processing is necessary in order to form these polymers into particular shapes and designs. PGFA does not require heat or solvent to flow but heat could be used to thermally crosslink PGFA into a solid structure. Future studies investigating thermal crosslinking and UV crosslinking agents are also being considered for a non-thermal crosslinking technique.
4.2.4 The Use of an Antibiotic as a Polymer Synthesis Initiator
Polymerization can be facilitated with a variety of initiators that can be environmentally induced or be a physical chemical compound. Environmental factors such as UV light or heat can facilitate polymerization. Photochemical activation utilizes
UV light by transferring energy to monomers via light absorption.175 Light is a powerful tool that is able to induce chemical transformations to alter chemical bonds to synthesize or modify polymers.176 Thermochemical activation applies the same principles of
151 photochemical activation however the energy source is heat. Many polymer types including polyesters for medical applications are photo or thermo-chemically polymerized. Polyurethanes for grafts, coatings, nanocapsules, and shape memory films have been formulated using photochemical initiation.175, 177 Modifications and synthesis of Poly(lactic acid) (PLA), Poly(glycolic acid) (PGA), and their copolymer ratios are widely used in medicine ranging from structural cardiovascular applications to therapeutic agent delivery.178-180 Recent advancements in photochemical crosslinking of
Poly(propylene fumarate) (PPF) have been developed for orthopedic and oncological applications.181-182
Regardless of these advancements, many of these medical grade polymers and their derivatives still lack MRI-visibility or radiopacity. The use of non-invasive imaging for medical diagnoses and treatment has become a vital technology that requires complementary polymers. The development of a new class of radiopaque and MRI- visible polymers using a new synthesis technique known as contrast medium initiated polymerization hold promise for fulfilling these needs.134 This technique substitutes a heavy element contrast medium as the polymerization initiator during synthesis. The direct attachment of the heavy element medium to the polymer chain renders the polymer visible using non-invasive imaging techniques. Contrast medium initiated polymerization has several advantages over other methods used to synthesize radiopaque polymer systems: 1) direct covalent bond formation between contrast agent and polymer chain, 2) homogeneous distribution of contrast agent, and 3) preservation of gross polymer structure. This methodology addresses many of the shortcomings of other radiopaque polymer systems that lead to imaging artifacts and material failure.104-105 As shown in
152 prior work, the metal chelate gadodiamide is able to facilitate polymerization of fumaric acid by providing free radicals from four available carboxylic acid groups, producing poly(gadodiamide fumaric acid) (PGFA).134
Using the same technique of initiator substitution, the antibiotic ciprofloxacin was investigated as a possible initiator with therapeutic potential. Ciprofloxacin has a lower molecular weight than gadodiamide and contains halogen fluorine. With only one available carboxylic acid end group ciprofloxacin can only add in linearly to the polymer chain. Linear addition can be confirmed with PCFA having a similar degradation profile to PFA. PCFA displays similar characteristics to PGFA despite the change in initiator.134
Ciprofloxacin was chosen as an initiator due to his broad antibacterial spectrum of activity. Other antibiotic compounds with reactive end groups can potentially be used as initiators using this synthesis technique.
4.2.5 Degradation of PGFA and Drug Release Kinetics of PCFA and PGCFA
Polymer degradation results from the chain scission process during which polymer chains are cleaved to form oligomers and finally form monomers.183 There are many different ways in which polymer degradation can occur: photo-, thermal-, mechanical, and chemical degradation.184 All biodegradable polymers contain hydrolysable bonds. Chemical degradation via hydrolysis and/or enzyme catalyzed hydrolysis is the most important degradation mechanism. The degradation of biodegradable polymers is complex. Water enters the polymer and can induce swelling.
Chemical degradation is then initiated by hydrolysis leading to progressive changes in the microstructure of the bulk polymer (cracks, pores, etc.). This process also produces a decrease of pH in and around these new microstructures due to the accumulation of acidic
153 degradation products. Some polymers such as PLLA degrade by bulk degradation in which autocatalysis of the center of the polymer degrades prior to the outer surface.
Others such as PLGA, PFA, and PGFA undergo surface degradation in which the polymer chains exposed at the exterior surface degrade at a rate that exceeds the degradation of the center.185 The release of oligomers and monomers leads to a decrease in molecular weight and weight loss of the polymer structure.
Polymer degradation kinetics is directly related to the polymer architecture. As shown in prior work with polyanhydrides, Mw of branched polymers were significantly higher than linear polymers of the same monomers.186 It was also noted that there were no noticeable changes in the physio-chemical or thermal properties in the branched polymer compared to the linear polymers.186 However, a difference in polymer degradation existed with the branched polyanhydride degrading significantly faster than the linear polymers.186 In this study, we observed similar results with PGFA and PFA. It is likely that PGFA was synthesized into a polymer network and can be directly compared to the parent polymer, PFA, which is a linear polymer. There was no significant difference in Tg or rheological behavior but PGFA has a higher Mw than PFA. Unlike results observed in the polyanhydrides, the linear polymer (PFA) degraded at a faster rate than the network
(PGFA) likely due to the inclusion of gadodiamide. Gadodiamide is very hydrophilic with four potential binding carboxylic acid side groups. These bonds will be hydrolyzed prior to fumaric acid chains thus hindering hydrolytic degradation of fumaric acid. The established network generated by the gadodiamide must be cleaved before the linear chain scission of fumaric acid.
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Therapeutic agent release will be dependent upon two factors, diffusion of the agent within the polymer and polymer chain degradation. Synthesizing the therapeutic agent directly into the polymer chain will require the formation of pores and the breaking of bonds to release the therapeutic agent. One key finding with regard to drug release was observed in the comparison of ciprofloxacin release between PCFA and PGCFA.
Ciprofloxacin concentration released from PGCFA was greater earlier than PCFA and the concentration released from PCFA was relatively constant throughout the experimental time period. This elucidates the role of gadodiamide in the structure of PGCFA. The faster release of the ciprofloxacin confirms that instead of binding to the fumaric acid backbone chain, ciprofloxacin more likely binds to one of the four available carboxylic acid groups in gadodiamide. The bonds of gadodiamide and ciprofloxacin are more susceptible to hydrolytic scission than bonds between fumaric acid and gadodiamide or another fumaric acid. For treatment in pediatric tracheomalacia, approximately one week of ciprofloxacin release will be appropriate to address the risk of post-surgical infection.
If there is increased risk of infection such as prior upper respiratory infection or chronic sinus complications, PCFA with a two week release may be more appropriate.
Regardless, local delivery of ciprofloxacin will greatly reduce the amount of ciprofloxacin needed and limit systemic exposure.
4.2.6 Current Stent Coatings and Coating Technologies
This work represents an effort to find solutions for inflammation and the exuberant proliferative response after the implantation of a stent or scaffold into a vessel.
The majority of stent implantations occur in the vascular system of the body and therefore research efforts regarding stent coatings are driven by cardiovascular needs.
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This work, although intended for the airway, must be compared to the current stent coating technologies that have been developed for cardiovascular applications at they are the most advanced and closest relevant clinical comparison.
The inflammatory response after implantation of a coronary stent has been well documented.187-190 The area around the stent struts is the most vulnerable for macrophage and leukocyte infiltration as well as excessive smooth muscle cell proliferation. The exact pathogenesis of this hyperproliferative response is still unknown. However, it is hypothesized that the abundance of inflammatory cells, particularly macrophages, may preserve the stimulus to cause hyperproliferation of smooth muscle cells.191
The inflammatory pathway for neointima growth around a stent is caused by catheter deployment, stent expansion and material interactions with the vessel lumen.
Studies suggested that novel devices such as self-expanding stents that do not require high balloon pressure inflation may reduce arterial injury.192 Another factor with the inflammation response after stent implantation is the biomaterial interaction with the arterial lumen. Topol et al 193 demonstrated that several aliphatic polyesters including
PLLA aggravate the vessel wall initiating the body’s inflammatory response. Others have investigated how the molecular weight of the polymer influences the inflammatory response and shown that low molecular weight PLLA has a higher inflammatory response in comparison to high molecular weight PLLA.194
Further studies attempted to address the ‘secondary’ inflammatory trigger by incorporating drug into the fibers of the stent. Su et al. incorporated curcumin to overcome the inappropriately high inflammatory response. 136 Although this method did
156 reduce cellular inflammatory responses, their closed-loop circulation model used a very dilute amount of blood cells and proteins in saline solution.
Metal stents have been coated with polymeric or naturally occurring biomaterials in order to improve hemocompatability and reduce neointimal thickening. Whelan et al. coated a metallic stent with phosphorylcholine to examine the potential of this biomimetic molecule in alleviating neointimal thickening and the inflammatory response associated with stent deployment. 195 Using a porcine model, implanted stents for 12 weeks indicated no significant differences between control (non-coated metallic stent) and coated stents in regards to intimal thickness, endothelializtion and the lack of stent thrombosis. This indicates that the phosphorylcholine coating had minimal role in the alleviation of inflammatory response.
Polymeric based coatings have demonstrated vast improvement in hemocompatability of metallic stents. With many clinical trials in progress, metallic stents with a PLGA based drug delivery coating is the most successful and widely used option in cardiac intervention. Significant advances in these polymeric coatings have further improved safety and clinical performance in newer-generation drug-eluting stents.106 Particularly the platinum-chromium stents with the PLGA everolimus-eluting coating are the most successful, dropping in-stent thrombosis rate to 0.4% at one year after deployment. Due to PLGA’s chemistry and degradation stability it provides an optimal vehicle for a stent coating. The fabrication of the double opposed helical PLLA in this study plays an important role in the biocompatibility of the stent. Minimal protein and platelet adhesion is observed in our robust closed-loop model of undiluted whole pig blood. Therefore the PLLA stent, even without any additional therapeutic agent coating,
157 has proven more biocompatible than a stent composed of drug-incorporated fibers or fiber containing drug reservoirs.
4.2.7 Effects of Coatings on Stent Fiber Mechanical Properties
Various studies have investigated incorporation of drug impregnation into fiber.112-116 This was proven to be difficult not only because many therapeutic agents cannot withstand the high temperatures necessary for processing but the end product fiber results with reduced mechanical properties.113-114 The same trend is observed in fibers that contain reservoirs for drug loading.115-117 As a result of these mechanical property shortcomings, studies began to investigate coatings consisting of therapeutic agents instead of direct incorporation into the polymeric structure. The use of coatings was particularly essential for improvement with inflammatory response management at the site of stent deployment.
Zilberman and Kraitzer developed a method that added a coating containing paclitaxel to fiber.135 They observed a decrease in mechanical strength in these fibers with the addition of this coating. A decrease in fiber mechanical strength was also observed in
Su et al. 136 with the addition of curcumin. On the contrary, Elsner et al. 137 developed a wound healing matrix with the aid of Bovine Serum Albumin (BSA). Their prior wound healing matrices displayed mechanical failure after three weeks but with the addition of
BSA demonstrated improvement of mechanical properties maintenance. In general, coatings have a significant role in abating negative side effects of injury; however, the method of which the therapeutic agent is incorporated with the device is crucial.
Our work has shown a critical finding among the mechanical properties of these coated fibers. In comparison to the control fiber group, there is no significant difference
158 in tensile strength between the two methods of coated fibers. Both methods expose the fiber to the solvent-dissolved coating mixture for only a few seconds, which is not enough time for solvent to facilitate a reaction between the polymers that may alter the mechanical properties. Future studies confirming whole stent mechanical properties with a coating will be necessary.
4.2.8 Characterization and Drug Release of Coated Stent Fibers
To achieve sustained drug release with no initial burst release, we relied on
PDLGA 7525 as shown to be successful in prior work.196 The 50/50 PDLGA copolymer contains more glycolic acid groups and fewer lactic acid groups along the polymer chain.
Therefore it is less hydrophobic than 75/25, enabling a quicker initial phase of release.
Biodegradation of PLGA proceeds by random hydrolytic chain scission of ester links.197 The PLGA polymer backbone consists of both C-C and C-O-C bonds.
Hydrolyzable groups such as a bond containing oxygen degrade much more efficiently and rapidly than C-C bonds. These C-C bonds generally require a catalyst such as heat, radiation, acids/bases or any combination of these factors to degrade.198 Initially PLGA undergoes surface degradation, wherein the polymer chains exposed at the exterior surface degrade faster than those at the center of the coating. In contrary, bulk degradation of semicrystalline PLLA degrades by autocatalysis with the surface remaining relatively resistant to degradation.199 For drug delivery, surface-eroding polymers offer two key advantages over semicrystalline polymers; the retention of mechanical integrity over the device’s lifetime and minimal toxic effects due to a low local concentration of degradation products and lower solubility.200
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Other polymer-based coatings containing phosphorylcholine coated stents release all drug content on the stent in a matter of days.201 The drug release kinetics achieved in the presented methods would be more favorable for maintaining a critical therapeutic window over the acute and beginning of the chronic phase of the inflammatory response.
The scope of this study however, was limited to the assessment of isolated PLLA coated fibers. There were no mechanical differences between coated and uncoated fibers; however, future studies investigating the fully coated stent are necessary.
4.2.9 Bioresorbable Devices Offer Better Interventions in Pediatric Airways
A temporary biocompatible, bioresorbable intraluminal device such as the DH BDS may be more suitable for use in pediatric tracheomalacia. Tracheomalacia patients typically develop increased tracheal stability with size and age, at which an intervention no longer becomes necessary.139 Bioresorbable materials would not require removal once the tracheal stability is achieved and would not create a growth impediment as seen with other permanent devices, such as metal stents. Traditional stenting with permanent materials, such as metal, can result in the creation of a long-standing foreign body capable of transmural erosion or delayed secondary infection.17 As shown in our results, the DH BDS does not elicit the same intensity of inflammation as the metal stent. The
Genesis® metal stent shows thickening of fibrotic tissue layers and disruption of the epithelium. Similar results were reported with Palmaz® stents in porcine tracheomalacia model.202 Using the Palmaz® stent, studies reported a 16-fold increase in airway resistance compared to a bioresorbable polymer scaffold and roughly 60% of animals had significant respiratory distress.139 Also drastic modification of tracheal layers was
160 observed (polypoid hyperplasia, loss of normal mucosal cilia, goblet cell hyperplasia) and secondary pulmonary infections (pneumonia and lung abscess).139
There remains controversy regarding the effectiveness of internal stenting compared to external stenting of the airway for tracheomalacia. Numerous reports have cited stent migration and collapse, tracheal stenosis from granulation tissue formation, and numerous secondary infections associated with internal stents.17, 203 It is well known that metal stents can lead to excessive inflammation after implantation comparative to bioresorbable stents in other applications such as cardiovascular interventions.46
Currently there are very limited studies that investigate internal stenting of the trachea with bioresorbable scaffolds. A bioresorbable stent, such as the DH BDS, offers a degradable option that is able to withstand the compressive forces of the trachea and the shear forces observed there. Internal stenting also does not require an open surgical procedure as is required for external stenting. Bioresorbable stents also offers the option of continued therapy beyond the device lifetime. For example, if the patient reaches the age and size in which the stent is no longer therapeutically beneficial (i.e. the stent is no longer the correct size, stent has degraded, etc.) and tracheal stability has not been reached; a second stent can be deployed with or without removal of the first stent.
The DH BDS is composed of PLLA and the proposed coating is composed of PLGA and PGCPF in unprocessed and nanoparticle form. Semicrystalline PLLA bulk degrades by autocatalysis with the surface remaining resistant to degradation.199 Prior in-vitro studies show degradation of the DH BDS degrades between 24-30 months.204 Other bioresorbable stent designs utilize weaker but faster degrading polymers such as polycaprolactone (PCL) and polyhydroxybutyrate (PHB).205-206 These designs in-vivo
161 show higher inflammatory responses comparative to PLLA and other metal stents. The
DH BDS will provide long-term structural integrity to facilitate the tracheal remodeling while degrading into low concentrations of lactic acid that can be absorbed and metabolized by nearby cells.
In contrast, the stent coating undergoes surface degradation. In surface degradation, polymer chains exposed at the exterior surface degrade prior to the center.185 This surface degradation is driven by random hydrolytic chain scission in which ester bonds are broken in aqueous environment. In therapeutic agent delivery, surface degrading polymers have two key advantages over semicrystalline bulk degrading polymers; they retain their mechanical properties over the polymer lifetime and minimal toxic effects are found locally due to the low concentrations of degradation products.200 Thus in this application, the coating should last no more than three months providing sufficient therapeutic agent delivery during acute and early chronic healing phases.
