ENABLING SOLID LIPID NANOPARTICLE DRUG DELIVERY TECHNOLOGY BY INVESTIGATING IMPROVED PRODUCTION TECHNIQUES
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Michael D. Triplett II, B.S.
* * * * *
The Ohio State University 2004
Dissertation Committee: Approved By Professor James F. Rathman, Adviser
Professor Jeffrey J. Chalmers Adviser Professor Kurt W. Koelling Graduate Program in Chemical Engineering
ABSTRACT
Industry estimates suggest that approximately 40% of lipophilic drug candidates fail due to solubility and formulation stability issues, prompting significant research activity in advanced lipophile delivery technologies. Solid lipid nanoparticle technology represents a promising new approach to lipophile drug delivery. Despite numerous research studies demonstrating improved therapeutic drug profiles, the commercialization of solid lipid nanoparticle technology remains limited. Physical instability and drug burst release have undermined performance while commercialization has been impeded by the lack of a large-scale, economically efficient production process.
Research has been conducted with the objective of advancing solid lipid nanoparticle production technology. Formulation and process effects on solid lipid nanoparticle size distribution, stability, drug loading, and drug release have been investigated, culminating in a novel solid lipid nanoparticle synthesis approach based on electrohydrodynamic aerosolization.
Utilizing a high-shear homogenization technique, effects of mixing speed, mixing time, and material concentrations were investigated using an experimental design approach. Experimentation showed stearic acid as the optimal lipid, sodium taurocholate as optimal cosurfactant, a 3:1 lecithin to sodium taurocholate ratio provided optimum performance, and mixing time and speed were inversely related to nanoparticle size and polydispersity.
β-Carotene was successfully incorporated into stearic acid nanoparticles. β-Carotene entrapment efficiency was shown to have a maximum of 80 % with a mean of 40 %. Entrapment efficiency decreased with increasing β-carotene concentration. β-carotene was retained in the nanoparticles for one month, the maximum time period examined. A maximum β-carotene concentration of 0.39 mg/ml was obtained in the nanoparticle suspension.
ii An electrohydrodynamic aerosolization device was designed and constructed for making solid lipid nanoparticles. Using water, sodium dodecyl sulfate, and potassium chloride, stable cone-jets were produced at voltages applied to the stainless steel needle ranging from -1.4 kV to - 6.2 kV. When commercial vegetable oil was substituted for water, stable cone-jets were produced at voltages from -
1.6 kV to - 5.7 kV. The use of electrohydrodynamic aerosolization to produce lipid nanoparticles was demonstrated. Using oleic acid, Pluronic F-68, and potassium chloride, particles possessing number distribution median diameters of 82 nm, 180 nm, and 210 nm were produced. Future electrohydrodynamic aerosolization device recommendations were included.
iii
Dedicated to Nanette for her unwavering support and encouragement, my parents for their nurturing and countless sacrifices on my behalf, and my family for whom I hope this accomplishment provides
fulfillment and inspiration
iv ACKNOWLEDGMENTS
I thank my adviser, Jim Rathman, for his intellectual support, creativity, and objectivity that made this work possible, and for his stimulating discussions in the art and beyond.
I am grateful to Jeff Chalmers for his unselfishness and informative insights. I thank Kurt
Koelling for his many years of advice and camaraderie. I thank Hal Walker, Linda Weavers, Bob
Brodkey, Jim Lee, and Dave Tomasko for sharing their research facilities and equipment.
I thank Shona Patel, Mei Yee, Grady Marcum, Erica Jones, Gary Koenig, Brian Kellogg, and
Patrick Bennett for their efforts as undergraduate researchers. This document would have been impossible without their tireless efforts and insights. I am privileged to have worked with so many talented people. I expect many great accomplishments in the years ahead from this group. On a special note, I thank Grady Marcum for his service to our great country, and I ask that God bless him during his imminent tour of duty in Iraq as part of the War on Terror.
I thank the United States Department of Defense, specifically the Office of Naval Research, and the American Society for Engineering Education for the National Defense Science & Engineering
Graduate Fellowship Program that supported my graduate studies. I hope that countless future
Americans experience the privilege of receiving a National Defense Science & Engineering Graduate
Fellowship. America’s national security and economic future depend on more Americans obtaining advanced science and engineering skills.
v VITA
February 23, 1975 …………………………. Born – East Liverpool, Ohio, USA
1997 ……………………………………….. B.S. Chemical Engineering, The Ohio State University.
1997 – 2000 ……………………………….. Engineer, Procter & Gamble Company, Cincinnati, Ohio, USA
2000 – 2001 University Fellow The Ohio State University
2001 – 2004 National Defense Science & Engineering Graduate Fellow, The Ohio State University
FIELDS OF STUDY
Major Field: Chemical Engineering
vi TABLE OF CONTENTS
Page Abstract…………………………………………………………………………………………... ii Dedication.……………………………………………………………………………………….. iv Acknowledgments……………………………………………………………………………….. v Vita………………………………………………………………………………………………. vi List of Tables..…………………………………………………………………………………… ix List of Figures.………………………………………………………………………………….... xi
Chapters:
1. Introduction…………………………………………………………………………………. 1
2. Review of solid lipid nanoparticle technology……………………………………………… 11
2.1. Solid lipid nanoparticle overview……………………………………………………... 11 2.2. Lipid nanoparticle synthesis techniques………………………………………………. 13 2.3. Effect of lipids and surfactants………………………………………………………... 23 2.4. Solid lipid nanoparticle stability………………………………………………………. 24 2.5. Lipophile loading and release in lipid nanoparticles………………………………….. 33 2.6. Pharmacological performance of solid lipid nanoparticle systems…………………… 36 2.7. Conclusions……………………………………………………………………………. 37
3. Material and process effects on solid lipid nanoparticle synthesis…………………………. 44
3.1. Introduction……………………………………………………………………………. 44 3.2. Materials and methods………………………………………………………………… 48 3.2.1. Materials……………………………………………………………………… 48 3.2.2. Lipid nanoparticle preparation………………………………………………... 50 3.2.3. Particle size analysis………………………………………………………….. 51 3.3. Results and discussion………………………………………………………………… 52 3.3.1. Assessment of microemulsion synthesis approach…………………………… 52 3.3.2. Effects of lipid and surfactant chemistry……………………………………... 55 3.3.3. Response surface modeling of stearic acid nanoparticles…………………….. 69 3.3.4. Investigation into the effects of lipid mass fraction and cholesterol addition... 75 3.4. Conclusions……………………………………………………………………………. 76
4. Incorporation of β-carotene into stearic acid nanoparticles………………………………… 78
4.1. Introduction……………………………………………………………………………. 78 4.2. Materials and methods………………………………………………………………… 79 4.2.1. Materials……………………………………………………………………… 79 4.2.2. Lipid nanoparticle preparation………………………………………………... 79 4.2.3. Particle size analysis………………………………………………………….. 79 4.2.4. Crystalline phase determination……………………………………………… 80 4.2.5. Drug loading and release determination……………………………………… 80 4.2.6. Atomic force microscopy…………………………………………………….. 80 vii 4.3. Results and Discussion………………………………………………………………... 80 4.3.1. Assessment of β-carotene incorporation using a 5 component mixture design 80 4.3.2. Investigation of ethanol as a cosurfactant…………………………………….. 84 4.3.3. Loading and release of β-carotene from stearic acid nanoparticles…………... 88 4.3.4. Effect of β-carotene loading on lipid crystallinity……………………………. 96 4.3.5. Comparison of a nutraceutical product to β-carotene loaded stearic acid nanoparticles………………………………………………………………….. 100 4.4. Conclusions……………………………………………………………………………. 101
5. Electrohydrodynamic Aerosolization as a novel approach for the preparation of solid lipid nanoparticles………………………………………………………………………………… 103
5.1. Introduction……………………………………………………………………………. 103 5.2. Materials and methods………………………………………………………………… 109 5.2.1. Materials……………………………………………………………………… 109 5.2.2. Particle size analysis………………………………………………………….. 109 5.2.3. Image capture…………………………………………………………………. 109 5.3. Results and discussion………………………………………………………………… 109 5.3.1. Design and construction of EHDA system…………………………………… 109 5.3.2. EHDA system start-up and aerosolization of water solutions………………... 115 5.3.3. Aerosolization of vegetable oil solutions…………………………………….. 119 5.3.4. Aerosolization of oleic acid solutions and demonstration of nanoparticle formation……………………………………………………………………… 123 5.3.5. Ethanol attack on acrylic housing…………………………………………….. 125 5.3.6. Electrical potential modeling and implications for future designs…………… 126 5.3.7. Composite nanoparticle modeling and implications for future research……... 132 5.4. Conclusions……………………………………………………………………………. 138 5.5. Recommendations for future research………………………………………………… 139
Appendices:
A. Sample photon correlation spectroscopy data file…..…………………………………. 141 B. Sample JMP journal file…..……………………………………………………………. 145 C. Matlab program used to model EHDA electrical potentials and fields……..…………. 151 D. Matlab program used to model drug release profiles………………………..…………. 154 E. Calibration of positive and negative dials used in EHDA experimentation…………… 159
List of References…………….……………………………………………………………….... 161
viii LIST OF TABLES
Table Page
1.1 Pharmaceutical criteria of colloidal drug delivery systems for parenteral administration. 4
2.1 Lipids and surfactants used in solid lipid nanoparticle production……………………… 13
2.2 Strengths and weaknesses of existing solid lipid nanoparticle synthesis techniques……. 22
2.3 Requirements of a solid lipid nanoparticle synthesis process…………………………… 23
2.4 Physical property dependence on triglyceride polymorph………………………………. 31
2.5 Drugs incorporated into solid lipid nanoparticles………………………………………... 34
3.1 Type of self-assembly predicted by surfactant number………………………………….. 46
3.2 Surfactant application according to Griffin’s HLB concept……………………………... 47
3.3 Effect of cosurfactant selection on lipid nanoparticle characteristics…………………… 53
3.4 Effect of storage temperature on lipid nanoparticle characteristics……………………… 54
3.5 Procedure used to prepare lipid nanoparticles…………………………………………… 55
3.6 Lauric acid data………………………………………………………………………….. 56
3.7 Stearic acid data………………………………………………………………………….. 56
3.8 Trilaurin data…………………………………………………………………………….. 57
3.9 Tristearin data……………………………………………………………………………. 57
3.10 ¼ fractional factorial screening experimental design intended to elucidate significant effects…………………………………………………………………………………….. 60
3.11 ANOVA table for the initial effective diameter model………………………………….. 62
3.12 ANOVA table for the reduced effective diameter model………………………………... 63
3.13 Coded coefficients and P-values for the reduced effective diameter model…………… 63
3.14 Coded coefficients and P-values for the reduced PI model……………………………… 64
3.15 Coded coefficients and P-values for d1 model…………………………………………… 67
3.16 Box-Behnken experimental design with data……………………………………………. 69 ix 3.17 JMP produced parameter estimates (uncoded) for d1 model derived from Box-Behnken DOE……………………………………………………………………………………… 71
3.18 Comparison of observed experimental diameters to predicted diameters……………….. 75
4.1 d1 (nm) model parameter estimates resulting from 5 component mixture experimental 81 design……………………………………………………………………………………..
4.2 d1 (nm) model parameter estimates with ethanol included as a cosurfactant……………. 85
4.3 Mole fractions used in determining entrapment efficiency and release properties……… 90
5.1 EHDA system factors……………………………………………………………………. 115
5.2 Experimental conditions and results for water EHDA screening design………………... 116
5.3 Lower cone-jet stability (-kV) model for water………………………………………….. 117
5.4 Experimental conditions and results for vegetable oil EHDA screening design………… 120
5.5 Lower cone-jet stability (-kV) model for water………………………………………….. 121
B.1 Customized mixture experimental design with corresponding data obtained by experiment……………………………………………………………………………….. 146
B.2 Summary of fit…………………………………………………………………………… 147
B.3 Analysis of variance (ANOVA) for obtained model…………………………………….. 147
B.4 Parameter estimates for obtained model…………………………………………………. 147
B.5 Effects test for obtained model…………………………………………………………... 148
B.6 Scaled estimates for factors included in significant model……………………………… 149
x LIST OF FIGURES
Figure Page
2.1 Solid lipid nanoparticles made from triglycerides and nonionic surfactants…………….. 11
2.2 Schematic representation of a solid lipid nanoparticle…………………………………... 12
2.3 Schematic representation of the configuration or a rotor-stator homogenizer…………... 15
2.4 Effect of homogenization rpm on emulsion droplet size………………………………… 16
2.5 Comparison of hot and cold high pressure homogenization processes………………….. 19
2.6 A physical representation and corresponding electrical potential of the double layer…... 27
2.7 An example of electrostatic repulsion producing a net repulsion between particles…….. 28
2.8 Activation energy of nucleation for a representative triglyceride system……………….. 30
2.9 Proposed redistribution of drug from molecularly dispersed state to enriched shell state, postulated as a cause of drug burst release phenomena observed in lipid nanoparticles... 35
2.10 Proposed structural models for drug loading profiles in lipid nanoparticles…………….. 36
3.1 Lauric acid structure……………………………………………………………………... 49
3.2 Stearic acid structure…………………………………………………………………….. 49
3.3 Trilaurin structure………………………………………………………………………... 49
3.4 Tristearin structure………………………………………………………………………. 49
3.5 Lecithin structure………………………………………………………………………… 49
3.6 DPPC structure…………………………………………………………………………... 49
3.7 Sodium glycocholate structure…………………………………………………………... 49
3.8 Sodium taurocholate structure…………………………………………………………… 49
3.9 Cholesterol structure……………………………………………………………………... 49
3.10 IKA Ultra-Turrax T 18 rotor-stator homogenizer used in lipid nanoparticle production... 50
3.11 Fatty acids produce smaller particles than triglycerides at equivalent molar formulations……………………………………………………………………………… 58
xi 3.12 Shorter triglyceride acyl chains produced smaller particles at equivalent molar formulations……………………………………………………………………………… 59
3.13 Lecithin to taurocholate ratio significantly impacts particle diameters………………….. 59
3.14 Trilaurin histograms illustrating the effect of increasing surfactant concentrations…….. 60
3.15 Pareto chart showing no significant effects in the initial effective diameter model……... 62
3.16 Residual analysis for effective diameter reduced model………………………………… 64
3.17 Pareto chart showing significance of effects in initial polydispersity model……………. 65
3.18 Pareto chart showing significance of effects in d1 model………………………………... 66
3.19 Residual analysis for d1 model…………………………………………………………... 67
3.20 Interactive effects present in d1………………………………………………………………………………………….. 68
3.21 Model residuals compared to experimental data………………………………………… 71
3.22 Interaction profiles for d1 model………………………………………………………… 72
3.23 Prediction profile for d1 model (t =120 s, SL =1.02, TP = 0.32)………………………… 72
3.24 Response surface for d1 model (t =120 s, SL =1.02, TP = 0.32)………………………… 73
3.25 Response surface for d1 model (t =120 s, SL =1.02, TP = 0.32; axes interchanged)……. 73
3.26 Twisting of response surface for d1 model (t =120 s, SL =1.02, TP = 0.32; t = y-axis)… 74
4.1 β-carotene structure……………………………………………………………………… 79
4.2 Plot of experimentally observed data versus diameters predicted by generated d1 formula…………………………………………………………………………………… 82
4.3 Residuals of model for d1 (nm) including β-carotene…………………………………… 82
4.4 Interaction profile for effects demonstrating significant relationships………………….. 83
4.5 Sodium taurocholate – ethanol interaction………………………………………………. 86
4.6 Sodium taurocholate – ethanol surface response profile (view 1)……………………….. 86
4.7 Sodium taurocholate – ethanol surface response profile (view 2)……………………….. 87
4.8 Sodium taurocholate – ethanol surface response profile (view 3)……………………….. 87
xii 4.9 β-carotene absorbance spectrum in the UV-Visible region……………………………… 89
4.10 d1 (nm) versus β-carotene concentration (mg/ml) in analytical sample…………………. 91
4.11 d1 (nm) means diamonds versus time along with two means comparison test results…... 92
4.12 Entrapment efficiency versus β-carotene concentration………….……………………… 93
4.13 Entrapment efficiency means diamonds versus time along with means comparison test results…………………………………………………………………………………….. 93
4.14 AFM image of nanoparticles produced according to Condition 3………………………. 95
4.15 AFM image of nanoparticles produced according to Condition 6………………………. 95
4.16 Heating and cooling cycles for each component comprising lipid nanoparticles……….. 97
4.17 DSC curve for a β-carotene loaded stearic acid nanoparticle with significant water……. 97
4.18 DSC curve for Condition 9 on day of preparation……………………………………….. 99
4.19 DSC curve for Condition 9 at one month after preparation……………………………… 99
4.20 Lycopene instability in a commercially available nutraceutical product………………... 100
4.21 Commercially available nutraceutical product (left) and lipid nanoparticle suspension (right)…………………………………………………………………….………………. 101
5.1 Force balance in EHDA flow……………………………………………………………. 105
5.2 Concentric EHDA flow………………………………………………………………….. 108
5.3 EHDA process flow diagram proposed at outset of design activities…………………… 111
5.4 Schematic of 1st generation EHDA process……………………………………………… 112
5.5 Front and 45° views of CAD depiction of 1st generation EHDA device………………… 113
5.6 Photo of 1st generation EHDA device…………………………………………………… 114
5.7 Interaction plot demonstrating the relationship between positive voltage and flow rate... 117
5.8 Water with no applied voltage…………………………………………………………… 118
5.9 Formation of a stable cone-jet mode…………………………………………………….. 118
xiii 5.10 Interaction plot demonstrating the relationship between positive voltage and flow rate for oil system…………………………………………………………………………….. 122
5.11 Oil with no applied voltage……………………………………………………………… 123
5.12 Formation of a stable oil cone-jet……………………………………………………….. 123
5.13 Oleic acid structure……………………………………………………………………… 123
5.14 Pluronic F-68 structure………………………………………………………………….. 123
5.15 Number and volume distributions of oleic acid nanoparticles formed by EHDA………. 125
5.16 EHDA structural failure…………………………………………………………………. 126
5.17 Close-up of EHDA structural failure……………………………………………………. 126
5.18 Electrical potential (V) distribution with -6 kV and 1 kV applied to the needle and positive electrode, respectively…………………………………………………………... 128
5.19 Electrical potential (V) distribution with -3 kV and 6 kV applied to the needle and positive electrode, respectively…………………………………………………………... 129
5.20 Electrical field (V/cm) generated around the needle (z = 2.5 cm) with -3 kV and 1 kV applied to the needle and positive electrode, respectively………………………………. 130
5.21 Electrical field (V/cm) generated around the positive electrode with -3 kV and 6 kV applied to the needle and positive electrode, respectively……………………………….. 131
5.22 Schematic representation of an enriched core – shell particle structure…………………. 132
5.23 Calculated release profiles as a function of diameter for a uniform initial drug profile… 135
5.24 Calculated release profiles as a function of diameter for enriched drug core profile……. 136
5.25 Calculated release profiles as a function of diameter for a uniform initial drug profile… 137
5.26 Pulsatile delivery profile demonstrated using a mixture of 100 nm particles…………… 138
B.1 Actual by predicted plot for model generated from analysis of experimental design…… 146
B.2 Residual by predicted plot for model generated from analysis of experimental design…. 148
B.3 Prediction profiler for generated model………………………………………………….. 149
B.4 Interaction profile………………………………………………………………………... 150
xiv E.1 Calibration of EHDA power supply dial settings to voltage measurements…………….. 160
xv
CHAPTER 1
INTRODUCTION
Sales of U.S. pharmaceutical formulations utilizing advanced drug delivery technologies exceeded
$38 billion, or roughly 18% of total pharmaceutical sales, in 2003. While U.S. pharmaceutical sales are expected to increase 14% to 18% in the next several years, Medco Health projects sales of pharmaceutical products utilizing advanced drug delivery technologies to grow 28% over the next 5 years. By 2007, pharmaceutical products utilizing advanced drug delivery technologies are projected to account for 39% of all pharmaceutical sales.[1]
The market shift toward advanced drug delivery formulations reflects both a strong societal desire to improve therapeutic efficacy and the economic pressures confronting the pharmaceutical industry.
Medical professionals continually seek better therapies and increasingly seek earlier detection capabilities.
Patients desire effective, inexpensive treatments that minimize harmful side effects. Employers seek to reduce illnesses and treatment costs that decrease employee productivity, increase health insurance costs, and often increase staffing needs. Faced with numerous upcoming expirations of patents covering blockbuster drugs, pharmaceutical companies now view advanced drug delivery technologies as a method to maintain patent protection of highly profitable drugs. By altering the therapeutic profile of existing drugs by combining the drugs with new advanced delivery technologies, the pharmaceutical companies can obtain new patents to extend the exclusivity of blockbuster drugs.[2] Companies can transform generic drugs into patentable pharmaceutics by reformulating generic drugs with advanced delivery technologies.
This practice is known as ‘branded generics’ and has become a very profitable business.
During the past decade, advanced drug delivery research and development activity has surged because of the aforementioned medical and economic driving forces. The emergence of nanotechnology and microtechnology and the growing capabilities of proteomics, genomics, and combinatorial chemistry 1 have provided scientists and engineers with new techniques for creating novel advanced drug delivery technologies. Commonly accepted criteria of advanced drug delivery systems include maximal drug bioavailability, tissue targeting, controlled release kinetics, minimal immune response, ease of administration for patient compliance, and the ability to deliver traditionally difficult drugs such as lipophiles, amphiphiles and biomolecules. The National Institutes of Health, in RFA: EB-03-011, summarized modern drug delivery requirements as the targeted and controlled delivery of drugs, proteins, and genes into cells with reduced side effects and easier administration.[3]
A drug’s therapeutic efficacy depends on four fundamental pathways of drug transport and modification within the body: absorption into the plasma from the administration site; distribution between the plasma and tissues, metabolism within the tissues; and elimination from the body. Absorption rate depends on many factors such as hydrophobicity, chemical environment, particle size, crystallinity, blood flow, absorptive surface area, and residence time at absorptive surface. Drug distribution largely depends on blood flow, capillary permeability such as in the blood-brain barrier, ligand binding, and hydrophobicity. Drug metabolism and elimination primarily depend on the aforementioned properties.[4]
The drug delivery system can greatly impact each pathway, and, therefore, the delivery system is a critical design component in pharmaceutical sciences.
Lipophiles, or poorly water soluble molecules, perform pivotal, beneficial roles in numerous biological processes. Many leading small molecule drugs are lipophilic. Anticancer drugs, including piposulfan, etoposide, camptothecin, and paclitaxel are lipophilic.[5] The leading antifungal agents such as amphotericin B, a polyene, and the newer azoles, fluconazole and itraconazole, are lipophilic.[6-8] Key antioxidants such as vitamin A, vitamin E, retinol, lycopene, and β-carotene also are lipophilic.[9-17] In recent years, scientists increasingly have demonstrated novel pharmaceutical, nutraceutical, biotechnological, and agricultural applications of lipophilic compounds.
As scientists increasingly discover lipophiles’ diverse capabilities in biological systems, more research effort is expended to develop novel approaches that utilize lipophiles to solve today’s most challenging pharmaceutical, nutraceutical, biotechnological, and agricultural issues. Laboratory demonstration of biological activity is insufficient for achieving new therapies; the lipophiles must be
2 formulated and delivered in a safe, efficacious, and cost effective manner. Lipophile delivery is extremely challenging and has long been a source of frustration in the pharmaceutical sciences.
Upon introduction to aqueous biological environments, lipophilic molecules exhibit instability, food interactions, reduced bioavailability, non-specific targeting, and toxic effects that produce undesired immunogenic responses and reduced efficacy. SkyePharma suggests that 10% of marketed drugs in 1999 were poorly water soluble.[18] Acusphere, Inc. claims that FDA-approved lipophilic pharmaceuticals constituted up to 40% of all drugs in development in 2002.[19] SkyePharma claims that 40% of all poorly water soluble drug candidates are abandoned due to solubility, stability, and delivery issues.[18] According to Acusphere, Inc., a well designed lipophilic drug delivery system provides increased drug tolerance, shorter and more amenable administration strategies, and improved efficacy.[19] Clearly, therapeutic and economic incentives exist for the development of novel technologies for the delivery of lipophilic drugs.
As a result, lipid-based delivery technologies have received and continue to receive significant attention from academia and industry.
Historically, most lipid-based delivery technology research has focused on the delivery of hydrophobic small molecule drugs for human medical applications. More recently, lipid-based delivery systems have suggested as biocompatible carriers of peptides, proteins, plasmic DNA, antisense oligonucleotides or ribozymes, for pharmaceutical, cosmetic, and biochemical purposes. The inherent biocompatibility of lipids, self-assembly capabilities, particle size versatility, and low costs make lipid- based delivery systems attractive and the subject of intense research.[20]
Despite the intense research efforts spanning several decades, targeted and controlled delivery of lipophilic drugs remains one of the more elusive objectives in the pharmaceutical sciences. Given the aforementioned challenges for delivering lipophilic drugs, most scientists prefer parenteral delivery targeted lipophilic drug delivery systems.[21] The choice of parental administration imposes strict particle size constraints on the delivery system. Since the smallest capillary is approximately 5 µm in diameter, parenteral delivery systems must be colloidal in nature. Table 1.1 lists the modern pharmaceutical criteria of colloidal drug delivery systems intended for parenteral administration.[22] This research effort focuses on the development of a lipid nanoparticle technology for the parenteral delivery of hydrophobic drugs.
3 Criteria 1. Nontoxic, biocompatible materials 2. Minimal reticuloendotheial system (RES) interaction 3. Submicron particle distributions 4. Sterile 5. Shelf life of 3 – 5 years 6. Targeting capability 7. Controlled drug release
Table 1.1: Pharmaceutical criteria of colloidal drug delivery systems for parenteral administration
Introduced in the 1950’s, oil-in-water emulsions for parenteral nutrition were the first commercial colloidal delivery carriers of lipophilic drugs.[7] Oil-in water emulsions were later extended to parenteral drug delivery. Although formulations containing diazepam and etomidate have been commercialized, parenteral delivery of emulsions exhibit several disadvantages. Oils approved for medical use generally exhibit low solubility for most drugs. The addition of the drug often destabilizes the emulsion. Emulsions exhibit drug burst release phenomena due to emulsion destabilization upon administration, the liquid state of the carrier offering little mass transfer resistance, and the inability to stabilize the drug in the oil phase.[7] These limitations have inhibited additional research and development of emulsions as controlled release drug delivery systems.
In the 1960’s, liposomes emerged as the second generation of colloidal drug delivery technology.
Liposomes are spherical phospholipid bilayer structures with an encapsulated aqueous core. This structure provides the ability to load either hydrophilic or lipophilic compounds in the aqueous core or in the lipid bilayer, respectively.[8, 9] The use of biological phospholipids provides inherent biocompatibility. Drug release kinetics, stability, and biodistribution can be tailored by varying size, surface charge, surface hydrophobicity, and membrane fluidity.[10] The addition of gangliosides or polyethylene glycols on the liposome surface prevents RES uptake, and the addition of surface ligands permits tissue targeting.
Liposome based drug carriers were commercialized in the 1980’s as topical anti-aging products and later in the early 1990’s as pulmonary delivery and intravenous delivery technologies.[7]
The promise of liposomal drug delivery has not been fully realized due to several shortcomings.
Chemical and physical stability issues greatly impair the use of liposomes for parenteral delivery. In
4 addition to providing low lipophilic drug loading capacity, liposomes often exhibit drug burst release kinetics. Finally, production costs make liposomes more expensive than other drug delivery systems, sometimes even several orders of magnitude more expensive.[12]
In the late 1980’s, the pharmaceutical community began investigating the potential of microemulsions, often referred to as swollen micelles, as hydrophobic drug delivery agents. Many researchers define microemulsions as single phase optically isotropic and thermodynamically stable liquid solutions comprised of water, oil, and an amphiphile.[13] As a result, unlike emulsions (macroemulsions) that are thermodynamically unstable, microemulsions do not require significant energy input to form. This produces a cost benefit and provides inherent stability. Microemulsions of pharmaceutical interest have been spherical with narrow size distributions, most often 50 – 250 nm in diameter.[14]
Unfortunately, microemulsion delivery systems exhibit the same deficiencies as emulsion systems.
Most problematically, burst release and destabilization occur upon administration, undermining the concept of controlled drug release. Upon administration, the electrostatic and concentration conditions change dramatically. As a result, the microemulsion is thermodynamically disrupted and often transforms into a macroemulsion or ‘dissolves’, leading to a rapid release of the drug.[14] Kinetically, the liquid state of the dispersed lipid provides little resistance to drug mass transfer. When combined with the small diffusion path, even if the microemulsion remains intact after administration, the drug rapidly diffuses into the bulk blood. To achieve controlled drug release, it has been estimated that the drug must be very lipophilic, having at least a 106:1 octanol-to-water partition coefficient.[15] For these reasons, no parenteral microemulsion formulation has been commercialized to date.
In the 1990’s, polymeric nanoparticles emerged as a fourth approach to parenteral drug delivery of lipophilic drugs. Synthetic, yet biocompatible, polymer carriers offer tremendous control over the polymer matrix due to the polymer industry’s many years of polymer production experience and continuing advances in microtechnology and nanotechnology. Hence, polymer carriers offer significant control over the drug release kinetics.
There are two main disadvantages of the use of synthetic polymers. First, synthetic polymers on the nanoparticle scale are highly cytotoxic because of polymer degradation upon endocytosis. The second
5 disadvantage is the lack of an efficient large-scale production method.[16] As a result of the cytotoxicity and production issues, few polymeric nanoscale particulate drug carrier systems are sold on the market today.
