AFFINITY-BASED DELIVERY OF RETINOIDS

STEVEN MICHAEL VESOLE

Submitted in partial fulfillment of the requirements For the degree of Master of Science

Department of Biomedical Engineering CASE WESTERN RESERVE UNIVERSITY

August, 2011

CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

__Steven Michael Vesole______candidate for the _MS Engineering______degree *.

(signed)______Horst von Recum, Ph.D.______

(Chair of the Committee)

______Zheng-Rong Lu, Ph.D.______

______Erin Lavik, Sc.D.______

______

______

______

(date) ______June, 30th 2011______

*We also certify that written approval has been obtained for any proprietary material contained therein.

I grant to Case Western Reserve University the right to use this work, irrespective of any copyright, for the University’s own purposes without cost to the University or to its students, agents and employees, I further agree that the University may reproduce and provide single copies of the work, in any format other than in or from microforms, to the public for the cost of reproduction.

______STEVEN M VESOLE______(sign)

Table of Contents List of Tables ...... 2 List of Figures ...... 3 Preface ...... 7 Acknowledgments ...... 8 Abstract ...... 9 Chapter 1 Background ...... 10 Age-Related Macular Degeneration ...... 10 Retinoids ...... 13 Drug Delivery Technologies/ Eye Delivery ...... 17 Cyclodextrins/Cyclodextrin Polymers/ with Retinoids ...... 24 Chapter 2 CDP Hydrogels: Synthesis and Mechanical Properties ...... 36 Results/Discussion ...... 42 Chapter 3 CDP Hydrogels: Understanding Drug Loading ...... 58 Results/Discussion ...... 60 Chapter 4 CDP Hydrogels: Understanding and Predicting Drug Interactions ...... 68 Results/Discussion ...... 71 Chapter 5 Retinoid Delivery with 1st Generation Cyclodextrin Hydrogel ...... 88 Results/Discussion ...... 90 Chapter 6 Retinoid Delivery with Cyclodextrin/Dextran Hydrogel Blends ...... 100 Results/Discussion ...... 106 Chapter 7 Retinoid Delivery with BSA/Cyclodextrin Hybrid Hydrogels ...... 119 Results/Discussion ...... 123 Chapter 8 Conclusions/Future Direction ...... 139 Retinoids and Lasting Impact ...... 148 Bibliography ...... 149

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List of Tables

Table # Page # Description Table 1 14 Examples of naturally occurring retinoids Table 2 20 Stakeholders in translational science and their objectives Table 3 23 Biodegradable and non-biodegradable ocular delivery platforms Table 4 24 Ocular delivery technologies and duration of release Table 5 27 Common cyclodextrins and several key properties Table 6 38 List of samples for analysis of gelation behavior Table 7 39 List of polymer blends in terms of weight percentage Table 8 46 Gelation time and storage modulus from isothermal cure Table 9 50 Density and polymer volume fraction as a function of CDP % Table 10 51 Parameters from log-log plot of shear modulus versus frequency Table 11 54 Network parameters for polymer blends Table 12 59 Rhodamine loading study sample list Table 13 64 Slope of gray level transition regime at 24 and 48 hours Table 14 73 Change in emission signal compared to DMSO control Table 15 75 Free energy of binding from simulation and experimental values Table 16 76 Free energy of binding from surrogate retinoid and therapeutic Table 17 97 Summary of fit parameters for two-phase release Table 18 98 Summary of pharmacokinetic parameters including half-life Table 19 102 List of polymer blends in terms of weight percentage Table 20 105 DSC method for analyzing dextran and CDP gels Table 21 120 Various compositions of BSA/CDP hybrid gels Table 22 137 Double exponential fit parameters for BSA/CDP gels Table 23 148 Retinoids that are used in other clinical applications

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List of Figures

Figure # Page # Description

Figure 1 12 Graphical depiction of dry AMD disease progression

Figure 2 14 Basic structure of a retinoid

Figure 3 15 Graphical representation of the cone-specific visual cycle

Figure 4 18 Questions that typically arise during drug delivery design

Figure 5 19 How to evaluate a delivery technology

Figure 6 25 Affinity delivery vs. typical first-order release

Figure 7 26 Structure of beta-cyclodextrin

Figure 8 28 Hydrophobic cavity of CD for loading and release of drug

Figure 9 31 Structure of BCD crosslinked with epichlorohydrin

Figure 10 32 Structure of CDP crosslinked with EGDE

Figure 11 45 Synthesis procedure for CDP hydrogel

Figure 12 45 Urethane bond formation during synthesis

Figure 13 46 Drug loading process after network formation

Figure 14 47 Gelation time and storage modulus of CDP

Figure 15 48 Gelation time and storage modulus of Dextran

Figure 16 49 Retinol’s hydroxyl shielded by CD during synthesis

Figure 17 50 Swelling equilibrium versus CDP content

Figure 18 52 Log-log plot of shear modulus versus frequency

Figure 19 53 Tan delta as a function of frequency

Figure 20 55 Linear regression for χ parameter versus calculated values

Figure 21 56 Strain sweep of CDP/Dextran blended gels

Figure 22 56 Stress relaxation for CDP/Dextran blended gels

Figure 23 61 Rhodamine B dye front through CDP and Dextran Hydrogels

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Figure 24 61 Figure 23 with black and white filter applied