4.2.10 Coatings Can Improve Bioresorbable Stents for Airway Interventions
Stents are primarily used for cardiovascular applications. Coatings were introduced on coronary stents to reduce neointimal proliferation. The area in which the stent struts make contact with the lumen are susceptible to inflammatory cell intrusion and smooth muscle cell proliferation.187 The exact pathogenesis of this response is likely initiated by cell damage caused by catheter deployment, expansion of the stent, and the forceful interactions between device materials and the blood vessel wall.
An analogous situation is faced in the airway. The primary clinical issue with tracheal healing is the overly exuberant scar formation that could lead to tracheal
162 stenosis.26 After internal implantation of a scaffold or stent, tracheal remodeling occurs in three phases: 1) tracheal wall recovery, 2) re-epithelialization of the tracheal wall and epithelial coverage of the device, and 3) scar tissue formation. Atmospheric exposure presents a secondary obstacle in the prevention of excessive inflammation and scar tissue formation. Tracheal intraluminal devices are in contact with inspired air that can contain airborne pathogens.23-24 This greatly increases infection risk. Therefore, it is attractive to provide therapeutic agents to control both post-surgical inflammation and infection.
The majority of medical devices designed for the airway do not have a coating. Some of the devices have a silicon coating. The silicon coating helps prevent growth into the stent struts and to seal anastomotic leaks.207 The coating does not contain any therapeutic agents. In recent studies, coated cardiovascular stents have been investigated for the airway. Hyaluronic acid (HA) coated stainless steel stents have shown a significant reduction in thrombosis and neointimal proliferation.208-209 Studies conducted in a rabbit model suggested that HA coated stents may help reduced tracheal stenosis in patients without airway injury.210 The coating had no significant advantages for post-traumatic tracheal injuries.210 Sirolimus is another antiproliferative agent used for the prevention of in-stent stenosis in coronary arteries. Sirolimus-coated stents have also been considered for airway intervention.211
Thus far, all drug delivery coatings for tracheal devices focus on the delivery of antiproliferative agents to prevent tracheal stenosis. None of these coatings address infection. Our coating design offers four main therapeutic advantages. First, this system is a tailored drug delivery vehicle based on polymer composition. The molecular weight of the PGCPF and the formulation techniques used to make the nanoparticles will control
163 degradation time of materials and subsequently the drug release. Second, the system is designed utilizing only polyester materials. This eliminates irritation and dehydration of surrounding tissues that can be associated with other materials, such as hydrogels. Third, the design allows for multiple drug delivery. Using PGCPF as a matrix carrier for nanoparticles produces a composite with additional therapeutic and diagnostic potential.
Controlled release of dexamethasone additionally provides inflammation relief. Finally, synthesized materials contain an MRI-contrast agent. This will allow for visualization of
DH BDS without the use of metallic leads.
Ciprofloxacin and dexamethasone are fitting candidates as airway therapeutic agents. Ciprofloxacin is commonly prescribed by otolarynologists for respiratory infections. It is well established that ciprofloxacin penetrates well into airway tissues; local airway delivery might also be more efficacious.88 Dexamethasone is commonly prescribed as an anti-inflammatory for the airway and vasculature.89 Though dexamethasone is very effective, long-term systemic exposure can lead to side effects such as osteoporosis, dermal thinning, ophthalmological complications, and reduced growth velocity in children.90 Thus local delivery of dexamethasone would reduce potential side effects from systemic exposure while providing inflammation suppression.
An FDA approved combination product of ciprofloacin and dexamethasone is currently available for middle and outer ear infection in pediatrics.83 There are also documented cases in which this product or the combination of both components are used for endoscopic airway management.92, 94
The coating formulation we designed will provide one-week delivery of ciprofloxacin and up to three-month delivery of dexamethasone from a stent. Local
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delivery of these therapeutic agents will significantly lower the required dosage needed
for therapeutic benefits. These doses will be magnitudes lower than what would be
prescribed orally or intravenously. This greatly reduces the potential risk of side effects
associated with these therapeutic agents.
Many tracheal implants and devices are radiopaque. This allows for direct
assessment of the device using fluoroscopy or X-ray imaging. Most of these implants and
devices are composed of permanent materials that require surgical removal once no
longer therapeutically beneficial. Bioresorbable polymers provide a temporary
intervention that can degrade into non-toxic substances overtime. Most bioresorbable
materials are not radiopaque. The radiological detectability of polymers used in medical
implants and devices is limited by their density.100 Radiopacifying agents such as heavy
metals and highly polar salt complexes can be introduced in resins. The formation of
homogeneous mixtures of the radiopacifying agents and the polymer is difficult to
achieve due to incompatibility of mixing agents.101 The risks associated with contrast
agent use are toxic side effects related to the leaching of radiopacifying agents and device
failure due to incomplete mixing of materials.162
To synthesize our radiopaque polymer, we use a MRI-contrast medium in place of a traditional polymerization initiator. This removes any manipulation of monomers prior to polymerization and no solvents are required. Our chemical structure analysis via
FTIR and 1H-NMR show we maintain critical polymer bonds found in the parent polymer
(PPF). Thus we preserve polymer gross structure and ensure that leaching of the contrast agent cannot occur due to its direct synthesis to the polymer chain. The novelty in this
165 method is such that this device is capable of being imaged via fluoroscopy/X-ray and MRI due to the nanoparticles incorporated into the composite coating.
Current devices used for intraluminal stenting for the treatment of pediatric
tracheomalacia largely originated from cardiovascular applications. These devices remain
controversial due to their limited biocompatibility and mechanical properties. They also
remain an issue for pediatrics because the majority of these devices are composed of
permanent materials, which are problematic in a growing patient. In our pediatric
tracheomalacia rabbit model, we show that a bioresorbable DH BDS stent produced less
inflammation up to one month after implantation compared to a metal stent.
To further improve the DH BDS, a multi-drug release coating can be applied to combat infection risk and inflammation. Novel polymer synthesis including MRI-contrast and therapeutic agents provide solvent-less techniques for coating design. Formulating these newly synthesized polymers into PTNPs allows for a novel, completely bioresorbable strategy. This coating design, used in conjunction with the DH BDS will potentially mitigate the long-term risk associated with permanent devices while providing therapeutic agents locally, facilitating recovery and imaging capabilities.
4.3 BIOCOMPATIBILITY OF POLYMERIC PARTICLES AND COATING
MATERIALS
4.3.1 Current Biocompatibility Standards for Polymeric Materials in Medical
Applications
Materials used in the construction of medical devices intended for use on or in
the body are typically known as biomaterials. At present, the term biomaterial as coined
166 by the National Institute of Heath is defined as “any substance (other than a drug) or combination of substances, synthetic or natural of origin, what can be used for any period of time, as a whole or as a part of a system which treats, augments, or replaces any tissue, organ, or function of the body.”212 The required material properties of the devices are determined by the application and functional lifetime of the device. Device lifetime can range from temporary to infinite (permanent) use. Device application can fall into one of five major categories: 1) blood contact, 2) soft tissue contact, 3) orthopedic and dental, 4) organ specific, or 5) tissue engineering.213 Due to the complexity of many of these devices one material property alone is unlikely to lead to a successful device, whereas a lack of a single key property can lead to device failure.
In selection of polymeric materials for medical devices or device components, a comprehensive set of requirements must be met. These include both regulatory and operational requirements as well as function, mechanical, and safety. The Food and Drug
Administration requires manufacturers, medical laboratories, and other concerned institutions to assess and inspect polymers and the final constructed devices to ensure proper quality, workmanship, and safety for use.
The FDA classifies medical devices into three classes that are determined by the
“intended use” and “indications for use” of the device.214 When determining device class, risk is also a major factor. The lowest risk devices are generally classified into Class I
(band-aids, medical gauze, etc.), and Class III (stents, catheters, etc.) includes those of the greatest risk.214 Coatings on medical devices fall into another FDA category known as a
“combination device.” Coatings that include bioactive materials must be examined by the
Office of Combination Products within the FDA to cover the broad responsibilities in the
167 regulation of these combination products. A series of ISO standards for biological evaluation of medical devices was generated in 1995.215 These twenty available tests provide guidelines and testing protocols to ensure the safety and efficacy of medical devices. An outline of these standards can be found in Helmus et al.213
For the development of a composite coating for a bioresorbable pediatric stent,
ISO 10993-5 “Tests for Cytotoxicity: In vitro Method” was utilized. It is essential that the coating proved to be biocompatibile in-vitro before testing the material in-vivo. As shown in Figure 105, a material is considered to be non-cytotoxic if viability of cells exposed to the materials for 24 hrs is greater than or equal to 80%.
Figure 105. Cytotoxicity scale according to ISO 10993-5 Tests for Cytotoxicity: In vitro methods.
The novel polyesters designed and synthesized in the development of the composite coating must meet these stringent functional requirements. Selection of materials and material concentrations made with function requirements in mind will increase the safety and efficacy of the device design. It is also advantageous to have understanding of the historic context of current and past materials used in medical device design. New databases, standards, and online toolboxes allow for the rapid review of biocompatibility and performance of materials used in past and current medical devices.
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4.3.2 Bioresorbable Polymers Demonstrate Superior Biocompatibility
Biocompatibility of fully crosslinked PGFA (which when crosslinked forms
PGPF) films was assessed and compared in both human dermal fibroblasts and tracheal epithelial cells. At low concentrations, the biocompatibility performance of PGPF surpassed PLLA and PLGA. This indicated that a coating of PGPF proves to be more biocompatibile in-vitro compared to a bare stent strut (PLLA) and a commonly used stent coating (PLGA). This finding is important as it shows the potential for device improvement with the addition of this coating even without therapeutic agent incorporation.
The design of this MRI-visible multi therapeutic agent delivery coating is not complete without the incorporation of the PTNPs. Biocompatibility testing of PGPF in combination with the PTNPs had to be performed to ensure no compounding of cytotoxicity effects. As expected and observed in the biocompatibility results with each component, viability below 80% was observed when a concentration of PGPF of 2 mg/mm3 was used with 1.00 mg/mL of PTNPs. A coating can perform without cytotoxic effects on surrounding tissues if the concentration of PGPF is 2 mg/mm3 or less in combination with PTNPS at a concentration of 0.75 mg/mL or less. Understanding these threshold concentrations is critical such that safety and efficacy can be ensured without hindrance to device performance.
PTNPs can be used in other applications beyond a composite stent coating. For example, the PTNPs can be used for imaging, systemic or local drug delivery, and biodistribution studies. Therefore, it would be beneficial to analyze the PTNPs separately in order to compare their biocompatibility performance to past and current theranostic
169 particle technologies. Bioresorbable PLGA/PGFA PTNPs demonstrated high biocompatibility compared to other described theranostic particle systems. Nanoparticles can effect biological systems via interactions with cellular components such as the plasma membrane, organelles, or macromolecules.216 Due to the diversity in design of other nanoparticle systems it is important that cytotoxicity studies are conducted for each system type as they can trigger distinctive biological responses. The most commonly used theranostic systems are designed using nanoparticles composed of carbon, gold, iron oxide, and cadmium based quantum dots. Carbon nanoparticles have unique physiochemical properties that have made them suitable systems for biomedical materials and devices including tissue scaffolds, drug delivery agents, and fluorescent contrast agents.217-218 In biocompatibility studies with HDFs, other groups have shown cytotoxicity of C60 nanoparticles at 20 ppb and one demonstrated no significant toxicity up to 226 μg/cm-2.219-221 Other theranostic systems use metals such as gold and iron oxide. Gold nanoparticles with diameters of 50 nm or less have demonstrated to be non- cytoxic up to 200 μg/mL.222-223 One study using coated gold nanoparticles with HDFs in a concentration range of 0-0.8 mg/mL demonstrated major adverse side effects on cell viability with intracellular presence of gold nanoparticles.224 Super-paramagnetic iron oxide nanoparticles have shown to be cytotoxic to Hela cells above a concentration of
0.05 mM (<80% viability) with a LD50 (50% viability) of 0.75mg/mL.225-226 Lastly, quantum dots composed of cadmium selenide or cadmium telluride have been used in biomedical applications, especially imaging. The development of quantum dots preceded establishment of cytotoxicity standards and has demonstrated limited biocompatibility.
Cadmium selenide quantum dots have been shown to elicit cell damage at a concentration
170 of 0.1 mg/mL or greater.227 Cadmium telluride quantum dots have provoked a decrease in cellular metabolic activity at a concentration of 1 μg/mL, and cell viability below 80% at
10 μg/mL.
PLGA/PGFA PTNPs offer a resorbable theranostic solution for biomedical applications with high in-vitro biocompatibility that other compositions lack. Our PTNPs exhibited non-cytotoxicity up to 1.00 mg/mL in cell media in both an unspecialized
(HDF) and specialized (TEC) cells. Using both cell types demonstrated that our PTNPs can be suitable for a range of biomedical applications with broad targets as well as show compatibility in a specialized target such as the trachea. In-vivo investigations will be performed in the future to determine biodistribution, clearance, and whole tissue effects via histology.
4.3.3 Advantages of Using PTNPs as Part of a Composite Stent Coating
Most theranostic delivery technologies have been designed for image-guided drug or gene therapy. Image guided drug delivery combines the functionality of diagnostics and the delivery of therapies that can be visualized in real time for biodistribution and quantification studies.228 There is a great diversity in composition, structure, and morphology of proposed theranostic systems, but engineered magnetic nanoparticles (MNPs) is one of the most common. MNPs are usually composed of iron, cobalt, nickel, or their oxides. MNPs are considered one of the most promising biomedical theranostic systems due to their nanoscale dimensions and their inherent ability to interact with an external magnetic field.229 A variety of polymer and copolymers have been coated or conjugated to MNPs in order to include therapeutic agents such as contrast mediums, drugs, proteins, or genes. A significant disadvantage of using MNPs is
171 that they are not biodegradable. There are various ongoing investigations regarding the toxicity, biodistribution, and clearance of MNPs. Many safety concerns still prevent these technologies from being used clinically.161
In addition to biocompatibility concerns, many MNP theranostic systems have limited drug loading capacities and delivery capabilities. Many MNP systems with amphiphilic block copolymers utilize both synthetic and natural polymers for therapeutic agent conjugation. A spectrum of both hydrophilic and hydrophobic therapeutic agents from antibiotics to cancer therapies are able to be combined with MNPs.229 Loading efficiencies of therapeutic components tend to be low due to chemical and geometric constraints. Most theranostic particle systems can only load agents onto the surface or inside of the core of a MNP. The advantage of using a fully polymeric system such as
PLGA/PGFA is that drug is loaded directly into polymer chains before PTNP formation.
This results in high loading efficiency and homogeneous distribution of therapeutic agent throughout the PTNP. Some MNP systems require external stimuli such as a change in pH, heat, or light to release their therapeutic contents.230-231 Often this triggers a burst release of most or all contents of the MNPs resulting in a nearly complete short-term release. The PTNP system is that drug release is not dependent on external stimuli for drug release. Drug release is controlled by diffusion and polymer degradation, which results in controlled long-term release of contents over the lifetime of the PTNP. A bioresorbable polymeric system such as PLGA/PGFA PTNP system overcomes many disadvantages of current theranostic particle systems.
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4.3.4 Dexamethasone-loaded PTNPs lower Inflammatory Cytokines In-Vitro
It is well recognized that to be qualified as a good biomaterial, especially a stent coating, it is essential to be compatible with surrounding cells and have minimal impact on local biologic processes.232 TNF-α and IL-1β are critical cytokines involved in inflammatory processes after stent implantation.233 TNF-α is an endogenous pyrogen that primarily serves as immune cell regulatory factor. IL-1β is a lymphocyte mitogen that is involved in cell proliferation, differentiation, and apoptosis. Together, both cytokines stimulate macrophage phagocytosis and the production of oxidants and inflammatory lipids. Dexamethasone is a glucocorticord agonist that modifies protein synthesis and interferes with the function of inflammatory mediators. Specifically, dexamethasone interferes with macrophage activity and subsequent cytokine production. During the early phases of healing after stent implantation, acute inflammatory cells are found around the stent struts. Significant accumulation of macrophages after trauma can trigger the production of smooth muscle cell growth factors leading to hyperproliferation.185
Regardless of a stent is deployed in the airway or in the cardiovascular system the release of dexamethasone locally will likely reduce inflammation.