Drug nanosuspensions, also known as nanoparticulates, represent a recent approach to parenteral drug delivery and consist of a poorly soluble crystalline drug with surface active agents (surfactants) adsorbed onto the drug aggregate surface.[17] Although straightforward in theory, this approach has suffered from solubilization and recrystallization of the drug particulates. These processes often lead to drug particulate aggregation and particulate size growth, both very undesirable for two reasons. First, drug toxicity increases as the drug is concentrated in larger particulates. Second, the increased particulate size could lead to extremely harmful vascular blockages.[6, 17] Due to the lack of control over drug particulate growth, drug nanosuspensions remain in the research and development realm.
Solid lipid nanoparticles recently emerged as a novel approach to parenteral drug delivery systems. In theory, solid lipid nanoparticles combine the advantages of lipid emulsion systems and polymeric nanoparticle systems while overcoming the temporal and in vivo stability issues that plague the aforementioned approaches. Utilizing biological lipids is theorized to minimize carrier cytotoxicity, and the solid state of the lipid is theorized to permit more controlled drug release due to increased mass transfer resistance.[6] Solid lipid nanoparticles generally are spherical in shape and are comprised of a solid lipid core stabilized by a surfactant interfacial region. The core lipids can be fatty acids, acylglycerols, waxes, and mixtures of the same. Biological membrane lipids such as phospholipids, sphingomyelins, bile salts such as sodium taurocholate, sterols such as cholesterol, and mixtures of the same are utilized as surfactant stabilizers. Polyethylene glycol incorporation can provide steric stabilization and inhibit immune clearance.[21, 23-25] Ligands can be conjugated to nanoparticles to promote tissue targeting.[26]
6 Scalable solid lipid nanoparticle synthesis processes such as high-shear homogenization, high- pressure homogenization, and microemulsion dilution have been demonstrated.[18, 19] Numerous combinations of lipids and lipophilic drugs have been formulated into solid lipid nanoparticles of various diameters. Extended drug release profiles have been observed in vitro. Animal studies have demonstrated desirable drug localization and clearance times. These early efforts generated significant excitement in solid lipid nanoparticles as a parenteral drug delivery system for hydrophobic compounds.
Despite the perceived advantages, the application of solid lipid nanoparticle technology has proved extremely difficult. Physical instability, characterized by particle growth, and drug burst release have plagued solid lipid nanoparticle systems. Given the solid nature of the lipid, particle size growth represents a potentially lethal complication when considering parenteral drug delivery. In the literature, the
“one at a time” experimental approach mostly has been applied to solid lipid nanoparticle synthesis and drug release studies. Typically, the types of materials, material composition, and process parameters have been varied until acceptable particle characteristics result with little consideration of the underlying scientific fundamentals. Such an approach coupled with inconsistent results has led some researchers to suggest that predicting solid lipid nanoparticle size and stability is nearly impossible.[27] As a result, people have begun to abandon parenteral drug delivery applications and, instead, have begun to focus on oral, topical, and ocular drug delivery applications.[12] Yet, parenteral delivery remains the most promising approach in realizing the ultimate goal of targeted, controlled drug release.
Given the successful demonstration of solid lipid nanoparticle technology, the underlying concept of solid lipid nanoparticles as a lipophilic delivery technology appears sounds. Commercialization has been impeded by the lack of a large-scale, economically efficient production process. Currently available technologies do not provide the necessary particle size control, long-term stability, drug loading capability, and reproducibility to fully realize the potential previously demonstrated in laboratory settings.
Additionally, the existing technologies are not cost efficient due to a high number of processing steps and high energy demand. In short, an improved production process is required to realize the full technological and economic potential of solid lipid nanoparticle technology. An investigation of solid lipid nanoparticle synthesis and the resulting effects on stability, drug loading, drug release, and efficacy has been conducted.
7 The primary objective of this research effort was to enable solid lipid nanoparticle technology to move beyond its current stability, release, and cost limitations. The central hypothesis was that existing limitations result from inappropriate chemical formulations and processing conditions. This research effort is divided into two categories: 1) the optimization of currently accepted solid lipid nanoparticle production techniques and 2) the development of a novel solid lipid nanoparticle production technique.
Currently accepted techniques evaluated include microemulsion dilution and high-shear homogenization
(HSH). The novel category focused on electrohydrodynamic aerosolization (EHDA) technology, also known as electrospray technology, as a means to produce solid lipid nanoparticles. Beyond the scope of this doctoral thesis, the long-term goal of this research is the development of a commercially viable novel solid lipid nanoparticle production process that provides robust particle size control, stability, high lipophile drug loading, superior targeted and controlled release capabilities, and delivery of sensitive biomolecules.
Future application interests include anticancer formulations, antifungal formulations, nutraceutical formulations, vaccine therapy, and gene delivery.
References 1. Hoffman, J. M., Shah, N. D., Vermeulen, L. C., Hunkler, R. J., and Hontz, K. M. (2004) Projecting Future Drug Expenditures--2004. Am J Health-Syst Pharm 61, 145-148
2. Bell, M. (2003) Unraveling the Pharmaceutical Industry. In p. 6, Arthur D. Little GmbH, Wiesbaden, Germany
3. NIH (2002) Development of novel drug and gene delivery systems and devices. RFA: EB-03- 011. In
4. Mycek, M. J., Harvey, R. A., and Champe, P. C. (2000) Pharmacology, Lippincott, Williams, & Wilkins, Philadelphia, PA
5. Merisko-Liversidge, E., Sarpotdar, P., Bruno, J., Hajj, S., Wei, L., Peltier, N., Rake, J., Shaw, J. M., Pugh, S., Polin, L., Jones, J., Corbett, T., Cooper, E., and Liversidge, G. G. (1996) Formulation and Antitumor Activity Evaluation of Nanocrystalline Suspensions of Poorly Soluble Anticancer Drugs. Pharmaceutical Research 13, 272-278
6. Mann, J. J. (1980) Antifungal agents, Johns Hopkins University, Baltimore, MD
7. Sarosi, G. A., and Davies, S. F. (1993) Fungal diseases of the lung, Raven Press, New York
8. Vieira, D. B., and Carmona-Ribeiro, A. M. (2001) Synthetic Bilayer Fragments for Solubilization of Amphotericin B. Journal of Colloid and Interface Science 244, 427-431
8 9. Blomhoff, R. (1994) Vitamin A in health and disease, M. Dekker, New York
10. Sies, H., and Krinsky, N. I. (1994) Second International Conference on Antioxidant Vitamins and Beta-Carotene in Disease Prevention. Proceedings of a symposium. Berlin, Germany, October 10- 12, 1994. American Journal of Clinical Nutrition 62, 1299-1540
11. Prasad, K. N., Santamaria, L., and Williams, R. M. (1995) Nutrients in cancer prevention and treatment, Humana Press, Totowa, NJ
12. Kumpulainen, J. T., and Salonen, J. T. (1996) Natural antioxidants and food quality in atherosclerosis and cancer prevention, Royal Society of Chemistry Information Service, Cambridge, UK
13. Baskin, S. I., and Salem, H. (1997) Oxidants, antioxidants, and free radicals, Taylor & Francis, Washington, DC
14. Garewal, H. S. (1997) Antioxidants and disease prevention, CRC Press, Boca Raton, FL
15. Basu, T. K., Temple, N. J., and Garg, M. (1999) Antioxidants in human health and disease, CABI Publishing, New York
16. Salonen, J. T., and Kumpulainen, J. T. (1999) Natural antioxidants and anticarcinogens in nutrition, health and disease, Royal Society of Chemistry, Cambridge, UK
17. Rosales, G. R. (2002) Carotenoid and fruit development effects on germination and vigor of tomato (Lycopersicon esculentum Mill.) seeds. In Horticulture and Crop Science p. 135, The Ohio State University, Columbus, OH
18. SkyePharma (1999) Solubilization. In Vol. 2003
19. Acusphere (2002) Background to Our Hydrophobic Drug Delivery System (HDDS™). In Vol. 2003
20. Ulrich, A. S. (2002) Biophysical Aspects of Using Liposomes as Delivery Vehicles. Bioscience Reports 22, 129-150
21. Müller, R. H., Mäder, K., and Gohla, S. (2000) Solid lipid nanoparticles (SLN) for controlled drug delivery – a review of the state of the art. European Journal of Pharmaceutics and Biopharmaceutics 50, 161-177
22. Westesen, K. (2000) Novel lipid-based colloidal dispersions as potential drug administration systems - expectations and reality. Colloid & Polymer Science 278, 0608-0618
23. Bocca, C., Caputo, O., Cavalli, R., Gabriel, L., Miglietta, A., and Gasco, M. R. (1998) Phagocytic uptake of fluorescent stealth and non-stealth solid lipid nanoparticles. International Journal of Pharmaceutics 175, 185-193
24. Gregoriadis, G. (1998) Targeting of drugs: strategies for stealth therapeutic systems, Plenum Press, New York
25. Fundarò, A., Cavalli, R., Bargoni, A., Vighetto, D., Zara, G. P., Paolo, G., and Gasco, M. R. (2000) Non-stealth and stealth solid lipid nanoparticles carrying doxorubicin: pharmacokinetics and tissue distribution after i.v. administration to rats. Pharmacological Research 42, 337-343
9 26. Schreier, H. (2001) Drug targeting technology: physical, chemical, biological methods, Marcel Dekker, New York
27. Powell, M. F., and Newman, M. J. (1995) Vaccine design: the subunit and adjuvant approach, Plenum Press, New York
10
CHAPTER 2
REVIEW OF SOLID LIPID NANOPARTICLE TECHNOLOGY
2.1 Solid Lipid Nanoparticle Overview
Solid lipid nanoparticles typically are spherical with average diameters between 50 to 500 nanometers. Solid lipid nanoparticles possess a solid lipid core matrix that can solubilize lipophilic molecules. The lipid core is stabilized by surfactants (emulsifiers). For pharmaceutical applications, all formulation excipients must have Generally Recognized as Safe (GRAS) status.[1] Figure 2.1 shows solid lipid nanoparticles imaged by SEM. Figure 2.2 illustrates the theoretical structure of a single solid lipid nanoparticle. In this schematic, a tristearin and stearic acid lipid mixture form the core, a lecithin and sodium taurocholate surfactant mixture stabilize the interface, and the model drug, amphotericin B, is solubilized by the lipid core.
Figure 2.1: Solid lipid nanoparticles made from triglycerides and nonionic surfactants [2]
11
Surfactants Lipids Drug
Lecithin Sodium Tristearin Stearic Acid Amphotericin B Taurocholate
Figure 2.2: Schematic representation of a solid lipid nanoparticle
To achieve and maintain a solid lipid particle upon administration, the lipid nanoparticle’s melting point must exceed body temperature (37 °C). High melting point lipids investigated include triacylglycerols (triglycerides), acylglycerols, fatty acids, steroids, waxes, and combinations thereof.
Surfactants investigated include biological membrane lipids such as lecithin, bile salts such as sodium taurocholate, biocompatible nonionics such as ethylene oxide/propylene oxide copolymers, sorbitan esters, fatty acid ethoxylates, and mixtures thereof.[3] Table 2.1 identifies the types of lipids and surfactants reported in solid lipid nanoparticle formulations.
12 Lipids Surfactants Triacylglycerols Phospholipids Tricaprin Soy lecithin Trilaurin Egg lecithin Trimyristin Phosphatidylcholine Tripalmitin Ethylene oxide/propylene oxide copolymers Tristearin Poloxamer 188 Acylglycerols Poloxamer 182 Glycerol monostearate Poloxamer 407 Glycerol behenate Poloxamine 908 Glycerol palmitostearate Sorbitan ethylene oxide/propylene oxide copolymers Fatty acids Polysorbate 20 Stearic acid Polysorbate 60 Palmitic acid Polysorbate 80 Decanoic acid Alkylaryl polyether alcohol polymers Behenic acid Tyloxapol Waxes Bile salts Cetyl palmitate Sodium cholate Cyclic complexes Sodium glycocholate Cyclodextrin [4] Sodium taurocholate para-acyl-calix-arenes [5] Sodium taurodeoxycholate Alcohols Ethanol Butanol
Table 2.1: Lipids and surfactants used in solid lipid nanoparticle production (adapted from [3])
2.2 Lipid Nanoparticle Synthesis Techniques In the 1980’s, Speiser and coworkers were the first to report making solid lipid particles for drug delivery applications.[6, 7] Speiser created an initial nanoemulsion by using high speed mixing or ultrasonication; the nanoemulsion was subsequently spray dried to produce the ‘nanopellets’. Domb later described a very similar process based on high speed mixing and ultrasonication to yield lipid particles, or
‘lipospheres’.[8-10] Both Speiser’s and Domb’s techniques yielded polydisperse populations that failed to produce many submicron particles.
Numerous research groups subsequently commenced research efforts to improve solid lipid nanoparticle synthesis. Most researchers have approached solid lipid nanoparticle synthesis as some variation of a two-step process: 1) the creation of a precursor oil-in-water ‘nano’ emulsion and 2) subsequent solidification of the dispersed lipid phase. As a result, traditional emulsion techniques and processing have received much attention. Emulsion droplet size is known to be a function of the shear forces exerted on the droplet surface, interfacial tension, the dispersed phase viscosity, and the continuous 13 phase viscosity. As a result, emulsification science has proceeded on the basis of reducing interfacial tension and viscosity through formulary development and by increasing the shear forces imparted on the liquid-liquid system.
Approaching solid lipid nanoparticle synthesis from an emulsion perspective is fraught with significant challenges. Most emulsions produce polydisperse droplets with many droplets sizes exceeding the desired submicron target. To overcome the polydispersity and larger than desired droplet sizes, researchers often subject the precursor emulsions to large mechanical forces such as high shear homogenization (HSH), high pressure homogenization (HPH), and ultrasonication. The high energy input increases operating expenses, increases mechanical contamination risks, and can inhibit the activity of mechanically and thermally sensitive biological molecules. In an effort to avoid the large mechanical energy inputs, some researchers pursue more chemically elegant approaches, namely microemulsions and solvent evaporation techniques. Given the inherent instability of many emulsion systems, the process for solidifying the dispersed phase creates thermodynamically challenging phase transitions that may contribute to polydispersity and particle instability. Finally, the overall formulation and process parameters depend on the chemical nature of the drug to be delivered. This lack of formulation and process robustness necessitates reformulation and process optimization efforts for each drug to be delivered, negatively impacting the economic vitality of the technology by increasing development costs and speed to market.
Despite the challenges of emulsion-based approaches, research efforts beginning in the early
1990’s have made substantial strides in successfully synthesizing solid lipid nanoparticles from emulsions.
Improving upon earlier work, the research groups of Ahlin and Domb produced solid lipid nanoparticles by high shear homogenization.[8, 11] Ahlin reported poloxamer (0.5 weight %) stabilized trimyristin nanoparticles with average particle sizes from 100-200 nm using high shear homogenization at room temperature and 25,000 rpm for 10 minutes.[11] Solidification was achieved by dispersing the emulsion in water (T = 16 °C) at 5000 rpm for 5 minutes.[11] Higher stirring rates reduced the polydispersity, but did not significantly reduce particle size. Ahlin et al. was unable to establish optimum emulsification and solidification conditions for the high shear homogenization approach. This reflects the complex and ultra sensitive relationship between formulation chemistry and process parameters.
14 High shear homogenization utilizes a rotor-stator homogenizer. Rotor-stator homogenizers were developed to increase shear forces while keeping power consumption to a reasonable level. The devices have gained significant use in the pharmaceutical and food industries for emulsion generation. The rotor- stator design consists of a rotor containing multiple blades and a stator with multiple slits oriented vertically or diagonally around the homogenizer shaft. The rotor is housed concentrically within the stator.
As the rotor rotates, liquid is centrifugally forced out through the stator slits. A vacuum results and induces bulk liquid to be drawn upward into the rotor region. A high level of mechanical energy is applied in a small space with minimal vortex formation. Figure 2.3, taken from Maa and Hsu, illustrates the basic rotor-stator homogenizer architecture.[12]
Figure 2.3: Schematic representation of the configuration or a rotor-stator homogenizer [12]
Two major forces act on the liquid. Centrifugal force causes mechanical impingement against the stator wall and can reduce droplet size. Second, and more importantly, the shear forces resulting from the highly turbulent region between the rotor and stator act to rip droplets apart. As the homogenization speed increases, the generated shear forces also increase. This increased shear force leads to a reduction in 15 emulsion droplet size. Figure 2.4, presented by Genentech researchers, Maa and Hsu, demonstrates the characteristic reduction in emulsion droplet size with increasing shear forces (homogenizer rpm).[12] In this specific example, Maa and Hsu mixed a 0.4 g/ml PMMA/MeCl2 solution in a 0.2 g/ml aqueous bovine serum albumin solution at a volumetric ratio of 10:2 (ml:ml). The ‘macro’ and ‘micro’ tip corresponded to two different rotor designs of different slit dimensions. Note that after 3 minutes, minimal, if any, droplet size reduction occurs.
Figure 2.4: Effect of homogenization rpm on emulsion droplet size [12]
High shear homogenization is a simple technique and has a low capital cost requirement. When scaling to industrial scale applications, the power consumption and energy dissipation rates may become extremely large. The disadvantages to HSH include the operating expenses associated with the high energy input, the potential shear damage to sensitive biomolecules, and the observed polydispersity that results from a non-uniform flow field. As with most delivery technology research, the delivery of sensitive biomolecules is highly desirable. Rotor-stator homogenizers have been reported to produce energy dissipation rates ranging from 103 – 105 W kg-1.[13] When assuming the bulk fluid density to be that of water, as in the case of oil-in-water emulsions, the reported energy dissipation rates range from 103 – 105 16 kW m-3. Energy dissipation rates as low as 103 W m-3 have been reported to damage living cells, with values of 103 – 105 kW m-3 causing significant cell damage.[14-17] Additionally, and more pertinent, shear-induced protein denaturation in agitated bioreactors has been reported.[18, 19]
Polydispersity directly follows the heterogeneous flow field, i.e. energy dissipation. Since dispersed phase droplet disruption is a function of local fluid forces, a heterogeneous flow field will produce heterogeneous droplet sizes. By design, the rotor-stator homogenizer generates a heterogeneous flow field, and, therefore, significant polydispersity is to be expected. Since rotor-stator homogenizers generate greater shear forces than impeller agitation and heterogeneous flow fields, rotor-stator homogenizers are not optimal devices for synthesizing solid lipid nanoparticle formulations when biomolecule delivery and monodispersity are desired.
Ultrasonic emulsification has proved capable of producing solid lipid nanoparticles, but is subject to several limitations. Siekman et al. produced tyloxapol and soy lecithin stabilized tripalmitin nanoparticles ranging from 30 to 180 nm.[20] More than 15 minutes of ultrasonication was required, raising serious concerns about metal contamination common to this technology. Like rotor-stator homogenizers, ultrasonication generates heterogeneous flow fields, yielding high degrees of polydispersity.
Additionally, polydispersity increases significantly with increasing lipid content.[3] These concerns have caused researchers to steer away from ultrasonication.
High pressure homogenizers, or microfluidizers, have been used extensively for many years in the production of nanoemulsions for parenteral nutrition and in the dairy industry. Naturally, several researchers turned their attention to this technique in hopes of producing superior solid lipid nanoparticles.
High pressure homogenizers apply basic principles of fluid mechanics to produce extremely high fluid pressures, and, thus energy dissipation rates in the form of high shear stresses and cavitation forces that act to reduce the size of dispersed phase in emulsions. By applying pressures from 100-2000 bar, the liquid stream accelerates to a high velocity (> 1000 km/hr) and eventually passes through a micron size gap (25 –
30 µm). The resulting shear stresses and cavitation forces reduce the size of the dispersed phase. Viscosity constraints generally limit lipid content to less than 10% v/v.[3] High pressure homogenizers have been reported to generate energy dissipation rates of 109 – 1012 kW m-3.[3, 13] Mehnert and Mader assert that
17 high pressure homogenizers generate homogeneous flow fields, as a result of the small gap.[3] Therefore, these researchers expect high pressure homogenizers to produce more uniform emulsion droplet size distributions than HSH and ultrasonic emulsification techniques.
Researchers have developed two approaches to HPH known as the hot homogenization technique and the cold homogenization technique. Using both methods, researchers have successfully synthesized numerous solid lipid nanoparticle formulations with mean diameters ranging from several hundred nanometers to less than 100 nm.[21] Hot HPH operates at a temperature above the lipid melting point. If desired, the drug is dissolved in the hot lipid melt. Then, the lipid-drug solution is mixed, typically using a rotor-stator homogenizer, with an aqueous-surfactant solution at equal temperature to form a ‘pre- emulsion’. This pre-emulsion is homogenized at temperatures above the lipid melting point to form a nanoemulsion.[3] Lander et al. reported that high homogenization temperatures produce smaller particle sizes due to the decreased dispersed phase viscosity.[22] The homogenization can be repeated up to five passes with particle size decreasing after each cycle. After five cycles, however, particle size tends to increase via coalescence, reasoned to be the result of increased kinetic energy.[20] To achieve the desired solid product, the nanoemulsion must be cooled to a temperature below the lipid melting point.
A shortcoming of hot HPH is the potential of a drug to partition into the aqueous phase during homogenization, resulting in a lack of solubilization and/or concentration of the drug at interface. This is particularly troublesome when a drug’s hydrophilicity increases with increasing temperature. To overcome the drug degradation and partitioning effects of hot HPH, some researchers have employed cold HPH.
Like hot HPH, the initial step of cold HPH blends the drug into the melted lipid. Then, the drug- lipid melt is rapidly cooled by dry ice or liquid nitrogen to form a solid. This solid mixture is then milled to produce particles ranging from 50-100 µm. Then, the solid particles are dispersed in an aqueous surfactant solution at a temperature below the lipid melting point, forming a ‘pre-suspension’. The pre-suspension is then subjected to HPH below the lipid melting temperature to further reduce the solid particle size.
Relative to hot HPH, cold HPH generally produces larger mean particle sizes and broader particle size distributions. Cold HPH addresses unwanted drug partitioning, but thermal and shear induced drug
18 degradation, broad particle size distributions, and stability remain weaknesses of the cold and hot HPH approaches.[3] Figure 2.5 offers a schematic comparison of hot and cold high pressure homogenization.
Figure 2.5: Comparison of hot and cold high pressure homogenization processes [12]
Although HPH has become a preferred approach of some researchers for solid lipid nanoparticle synthesis, several specific shortcomings exist. First, HPH equipment has a significant capital cost, and the operating conditions are energy intensive. Second, contrary to other researchers’ claims, the highly turbulent flow regimes generated by high pressure homogenizers fundamentally cannot produce homogeneous energy dissipation distributions within the fluid. This lack of uniformity contributes to an undesirable polydispersity index. Third, drug degradation may result from exposure to high temperatures and high fluid stresses. HPH has been shown to reduce molecular weights of polymers and to degrade
19 DNA and albumin.[23, 24] Fourth, as discussed previously, drug hydrophilicity may increase with increasing temperature, causing unwanted drug partitioning into the aqueous phase during homogenization.[3]
Some researchers have focused their efforts on developing more chemically driven processes that reduce the energy input requirement for emulsification. Sjöström first described a solvent emulsification and subsequent evaporation process to synthesize solid lipid nanoparticles.[25] A lipid material was blended with a water immiscible solvent such as cyclohexane, and then the hydrophobic blend was emulsified in water. The resulting emulsion was subjected to reduced pressure (~ 50 mbar) to evaporate the solvent. Upon evaporation, the remaining lipid precipitated to form an aqueous dispersion. Using cholesterol acetate as the lipid and a lecithin/sodium glycocholate surfactant mixture, Sjöström produced solid lipid nanoparticles with a mean particle size of 25 nm.[25] Using a similar method, Siekmann produced tripalmitin/lecithin/cosurfactant nanoparticles with mean particle sizes ranging from 30-100 nm.[26] In general, this process has consistently yielded small particle diameters and narrow distributions.[27]
This solvent evaporation process avoids thermal stresses on potential drug molecules and avoids the solidification step necessary in the hot HPH, high shear homogenization, and ultrasonication processes.
However, the use of organic solvents presents a major toxicological disadvantage. Sjöström et al. have calculated toluene residues as high as 100 ppm.[27] Small particle sizes were attainable only with very low lipid concentrations, an economic disadvantage.[3] To date, no examples of drug loading have been reported using this technique.
Microemulsions, or swollen micelles, represent an intuitively interesting approach for producing solid lipid nanoparticles. As elaborated by Moulik and Paul, microemulsions are thermodynamically stable, isotropic, and clear systems comprised of water, lipids, and surfactants.[28] Given appropriate conditions, the lipid/surfactant constituents of microemulsions self-assemble into spherical particles typically ranging from 5-100 nm. These particles are polydisperse in nature, but polydispersity decreases with decreasing particle size. Gasco et al. has optimized the synthesis of produce solid lipid nanoparticles from microemulsions.[29-32] Microemulsions using stearic acid and surfactants were formed at 65-70ºC,
20 and then were dispersed into near freezing water at ratios of 1:25-1:50 hot microemulsion to cold water to crystallize the lipid phase.[29] Cavalli et al. reported stearic acid mean particle sizes of 70 + 2 nm and 200
+ 5 nm when stabilized by ionic and nonionic surfactants, respectively.[32]
The minimal energy requirement for microemulsion formation is a significant advantage. The theoretical stability is quite attractive for long-term retardation of phase separation phenomena which give rise to storage instabilities, i.e. particle size growth. Thus, lipid nanoparticles formed by microemulsions are amenable to sensitive biomolecules. The capital and operating expenses should be lower than high shear homogenization and high pressure homogenization techniques. The major disadvantage of the microemulsion approach is the sensitivity of microemulsion systems to minor changes in composition or thermodynamic variables, which can cause significant phase transitions. A process optimized for a particular system may no longer work if the composition is modified only slightly; this lack of robustness leads to unacceptably high development costs. Additionally, the solidification process shifts the system to a thermodynamically unstable state, undermining the very advantage of a microemulsion approach.
Solid lipid nanoparticle researchers seemingly have settled on high pressure homogenization and the microemulsion approach as the techniques of choice. As a result, process scale up investigations have occurred only on high pressure homogenization and microemulsion systems.[27] Researchers developed and validated a GMP high pressure homogenization batch process capable of producing 2 – 10 kg solid lipid nanoparticle dispersions. By placing two high pressure homogenizers in series, solid lipid nanoparticle dispersion production rates of 50 – 150 kg hr-1 have been demonstrated.[33-37] Further evidence of the scalability and commercial potential of solid lipid nanoparticles produced by high pressure homogenization, the first commercial product, a topically administered moisturizer, recently was introduced in Poland.[27] Similarly, for the microemulsion technique, a process has been developed to produce 100 ml microemulsion batches that are dispersed into cold water at ratios of 1:1 to 1:10.[37]
No single synthesis technique has demonstrated exceedingly superior performance. Each technique possesses strengths and weaknesses. Table 2.2 summarizes the reviewed synthesis techniques.
21
Technique Strengths Weaknesses High shear homogenization Low capital cost Energy intensive process Demonstrated at lab scale Biomolecule damage Polydisperse distributions Unproven scalability
Ultrasonication Reduced shear stress Metal contamination potential Energy intensive process Polydisperse distributions Unproven scalability
High pressure homogenization Scalable Extremely energy intensive process Mature technology Polydisperse distributions Continuous operation Biomolecule damage Commercially demonstrated
Solvent evaporation No dilution solidification Residual organic solvent Monodisperse distributions
Microemulsion Low mechanical energy input Extremely sensitive to change Theoretical stability Labor intensive formulation work Low nanoparticle concentrations
Table 2.2: Strengths and weaknesses of existing solid lipid nanoparticle synthesis techniques
The existing synthesis techniques are adequate for laboratory settings, but may not provide the needed characteristics for achieving widespread market penetration. This is particularly true for the case of parenteral delivery, the primary and original driving force behind solid lipid nanoparticle research and development efforts. The ultimate production technique must cost effectively provide stable, efficacious solid lipid nanoparticles. Safety is the first consideration and encompasses particle stability (i.e. size as a function of time), GRAS excipients, and surfactants at tolerable concentrations. Efficacy is the second consideration, and, in the context of lipid nanoparticles, efficacy is characterized by precise particle size control according to the needs of the intended application, surface functionalization to accomplish desired biodistributions, controlled release of therapeutically meaningful drug concentrations. Third, the synthesis technique should permit continuous operation, require minimal mechanical energy, and provide process control mechanisms. Table 2.3 summarizes these requirements. Clearly, none of the existing techniques satisfies all requirements. Therefore, additional research and development is essential.
22 Requirements 1. Use only GRAS excipients 2. Minimizes surfactant concentrations 3. Ability to produce multiple particle sizes without significant formulation changes 4. Permits surface functionalization 5. Minimizes mechanical and thermal energy inputs 6. Permits control of drug localization in nanoparticles to effect desired release kinetics 7. Continuous operation
Table 2.3: Requirements of a solid lipid nanoparticle synthesis process
2.3 Effect of Lipids and Surfactants
The effects of lipid type have been studied, and average particle size has been shown to increase with increasing lipid melting temperature for both high pressure homogenization and high shear homogenization techniques.[11, 38, 39] Mehnert and Mäder suggest this behavior is due to the increased viscosity of the dispersed phased.[3] Alternative explanations could include the increasing molecular size with increasing melting temperature, particularly true for saturated acyl chains, the increasing hydrophobicity of higher melting temperature lipids, varying crystallization rates, and varying lipid crystal structures.[3] Larger particles and increased polydispersity results when lipid content exceeds 10% of the emulsion/dispersion. Increased emulsion viscosity and increased rate of droplet agglomeration are thought to cause this behavior, which has been observed in lipid nanoemulsions as well.[20, 38, 40]
Surfactant properties and concentrations greatly affect the quality and efficacy of lipid nanoparticles. Surfactants possess surface activity, meaning they preferentially locate in interfacial regions.