Figure 25 63 Gray level as a function of distance along gel during load

Figure 26 63 Gray level difference between 3min and 24 hours of loading

Figure 27 65 Gray level transition region slope calculation at 24,48 hours

Figure 28 66 Full time course for loading a gel with Rhodamine B

Figure 29 72 Emissions scan for various polymers doped with retinol

Figure 30 74 Emissions signal versus temperature with different polymers

Figure 31 76 Linear regression of experimental values versus simulation

Figure 32 78 Effect of BCDP concentration on retinol emissions

Figure 33 78 Effect of retinol emissions versus temperature

Figure 34 80 Double reciprocal plot to determine equilibrium constant

Figure 35 81 Arrhenius plot of ln(K) versus 1/T

Figure 36 82 Boundary conditions for drug delivery system

Figure 37 85 Predicted release from dextran hydrogel

Figure 38 85 Predicted release from CDP hydrogel

Figure 39 92 Model of BSA’s hydrophobic domains

Figure 40 93 BSA fluorescence is quenched by retinol presence

Figure 41 94 Stern-Volmer plot for retinol quenching

Figure 42 95 Retinol release from 1st Generation BCDP and Dextran Gels

Figure 43 96 Diagram of biphasic release process

Figure 44 97 Fitted release profile based on biphasic model

Figure 45 106 Loading dependence related to loading sink concentration

Figure 46 107 CDP/Dextran blended gels after loading

Figure 47 107 Drug loading percent based on mass after loading

Figure 48 108 Drug loading percent based on drug extracted and released

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Figure 49 109 TGA curves for CDP hydrogel +/- retinol

Figure 50 109 TGA curves for dextran hydrogel +/- retinol

Figure 51 111 DSC thermogram for bulk retinol

Figure 52 112 DSC thermogram for CDP hydrogel +/- retinol

Figure 53 112 DSC thermogram for dextran hydrogel +/- retinol

Figure 54 113 Cumulative release from CDP/Dextran blended gels

Figure 55 114 Logarithmic fit for release from CDP/Dextran blended gels

Figure 56 115 Calibration curve based on stoichiometric ratios of BSA:drug

Figure 57 116 Distribution of data in terms of BSA:drug ratio

Figure 58 117 SDS-PAGE of BSA in release medium after release

Figure 59 124 Schematic of BSA/CDP crosslinked gel

Figure 60 125 CDP/BSA hybrid gels at swelling equilibrium

Figure 61 126 Simulation image of Evan’s Blue dye with BCD

Figure 62 127 Calibration of Evan’s Blue dye in PBS

Figure 63 127 Release of Evan’s Blue dye from hybrid gels in PBS

Figure 64 128 Evan’s Blue dye immediately diffusing out in 10% BSA sink

Figure 65 129 Calibration of Evan’s Blue dye in PBS +10% BSA

Figure 66 129 Release of Evan’s Blue dye from hybrid gels in 10% BSA

Figure 67 131 Competitive binding of retinol to BSA and CDP

Figure 68 133 Hybrid gels before loading and in loading sink

Figure 69 134 Hybrid gels after loading and after 20 days of release

Figure 70 135 Calibration curve of retinol in 2.5% BSA release sink

Figure 71 135 Cumulative release of retinol from CDP/BSA hybrid gels

Figure 72 136 Logarithmic plot of cumulative % released

Figure 73 137 Double exponential fit of retinol release from hybrid gels

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Figure 74 141 Regeneration of visual pigment versus age

Figure 75 142 Rate of visual pigment loss versus average release rate

Figure 76 144 Pluronic®-cyclodextrin intelligent gel, thermally reversible

Figure 77 145 Tracking reversible gelation using Rhodamine B

Figure 78 146 Prospective design of reloadable CDP hydrogel device

Figure 79 147 Tracking loading using solid state fluorescence

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Preface

With all drug delivery work it is important to maintain a clear conscience for why the work is being done: is one trying to observe, document, and verify product features that advance the general understanding of drug delivery materials, and/or is one concerned with building a platform that is clinically translatable. It is my opinion that in doing good life sciences research one has to be truthful to personal motivations, scientific interests, and realistic with the capabilities of the technology at hand. In the following body of work I seek to provide answers to basic scientific questions as well as set the stage for a technology that may have a lasting impact on society. I therefore dedicate this work to the scientific community from which this work was based upon, my friends, family, and colleagues who have supported me during this time of scientific exploration, as well as those individuals whom I don’t know that in the future may benefit directly or indirectly from some aspect of this work.