There have been many developments in delivery systems aimed at modulating inflammation in disease states such as cardiovascular, orthopedic, and oncological applications.234 Most dexamethasone delivery systems are composed of polymeric materials with PLGA used most commonly. Polyesters like PLGA are widely utilized for drug delivery systems due to their affinity to bind proteins, peptides, nucleic acid, and other hydrophobic molecules.235 Recently Son et al. developed a dexamethasone microcapsule using PLGA 50:50 for rheumatoid arthritis treatment.236 Using
173 immunohistological staining of articular knee joints in rats with rheumatoid arthritis, they observed a significant decrease in TNF- α positive cells with microcapsule treatment over a period of six weeks.236
4.3.5 Feasibility of Coating Bioresorbable Stents with PTNPs
Synthetic bioresorbable polymers such as Poly(L-lactic acid) (PLLA), poly(D,L- lactide) (PDLLA) and PLGA have been investigated as both stent structural fibers and stent coating materials.237 Blending bioresorbable polymers allows tailoring mechanical strength and degradation rate of the device. Using these polyesters also provides a vehicle for therapeutic agent delivery. Prior designs have utilized polymer microporosity and reservoirs for the local delivery of anti-inflammatory agents, genes, and other therapeutic agents.185, 238-239 Currently, all approved drug eluting metal stents use a thin polymer coating for the delivery of therapeutic agents, however the degree of control on the release profiles is limited. Due to the major developments in nanoparticle technology, a nanoparticle eluting drug coated stent technology holds great potential.
The use of nanoparticles on stents was first investigated with metal stent platforms. Both polymeric and metallic nanoparticles have been applied to metal stents with electrodeposition or spray coating techniques. In electrodeposition, cathodic stents are placed in a solution of nanoparticles in distilled water with current maintained in the solution. The electrostatic attraction deposits the nanoparticles onto the stent struts.
Recently, electrodeposition of pitavastatin loaded PLGA nanoparticles onto a stainless steel balloon expandable stent has been designed for endothelial regeneration.240 This design demonstrated the attenuation of in-stent stenosis as effectively as a polymer coated sirolimus eluting stent in a porcine coronary artery model. Other studies use spray coating
174 techniques that deposit fine mists of nanoparticles onto the stent struts. Spray coating techniques are more commonly used when nanoparticle cargo consists of delicate molecules such as proteins and genes that can be affected by electrical charges. Using
PLGA as a bilayer nanoparticle platform, one study successfully delivered paclitaxel and vascular endothelial growth factor (VEGF) to promote revascularization and re- endothelialization post stent implantation.241 They were able to show that stents coated with paclitaxel/VEGF bilayer nanoparticles promoted early endothelial healing and inhibition of excessive smooth muscle cell proliferation after one month.
Despite some improvements with nanoparticle coatings on metal stents, bioresorbable stents have demonstrated superior biocompatibility in both tracheal and cardiovascular applications.138, 242 Bioresorbable stents also fulfill many unmet needs for pediatric interventions that permanent devices such as metal stents cannot satisfy. For example, most pediatric patients with tracheal obstruction that require surgical intervention only require a temporary intervention until the patient is able to “grow out of” the condition. Secondary surgical procedures to remove permanent devices such as metal stents and silicone tubes can result in increased tracheal damaged. Using a bioresorbable stent can eliminate retrieval procedures due to the fact the device dissolves into non-toxic substances over time. Drug eluting bioresorbable stents are a new technology that is currently undergoing clinical evaluation. Encouraging results have been shown in both safety and efficacy using bioresorbable stent systems including the
Absorb BVS stent (Abbot Vascular, IL, USA), Xience stent (Abbot Vascular, IL, USA),
DESolve 100 stent (Elixir Medical, Sunnyvale, CA, USA), Igaki Tamai (Igaki Medical
Planning Company, Kyoto, Japan) ReZolveTM (REVA Medical, San Diego, CA, USA)
175 and the DH BDS (University of Texas SMC, Dallas, TX, USA).243 The theoretical gains of drug eluting bioresorbable stents are enticing, however at this time there have been few studies presented that clearly show an advantage of bioresorbable stents over existing metal stent systems. Current bioresorbable stent systems also lack whole device imaging post-implantation. Improvements in coating materials can overcome current bioresorbable stent limitations.
The next generation of bioresorbable stents is evolving to incorporate nanotechnology in a similar evolutionary path as was seen for drug eluting metal stents.
Bioresorbable stents coated with nanoparticles/microparticles loaded with therapeutic agents can successfully create a temporary device with sufficient mechanical properties and localized controlled drug release. An interesting prospect in bioresorbable stent design is to use theranostic nanoparticles as coating material. Bioresorbable stents with theranostic nanoparticle coatings could then be imaged using non-invasive techniques and also provide important therapeutic agents locally. Many current theranostic nanoparticle system technologies however still come with a significant disadvantage in that they are formulated with metallic materials. With existing limited knowledge in biodistribution and clearance of metallic nanoparticles, their addition could result in similar outcomes to using metallic stents. At present, a completely bioresorbable stent strategy that incorporates therapeutic agent delivery and imaging capabilities does not exist. However, the PLGA/PGFA bioresorbable PTNP system that is not formulated with a metallic core or shell could be used, eliminating concerns with aggressive local inflammation observed with implanted metallic materials. Coating a bioresorbable stent with our PTNP design would generate an imaging capable polymeric stent design that
176 will degrade into non-toxic products over time without the disadvantages associated with conventional contrast agents or metallic materials.
4.3.6 Bioresorbable Antimicrobial Polymers and Their Use in Medical Applications
Microbial contamination is a serious issue for implantable medical devices. The generation of microbial films are recognized to play a pivotal role in post-surgical infections associated with cardiovascular devices, urinary catheters, and orthopedic implants.244 Biofilms are formed in a three stage process: 1) microorganism adhesion to surface, 2) exopolysaccharide production and 3D biofilm development, followed by 3) detachment of microorganism(s) from biofilm via single or clustered cells.244 Due to this three stage developmental process, biofilm formation is targeted with three strategies: inhibition of microbial adhesion, interference with biofilm development signaling molecules, or disaggregation of biofilm matrix.
The inhibition of microorganism adhesion to medical device surfaces has been addressed with coatings. Microbial adhesion is strongly dependent on the physiochemical properties of the medical device surface with the most prominent being surface hydrophobicity and surface charge.244 Others have shown coating medical devices with hydrophilic polymers can significantly prevent bacterial adhesion.245 Hyaluronic acid and poly-N-vinylpyrrolidone, both hydrophilic polymers, have shown success as coatings for polyurethane catheters and silicon shunts.246-247 Other coatings incorporate or entrap one or more antimicrobial substances to combat a wide spectrum of microbial activity. For example, heparin coatings for central venous lines generate a negatively charged surface that not only prevented thrombosis but also reduced microorganism colonization.248
177
The design of PCFA and PGCFA not only includes antibiotic ciprofloxacin, but also generates a hydrophilic surface. Ciprofloxacin actively recruits water to the polymer chains due to its inherent hydrophilicity further enhancing surface hydrophilicity. The generation of a hydrophilic surface prevents adhesion of microorganisms on the device surface while also promoting hydrolytic chain scission. This is critical due to the ever evolving microorganism antibiotic resistance genetic selection. Regardless if the microorganism is susceptible to ciprofloxacin or not, biofilms cannot be formed if microorganisms cannot adhere to the surface.
4.3.7 Multifunctional Polymers and PTNPs as a Coating for Airway Stents
The majority of stents used for the airway are bare metal stents or silicon tubes.
Most of these designs do not have a therapeutic agent coating. Some of the cardiovascular stent designs that have a coating contain antiproliferative agents for prevention of neointimal hyperplasia.187 Other investigations have examined dexamethasone eluting vascular stents.185, 249-250 Thrombosis and stenosis are common issues associated with cardiovascular stenting that have been addressed with formulation of stent coatings. Tracheal wound healing post-stent implantation follows an acute and chronic phase response.26 First, the tracheal wall recovers from the mechanical impact of the surgical procedure. Second, re-epithelialization of the tracheal wall occurs including the formation of epithelium on the device. Finally, scar tissue is formed. Excessive scar tissue formation can lead to stenotic regions that obstruct normal respiratory processes.26
Cardiovascular stents with antiproliferative agent coatings have been used to prevent tracheal stenosis. However, the tracheal environment is not a closed blood system but rather is a mucus coated lumen exposed to respired air. The risk of infection is greatly
178 increased due to the potential contact of the medical device area with airborne pathogens.23-24 Currently none of the stents being used for tracheal interventions have a coating that addresses infection. Using PGCFA as a coating on a bioresorbable stent can address this unmet need in airway intervention. PGCFA offers not only protection from microorganisms and biofilm formation but also adds a contrast imaging medium, rendering the device visible via MRI, illuminating the entire stent structure. Any concern regarding the length of antibiotic growth inhibition can be addressed by modfying coating layer thickness and coating application technique. PGCFA offers a burst release of ciprofloxacin at up 4 days while PCFA offers a more sustained release for 14 days. The advantage of PGCFA is the inclusion of the contrast agent for imaging purposes, but its short term release is a significant disadvantage. Multiple layers of PCFA and PGCFA would be able to extend the antimicrobial protection while still providing imaging capability. Future investigations will examine layer-by-layer coating techniques to achieve both a sustained ciprofloxacin release and MRI visibly capability.
Recently, a bioresorbable stent composed of PLLA has shown success in a rabbit model for pediatric airway malacia.138, 242 In the rabbit model, the Double Opposed
Helical bioresorbable stent (DH BDS) elicited less inflammation for up to one month after implantation compared to a metal stent. Using a coating of PGCFA on the DH BDS would hopefully further improve the design by adding imaging capabilities, to the entire stent and providing therapeutic agent to combat infection risk and inflammation.
Synthesis of both PCFA and PGCFA provide a solvent-less synthesis scheme that eliminates toxicity issues associated with other coating materials and techniques. Using
PGCFA as a coating on the DH BDS would produce an MRI-visible fully bioresorbable,
179 device composed of polyester materials that fulfills a significant unmet need in pediatric airway intervention.
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CHAPTER FIVE Conclusion
The rationale behind this research can be summarized as follows: currently there are no interventional technologies available that are specifically designed for the treatment of pediatric tracheomalacia. This research designed a multi-functional polymeric composite coating to be combined with a bioresorbable polymeric stent for such treatment. The development of the multi-functional polymeric composite coating can add imaging capabilities and local delivery of multiple therapeutic agents to improve clinical outcomes. The corresponding three aims of the research investigated: 1) the optimal particle formulation from a factorial design for anti-inflammatory release; 2) the design of a multi-drug release coating for a bioresorbable stent; and 3) the in-vitro biocompatibility of optimal particle formulation, polymeric materials, and composite coating.
General hypotheses were proposed with which to analyze the significance of each of these hypotheses.
Hypothesis 1.1: There will be no significant difference in particle characteristics in particles formulated using the same copolymer ratio from two different manufacturers. Particles formulated using PLGA 50:50 will have a larger effective hydrodynamic diameter, lower zeta potential, lower Tg, higher drug loading efficiency, and shorter drug release lifetime compared to particles formulated with
PLGA 75:25.
As determined by various particle formulations and detailed measurements of nanoparticle characteristics, it was found that:
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Particles formulations from all copolymers were spherical in morphology
Regardless of manufacturer, cumulative dexamethasone release was faster from
the PLGA 50:50 copolymer ratio than the PLGA 75:25 copolymer ratio
Cumulative dexamethasone release was faster in polymers from Corbion Purac®
than Evonik Resomer® despite all copolymers having an equivalent mean
inherent viscosity
Particles formulated with PLGA 50:50 had higher zeta potential values than
PLGA 75:25; meaning they are more stable
Particles formulated with PLA 50:50 had lower Tg values than PLGA 75:25
There was no clear relationship between manufacturers or copolymer ratio and
drug loading efficiency or effective hydrodynamic diameter
Formulating nanoparticles using a solvent displacement technique elucidated clear differences in particles formulated from the same copolymer ratios from different manufacturers contradicting the hypothesis. The second statement of the hypothesis was confirmed; particles formulated using PLGA 50:50 did have a larger effective hydrodynamic diameter, lower zeta potential, lower Tg, higher drug loading efficiency and shorter drug release lifetime compared to particles formulated with
PLGA 75:25.
182
Hypothesis 1.2: Thermally processing particles with solvent removal via distillation will produce an increase in particle effective hydrodynamic diameter, Tg, zeta potential, drug loading efficiency, and drug release lifetime compared to particles with solvent removal via evaporation at room temperature.
As determined by various particle formulations and detailed measurements of nanoparticle characteristics, it was found that:
Thermally processing particles does not affect particle morphology; all particles
were spherical
For both PLGA 50:50 and 75:25 copolymer ratios, effective hydrodynamic
diameter increased as distillation time increased
Particles formulated with PLGA 75:25 were larger in effective hydrodynamic
diameter than those formulated with PLGA 50:50
Extracting solvent via distillation had no effect on the zeta potential for groups
formulated with PLGA 50:50
For groups formulated with PLGA 75:25, zeta potential increased as distillation
time increased
Particle groups formulated with PLGA 50:50 with distillation had increased drug
loading efficiencies compared to the control
There was no clear relationship between drug loading efficiency and distillation
in particle group formulated with PLGA 75:25
Solvent removal via distillation considerably increased the drug delivery lifetime
and Tg of all particle groups irrespective of copolymer ratio
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Thermally processing the particles by removing solvent via distillation confirmed the claims in this hypothesis.
Hypothesis 1.3: Blending PGFA with PLGA to formulate particles will decrease particle effective hydrodynamic diameter, Tg, zeta potential, and drug release lifetime. Blending will increase drug loading efficiency.
As determined by various particle formulations and detailed measurements of nanoparticle characteristics, it was found that:
Blending PLGA with PGFA regardless of copolymer ratio decreased particle
effective hydrodynamic diameter
Blending with PGFA decreased the magnitude of zeta potential and Tg for all
formulation groups
Drug release lifetime decreased for all particle groups when PGFA was blended
except for Evonik Resomer® 50:50
Drug loading efficiency increased when PLGA was blended with PGFA except
for the Corbion Purac® PLGA 50:50 group
Blending PLGA with PGFA to make particles confirmed most of the claims postulated in this hypothesis. Some minor discrepancies, no effect on drug release lifetime with Evonik
Resomer® 50:50 and no effect on drug loading efficiency for Corbion Purac® 50:50, were observed that do not effect further results.
184
Hypothesis 1.4: Increasing the sonication time will generate particles of small effective hydrodynamic diameter without compromising other particle characteristics.
As determined by altering one parameter in the formulation technique and detailed measurements of nanoparticle characteristics, it was found that:
Sonication time decreased particle size until a threshold was reached with 45 min
which resulted in no further decrease in particle effective hydrodynamic diameter
Increasing the sonication time during particle formation decreased particle hydrodynamic diameters and did not affect other particle characteristics confirming the claims in this hypothesis.
Hypothesis 2.1: Poly(fumaric acid) is significantly different both chemically and exhibited material properties compared to poly(propylene fumarate).
As determined by detailed measurements obtained during polymer characterization, it was found that:
PFA synthesis resulted in a viscous liquid unlike previous descriptions of PPF
which is a solid
PFA was discernible from PPF via 1H-NMR where associated peaks for PG and
a hydroxyl group that is not present in PPF
The hydroxyl peak in PFA not present in PPF was also shown in the FTIR
spectra at 3448 cm-1
The chemical and material properties of the polymer product synthesis (PFA) were
proven to be distinctly different than previously described for PPF validating the
claims of this hypothesis.
185
Hypothesis 2.2: Using other therapeutic agents as initiators in poly(fumaric acid) synthesis will alter chemical and material properties, especially rheological behavior.