By their amphiphilic nature, surfactants lower the interfacial tension between lipid and aqueous phases. In emulsions, the water-lipid interfacial area increases as oil droplets size is reduced. Any expansion of the interface between two immiscible phases is thermodynamically unfavorable. Without appropriate stabilization of the interface by surfactants, a characteristic phase separation process occurs. So called
Ostwald ripening whereby larger oil droplets grow at the expense of smaller droplets occurs first, followed by droplet flocculation, droplet coalescence, and finally, phase separation. Surfactants inhibit the phase separation process by reducing interfacial tension and the imparting steric hindrance. When optimized, the surfactants can ‘stabilize’ emulsions over the useful life.
23 Few patterns exist in the literature with regard to surfactant composition and quality of solid lipid nanoparticle dispersions. The optimum surfactant concentration must be determined on a case by case situation. For acceptable tribehenin nanoparticles, zur Mühlen determined that 5 w% sodium cholate or
Poloxamer 188 was required.[38] Siekmann et al. determined that 10 w% tyloxapol stabilized 85 nm tripalmitin nanoparticles while 2 w% tyloxapol failed to stabilize the suspension.[20] Likewise, for optimum nanoparticle quality, homogenization parameters may vary according to choice of surfactant. In one HPH study, optimal lipid nanoparticles were formed at 500 bar and three cycles when using Poloxamer
188, but lecithin stabilized particles of the same lipid required 1500 bar operating pressures.[41] The varying rates of adsorption onto the interface, hydrophile-lipophile balance (HLB) numbers, and surfactant numbers may account for the plethora of observed behaviors when surfactants are interchanged.
One clear trend is the beneficial role of cosurfactants. Solid lipid nanoparticles stabilized by surfactant mixtures, such as lecithin/Poloxamer 188 and lecithin/tyloxapol, resulted in more stable, smaller particle sizes than formulations of the same lipid and a single surfactant.[38] When using lecithin as the surfactant with taurodeoxycholate and monooctylphosphate as cosurfactants, Cavalli et al. produced stearic acid nanoparticles having 70 + 2 nm diameters.[32] Surfactant mixtures often reduce interfacial tension more than single surfactant formulations on a mole per mole basis, particularly if the cosurfactant head group is significantly smaller than the surfactant head group. This phenomenon is largely due to an increased surfactant concentration at the interface, or surface excess, made possible by the minimization of repulsion forces of closely packed, like surfactant molecules.[42]
2.4 Solid Lipid Nanoparticle Stability
Lipid nanoparticle stability must be considered from two perspectives, the particle size distribution and the lipid crystalline state. Particle size is a critical safety factor for parenteral administrated and self life, as noted previously. Particle size greatly affects biodistribution and RES clearance mechanisms.
Particle size also affects the visible appearance of the product, since the human eye can only detect light scatter by particles greater than ~ 1 µm.[42] The degree of polydispersity can impact particle size growth via Ostwald ripening and can impact the overall drug release kinetics. The lipid crystalline state strongly correlates with drug incorporation, drug release, and the particle geometry, i.e. spherical versus prolate.[3] 24 As noted previously, mechanical shearing forces are primarily responsible for emulsion droplet size reduction. For two immiscible liquids, such as lipids and waters, this reduction in droplet size corresponds to a thermodynamically unfavorable interfacial area expansion. Once the mechanical shearing force is removed, the emulsion droplet’s ability to remain a discrete droplet of constant size depends of the dispersing capability of the surfactants present at the lipid-water interface. An emulsion droplet will appear stable over extended periods of time if the surfactants’ dispersing capability is sufficient, and, consequently an emulsion system will exist for an extended period. However, if the surfactants’ dispersing capability is insufficient for the emulsion system, then characteristic phase separation processes will begin immediately.
Phase separation processes include creaming, Ostwald ripening, flocculation, and coalescence.
Creaming is the gravity driven process by which a less dense phase rises to the top of a multiphase system.
By definition, creaming does not result in particle size changes and should be of little concern in solid lipid nanoparticle systems. However, creaming does bring emulsion droplets closer to one another which could assist Ostwald ripening, flocculation, and coalescence. Flocculation is the process by which emulsion droplets aggregate together in loose groups in which the identity of each drop is maintained. Flocculation is driven by attractive intermolecular van der Waals forces that arise from the mutual attraction of hydrophobic molecules up to 10 nm. Coalescence is the fusion of individual droplets to form larger droplets. Coalescence will continue until all droplets have fused together to form a second continuous phase. Coalescence depends on the distance between droplets and then on the balance of disruptive and stabilizing interfacial forces. Van der Waals forces work in favor of coalescence and the Gibbs-Marangoni effect will oppose coalescence due to the distortion and increase in surface area as the droplets approach each other. Ostwald ripening is the process by which lipophilic molecules in smaller particles diffuse to larger particles, if the lipophilic molecule has some degree of water solubility. Ostwald ripening occurs because smaller particles have higher energy states than do larger particles. Smaller particles possess a higher degree of curvature than do larger particles, and, therefore, interfacial molecules of small particles are more exposed to the continuous phase. This results in a lower net attractive force to the internal bulk lipid phase which can lead molecules to diffuse to lower energies found in larger lipid droplets.[20, 42]
Ostwald ripening results in the growth of larger lipid droplets at the expense of smaller lipid droplets.
25 Creaming can be prevented by minimizing the density difference between phases. Ostwald ripening cannot be prevented, but it can be slowed by minimizing polydispersity. Flocculation and coalescence are concerns for solid lipid nanoparticles. Surfactants inhibit flocculation in emulsions and suspensions by countering phase separation processes with two mechanisms, electrostatic repulsion between droplets/particles and steric hindrance between droplets/particles.[42]
Electrostatic repulsion results from the formation of an electrical double layer at the lipid-water interface. Ionic surfactants, such as negatively charged lecithin, adsorbed at the interface attract solution counter ions, cations in the case of lecithin, into the interfacial region. The counter ions effectively adsorb onto the oppositely charged interface. The net charge at the interface affects the ion distribution in the nearby region, increasing the concentration of counter ions close to the interface. Thus, an electrical double layer is formed in the interfacial region.
This double layer consists of two parts: an inner region known as the Stern layer that includes ions bound relatively tightly to the interfacial surfactant ions, and an outer region known as the Diffuse layer where a balance of electrostatic forces and random thermal motion determines the ion distribution. The electrical potential decreases linearly from the interface to the Stern layer. The potential in the Diffuse layer decays exponentially with increasing distance from the Stern layer until eventually reaching the bulk solution value, zero in most instances. The physical situation and electrical potential decay are depicted in
Figure 2.6.[43]
The particle and adsorbed ions of the Stern layer exist functionally as a single unit when moving through fluid. The surface of shear, or slipping plane, is the boundary at which the relative motion commences between the immobilized layer and the mobile fluid of the surrounding environment. The potential at the surface of shear is known as the zeta potential, ξ, and is measured in millivolts (mV). The magnitude of zeta potential has been correlated to stability of particle and emulsion droplets. As zeta potential increases, electrostatic repulsion between two particles increases. If the electrostatic repulsion exceeds the attractive forces due to van der Waals’ interactions, then the colloidal system will be stable. If not, flocculation followed by coalescence will lead to phase separation.[44] Zeta potential values more electronegative than -30 mV generally represent sufficient electrostatic repulsion for stability, and stability
26 is assured in most instances at zeta potentials between -45 to -70 mV.[45] Frietas et al. demonstrated solid lipid nanoparticle stability and instability when zeta potentials were -25 mV.[46] Electrical potential as function of separation distance between two particles for a stable colloidal system is depicted in Figure 2.7.
Figure 2.6: A physical representation and corresponding electrical potential of the double layer [43]
27
Figure 2.7: An example of electrostatic repulsion producing a net repulsion between particles [47]
Zeta potential is a function of the surface charge of the particle, any adsorbed layer at the interface, and the nature and composition of the surrounding environment. When a layer of macromolecules is adsorbed on the particle's surface, it shifts the shear plane farther from the surface and alters the zeta potential. The presence of ‘indifferent’ ions can greatly impact zeta potential by suppressing the double layer potential. Flocculation induced by increasing the concentration of indifferent ions is known as ‘salting out’. Salinity is a major factor in the stability of emulsions and dispersions stabilized by ionic surfactants.
Steric stabilization prevents two particles from approaching to the short distances required for flocculation and coalescence. Nonionic surfactants operate by steric stabilization, and ethylene oxide/propylene oxide copolymers are routinely employed for their steric stabilization capabilities. The polyoxypropylene chain adsorbs onto the hydrophobic interface, and the polyoxyethylene chain extends into the aqueous phase in a coil configuration. Given sufficient surfactant concentration and hydrophilic chain length, often > 20 ethylene oxide units, the hydrophilic coils extending outward from the surface maintain other particles at distances required for stability.[42]
Unlike systems stabilized by ionic surfactants, those stabilized by nonionic surfactants are independent of bulk electrolyte concentration. However, nonionics are affected by temperature.
Hydrophilicity of the polyoxyethylene chain decreases with increasing temperature as chain dehydration occurs. As dehydration increases, the polyoxyethylene chain adsorbs more strongly on the hydrophobic 28 surface, reducing the steric boundary around the particle. Above a critical temperature, known as the critical flocculation temperature, flocculation occurs as the steric hindrance no longer exceeds the van der
Waals attraction between particles. In emulsion systems, the temperature dependency of nonionic surfactants can give rise to a phase inversion, i.e. from oil-in-water to water-in-oil. This temperature is referred to as the phase inversion temperature (PIT).
Often, the best stabilization strategy is to invoke both electrostatic and steric approaches. This strategy has been widely used in liposome science.[48, 49] Several researchers have successfully applied this approach to solid lipid nanoparticles, as well.[50-52] However, one must remain cognizant of the effect of steric stabilization on the zeta potential. Adsorption of the steric stabilizer shifts the shear plane outward, reducing the zeta potential. A proper balance between electrostatic repulsion and steric stabilization must be obtained for long-term stability, if stability depends on both mechanisms.
Lipid crystallinity is another dimension of lipid nanoparticle stability, significantly impacting lipid nanoparticle drug incorporation and release characteristics. Crystallization is a balance between attractive intermolecular forces and entropic factors. In lipids, van der Waals forces drive non-polar molecules closer to one another. Entropy favors increased molecular disorder, driving molecules farther apart. As intermolecular attraction increases, or entropy decreases, liquids crystallize more readily. As temperature decreases, entropy decreases and the intermolecular distance decreases. Therefore, intermolecular attraction outweighs entropy, and crystallization will commence at a site of nucleation. Historically, four states of crystallinity are associated with lipids, but more recent research has revealed numerous varieties of crystalline structures in lipids.[21]
Most lipids possess more than one solid phase under normal thermodynamic conditions. These phases result from the multitude of molecular arrangements made possible by the complex interactions of the nonpolar acyl chains and the polar regions of lipids. These solid phases are classified according to polymorphism and polytypism. Polymorphism is critically defined as the ability to reveal different unit cell structures in crystal, originating from a variety of molecular conformations and packings.[53] Simply put, polymorphism is the existence of multiple crystal forms. Polytypism can be defined as one-dimensional polymorphism where the variation is found in the vertical stacking patterns while the other two dimensions
29 remain constant.[54] The fundamentals of polymorphic and polytypic thermodynamics and kinetics are too complex and lengthy for a thorough discussion in this work. However, understanding the practical implications of polymorphism and polytypism, particularly polymorphism, on lipid nanoparticle synthesis and stability is critical.
Most is known and written about the crystallinity behavior of acylglycerols, particularly triglycerides. Thermodynamic stability increases, lipid packing density increases, and drug incorporation rates decrease in the following order of polymorphism: α crystal <β′ crystal < β crystal. Initial crystalline structure is often dependent on the rate of cooling, indicating the kinetic dependency of crystallization.
When cooled very slowly, pure lipids will organize themselves into the lowest energy form, the β crystal.
When cooled rapidly, pure lipids initially crystallize in the disordered, higher energy α crystal.
Intermediate cooling rates yield the intermediate β′ crystal. This behavior can be explained in terms of the activation energy of nucleation, as indicated in Figure 2.8. The rapid cooling minimizes the energy input into the system, inhibiting the crystallization of more ordered forms, and vice versa.
Figure 2.8: Activation energy of nucleation for a representative triglyceride system [55]
Polymorphism can produce profound effects on physical properties. Table 2.4 summarizes the melting points and heats of fusion of triglyceride polymorphs reported in solid lipid nanoparticle literature.
30 Fatty Acid Melting Point (°C) Heat of Fusion (kJ/mol) Chain Length α β′ β α β′ β 10 -10 13 33 56 92 12 14 30 46 72 82 116 14 31 41 56 85 100 137 16 46 53 66 103 132 166 18 55 61 73 112 143 192 20 64 69 78 122 160 221
Table 2.4: Physical property dependence on triglyceride polymorph [55]
In addition to the multiple polymorphs, supercooled liquids can result from extremely fast cooling, especially at smaller diameters. If the surface curvature is significant, as in the case of small diameter particles, then the internal bulk phase may be geometrically strained and less shielded from the external environment. The resulting higher energy state contributes to crystallization temperature depression.[21]
Westesen et al. showed trilaurin nanoparticles remained in a supercooled state if not artificially induced to crystallize.[56]
In many systems, particularly in single component lipid formulations, the crystallization process continues after initial solidification. Striving for the lowest energy configuration, the lipid molecules will continue to reorganize over time until reaching the β crystal state. This process, known as solid-state phase transformation, undermines solid lipid nanoparticles’ use as a drug delivery technology. As lipid packing density increases, less volume is available for drug molecules. For stearic acid solid-state phase transformations, the reduction in crystal cell unit volume exceeds 50%.[57] More importantly, the presence of drug molecules can disrupt the thermodynamically favored transitions toward the β′ crystal and the β crystal, and, as a result, the drugs can be expelled from the lipid core. However, if solid-state transformations can be controlled, the process could be used as a drug release mechanism.
Lipid polymorphism is associated with a change in particle shape.[3] Typically, triglycerides crystallize as a sphere in the α crystal state.[58] Westesen used transmission electron microscopy (TEM) to demonstrate that tripalmitin and tristearin solidified in the α crystal form spheres, but the transformation to the β crystal is accompanied by the onset of a platelet structure.[59] The platelet structure possesses greater surface area than a sphere, thus requiring higher surfactant concentrations to maintain stability. The
31 transformation to the β crystal, therefore, can lead to particle growth. The platelet structure also brings loaded drug molecules closer to the interface, promoting drug release. For these reasons, producing and maintaining the α crystal structure is highly desirable in solid lipid nanoparticle applications.
Lipid mixtures, surfactant mixtures, and rapid cooling techniques promote the α crystal structure. Using lipids of dissimilar geometries inhibits closely packed, highly ordered crystal structures.
For example, introducing oleic acid, cis-9-octadecanoate, into tripalmitin inhibits close, ordered acyl chain packing due to the cis double bond of oleic acid. Likewise, introducing surfactants whose hydrophobic tails are geometrically dissimilar to the core lipid inhibits highly ordered crystal formation. Sterols, such as the bile salts like sodium taurocholate, possess a bulky, five ring hydrophobic region which does not permit close highly ordered crystal formation, at least near the interface. As noted before, rapid cooling does not provide adequate time for the crystallization process to form the more highly ordered β crystal. These techniques provide researchers with opportunities to produce solid lipid nanoparticles in the α crystal form.
Despite the stability challenges, optimized solid lipid nanoparticle dispersions can be stable for more than one year.[60, 61] By photon correlation spectroscopy (PCS) analysis, Müller et al. demonstrated that glycerol palmitostearate and tribehenate nanoparticles were stable for 3 years.[33] To avoid instability issues in aqueous dispersions, researchers have demonstrated spray drying and lyophilization techniques with successful reconstitution for long-term solid lipid nanoparticle stability.[33, 62-65] Solid lipid nanoparticle stability is a function of formulation and processing parameters, providing several options to researchers and developers.
Interested in solid lipid nanoparticle long-term storage and product shelf-life, researchers have investigated spray-drying, lyophilization, and sterilization. Freitas et al. and Müller et al. demonstrated the ability to spray-dry and reconstitute solid lipid nanoparticles.[33, 62] The effects of lipid type, lipid concentration, carbohydrate type, carbohydrate concentration, redispersion medium, and spraying medium have been investigated. For effective reconstitution, the lipid melting point had to exceed 70 °C, cryoprotective additives such as trehalose had to be added, and the solid lipid nanoparticle concentration had to be no greater than 1% in the dispersion. Successful lyophilization and reconstitution was demonstrated by several researchers.[33, 63-65] The effects of operating conditions, lipid concentration,
32 cryoprotectant type, cryoprotectant concentration, and redispersion conditions have been investigated.
Trehalose was determined to be the best cryoprotectant at a sugar/lipid ratio of 1:3.[64]
Sterilization is critically important to solid lipid nanoparticle efficacy. Autoclaving of solid lipid nanoparticles has been investigated by Schwarz et al.[66] Poloxamer 188 stabilized solid lipid nanoparticles did not withstand autoclaving because of the nonionic surfactant’s critical flocculation temperature; lecithin stabilized solid lipid nanoparticles were capable of withstanding autoclaving without destabilization. Wissing et. al suggest gamma irradiation and sterile filtration as possible, less stressful sterilization techniques.[27] Sterile filtration is only relevant if the particle size is less than 200 nm. With proper formulation and process selection, temporal stability and sterilization of solid lipid nanoparticles does not appear to be a significant barrier to the technology.
2.5 Lipophile Loading and Release in Lipid Nanoparticles
A variety of drugs, including agents for treating cancer, AIDS, fungal infections, high blood pressure, mental illness, skin disease, and imaging have been loaded into solid lipid nanoparticles. Table
2.5 lists several drugs that have been incorporated into lipid nanoparticles. For efficiency and efficacy reasons, the amount of drug that can be loaded is very important. Calculated as the ratio of drug weight to the sum of drug and lipid weight, loading capacity typically ranges from 1-5%.[21] Using HPH, Westesen obtained loading capacities up to 50% for Ubidecarenone, 20% for Tetracaine and etomidate, and 25% for cyclosporin.[41, 60, 67-69] For HPH, Müller suggests that capacity is determined by the drug solubility in the melted lipid, the miscibility of the melted drug and melted lipid, and the physiochemical structure of the solid lipid.[21]
33 Compound Clinical use Timolol High blood pressure and recurrent heart disease Deoxycorticosterone Hormone replacement therapy Doxorubicin Various cancers Idarubicin Leukemia [D-Trp-6]LHRH Endocrinology and cancer Pilocarpine Glaucoma Thymopentin AIDS 3′-Azido-3′ deoxythmidine (AZT) AIDS Diazepam Anxiety and epilepsy Gadolinium (III) complexes MRI contrast agents Progesterone Hormone replacement therapy Hydrocortisone Asthma, skin conditions, and arthritis Paclitaxel Ovarian, breast, lung, and Kaposi’s sarcoma cancers Retinol Anti-aging and skin conditions Coenzyme Q10 (Ubidecarenone) Cardiovascular recovery and cancer Vitamin E palmitate Antioxidant Aciclovir Herpes and viral infections Prednisolone Inflammation and arthritis Tetracaine Ophthalmic treatment Etomidate Anesthetic Cyclosporine Prophylaxis in organ transplants Nimesulide Anti-aging Oxazepam Anxiety and depression Magnetite Magnetic targeting Menadione (Vitamin K) Blood clotting and bone calcification Cortisone Inflammation and arthritis Betamethasone valerate Dermatoses of the scalp Camptothecin Cancer Piribedil Parkinson’s disease
Table 2.5: Drugs incorporated into solid lipid nanoparticles ([21, 27])
The drug can locate between fatty acid chains, between lipid layers, in lipid crystal imperfections.
As noted previously, the chemical properties of the lipid affect the crystallinity of solid particle. It has been suggested that lipids that form more perfect crystalline solids, such as monoacid triglycerides having a β crystal structure, expel solubilized drugs and that those lipids that form less perfect crystalline structures, such as triglyceride mixtures, possess higher loading capacities.[60, 70]
Obtaining controlled drug release from lipid nanoparticles remains elusive as, more often than not, burst release kinetics have been observed.[21] Müller reported the first controlled release of a drug, prednisolone, from HPH produced solid lipid nanoparticles.[71] In vitro drug release was obtained for 7 weeks. Release kinetics were dependent on the lipid matrix, surfactant concentration, and HPH production
34 parameters, but were independent of particle size.[71] The size independence suggests the mass transfer was not diffusion limited. Burst release increased with increasing processing temperature and increasing surfactant concentration, leading Müller to suggest that drug partitioning into the aqueous phase during homogenization negatively affects sustained release.[21] As temperature and surfactant levels increase, drug solubility in water increases. As temperature decreases, Müller rationalizes that the lipid crystallizes initially in the center, and the drug tends to repartition into the lipid; however, because the lipid core has already crystallized, the core is unavailable to the drug. As the system continues to cool, the drug solubility in the water diminishes, and the drug is concentrated in the lipid ‘shell’ region. This enriched shell profile then promotes drug burst release.[21] These events are depicted in Figure 2.9.
During During Homogenization Cooling
Figure 2.9: Proposed redistribution of drug from molecularly dispersed state to enriched shell state, postulated as a cause of drug burst release phenomena observed in lipid nanoparticles (modified from [21])
To obtain sustained drug release, Müller suggests a diffusion controlled release mechanism and a uniform drug profile throughout the lipid shell or an enriched ‘core’.[21] If a delayed release profile is desirable, the proposed drug-enriched core/lipid shell model represents an interested option. Figure 2.10 depicts the commonly proposed drug loading profiles thought to be possible in lipid nanoparticles.
Existing synthesis techniques do not provide precise control, for constructing prescribed desired drug 35 profiles. The process remains a trial-and-error based approach in which changing drugs, lipids, surfactants, concentrations, and process parameters lead to unpredictable results. An improved production technology is required to provide this level of control.
Figure 2.10: Proposed structural models for drug loading profiles in lipid nanoparticles [21]
2.6 Pharmacological Performance of Solid Lipid Nanoparticle Systems
Due to their nanoscale size, lipid nanoparticles can be administered intravenously. Without proper surface modification the cells of the reticuloendothelial system, particularly the liver and spleen, rapidly clear colloidal particles. To increase circulation times, reticuloendothelial system avoidance (“stealth”) can be accomplished by incorporating polyoxyethylene, utilized extensively in liposomal and polymeric microparticle technologies. To accomplish the incorporation, researchers add polyoxyethylene
(hydrophilic) and polypropylene (hydrophobic) block copolymers into the formulation. Incorporation of polyoxyethylene and polypropylene block copolymers has increased lipid nanoparticle tumor accumulation, antibacterial activity of antifungal drugs, and extravasation of the blood brain barrier (BBB) of anticancer drugs normally incapable of crossing the BBB. [52, 72, 73]
Lipid nanoparticle drug formulations have shown to produce improved pharmacokinetic profiles versus traditional drug formulations.[52, 74] When formulated in lipid nanoparticles, doxorubicin plasma concentrations increased 3-5 times, showed a bi-exponential curve with high Area Under the Curve (AUC),
36 exhibited longer circulation half-lives, decreased the volume of distribution, and reduced toxic side effects in rats.[52, 75] Surprisingly, similar circulation half-lives and pharmacokinetics were observed for stealth and non-stealth lipid nanoparticle formulations of doxorubicin.[52]
Drug targeting can be accomplished by ligand mediated attachment, exploiting physiological conditions as in cancer’s leaky vasculature, and utilizing the immune system’s affinity for hydrophobic colloidal particles. As noted earlier, a role of Kupffer cells in the liver is the removal of hydrophobic colloidal particles. If liver targeting is desirable, passive targeting can be accomplished in the case of non- stealth nanoparticles. Similarly, macrophages throughout the circulation naturally remove nanoparticles, offering a passive targeting opportunity. Typically, however, targeting of the liver and macrophages is undesirable and is avoided through the use of stealth technology. Passive targeting of cancer tumors is made possible by the leaky vasculature associated with cancer. The leaky vasculature generated during cancer driven angiogenesis (to feed the tumor cells) allows extravasation of colloidal particulates.
Interestingly, stealth lipid nanoparticles have demonstrated a strong propensity to accumulate in the brain.
BBB penetration is extremely difficult and one of the critical challenges facing pharmaceutical therapeutics and imaging today. Lipid nanoparticle accumulation in the brain may be blood protein mediated. Adsorption of blood proteins such as apolipoproteins on lipid nanoparticle surfaces may lead to interactions with endothelial cells that facilitate crossing the BBB.[27] Olbrich et. al demonstrated the brain accumulation of diminazen aceturate when formulated in lipid nanoparticles, leading to reduced infection of Trypanosoma brucei.[76] Diminazen aceturate alone does not cross the BBB because of its hydrophilicity. Lipid nanoparticle enhanced BBB transport also has been demonstrated for tobramycin, doxorubicin, and idarubicin.[73, 75, 77] Drug delivery to the brain is an exciting area of possible application for lipid nanoparticle technology.
2.7 Conclusions
Lipid nanoparticle drug delivery technology presents significant opportunities for improving medical therapeutics, but the technology’s potential remains unrealized. Several technology challenges remain unsolved: appropriate control of particle size and size distribution, short-term and long-term lipid crystallinity, drug loading profile, drug release kinetics, and greater control of biodistribution once 37 administered. This research effort focused on lipid nanoparticle synthesis, improving the formulators control over particle size, size distribution, and drug loading profile through processing and material formulation variables.
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65. Lim, S.-J., and Kim, C.-K. (2002) Formulation parameters determining the physiochemical characteristics of solid lipid nanoparticles loaded with all-trans retinoic acid. International Journal of Pharmaceutics 243, 135-146
66. Schwarz, C., Mehnert, W., Lucks, J. S., and Müller, R. H. (1994) Solid lipid nanoparticles (SLN) for controlled drug delivery: I. Production, characterization and sterilization. Journal of Controlled Release 30, 83-96
67. Iscan, Y. Y. (1999) Formulation and characterization of solid lipid nanoparticles for skin delivery. In Lipid and Surfactant Dispersed Systems Vol. Proceedings Book pp. 63-166, Moscow
68. Schwarz, C., Freitas, C., Mehnert, W., and Muller, R. H. (1995) Sterilisation and physical stability of drug-free and etomidate-loaded solid lipid nanoparticles. In 22nd International Symposium on Controlled Release of Bioactive Materials Vol. 3304 pp. 766-767, Seattle, WA
69. Dingler, A. (1998) Feste Lipid-Nanopartikel als kolloidale Wirkstoffträgersysteme zur dermalen Applikation. In, Free University of Berlin, Berlin, Germany
70. Runge, S. A. (1998) Feste Lipid-Nanopartikel (SLN) als kolloidaler Arzneistoffträger zur Cyclosporin. In, Free University of Berlin, Berlin, Germany
71. zur Mühlen, A., and Mehnert, W. (1998) Drug release and release mechanism of prednisolone loaded solid lipid nanoparticles. Pharmazie 53, 552
72. Chen, D. B., Yang, T. Z., Lu, W. L., and Zhang, Q. (2001) In vitro study of two types of long- circulating solid lipid nanoparticles containing paclitaxel. Chemical & Pharmaceutical Bulletin 49, 1444-1447
73. Bargoni, A., Cavalli, R., Zara, G. P., Fundarò, A., Caputo, O., and Gasco, M. R. (2001) Transmucosal transport of tobramycin incorporated in solid lipid nanoparticles (sln) after duodenal administration to rats. Part II—Tissue distribution. Pharmacological Research 43, 497-502
74. Miglietta, A., Cavalli, R., Bocca, C., Gabriel, L., and Rosa Gasco, M. (2000) Cellular uptake and cytotoxicity of solid lipid nanospheres (SLN) incorporating doxorubicin or paclitaxel. International Journal of Pharmaceutics 210, 61 - 67
75. Zara, G. P., Cavalli, R., FUNDARÒ, A., BARGONI, A., CAPUTO, O., and GASCO, M. R. (1999) Pharmacokinetics of doxorubicin incorporated in solid lipid nanospheres (SLN). Pharmacological Research 40, 281-286
42 76. Olbrich, C., Geßner, A., O. Kayser, and Müller, R. H. (2002) Lipid--drug conjugate (LDC) nanoparticles as novel carrier system for the hydrophilic antitrypanosomal drug diminazenediaceturate. Journal of Drug Targeting 10, 387-396
77. Zara, G. P., Bargoni, A., Cavalli, R., Fundarò, A., Vighetto, D., and Gasco, M. R. (2002) Pharmacokinetics and tissue distribution of idarubicin-loaded solid lipid nanoparticles after duodenal administration to rats. Journal of Pharmaceutical Sciences 91, 1324 - 1333
43
CHAPTER 3
MATERIAL AND PROCESS EFFECTS ON LIPID NANOPARTICLE SYNTHESIS
3.1 Introduction
As noted in the previous chapter, most researchers have approached solid lipid nanoparticle synthesis as a two-step process: 1) the creation of a precursor oil-in-water ‘nano’ emulsion and 2) subsequent solidification of the dispersed lipid phase. In this model, the quality of lipid nanoparticles produced is a function of the precursor emulsion quality where emulsion stability impacts particle stability and emulsion polydispersity impacts particle polydispersity. As a result, traditional emulsion techniques and processing have received much attention. Emulsion droplet size is known to be a function of the shear forces exerted on the droplet surface, interfacial tension, the dispersed phase viscosity, and the continuous phase viscosity. As a result, emulsification science has proceeded on the basis of reducing interfacial tension through formulary development and by increasing the shear forces imparted on the liquid-liquid system.