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Acknowledgments

I would first like to thank the Case-Coulter Translation Research Program for funding this work. There are many individuals who have supported me in research functions including Lauren Buerkle (Rheology), Tiffani Abernathy (TGA, DSC), and

Casey Johnson (GPC). I would also like to thank Dr. Thimma Reddy Thatiparti who served as an invaluable resource for this work, as well as Dr. Pamela Wilson for her assistance when my project was just starting off. Amy Wen’s coding contribution in

MATLAB simulations were an irreplaceable and necessary asset. A special thanks to the equipment owners who permitted me use of their facilities: Prof. Erin Lavik, the

Biomedical Engineering Department, and the Macromolecular Science and Engineering

Department at Case Western Reserve University.

Throughout the course of this work I was further encouraged and motivated by my fellow lab mates in the Center for Delivery of Molecules and Cells. I’d especially like to thank my graduate supervisor, Prof. Horst von Recum, for his advice, guidance, and financial support during this work. His approachability and management style allowed me to explore creative aspects of the work that were not necessarily true to the core of the project, such tangents aided me in my own professional development.

I’d finally like to thank my friends and family for their moral support and pleasant distractions during this period of academic trial.

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Affinity-Based Delivery of Retinoids

Abstract by

Steven Michael Vesole

Age-related Macular Degeneration (AMD) is a debilitating disease that deteriorates central vision. Retinoid analogues have shown promise in treating AMD and other ocular conditions by circumventing a faulty phototransduction pathway found in unhealthy cells. A cyclodextrin (CD) molecule is a well-known cyclic oligosaccharide that has been used in the food, pharmaceutical, and chemical industries for its unique chemical structure containing a hydrophobic cavity that can increase the solubility of hydrophobic molecules. This hydrophobic interaction, hereby deemed an affinity inclusion complex, is exploited in several hydrogel drug delivery formulations; sustained release was maintained for more than 30 days. Affinity complexes are also explored through spectroscopic analysis, mathematical modeling, and simulation.

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Chapter 1: Background

Age-Related Macular Degeneration

Age-related Macular Degeneration (AMD) is a debilitating disease that results in a dramatic reduction in quality of life. Quality of life impacts include the inability to complete tasks such as reading, watching TV, driving, and recognizing faces.i In the

Western world the disease represents the leading cause of blindness for those over the age of 60 ii and in 2004 the prevalence in the United States was 1.75 million people, or 1.47% of the populationiii. A future prediction indicates an increasing prevalence and places the incidence of AMD at 3 million individuals by the year 2020.iii

Epidemiological studies show that AMD is increasingly being recognized as a complex genetic disorder that is provoked by environmental factors. Such a theory suggests that one or multiple genes, plus environmental risk factors such as alcohol consumption and/or obesity, increase the susceptibility of one developing the condition.iv

Those studying the epidemiology objectively admit that the knowledge base is limited and that the disease’s origin and respective risk factors are not fully understood.v For example, the presence of a coding variant of chromosome 1 may result in an inappropriate inflammatory response that could increase the risk of developing AMD.vi

Furthermore, many therapeutic agents, both approved and being developed, typically target the pathological consequences of the disease and not the genetic origin.

Quality of life impacts are directly related to the disease pathophysiology. AMD primarily affects the central region of the eye, which is also the zone of highest visual acuity, and therefore the worst possible location. vii There are two main forms of AMD: a dry form, characterized by geographic atrophy of the retinal pigment epithelial cells

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(RPE), and a wet form, characterized by RPE cell detachment, hemorrhages, and/or scars similar to those found in the dry form.viii Additionally, there is a division in the pathophysiology of an early stage disease versus a late stage disease related to its dry or wet classification. An early stage is associated with a drusen deposition seen as pigmentary abnormalities, while a late-stage disease is described as the development of well-defined areas of RPE cell loss, and/or choroidal neovascularization.ix

The histopathological features of drusen are critically important in the diagnosis of the disease. Drusen are RPE extracellular deposits located between the RPE and inner collagenous zone of Bruch’s membrane (basement membrane). The classification of drusen is divided between hard drusen, hard drusen cluster, soft cluster-derived drusen, soft membranous drusen, and basal laminar drusen.x Small hard drusen typically precede the development of late stage dry atrophic AMD, while large soft drusen precede the development of RPE detachment and choroidal neovascularization.x Through the study of drusen’s composition and origin, theories have evolved concerning AMD disease progression. One such theory is that drusen formation results in RPE atrophy (apoptosis), followed by death of overlying rod cell photoreceptors, then the loss of cone photoreceptors.xi This theory is graphically depicted in Figure 1.