As determined by various polymer synthesis formulations and detailed measurements taken during polymer characterization, it was found that:
A novel polymer synthesis known as contrast medium initiated polymerization
has been developed to produce bioresorbable radiopaque and MRI-visible
polymers
Contrast mediums such as gadodiamide and potassium iodide and therapeutic
agents such as ciprofloxacin, and a combination thereof, can successfully drive
polymerization
Homogeneous distribution of contrast medium or therapeutic agent can be
achieved with direct addition into the polymer chain
Incorporation of contrast medium or therapeutic agent was confirmed using FTIR
and sometimes by 1H-NMR depending on hydrogens in initiator chemical
structure
Glass transition temperatures were significantly different in the resulting polymer
formulated with an non-traditional initiator
All polymers produced, regardless of initiator, resulted in polymers with Non-
Newtonian pseudoplastic behavior
With polymers that contained ciprofloxacin, cumulative drug release kinetics was
shown to be directly related to degradation kinetics
186
All polymers synthesized to a molecular weight of 2000 Daltons or less were
amber liquids at room temperature and above 5000 Daltons the polymer was an
unmalleable solid
The full characterization of these novel polymers confirmed all claims in the
hypothesis.
Hypothesis 2.3: Porous and non-porous PLGA coatings will not alter PLLA fiber mechanical properties. A porous PLGA coating will release dexamethasone faster than a non-porous coating.
As determined by detailed measurements of fiber mechanical properties and cumulative drug release, it was found that:
Dexamethasone incorporation into the PLGA porous and nonporous coating was
confirmed via FTIR
The addition of a porous or nonporous coating did not generate a significantly
different elastic modulus however the yield stress was significantly different for
the coated fibers compared to the control
Overall porous coated fibers had weaker mechanical properties
Porous coating had a higher total amount of dexamethasone compared to the
nonporous coating
The porous coating has a significantly greater release of dexamethasone during
weeks two through eight compared to the nonporous coating
A significant reduction in coating thickness from initial to week eight was
observed in porous coated fibers but not nonporous coated fibers
187
There was no significant difference in the concentration of dexamethasone
calculated per fiber
Significant changes in some mechanical properties of porous coated fiber did not
support this hypothesis. However, the porous coated fiber did not have any changes
in mechanical properties compared to the control. The second claim in the hypothesis
was confirmed that the porous coated fiber released dexamethasone faster than the
nonporous coating.
Hypothesis 2.4: The composite coating design will controllably deliver ciprofloxacin for at least one week and dexamethasone for at least three months fulfilling the optimal treatment for pediatric tracheomalacia.
As determined by detailed measurements of cumulative drug release from polymeric materials and nanoparticles, it was found that:
PTNPs formulated with Corbion Purac® PLGA 75:25 blended with PGFA
released 97% of loaded dexamethasone at three months
The concentration of dexamethasone released from the PTNPs fell within the
therapeutic window for pediatric airway clinical dosage
Poly(ciprofloxacin fumaric acid) and poly(gadodiamide fumaric acid) both
released ciprofloxacin for one or two weeks based on formulation, however the
total cumulative concentration released was not significantly different between
formulations
The concentrations of ciprofloxacin released from PGCFA and PCFA was above
the required minimum inhibitory concentration for common bacterial strains and
188
significantly lower than the required clinical dosage for systemic delivery of
antibiotic
The characterization of drug release kinetics for PTNPs, PCFA, and PGCFA
confirmed the claims of this hypothesis.
Hypothesis 3.1: PGPF and PTNPs will comply with ISO 10993 and show a biocompatibility of 80% or higher with human dermal fibroblasts. PGPF and
PTNPs will be less cytotoxic than PLLA and PLGA.
As determined by detailed measurements from rigorous biocompatibility assessment of materials with human dermal fibroblasts, it was found that:
XTT assay showed that human dermal fibroblast viability remained well above
the required 80% when indirectly contacted with PGPF
XTT assay showed human dermal fibroblast viability remained above the
required 80% when directly contacted with PGPF with concentrations 2mg/mm3
or less but not 5mg/mm3 or higher
Live/Dead fluorescence staining showed that human dermal fibroblast viability
remained well above 80% when directly contacted with PGPF to a concentration
of 5 mg/mm3 but not 10 mg/mm3 or higher
XTT assay showed that human dermal fibroblasts directly contacted with PTNPs
at concentrations of 0.25-1.00 mg/mL cell viability remained well above the
required 80%
Live/Dead fluorescence staining showed that human dermal fibroblast viability
remained well above 80% when directly contacted with PTNPs at concentrations
of 0.25-1.00 mg/mL
189
In-vitro biocompatibility assays completed with human dermal fibroblasts confirmed
the claims of this hypothesis. Cytotoxicity is observed at high concentrations of
PGPF which is not unexpected; these concentrations are well above the expected
amount intended to be used for coating of a stent.
Hypothesis 3.2: PGPF, PTNPs, and a composite mixture of the two will comply with
ISO 10993 and show a biocompatibility of 80% or higher with human tracheal epithelial cells. PGPF, PTNPs, and the composite mixture will be less cytotoxic than
PLLA.
As determined by detailed measurements from rigorous biocompatibility assessment of materials with tracheal epithelial cells, it was found that:
XTT assay showed tracheal epithelial cell viability remained above the required
80% when directly contacted with PGPF with concentrations 2mg/mm3 or less
but not 5mg/mm3 or higher
Live/Dead fluorescence staining showed that human dermal fibroblast viability
remained above 80% when directly contacted with PGPF to a concentration of 2
mg/mm3 but not 5 mg/mm3 or higher
XTT assay showed that tracheal epithelial cells directly contacted with PTNPs at
concentrations of 0.25-1.00 mg/mL cell viability remained well above the
required 80%
Live/Dead fluorescence staining showed that tracheal epithelial cell viability
remained well above 80% when directly contacted with PTNPs at concentrations
of 0.25-1.00 mg/mL
190
XTT assay showed tracheal epithelial cell viability remained above the required
80% when directly contacted with the composite coating with concentrations of
PGPF 2mg/mm3 or less and PTNPs 0.75 mg/mL or less.
Live/Dead fluorescent staining showed tracheal epithelial cell viability remained
above the required 80% when directly contacted with the composite coating with
concentrations of PGPF 2mg/mm3 or less and PTNPs 0.75 mg/mL or less.
PGPF, PTNPs, and the composite coating mixture were less cytotoxic than PLLA
for each given concentration
In vitro biocompatibility assays completed with tracheal epithelial cells confirmed
the claims in the hypothesis. Cytotoxicity is observed at high concentrations of PGPF
and the composite coating, which is not unexpected; these concentrations are well
above the expected amount intended to be used.
Hypothesis 3.3: All four bacteria strains, Escherichia coli, Klebsiella pneumoniae,
Moraxella catarrhalis, and Pseudomonas aeruginosa, will be susceptible to PCFA and PGCFA but not PFA. Inhibition zone length will mirror drug release kinetics;
PGCFA will have large inhibition zones at early time points and PCFA will have consistent inhibition zone lengths throughout the experiment.
As determined by detailed measurements from a sensitivity assay and cumulative drug release analysis of polymers, it was found that:
All four bacterial strains were susceptible to PCFA and PGCFA with
susceptibility ranking (most to least) as follows; Escherichia coli, Moraxella
catarrhalis, Klebsiella pneumoniae, and Pseudomonas aeruginosa
191
All bacterial strains except Moraxella catarrhalis were not susceptible to the
control polymer PFA (no antibiotic present)
Measured inhibition zone lengths and subsequently calculated biological active
concentration of ciprofloxacin from PCFA were all above the minimum
inhibitory concentration for all bacterial strains up to day 14
Measured inhibition zone lengths and subsequently calculated biological active
concentration of ciprofloxacin from PCFA were all above the minimum
inhibitory concentration for all bacterial strains up to day 4 then dropped below
the required growth inhibiting concentration
Inhibition zone lengths measured for PCFA and PGCFA and the subsequent
calculated biological active concentrations of ciprofloxacin positively correlated
with prior drug release kinetics
All claims in this hypothesis were validated except that one bacterial strain was
unexpectedly susceptible to PFA. M. catarrhalis, unlike the other strains, is gram
negative. This means M. catarrhalis has a different cellular membrane structure
compared to the other bacterial strains possibly explaining why it was susceptible to
the polymer degradation products without any antibiotic.
Hypothesis 3.4: After 24 hr exposure, PTNPs loaded with dexamethasone will effectively lower inflammatory cytokine concentrations in mouse macrophages comparable to free dexamethasone.
As determined by detailed measurements from comprehensive inflammation assessment of nanoparticles with mouse macrophages, it was found that:
192
After 24 hr exposure PTNPs can release dexamethasone to have a comparable
effect to free dexamethasone found in cell culture media
TNF-α concentration is equivalent to the control after LPS stimulation in the
presence of PTNPs in the concentration range of 0.50-1.00 mg/mL
IL-1β concentration is equivalent to the control after LPS stimulation in the
presence of PTNPs in the concentration range of 0.75-1.00 mg/mL
Optimal PTNP concentration for composite coating is 0.75 mg/mL
In-vitro inflammation assessment of mouse macrophage cells with LPS stimulation utilizing mouse specific cytokine ELISA kits confirmed the claims of this hypothesis.
This research contributes significant findings in the field of engineering, basic science, and clinical science. The work of Aim 1 facilitated more understanding of polymeric nanoparticles including novel processing techniques and formulations. It also contributes to the field of theranostics with the design of the first fully biodegradable theranostic nanoparticle. The work of Aim 2 enhances the field of polymer science with the development of contrast medium initiated polymerization and the use of therapeutic agents as polymerization initiators. It also explores novel polymer syntheses specific for clinical airway stent applications. This work as a whole provides a multi-functional coating that may conquer the ineffective of current “off-label” cardiovascular stents for airway applications. Lastly, this work creates a new advance in stent and coating technology to undergo pre-clinical evaluation.
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CHAPTER SIX Future Work
The research presented here has laid the foundation of future in-vivo studies for pre-clinical evaluation. At present, a tracheomalacia animal model utilizing the New
Zealand White Rabbit is being developed in accordance to Guidelines for the Care and
Use Committee of Laboratory Animals and University of Texas Southwestern Medical
Center at Dallas institutional boards. The surgical procedure removes approximately
80% of the tracheal cartilaginous rings within a 10 mm segment of the trachea
(approximately 5 rings) while preserving the inner mucosal layer (Figure 106).
Figure 106. Showing the removal of the tracheal rings in a New Zealand White Rabbit with preservation of inner mucosal layer.
Surgical creation of a tracheomalacia model has been successful in several rabbits. Confirmation of airway malacia was obtained using two in-vivo imaging techniques and post-study excision with histology (Figure 107).
194
Figure 107. Malacic region in rabbit as indicated by arrows using (Top Left) 3D reconstruction of CT scan slices, (Top Right) X-ray, and (Bottom Left) post-study excision. (Bottom Right) Histological section of malacic region shows irregularity of tracheal layers and collapse of lumen.
Significant narrowing in the trachea is noted in both CT scan data and standard X-ray near the surgically placed clip. After sacrifice, the trachea of the rabbit was removed, and found to clearly demonstrate narrowing at the surgical region. Histological examination revealed severe irregularity in the tracheal layers and collapse of the tracheal lumen.
With successful creation of this animal model, a pilot stenting study was performed to compare the performance of the DH BDS to a commonly used Cordis balloon expandable Genesis® metal stent. Both the metal control and the DH BDS stent were successfully deployed into the malacic airway rabbit model (Figure 108). One week
195 post-implantation, bronchoscopy, revealed the presence of aggressive inflammation
(bleeding, tissue granulation) in the airway with the metal stent. The DH BDS stented airway showed little inflammation and some tissue coverage of the stent struts.
Figure 108. Successful implantation of metal and DH BDS stent were confirmed via bronchoscope. After one week the metal stent shows signs of inflammation while the bioresorbable stent does not.
One month post-implantation, animals were sacrificed, tracheal specimens were removed, and histological staining was performed (Figure 109). The metal stented airway showed fibrosis and granulation tissue around the stent struts. As shown at higher magnification, active heterophils with red granular cytoplasm and some macrophages were present at the
196 stent struts. There was inconsistent embedding of the stent struts within the tracheal lumen. Epithelium near the struts also showed variation.
*
Figure 109. Histological sections of metal and DH BDS stent in trachea. Tracheal epithelium is disrupted and fibrotic cells more prominent in metal stented specimen than DH BDS. Some areas of epithelium showed re-epithelialization with normal cell morphology while others showed quiescent or damaged epithelium. During the histological processing
(epoxy embedding) the DH BDS stent became dislodged from the lumen wall. It is possible that a chemical reaction may have occurred between the exposed PLLA and the epoxy during the curing stage. However, indented sections of the tissue are noticed in areas where stent struts were likely present. The DH BDS showed some collapse but the malacic region remained open. Collapse at this cranial end of the stent was most likely
197 caused by torsion and bending at the neck. The DH BDS showed significant penetration of heterophils around the stent struts. The epithelium appeared continuous and compressed into a non-ciliated squamous form. The areas indented from the stent struts showed many heterophils and other inflammatory cells present indicating a mild inflammatory response.
Mild signs of the inflammatory response are noted with the DH BDS, however significant improvement compared to the current metal stent technologies was demonstrated in this pilot study. A composite coating that can deliver both antibiotic and anti-inflammatory agents can potentially reduce these side effects. Following similar protocol, a stent coated with the composite coating will be deployed in the malacic rabbit model and compared to these prior results. Optimization of the coating technique (robotic microspray) will have to be performed in order to provide an optimal and repeatable coating method for the DH BDS. Furthermore, MRI-visualization studies of the coated stents can be performed to improve post-surgical non-invasive monitoring.
In stenting applications, there is a significant concern regarding stent migration after implantation, especially in the airway. Many of the current technologies fail due the inability of the stent to remain at the implantation site. The trachea is a complicated moist environment that undergoes more shear force and movement at the wall than other stented vessels such as arteries and veins. Some stent designs are stitched into the tracheal wall or they have grooves or ridges to help adherence and prevent migration.
These grooves, ridges, and especially stitches can lead to serious complications. These designs require that the stent does not migrate, however they must also be easily removed after an extended period of time. The uniqueness of this coating is that it is very “sticky”
198 and can help the adherence of the stent to the tracheal wall. Pre-clinical studies are necessary to determine if the formulation, as described, is able to withstand tracheal wall shear forces and help prevent stent migration. If this coating is not able to overcome these forces, future studies could investigate blending bioresorbable polymeric adhesives with the coating for improved adherence qualities.
Another potential modification to the coating to investigate is to use thermal or
UV-crosslinking to prolong drug release lifetime as well as enhance coating adhesive properties. Crosslinking of PGFA can be completed using heat or UV-light with an initiator. The temperature for thermal crosslinking (~140°C) would likely damage the stent therefore UV-crosslinking is likely the best option for this application. The degree of polymer crosslinking is directly related to the UV power output (wattage) and the length of curing time. Future studies can look into quantitatively establishing the relationships between UV-crosslinking, degree of crosslinking, polymer stickiness, and drug release lifetime. Studies will also need to confirm maintenance of stent mechanical properties and performance with the addition of coating crosslinking.
Beyond pre-clinical studies, more research regarding the antimicrobial efficacy of the materials should be investigated. In this work, only one assay (Kirby-Bauer Disk
Diffusion) was used to quantify the antimicrobial efficacy of the degrading polymers.
This assay investigated the antimicrobial effect of polymer degradation products in a robust study that utilized four different strains of airway bacteria. However, a secondary assay would be beneficial for confirmation of antimicrobial activity as well as aid in future FDA approval. Assays such as broth dilution methods, E-test, mechanism- or geneotypic-specific tests could be utilized.
199
One final clinical concern regarding antimicrobial materials is the development of antibiotic drug resistance. In this design, ciprofloxacin was the only antibiotic investigated. Ciprofloxacin was chosen because the chemical structure provides a predictable polymerization pattern and its broad spectrum of activity against various microbes is suitable for the airway. Future work should investigate other antibiotic compounds as polymerization initiators. Creating a library of bioresorbable antimicrobial polymers could aid to create multiple treatment options. As observed in this work, it possible that some polymeric materials already possess antimicrobial properties without the addition of antibiotics. As long as the material remains biocompatible with human cells and tissues, new non-drug antimicrobial treatments and devices are possible. There are very limited studies that investigate this symbiotic amensalism, however this relationship was demonstrated in the sensitivity assay with PFA and Moraxella catarrhalis. PFA and potentially other formulations could be a valid candidate for treating gram-positive bacterial infections. Future studies looking into this phenomenon could reveal more basic science relationships and clinical applications to address the evolution of antibiotic resistance of microbes.