Emulsion formation and stability are governed by the following thermodynamic relationship for
Gibbs Free Energy at constant temperature, pressure, composition:
∆ = − ∆ + γ GMIX T ,P,φ T S MIX T ,P,φ AS
Where ∆GMIX is the Gibbs free energy of mixing, T is the temperature, ∆SMIX is the entropy of mixing, γ is the interfacial tension, and the AS is the total interfacial area. For two immiscible phases, an expansion of interfacial area represents a negative entropy of mixing. The net molecular repulsion between the two
44 immiscible molecules limits the conformational and directional freedom of each molecule, leading to increased molecular order. Since temperature, interfacial tension, and interfacial area are always positive values, the Gibbs free energy of mixing for two immiscible materials is positive, i.e. thermodynamically unfavorable. Therefore, emulsion production requires external energy, and emulsions ultimately exhibit instability in the form of the phase separation process: flocculation, coalescence, and phase separation.
To overcome the thermodynamic barrier to emulsification, mechanical energy is required to expand the interfacial area, i.e. disperse one immiscible phase in the other as in the case of oil-in-water emulsions. As noted previously, many forms of mechanical energy are used including magnetic stir bars, impeller agitation, high-pressure homogenization (microfluidization), rotor-stator homogenization, and ultrasonication. In part because these processes do not produce uniform shear stresses in the fluid, the resulting emulsions exhibit undesirable polydispersity.
Appropriate surfactant choice can reduce the interfacial tension and produce apparent emulsion stability for extended periods. Optimized surfactant types and concentrations can lead to reduced particle size and decreased polydispersity. Surfactants are characterized by being either ionic or nonionic, their
Hydrophile-Lipophile Balance (HLB) number, and surfactant number (NS). Ionic surfactants provide electrostatic stability to emulsion droplets, whereas nonionic surfactants provide steric stability. Ionic surfactant activity is dependent on electrolyte concentration, pH, and temperature. Increased bulk electrolyte concentration suppresses the electrostatic double layer produced by the ionic surfactants and indifferent ions that extends beyond the droplet surface. The double layer’s magnitude, measured by the zeta potential (ζ), and effective distance determine the approach length between two emulsion droplets.
The closer two droplets are the more probable flocculation and coalescence. Ionic surfactants become more hydrophilic with increasing temperature. On the contrary, nonionic surfactants become more hydrophobic with increasing temperature due to reduced hydrogen bonding between water and ethylene oxide group s of the surfactant. Nonionic surfactants are often characterized by a phase inversion temperature (or cloud point), defining the temperature when the surfactant transitions from being more soluble in water to being more soluble in oil.
45 The surfactant number, also known as the critical packing parameter (CPP), characterizes a surfactant’s packing/micellar structure using geometric arguments. NS is defined as the ratio of the hydrophobic group area to the hydrophilic group area. The premise of NS is that the relative sizes of the hydrophilic and hydrophobic groups determine micellar shape and size. For ionic hydrophilic (head) groups, a0, the effective size is the actual physical size plus the electrostatic double layer. For nonionic head groups, a0, the effective size is the actual physical size plus the water associated through hydrogen bonds. For ionic and nonionic, the hydrophobic group area is defined as the alkyl chain volume, v, divided by the maximum length of the alkyl chain, lC. Therefore, the surfactant number is defined as:
= v N S a0lC
Based on geometrical arguments, the shape of the self-assembly structure can be predicted by NS, as shown in Table 3.1.[1, 2]
Surfactant Number, NS Structure < 0.33 Spherical micelles 0.33 – 0.50 Non spherical micelles (e.g. cylinders, rods, etc.) 0.50 – 1.00 Bilayers (vesicles, liposomes, membranes, etc.) > 1.00 Inverted micelles
Table 3.1: Type of self-assembly predicted by surfactant number
The Hydrophile-Lipophile Balance (HLB) has proved useful for guiding surfactant selection according to desired performance. In 1949, Griffin proposed that the HLB value for a nonionic surfactant be determined by dividing by 5 the percentage of the overall surfactant molecule represented by hydrophilic ethylene oxide. Thus, the maximum HLB number could be 20 for a completely water soluble molecule. Davies later extended the HLB concept to nonionic surfactants, assigning HLB values to chemical groups composing a surfactant. A surfactant’s HLB value became the sum of the individual groups’ HLB values. Through experiment, HLB values have been empirically related to the type of emulsion system formed. Table 3.2 indicates a surfactant’s application according to its HLB value.[3]
46 HLB value Application 3 – 6 Water-in-oil emulsifier 7– 9 Wetting agent 8 – 18 Oil-in-water emulsifier 13 – 15 Detergent 15 – 18 Solubilizer
Table 3.2: Surfactant application according to Griffin’s HLB concept
Despite intensive research for over a century, emulsion science frustratingly remains driven by empirical explanations. Other than basic thermodynamic statements, emulsion systems cannot be explained and controlled by first principles. Quite possibly, advances in molecular simulation capabilities will provide more fundamental insight into emulsions in the future. For now, however, researchers are left to trial-and-error approaches to emulsion formulation. The situation is complicated tremendously for lipid nanoparticles because of the desire to solidify the dispersed oil (lipid) phase. Even if a stable emulsion can be formed at elevated temperatures, the transition to cooler temperatures results in thermodynamic property changes that can render an emulsion unstable. Therefore, for lipid nanoparticles produced by the precursor emulsion approach, formulation of a stable emulsion is not sufficient. At best, in this situation, lipid nanoparticle work can only proceed on an empirical basis.
In this work, the objective was to evaluate material properties and processing conditions on lipid nanoparticle synthesis and stability. The decision was made to initially consider lipid nanoparticle synthesis as an emulsion followed by solidification two-step process, in agreement with the leading research groups working in the field. The materials and processing conditions were self-limited to ensure cost efficiency and biocompatibility. If successful in producing monodisperse nanoparticles at desired sizes, the material and processes could be easily scaled without reformulation. Therefore, the central question of this work was to determine whether improved nanoparticle synthesis and stability could be achieved via optimized formulations and process conditions given extensive design restrictions.
47 3.2 Materials & Methods
3.2.1 Materials
All chemicals were purchased from Sigma-Aldrich. Investigated lipids included fatty acids, stearic acid (MW = 284.5; MP = 69 °C) and lauric acid (MW = 200.3; MP = 43 °C), and triglycerides, tristearin (MW = 891.5; MP = 72 °C) and trilaurin (MW = 639.0; MP = 46.5 °C). Investigated surfactants included L-α-phosphatidylcholine (lecithin), dipalmitoyl phosphatidylcholine (DPPC), sodium taurocholate, sodium glycocholate, and cholesterol. Double distilled water was obtained onsite from a
Nanopure water purification system.
Stearic acid and tristearin are comprised of one and three acyl chains of 18 carbon atoms in length, respectively. Lauric acid has one and trilaurin has three acyl chains of 12 carbon atoms in length. Lecithin possesses a glycerol backbone to which one or two acyl chains are attached, forming the hydrophobic tail.
Lecithin’s polar head group, a phosphate bonded to a choline group, is found at the third glycerol hydroxyl group. Sigma-Aldrich’s P5394 lecithin is derived from egg yolk and is greater than 60% phosphatidylcholine, with the remaining 40% consisting of mostly phosphatidylethanolamine plus other phospholipids as well as traces of triglycerides and cholesterol. According to www.sigmaaldrich.com, purified egg yolk phosphatidylcholine typically have fatty acid contents of approximately 33% C16:0
(palmitic), 13% C18:0 (stearic), 31% C18:1 (oleic), and15% C18:2 (linoleic), which gives an average molecular weight of approximately 768. DPPC is a pure (> 95%) phosphatidylcholine having only C16:0
(palmitic) fatty acid tails.
The bile salts, sodium taurocholate and sodium glycocholate, and cholesterol are sterols and perform critical functions in mammalian processes. Sodium taurocholate and sodium glycocholate derive their amphiphilicity from the hydrophobic ring backbone and a polar head group combined with several hydroxyl groups located in the same plane. Cholesterol is only slightly amphiphilic due to a single hydroxyl group. Figure 3.1 through 3.9 show the molecular structure of each molecule.
48
Figure 3.1: Lauric acid structure Figure 3.2: Stearic acid structure
Figure 3.3: Trilaurin structure Figure 3.4: Tristearin structure
Figure 3.5: Lecithin structure Figure 3.6: DPPC structure
Figure 3.7: Sodium glycocholate structure Figure 3.8: Sodium taurocholate structure
Figure 3.9: Cholesterol structure
49 3.2.2 Lipid Nanoparticle Preparation
The procedure for producing lipid nanoparticles was similar to the method used by Bocca et al.[4]
The lipid (lauric acid, stearic acid, trilaurin, or tristearin) was maintained at ~ 75 °C and allowed to melt completely. Separately, double distilled water was heated to 75 °C. Typically, surfactants were added to the water under magnetic stirring and allowed to equilibrate at 75 °C. Next, the water – surfactant solution was added to the melted lipid and once again allowed to equilibrate at 75 °C. If desired to create the emulsion (i.e., no spontaneous emulsification as in the case of microemulsions), external mechanical energy then was added in the form of an IKA Ultra-Turrax T 18 rotor-stator homogenizer. The Ultra-
Turrax T 18 homogenizer, equipped with the 19 mm dispersing tool, has a speed range of 6,000 - 30,000 rpm and an operational volume range of 10 - 2000 ml. The homogenizer motor produces 160 W of power.
The homogenizer only was operated in a batch set-up. Figure 3.10 shows the IKA Ultra-Turrax T 18 rotor- stator homogenizer.
Figure 3.10: IKA Ultra-Turrax T 18 rotor-stator homogenizer used in lipid nanoparticle production
Once mixed, the dispersed lipid phase of the emulsion required solidification in order to produce the solid lipid nanoparticles. This was accomplished several ways. First, the emulsion was allowed to cool slowly at room temperature (~ 25 °C) or more rapidly in the refrigerator (~ 2 °C). Alternatively, small
50 emulsion aliquots were added to cold water in ratios ranging between 1:10 to 1:50 (emulsion:water). The cooling (or dilution) water was in the temperature range of 2 – 25 °C, far below the lipids’ melting temperatures. Typically, 1 mL of emulsion would be added to the cooling water, forming a suspension of solid lipid nanoparticles. Multiple lipid nanoparticle samples were prepared from one emulsion batch.
3.2.3 Particle Size Analysis
Photon correlation spectroscopy (PCS), also known as dynamic light scattering (DLS), was employed to measure particle size distributions and zeta potentials. Specifically, a Brookhaven Instruments
90Plus system was used. DLS makes use of particles’ ability to scatter light and their natural Brownian motion when suspended in a fluid, water in this case. Particle size is calculated based on an estimate of the particles’ diffusion coefficient while suspended in a medium. Particle diffusion rates are inversely proportional to particle size. The time variation of the scattered light is analyzed by examining their auto- correlation. From this a diffusion coefficient can be derived. The autocorrelation function, C(τ), is
− Γ C()τ = Ae 2 t + B
Γ = q 2 D
θ 4πnsin( ) q = 2 λ
k T D = B 3πηd
where A and B are constants specific to the machine, q is the scattering vector, D is the diffusion coefficient, n is the refractive index, θ is the scattering angle, λ is the light wavelength, kB is the Boltzmann
Constant, T is the temperature (K), η is the viscosity, and d is the particle diameter.[5]
For measuring zeta potential, the instrument uses electrophoretic light scattering and the Laser
Doppler Velocimetry (LDV) method to determine particle velocity and, from this, the zeta potential.[6]
For both zeta potential and particle size analyses, lipid nanoparticle suspensions were placed in a 4 ml 51 acrylic cuvette. Measurement temperature was investigated early, but most measurements were performed at 25 °C. The light incidence angle was fixed at 90°. The solvent was double distilled water at all times.
Measurement intensity was allowed to self-adjust, permitting optimum measurement capabilities.
3.3 Results and Discussion
3.3.1 Assessment of Microemulsion Synthesis Approach
Due to the attractiveness of the minimal external energy input required to form microemulsions, attempts were made at duplicating the results of Bocca et al. who successfully made lipid nanoparticles from microemulsions. Using 0.70 mmol stearic acid, 0.69 mmol cosurfactant, 0.14 mmol lecithin, and
110.0 mmol water, Bocca et al. produced clear, presumably microemulsions from which 1 ml aliquots were removed and injected into ~ 2 °C water (1:10 v/v ratio, i.e. 10 ml of water) under gentle magnetic bar stirring. The researchers used sodium taurocholate and sodium glycocholate as the cosurfactants. Using a
Coulter N4 device at a 90° laser angle and 25 °C, sodium taurocholate containing lipid nanoparticles had a diameter of 45 nm (+ 3 nm) and a polydispersity index (PI) of 0.16 (+ 003). Sodium glycocholate containing lipid nanoparticles had a diameter of 90 nm (+ 3 nm) and a polydispersity index (PI) of 0.14 (+
003).[4]
Using the same molar ratios as Bocca et al., lipid nanoparticles were produced in accordance with the microemulsion-based procedure stated previously. DPPC was used instead of lecithin. A third cosurfactant, cholesterol, was studied. Particle size analysis was performed using a Brookhaven
Instruments 90Plus system operated at a 90° laser angle and 25 °C. The confidence interval of the mean diameter for sodium glycocholate containing lipid nanoparticles overlapped with Bocca’s data. The mean diameter for sodium taurocholate containing lipid nanoparticles was significantly larger than Bocca’s nanoparticles using sodium taurocholate as the cosurfactant. The large PI suggested a multimodal distribution and necessitated an examination of the raw data. A histogram revealed a bimodal distribution of the diameter with the modes corresponding to 46 nm and 215 nm, respectively. Mode 1 was in excellent agreement with the Bocca’s reported 45 nm mean diameter for taurocholate containing lipid nanoparticles.
When cholesterol was used as the cosurfactant, particle diameters increased significantly (over 500 nm).
52 Cosurfactant Diameter (nm) Polydispersity Index Sodium taurocholate 99 + 4.3 0.30 + 0.01 Sodium glycocholate 110 + 12 0.01 + 0.00 Cholesterol 620 + 140 0.01 + 0.00
Table 3.3: Effect of cosurfactant selection on lipid nanoparticle characteristics
Formation of an oil-in-water (o/w) emulsion out of stearic acid (oil) requires a system HLB value of 15. DPPC possesses an HLB value of approximately 8. Given the equimolar ratio of surfactant to cosurfactant use in the systems studied here, a proper cosurfactant would possess an HLB value of 22 according to HLB theory. Indeed, bile salt HLB values have been reported in the range of 20 -25.[3]
Cholesterol, on the other hand, possesses a HLB value around 2 -3, and, therefore, the significant increase in particle size is in agreement with the theory. In fact, the cholesterol containing system never produced very clear to slightly turbid emulsion like the taurocholate or glycocholate containing systems.
The difference between taurocholate and glycocholate diameters cannot be explained totally on
HLB grounds. Sodium glycocholate is slightly more hydrophilic than sodium taurocholate, as evidenced by sodium glycocholate’s higher critical micelle concentration (7.1 mM versus 3.3 mM). According to
Bancroft’s theory, the hydrophilicity directly relates to a surfactant’s ability to solubilize oil. This suggests that sodium glycocholate should produce smaller particle diameters which were not observed. Sodium taurocholate has a larger aggregation number and is a larger molecule than sodium glycocholate. Both facts intuitively suggest that sodium taurocholate should produce larger particles. On the contrary, the large size may enhance sodium taurocholate’s cosurfactant contributions, thus resulting in smaller particles.
The larger sodium taurocholate molecule may increase the distance between two DPPC polar groups at the surface. The net effect would be a lowering of the system’s interfacial tension because of a reduction of repulsive forces between two like charged phosphatidylcholine head groups, offering a plausible explanation. Unfortunately in emulsion science, pure surfactant theory does not translate well in predicting behaviors of multicomponent surfactant formulations.
Significant difficulties were encountered in producing the lipid nanoparticles according to Bocca’s method. Bocca’s work was performed on a much larger scale than what was affordable to the researchers
53 conducting this work. Milligram quantities were difficult to measure accurately, transfer, mix, and heat.
Maintaining temperature control was very challenging with the available heated magnetic stirrers and given the necessary container transfers when combining the water solution with the lipid solution. Several trials were aborted to obvious errors and detrimental observations such as lipid resolidification and phase separation during supposed equilibration. Despite the difficulties, several trials were completed that provided additional insight.
The effect of storage temperature was one insight elucidated. Because the Brookhaven PCS system was housed in another building and controlled by a separated research program, the time between lipid nanoparticle production and particle size analysis was an uncontrollable factor. The temperature at which the lipid nanoparticle suspensions were stored was theorized to be a possible factor. Lipid nanoparticles produced from a single emulsion were stored for 24 hours at two different temperatures, 4 °C and 21 °C. As shown in Table 3.4, the particles stored at 4 °C were significantly larger. This is most likely a result of solid state polymorphic phase transitions in stearic acid. Given the lack of replicates, no definitive conclusion can be drawn from these results. A possible downside associated with maintaining an elevate storage temperature is increased microbial growth in the absence of aseptic production techniques, as in the case in this laboratory research. Microbial growth was a recurrent problem throughout these research efforts and was controlled only by low temperature storage.
Storage Temperature (°C) Cosurfactant Diameter (nm) Polydispersity Index 4 Sodium taurocholate 150 + 2.2 0.30 + 0.01 21 Sodium taurocholate 58 + 31 0.26 + 0.11
Table 3.4: Effect of storage temperature on lipid nanoparticle characteristics
Consistency and reproducibility were difficult to achieve using the microemulsion approach given the operating constraints. Use of DPPC was cost prohibitive, even at small scales. The decision was made to replace DPPC with egg lecithin in future experiments. After considering emulsion quality and the relative costs, sodium taurocholate was selected over sodium glycocholate as the cosurfactant of choice.
The microemulsion approach was deemed unacceptable according to criteria presented in earlier chapters 54 of this work. The extreme sensitivity exhibited by the systems was a primary concern. Seemingly subtle changes in material weight, processing, handling, etc. created significant deviations from previous data and the standard established by Bocca et al. To overcome these sensitivities, the decision was made to introduce in future experiments mechanical energy in the form of an Ultra-Turrax rotor-stator homogenizer.
3.3.2 Effects of Lipid and Surfactant Chemistry
Lipid chemistry, surfactant to lipid molar ratio, and surfactant to cosurfactant molar ratio were investigated to determine effects on lipid nanoparticle size distribution and stability. Four lipids were investigated: lauric acid, stearic acid, trilaurin, and tristearin. Lecithin (surfactant) to sodium taurocholate
(cosurfactant) molar ratio was varied from 1:3 (25% lecithin) to 3:1 (75% lecithin). Total surfactant
(lecithin + sodium taurocholate) to lipid molar ratio was varied from 0.25 to 1.50. The lipid nanoparticles were prepared according to the emulsion approach described earlier (Table 3.5). Upon combining the water and lipid solutions, the system was mixed for 120 seconds using the Ika Ultra-Turrax T 18 homogenizer at 30,000 rpm.
1. Set water bath to ~ 80 °C 2. Measure lipids, surfactants, and water per experimental formula 3. Add lipid and lecithin to empty jacketed vessel 4. Add sodium taurocholate to water 5. Heat water solution to 75 °C on heated plate 6. Once lipid has melted, add water solution to jacketed vessel 7. Allow combined lipid-water solution to equilibrate at 75 °C 8. Disperse solution on dial setting "6" (30,000 rpm) for 2 minutes 10. Add 1 ml of emulsion to 20 ml of 2 °C diluent in glass vial 11. Store lipid nanoparticle product at 2 °C 12. Measure particle size
Table 3.5: Procedure used to prepare lipid nanoparticles
Particle size measurements were made on day of production (Day 0) and 1 week after production
(Day 7). The effective diameter (deff) and modes present in a multimodal distribution were recorded.
Brookhaven’s 90Plus PCS system assumes a normal distribution in estimating deff, a single mode system following a Gaussian distribution. Most emulsion and particulate systems do not follow Gaussian
55 distributions, either being lognormal or multimodal. Brookhaven’s 90Plus PCS system permits collection of raw data for multimodal analysis. Four methods for estimating multimodal distributions are provided in the software: number, volume, surface, and scattering intensity. Number was selected because an absolute count seemed most appropriate for tracking physical effects and material recycling was deemed viable in lipid nanoparticle production. Raw data are provided according to lipid type in Tables 3.6 through 3.9.
Gaussian Multimodal Surfactant:lauric acid Lecithin:taurocholate Day deff (nm) PI d1 (nm) d2 (nm) 0.25 3.00 0 230 0.16 150 810 1.00 3.00 0 230 0.29 120 490 1.50 3.00 0 240 0.22 80 320 0.25 0.33 0 5500 0.53 490 8500 1.50 0.33 0 2200 0.59 240 8600 0.25 3.00 7 660 0.42 340 8400 1.00 3.00 7 220 0.25 210 1900 1.50 3.00 7 180 0.21 80 260 0.25 0.33 7 12000 0.19 9700 - 1.00 0.33 7 4500 0.42 80 7000 1.50 0.33 7 1600 0.43 110 7200
Table 3.6: Lauric acid data
Gaussian Multimodal Surfactant:stearic acid Lecithin:taurocholate Day deff (nm) PI d1 (nm) d2 (nm) 0.25 3.00 0 530 0.36 180 4000 1.00 3.00 0 280 0.29 190 2200 1.75 3.00 0 180 0.22 130 530 0.25 0.33 0 530 0.30 240 2100 1.00 0.33 0 280 0.25 160 830 1.75 0.33 0 370 0.27 110 510 0.25 3.00 7 600 0.26 310 5700 1.00 3.00 7 370 0.31 180 1700 1.75 3.00 7 240 0.20 160 850 0.25 0.33 7 470 0.32 320 2700 1.00 0.33 7 310 0.21 180 620 1.75 0.33 7 290 0.22 110 440
Table 3.7: Stearic acid data
56 Gaussian Multimodal Surfactant:trilaurin Lecithin:taurocholate Day deff (nm) PI d1 (nm) d2 (nm) 0.25 3.00 0 1270 0.35 700 5400 1.00 3.00 0 380 0.22 300 300 1.75 3.00 0 250 0.05 220 4000 0.25 0.33 0 1500 0.20 760 9100 1.00 0.33 0 640 0.23 450 3000 1.75 0.33 0 370 0.23 230 790 0.25 3.00 7 1200 0.41 430 8900 1.00 3.00 7 360 0.29 110 5400 1.75 3.00 7 210 0.12 140 520 0.25 0.33 7 1200 0.51 360 4400 1.00 0.33 7 510 0.33 130 3800 1.75 0.33 7 370 0.28 230 2100
Table 3.8: Trilaurin data
Gaussian Multimodal Surfactant:tristearin Lecithin:taurocholate Day deff (nm) PI d1 (nm) d2 (nm) 0.25 3.00 0 1600 0.37 - - 1.00 3.00 0 560 0.19 - - 1.75 3.00 0 500 0.17 - - 0.25 0.33 0 2000 0.33 - - 1.00 0.33 0 830 0.29 - - 1.75 0.33 0 580 0.27 - - 0.25 3.00 7 1300 0.44 240 831 1.00 3.00 7 300 0.30 160 756 1.75 3.00 7 240 0.25 154 2044 0.25 0.33 7 1800 0.42 232 615 1.00 0.33 7 340 0.33 144 823 1.75 0.33 7 270 0.23 133 634
Table 3.9: Tristearin data
Several interesting trends emerge from the data. First, fatty acids produce significantly smaller particles than triglycerides, given the range of mole fractions employed here (Figure 3.11). This result agrees with one’s intuition in that fatty acid molecules are ~ 1/3 the size of triglycerides composed of the same fatty acids. Second, although not consistent throughout the data, smaller acyl chains seem predisposed to produce smaller particle diameters (Figure 3.12). Third, a 3:1 lecithin to sodium taurocholate ratio significantly improves nanoparticle quality and reduces diameter (Figure 3.13). A 3:1 lecithin to sodium taurocholate was found to impart maximum emulsion stability by other researchers. [7]
57 Fillery-Travis et al. proposed and produced evidence that sodium taurocholate functioned as a traditional anionic cosurfactant, i.e. reducing interfacial tension and increasing negative surface charge, and formed a liquid crystalline pospholipid interface at lecithin to taurocholate molar ratios of 3:1.[7, 8] The liquid crystalline structure was unique to the 3:1 lecithin to taurocholate ratio and imparted maximum emulsion stability. Finally, total surfactant to lipid ratio dramatically impacted particle size and distribution characteristics. Figures 3.11 – 3.13 indicate the reduction in diameter mode with increasing surfactant concentrations. Figure 3.14 clearly illustrates surfactant concentration effect on particle distributions. Note the reduction in first mode diameters and the relative frequency shift from larger diameters to smaller diameters.
Tristearin Stearic Acid
2000 1600 1500
1000 530 560 500
0 day (nm)deff @ 500 280 180
0 0.25 1.00 1.75 Total surfactant to lipid ratio
Figure 3.11: Fatty acids produce smaller particles than triglycerides at equivalent molar formulations
58
n = 18 n = 12
2000 1600
0 1500 1270
1000
560 500
eff (nm) @ day (nm)eff @ 500 380 d 250
0 0.25 1.00 1.75
Total surfactant to lipid ratio
Figure 3.12: Shorter triglyceride acyl chains produced smaller particles at equivalent molar formulations
1:3 lecithin to taurocholate ratio 3:1lecithin to taurocholate ratio 15000 12300 12000
9000
6000 4500
7 day (nm)deff @ 3000 1600 660 220 180 0 0.25 1.00 1.50
Total surfactant to lipid ratio
Figure 3.13: Lecithin to taurocholate ratio significantly impacts particle diameters
59 0.25 surfactant to trilaurin ratio
700Diameter (nm) 5400
1.00 surfactant to trilaurin ratio
410Diameter (nm) 3000
1.75 surfactant to trilaurin ratio
220 Diameter (nm) 4000
Figure 3.14: Trilaurin histograms illustrating the effect of increasing surfactant concentrations
To increase experimental efficiency in future experiments, a design of experiments methodology was incorporated. The previous work examining lipid chemistry, surfactant to lipid molar ratio, and surfactant to cosurfactant molar ratio was repeated in the form of ¼ fractional factorial screening design.
The experimental design and analysis were conducted using Minitab software. Four lipids were investigated: lauric acid, stearic acid, trilaurin, and tristearin. The acyl chain length was designated as n.
Lecithin ([S1]) to sodium taurocholate ([S2]) molar ratio was varied from 0.33 (25% lecithin) to 3.00 (75% lecithin). Total surfactant ([Stot]) to lipid ([Lipid]) molar ratio was varied from 0.25 to 2.50. The lipid nanoparticles were prepared according to the emulsion approach described earlier (Table 3.5), except mixing time (t) and mixing speed (RPM) were varied between 20 – 120 seconds and 6,000 – 30,000 rpm, 60 respectively. Due to cost constraints, no replicates were run which limited the power and resolution of the design. Six factors in total for a ¼ fractional factorial design required 16 runs. No center points were included. The objective was to quantify and verify observations observed in the preceding set of experiments. Table 3.10 shows the experimental design.
Run Lipid n [Stot]:[Lipid] [S1]:[S2] t (s) RPM 1 Stearic Acid 18 0.25 0.33 120 30000 2 Tristearin 18 2.50 3.00 120 30000 3 Trilaurin 12 0.25 3.00 20 30000 4 Trilaurin 12 2.50 0.33 120 30000 5 Tristearin 18 0.25 3.00 120 6000 6 Stearic Acid 18 2.50 0.33 120 6000 7 Stearic Acid 18 2.50 3.00 20 30000 8 Trilaurin 12 2.50 3.00 20 6000 9 Tristearin 18 2.50 0.33 20 6000 10 Lauric Acid 12 2.50 0.33 20 30000 11 Stearic Acid 18 0.25 3.00 20 6000 12 Lauric Acid 12 0.25 3.00 120 30000 13 Tristearin 18 0.25 0.33 20 30000 14 Lauric Acid 12 0.25 0.33 20 6000 15 Lauric Acid 12 2.50 3.00 120 6000 16 Trilaurin 12 0.25 3.00 120 6000 17 Stearic Acid 18 0.25 0.33 120 30000
Table 3.10: ¼ fractional factorial screening experimental design intended to elucidate significant effects
Minitab analysis provided considerable amounts of useful information, reaffirming the utility of statistical design of experiments (DOE) and corresponding analyses. Minitab 14 includes all estimable effects in its default analysis of variance (ANOVA) routine. In this case, even with ¼ resolution and inadequate power because of zero replicates, Minitab was capable of estimating all two-way interactions and even one three-way interaction. The three-way interaction terms were removed from the initial
ANOVA to determine significant effects. Two-way interactions were kept to assess their significance, although all two-way interactions were confounded, or aliased, with other two-way interactions. This confounding prevented identification of the specific interaction, leaving only the qualitative assessment of the presence of two-way interactions.
61 The initial, inclusive effects model, i.e. all main effects and two-way interactions included, for
2 2 effective diameter (deff) produced no significant effects (p-values > 0.05) despite yielding R and R adj values of 0.990 and 0.916, respectively. Figure 3.15 confirms the lack of significance while indicating relative effects on deff. Table 3.11 shows the lack of significance for the main effects (P-value = 0.187) and two-way interactions (P-value = 0.209). Removing insignificant terms produced a significant model for
2 2 deff, with R and R adj values of 0.968 and 0.924, respectively. The ANOVA output (Table 3.12) shows the significance for the main effects (P-value = 0.002) and two-way interactions (P-value = 0.001).