APPENDIX A Nanoparticle Characterization Theories and Techniques
Particle Characterization
Particle Morphology
Morphology, size, and material chemistry are the critical factors that determine drug content and release. There is evidence that morphology/shape has a significant impact in drug delivery systems that rely on polymer degradation for therapeutic agent release in tablets and films.251 Beyond drug loading and release kinetics, particle morphology will also effect the transportation in the body. Spherical particles in a fluid environment are somewhat predictable due to their radial symmetry. Particles of other geometries may tumble or align in filtering organs such as the spleen or liver, causing blockages or vessel damage.252 The precise role of particle shape in drug delivery still remains ambiguous, due to the lack of easily replicated methods available to control particle shape.
Scanning Electron Microscopy (SEM) or Transmission Electron Microscopy
(TEM) usually performed to characterize nanoparticle morphology. TEM is an older approach that relies on transmitting electrons through a thin sample to generate an image.
Though it has higher resolution than SEM, a very thin sample is required and only a small area can be analyzed in two dimensions. SEM is more available and more commonly used for particle morphology assessment. SEM uses the scattering of electrons to provide a three dimensional image of a relatively large sample area. Surface characteristics and gross shape of particles can be assessed and measurements can be taken.
200 201
Particle Size Distribution Theory
Dynamic Light Scattering (DLS) is a common technique that uses light scattering to size particles down to 1 nm diameter.253 When light from a laser source illuminates small particle in suspension the light intensity fluctuates over time. This fluctuation is a result of the particles in solution undergoing Brownian motion. Brownian motion is the random movement of particles due to the surrounding liquid molecules and can be described by the following equation:254
where ρ is density, t is time, x is particle starting point, and D is mass diffusivity. DLS measures the motion of the particles in suspension and related its velocity to the size of the particle according to the Stokes-Einstein equation:
where d(H) is hydrodynamic diameter, D is the translational diffusion coefficient determined from Brownian motion, k is Boltzmann’s constant, T is absolute temperature, and η is viscosity.254
Zeta Potential Theory
DLS can also be utilized to determine zeta potential. Zeta potential is the measure of the electro-kinetic potential of a particle.255 The liquid layer that surrounds a particle exists in two parts; an inner part known as the Stern layer and an outer, diffuse layer.
Ions are bound in the Stern layer and less firmly attached in the outer layer. The potential
202 that exists at the boundary between these two layers is the zeta potential. DLS can determine the relationship between particle mobility and zeta potential using the following equation:
where μ is electrophoretic mobility, ζ is the zeta potential, ε is the dielectric constant, η is viscosity, and f (ka) is the order of unity. Zeta potential is then expressed in millivolts.
Zeta potential is an indicator of particle stability and particle interaction with the environment.256 Depending on the intended target, a positive or negative zeta potential is favorable for particle interaction. Particles formulation with unmodified PLGA have a negative zeta potential due to the negative charge of the –COOH (carboxyl) end group.
Significant colloidal or particle stability is maintained with a zeta potential of ±30 mV or greater.70
Drug Loading Efficiency Calculation
When particles are used for drug delivery purposes, the ability of the particles to capture and release drug is essential for therapeutic outcomes. Both hydrophobic and hydrophilic drugs can be encapsulated, absorbed, dispersed, or tagged to polymeric particles.257-260 Depending on the method of incorporation, drug loading efficiency (DLE) can be calculated from standard equations.261 Utilizing a solvent displacement technique, drug is dissolved into the mixture of polymer and solvent; facilitating the binding of the drug directly into the polymer chain. Therefore, drug loading efficiency is calculated with the following equation:
203
where A1 is measured amount of drug and A0 is initial amount of drug.
204
APPENDIX B Polymer Characterization: Differential Scanning Calorimetry Theory
Differential Scanning Calorimetry
Differential scanning calorimetry (DSC) is a powerful technique in which phase transitions and chemical reactions of polymers can be observed as a function of temperature. This instrument measures heat flow as a function of temperature. Two aluminum pans are placed into the machine, one in which contains a sealed sample and one is empty that serves as a reference. The temperature of these aluminum pans is increased at a constant defined rate at a constant pressure. Therefore, heat flow is equivalent to enthalpy changes:
where q is enthalpy and H is heat flow. As stated, the change in heat flow is reported as the value of the sample with the value of the reference subtracted:
The value calculated in the heat flow equation can be positive or negative. Most phase transitions are endothermic processes, which means heat is absorbed and therefore heat flow into the sample is higher than that of the reference. Crystallization, some crossing linking processes and decomposition reactions are exothermic processes and will result in a negative value for heat flow. A typical DSC schematic is shown in Figure 110 and output curve in Figure 111.262
205
Figure 110. Differential Scanning Calorimetry setup with a linear temperature scan rate.263
After a scan has been completed, valuable information regarding the temperatures in which specific processes occur in polymers will be known. The glass transition temperature (Tg) is a reversible transition that occurs in amorphous materials. At this temperature, the amorphous material transitions from a solid-like state to a liquid state in which is it able to flow. Crystalline and semi-crystalline polymer will exhibit a crystallization temperature (Tc). This is an exothermic process in which the integral value can be used to calculate the percent crystallinity of a polymer:
where ΔHf is the enthalpy of melting and ΔHf100 is enthalpy of melting for a fully crystalline polymer. The final transition that can be observed is the melting temperature
(Tm). This is the temperature at which a polymer is in a complete liquid phase.
206
Figure 111. Typical Differential Scanning Calorimetry curve.263
Other than transition temperatures, enthalpy changes can also be calculated and compared from sample to sample. Integrating the area under a peak above the baseline of the scan calculates total enthalpy change for the given process:
Heat capacities and changes in heat capacities can be calculated from the shift in the baseline of the DSC curve. The difference between the heat capacity of sample and the reference is calculated then can be used in the left hand side of the following equation:
where Cp is heat capacity and T is temperature.
Not all polymers exhibit all of these transition temperatures. For example, PLGA only exhibits a Tg because it is amorphous and does not have a melt temperature or detectable crystalline structure. DSC will be a critical technique in the particle factorial
207 design that incorporates thermal treatment of particles in particle formulation. For PLGA,
Tg will be the indicator of changes in molecular mobility. The higher the Tg, the more hindered the motion of the chains around the polymer backbone.264 Conversely, the lower the Tg the polymer side chains are more flexible, providing ample motion leading to limited stability, leading to faster degradation. With faster degradation comes the risk of adverse toxic effects, which include increased local acidity, uncontrollable drug release kinetics, and decreased device lifetime due to faster degradation by autocatalysis.
208
APPENDIX C Polymer Characterization: Rheology
Rheology of Structured Polymers
Rheology Theory
Before a coating method can be developed, the rheological properties of the coating material must be characterized. Rheology is an extension of continuum mechanics that is used to characterize material flow, which is a combination of elastic, viscous and plastic behaviors. In fluid mechanics, constitutive equations provide the needed relations between the shear stress and fluid velocity. Unlike a conservation relationship, a constitutive relationship is not universal and only applies to a limited class of fluids. Therefore, experimental measurements are needed to derive these constitutive relationships to explain behavior. In rheology, a series of experiments are used to determined gross fluid behavior. The simplest behavior a fluid can exhibit is Newtonian behavior. When a fluid is Newtonian, there is a linear relationship between shear stress
(τ) and shear rate (γ):265
Newtonian fluid behavior is characterized by a single coefficient of viscosity for a specific temperature; that may change with temperature but is independent of strain rate.266 When a fluid does not exhibit this linear relationship it is considered Non-
Newtonian. Non-Newtonian fluid do not undergo strain rates proportional to the applied shear stress.267 If the fluid is Non-Newtonian it is then subcategorized as either a
Bingham Plastic or a Power Law Fluid. A Bingham Plastic exhibits material behavior
209 that has both solid and fluid-like properties. The material will not flow until applied stress exceeds the yield stress for the material.265
Above the yield stress, the relationship between shear stress and shear rate is linear.265
A Power Law Fluid exhibits non-linear behavior. The slope of the shear stress versus shear rate curve is the apparent viscosity. The relationship of apparent viscosity (ηapp) can be explained as a function of shear rate multiplied by the consistency index (m) raised to a dimensionless power.265
These relationships are illustrated in Figure 112. Using a linear scaled plot, a Newtonian and Bingham Plastic behave linearly while and Power Law Fluid is non-linear (Figure
112A). To simplify the analysis of a Power Law Fluid, a log-log plot is used to generate linear relationships (Figure 112B).
210
Figure 112. Showing (A) Shear rate versus shear stress relationship and (B) apparent viscosity versus shear rate for Newtonian and Non-Newtonian fluids.265
Temperature Dependence
To fully characterize a polymer, especially a “fluid-like” low molecular weight polymer, rheological analysis will provide essential information. Rheological testing can be performed on thermoplastic materials that are solid, melted, or semi-solid state. Solid- state polymer can be dissolved in solvent to create fluid state before testing on a rheometer. For the polymers in this research, no solvent will be required, as they are liquids at room temperature
Primarily, temperature dependence of a polymer must be determined. A dynamic temperature ramp study can simulate manufacturing conditions, processing conditions, storage, and in-vivo environment. A temperature ramp can also evaluate the long term stability of a sample. A temperature dependence study can be performed on a small sample; therefore an entire batch of material does not need to be sacrificed to predict behavior. Assessing polymer characteristics (such as viscosity, compliance, storage and loss modulus, etc.) is essential in designing a medical grade device or coating. During the
211 formulation of a coating, the materials will experience several temperature environments: synthesis conditions, bench-top assessment conditions, in-vivo conditions, and storage conditions. Fluid behavior and stability of the polymer must be assessed in each of these conditions to ensure there is no loss in structural integrity during the formulation process.
Stress Sweep
A stress (τ) sweep will determine the linear viscoelastic region, and determine if a yield stress is present or not. The linear viscoelastic region is where the polymer behavior (elastic modulus G’) is independent of imposed stress and further rheological examination requires testing within this region. Below the yield stress the fluid is considered “fully” elastic. Above this value the structure of the materials breaks and allows for flow to occur.
Figure 113. Yield stress measurement of a cosmetic cream from a stress sweep experiment.153
Strain Sweep
A strain sweep will further confirm the presence or absence of a yield stress and determine the critical strain level. The critical strain level is the strain % (γ) at which the behavior becomes non-linear. The rheological properties of a viscoelastic material are
212 independent (linear) of strain below the critical strain level. Above this strain, the material behaves non-linearly and the storage modulus (G’) decreases. If storage modulus
(G’) is greater than loss modulus (G’’) then the material is highly structured like a solid.
If G’ is less than G’’ than the material behaves like a fluid with little structure present.
Figure 114. Assessment of critical strain level of a water-based acrylic coating from a strain sweep experiment.153 Frequency Sweep
A frequency (ω) sweep is used to determine the gross fluid structure. This sweep provides information about the effect of colloidal forces and interactions of particles or droplets in the sample. It will be critical to evaluate the stability of the coating solution with the addition of particles. After the linear viscoelastic region of the fluid has been identified, the frequency sweep must be run below the critical strain or stress level.
Measurements during this test are made over a range of oscillation frequencies at a constant oscillation amplitude and temperature. Below the critical strain, G’ is often independent of frequency if the fluid is highly structured. The more frequency dependent
G’ is, the more fluid-like the material structure is.
213
Figure 115. Frequency sweep test on simulated rocket propellant material. At high strain amplitudes (blue) G’’>G’ and the material behaves more like a fluid and a low strains G’>G’’ and the material behaves more like a solid.153
Time-Dependent Behavior
Finally, a time-dependent test is used to evaluate compliance and zero shear viscosity, which are both measures of elastic recoil of the material. This test is very similar to a creep test. After a sample is allowed to creep under load, relieving the imposed stress, measuring sample recovery can obtain the elastic behavior of the material.
Figure 116. Time-dependent “creep” test of cookie dough.153
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The Important of Rheological Characterization of Polymers
The rheological properties of the coating material will be important for a stent coating system. Viscosity measurements at relevant temperatures will be essential to ensure appropriate delivery of the material through a coating system. These steps are essential to designing a system that will provide uniform coatings reproducibly. This information is also important for delivery of the material with an injection methodology.
A subcutaneous injection of this composite coating can serve as another method of drug delivery for applications othen than those associated with a stent. For the scope of this study, rheological properties will be assessed for coating system design.
215
APPENDIX D Drug Release Theory and Mathematical Modeling
Drug Release Theory
Controlled drug release from particles and subsequent biodegradation are essential for developing a successful particle drug delivery system. Release rates of particles depend upon: 1) desorption of the surface-bound/adsorbed drug; 2) diffusion through the particle matrix; 3) particle matrix erosion; and 4) a combined erosion/diffusion process.268 Therefore, diffusion and biodegradation govern drug release in polymeric particles. For solvent displacement technique, drug is uniformly distributed in the polymer matrix by dissolving in the polymer-solvent mixture. If diffusion of the drug through the polymer matrix is faster than degradation of the matrix, then drug release occurs mainly by diffusion, otherwise it depends on degradation as well.268
Mathematical Modeling of Drug Release Kinetics
Drug release kinetics mathematical modeling is the most dependable prediction of drug release behavior. Experimental observations from prior works have exhibited both linear and nonlinear release functions that best correlate to particle drug release
(Table 23).269-270
Table 23 Mathematical equations of the models used to characterize cumulative dexamethasone release.
Model Equation Zero Order
First Order
Hixson-Crowell
Higuchi
Korsmeyer-Peppas
216
A Zero Order model is used when drug release is solely governed by diffusion with a rate constant (K0) and an initial drug amount (Q0). In this model, diffusion occurs independently of concentration. Though the simplest model, a Zero Order model is generally not the most accurate because many particle systems are composed of biopolymers that degrade. This model does not take into account the degradation effects on drug release kinetics (pH, surface area, erosion mechanism, etc.).
A First Order model is used when in place of a Zero Order when concentration of the drug plays a role in its diffusion. The changes in concentration of the drug from the vehicle and into the environment can alter the diffusive rate constant (K1). For example, if the concentration of drug in the environment is increasing to a point in which equilibrium or higher concentration is achieved, it is likely the diffusion constant will decrease. A
First Order model does not take into account degradation of delivery device if degradable materials are used.
The Hixson-Crowell model builds upon a First Order model taking into consideration changes in geometry of the delivery vehicle (surface area and diameter changes in particles or tablets during degradation). In particular for particles, Hixson and
Crowell recognizes the particles’ regular area is proportional to the cube root of its volume, therefore leading to the above equation derivation.271 The Hixson-Crowell model equation is also derived from the principle that dissolution occurs in planes that are parallel to the drug surface if the drug vehicle diminishes proportionally, in such a manner that the initial geometric form is kept constant.271 In the case of a particle, a spherical particle always remains spherical; however the diameter of the particle is decreasing over this time decreasing surface area.
217
The Higuchi model was the first model aimed to describe drug release from a matrix system that allowed for considerations in porosity and various geometries than do not follow a constant geometry as it degrades.272 This model relies upon the following hypotheses to be accurate: 1) initial drug concentration in the matrix is much higher than drug solubility; 2) drug diffusion takes place only in one dimension (no edge effect); 3) drug particles are smaller than system thickness; 4) matrix swelling and dissolution are negligible; 5) drug diffusivity is constant; and 6) perfect sink conditions are always attained in the release environment.272 This model can be altered for dissolution through pores by modifying the diffusion coefficient by multiplying it by the porosity (δ) of the matrix and dividing by the tortuosity (τ) of the matrix yielding .
Korsmeyer-Peppas developed their model specifically for polymeric systems.
This model takes into consideration that both diffusion and erosion control drug release by adjusting the release exponent.273 It is very similar to a power law with the exception of interpretation of the exponent (Table 2).
Table 24 Interpretation of diffusion exponent for drug release from polymeric matrices.269
Drug Transport Rate as a Function of Release Exponent Mechanism Time 0.5 Fickian Diffusion t-0.5 0.45 < n = 0.89 Non-Fickian Transport tn-1 0.89 Case II Transport Zero Order Release >0.89 Super Case II Transport tn-1
218
In this model, the value of the exponent (n) characterizes the release mechanism of the drug into four categories. Fickian diffusion follows the two laws presented by Fick. The first relates the diffusion rate to the concentration under the assumption that steady state is achieved:
Where J is diffusion rate, D is diffusivity, ϕ is concentration and x is position. In more general terms, Fick’s first law confirms that diffusion of a solute occurs from regions of high concentration to a region of low concentration across a gradient. Fick’s second law predicts how diffusion affects concentration over time (t):
This law is derived from Fick’s first law and the conservation of mass equation in the absence of chemical reactions (analogous to heat diffusion equation). If these laws are not satisfied then solute movement is considered Non-Fickian Transport. Case II transport is a particular case of Non-Fickian diffusion that in some cases polymer systems has distinct, sharp boundaries, which are moved linearly with time, exist between the swollen and unswollen regions.274 Therefore, Zero Order release is observed in these swollen regions. The Super Case II transport case occurs when the boundaries are not distinct and sharp, and the release can no longer be expressed linearly. Therefore, a non-linear release kinetic is observed.
These mathematical models will be correlated to raw experimental drug release data from particles to generate predictions of drug release lifetime and drug release
219 mechanism. The addition of post-processing technique and novel polyester blends will be compared to control PLGA particles.
APPENDIX E High Pressure Liquid Chromatography Standards
Dexamethasone Standard
Table 25. Dexamethasone calibration standards. Data shown with average results for each standard.
Retention Area Height Amount Concentration Sample Time (min) (mAU*min) (mAU) (μg) (μg/μL)
STD 1 5.100 195.984 1085.630 8.0000 1.000
STD 2 5.090 98.386 543.691 2.0000 0.5000
STD 3 5.080 49.258 270.884 0.5000 0.2500
STD 4 5.073 24.760 135.548 0.1250 0.1250
STD 5 5.073 12.277 67.179 0.0313 0.0625
STD 6 5.070 6.042 33.075 0.0078 0.0313
STD 7 5.157 2.973 16.270 0.0020 0.0156
STD 8 5.100 1.544 8.411 0.0005 0.079
220 221
STD 9 5.103 0.568 3.103 0.0001 0.0039
STD 10 5.100 0.1237 0.68 <0.0001 0.0020
Figure 117. Dexamethasone standard chromatograms via HPLC.
222
Figure 118. Dexamethasone calibration curve. Data shown mean±SD and calibration curve with 95% confidence interval indicated by dotted line.
Ciprofloxacin Standard
Varying the concentration of TFA is critical in analyzing the drug without creating an acidic environment that will degrade the drug into multiple components. In highly acidic conditions ciprofloxacin is degraded into two parts which are visible by two peaks on the chromatogram (Figure 119). However, even with a mild increase in pH from
1.7 to 1.9 the second degradation peak decreases. This indicated a more neutral pH is necessary. A pH of 3.1 is not neutral enough to generate one peak for ciprofloxacin but the integral of the second peak is greatly reduced (Figure 120). It was determined that pH
5.05 was the optimal pH for ciprofloxacin detection. There is no second peak of degradation product at this pH. The small peak observed in the chromatogram has an integral equivalent to the impurity found in the chemical provided by manufacturer.
223 224
A B C
Figure 119. Ciprofloxacin chromatograms from highly acidic mobile phase pH (A) 1.7, (B) 1.8, (C) 1.9.
A B
Figure 120. Ciprofloxacin chromatograms from moderately acidic mobile phase pH (A) 3.1 and (B) pH 5.05.
225
Table 26 Ciprofloxacin calibration standards. Data shown with average results for each standard.
Retention Area Height Amount Concentration Sample Time (min) (mAU*min) (mAU) (μg) (μg/ μL)
STD 1 1.510 456.915 2378.543 8.0000 1.0000
STD 2 1.433 256.273 1872.521 2.0000 0.5000
STD 3 1.383 134.274 1133.374 0.5000 0.2500
STD 4 1.353 68.225 592.507 0.1250 0.1250
STD 5 1.333 34.207 282.692 0.0313 0.0625
STD 6 1.323 17.170 135.159 0.0078 0.0313
STD 7 1.333 8.725 66.686 0.0020 0.0156
STD 8 1.503 4.292 33.253 0.0005 0.0079
STD 9 1.313 1.610 12.127 0.0001 0.0039
STD 10 1.313 0.362 2.697 <0.0001 0.0020
226
Figure 121. Ciprofloxacin standard chromatograms via HPLC.
227
Figure 122. Ciprofloxacin calibration curve. Data shown mean±SD and calibration curve with 95% confidence interval indicated by dotted line.
228
Simultaneous Detection of Dexamethasone and Ciprofloxacin Standard
Table 27. Ciprofloxacin calibration results from simultaneous detection. Data shown with average results for each standard.
Retention Area Height Amount Concentration Sample Time (min) (mAU*min) (mAU) (μg) (μg/μL)
STD 1 1.706 478.927 2134.093 3.000 1.000
STD 2 1.717 437.859 2480.200 1.500 0.500
STD 3 1.717 314.321 2278.397 0.750 0.250
STD 4 1.717 171.790 1343.850 0.375 0.125
STD 5 1.717 88.172 696.403 0.188 0.063
STD 6 1.717 45.840 366.913 0.100 0.033
229
Table 28. Dexamethasone calibration results from simultaneous detection. Data shown with average results for each standard.
Retention Area Height Amount Concentration Sample Time (min) (mAU*min) (mAU) (μg) (μg/μL)
STD 1 2.869 123.932 867.827 3.000 1.000
STD 2 2.863 62.746 446.957 1.500 0.500
STD 3 2.860 32.221 232.660 0.750 0.250
STD 4 2.860 16.345 118.143 0.375 0.125
STD 5 2.860 8.332 60.397 0.188 0.063
STD 6 2.860 4.083 31.137 0.100 0.033
Figure 123. Standard calibration curve for simultaneous release of ciprofloxacin (blue) and dexamethasone (black). Data shown mean±SD with ciprofloxacin nonlinear regression and dexamethasone linear regression including 95% confidence interval indicated by dotted line.
230
Equations determined from the calibration curves were solved such that independent and dependent variables were reversed; i.e. drug amount is solved for in terms of peak area.
To solve for ciprofloxacin amount in nonlinear relation to peak area the following equation was used:
y(x) = 0.091e0.0069x r2 = 0.975
To solve for dexamethasone amount in linear relation to peak area the following equation was used:
y(x) = 0.024x-0.017 r2 = 0.99
APPENDIX F XTT Assay Standard Calibration Determination
Table 29. 96-well plate arrangement for XTT assay calibration. Values are cells per well.
1 2 3 4 5 6 7 8 9 10 11 12 A 1X106 5X105 1X105 5X104 1X104 5X103 1X103 MEDIA BLANK B 1X106 5X105 1X105 5X104 1X104 5X103 1X103 MEDIA BLANK C 1X106 5X105 1X105 5X104 1X104 5X103 1X103 MEDIA BLANK
24 Hour Cell Inoculation
2 hour Incubation
Table 30. Raw data absorbance reading at 450 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.671 0.720 0.795 0.654 0.541 0.499 0.482 0.425 0.049 B 0.765 0.882 1.233 0.912 0.688 0.523 0.518 0.423 0.049 C 0.691 1.022 0.785 0.831 0.587 0.507 0.515 0.412 0.049
Table 314. Raw data absorbance reading at 475 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.644 0.647 0.753 0.583 0.508 0.441 0.419 0.362 0.047 B 0.661 0.677 0.824 0.831 0.585 0.486 0.449 0.362 0.047 C 0.628 0.854 0.730 0.654 0.525 0.452 0.444 0.352 0.048 H
231 232
Table 325. Raw data absorbance reading at 500 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.528 0.513 0.578 0.488 0.450 0.363 0.349 0.301 0.047 B 0.526 0.532 0.600 0.598 0.439 0.386 0.362 0.303 0.046 C 0.506 0.672 0.573 0.512 0.423 0.371 0.361 0.294 0.047
Table 33. Raw data absorbance reading at 630 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.051 0.049 0.050 0.050 0.050 0.046 0.048 0.047 0.046 B 0.050 0.050 0.050 0.050 0.050 0.048 0.051 0.044 0.045 C 0.052 0.125 0.052 0.052 0.049 0.053 0.048 0.044 0.044
Table 34. Raw data absorbance reading at 660 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.042 0.040 0.041 0.041 0.041 0.039 0.042 0.041 0.045 B 0.041 0.041 0.043 0.040 0.042 0.041 0.044 0.038 0.045 C 0.043 0.111 0.045 0.042 0.041 0.046 0.041 0.038 0.044
Table 35. Raw data absorbance reading at 690 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.039 0.037 0.038 0.038 0.039 0.037 0.039 0.039 0.045 B 0.038 0.037 0.040 0.037 0.039 0.038 0.041 0.036 0.044 C 0.040 0.105 0.042 0.039 0.038 0.044 0.038 0.036 0.044
233
4 hour Incubation
Table 36. Raw data absorbance reading at 450 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.097 1.158 1.277 0.943 0.801 0.644 0.590 0.490 0.049 B 1.026 1.224 1.345 1.199 0.830 0.713 0.642 0.489 0.049 C 1.070 1.293 1.225 1.161 0.796 0.706 0.640 0.476 0.049
Table 37. Raw data absorbance reading at 475 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.073 1.139 1.260 0.944 0.763 0.601 0.544 0.435 0.047 B 1.000 1.195 1.335 1.206 0.790 0.668 0.596 0.437 0.047 C 1.044 1.276 1.175 1.134 0.772 0.665 0.591 0.425 0.048
Table 38.Raw data absorbance reading at 500 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.854 0.908 0.995 0.778 0.622 0.490 0.448 0.360 0.047 B 0.787 0.914 1.164 0.959 0.630 0.542 0.486 0.363 0.046 C 0.829 1.029 0.945 0.893 0.630 0.542 0.483 0.351 0.047
Table 39. Raw data absorbance reading at 630 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.058 0.057 0.059 0.057 0.053 0.049 0.051 0.049 0.046 B 0.056 0.058 0.066 0.059 0.054 0.052 0.054 0.047 0.045 C 0.060 0.129 0.061 0.060 0.053 0.058 0.052 0.047 0.044
234
Table 40. Raw data absorbance reading at 660 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.043 0.042 0.043 0.043 0.043 0.040 0.042 0.042 0.045 B 0.043 0.042 0.046 0.043 0.043 0.042 0.045 0.040 0.044 C 0.046 0.110 0.046 0.044 0.042 0.048 0.042 0.039 0.044
Table 41. Raw data absorbance reading at 690 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.040 0.038 0.038 0.039 0.039 0.037 0.039 0.040 0.045 B 0.039 0.038 0.041 0.038 0.040 0.039 0.042 0.037 0.044 C 0.042 0.104 0.042 0.040 0.039 0.045 0.038 0.037 0.044
5 hour Incubation
Table 42. Raw data absorbance reading at 450 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.292 1.369 1.541 1.118 0.918 0.714 0.647 0.523 0.049 B 1.212 1.442 1.661 1.449 0.950 0.797 0.712 0.520 0.049 C 1.277 1.486 1.422 1.361 0.917 0.796 0.714 0.509 0.049
Table 43. Raw data absorbance reading at 475 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.276 1.363 1.541 1.108 0.896 0.679 0.610 0.476 0.047 B 1.191 1.434 1.649 1.453 0.922 0.768 0.673 0.476 0.047 C 1.261 1.468 1.412 1.356 0.893 0.762 0.673 0.464 0.048
235
Table 44.Raw data absorbance reading at 500 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.008 1.082 1.213 0.896 0.733 0.564 0.508 0.397 0.047 B 0.941 1.127 1.293 1.139 0.752 0.626 0.554 0.398 0.046 C 0.991 1.179 0.129 1.090 0.731 0.623 0.554 0.386 0.047
Table 45. Raw data absorbance reading at 630 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.061 0.060 0.063 0.058 0.055 0.050 0.052 0.050 0.045 B 0.059 0.061 0.067 0.063 0.057 0.053 0.056 0.048 0.045 C 0.062 0.129 0.066 0.063 0.055 0.059 0.053 0.047 0.044
Table 46. Raw data absorbance reading at 660 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.044 0.043 0.044 0.044 0.043 0.041 0.043 0.042 0.045 B 0.044 0.043 0.047 0.044 0.044 0.043 0.046 0.041 0.045 C 0.046 0.108 0.047 0.045 0.043 0.048 0.042 0.040 0.044
Table 47. Raw data absorbance reading at 690 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.040 0.038 0.039 0.039 0.039 0.037 0.040 0.039 0.045 B 0.039 0.038 0.042 0.039 0.040 0.039 0.043 0.038 0.044 C 0.042 0.101 0.042 0.040 0.039 0.045 0.038 0.037 0.044
236
24 Hour Inoculation Analysis
2 Hour Incubation
Table 48. Calculated specific absorbance using raw data readings from 450 nm and 630 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.195 0.292 0.227 0.238 0.049 5x105 0.246 0.409 0.485 0.380 0.122 1x105 0.319 0.757 0.319 0.465 0.253 5x104 0.179 0.439 0.367 0.328 0.134 1x104 0.066 0.215 0.126 0.136 0.075 5x103 0.028 0.052 0.042 0.041 0.012 1x103 0.009 0.044 0.055 0.036 0.024
Table 49. Calculated specific absorbance using raw data readings from 475 nm and 660 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.240 0.258 0.233 0.244 0.013 5x105 0.245 0.274 0.391 0.303 0.077 1x105 0.350 0.419 0.333 0.367 0.045 5x104 0.180 0.429 0.260 0.290 0.127 1x104 0.105 0.181 0.132 0.139 0.039 5x103 0.040 0.083 0.054 0.059 0.022 1x103 0.015 0.043 0.051 0.036 0.019
237
Table 50. Calculated specific absorbance using raw data readings from 500 nm and 690 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.176 0.173 0.160 0.170 0.009 5x105 0.163 0.179 0.253 0.198 0.048 1x105 0.226 0.244 0.225 0.232 0.011 5x104 0.137 0.245 0.166 0.183 0.056 1x104 0.099 0.086 0.080 0.088 0.010 5x103 0.016 0.035 0.024 0.025 0.010 1x103 0.000 0.008 0.019 0.009 0.010
4 Hour Incubation
Table 51. Calculated specific absorbance using raw data readings from 450 nm and 630 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.549 0.481 0.534 0.521 0.036 5x105 0.611 0.677 0.688 0.659 0.042 1x105 0.728 0.790 0.688 0.735 0.051 5x104 0.396 0.651 0.625 0.557 0.140 1x104 0.258 0.287 0.267 0.271 0.015 5x103 0.105 0.172 0.172 0.150 0.039 1x103 0.049 0.099 0.112 0.087 0.033
238
Table 52. Calculated specific absorbance using raw data readings from 475 nm and 660 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.595 0.520 0.573 0.563 0.039 5x105 0.662 0.716 0.741 0.706 0.040 1x105 0.782 0.852 0.704 0.779 0.074 5x104 0.466 0.726 0.665 0.619 0.136 1x104 0.285 0.310 0.305 0.300 0.013 5x103 0.126 0.189 0.192 0.196 0.037 1x103 0.067 0.114 0.124 0.102 0.030
Table 53. Calculated specific absorbance using raw data readings from 500 nm and 690 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.0454 0.85 0.436 0.425 0.036 5x105 0.510 0.513 0.574 0.532 0.036 1x105 0.597 0.760 0.552 0.636 0.109 5x104 0.379 0.558 0.502 0.480 0.092 1x104 0.223 0.227 0.240 0.230 0.009 5x103 0.093 0.140 0.146 0.126 0.029 1x103 0.049 0.081 0.094 0.075 0.023
239
5 Hour Incubation