Pareto Chart of the Standardized Effects (response is deff, Alpha = .05) 12.71 Factor Name AC A Lipid Bn AE C [S 1]:[S 2] F D [Stot]:[Lipid] Et D FRPM A C Term AB BD E B AD
0 2 4 6 8 10 12 14 Standardized Effect
Figure 3.15: Pareto chart showing no significant effects in the initial effective diameter model
Source DF Sequential SS Adjusted SS Adjusted MS F P-value Main Effects 6 161969248 204733304 34122217 16.43 0.187 Two-Way Interactions 5 132496827 132496827 26499365 12.76 0.209 Residual Error 1 2076722 2076722 2076722 Total 12 296542797
Table 3.11: ANOVA table for the initial effective diameter model 62 Source DF Sequential SS Adjusted SS Adjusted MS F P-value Main Effects 5 159109851 227771827 45554365 24.37 0.002 Two-Way Interactions 2 128086734 128086734 64043367 34.26 0.001 Residual Error 5 9346212 9346212 1869242 Total 12 296542797
Table 3.12: ANOVA table for the reduced effective diameter model
Term Effect Coefficient SE Coefficient t P-value Constant 6275 503.1 12.47 0.000 Lipid 4790 2395 418.6 5.72 0.002 [S1]:[S2] 3982 1991 394.7 5.04 0.004 [Stot]:[Lipid] -5518 -2759 503.1 -5.48 0.003 t -740 -370 418.6 0.88 0.417 RPM -6167 -3084 503.1 -6.13 0.002 Interaction 1 5034 2517 394.7 6.38 0.001 Interaction 2 6466 3233 503.1 6.43 0.001
Table 3.13: Coded coefficients and P-values for the reduced effective diameter model
The P-values in Table 3.13 demonstrate the significance of the model parameters, except for time
2 (t). Time was maintained in the reduced version because R adj decreased and the ANOVA P-value for two- way interactions increased when time was removed. These observations suggest that time is a factor in one of the two significant two-way interactions. The positive coefficient for lipid, a categorical factor, implies that triglycerides produce larger nanoparticles than fatty acids, agreeing with earlier experimental observations already discussed. Interestingly and contrary to previously observed trends, the positive coefficient for lecithin to sodium taurocholate ratio implies that nanoparticle size increases with increasing lecithin to sodium taurocholate ratio. The negative coefficient for surfactant to lipid ratio suggests an inverse relationship with nanoparticle size, consistent with earlier observations and intuitively consistent with greater dispersing capability. Mixing time and RPM both exhibit an inverse relationship with particle size, as one would expect. The unidentifiable interactions both possess positive coefficients, suggesting a directly proportional relationship with particle size. The residuals for the reduced model indicate no significant problems inherent to the model (Figure 3.16).
63 Residual Plots for deff Normal Probability Plot of the Residuals Residuals Versus the Fitted Values 99 2000
90 1000
50 0 Percent Residual 10 -1000 1 -2000 -1000 0 1000 2000 0 3000 6000 9000 12000 Residual Fitted Value
Histogram of the Residuals Residuals Versus the Order of the Data
3 2000
1000 2
0 1 Residual Frequency
-1000 0 -1000 -500 0 500 1000 1500 1 2 3 4 5 6 7 8 9 10 11 12 13 Residual Observation Order
Figure 3.16: Residual analysis for effective diameter reduced model
Polydispersity (PI) was also investigated. Figure 3.16 shows the significance of effects in initial, inclusive polydispersity model. The Pareto chart indicates that two interaction terms were significant and
2 2 that mixing RPM was significant. Model R and R adj values were 0.999 and 0.986, respectively, and the P- values for main effects and two-way interactions were 0.079 and 0.082, respectively. The reduced PI
2 2 model was significant, yielding R and R adj values of 0.978 and 0.936, respectively, and the P-values for main effects and two-way interactions of 0.005 and 0.002, respectively. The lipid type, acyl chain length
(n), mixing RPM, and two interactions are significant in the reduced model (Table 3.14). Again, triglycerides reduce the emulsion desirability, i.e. increase the PI. The length of the acyl chain is inversely proportional to PI. RPM strongly affects PI in an inversely proportional relationship. As with effective diameter, two two-way interactions were significant but unidentifiable. Figure 3.17 shows the significance of effects in initial, inclusive polydispersity model.
64 Pareto Chart of the Standardized Effects (response is PI, Alpha = .05) 12.71 Factor Name AC A Lipid Bn F C [S 1]:[S 2] AE D [Stot]:[Lipid] Et A FRPM B D Term AB BD AD C E
0 2 4 6 8 10 12 14 16 18 Standardized Effect
Figure 3.17: Pareto chart showing significance of effects in initial polydispersity model
Term Effect Coefficient SE Coefficient t P-value Constant 0.3425 0.01869 18.32 0.000 Lipid 0.1586 0.0793 0.01499 5.29 0.006 n -0.1396 -0.0698 0.01499 -4.66 0.010 [S1]:[S2] 0.0341 0.0171 0.01499 1.14 0.318 [Stot]:[Lipid] -0.0940 -0.0470 0.01869 -2.51 0.066 t -0.0121 -0.0061 0.01499 -0.40 0.707 RPM -0.3097 -0.1549 0.01869 -8.29 0.001 Interaction 1 0.2180 0.1090 0.01413 7.71 0.002 Interaction 2 0.2612 0.1306 0.01869 6.99 0.002
Table 3.14: Coded coefficients and P-values for the reduced PI model
The multimodal distribution also was investigated, with particular interest in the first mode (d1).
As noted previously, d1 better represents the nanoparticle preparation because deff is the result of a forced
Gaussian distribution. Figure 3.18 shows the significance of effects in the d1 model. The Pareto chart indicates that four interaction terms were significant while all main effects except mixing time and 65 2 2 surfactant to lecithin ratio were significant at 95% confidence. Model R and R adj values were 1.000 and
0.999, respectively, and the P-values for main effects and two-way interactions were 0.018 and 0.028, respectively. The full model was significant. Removal of mixing time and surfactant to lecithin ratio
2 2 reduced R and R adj values and increased P-values, and, therefore, no model reduction was enacted.
Unlike deff, the model for d1 suggests that triglycerides produce smaller particles. Increasing the lecithin to sodium taurocholate ratio produces smaller particles, in agreement with earlier experimental observations.
Four unidentifiable interactions were significant. Model residuals were randomly distributed, indicating no obvious model deficiencies (Figure 3.19). Figure 3.19, an interaction plot for d1, shows the significant degree of interaction present in the experimental data. Because of confounding in the experimental design, positive identifications cannot be made. However, the interaction plots demonstrates the high degree of interaction, suggesting future experiments have higher resolution and power in order to capture the identities of the interactive effects.
Pareto Chart of the Standardized Effects (response is d1, Alpha = .05) 12.71 Factor Name C A Lipid Bn B C [S 1]:[S 2] AE D [Stot]:[Lipid] Et A FRPM AC BD Term AB F AD E D
0 10 20 30 40 50 60 70 80 90 Standardized Effect
Figure 3.18: Pareto chart showing significance of effects in d1 model 66 Term Effect Coefficient SE Coefficient t P-value Constant 240.4 1.984 121.14 0.005 Lipid -142.3 -71.1 1.500 -47.42 0.013 n -207.8 -103.9 1.500 -69.25 0.009 [S1]:[S2] -243.2 -121.6 1.500 -81.08 0.008 [Stot]:[Lipid] 0.0 0.0 1.984 0.00 1.000 t -15.3 -7.6 1.500 -5.08 0.124 RPM -70.5 -35.2 1.984 -17.76 0.036 Interaction 1 54.2 27.1 1.299 20.88 0.030 Interaction 2 102.7 51.4 1.299 39.55 0.016 Interaction 3 -31.5 -15.7 1.500 -10.50 0.060 Interaction 4 198.8 99.4 1.984 50.08 0.013 Interaction 5 106.0 53.0 1.500 35.33 0.018
Table 3.15: Coded coefficients and P-values for d1 model
Residual Plots for d1 Normal Probability Plot of the Residuals Residuals Versus the Fitted Values 99
90 1
50 0 Percent Residual 10 -1
1 -3.0 -1.5 0.0 1.5 3.0 0 150 300 450 600 Residual Fitted Value
Histogram of the Residuals Residuals Versus the Order of the Data
4.8 1 3.6 0 2.4 Residual Frequency 1.2 -1
0.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 1 2 3 4 5 6 7 8 9 10 11 12 13 Residual Observation Order
Figure 3.19: Residual analysis for d1 model
67 Interaction Plot (data means) for d1
12 18 0.33 3.00 0.25 2.50 20 120 1 6
400 Lipid FA Lipid 200 TG
0 400 n 12 n 200 18
0 400 [S1]:[S2] 0.33 [S1]:[S2] 200 3.00
0 400 [Stot]:[Lipid] 0.25 [Stot]:[Lipid] 200 2.50
0 400 t 20 t 200 120
0
RPM
Figure 3.20: Interactive effects present in d1
The response optimizer feature in Minitab was employed to assist in deciding on the best course forward. In order to achieve a target of 100 nm for deff and d1 and a target of 0.10 for PI, stearic acid was indicated as the lipid to use. A lecithin to sodium taurocholate ratio of 2.3, a surfactant to lipid ratio of 1.4, a mixing time of 70 seconds, and a mixing speed of 18,000 RPM were indicated. The choice of stearic acid was confirmed by empirical knowledge obtained from observation during lipid nanoparticle preparation and subsequent analyses. The stearic acid emulsions appeared less turbid and the resulting nanoparticles less susceptible to flocculation. Therefore, stearic was used exclusively from this point forward. The other factors were not fixed at this point, but the suggested values were used as starting points for future experimentation. The decision was made to continue use of the Ultra-Turrax rotor-stator homogenizer, given the positive effect on particle size and PI. The next step was the design and execution of a Box-
Behnken response surface experimental design to create predictive 2nd order models, if possible.
68 3.3.3 Response Surface Modeling of Stearic Acid Nanoparticles
A Box-Behnken design was employed to derive predictive models for stearic acid nanoparticles.
Three variables were investigated: mixing time (t), surfactant to lipid molar ratio (SL, formerly
[Stot]:[Lipid]), and sodium taurocholate to lecithin molar ratio (TP, formerly 1/[S1]:[S2]). Mixing speed was fixed at 30,000 rpm. Mixing time ranged from 30 to 150 seconds. Surfactant to lipid molar ratio ranged from 0.5 to 2.5. Sodium taurocholate to lipid molar ratio ranged from 0.1 to 10. Zero replicates were run because of cost concerns, somewhat limiting the DOE’s power. The same emulsion-based procedure described earlier was used to prepare the lipid nanoparticles. Table 3.16 shows the experimental pattern, corresponding factor values, and the obtained data.
Run Pattern t SL TP deff (nm) PI d1 (nm) d2 (nm) 1 --0 30 0.5 5.05 231.4 0.173 180.6 . 2 0++ 90 2.5 10 2670 0.218 169.6 1050 3 -0- 30 1.5 0.1 350.1 0.089 272.7 . 4 000 90 1.5 5.05 358.1 0.005 265.3 . 5 -0+ 30 1.5 10 229.8 0.021 164.0 424.0 6 0+- 90 2.5 0.1 158.9 0.005 140.7 . 7 +-0 150 0.5 5.05 198.5 0.111 148.5 . 8 0-+ 90 0.5 10 265.2 0.142 199.4 . 9 000 90 1.5 5.05 415.7 0.005 369.8 . 10 0-- 90 0.5 0.1 210.1 0.174 164.1 . 11 +0- 150 1.5 0.1 174.0 0.005 151.5 . 12 -+0 30 2.5 5.05 399.7 0.282 250.7 1280 13 +0+ 150 1.5 10 587.0 0.159 399.1 1170 14 000 90 1.5 5.05 425.1 0.196 323.5 . 15 ++0 150 2.5 5.05 372.5 0.220 154.5 568.9
Table 3.16: Box-Behnken experimental design with data
Effective diameter ranged from a high of 2700 nm to a low of 160 nm. The PCS instrument detected bimodal distributions in only 5 runs. All 15 runs produced nanoscale particles, but turbidity varied greatly by run. All data reported are Day 0 measurements, i.e. the analysis was performed on the day of preparation. Being the most accurate and realistic means to characterize the nanoparticle formulations, only d1 and PI were analyzed.
69 2 2 A significant model for d1 resulted from the JMP stepwise model generator function. R and R adj values were 0.807 and 0.662, respectively. The model P-value was 0.015, exceeding the desired 95% confidence level. The model had six parameters, exhibiting interactive and curvature effects. Although all main effects were retained in the model, their interactive and curvature terms were significant, but not the individual terms. Table 3.17 shows the significance of t*TP, SL*SL, and TP*TP. TP*TP, P-value = 0.144, was retained because its removal dramatically reduced model quality. The resulting model for d1 explicitly written was found to be the following:
= − ()+ ( )+ ( )+ ()( )− ( 2 )− ( 2 ) d1 302 1.80 t 2.86 SL 25.4 TP 89.1 t TP 105 SL 41.7 TP
The model’s residuals were randomly distributed, indicating no problems with the result (Figure
3.21). Interactions and curvature are demonstrated in Figure 3.22, with t and TP strongly interacting as evidenced by the large angle of intersection. Using the prediction profiler function in JMP, formulations can be estimated to achieve desired particle diameters. Figure 3.23 illustrates an example of one such prediction. For t =120 s, SL =1.02, TP = 0.32, the predicted diameter is 170 nm + 68 nm. The wide confidence interval is a reflection of the experiment’s low power resulting from the lack of replicates. The confidence interval normally would decrease with increased number of replicates. Figure 3.24 – 3.26 provide a snapshot of the response surface profile for d1 against two of the three main effects (t =120 s, SL
=1.02, TP = 0.32 in all three figures). In Figures 3.24 and 3.25, the x and y axes are SL and TP, and a net curvature (parabolic) effect can be seen in both surfaces. Figure 3.26 replaces SL with t (not seen in order to show twisting), a dramatic twisting of the surface can be seen. The combination of these figures can aid optimization activities.
70 Term Estimate SE Estimate t P-value Intercept 301.81538 23.83953 12.66 <.0001 t -1.80 17.54542 -0.10 0.9208 SL 2.8625 17.54542 0.16 0.8744 TP 25.3875 17.54542 1.45 0.1859 t*TP 89.075 24.81297 3.59 0.0071 SL*SL -104.9519 25.74964 -4.08 0.0036 TP*TP -41.70192 25.74964 -1.62 0.1440
Table 3.17: JMP produced parameter estimates (uncoded) for d1 model derived from Box-Behnken DOE
80
60 40 20
0 -20 -40 Diameter Mode 1 Residual -60 100 150 200 250 300 350 400 Diameter Mode 1 Predicted
Figure 3.21: Model residuals compared to experimental data
71 400 150
300
t t 200 15030 30 Mode 1 Diameter
100
400
300 SL SL 200 0.52.5 2.5 Mode 1 0.5 Diameter
100
400 10
300 TP TP 200 10 Mode 1 Diameter 0.1 0.1 100
30 60 90 120 150 180.5 1 1.5 2 2.5 2 4 6 8 10
Figure 3.22: Interaction profiles for d1 model
467.994
170.4658 ±68.379 Mode 1 Diameter
7.41661 30 10 0.5 2.5 0.1
120 150 1.02 0.32
t SL TP
Figure 3.23: Prediction profile for d1 model (t =120 s, SL =1.02, TP = 0.32)
72
Figure 3.24: Response surface for d1 model (t =120 s, SL =1.02, TP = 0.32)
Figure 3.25: Response surface for d1 model (t =120 s, SL =1.02, TP = 0.32; axes interchanged)
73
Figure 3.26: Twisting of response surface for d1 model (t =120 s, SL =1.02, TP = 0.32; t = y-axis)
To evaluate the model’s predictive capability, five sets of formulation conditions were run to compare the observed diameters (d1) to the predicted diameters. Table 3.18 lists the observed and predicted values. Reasonable agreement is achieved in Runs 2-5. Run 1, an extreme position in the Box-Behnken design space, severely under predicts the diameter. Given the extreme position, this result is not surprising and reinforces the concept that models generated according to DOE techniques are valid only within the ranges of testing. Models resulting from DOE activities cannot be extrapolated outside the original design space without proper experimental augmentation. The results shown in Table 3.18 provide a reasonable level of confidence in the stearic acid diameter model derived from the Box-Behnken DOE.
74 Run t SL TP Observed d1 (nm) Predicted d1 (nm) ∆ (nm) 1 150 0.5 0.1 150 36 -110 2 150 0.9 0.1 80. 100 20. 3 140 1.25 0.1 170 150 -20. 4 120 1.5 0.1 170 190 20. 5 150 0.6 2.5 180 140 -40
Table 3.18: Comparison of observed experimental diameters to predicted diameters
3.3.4 Investigation into the Effects of Lipid Mass Fraction and Cholesterol Addition
The effects of lipid mass fraction and cholesterol presence were investigated. As noted in previous chapters, the addition of cholesterol could serve as a controlled release and targeting mechanism in amphotericin B therapeutics. Therefore, understanding the influence of cholesterol was paramount.
Secondly, lipid mass fraction is a lipid nanoparticle quality and economic factor. Lipid mass fraction is important in emulsion science because volume fraction of the oil phase greatly impacts emulsion quality. If the lipid mass fraction is too high, the system will become very viscous or may simply invert. Both effects will negatively affect lipid nanoparticle preparation. On the contrary, increasing lipid mass fraction serves to increase the economic value per preparation. Therefore, determination of an optimum lipid mass fraction is desirable.
A customized experimental design was created and executed. Stearic acid, lecithin, sodium taurocholate, and cholesterol mole fractions were treated as mixture factors. These four mixture factors were combined with lipid mass fraction which was treated as a continuous factor. Stearic acid ranged from
0.56 to 0.95. Lecithin ranged from 0.038. Sodium taurocholate ranged from 0 to 0.19. Cholesterol ranged from 0.012 to 0.10. Lipid mass fraction ranged from 0.036 to 0.084. Homogenizer mixing speed and time were fixed at 30,000 RPM and 12 seconds, respectively.
2 2 The full main effects model was significant. Model R and R adj values were 0.76 and 0.71, respectively, and the model P-value was less than 0.0001. Within the tested ranges, lipid mass fraction, lecithin mole fraction, cholesterol mole fraction were not significant (P-value > 0.05). Stearic acid and sodium taurocholate mole fractions were significant.
75 3.4 Conclusions
Lipid nanoparticles were successfully produced. Lipid varieties included fatty acids, stearic acid and lauric acid, as well as triglycerides, trilaurin and tristearin. Lecithin, DPPC, sodium taurocholate, sodium glycocholate, and cholesterol were evaluated as cosurfactants. Magnetic stirring and rotor-stator mixing were examined for effects on emulsion and nanoparticle quality. Experimentation showed stearic acid to be the choice lipid, sodium taurocholate to be the choice cosurfactant, and rotor-stator mixing to be necessary for quality emulsion preparation.
Statistical design of experiments and analysis methodology proved its worth. A lecithin to sodium taurocholate ratio of 3:1 was shown to be most effective. Surfactant to lipid molar ratio was inversely related to lipid nanoparticle size and polydispersity. Mixing time and speed were shown to be inversely related to lipid nanoparticle size and polydispersity. A model was derived that reasonably predicted lipid nanoparticle diameters. Finally, lipid mass fraction (0.036 - 0.084) and cholesterol mole fraction (0.012 to
0.10) were shown to be insignificant. These findings provide a solid foundation to base future experimental endeavors in formulating stearic acid nanoparticles.
References
1. Israelachvili, J. N., Mitchell, D. J., and Ninham, B. W. (1976) Theory of self-assembly of hydrocarbon amphiphiles into micelles and bilayers. Journal of the Chemical Society: Faraday Transactions II 72, 1525-1568
2. Porter, M. R. (1994) Handbook of Surfactants, Chapman & Hall, London
3. Holmberg, K., Jonsson, B., Kronberg, B., and Lindman, B. (1998) Surfactants and Polymers in Aqueous Solution, John Wiley & Sons Ltd., West Sussex, England
4. Bocca, C., Caputo, O., Cavalli, R., Gabriel, L., Miglietta, A., and Gasco, M. R. (1998) Phagocytic uptake of fluorescent stealth and non-stealth solid lipid nanoparticles. International Journal of Pharmaceutics 175, 185-193
5. Brookhaven (2004) Dynamic Light Scattering. In Vol. 2004, http://www.bic.com/DLSBasics.html
6. Corporation, B. I. (2004) What is Zeta Potential? In Vol. 2004, http://www.bic.com/WhatisZetaPotential.html
76 7. Fillery-Travis, A. J., Foster, L. H., and Robins, M. M. (1995) Stability of emulsions stabilised by two physiological surfactants: L-a-phoshatidylcholine and sodium taurocholate. Biophysical Chemistry 54, 253-260
8. Fängmark, I., and Carpin, J. C. (1996) Protein nebulization. Journal of Aerosol Science 27, S231- S232
77
CHAPTER 4
INCORPORATION OF β-CAROTENE INTO STEARIC ACID NANOPARTICLES
4.1 Introduction
The ultimate objective of this research was to enable lipid nanoparticle drug delivery technology.
Limiting research activities to formulation and preparation studies was insufficient in pursuit of this ultimate objective. Drug delivery development must include loading and release modeling. In this next section of work, the drug loading and release characteristics of stearic acid nanoparticles were studied.
Objectives included the determination of drug loading effects on lipid nanoparticle structure and stability, loading capacity given a model lipophile to be delivered, and determination of lipophile release kinetics.
For this work, β-carotene was selected as the model lipophile. β-carotene delivery is of interest in the food industry, the growing nutraceutical industry, the skin care industry, and marketers of antioxidant medications. The human body converts one β-carotene molecule into two vitamin A molecules, hence the synonym pro-vitamin A. Based on research findings, β-carotene is marketed by the nutraceutical industry as beneficial for vision health, promoting healthy tissue growth, enhancing the immune system, cancer prevention, and cardiovascular disease prevention.[1-4] From a more practical experimental standpoint, β- carotene was economical and presented low occupational risks.
β-carotene belongs to a group of plant compounds called carotenoids. Carotenoids consist of an isoprene polymer backbone. With a molecular formula of C40H56, β-carotene possesses a molecular weight of 536.90 g/mol. β-carotene is insoluble in water, making it an excellent model small molecule lipophile drug. β-carotene is very stable until decomposing at its melting point of 176 – 183 °C. Figure 4.1 shows the molecular structure of β-carotene.
78
Figure 4.1: β-carotene structure
4.2 Materials & Methods
4.2.1 Materials
Stearic acid, L-α-phosphatidylcholine (lecithin), sodium taurocholate, cholesterol, and β-carotene were purchased from Sigma-Aldrich. Double distilled water was obtained locally from a Nanopure water purification system.
4.2.2 Lipid Nanoparticle Preparation
Lipid nanoparticles were produced according to procedure developed in Chapter 3. Stearic acid was maintained at ~ 75 °C and allowed to melt completely. Separately, double distilled water was heated to 75 °C. Typically, surfactants were added to the water under magnetic stirring and allowed to equilibrate at 75 °C. Next, the water – surfactant solution was added to the melted lipid and once again allowed to equilibrate at 75 °C. Having previously established the need for external mechanical energy, an IKA Ultra-
Turrax T 18 rotor-stator homogenizer was employed to achieve adequate mixing. Once mixed, the dispersed lipid phase of the emulsion required solidification in order to produce the solid lipid nanoparticles. 1 ml emulsion aliquots were added to cold water (~ 2 °C) at a ratios of 1:20
(emulsion:water). Final product was stored at 4 °C. Multiple lipid nanoparticle samples were prepared from one emulsion batch.
4.2.3 Particle Size Analysis
Photon correlation spectroscopy (PCS), also known as dynamic light scattering (DLS), was employed to measure particle size distributions and zeta potentials. Specifically, a Brookhaven Instruments
90Plus system was used.
79 4.2.4 Crystalline Phase Determination
Differential Scanning Calorimetry (DSC) was employed to determine crystalline states. Standard aluminum DSC pans were used. Nitrogen gas was transported through the DSC chamber at a rate of 50 ml/minute. The temperature was increased 5 °C/minute between 25 °C – 90 °C. 5 – 10 mg samples were added to the aluminum pans for the analysis. Lipid nanoparticle samples were prepared via centrifugation.
4.2.5 Drug Loading and Release Determination
UV-Visible photospectroscopy (UV-Vis) was employed to measure drug loading and release. An empirical model was created to determine β-carotene concentrations in stearic acid nanoparticles. Ethanol served as the solvent throughout the studies, dissolving all lipid nanoparticles to eliminate secondary scattering and absorption errors. All absorption readings were conducted at a 450 nm, the wavelength at whcih β-carotene absorbs most strongly relative to the other light absorbing molecules used in these studies. Other molecules with detectable absorbance at 450 nm included sodium taurocholate, cholesterol, and lecithin. Ensuring Beer’s law remained valid (i.e. linear relationship between absorbance and concentration), a calibration curve for each absorbing molecule was created. Assuming linearity, an overall calibration model was creating by summing the individual models. Given known concentrations of all components except β-carotene, β-carotene concentration was calculated from the absorbance.
4.2.6 Atomic Force Microscopy
Atomic force microscopy (AFM) was employed to obtain physical images of the lipid nanoparticles.
4.3 Results and Discussion
4.3.1 Assessment of β-Carotene Incorporation Using a 5 Component Mixture Design
β-Carotene incorporation was studied using a 5 component mixture experimental design. Lipid mass fraction, mixing speed, and mixing time were fixed at 0.1, 30,000 RPM, and 150 seconds,
80 respectively. The five components, with studied mole fractions ranges in parentheses, were stearic acid
(0.52 – 0.90), lecithin (0.025 – 0.25), sodium taurocholate (0 – 0.1), cholesterol (0.025 – 0.21), and β- carotene (0.025 – 0.20). 20 conditions (runs) were included in the design with zero replicates, again with the intent to minimize costs. All PCS analyses were performed on the same day as nanoparticle preparation. Observed diameters ranged from 12 to 318 nm.
Using JMP statistical software, a significant model of first mode diameter, d1 (nm), was created
2 2 that included all five components and four factor interaction terms (Table 4.1). Model R and R adj values were 0.96 and 0.92, respectively, and the model P-value was less than 0.0001. β-Carotene was present in three of the significant effects: β-carotene mole fraction, stearic acid mole fraction-β-carotene mole fraction interaction, and cholesterol mole fraction-β-carotene mole fraction interaction. The resulting model reasonably captured the observed experimental data (Figure 4.2), and the residuals were normally distributed (Figure 4.3). Figure 4.4 demonstrates the extent of interactions present.
Term Estimate SE Estimate t-ratio P-value Power Stearic Acid -37 32.5 -1.15 0.2765 0.055 Lecithin 6600 1082.6 6.09 0.0001 0.997 Sodium Taurocholate -590 160.5 -3.64 0.0044 0.798 Cholesterol 670 160.5 4.16 0.0019 0.898 β-carotene -9200 1675.8 -5.51 0.0002 0.991 Stearic Acid*Lecithin -7100 1489.5 -4.75 0.0007 0.960 Stearic Acid*β-carotene 14000 2481.7 5.74 0.0001 0.994 Lecithin*Cholesterol -12000 1861.1 -6.38 < 0.0001 0.998 Cholesterol*β-carotene 4900 1833.2 2.68 0.0230 0.503
Table 4.1: d1 (nm) model parameter estimates resulting from 5 component mixture experimental design
81 350 300
250 200
150 d(m) Actual 100 50
0 0 100 200 300 400 500 d(m) Predicted P<.0001 RSq=0.96 RMSE=19.899
Figure 4.2: Plot of experimentally observed data versus diameters predicted by generated d1 formula
40 30 20 10 0 -10 d(m) Residual -20 -30 -40 0 100 200 300 400 500 d(m) Predicted
Figure 4.3: Residuals of model for d1 (nm) including β-carotene
82 400 0.525 0.9 0.9 300 Stearic Acid Stearic
200 Stearic d(m) 0.9 Acid 100 0.525
0 0.525 400 0.025 0.25 300 0.025
0.025 Lecithin 200 Lecithin d(m) 0.25 100 0.25 0 400 0.2075 0.025 0.2075 300 0.025 0.025 Cholesterol 200 Cholesterol d(m) 100 0.2075 0 400 0.2009 0.2009 0.2009
300 Beta-carotene
200 Beta-carotene
d(m) 0.025 100 0.025 0.025 0
.55 .6 .65 .7 .75 .8 .85 .9 .95 .05 .1 .15 .2 .25 .05 .1 .15 .2 .05 .1 .15 .2 .25
Figure 4.4: Interaction profile for effects demonstrating significant relationships
This experimental design showed the significant impact of β-carotene incorporation on d1, although β-carotene incorporation did not negatively impact the observed size of stearic acid nanoparticles.
Through use of the model generated by the JMP analysis, particle size and β-carotene loading can be optimized (or at least approximated) to desired specifications.
In a broader sense, the significant effect that β-carotene produced on particle diameters is undesirable. β-carotene’s significant effect suggests that a new model must be created for each new drug or component to be incorporated. This lack of robustness increases formulation and development work when extending stearic acid nanoparticles to deliver different drugs. Thus, costs increase and speed to market decreases, both having negative effects on business performance. Lack of formulary robustness represents a considerable deficiency of the emulsion based lipid nanoparticle preparation approach.