Table 54. Calculated specific absorbance using raw data readings from 450 nm and 630 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 1.182 1.104 1.166 1.151 0.041 5x105 1.260 1.332 1.308 1.300 0.037 1x105 1.429 1.545 1.307 1.427 0.119 5x104 1.011 1.337 1.249 1.199 0.169 1x104 0.814 0.844 0.813 0.824 0.018 5x103 0.615 0.695 0.688 0.666 0.044 1x103 0.546 0.607 0.612 0.588 0.037
Table 55. Calculated specific absorbance using raw data readings from 475 nm and 660 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 1.183 1.098 1.166 1.149 0.045 5x105 1.271 1.342 1.311 2.308 0.036 1x105 1.448 1.553 1.316 1.439 0.119 5x104 1.015 1.360 1.262 1.212 0.178 1x104 0.804 0.829 0.801 0.811 0.015 5x103 0.589 0.676 0.665 0.643 0.047 1x103 0.518 0.578 0.582 0.559 0.036
240
Table 56. Calculated specific absorbance using raw data readings from 500 nm and 690 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.919 0.853 0.900 0.890 0.034 5x105 0.995 1.040 1.029 1.021 0.023 1x105 1.125 1.202 1.038 1.122 0.082 5x104 0.808 1.051 1.001 0.953 0.128 1x104 0.645 0.663 0.643 0.650 0.011 5x103 0.478 0.538 0.529 0.515 0.032 1x103 0.419 0.462 0.467 0.449 0.026
48 Hour Cell Inoculation
2 hour Incubation
Table 57. Raw data absorbance reading at 450 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.183 1.024 0.997 1.041 0.748 0.604 0.522 0.380 0.045 B 1.022 0.982 1.049 1.205 0.805 0.613 0.549 0.414 0.044 C 1.068 1.041 1.009 0.924 0.822 0.605 0.524 0.439 0.044
Table 58. Raw data absorbance reading at 475 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.129 0.955 0.986 0.947 0.678 0.550 0.463 0.322 0.045 B 0.911 0.920 0.908 0.908 0.747 0.568 0.479 0.350 0.044 C 1.015 1.043 0.959 0.873 0.764 0.555 0.464 0.372 0.047
241
Table 59. Raw data absorbance reading at 500 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.974 0.805 0.853 0.806 0.567 0.463 0.389 0.270 0.045 B 0.882 0.797 0.804 0.716 0.680 0.492 0.403 0.292 0.044 C 0.860 0.864 0.792 0.711 0.638 0.488 0.392 0.301 0.048
Table 60. Raw data absorbance reading at 630 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.295 0.148 0.067 0.062 0.057 0.049 0.052 0.048 0.045 B 0.060 0.116 0.076 0.057 0.082 0.051 0.052 0.048 0.044 C 0.063 0.075 0.065 0.056 0.055 0.052 0.049 0.049 0.046
Table 61. Raw data absorbance reading at 660 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.279 0.134 0.053 0.048 0.046 0.040 0.045 0.042 0.045 B 0.044 0.102 0.061 0.044 0.0471 0.042 0.043 0.041 0.042 C 0.049 0.060 0.051 0.043 0.044 0.044 0.042 0.040 0.044
Table 62. Raw data absorbance reading at 690 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.270 0.127 0.049 0.044 0.042 0.038 0.043 0.040 0.045 B 0.040 0.098 0.056 0.041 0.067 0.039 0.040 0.039 0.043 C 0.045 0.055 0.048 0.040 0.041 0.040 0.039 0.038 0.044
242
4 hour Incubation
Table 63. Raw data absorbance reading at 450 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.724 1.604 1.619 1.692 1.153 0.901 0.687 0.436 0.045 B 1.759 1.665 1.680 1.716 1.299 0.951 0.717 0.477 0.046 C 1.889 1.740 1.681 1.504 1.284 0.845 0.697 0.488 0.044
Table 64. Raw data absorbance reading at 475 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.691 1.585 1.661 1.660 2.156 0.890 0.652 0.385 0.045 B 1.743 1.618 1.672 1.625 1.275 0.902 0.667 0.419 0.045 C 1.863 1.742 1.635 1.499 1.261 0.808 0.653 0.431 0.044
Table 65.Raw data absorbance reading at 500 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.384 1.280 1.445 1.367 0.933 0.714 1.532 0.322 0.045 B 1.375 1.291 1.331 1.277 1.011 0.729 0.548 0.350 0.044 C 1.440 1.379 1.303 1.195 1.011 0.667 0.538 0.360 0.043
243
Table 66. Raw data absorbance reading at 630 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.305 0.142 0.078 0.072 0.062 0.053 0.053 0.049 0.045 B 0.069 0.137 0.074 0.065 0.069 0.056 0.053 0.051 0.044 C 0.087 0.085 0.072 0.064 0.062 0.056 0.052 0.050 0.045
Table 67. Raw data absorbance reading at 660 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.282 0.112 0.053 0.050 0.045 0.041 0.043 0.042 0.045 B 0.047 0.116 0.053 0.045 0.045 0.043 0.043 0.043 0.044 C 0.065 0.062 0.051 0.045 0.045 0.044 0.042 0.043 0.046
Table 68. Raw data absorbance reading at 690 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.217 0.115 0.048 0.044 0.041 0.037 0.040 0.039 0.047 B 0.042 0.110 0.047 0.040 0.040 0.039 0.040 0.040 0.044 C 0.059 0.056 0.045 0.040 0.041 0.040 0.039 0.039 0.044
244
5 hour Incubation
Table 69. Raw data absorbance reading at 450 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.992 1.865 2.007 2.027 1.403 1.033 0.763 0.457 0.045 B 2.074 1.911 2.014 1.950 1.498 1.070 0.794 0.500 0.043 C 2.222 2.074 1.954 1.758 1.463 0.934 0.771 0.510 0.044
Table 70. Raw data absorbance reading at 475 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.958 1.877 2.005 2.025 1.391 1.024 0.733 0.411 0.045 B 2.054 1.911 1.997 1.929 1.493 1.048 0.755 0.447 0.042 C 2.189 2.055 1.950 1.760 1.455 0.910 0.735 0.458 0.044
Table 71.Raw data absorbance reading at 500 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 1.573 1.497 1.599 1.582 1.127 0.829 0.599 0.346 0.046 B 1.604 1.512 1.575 1.531 1.183 0.844 0.619 0.376 0.044 C 1.702 1.622 1.540 1.401 1.153 0.745 0.606 0.386 0.044
Table 72. Raw data absorbance reading at 630 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.310 0.147 0.079 0.075 0.065 0.060 0.054 0.050 0.045 B 0.071 0.138 0.077 0.070 0.066 0.058 0.054 0.052 0.043 C 0.083 0.089 0.076 0.068 0.064 0.056 0.054 0.051 0.042
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Table 73. Raw data absorbance reading at 660 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.284 0.124 0.053 0.049 0.046 0.045 0.043 0.043 0.045 B 0.046 0.114 0.053 0.046 0.046 0.043 0.042 0.044 0.043 C 0.057 0.063 0.052 0.046 0.045 0.043 0.043 0.043 0.044
Table 74. Raw data absorbance reading at 690 nm.
1 2 3 4 5 6 7 8 9 10 11 12 A 0.273 0.115 0.047 0.043 0.041 0.041 0.040 0.040 0.045 B 0.040 0.107 0.046 0.040 0.041 0.039 0.039 0.041 0.046 C 0.050 0.057 0.045 0.040 0.040 0.039 0.039 0.039 0.044
48 Hour Inoculation Analysis
2 Hour Incubation
Table 75. Calculated specific absorbance using raw data readings from 450 nm and 630 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.508 0.548 0.556 0.541 0.030 5x105 0.496 0.452 0.527 0.492 0.038 1x105 0.550 0.559 0.505 0.538 0.029 5x104 0.572 0.734 0.429 0.578 0.153 1x104 0.311 0.309 0.328 0.316 0.010 5x103 0.175 0.148 0.114 0.146 0.030 1x103 0.090 0.083 0.036 0.070 0.029
246
Table 76. Calculated specific absorbance using raw data readings from 475 nm and 660 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.528 0.517 0.594 0.546 0.042 5x105 0.499 0.468 0.611 0.526 0.075 1x105 0.611 0.497 0.536 0.548 0.058 5x104 0.577 0.514 0.458 0.516 0.060 1x104 0.310 0.326 0.348 0.328 0.019 5x103 0.188 0.176 0.139 0.168 0.025 1x103 0.096 0.086 0.050 0.077 0.024
Table 77. Calculated specific absorbance using raw data readings from 500 nm and 690 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.434 0.490 0.514 0.479 0.041 5x105 0.408 0.407 0.508 0.441 0.058 1x105 0.534 0.456 0.443 0.478 0.049 5x104 0.492 0.383 0.370 0.415 0.067 1x104 0.255 0.321 0.296 0.291 0.033 5x103 0.115 0.161 0.147 0.154 0.007 1x103 0.076 0.071 0.052 0.066 0.013
247
4 Hour Incubation
Table 78. Calculated specific absorbance using raw data readings from 450 nm and 630 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.983 1.213 1.314 1.170 0.170 5x105 1.026 1.051 1.167 1.081 0.075 1x105 1.105 1.129 1.121 1.118 0.012 5x104 1.184 1.174 0.952 1.103 0.131 1x104 0.655 0.753 0.734 0.714 0.052 5x103 0.412 0.418 0.301 0.377 0.066 1x103 0.198 0.187 0.157 0.181 0.021
Table 79. Calculated specific absorbance using raw data readings from 475 nm and 660 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 1.024 1.277 1.367 1.223 0.178 5x105 1.078 1.083 1.249 1.137 0.097 1x105 1.223 1.200 1.153 1.192 0.036 5x104 1.225 1.161 1.023 1.136 0.103 1x104 0.726 0.811 0.785 0.774 0.044 5x103 0.464 0.440 0.333 0.412 0.070 1x103 0.224 0.205 0.180 0.203 0.022
248
Table 80. Calculated specific absorbance using raw data readings from 500 nm and 690 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 0.791 0.983 1.021 0.932 0.123 5x105 0.843 0.831 0.963 0.879 0.073 1x105 1.075 0.934 0.898 0.969 0.093 5x104 1.001 0.887 0.795 0.894 0.103 1x104 0.570 0.621 0.610 0.600 0.027 5x103 0.355 0.340 0.267 0.321 0.047 1x103 0.170 0.158 0.139 0.156 0.016
5 Hour Incubation
Table 81. Calculated specific absorbance using raw data readings from 450 nm and 630 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 1.682 2.003 2.139 1.941 0.234 5x105 1.718 1.773 1.958 1.816 0.126 1x105 1.928 1.937 1.878 1.914 0.032 5x104 1.952 1.880 1.690 1.841 0.135 1x104 1.338 1.432 1.399 1.390 0.048 5x103 0.973 1.012 0.878 0.954 0.069 1x103 0.709 0.740 0.717 0.722 0.016
249
Table 82. Calculated specific absorbance using raw data readings from 475 nm and 660 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 1.674 2.008 2.132 1.938 0.237 5x105 1.753 1.797 1.992 1.847 0.127 1x105 1.952 1.944 1.898 1.931 0.029 5x104 1.976 1.883 1.714 1.858 0.133 1x104 1.345 1.447 1.410 1.401 0.052 5x103 0.979 1.005 0.867 0.950 0.073 1x103 0.690 0.713 0.692 0.698 0.013
Table 83. Calculated specific absorbance using raw data readings from 500 nm and 690 nm.
Cell Calculated Calculated Calculated Standard Average Concentration Read 1 Read 2 Read 3 Deviation 1x106 1.300 1.564 1.652 1.505 0.183 5x105 1.382 1.405 1.565 1.451 0.100 1x105 1.552 1.529 1.495 1.525 0.029 5x104 1.539 1.491 1.361 1.464 0.092 1x104 1.086 1.142 1.113 1.114 0.028 5x103 0.788 0.805 0.706 0.766 0.053 1x103 0.559 0.580 0.567 0.569 0.011
Combined Analysis
Figure 124. Standard curves from 24 hour inoculation pre-optimization. Data shown mean±SD, n=3.
Figure 125. Standard curves from 48 hour inoculation pre-optimization. Data shown mean±SD, n=3.
250 251
Figure 126. Linear region of standard curve from 24 hour inoculation pre-optimization. Data shown with each replicate and linear regression model with 95% confidence band.
Table 84. Linear regression analysis of 24 hour inoculation pre-optimization.
Time Slope Y-intercept r2 (hours) (x10-5) 2 1.16 0.02 0.74 4 2.22 0.07 0.91 5 2.82 0.52 0.91
252
Figure 127. Linear region of standard curve from 48 hour inoculation pre-optimization. Data shown with each replicate and linear regression model with 95% confidence band.
Table 85. Linear regression analysis of 48 hour inoculation pre-optimization.
Time Slope Y-intercept r2 (hours) (x10-5) 2 2.50 0.04 0.96 4 6.38 0.12 0.96 5 7.85 0.60 0.97 *bold indicates selected calibration curve from pre-optimization
253
APPENDIX G BACTERIAL SENSITIVITY ASSAY COMPLETE ANALYSIS
Escherichia coli
Figure 128. E. coli standard ciprofloxacin standard.
Table 86. Escherichia coli standard measurements.
Ciprofloxacin Amount Inhibition Zone Length (mm) (ng) 0 0 0 0 1.25 6 7 7 2.50 8 8 8 5.00 10 9 9 10.00 11 10 10 20.00 12 11 11 40.00 13 12 12
254
Figure 129. Linear region of Escherichia coli standard curve using average IZL. Data shown mean with linear regression linear and 95% confidence interval, n=3.
255
Figure 130. Replicate one of Escherichia coli ciprofloxacin sensitivity via disk diffusion method.
256
Figure 131. Replicate two of Escherichia coli ciprofloxacin sensitivity via disk diffusion method.
257
Figure 132. Replicate three of Escherichia coli ciprofloxacin sensitivity via disk diffusion method.
258
Table 87. Measured Escherichia coli IZL on Day 2 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 7 6.5 6 8 8 9 2 8 7.5 9 9 9.5 10 5 9 10 10 10 10 11 10 11 10 11 11 11 11
Figure 133. Power Law regression fit of Day 2 Escherichia coli inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
259
Table 88. Calculated ciprofloxacin BAC of Escherichia coli on Day 2 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 4.84 - - 2.34 2.34 9.52 2 1.17 - 4.76 4.76 6.56 8.35 5 1.90 3.34 3.34 3.34 3.34 4.78 10 2.39 1.67 2.39 2.39 2.39 2.39
Figure 134. Escherichia coli ciprofloxacin BAC is not significantly different on Day 2 between PCFA and PGCFA.
260
Table 89. Measured Escherichia coli IZL on Day 4 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 6 6.5 6 4 6 7 2 7 7 7.5 6 7 7.5 5 8 8 10 8 8.5 9 10 8 9 10 9 9.5 10
Figure 135. Power Law regression fit of Day 4 Escherichia coli inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
261
Table 90. Calculated ciprofloxacin BAC of Escherichia coli on Day 4 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 ------2 2.42 - - 6.01 - - 5 0.47 0.47 3.34 0.47 1.19 1.90 10 0.23 0.95 1.67 0.95 1.31 1.67
Figure 136. Escherichia coli ciprofloxacin BAC is not significantly different on Day 4 between PCFA and PGCFA.
262
Table 91. Measured Escherichia coli IZL on Day 7 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 5 6 6 4 4 5 2 6 8 8 4.5 6 6 5 7.5 9 9 6 7 7.5 10 8 9.5 9.5 7 8 8
Figure 137. Power Law regression fit of Day 7 Escherichia coli inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
263
Table 92. Calculated ciprofloxacin BAC of Escherichia coli on Day 7 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 ------2 - 1.17 1.17 - - - 5 0.25 1.90 1.90 - 0.97 0.25 10 0.23 1.31 1.31 0.48 0.23 0.23
Figure 138. Escherichia coli ciprofloxacin BAC is significantly different on Day 7 between PCFA and PGCFA.
264
Table 93. Measured Escherichia coli IZL on Day 14 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 6 7 7 4 4 6 2 7 9 9 6 6 6.5 5 9 10 9.5 7 7 8 10 9 10 10 7 8 9
Figure 139. Power Law regression fit of Day 14 Escherichia coli inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
265
Table 94. Calculated ciprofloxacin BAC of Escherichia coli on Day 14 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 - - 4.84 - - - 2 2.42 4.76 4.76 - - - 5 1.90 3.34 2.62 0.97 0.97 0.47 10 0.95 1.67 1.67 0.48 0.23 0.95
Figure 140. Escherichia. coli ciprofloxacin BAC is significantly different on Day 14 between PCFA and PGCFA.
266
Klebsiella pneumoniae
Figure 141. Klebsiella pneumoniae ciprofloxacin standard.
Table 95. Klebsiella pneumoniae standard measurements.
Ciprofloxacin Amount Inhibition Zone Length (mm) (ng) 0 0 0 0 50 3 3 3 150 5 5 5 300 6.5 7 6 600 8 8 8 1000 8 8 8
267
Figure 142. Linear region of Klebsiella pneumoniae standard curve using average IZL. Data shown mean with linear regression linear and 95% confidence interval, n=3.