83 4.3.2 Investigation of ethanol as a cosurfactant
Ethanol is routinely employed as a cosurfactant in many emulsion systems. Ethanol’s amphiphilic properties result from the hydroxyl group and the short, two carbon acyl chain. Ethanol demonstrates substantial solubility in both water and organic solvents. Ethanol’s effectiveness as a cosurfactant resides in the combination of amphiphilicity and its small structure. Because of ethanol’s size, an ethanol molecule can fit into unoccupied locations at the oil-water interface otherwise unavailable to much larger amphiphiles such as lecithin, sodium taurocholate, and nonionic surfactants like Tweens. Because of the ethanol’s small size, ethanol can locate at the interface without crowding out existing amphiphiles, i.e. induce repulsion between neighboring amphiphiles. The net result is an increase in interfacial concentration of surfactants which produce a reduction of interfacial tension. As noted previously, reduced interfacial tension promotes smaller droplet size and stability.
A custom experimental design was generated that included 8 factors, two mixing factors and six composition factors. Mixing speed and mixing time ranged from 12,000 - 30,000 RPM and 20 - 60 seconds, respectively. The six components, with studied mole fractions ranges in parentheses, were water
(0.985 – 0.995), stearic acid (0.0005 – 0.005), lecithin (0.0001 – 0.005), sodium taurocholate (0.0001 –
0.005), ethanol (0 – 0.005 ), and β-carotene (0.00001 – 0.001). 21 runs were chosen with one replicate, generating 42 experimental conditions. All PCS analyses were performed on the same day as nanoparticle preparation. Observed diameters ranged from 130 to 1600 nm.
Using JMP statistical software, a significant model of first mode diameter, d1 (nm), was created that included mixing time, water mole fraction, stearic acid mole fraction, lecithin mole fraction, sodium taurocholate mole fraction, ethanol mole fraction , β-carotene mole fraction and two factor interaction terms, mixing time-β-carotene mole fraction interaction and sodium taurocholate-ethanol mole fraction
2 2 interaction (Table 4.2). Model R and R adj values were 0.65 and 0.57, respectively, and the model P-value
2 was less than 0.0001. The low R adj value suggests the model poorly captures the system behavior.
84 Term Estimate SE Estimate t-ratio P-value Power Mixing Time -120 34.2 -3.39 0.0018 0.859 Water -1000 419.7 -2.49 0.0180 0.568 Stearic Acid 1.1 x 105 76169.2 1.44 0.1589 0.157 Lecithin 1.8 x 105 69060.6 2.57 0.0148 0.601 Sodium Taurocholate 3.4 x 104 32957.8 1.04 0.3035 0.052 Ethanol 6.8 x 104 48886.2 1.38 0.1753 0.140 β-carotene 5.8 x 106 1987660.7 2.90 0.0065 0.723 Mixing Time*β-carotene 8.5 x 106 1590137.8 5.31 <0.0001 0.998 Sodium Taurocholate*Ethanol 1.8 x 108 54313636.4 3.27 0.0024 0.832
Table 4.2: d1 (nm) model parameter estimates with ethanol included as a cosurfactant
This experimental design reaffirmed the ability to successfully incorporate β-carotene in the stearic acid lipid nanoparticles and the significance of β-carotene in determining particle diameters (P- values < 0.05). Mixing time, once again, was shown to have a significant effect. As expected, mixing time is inversely related to particle diameter. Stearic acid was included in the final model, despite having a P- value = 0.1589, because the model parameters decline dramatically when stearic acid was removed from the model.
Ethanol was not significant as a main effect, but its interaction with sodium taurocholate mole fraction was significant. This interaction proved to be counterproductive. As ethanol content was increased, the addition of sodium taurocholate increased particle size. When ethanol was absent from the system, increasing sodium taurocholate was observed to reduce particle size (Figure 4.5). Therefore, one must conclude that the presence of ethanol produced a negative effect at the interface, i.e. increased the interfacial tension. One possible explanation could be the ethanol excluded the sodium taurocholate from the interface because of electrostatic repulsion between ethanol’s hydroxyl group and sodium taurocholate’s plane of hydroxyl groups.
85 1500 0.005
1000 Ethanol
d1(nm) 500
0 0 .001 .003 .005
Taurocholate
Figure 4.5: Sodium taurocholate – ethanol interaction
Interestingly, the interaction between ethanol and sodium taurocholate is a classic saddle response surface (Figures 4.6 – 4.8). The largest particle diameters are predicted when both ethanol and sodium taurocholate are at intermediate values (relative to this experimental design). Saddle points are generally to be avoided because their ‘instability’. The instability arises from the potential to ‘fall off the saddle’ on either side with subtle composition changes. Therefore, the major conclusion from this particular experimental design was to remove ethanol from all future formulations using the emulsion approach.
Figure 4.6: Sodium taurocholate – ethanol surface response profile (view 1)
86
Figure 4.7: Sodium taurocholate – ethanol surface response profile (view 2)
Figure 4.8: Sodium taurocholate – ethanol surface response profile (view 3)
87 4.3.3 Loading and release of β-carotene from stearic acid nanoparticles
After establishing the ability to successfully produce lipid nanoparticles that contained β-carotene, it was critical to study the β-carotene loading and release characteristics. Maximizing the so called drug- payload, i.e. amount of drug incorporated per particle, and controlling the rate of release are universal goals of advanced drug delivery technologies.
The d1 models generated by earlier experiments provide some insight into the drug loading capability for these systems. However, the d1 models all assumed that 100% of the β-carotene prescribed by the formulation was incorporated into the lipid nanoparticles. 100% entrapment efficiency (EE) is rarely observed in lipophilic delivery. Saturation effects and unwanted partitioning during preparation can limit EE. EE is defined as,
⎛ ()Drug − Drug ⎞ ()≡ = ⎜ − Added Measured ⎟× EntrapmentEfficiency % EE ⎜1 ⎟ 100% ⎝ Drug Added ⎠
Where the drug added is the amount of drug prescribed by the formulation and drug measured is the amount of drug analytically determined to be present after production.
EE determination was made by UV-Vis spectroscopy. According to the literature, β-carotene produces maximum absorbance between 440 – 490 nm (Figure 4.9). The absorbance in the blue-green region is the result of the repeating double bonds (isoprene blocks) in the β-carotene backbone. The slight peak present below 300 nm is due to the carboxyl double bond. Dissolved in ethanol, β-carotene produced a maximum absorbance at 450 nm using the available Cary UV-Vis photospectrophotometer.
88
Figure 4.9: β-carotene absorbance spectrum in the UV-Visible region (adopted from [5])
Each component comprising the lipid nanoparticles were dissolved in ethanol and subjected to 450 nm light. Sodium taurocholate and lecithin produced measurable absorbance values at 450 nm.
Examination of the full absorbance spectrum for each component indicated that 450 nm provided the greatest relative difference between β-carotene and the other components. Therefore, 450 nm was selected as the wavelength for all future UV-Vis spectroscopy measurements.
A calibration curve was generated for all three components at 450 nm. A regression analysis produced a linear model for all three components. Those three models were combined to yield an overall absorbance versus concentration model which was then used to estimate the amount of β-carotene present at a given set of compositions and measured absorbance. The equations were the following:
= []β + AβC 19 C 0.010
= []− ANaT 0.17 NaT 0.0052
= []− AL 0.0046 L 0.00016
89 = []β + [ ]+ []+ ATotal 19 C 0.17 NaT 0.0046 L 0.0046
Where A, βC, NaT, L, and [] represent absorbance, β-carotene, sodium taurocholate, lecithin, and concentration in mg/ml, respectively.
An experiment was designed that included 12 conditions representing six different β-carotene loading amounts. The lipid mass fraction was fixed at 0.05. Relative to the lipid present, stearic acid mole fraction, lecithin mole fraction, and sodium taurocholate mole fraction were fixed at 0.710, 0.210, and
0.069, respectively. β-carotene mole fraction was increased every third condition, and cholesterol mole fraction was decreased accordingly to maintain an overall lipid mass fraction of 0.05. Mixing time and speed were fixed at 150 seconds and 30,000 RPM, respectively. Table 4.3 shows the experimental conditions used to determine EE and β-carotene release over one month.
Condition Stearic Acid Lecithin Taurocholate Cholesterol β-carotene Mole Fraction Mole Fraction Mole Fraction Mole Fraction Mole Fraction 1 0.710 0.210 0.069 0.011 0.0000 2 0.710 0.210 0.069 0.011 0.0000 3 0.710 0.210 0.069 0.010 0.0010 4 0.710 0.210 0.069 0.010 0.0010 5 0.710 0.210 0.069 0.010 0.0015 6 0.710 0.210 0.069 0.010 0.0015 7 0.710 0.210 0.069 0.009 0.0020 8 0.710 0.210 0.069 0.009 0.0020 9 0.710 0.210 0.069 0.009 0.0025 10 0.710 0.210 0.069 0.009 0.0025 11 0.710 0.210 0.069 0.008 0.0030 12 0.710 0.210 0.069 0.008 0.0030
Table 4.3: Mole fractions used in determining entrapment efficiency and release properties
Particle size and drug entrapment efficiency were measured weekly for four weeks.
Measurements beyond four weeks were rendered impossible because mold grew in the 25 ml vials. Future experiments should be conducted using aseptic techniques, and addition of preservatives should be considered to lengthen the life of lipid nanoparticle suspensions created in the lab. As noted in Table 4.3, each compositional condition was repeated once. Measurements were made in triplicate, in most cases. 90 β-Carotene mole fraction was determined not to be a significant factor in particle diameter, d1
(nm). A linear equation provided the best fit to the data, but the model P-value was 0.306 (Figure 4.10).
Therefore, at 95% confidence, the conclusion was reached that the model was not significant. The overall mean diameter was 328 nm for all conditions. The d1 model previously developed (shown in Table 4.1) predicted only a 5 nm difference over the full range of experimental conditions. The lack of significance, therefore, is not surprising.
450
400
350
300 MM Mode 1 Diameter
250
0 .001 .002 .003 .004 Fit Mean [bC]real Linear Fit
Figure 4.10: d1 (nm) versus β-carotene concentration (mg/ml) in analytical sample
Particle diameter, d1 (nm), was also shown not to depend significantly on time. The ANOVA corresponding to Figure 4.11 returned a P-value of 0.416, well above the 0.05 value required for 95% confidence. d1 (nm) was 320 nm on week 0, 1, and 4 and was 350 nm on week 2. This lack of particle size growth indicates stable formulations during the first month.
91 450
400
350
300 MM Mode 1 Diameter
250
0 1 24All Pairs Each Pair Tukey-Kramer Student's t Week Number 0.05 0.05
Figure 4.11: d1 (nm) means diamonds versus time along with two means comparison test results
Entrapment efficiency and release measurements were the primary reasons for conducting this set of experiments. The mean entrapment efficiency was 40%, and the maximum obtained was 84%.
Therefore, on average, 40% of the β-carotene added to the original formulation was analytically determined to be present in the nanoparticles. 40% entrapment efficiency at the β-carotene concentrations utilized in this experiment was quite impressive. Conditions 11 and 12 represented the maximum β-carotene concentration of 0.22 mg/ml. The concentrations investigated were limited by an inability to accurately measure microgram quantities of β-carotene.
Figure 4.12 clearly demonstrates that entrapment efficiency decreased with formulation
2 2 prescribed β-carotene concentration. Although the model R and R adj values were 0.35 and 0.35, respectively, the model P-value was less than 0.0001. Therefore, the relationship between entrapment efficiency decreased and prescribed β-carotene concentration was significant. This behavior most likely can be explained by β-carotene having a finite solubility in the lipid solution and/or lipid crystal. If Figure
4.12 were extended to higher concentrations, a saturation plateau would be expected.
92 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2
Loading Efficiency - Corrected 0.1 0 0 .001 .002 .003 .004 [bC]real
Figure 4.12: Entrapment efficiency versus β-carotene concentration
β-carotene concentration did not significantly decrease over the course of one month, suggesting that β-carotene was retained in the stearic acid nanoparticles for at least one month. Given the propensity of lipid nanoparticles to exhibit drug burst release phenomena, the absence of β-carotene burst release was a major accomplishment. This was particularly exciting when considering the very lipophilic nature of β- carotene. Figure 4.13 shows the lack of observed β-carotene concentration reduction over the four weeks.
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Loading Efficiency - Corrected 0 -0.1 0 1 24Each Pair All Pairs Student's t Tukey-Kramer Week Number 0.05 0.05
Figure 4.13: Entrapment efficiency means diamonds versus time along with means comparison test results
93 Interestingly, the data shown in Figure 4.13 suggest that β-carotene concentration actually increased with time. In fact, the ANOVA (P-value < 0.0001) confirmed that there were significant differences between the weeks. This result is counterintuitive and calls the data into question. There is no known rationale for why concentration would actually increase. The only conceivable explanation could be that β-carotene not originally incorporated into the nanoparticle later adsorbed onto the lipophilic external nanoparticle surface to minimize its exposure to water. Even so, the results of this study need to be confirmed in the future.
AFM images were taken to provide evidence of the spherical structure assumed by the PCS analyses. Figure 4.14 shows a particle produced during preparation of Condition 2, i.e. without β-carotene.
The particle captured by AFM had a diameter of 391 nm, whereas PCS measured d1 = 355 nm.
Figure 4.15 shows a particle produced during preparation of Condition 6, i.e. a β-carotene mole fraction of
0.0015. The particle captured by AFM had a diameter of 266 nm, whereas PCS measured d1 = 226 nm.
For both conditions, the particle captured by AFM was ~ 40 nm larger than the median diameter determined by PCS. This may be attributed to the AFM sample preparation. Small quantities of lipid nanoparticle suspension were placed on the substrate and allowed to air dry. This crude sample preparation may have allowed the lipid nanoparticles to flatten during the cooling process, increasing the observed horizontal dimension. Supporting this explanation was the fact that both particle heights (as measured by
AFM) were smaller than the horizontal dimension.
Several smaller nanoparticles are present in Figure 4.14, providing evidence of significant polydispersity in the product. This could have been true polydispersity, or it could represent agglomeration of individual nanoparticles. SEM images would provide the best analytical tool in determining the extent of nanoparticle agglomeration. Unfortunately, the available SEM facilities conducted analyses or sample preparations at temperatures exceeding the melting of stearic acid, thereby compromising the integrity of the nanoparticles. The large masses on the left side of Figure 4.15 were not identifiable and were considered artifacts of the sample preparation.
94
Figure 4.14: AFM image of nanoparticles produced according to Condition 3
Figure 4.15: AFM image of nanoparticles produced according to Condition 6
95 4.3.4 Effect of β-carotene loading on lipid crystallinity
Verification of the solid state in lipid nanoparticles is essential. As detailed in Chapter 2, supercooling has been observed in lipid nanoparticle systems, and the thermodynamic probability of supercooling increases with decreasing particle diameters. Supercooled particles undermine the benefits of the solid lipid, namely the physical stability and increased resistance to drug release. As noted earlier, lipid crystalline structure has been correlated to drug loading and release behavior. Therefore, characterizing the lipid crystalline structure is critical to describing loading and release behavior.
Differential scanning calorimetry (DSC) was employed to investigate the crystalline structure of the β-carotene loaded stearic acid nanoparticles detailed in Table 4.3. First, it was necessary to investigate the calorimetry profiles of each component in the lipid nanoparticle formulations. Figure 4.16 shows one heating and one cooling cycle for each component present in the stearic acid nanoparticles of interest.
Other than stearic acid, the components do not demonstrate much phase transition activity between 25 °C –
90 °C. This observation is not surprising since only stearic acid (red) has a melting point (~ 70 °C) within the temperature range of interest.
Pure stearic acid exhibited a melting point of 72.3 °C during the heating cycle, higher than the commonly reported 70 °C in literature. The increased melting point was confirmed in two additional heating cycles, representing an instrument offset or a more stable stearic acid structure (i.e. β crystal conformation). Stearic acid exhibited a freezing point of 67.8 °C during the cooling cycle. Freezing point depression (relative to melting) is common in lipids where several crystal conformations are possible.
96 6 Temperature (C)
67.8 4
2 Taurocholic Acid Lecithin 0 Beta Carotene 25 35 45 55 65 75 85 Steric Acid Choles terol -2 Heat Flow, exo up (W/g) up exo Flow, Heat
-4
-6 72.3
Figure 4.16: Heating and cooling cycles for each component comprising lipid nanoparticles
5
Temperature (C)
0 25 35 45 55 65 75 85
61.5 -5
Lipid Nanoparticle 62.9 -10 64.3
HeatFlow, exo up (W/g) 67.2 -15
-20
Figure 4.17: DSC curve for a β-carotene loaded stearic acid nanoparticle with significant water 97 Figure 4.17 shows a DSC curve for a β-carotene loaded stearic acid nanoparticle. This curve shows that the lipid nanoparticles were solid indeed. A considerable amount of water was present in the sample, as evidenced by the dramatically energy increase beginning around 60 °C. The stearic acid nanoparticle measurement was superimposed on the water liquid-gas phase transition, i.e. the vaporization process. The subtle cooling observed during the lipid melting (i.e. peaks) seems to be a software glitch.
Four distinct peaks observed in the heating cycle were attributed to the stearic acid nanoparticles:
61.5 °C, 62.9 °C, 64.3 °C, and 67.2 °C. These peaks, all below 72.3 °C which was observed for pure stearic acid, suggest that multiple crystal phases were present in the sample, decreasing in crystalline order with decreasing temperature. As noted in previous chapters, obtaining the highly ordered β crystal conformation becomes more improbable as the number of components and the chemical heterogeneity increase. As noted previously, preventing the β crystal conformation is desirable because the β crystal conformation is associated drug release. As the crystal more ordered, less space is available for dissimilar molecules that serve to disrupt the thermodynamically preferred crystal ordering. Therefore, Figure 4.17 represents an extremely desirable DSC profile, a solid state in a less ordered crystal structure than the β crystal conformation.
Figures 4.18 and 4.19 represent Condition 9 (see Table 4.3) on the day of preparation and one month after preparation, respectively. In both cases, the lipid nanoparticles were in a solid state possessing less order than the β crystal conformation. After one month, the lowest melting peak in Figure 4.18 (52.5
°C) was no longer observable. This suggests that lipid phase transitioned to a more ordered state, possibly represented by the 63.8 °C peak observed in Figure 4.19. The highest temperature peak’s shift over one month from 65.5 °C to 67.1 °C also suggest more ordering in the highest ordered phase present.
Figures 4.16 – 4.19 support the observed lack of significant reduction in β-carotene concentration during the first month. Burst release has been correlated to solid state lipid transformations. After one month, the lipid nanoparticle formulations inhibited transition to the highest ordered crystal structure, represented by 72.3 °C in Figure 4.14. Had the lipid phase reached the highest ordered crystal structure, the β-carotene would have been released from the lipid nanoparticles.
98 Temperature (C)
20 30 40 50 60 70 80 90
52.5 Heat Flow (mW) Heat 65.5
Figure 4.18: DSC curve for Condition 9 on day of preparation
Temperature (C)
20 30 40 50 60 70 80 90 Heat FlowHeat (mW) 63.8 67.1
Figure 4.19: DSC curve for Condition 9 at one month after preparation
99 No discernable trends were observed to suggest that β-carotene concentration in the initial formulation altered the crystalline structure. Therefore, β-carotene loading seemingly was limited by solubility.
4.3.5 Comparison of a nutraceutical product to β-carotene loaded stearic acid nanoparticles
To emphasize the difficulty encountered in lipophile delivery, Figure 4.20 shows lycopene instability in a commercially available nutraceutical product. The red rings noticeable at the top and bottom of the bottle were lycopene coming out of solution. Lycopene, or ψ-carotene, is a carotenoid analog of that β-carotene. This particular product utilized liposomes (according to the ingredients list) to solubilize, stabilize, and deliver the lycopene. The manufacturer stated the lycopene concentration as 0.002 mg/ml. Obviously, significant lycopene quantities leaked from the liposomes. Only a few weeks later in local retail outlets, the lycopene had been removed from the same product.
Figure 4.20: Lycopene instability in a commercially available nutraceutical product
100 Conditions 11 and 12 as listed in Table 4.3 represent final a β-carotene concentration of 3.9 mg/ml in the original formulation. Assuming an entrapment efficiency of 10% reduces this concentration to 0.39 mg/ml, an order of magnitude greater than the commercially available product show in Figure 4.20. Even at an order of magnitude larger concentration, stearic acid nanoparticles demonstrated superior stability than the commercially available product. Figure 4.21 shows the commercially available product on the left and β-carotene loaded stearic acid nanoparticles on the right.
Figure 4.21: Commercially available nutraceutical product (left) and lipid nanoparticle suspension (right)
4.4 Conclusions
β-Carotene was successfully incorporated into stearic acid nanoparticles using lecithin and sodium taurocholate as the principal surfactants. Cholesterol was also included with the long-term purpose being related amphotericin B delivery. Ethanol was shown to reduce lipid nanoparticle quality and to have an undesirable interaction with sodium taurocholate. Significant models predicting first mode diameter, d1
(nm), were generated.
Stearic acid nanoparticles existed in the solid state, according to differential scanning calorimetry analyses. At one month, the solid state remained less ordered than pure stearic acid, a positive result from a drug loading and release perspective. β-Carotene entrapment efficiency was shown to have a maximum of
80 % and a mean of 40 %. Entrapment efficiency decreased with increasing β-carotene concentration.
Measured β-carotene concentration in the nanoparticles did not significantly decrease in one month.
101 References
1. Sies, H., and Krinsky, N. I. (1994) Second International Conference on Antioxidant Vitamins and Beta-Carotene in Disease Prevention. Proceedings of a symposium. Berlin, Germany, October 10- 12, 1994. American Journal of Clinical Nutrition 62, 1299-1540
2. Albanes, D. (1996) Alpha tocoperol and beta carotene supplements and lung cancer incidence in the alpha tocopherol, beta-carotene cancer prevention study: effects of base line characteristics and study compliance. Journal of National Cancer Institute 88, 1560-1570
3. Nguyen, M. L. (2000) Physical and chemical stability of all-trans Lycopene and other Tomato Carotenoids in vitro. In p. 200, The Ohio State University, Columbus, OH
4. Rosales, G. R. (2002) Carotenoid and fruit development effects on germination and vigor of tomato (Lycopersicon esculentum Mill.) seeds. In Horticulture and Crop Science p. 135, The Ohio State University, Columbus, OH
5. Isler, O., Gutmann, H., and Solms, U., eds (1971) Carotenoids, Birkhäuser-Verlag, Stuttgart, Germany
102
CHAPTER 5
ELECTROHYDRODYNAMIC AEROSOLIZATION AS A NOVEL APPROACH FOR THE PREPARATION OF SOLID LIPID NANOPARTICLES
5.1 Introduction
In theory, solid lipid nanoparticles solubilize hydrophobic molecules, transport the solubilized molecules to the desired tissue, and then release the solubilized molecules either extracellularly or intraceullarly via a passive diffusion mechanism or metabolic degradation. Scientists have demonstrated sustained release kinetic profiles for solid lipid nanoparticles formulations, occasionally achieving sustained release over a period of weeks. Unfortunately, most solid lipid nanoparticle formulations have exhibited burst release kinetics, characterized by a concentration spike followed by an exponential decay of concentration. The undesired burst release phenomenon most often has been associated with particle instability as indicated by rapid particle growth and lipid crystallinity changes.
The limited commercial development of solid lipid nanoparticle technology indicates that more development is required to realize the technology’s theoretical potential. Solid lipid nanoparticle research has been plagued by an inability to produce particles of desired sizes, a lack of particle stability over time, polydisperse distributions, limited drug loading, burst release kinetics, and the lack of an economically viable production process. This research aimed to address these shortcomings by simultaneously investigating the chemical formulation and a novel production process based on electrohydrodynamic aerosolization.
Most researchers have approached solid lipid nanoparticle synthesis as a two-step process: 1) creation of a precursor oil-in-water emulsion and 2) solidification of dispersed lipid phase. As a result, traditional emulsion techniques and processing have received much attention. Significant challenges
103 confront this approach. First, most emulsions produce micron size dispersed phases, far above the desired submicron region. Second, to reduce the dispersed phase size, the precursor emulsions are subjected to large mechanical forces such as high shear homogenization (HSH), high pressure homogenization (HPH), and ultrasonic energy. The high energy input increases operating expenses and can inhibit the activity of environmentally sensitive biological molecules. Third, the solidification process creates thermodynamically challenging transitions within the complicated multicomponent phase diagrams.
Fourth, formulation and process parameters are highly dependent on the chemical nature and composition of the drug or molecule to be delivered. This lack of robustness increases development costs and time to market, both negatively impacting the economic vitality of the technology. New production process and formulations are required.
In a novel approach for lipid nanoparticle preparation, electrohydrodynamic aerosolization
(EHDA) technology was developed to generate lipid nanoparticles. When operated in the so-called cone- jet mode, EHDA is known to produce monodisperse aerosol distributions. EHDA provides several formulation parameters and process parameters by which aerosol size can be controlled. In summary,
EHDA utilizes electric charge to aerosolize liquids by overcoming the liquid’s surface tension. The liquid to be aerosolized is delivered to a nozzle, often a stainless steel capillary, maintained at high electrical potential. As the fluid passes through the nozzle, the electric field induces free charge at the liquid’s surface. The free charge on the surface generates electric stress that causes the liquid to accelerate away from the nozzle, thereby producing a so-called Taylor cone and electric current at the liquid’s surface. At the cone apex where the free charge is highly concentrated, a liquid jet with high charge density is formed.
At appropriate conditions, the jet will disintegrate into highly charged aerosol droplets.[1]
104
Figure 5.1: Force balance in EHDA flow [1]
Despite the expansive research into electrostatically generated aerosols, limited literature exists on the subject of nanoparticle formation via EHDA. In 1999, DuDout et al reported making calcium chloride nanoparticles from a solution of 1% calcium chloride in ethanol.[2] In 2001, Loscertales et al reported the production of olive oil and water nanoparticles by EHDA.[3] EHDA has been applied to pulmonary drug delivery by Battelle Memorial Institute. Ventaira, a successful start-up company founded by Battelle
Memorial Institute, has been awarded numerous patents for the use of EHDA for handheld pulmonary delivery devices and is currently in commercialization partnerships with Abbott Laboratories,
GlaxoSmithKline, Pfizer, Pharmacia, and other pharmaceutical companies.
Three steps define EHDA: 1) acceleration of the liquid in the liquid cone and subsequent jet formation; 2) the jet disintegration into aerosol droplets; 3) droplet evolution after formation. Beginning with Zeleny in 1914, several attempts have been made to analytically model these three processes.[4] The liquid acceleration and cone formation process is governed by a balance of surface tension, gravity, surface electric stress, inertial stress, and viscous stress. [5] Most recently, Yan et al. proposed a two-dimensional axisymmetric model of the flow and electric fields produced both inside and outside the liquid during electrostatically driven aerosolization.[6]
If the electrical relaxation time, te = βε0/K, is much less than the hydrodynamic time, th ~ L/U, it can be assumed that the liquid bulk is neutral and the free charges are isolated at the liquid surface. The parameters in these equations are liquid permittivity (β), the permittivity of a vacuum (ε0), the liquid
105 conductivity (K), the characteristic length (L), and the characteristic velocity (U). For a Newtonian fluid, the continuity equation in cylindrical coordinates reduces to:
∂ ∂ (rv) + (ru) = 0 . ∂r ∂z
Where v and u denote the axial and radial velocity components. The radial and axial momentum equations reduce to:
∂v ∂v 1 ∂p ⎛ ∂ 2v 1 ∂v v ∂ 2v ⎞ v + u = − +η⎜ + − + ⎟ ∂ ∂ ρ ∂ ⎜ 2 ∂ 2 2 ⎟ r z r ⎝ ∂r r r r ∂z ⎠ ∂u ∂v 1 ∂p ⎛ ∂ 2u 1 ∂u ∂ 2u ⎞ v + u = − +η⎜ + − ⎟ − g . ∂ ∂ ρ ∂ ⎜ 2 ∂ 2 ⎟ r z z ⎝ ∂r r r ∂z ⎠
Where p , g , ρ , and η are the pressure, gravitational constant, density, and kinematic viscosity, respectively. Gauss’ equation reduces to:
1 ∂ ⎛ ∂ϕ ⎞ ∂ ⎛ ∂ϕ ⎞ ⎜r ⎟ + ⎜ ⎟ = 0 . r ∂r ⎝ ∂r ⎠ ∂z ⎝ ∂z ⎠
Where ϕ denotes the electrical potential.[6]
Using a numerical iteration scheme, Yan et al., Hartman et al., and Gañán-Calvo et al. have independently achieved reasonable agreement between cone-jet dimensions and electrical current determined via numerical simulation results and observed experimental results.[1, 5, 7] The researchers assumed the radial momentum transfer to be insignificant compared to the axial momentum transfer. The jet was considered to have a flat velocity profile and to have negligible viscous drag forces. All simulations and experiments were conducted at room temperature and atmospheric pressure. Thermal and evaporative effects were neglected, as were nozzle exit effects and possible electric discharges into the atmosphere.