268
Figure 143. Replicate one of Klebsiella pneumoniae ciprofloxacin sensitivity via disk diffusion method.
269
Figure 144. Replicate two of Klebsiella pneumoniae ciprofloxacin sensitivity via disk diffusion method.
270
Figure 145. Replicate three of Klebsiella pneumoniae ciprofloxacin sensitivity via disk diffusion method.
271
Table 96. Measured Klebsiella pneumoniae IZL on Day 2 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 3 1.5 2 4 3 4.5 5 5 5 5 7 7 6.5 10 6 7 6.5 8 8 8 15 7 8 7.5 9 9 9 20 9 8 9 10 9 10
Figure 146. Power Law regression fit of Day 2 Klebsiella pneumoniae inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
272
Table 97. Calculated ciprofloxacin BAC of Klebsiella pneumoniae on Day 2 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 ------5 ------10 24.86 30.62 28.38 38.92 38.92 38.92 15 21.26 22.97 23.60 30.63 30.62 30.63 20 22.98 19.38 22.97 26.49 22.97 26.49
Figure 147. Klebsiella pneumoniae ciprofloxacin BAC is significantly different on Day 2 between PCFA and PGCFA.
273
Table 98. Measured Klebsiella pneumoniae IZL on Day 4 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 1 1 2.5 1 1 2 5 4 3 5 4 3 4 10 6 5 7 6 4 5 15 8 6.5 7.5 7 5 6 20 8 8 8 8 7 7
Figure 148. Power Law regression fit of Day 4 Klebsiella pneumoniae inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
274
Table 99. Calculated ciprofloxacin BAC of Klebsiella pneumoniae on Day 4 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 ------5 21.62 - - 20.55 - - 10 24.86 19.46 31.89 24.86 22.97 15.94 15 25.94 14.60 23.60 21.26 19.38 17.83 20 19.45 14.60 19.45 19.46 19.38 16.57
Figure 149. Klebsiella pneumoniae ciprofloxacin BAC is significantly different on Day 4 between PCFA and PGCFA.
275
Table 100. Measured Klebsiella pneumoniae IZL on Day 7 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 2 1 3 0 0 0 5 5 5.5 4.5 2 2 2 10 8 7 6 4 4 3 15 8 8 8 5 5 4 20 9 9 9 5 6 5
Figure 150. Power Law regression fit of Day 7 Klebsiella pneumoniae inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
276
Table 101. Calculated ciprofloxacin BAC of Klebsiella pneumoniae on Day 7 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 ------5 35.67 31.89 28.64 - 10.81 - 10 38.92 25.94 24.86 10.81 11.89 3.78 15 25.94 22.97 25.94 11.89 12.43 7.21 20 22.97 22.97 22.97 8.92 12.43 8.92
Figure 151. Klebsiella pneumoniae ciprofloxacin BAC is significantly different on Day 7 between PCFA and PGCFA.
277
Table 102. Measured Klebsiella pneumoniae IZL on Day 14 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 2 3 3.5 0 0 0 5 4 6 5 2 2.5 2 10 7 8 7 4 4 3 15 8 9 8 5 5 4 20 8 10 8 4 5.5 4
Figure 152. Power Law regression fit of Day 14 Klebsiella pneumoniae inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
278
Table 103. Calculated ciprofloxacin BAC of Klebsiella pneumoniae on Day 14 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 1 ------5 ------10 21.62 37.81 31.89 10.81 10.81 3.78 15 25.94 30.62 25.94 11.89 11.89 7.21 20 19.46 26.49 19.46 5.40 10.67 5.40
Figure 153. Klebsiella pneumoniae ciprofloxacin BAC is significantly different on Day 14 between PCFA and PGCFA.
279
Pseudomonas aeruginosa
Figure 154. Pseudomonas aeruginosa ciprofloxacin standard.
Table 104. Pseudomonas aeruginosa standard measurements.
Ciprofloxacin Amount Inhibition Zone Length (mm) (ng) 0 0 0 0 50 0.5 0.5 1 150 1.0 1.5 1.0 300 2.0 2.0 2.0 600 3.0 3.0 3.0 1000 4.0 4.0 4.0
280
Figure 155. Linear region of Pseudomonas aeruginosa standard curve using average IZL. Data shown mean with linear regression linear and 95% confidence interval, n=3.
281
Figure 156. Replicate one of Pseudomonas aeruginosa ciprofloxacin sensitivity via disk diffusion method.
282
Figure 157. Replicate two of Pseudomonas aeruginosa ciprofloxacin sensitivity via disk diffusion method.
283
Figure 158. Replicate three of Pseudomonas aeruginosa ciprofloxacin sensitivity via disk diffusion method.
284
Table 105. Measured Pseudomonas aeruginosa IZL on Day 2 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 15 2 2 2 4 4 4 20 3 2.5 2.5 5 4 5 30 4 3.5 4 6 4.5 5.5 35 4 4 5 7 5 6
Figure 159. Power Law regression fit of Day 2 Pseudomonas aeruginosa inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
285
Table 106. Calculated ciprofloxacin BAC of Pseudomonas aeruginosa on Day 2 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 15 29.37 29.38 29.38 60.71 60.71 60.71 20 33.78 29.91 29.91 57.29 45.53 57.28 30 30.35 26.43 30.36 46.02 34.27 42.11 35 26.02 26.09 39.45 46.16 32.73 39.44
Figure 160. Pseudomonas aeruginosa ciprofloxacin BAC is significantly different on Day 2 between PCFA and PGCFA.
286
Table 107. Measured Pseudomonas aeruginosa IZL on Day 4 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 15 3 3 3 1 1.5 1 20 4 3.5 4 2 2 2 30 4.5 5 4.5 2.5 3 3 35 5 4.5 5 3 3.5 3.5
Figure 161. Power Law regression fit of Day 4 Pseudomonas aeruginosa inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
287
Table 108. Calculated ciprofloxacin BAC of Pseudomonas aeruginosa on Day 4 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 15 45.05 45.04 45.04 13.71 21.54 13.71 20 45.53 39.66 45.53 22.03 22.03 22.03 30 34.27 38.19 34.27 18.61 22.52 22.52 35 32.73 29.76 32.73 19.30 22.66 22.66
Figure 162. Pseudomonas aeruginosa ciprofloxacin BAC is significantly different on Day 4 between PCFA and PGCFA.
288
Table 109. Measured Pseudomonas aeruginosa IZL on Day 7 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 15 3 2 2.5 0 0 0 20 3 3 3 0 0 0 30 4 4 3.5 0 0.5 0.5 35 5 5 5 0 0.5 1
Figure 163. Power Law regression fit of Day 7 Pseudomonas aeruginosa inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
289
Table 110. Calculated ciprofloxacin BAC of Pseudomonas aeruginosa on Day 7 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 15 45.04 29.38 37.21 0 0 0 20 33.78 33.78 33.79 0 0 0 30 30.35 30.36 26.44 0 2.34 2.94 35 32.78 32.73 32.74 0 2.52 5.88
Figure 164. Pseudomonas aeruginosa ciprofloxacin BAC is significantly different on Day 14 between PCFA and PGCFA.
290
Table 111. Measured Pseudomonas aeruginosa IZL on Day 14 of polymer degradation.
Inhibition Zone Length (mm) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 15 4 4 3 0 0 0 20 5 4.5 4 0 0 0 30 5.5 5 5 0.5 1 0.5 35 7 5 6 1 1 1
Figure 165. Power Law regression fit of Day 14 Pseudomonas aeruginosa inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
291
Table 112. Calculated ciprofloxacin BAC of Pseudomonas aeruginosa on Day 14 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PCFA PGCFA (μL) 0 0 0 0 0 0 0 15 60.71 60.71 45.04 0 0 0 20 57.28 51.41 45.53 0 0 0 30 42.11 38.19 38.18 2.94 6.86 2.94 35 46.16 32.73 39.45 5.86 5.88 5.88
Figure 166. Pseudomonas aeruginosa ciprofloxacin BAC is significantly different on Day 14 between PCFA and PGCFA.
292
Moraxella catarrhalis
Figure 167. Moraxella catarrhalis ciprofloxacin standard.
Table 113. Moraxella catarrhalis ciprofloxacin standard measurements.
Ciprofloxacin Amount Inhibition Zone Length (mm) (ng) 0 0 0 0 5 5 4.5 5.5 10 7.5 8 7 20 9.5 10 9.5 40 11 11 11.5 80 12.5 13 12.5
293
Figure 168. Linear region of Moraxella catarrhalis standard curve using average IZL. Data shown mean with linear regression linear and 95% confidence interval, n=3.
294
Figure 169. Replicate one of Moraxella catarrhalis ciprofloxacin sensitivity via disk diffusion method.
295
Figure 170. Replicate two of Moraxella catarrhalis ciprofloxacin sensitivity via disk diffusion method.
296
Figure 171. Replicate three of Moraxella catarrhalis ciprofloxacin sensitivity via disk diffusion method.
297
Table 114. Measured Moraxella catarrhalis IZL on Day 2 of polymer degradation.
Inhibition Zone Length (mm) Volume PFA PCFA PGCFA (μl) 0 0 0 0 0 0 0 0 0 0 1 0 0 0.5 2 2 3 5.5 8 8 2 1 2 1.5 4 5 6 9.5 11 11.5 5 3 4.5 4 7 9 9 11 14 18 10 5 8.5 7 9.5 11 11 13 20 20
Figure 172. Power Law regression fit of Day 2 Moraxella catarrhalis inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
Table 115. Power Law coefficients from regression fit (y(x) = AxB) of Day 2 Moraxella catarrhalis inhibition zone length dependent on volume.
Polymer Type A B r2 PFA 0.72 0.98 0.91 PCFA 3.23 0.53 0.95 PGCFA 7.87 0.36 0.89
298
Table 116. Calculated ciprofloxacin BAC of Moraxella catarrhalis on Day 2 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PFA* PCFA PGCFA (μl) 0 0 0 0 0 0 0 0 0 0 1 ------2 - - - - - 3.15 - - 15.78 5 - - - 2.21 4.10 4.10 - 15.78 16.19 10 1.58 1.82 1.10 2.29 3.00 3.00 11.45 15.00 12.64 *PFA contains no ciprofloxacin and calculations are revealing equivalency of effectiveness in terms of ciprofloxacin concentration
Figure 173. Moraxella catarrhalis ciprofloxacin BAC is significantly different on Day 2 between PFA and PGCFA and PCFA and PGCFA. PFA and PCFA are not significantly different.
299
Table 117. Measured Moraxella catarrhalis IZL on Day 4 of polymer degradation.
Inhibition Zone Length (mm) Volume PFA PCFA PGCFA (μl) 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 2 1.5 2 1 2 0 0.5 2 4 6 5 4.5 5 4 5 1.5 5 3 8 9 7 7.5 9 8 10 5 5.5 5 11 12 11 10 11 10
Figure 174. Power Law regression fit of Day 4 Moraxella catarrhalis inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
Table 118. Power Law coefficients from regression fit (y(x) = AxB) of Day 4 Moraxella catarrhalis inhibition zone length dependent on volume.
Polymer Type A B r2 PFA 0.52 1.02 0.85 PCFA 2.91 0.60 0.96 PGCFA 2.71 0.60 0.95
300
Table 119. Calculated ciprofloxacin BAC of Moraxella catarrhalis on Day 4 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PFA PCFA PGCFA (μl) 0 0 0 0 0 0 0 0 0 0 1 ------2 - - - - 3.15 - - - - 5 - - - 3.16 4.10 2.21 2.68 4.10 3.16 10 0.16 0.31 0.16 3.00 3.47 3.00 2.53 3.00 2.53
Figure 175. Moraxella catarrhalis ciprofloxacin BAC is significantly different on Day 4 between PFA and PCFA and PGCFA. PCFA and PGCFA are not significantly different.
301
Table 120. Measured Moraxella catarrhalis IZL on Day 7 of polymer degradation.
Inhibition Zone Length (mm) Volume PFA PCFA PGCFA (μl) 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1.5 0 0 0 0 2 0 0 2 3 4.5 4 0.5 0 0 5 1 2 2.5 6 8 6.5 2 2 2 10 2.5 4 3.5 9 10 9 4 5.5 5
Figure 176. Power Law regression fit of Day 7 Moraxella catarrhalis inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
Table 121. Power Law coefficients from regression fit (y(x) = AxB) of Day 7 Moraxella catarrhalis inhibition zone length dependent on volume.
Polymer Type A B r2 PFA 0.32 1.03 0.82 PCFA 2.07 0.67 0.93 PGCFA 0.17 1.47 0.97
302
Table 122. Calculated ciprofloxacin BAC of Moraxella catarrhalis on Day 7 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PFA PCFA PGCFA (μl) 0 0 0 0 0 0 0 0 0 0 1 ------2 ------5 - - - 1.26 3.16 1.73 - - - 10 1.03 0.32 0.56 2.05 2.53 2.05 0.32 0.39 0.16
Figure 177. Moraxella catarrhalis ciprofloxacin BAC is significantly different on Day 7 between PFA and PCFA and PCFA and PGCFA. PFA and PGCFA are not significantly different.
303
Table 123. Measured Moraxella catarrhalis IZL on Day 14 of polymer degradation.
Inhibition Zone Length (mm) Volume PFA PCFA PGCFA (μl) 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1.5 1 1.5 0 0 0 2 2 0.5 1 3 4 4.5 0 0 0 5 1 1 2 7 8.5 8 2.5 2.5 1.5 10 2.5 3 3 9 10.5 9.5 3.5 5 4
Figure 178. Power Law regression fit of Day 14 Moraxella catarrhalis inhibition zone length dependent on pipetted volume of polymer degradation products. Data shown mean±SD with nonlinear regression equation and 95% confidence interval, n=3.
Table 124. Power Law coefficients from regression fit (y(x) = AxB) of Day 7 Moraxella catarrhalis inhibition zone length dependent on volume.
Polymer Type A B r2 PFA 0.38 0.87 0.82 PCFA 2.43 0.63 0.94 PGCFA 0.21 1.31 0.92
304
Table 125. Calculated ciprofloxacin BAC of Moraxella catarrhalis on Day 14 of polymer degradation.
Biologically Active Concentration (ng/μL) Volume PFA PCFA PGCFA (μl) 0 0 0 0 0 0 0 0 0 0 1 ------2 ------5 - - - 2.21 3.63 3.16 - - - 10 0.03 0.79 0.87 2.05 2.76 2.29 0.56 0.16 0.32
Figure 179. Moraxella catarrhalis ciprofloxacin BAC is significantly different on Day 14 between PFA and PCFA and PCFA and PGCFA. PFA and PGCFA are not significantly different.
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VITAE
Amy Claire Goodfriend was born in Morristown, New Jersey, on July 12, 1989, the daughter of Linda Jane Goodfriend and Jeffery Thomas Goodfriend, sister to Brian Joseph Goodfriend. After completing her education at Ridgefield High School, Ridgefield, Connecticut in 2007, she entered Bucknell University at Lewisburg, Pennsylvania. Amy was an undergraduate research assistant in the Function Morphology laboratory mentored by C. Tristan Stayton, Ph.D. from 2009 until graduation in 2011. During summer 2008 she interned at Progenics Pharmaceutical Inc. in Tarrytown, New York. In summer 2009 she received a summer research fellowship with Bucknell University to continue her research mentor by C. Tristan Stayton, Ph.D. at Bucknell University. During her last summer during undergraduate education, Amy completed an internship at the University of Pittsburgh Graduate School of Public Health under the direction of Dr. Aaron Barchowsky, Ph.D. She received the degree of Bachelor of Science with a major in Biology and a minor in Spanish from Bucknell University on May 22, 2011. In July, 2011 she entered the Graduate School of Biomedical Sciences at the University of Texas Southwestern Center at Dallas in the Biomedical Engineering program in the track of Biomaterials, Mechanics and Tissue engineering. Amy completed her research in the Department of Cardiovascular and Thoracic Surgery in the Division of Pediatric Cardiothoracic Surgery mentored by Joseph M. Forbess, M.D. Upon receiving her doctoral degree Amy will be pursuing a postdoctoral fellowship at Yale University.
Permanent Address: 503 Oak Island Dr. Cary, NC 27513
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