Pure component systems were studied, with only minimal amounts of simple electrolytes added to adjust liquid conductivity properties. These simplifications, necessary to perform the tedious numerical simulations, do not capture the complexity of the multicomponent, higher melting point systems of interest in this proposal. Nonetheless, useful EHDA scaling laws have been proposed resulting from these analytical efforts. 106 Fernández de la Mora first derived the EHDA scaling laws in 1994, useful in the design of EHDA systems.[8] First, Gañán-Calvo in 1997 and then Hartman in 1999 further refined the EHDA scaling laws for electric current generated in the cone-jet and droplet diameter.[6, 7] Derived from the equations listed above and the simplifying assumptions, Hartman proposed the following scaling law for liquid cones and jets with flat radial profiles of axial velocity:
I ~ ()γKQ 0.5
Where I , γ , K , and Q denote the electric current, the surface tension, and volumetric liquid flow rate, respectively. Hartman proposed the following scaling law for droplet diameter: [1]
1 1 ⎛ ρε Q 4 ⎞ 6 ⎛ ρε Q3 ⎞ 6 d ~ ⎜ 0 ⎟ ~ ⎜ 0 ⎟ d ⎜ 2 ⎟ ⎜ γ ⎟ ⎝ I ⎠ ⎝ K ⎠
The scaling laws provide guidance in formulating and operating an EHDA process; however, the literature dedicated to EHDA applications suggests that these scaling laws alone are insufficient for designing an EHDA process. The literature suggests several additional parameters must be considered including: liquid permittivity, capillary material and dimensions, magnitude of electric potential, electrode configuration and charge polarity, and ambient gas properties, temperature, pressure, and flow field conditions.[9-12] Using the scaling laws for guidance, a systematic experimental approach is necessary to apply EHDA technology.
Since solid nanoparticles, not liquid droplets, are the desired final product, additional downstream processing must be performed to effectively achieve particle solidification. The aerosolization must be performed at a temperature above the lipid formulation’s melting point, and the solidification can be accomplished by evaporative drying, passing the particles through a cold temperature region, or exhausting the liquid droplets into a cold solution where they can also be collected. As in spray drying operations,
107 solid lipid nanoparticles can be collected by known recovery techniques such as cyclone separators, membrane filtration, fluid impaction, and electrostatic precipitators.
An additional and important feature of EHDA is the ability to generate composite particles. By creating concentric flows of unique materials, composite particles can be aerosolized and solidified. The option to create composite particles offers increased drug loading and release design options. Loscertales et al. demonstrated an ethylene glycol - Somos® 6120 (epoxy-based resin for stereolithography) concentric flow configuration (Figure 5.2) and an olive oil – water concentric flow configuration.[3] Extending composite constructions to lipid materials should be straightforward.
Core Fluid
Shell Fluid
Figure 5.2: Concentric EHDA flow (adopted from [3])
This research aims to develop a new solid lipid nanoparticle production process that cost efficiently provides robust particle size control, stability, high lipophile loading, and lipophile targeting capabilities. The first objective was to evaluate electrohydrodynamic aerosolization (EHDA) as a novel solid lipid nanoparticle production process. The second research objective was to characterize the loading and release kinetics of model lipophiles produced via EHDA. Ultimately, if successful, this research will generate a novel production process that enables the full potential of solid lipid nanoparticle technology to be realized.
108 5.2 Materials & Methods
5.2.1 Materials
Stearic acid, L-α-phosphatidylcholine (lecithin), sodium taurocholate, potassium chloride (KCl), and sodium dodecyl sulfate (SDS) were purchased from Sigma-Aldrich. Pluronic F68 was obtained from
BASF. Oleic acid was purchased from Fisher. Crisco® vegetable oil was obtained from a local retail outlet. Double distilled water was obtained locally from a Nanopure water purification system.
5.2.2 Particle Size Analysis
Photon correlation spectroscopy (PCS), also known as dynamic light scattering (DLS), was employed to measure particle size distributions and zeta potentials. Specifically, a Brookhaven Instruments
90Plus system was used.
5.2.3 EHDA Image Capture
A Redlake high speed camera was used to capture images of the cone-jet structures and resulting sprays. A frame rate of 60 frames per second was used. Data were stored on a customized desktop computer system with enhanced memory capacity.
5.3 Results and Discussion
5.3.1 Design and Construction of EHDA System
Without any existing designs to work from, several EHDA process design iterations were anticipated at the outset. The initial thinking was to charge a stainless steel capillary by an external power source to create an electric field at the nozzle outlet. A syringe pump was to supply the lipid solution to the stainless steel capillary. The lipids were to be melted using a controlled temperature water bath. The lipids were to be pre-mixed by either rotor-stator homogenization or simple impeller rotation. Flowing an inert gas such as air, carbon dioxide, or nitrogen concurrently around the stainless steel capillary possibly would help prevent aerosol build-up just downstream of the jet break up. The droplets rapidly decelerate after
109 formation often causing disruption of the steady cone-jet operation. It was thought that a sheath gas flow may prevent this issue by carrying the droplets farther downstream by convection. The solidification process was least defined, but it was thought solidification could be accomplished a number of techniques such as reducing the temperature downstream of the aerosol formation, flashing volatile components like water and ethanol as a form of evaporative cooling, and discharging into a cold solution to create a suspension. Figures 5.3 and 5.4 show a process flow diagram proposed at outset of design activities and a schematic representation of the process flow diagram, respectively.
110 Lipids
Aerosol adjusting materials Drugs
Melt
Mix
Capillary Sheath gas
- Voltage to aerosolize
Solidify
+ Voltage to neutralize
Filtration Solid lipid nanoparticles
Exhaust gas
Figure 5.3: EHDA process flow diagram proposed at outset of design activities
111 Inert gas source
(e.g. air, CO2, etc.) Syringe pump to to form stabilizing accurately deliver sheath flow slow, viscous flows
Preferably nonconductive capillary ranging in diameters from microns to millimeters and lengths in the millimeters
Lipids, amphiphiles, “aerosol adjusting” materials heated to > 1 External voltage source 70 °C & mixed Material capable of capable of up to 100 kV conducting charge (to of negative potential generate electric field across nozzle outlet) Taylor cone
Jet 1. Aerosolization module 2. Neutralization module 3. Separation module Aerosol cloud
Electrode at positive potential relative to nozzle outlet; necessary to neutralize particles 2
Filter to remove solid Solid lipid nanoparticle product particles 3 Exhaust gas
Figure 5.4: Schematic of 1st generation EHDA process
With the assistance of professional engineering staff, the ideas presented in Figures 5.3 and 5.4 were transformed into CAD drawings (Figure 5.5) and then real, functioning systems (Figure 5.6). In the
1st generation device, a stainless steel needle, 0.5 mm ID, served as the negative electrode and as the inlet cylinder introducing the lipid solution to the aerosolization chamber. A wire carrying negative charge from a power supply was attached to the needle. The maximum achievable potential was – 6 kV. Upstream, the lipids were melted using heating tape or a jacketed tube kept at elevated temperatures. The lipophile to be delivered was mixed in the melted lipid, and then the lipid solution was delivered to the needle using a syringe pump. The ground and positive electrodes were attached to same power supply noted above, and the maximum positive potential was + 6 kV. The exhaust consisted of a flexible tube attached to the outlet port that could be directed to any desired location. The housing was comprised of available cast acrylic.
112 Lipid solution inlet
Sheath fluid inlet port
Negatively charged stainless steel needle
4.5 cm
Stainless steel ground electrode
Front 45°
11.7 cm
2.59 cm ID Positively charged stainless steel frit supporting filter
Exhaust
Figure 5.5: Front and 45° views of CAD depiction of 1st generation EHDA device
113
Figure 5.6: Photo of 1st generation EHDA device
114 5.3.2 EHDA System Start-up & Aerosolization of Water Solutions
Once the device had been constructed, demonstrating an ability to aerosolize a fluid was a necessary first step. Water was selected as the model fluid because of its low cost, low viscosity, and high salt solubility. With the long-term objective of solid lipid nanoparticle production, several factors were identified for the EHDA system based on the governing equations detailed previously. Not all of the factors listed in Table 5.1 are independent from one another. Obviously, material chemistry and concentrations impact phenomenological properties such as interfacial tension, viscosity, conductivity, etc.
The factors are evenly composed of material and process properties, giving tremendous control levers to a designer. The large number of factors is well suited to experimental design methodologies.
EHDA Factors Lipid chemistry Membrane lipid chemistry Aerosol adjusting material chemistry Material concentrations Interfacial tension Liquid conductivity Relative permittivity Viscosity Temperature Pressure Stainless steel needle inside diameter Distance between needle nozzle and ground electrode Negative voltage on needle Positive voltage on counter electrode Lipid solution volumetric flow rate Sheath fluid chemistry Sheath fluid flow rate Pre-mixing conditions Solidification conditions
Table 5.1: EHDA system factors
To prove the EHDA system’s ability to aerosolize water, a screening experimental design was employed with the following factors: SDS concentration, KCl concentration, water solution volumetric flow rate, and positive electrode voltage. The dependent variables were the negative voltage at the onset of a stable cone-jet and the negative voltage (lower cone-jet stability) at the onset of cone-jet instability (upper cone-jet stability). SDS was included to assess the impact of interfacial tension on cone-jet stability. SDS 115 was varied between 0 mM – 8 mM (critical micelle concentration of SDS in water at 25 °C). KCl was included to assess the impact of conductivity on cone-jet stability and was varied from 0 M – 1 M. The volumetric flow rate was varied between 0.01 ml/min – 1 ml/min. The positive potential on the counter electrode was varied between 0 kV – 6 kV (corresponding to a dial setting range of 8). A carbon dioxide environment was established by purging the system prior to start-up.
Table 5.2 shows the experimental design and corresponding results obtained. Stable cone-jets were obtained for each experimental condition. The minimum and maximum negative voltages producing stable cone-jets were -1.4 kV and -6.2 kV, respectively. The minimum voltage (-1.4 kV) occurred at the minimum flow rate, maximum SDS concentration, and intermediate KCl concentration. The maximum voltage occurred at the maximum flow rate in the absence of SDS and KCl.
Run Pattern Block [SDS] [KCL] Flow rate Positive Lower cone-jet Upper cone-jet (mM) (mM) (ml/min) voltage stability stability (dial) (-kV) (-kV) 1 --+- 1 0 0 1 0 3.9 6.2 2 +++- 1 8 0.01 1 0 2.1 2.7 3 +-++ 1 8 0 1 8 1.6 2.5 4 +--- 1 8 0 0.01 0 1.6 1.9 5 ---+ 1 0 0 0.01 8 2.0 2.4 6 -+-- 1 0 0.01 0.01 0 2.2 2.6 7 ++-+ 1 8 0.01 0.01 8 1.7 2.5 8 -+++ 1 0 0.01 1 8 1.9 2.2 9 --++ 2 0 0 1 8 2.1 2.9 10 +-+- 2 8 0 1 0 1.8 2.2 11 ++++ 2 8 0.01 1 8 1.6 2.2 12 ++-- 2 8 0.01 0.01 0 1.4 1.8 13 ---- 2 0 0 0.01 0 2.0 2.3 14 +--+ 2 8 0 0.01 8 1.7 2.0 15 -++- 2 0 0.01 1 0 2.0 2.7 16 -+-+ 2 0 1 0.01 8 2.1 2.6
Table 5.2: Experimental conditions and results for water EHDA screening design
2 2 A significant model of voltage at lower cone-jet stability was obtained. Model R and R adj values were 0.67 and 0.54, respectively, and the model P-value was 0.0162 (Table 5.3). Only SDS was significant at a 95% confidence level. SDS alone did not produce a significant model, so flow rate, positive voltage, and the interaction between flow rate and positive voltage were included in the model to produce a 116 significant model. Consistent with the scaling laws, SDS presence depressed the onset of a stable cone-jet, presumably due to a reduction in interfacial tension. Again consistent with the scaling laws, the minimum voltage required for a stable cone-jet also increased as flow rate increased. The minimum voltage required for a stable cone-jet decreased as positive voltage increased. The rationale for this observation is that the positive voltage induces transport of the negatively charged aerosol droplets away from the nozzle outlet and toward the positive field downstream. Aerosol droplet deceleration and accumulation near the nozzle is a known complication of EHDA operations. A significant (at 90% confidence) interaction existed between flow rate and positive voltage, likely the result of increased negatively charged aerosol cloud density at higher flow rates. Positive charge downstream becomes more important as flow rate increases.
This reality can be more easily viewed in the interaction plot (Figure 5.7).
Term Estimate SE Estimate t-ratio P-value Intercept 1.9 0.065 -29.3 < 0.0001 [SDS] -0.19 0.044 -4.2 0.0017 Flow rate 0.028 0.044 0.65 0.5302 Positive voltage -0.0068 0.011 -0.62 0.5499 Flow rate * Positive voltage -0.021 0.011 -1.9 0.0851
Table 5.3: Lower cone-jet stability (-kV) model for water
2.4 Flow Rate (ml/min) Rate Flow 2.2
2.0 Flow Rate 0.01 1.8 1 (ml/min)
Lower Cone 1.6 Jet Bound (kV) 1.4 2.4
Voltage Positive 2.2 0 2.0 Positive 1.8 8 Voltage
Lower Cone 1.6 Jet Bound (kV) 1.4
.2 .4 .6 .8 1 08
Figure 5.7: Interaction plot demonstrating the relationship between positive voltage and flow rate 117 2 2 A significant model of voltage at upper cone-jet stability was obtained. Model R and R adj values were 0.44 and 0.34, respectively, and the model P-value was 0.0314. SDS concentration was once again the only factor significant at 95% confidence (P-value = 0.0295, estimate = -0.16). Flow rate was significant at 90% confidence (P-value = 0.0713, estimate = 0.13).
Figures 5.8 and 5.9 show the same water solution (containing KCl and SDS) flowing at rate of 1 ml/min with 0 voltage and approximately – 3 kV applied to the needle, respectively. Figure 5.8 demonstrates a dripping mode balancing interfacial tension and gravity forces. Figure 5.9 clearly shows a stable cone-jet transport mode. The disintegration of the jet is also apparent in the image. The jet diameter is large relative to the cone. This can be attributed to the relatively large flow rate, elevated interfacial tension, and low applied voltage. Figures 5.8 and 5.9 clearly demonstrate the EHDA device’s ability to create stable cone-jets and subsequent aerosols in water solutions.
Figure 5.8: Water with no applied voltage Figure 5.9: Formation of a stable cone-jet mode
118 5.3.3 Aerosolization of Vegetable Oil Solutions
With ultimate objective being aerosolization of lipid solutions, vegetable oil was chosen as intermediate material to investigate prior to investigating lipids of interest in solid lipid nanoparticles.
Vegetable oil was chosen for its low cost, relatively low viscosity at room temperature, liquid state at room temperature, and chemical similarity to fatty acids or interest later.
A screening experimental design with vegetable oil as the primary fluid was employed with the following factors: SDS concentration, KCl concentration, vegetable oil solution volumetric flow rate, and positive electrode voltage. The dependent variables were the negative voltage at the onset of a stable cone- jet and the negative voltage (lower cone-jet stability) at the onset of cone-jet instability (upper cone-jet stability). SDS was included to assess the impact of interfacial tension on cone-jet stability. SDS was varied between 0 mM – 8 mM. KCl was included to assess the impact of conductivity on cone-jet stability and was varied from 0 M – 0.01 M. The volumetric flow rate was varied between 0.01 ml/min – 0.5 ml/min. The positive potential on the counter electrode was varied between 0 kV – 6 kV (corresponding to a dial setting range of 8). A carbon dioxide environment was established by purging the system prior to start-up.
Table 5.4 shows the experimental design and corresponding results obtained. Stable cone-jets were obtained for each experimental condition. The minimum and maximum negative voltages producing stable cone-jets were -1.6 kV and -5.7 kV, respectively.
119 Run Pattern Block Flow rate [SDS] [KCL] Positive Lower cone-jet Upper cone-jet (ml/min) (mM) (mM) voltage stability stability (kV) (-kV) (-kV) 1 +--- 1 0.5 0 0 0 1.9 2.7 2 ++-+ 1 0.5 8 0 6.3 1.6 2.9 3 --+- 1 0.01 0 0.01 0 1.8 3.1 4 +-++ 1 0.5 0 0.01 6.3 1.8 3.1 5 +++- 1 0.5 8 0.01 0 2.0 3.6 6 -+++ 1 0.01 8 0.01 6.3 2.3 3.5 7 -+-- 1 0.01 8 0 0 1.6 3.1 8 ---+ 1 0.01 0 0 6.3 2.3 3.6 9 ++-- 2 0.5 8 0 0 2.7 3.6 10 -++- 2 0.01 8 0.01 0 1.8 2.7 11 -+-+ 2 0.01 8 0 6.3 2.0 2.6 12 ++++ 2 0.5 8 0.01 6.3 2.2 3.2 13 +-+- 2 0.5 0 0.01 0 4.4 5.7 14 --++ 2 0.01 0 0.01 6.3 2.7 3.6 15 +--+ 2 0.5 0 0 6.3 4.1 5.3 16 ---- 2 0.01 0 0 0 2.2 3.5
Table 5.4: Experimental conditions and results for vegetable oil EHDA screening design
2 2 A significant model of voltage at lower cone-jet stability was obtained. Model R and R adj values were 0.92 and 0.80, respectively, and the model P-value was 0.0210 (Table 5.5). Inconsistent with the scaling laws, SDS presence increased the voltage necessary to produce a stable cone-jet. This is most likely associated with the poor solubility of SDS in the oil, as evidenced by the precipitation of SDS from the oil during the experiments. SDS was a poor choice of surfactant. A more oil soluble surfactant should be used in the future, such as Pluronic F-68. Again inconsistent with the scaling laws, the minimum voltage required for stable cone-jet formation decreased as flow rate increased. Again inconsistent with the scaling laws, minimum voltage required for a stable cone-jet increased with increasing KCl concentrations.
The minimum voltage required for a stable cone-jet decreased as positive voltage increased. The rationale for this observation is that the positive voltage induces transport of the negatively charged aerosol droplets away from the nozzle outlet and toward the positive field downstream. Aerosol droplet deceleration and accumulation near the nozzle is a known complication of EHDA operations.
120 Term Estimate SE Estimate t-ratio P-value Intercept 2.1 0.104 20.42 <.0001 Flow Rate -0.11 0.097 -1.15 0.3038 [SDS] 0.16 0.079 1.99 0.1037 [KCl] 0.045 0.043 1.03 0.3486 Positive Voltage -0.05 0.021 -2.41 0.0608 Flow Rate*[SDS] 0.33 0.097 3.36 0.0201 Flow Rate*Positive Voltage -0.12 0.027 -4.51 0.0063 [KCl]*Positive Voltage 0.045 0.015 2.95 0.0319 Block -0.11 0.048 -2.39 0.0627
Table 5.5: Lower cone-jet stability (-kV) model for water
Significant interactions existed between flow rate and SDS concentration, flow rate and positive voltage, and KCl concentration and positive voltage. Only the flow rate * positive voltage interaction contributed a reduction in the minimum voltage required for stable cone-jet formation (Figure 5.10).
Interestingly, the [KCl]*positive voltage interaction contributed to an increase in onset voltage. One possible explanation is that the presence of potassium ion reduces the net pull of the positive field created by the counter electrode. A block accounting for experimentation performed on two days was significant in the model (at a 90% confidence interval).
121 2.75 Flow Rate (ml/min) 2.50 1 0.01 2.25 Flow Rate 0.01 2.00 (ml/min) 0.01 1 1.75 Bound (kV) Lower Cone Jet 1.50 1 2.75 SDS conc (mM) SDS conc 2.50 8 2.25 8 SDS 2.00 8 conc (mM) 0 1.75 0 Bound (kV) Lower Cone Jet 1.50 0 2.75 2.50 (M) conc KCL 2.25 0.01 0 KCL 0.01 2.00 0.01 0 conc (M) 1.75 0 Bound (kV) Lower Cone Jet 1.50 2.75 Positive Voltage 2.50 0 2.25 0 Positive 2.00 6.3 6.30 Voltage 1.75 Bound (kV) Lower Cone Jet 1.50 6.3
.1 .3 .5 .7 .9 1.1 0 2 4 6 8 0 .0025 .0075 0 1 2 3 4 5 6 7
5.10: Interaction plot demonstrating the relationship between positive voltage and flow rate for oil system
Figures 5.11 and 5.12 show the same oil solution (containing KCl and SDS) flowing at rate of 0.5 ml/min with 0 voltage and approximately – 3 kV applied to the needle, respectively. Figure 5.11 demonstrates a dripping mode balancing interfacial tension and gravity forces. Figure 5.12 clearly shows a stable cone-jet transport mode. The disintegration of the jet is also apparent in the image, but the aerosolization is less complete than the comparable image for the water solution in Figure 5.9. The jet diameter is large relative to the cone. This can be attributed to the relatively large flow rate, elevated interfacial tension, and low applied voltage. Interestingly, the oil system exhibited a wider range over which the cone-jet was stable than did the water system. This observation should be thoroughly investigated in the future. Figures 5.11 and 5.12 clearly demonstrate the EHDA device’s ability to create stable cone-jets and subsequent aerosols in vegetable oil solutions.
122
Figure 5.11: Oil with no applied voltage Figure 5.12: Formation of a stable oil cone-jet
5.3.4 Aerosolization of Oleic Acid Solutions and Demonstration of Nanoparticle Formation
After successfully demonstrating the aerosolization of vegetable oil, the next activity was to demonstrate lipid nanoparticle formation. Oleic acid (cis-9-octadecanoic acid) was chosen as the lipid to investigate (Figure 5.13) because oleic acid’s freezing temperature is ~ 13 °C. Lacking sufficient temperature control on the 1st generation EHDA device, operating at room temperature was essential.
Solidification was accomplished by bubbling the aerosol exhaust through near 0 °C water. Pluronic F-68, a polyoxyethylene-polyoxypropylene block copolymer with a molecular weight of 8400, was chosen as the surfactant for its considerable solubility in oils (Figure 5.14).
Figure 5.13: Oleic acid structure Figure 5.14: Pluronic F-68 structure
123 0.04 mM Pluronic F-68 and 0.01 mM KCl were added to oleic acid. The oleic acid solution was pumped through the needle at 0.5 ml/min. To ensure transport into the water, carbon dioxide was pumped through the EHDA device at ~ 100 ml/min. The needle was charged to – 4 kV. The positive electrode was disconnected (i.e. 0 potential) due to shock concerns given the close proximity of water. The aerosolization was conducted for approximately 5 minutes at room temperature.
After 5 minutes run time, the water remained clear. The temperature was maintained near 0 °C prior to PCS analysis. Three samples were analyzed by PCS for particle size distribution. The analyses showed first mode diameters, d1, of 82 nm, 210 nm, and 180 nm, respectively. Bubble formation during the analyses prevented quality estimates of effective diameter and polydispersity. The particle size distribution can be assessed by the representative histogram presented in Figure 5.15. Both distributions, number and volume, represent the third sample (d1 = 180 nm). As expected the volume distribution is larger than the number distribution. These histograms provide strong evidence of solid lipid nanoparticle production. Demonstrating the ability to produce solid lipid nanoparticles with the 1st generation EHDA device was a tremendous success. Yet, many improvements can and must be made to the EHDA system.
Significant research remains to be undertaken.
124 Volume distribution Number distribution
180 208
101 117 135 156 180 208 240 277 319 369 425 491 567 d (nm)
Figure 5.15: Number and volume distributions of oleic acid nanoparticles formed by EHDA
5.3.5 Ethanol Attack on Acrylic Housing
After successfully demonstrating oleic acid nanoparticle production, the next logical step was to prepare nanoparticles using stearic acid. Using heating tapes and jacketed columns, adequate temperature control was provided to maintain stearic acid as a liquid through the needle. To reduce the viscosity and provide sufficient solubility for lecithin, sodium taurocholate, and KCl, ethanol had to be added to the stearic acid melt as an aerosol adjusting material.
Unfortunately, the ethanol attacked the acrylic material comprising the EHDA cylinder and flanges. Within seconds of flowing the ethanol solution through needle, stress cracks appeared. After a few minutes, the column broke in two pieces. In consulting with the acrylic supplier, ethanol was confirmed as the causative agent. Figures 5.16 and 5.17 show the structural failure experienced by the
125 EHDA device after exposure to ethanol. Alternative materials like polycarbonates and, if necessary, ceramics must be investigated to find a suitable material for building future EHDA devices.
Figure 5.16: EHDA structural failure Figure 5.17: Close-up of EHDA structural failure
5.3.6 Electrical Potential Modeling and Implications for Future Designs
The electrical potential, ϕ, distribution generated by the 1st generation EHDA device was modeled.
The objective of the modeling exercise was to assess the 1st generation EHDA design. More specifically, geometric effects and the effects of the electrical potential differences (voltage) applied to the electrodes were investigated.
The EHDA device was modeled in two dimensions, assuming radial symmetry. The two dimensional simplification allowed the use of Cartesian coordinates, x and y versus the cylindrical equivalents, r and z. Therefore, the general electrical potential equation was reduced to
126 ∂ 2ϕ ∂ 2ϕ ∇ 2ϕ = + . ∂x 2 ∂y 2
Using a finite element approach, the previous equation was discretized and calculations were performed in Matlab according to an iterative algorithm. Input parameters included the negative voltage applied to the needle, the positive voltage applied to the positive (counter) electrode, and the distance from the needle nozzle and the ground electrode. All dimensions aside from the distance between the nozzle and the ground electrode were fixed at the original design conditions. The needle was modeled as a point charge based on the assumption that all charge would collect at the terminal point of the conductor. In two dimensions, the ground and positive electrodes were modeled as symmetrical rectangles of each electrical potential. Once the electrical potential was calculated, the resulting electrical field, E, was determined using
E = −∇ϕ .
Figure 5.18 shows the electrical potential (V) distribution with -6 kV and 1 kV applied to the needle and positive electrode, respectively. The model shows the needle as a point charge with a maximum of -6 kV and rapidly decaying in the spherical direction. The positive electrical potential emanating from the positive electrode is shown downstream. The model confirms the electrical potential distributions intended in the 1st generation EHDA device design. The negative potential at the needle outlet is required to aerosolize the fluid flowing through the needle. The positive electrode (positive potential) serves two purposes: 1) inducing negatively charged aerosol droplets to flow down stream and 2) neutralizing negatively charged aerosol droplets for handling purposes and possible administration.
127 Needle nozzle
Outer wall of EHDA housing
Positive electrode
Figure 5.18: Electrical potential (V) distribution with -6 kV and 1 kV applied to the needle and positive electrode, respectively
Figure 5.19 shows the electrical potential (V) distribution with -3 kV and 6 kV applied to the needle and positive electrode, respectively. Figure 5.19 qualitatively shows an identical distribution as
Figure 5.18, but the magnitudes of the applied voltages vary. Based on the water and oil EHDA experiments discussed previously, Figure 5.19 represents a likely scenario for achieving stable cone-jet flows. One potentially problematic observation common to both figures is the extension of the electrical potential distribution beyond the walls of the EHDA device. The EHDA outer wall, made from cast acrylic, was not included in the model because cast acrylic is a non-magnetic and non-conductive material.
The model assumed the cast acrylic to have a magnetic permeability of free space.
128 In reality, the electric field generated by the electrodes distorts the electron density of the acrylic atoms, making tiny dipoles with small opposing electric fields. As a result, some loss occurs when the electrical fields pass through the acrylic material. Reflection does not occur with a static electric field. A truer representation of the electrical potential and electrical fields would show some flattening outside of the acrylic housing. Nonetheless, the presence of the electrical potential and electrical field outside of the device presents health and safety concerns. Future EHDA devices should incorporate a grounded external housing to eliminate this risk to operators.
Needle nozzle
Outer wall of EHDA housing
Positive electrode
Figure 5.19: Electrical potential (V) distribution with -3 kV and 6 kV applied to the needle and positive electrode, respectively
Figure 5.20 shows a negative electrical field (V/cm) generated around the needle with -3 kV and 1 kV applied to the needle and positive electrode, respectively. The distance between the needle nozzle and
129 the ground electrode, z, was 2.5 cm versus the 4.5 cm of the original design. Once again, the model suggests the electrical field extends to the exterior of the EHDA device by a few centimeters. Still, this external field represents a health and safety risk. The effect of reducing the distance between the needle and the neutral electrode is demonstrated by the electrical field deflection and the steeper gradients near the neutral electrode. Negatively charged aerosol droplets are more likely to travel down the steeper negative electrical field gradient (i.e. toward the positive electrode). If the electrical field is symmetrical about the needle nozzle, the negatively charged aerosol particles experience an equal electrostatic driving force in all directions. The equal directional electrostatic driving force does not assist downstream movement of the particles, reinforcing the need for the positive electrode and a sheath fluid to carry the particles downstream.
Outer wall of EHDA housing
Needle nozzle
Neutral electrode
Figure 5.20: Electrical field (V/cm) generated around the needle (z = 2.5 cm) with -3 kV and 1 kV applied to the needle and positive electrode, respectively
130 Figure 5.21 shows the electrical field (V/cm) generated around the positive electrode with -3 kV and 6 kV applied to the needle and positive electrode, respectively (z = 4.5 cm). Two observations can be made from Figure 5.21. At higher electrical potential, the resulting field extends several centimeters beyond the device. Using the cylinder electrode configuration, the steepest electrical field gradients occur at the edges of the electrodes while near zero gradients exists along the device centerline. This situation would suggest that the aerosol particles should concentrate near the cylinder walls, which was routinely observed experimentally. The accumulation near the cylinder walls represents a design flaw in the 1st generation EHDA device, leading to reduced efficiency and increased particle sizes. New electrode geometries should be considered in future design generations.
Positive electrode
Outer wall of EHDA housing
Figure 5.21: Electrical field (V/cm) generated around the positive electrode with -3 kV and 6 kV applied to the needle and positive electrode, respectively
131 5.3.7 Composite Nanoparticle Modeling and Implications for Future Research
The ability to produce composite nanoparticles is an attractive feature of EHDA technology.
Specifically, a core-shell structure can provide a delayed release of drug, providing adequate time to allow the particle to travel to a desired location in the body before releasing the drug. In the core-shell structure, the drug comprises an enriched core that is surrounded by drug deficient shell (Figure 5.22). This provides more effective therapy by targeting the drug release to the intended site of action, as opposed to a systemic release that often results in side effects.
Figure 5.22: Schematic representation of an enriched core – shell particle structure ([13]
To examine the effect of particle size diameter, structure, and core to shell ratios in composite particles, nanoparticle drug release was modeled. The general transient diffusion equation without internal convection is represented by
∂C a = D ⋅∇ 2C , ∂t ab a
Where Ca and Dab represent the drug concentration inside the nanoparticle and the drug diffusion coefficient in the solid lipid nanoparticle, respectively. In cylindrical coordinates assuming drug concentration varies
132 only with radial position, the previous equation takes the following form in r represents the nanoparticle radius
∂ ∂ ∂ Ca = ⋅ −2 ⋅ ⎛ 2 ⋅ Ca ⎞ Dab r ⎜r ⎟ ∂t ∂r ⎝ ∂r ⎠
In drug delivery, sink conditions are commonly applied when a small amount of drug is introduced to a much larger volume. For this case, a common boundary condition is that diffusion at the outer particle surface equals the rate of convection carrying drug away from the surface. This condition is represented by
− ⋅∇ = ⋅( s − ∞ ) Dab Ca k Ca Ca
s ∞ Where k, Ca , and Ca represent the convective transfer coefficient, the drug concentration at the particle surface, and the drug concentration in the external bulk (i.e. 0 for sink conditions), respectively. Given the radial diffusion only assumption, the previous equation reduces to the following R equals the particle radius
∂C − D ⋅ a = k ⋅C s @ r = R ab ∂r a
The second boundary condition is derived from the spherical geometry and the associated symmetry requirement giving
∂C a = 0 @ r = 0 ∂r
The initial condition was always an initial concentration profile in the drug, a uniform concentration, or a step profile representing a core-shell structure. 133 Calculations were performed using Matlab. Figure 5.23 shows the fraction of drug released from a single particle having a uniform initial drug profile versus time. Several particle diameters are shown. A typical first order release profile is common to all curves. Clearly, the rate of release decreases with increasing particle size, and the onset of release begins at time zero. Figure 5.24 shows the fraction of drug released from a single particle having a enriched core initial drug profile versus time. The core’s radius was set to 50% of the overall particle radius, corresponding to the core occupying 1/8 of the overall particle. Several particle diameters are shown. The delayed response is obvious in Figure 5.24, demonstrating the additional design control provided by the ability to create composite particles. The overall particle diameter greatly influences the release lag time. If zero order kinetics is desirable, Figure
5.24 suggests that administering an optimum mixture of particle diameters could provide the required staging for constant, cumulative drug concentration versus time.
Figure 5.25 shows the effect of core size on the drug flux. The dashed red line (100 %) corresponds to a uniform initial distribution. Notice the spike followed by exponential decay behavior, characteristic of first order kinetics. The bottom solid red line (84%) corresponds to a core-shell composite structure in which the core radius is 84% of the overall radius (59% of the overall volume). Even when the core represents 59% of the volume, the reduction in the initial drug release spike is reduced by an order of magnitude.
Figure 5.26 shows a crude attempt at formulating a pulsatile drug release profile, increasingly of interest in cancer therapy. The pulsatile profile was obtained by mixing several particle diameters and initial drug profiles. Figure 5.26 further illustrates the design flexibility enabled by EHDA produced nanoparticles.
134
Figure 5.23: Calculated release profiles as a function of diameter for a uniform initial drug profile
135
Figure 5.24: Calculated release profiles as a function of diameter for enriched drug core profile
136
Figure 5.25: Calculated release profiles as a function of diameter for a uniform initial drug profile
137
Figure 5.26: Pulsatile delivery profile demonstrated using a mixture of 100 nm particles
5.4 Conclusions
An electrohydrodynamic aerosolization (EHDA) device was designed and constructed for making solid lipid nanoparticles. Using water, sodium dodecyl sulfate, and potassium chloride, stable cone-jets were produced at voltages applied to the stainless steel needle ranging from -1.4 kV to - 6.2 kV. Using commercial vegetable oil, sodium dodecyl sulfate, and potassium chloride, stable cone-jets were produced at voltages applied to the stainless steel needle ranging from -1.6 kV to - 5.7 kV. Significant models predicting the onset of a stable cone-jet were generated for both the water and oil systems.
Ethanol was shown to chemically degrade cast acrylic, leading to structural failure of the first generation device. Electrical modeling showed that generated electrical potential distributions and
138 electrical fields extend beyond the walls of the EHDA device. This finding raises health and safety concerns regarding the 1st generation EHDA device that must be rectified in future designs. Drug release modeling demonstrated the potential of composite (core-shell) particle designs. The drug release profiles were modeled as functions of particle size and percentage core. Using a mixture of particles, a crude depiction of a pulsatile delivery system was demonstrated.
The use of EHDA to produce lipid nanoparticles was demonstrated. Using oleic acid, Pluronic F-
68, and potassium chloride, particles possessing a number distribution median diameter of 82 nm, 180 nm, and 210 nm were produced. EHDA production of lipid nanoparticles offers many exciting advantages compared to traditional production techniques. Significant research remains to be performed in order to fully exploit the potential of EHDA technology in lipid nanoparticle systems.
5.5 Recommendations for Future Research
• Determine an ethanol resistant material suitable for building future EHDA generations;
• Investigate improved electrode configurations that concentrate and orient electrical potentials and
electrical fields such that optimum performance can be achieved;
• Implement precise temperature control systems to maintain constant material properties prior to
aerosolization and to solidify the liquid aerosol droplets;
• Investigate supercritical carbon dioxide environments and/or sheath flows to reduce interfacial tension
between the lipid solution to be aerosolized and the surrounding environment;
• Investigate alternative needle designs to determine optimum performance:
o Inside diameter
o External and internal Teflon coatings
o Annular needle systems to create composite nanoparticle structures;
• Incorporate an inline differential mobility analyzer to permit studies of aerosol evolution;
• Continue experimentation aimed at increasing fundamental understanding of EHDA technology
applied to lipid nanoparticle synthesis and at optimizing nanoparticle design empirical knowledge.
139 References
1. Hartman, R. P. A., Brunner, D. J., Camelot, D. M. A., Marijnissen, J. C. M., and Scarlett, B. (1999) Electrohydrodynamic Atomization in the Cone-jet Mode: Physical Modeling of the Liquid Cone and Jet. Journal of Aerosol Science 30, 823-849
2. Dudout, B., Marijnissen, J. C. M., and Scarlett, B. (1999) Use of EHDA for the production of nanoparticles. Journal of Aerosol Science 30, S687-S688
3. Loscertales, I. G., Cortijo-bon, R., Barrero, A., Guerrero, I., and Gañán-Calvo, A. M. (2001) A novel technique to produce multicomponent micro/nano capillary jets and micro/nano capsules by electrohydrodynamic forces. Journal of Aerosol Science 32, 611-622
4. Zeleny, J. (1915) The electrical discharge from liquid points and a hydrostatic method of measuring the electric intensity at their surface. Physical Review 3, 69-91
5. Taylor, G. I. (1964) Disintegration of water drops in an electric field. Proceedings of Royal Society A 280, 383-397
6. Yan, F., Farouk, B., and Ko, F. (2003) Numerical modeling of an electrostatically driven liquid meniscus in the cone–jet mode. Journal of Aerosol Science 34, 99-116
7. Gañán-Calvo, A. (1998) The surface charge in electrospraying: its nature and its universal scaling laws. Journal of Aerosol Science 30, 863-872
8. Mora, J. F. d. l., and Loscertales, I. G. (1994) The current transported by highly conducting Taylor cones. Journal of Fluid Mechanics 335, 165-188
9. Thurston, R., Browning, J., Shah, P., and Placke, M. (2003) Compositions for aerosolization and inhalation. In, BattellePharma, Inc., USA
10. Busick, D. R., Dvorsky, J. E., Trees, G. A., and Saunders, J. H. (2002) High mass transfer electrosprayer. In, BattellePharma, Inc, United States
11. Dvorsky, J. E., and Chongsiriwatana, S. (2001) Directionally controlled EHD aerosol sprayer. In, Battelle Pulmonary Therapeutics, Inc., United States
12. Zimlick, W. C., Dvorsky, J. E., Busick, D. R., and Peters, R. D. (2002) Pulmonary aerosol delivery device and method. In, Battelle Pulmonary Therapeutics, Inc., United States
13. Müller, R. H., Mäder, K., and Gohla, S. (2000) Solid lipid nanoparticles (SLN) for controlled drug delivery – a review of the state of the art. European Journal of Pharmaceutics and Biopharmaceutics 50, 161-177
140
APPENDIX A
SAMPLE PHOTON CORRELATION SPECTROSCOPY DATA FILE
141 **** Brookhaven Instruments Corp.**** 90Plus Particle Sizing Software Version 2.18 Sample Identification: Exp 1102.1 Run 10 (Combined) Operator Identification: Triplett Notes: 11-19-2002, produced 11/19/2002 by Triplett Measurement Date: Nov 19, 2002 Measurement Time: 14:24:05 Batch: 0
**** Measurement Parameters **** Temperature = 10.0 Suspension = Aqueous Viscosity = 1.307 cp Ref.Index Fluid = 90.000 Angle = 1.33 Wavelength = 678.0 nm Runs Completed = 3 Run Duration = 00:01:00 Total Elapsed Time = 00:03:00 Average Count Rate = 407.5 kcps Ref.Index Real = 1.000 Ref.Index Imag = 0.000
**** Measurement Results **** Exp 1102.1 Run 10 (Combined) Effective Diameter: 198.5 Polydispersity: 0.111 Sample Quality: 9.9 Elapsed Time = 00:03:00
Run Eff. Diam. (nm) Half Width (nm) Polydispersity Sample Quality ------1 95.5 29.1 0.093 7.8 2 214.4 15.2 0.005 8.2 3 368.3 26.0 0.005 5.5 ------Mean 226.1 23.4 0.034 7.2 Std.Error 78.9 4.2 0.029 0.8 Combined 198.5 66.1 0.111 9.9
**** Lognormal Size Distribution Results **** GSD: 1.383 d(nm) G(d) C(d) | d(nm) G(d) C(d) | d(nm) G(d) C(d) ------116.5 26 5 | 182.9 97 40 | 247.0 80 75 131.0 44 10 | 190.6 99 45 | 260.8 70 80 141.9 58 15 | 198.5 100 50 | 277.7 58 85 151.1 70 20 | 206.8 99 55 | 300.8 44 90 159.6 80 25 | 215.5 97 60 | 338.3 26 95 167.5 87 30 | 226.9 92 66 | 175.2 93 35 | 235.3 87 70 |
142 **** Multimodal Size Distribution Results **** Mean Diameter: 158.916 Relative Variance: 0.027 Skew: 19.584
Percent | Lower Upper ------|------By Intensity: | 0 0 By Volume: | 0 0 By Number: | 0 0
d(nm) G(d) C(d) | d(nm) G(d) C(d) | d(nm) G(d) C(d) ------95.0 0 0 | 324.8 0 100 | 1110.2 0 100 106.2 0 0 | 363.2 0 100 | 1241.4 0 100 118.8 0 0 | 406.1 0 100 | 1388.2 0 100 132.8 69 18 | 454.1 0 100 | 1552.3 0 100 148.5 100 46 | 507.8 0 100 | 1735.9 0 100 166.1 96 77 | 567.8 0 100 | 1941.1 0 100 185.7 47 93 | 635.0 0 100 | 2170.6 0 100 207.7 16 99 | 710.0 0 100 | 2427.2 0 100 232.3 1 100 | 794.0 0 100 | 2714.2 0 100 259.7 0 100 | 887.8 0 100 | 3035.1 0 100 290.4 0 100 | 992.8 0 100 | 3393.9 0 100
**** Lognormal Size Distribution Results/Spreadsheet Format **** 116.5, 26, 5 131.0, 44, 10 141.9, 58, 15 151.1, 70, 20 159.6, 80, 25 167.5, 87, 30 175.2, 93, 35 182.9, 97, 40 190.6, 99, 45 198.5,100, 50 206.8, 99, 55 215.5, 97, 60 226.9, 92, 66 235.3, 87, 70 247.0, 80, 75 260.8, 70, 80 277.7, 58, 85 300.8, 44, 90 338.3, 26, 95
**** Multimodal Size Distribution Results/Spreadsheet Format **** 95.0, 0, 0 106.2, 0, 0 118.8, 0, 0 132.8, 69, 18 148.5,100, 46 143 166.1, 96, 77 185.7, 47, 93 207.7, 16, 99 232.3, 1,100 259.7, 0,100 290.4, 0,100 324.8, 0,100 363.2, 0,100 406.1, 0,100 454.1, 0,100 507.8, 0,100 567.8, 0,100 635.0, 0,100 710.0, 0,100 794.0, 0,100 887.8, 0,100 992.8, 0,100 1110.2, 0,100 1241.4, 0,100 1388.2, 0,100 1552.3, 0,100 1735.9, 0,100 1941.1, 0,100 2170.6, 0,100 2427.2, 0,100 2714.2, 0,100 3035.1, 0,100 3393.9, 0,100
144
APPENDIX B
SAMPLE JMP JOURNAL FILE
145
Stearic Lecithin Sodium Cholesterol Beta- Block d(c) PI d(m) Pred Formula d(m) Acid Taurocholate carotene 0.820 0.035 0.048 0.062 0.035 1 88 0.005 79.6 84.2 0.646 0.049 0.068 0.089 0.148 1 163.6 0.005 101 95.7 0.655 0.100 0.069 0.126 0.050 1 230.6 0.005 98.8 105.6 0.688 0.052 0.000 0.207 0.052 1 108.3 0.005 152.7 123.2 0.831 0.070 0.000 0.063 0.035 1 113.2 0.005 88 146.0 0.646 0.099 0.068 0.089 0.099 1 153 0.005 97.1 118.3 0.664 0.051 0.070 0.164 0.051 1 222.1 0.005 96.2 69.4 0.678 0.103 0.000 0.167 0.052 1 180.6 0.005 142.4 159.9 0.659 0.100 0.000 0.090 0.151 1 70.2 0.044 234.2 184.2 0.884 0.031 0.000 0.055 0.031 1 153.7 0.152 93.2 120.4 0.748 0.081 0.057 0.073 0.041 1 178.8 0.005 127 107.6 0.659 0.050 0.000 0.090 0.201 1 100.4 0.005 182.3 161.2 0.600 0.250 0.100 0.025 0.025 2 103.7 0.005 . 183.2 0.650 0.025 0.100 0.025 0.200 2 21.1 0.167 11.4 70.9 0.900 0.025 0.000 0.050 0.025 2 140.2 0.034 89.5 116.2 0.525 0.250 0.000 0.200 0.025 2 268.9 0.188 220.3 246.1 0.525 0.250 0.000 0.025 0.200 2 336.3 0.275 318 296.3 0.650 0.025 0.100 0.200 0.025 2 74.1 0.005 68.1 20.7 0.575 0.025 0.000 0.200 0.200 2 127.5 0.005 108.4 133.8 0.900 0.025 0.000 0.025 0.050 2 228.7 0.005 180.8 123.4
Table B.1: Customized mixture experimental design with corresponding data obtained by experiment
350 300
250 200
150 d(m) Actual 100 50
0 0 100 200 300 400 500 d(m) Predicted P<.0001 RSq=0.96 RMSE=19.899
Figure B.1: Actual by predicted plot for model generated from analysis of experimental design
146
RSquare 0.956018 RSquare Adj 0.920833 Root Mean Square Error 19.89927 Mean of Response 131 Observations (or Sum Wgts) 19
Table B.2: Summary of fit
Source DF Sum of Squares Mean Square F Ratio Model 8 86072.970 10759.1 27.1708 Error 10 3959.810 396.0 Prob > F C. Total 18 90032.780 <.0001
Tested against reduced model: Y=mean
Table B.3: Analysis of variance (ANOVA) for obtained model
Term Estimate Std Error t Prob>|t| LSV.05 LSN.05 AdjPower.05 Ratio Intercept (zeroed) 0 0 . . 0 . . Stearic Acid(Mixture) -37.44924 32.53612 -1.15 0.2765 72.49499 57.919 0.0557 Lecithin(Mixture) 6602.4632 1082.673 6.10 0.0001 2412.346 10.866 0.9977 Sodium -585.6119 160.59 -3.65 0.0045 357.8169 12.466 0.7986 Taurocholate(Mixture) Cholesterol(Mixture) 669.4187 160.5805 4.17 0.0019 357.7957 11.835 0.8982 Beta-carotene(Mixture) -9248.255 1675.811 -5.52 0.0003 3733.939 11.048 0.9912 Stearic Acid*Lecithin -7084.735 1489.581 -4.76 0.0008 3318.993 11.399 0.9602 Stearic Acid*Beta-carotene 14267.803 2481.785 5.75 0.0002 5529.762 10.969 0.9947 Lecithin*Cholesterol -11879.89 1861.19 -6.38 <.0001 4146.99 10.794 0.9989 Cholesterol*Beta-carotene 4916.8866 1833.298 2.68 0.0230 4084.841 15.374 0.5040
Table B.4: Parameter estimates for obtained model
147
Source Nparm DF Sum of Squares F Ratio Prob > F Stearic Acid(Mixture) 1 1 524.601 1.3248 0.2765 Lecithin(Mixture) 1 1 14726.228 37.1892 0.0001 Sodium Taurocholate(Mixture) 1 1 5265.711 13.2979 0.0045 Cholesterol(Mixture) 1 1 6881.517 17.3784 0.0019 Beta-carotene(Mixture) 1 1 12059.908 30.4558 0.0003 Stearic Acid*Lecithin 1 1 8957.635 22.6214 0.0008 Stearic Acid*Beta-carotene 1 1 13087.605 33.0511 0.0002 Lecithin*Cholesterol 1 1 16133.111 40.7421 <.0001 Cholesterol*Beta-carotene 1 1 2848.320 7.1931 0.0230
Table B.5: Effects test for obtained model
40 30 20 10 0 -10 d(m) Residual -20 -30 -40 0 100 200 300 400 500 d(m) Predicted
Figure B.2: Residual by predicted plot for model generated from analysis of experimental design
148
Continuous factors centered by mean, scaled by range/2 Term Scaled Plot Estimate Std Error t Ratio Prob>|t| Estimate Intercept 0 0 0.00 1.0000 Stearic Acid(Mixture) -7.021733 6.100522 -1.15 0.2765 Lecithin(Mixture) 742.77711 121.8007 6.10 0.0001 Sodium Taurocholate(Mixture) -29.2806 8.029502 -3.65 0.0045 Cholesterol(Mixture) 61.069692 14.64943 4.17 0.0019 Beta-carotene(Mixture) -813.4064 147.3916 -5.52 0.0003 Stearic Acid*Lecithin -149.4436 31.42085 -4.76 0.0008 Stearic Acid*Beta-carotene 235.29146 40.92732 5.75 0.0002 Lecithin*Cholesterol -121.9251 19.10166 -6.38 <.0001 Cholesterol*Beta-carotene 39.451713 14.70986 2.68 0.0230
Table B.6: Scaled estimates for factors included in significant model
9014.81
166.6596
d(m) ±20.858
-1.3e+4 0 1 0 1 0 1 0 1 0 1 0.70004 0.07616 0.03062 0.10533 0.08784
Stearic Lecithin Sodium Cholesterol Beta-carotene Acid Taurocholate
Figure B.3: Prediction profiler for generated model
149 350 0.525 0.9 0.9 300 Stearic Acid 200 150 Stearic
d(m) 0.9 100 Acid 0.525 50 0 0.525 350 0.025 0.25 300 0.025 0.025 200 Lecithin 150 Lecithin
d(m) 0.25 100 50 0.25 0
350 0.2075 0.025 0.2075 300 0.025
0.025 Cholesterol 200 150 Cholesterol d(m) 100 50 0.2075 0
350 0.2009 0.2009 0.2009 300 Beta-carotene
200 150 0.025 Beta-carotene d(m) 100 0.025 50 0.025 0
.55 .6 .65 .7 .75 .8 .85 .9 .95 .05 .1 .15 .2 .25 .05 .1 .15 .2 .05 .1 .15 .2
Figure B.4: Interaction profile
150
APPENDIX C
MATLAB PROGRAM USED TO MODEL EHDA ELECTRICAL POTENTIALS AND FIELDS
151 function laplace
%Variables EH1 = 2.540; %Electrode 1 height EH2 = 2.540; %Electrode 2 height EW = 0.266; %Electrode width IR = input('Enter IR, inner radius (cm): '); EP1 = input('Enter EP1, position of ground electrode (cm): '); EP2 = input('Enter EP2, position of positive electrode (cm): '); GND = 0; %Voltage on electrode 1 POS = input('Enter POS, electric potential of positive electrode (V): '); NEG = input('Enter NEG, electric potential of needle (V): '); h = EW/4; %Delta x = 0:h:3*(EP2+EH2); %Horizontal coordinate vector y = x; %Vertical coordinate vector
%Initialize potential matrix A = zeros(size(y'*x));
%Convert variables to matrix indices EH10 = floor(EH1/h); EH20 = floor(EH2/h); EW0 = floor(EW/h); IR0 = floor(IR/h); EP10 = floor(EP1/h); EP20 = floor(EP2/h);
%Create reference point (where needle will be placed) a = size(A); x0 = ceil(a(1,2)/2); %Needle column y0 = 2*(EH20+EP20); %Needle row
%Set boundary conditions///////////////////////////////////////////////////
%Electrode 1 stays grounded
%Electrode 2 for j = y0-EP20:-1:y0-EP20-EH20, for k = x0-IR0:-1:x0-IR0-EW0, A(j,k) = POS; end for k = x0+IR0:x0+IR0+EW0, A(j,k) = POS; end end
%Needle A(y0,x0) = NEG;
%Iteration of discretized Laplace equation///////////////////////////////// for i = 1:1000, for j = 2:a(1,1)-1, for k = 2:a(1,2)-1, 152 if ((j~=y0)|(k~=x0)) & (((j>(y0-EP10))|(j<(y0-EP10-EH10)))|((k>(x0-IR0))|(k<(x0-IR0-EW0)))) & (((j>(y0-EP10))|(j<(y0-EP10-EH10)))|((k>(x0+IR0+EW0))|(k<(x0+IR0)))) & (((j>(y0-EP20))|(j<(y0- EP20-EH20)))|((k>(x0-IR0))|(k<(x0-IR0-EW0)))) & (((j>(y0-EP20))|(j<(y0-EP20- EH20)))|((k>(x0+IR0+EW0))|(k<(x0+IR0)))) A(j,k) = (A(j+1,k) + A(j-1,k) + A(j,k+1) + A(j,k-1))/4; end end end end
%Output figure surface(x',y,A) view(0,90) shading interp colorbar xlabel('(cm)') ylabel('(cm)') title('Electrostatic Potential (V)')
%figure %[FX,FY] = gradient(A,1,1); %surface(x',y,A) %view(0,90) %shading interp %colorbar %hold on %quiver(x,y,-100*FX,-100*FY) %grid off %hold off
%figure %surface(x',y,E) %view(0,90) %shading interp %colorbar %xlabel('(cm)') %ylabel('(cm)') %title('Static Electric Field (V/cm)')
153
APPENDIX D
MATLAB PROGRAM USED TO MODEL DRUG RELEASE PROFILES
154 function SphericalDiffusivityModel clear; clc; warning('off'); redo=1; while redo==1 a=input('Enter initial concetration profile type:\n enter 1 for core/shell type,\n enter 2 for uniform type,\n enter 3 for decay type,\n enter 4 for step function type.\n '); Ca_inf=input('Enter concentration of solute in the outside medium (mol/L) '); Ca_inf=Ca_inf*1000; DABeff=input('Enter effective diffusivity coefficient (m^2/s) '); k=input('Enter convective mass transfer coefficient (m/s) '); time=input('Enter amount of diffusion time (hr) '); time=time*3600; cor_rad=eps; num_step=1; m=2; switch(a) case 1 cor_rad=input('Enter core radius (nanometer) '); cor_rad=cor_rad*10^-9; shell_rad=input('Enter shell radius (nanometer)\n NOTE: This value should be the actual radius, not the distance from the core to the surface!\n'); shell_rad=shell_rad*10^-9; cor_conc=input('Enter core solute concentration (mol/L) '); cor_conc=cor_conc*1000;
%checks to make sure shell radius is larger than core radius. % ------check=1; while check==1 check=0; if shell_rad<=cor_rad disp('The shell radius must be greater than the core radius!'); shell_rad=input('ReEnter shell radius (nanometer) '); shell_rad=shell_rad*10^-9; check=1; end end % ------part_size=shell_rad; case 2 part_size=input('Enter radius of solute particle (nanometer) '); part_size=part_size*10^-9; cor_conc=input('Enter solute concentration inside spherical particle (mol/L) '); cor_conc=cor_conc*1000;
case 3 part_size=input('Enter radius of solute particle (nanometer) '); part_size=part_size*10^-9; cor_conc=input('Enter solute concentration at the center of the spherical particle (mol/L) '); 155 cor_conc=cor_conc*1000;
r=linspace(0,part_size); t=linspace(0,time); sol=pdepe(m,@pdefun,@icfun,@bcfun,r,t,[],a,DABeff,k,Ca_inf,part_size,cor_rad,cor_conc); [row col depth]=size(sol); case 4 part_size=input('Enter radius of solute particle (nanometer) '); part_size=part_size*10^-9; num_step=input('Enter the number of layers '); cor_conc=input('Enter solute concentration inside layers of high concenteration (mol/L) '); cor_conc=cor_conc*1000; end r=linspace(0,part_size); t=linspace(0,time); sol=pdepe(m,@pdefun,@icfun,@bcfun,r,t,[],a,DABeff,k,Ca_inf,part_size,cor_rad,cor_conc,num_step); [row col depth]=size(sol);
%stores all concentration profiles into new matrix 'conc_profs'. % ------x=1; for u=1:1:length(t) conc_profs(x,:)=sol(u,:,1)/1000; x=x+1; end % ------
%for all times, calculates total amount of drug in shells of l nanometer length from core %to particle boundary. Then these shells are summed to reveal %total amount of drug in the particle at all times. % ------for j=1:row for k=1:col rad_dif_fun(k)=((k-1)*part_size/100)^2*sol(j,k,1)*part_size/100; %takes the form of r^2*Ca(r)*dr end integrand(j)=4*pi*sum(rad_dif_fun); end % ------percent_gone=1-integrand/integrand(1);
%calculates amount leaving per time by subtracting amount present at time %'n+1' from amount present at time 'n' and dividing by the time interval. % ------for n=1:row-1 flow(n+1)=(integrand(n)-integrand(n+1))/(t(n+1)-t(n)); end flow(1)=0; % ------figure(1) plot(t/3600,percent_gone) title('Drug Delivery vs Time') 156 xlabel('Time (hr)') ylabel('Fraction Delivered') figure(2) plot(t/3600,flow*6.02e23) title('Flow vs Time') xlabel('Time (hr)') ylabel('Flow (particles/s)') figure(3) surf(r/10^-9,t/3600,sol(:,:,1)/1000) title('Spherical Diffusion Model Surface Plot') xlabel('Radius (Nanometers)') ylabel('Time (hr)') zlabel('Concentration (Molarity)') figure(4) h=plot(r/10^9,conc_profs(1,:)); title('Animation of Concentration Change with Time') xlabel('Radius (Nanometers)') ylabel('Concentration (Molarity)') axis([0 part_size*10^9 0 cor_conc/1000]) for w=2:x-1 set(h,'XData',r/10^-9,'YData',conc_profs(w,:)) pause(.3) end clear; redo=input('\nWould you like to rerun this program?\n if so, enter 1\n if not, enter 0\n'); clc; end function [c,f,s]=pdefun(r,t,Ca,dCa_dr,a,DABeff,k,Ca_inf,part_size,cor_rad,cor_conc,num_step) c=1; f=DABeff*dCa_dr; s=0; function [p1,q1,pR,qR]=bcfun(r1,Ca1,rR,CaR,t,a,DABeff,k,Ca_inf,part_size,cor_rad,cor_conc,num_step) p1=0; q1=1; pR=k*(CaR-Ca_inf); qR=1; function Ca0=icfun(r,a,DABeff,k,Ca_inf,part_size,cor_rad,cor_conc,num_step) switch(a) case 1 if r<=cor_rad Ca0=cor_conc; else Ca0=0; end case 2 Ca0=cor_conc; case 3 157 Ca0=cor_conc*exp(-2*r/part_size); case 4 step=linspace(0,part_size,num_step+1); remainder=rem(num_step,2); switch(remainder) case 0 for w=1:(length(step)-1)/2 if ((r>=step(2*w-1)) & (r<=step(2*w))) Ca0=cor_conc; elseif ((r>=step(2*w)) & (r<=step(2*w+1))) Ca0=0; end end case 1 for w=1:(length(step)-2)/2 if ((r>=step(2*w-1)) & (r<=step(2*w))) Ca0=cor_conc; elseif ((r>=step(2*w)) & (r<=step(2*w+1))) Ca0=0; end end
if ((r>=step(length(step)-1)) & (r<=step(length(step)))) Ca0=cor_conc; end end end
158
APPENDIX E
CALIBRATION OF POSITIVE AND NEGATIVE DIALS USED IN EHDA EXPERIMENTATION
159
7
6
5
4 Positive Voltage (kV) Negative Voltage (kV) 3 |Voltage| (kV)
2
1
0 012345678 Dial Unit
Figure E.1: Calibration of EHDA power supply dial settings to voltage measurements
160
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