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Figure 1. Graphical depiction of dry AMD disease progression.

While this generalized progression may not be realized throughout the entire patient population, transition of an early to late disease state is well correlated with the advancement of a dry state to an advanced dry or even wet classification. In other words, all wet forms of the disease started as the dry form, and consequentially, wet AMD represents a smaller patient population. In 2001 the wet form of AMD amounted to only

10% of the total diseased population.xii

Although wet AMD represents a minority percent of the patient population, the only clinically available therapeutic regimens are for those with a rapidly deteriorating choroidal neovascularized disease state. Approved therapies include bevacizumab

(Genentech/Roche), pegaptanib (Merck), ranibizumab (Genentech/Roche), and

12 photodynamic therapy with verteporfin (GLT Opthalmics). Except for photodynamic therapy, these drugs all function as anti-angiogenic agents by inhibiting a variant of

VEGF.xiii The cost effectiveness of these current therapeutic options is also a topic that is widely discussed and debated.xiv

As mentioned previously, the dry form of AMD is presently without an approved therapeutic option. Even though a wet disease state is more aggressive than its advanced dry counterpart, late stage dry AMD presents an outstanding clinical opportunity. A therapeutic that can effectively treat a late dry form could also potentially advance a therapy for the millions of people with an early stage less symptomatic disease.

Furthermore, the development of a therapeutic that can arrest, or regress, an early stage disease may eliminate the need to develop therapeutics that are intended to treat a late stage disease.

Retinoids

Retinoids are a family of chemical compounds which include Vitamin A.

Retinoids share a common monocyclic, double-bonded chemical structure with a terminal functional group (Figure 2).xv Naturally-occurring retinoids include retinal, retinyl esters, retinoic acid, their metabolic intermediates, as well as different trans and cis configurations (Table 1). Vitamin A is important in maintaining biologic functions in such as growth, immunity, cellular differentiation and proliferation as well as vision.xv

Major sources of Vitamin A include eggs, meat, and dairy products. Beta-carotene, a carotenoid, is a precursor to Vitamin A found in many fruits and vegetables. Carotenoids supplement many of the biological functions of Vitamin A including antioxidant, immune enhancement, and anticancer effects.xvi

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Figure 2. Basic structure of a retinoid.

Table 1. Examples of naturally occurring retinoids.

Retinoids in the Visual Cycle

The visual cycle is a complex process that begins with the focusing of light

(photons) through the convex lens of the eye and ends with ganglion cells sending an electrical signal to the optic nerve. After photons pass through the vitreous they are received by photoreceptor cells; cone photoreceptors are responsible for color vision, and rods maintain nighttime vision.xvii When a photon interacts with a retinal chromophore on

14 a cone photoreceptor cell it isomerizes from an 11-cis-retinal to an all-trans-retinal configuration. This isomerization initiates a phototransduction cascade that ultimately causes a change in polarization and an electrical signal. The isomerization of the chromophore means that a new supply of 11-cis-retinal needs to be made available for the visual cycle to repeat. In the canonical visual cycle this demand is met by the RPE cells that recycle the chromophore, converting all-trans-retinal back to 11-cis-retinal.xvii The enzymes involved in this conversion have a stereospecific preference for certain retinoid structures.xviii A simplified process is depicted by Figure 3.

Figure 3. Graphical representation of the cone-specific visual cycle.

A dysfunction in this recycling is attributed to two types of retinal dystrophy: one in which the chromophore cannot be synthesized, and another in which there is an

15 accumulation of products derived from all-trans-retinal.xix Chromophore synthesis dysfunction is present in both Leber’s Congenital Amaurosis and Retinitis Pigmentosa, two related degenerative retinopathies.xix A therapeutic strategy is suggested for these 11- cis-retinal deficient patients: to systemically administer (orally) a dose of a retinal prodrug, 9-cis-retinyl-acetate, that can then convert in the liver to another pro-drug, 9-cis- retinyl palmitate, and further transform in the eye to a photoactive 9-cis-retinal pigment.xx

This pigment can initiate a similar phototransduction cascade compared to the one activated by 11-cis-retinal.xxi

A 9-cis-retinoid treatment may also reduce the age-dependent inflammatory reactions that were previously stated as a possible cause for AMD. By administering a 9- cis-retinoid, debilitated biochemical pathways, such as the ones that generate waste products, free radicals, and eventually drusen, may be improved. The reduction in these byproducts may consequentially prevent AMD disease progression.xxii In similar studies, retinylamine, a long-lasting retinoid cycle inhibitor, was shown to slow the flow of all- trans-retinal through the visual cycle, which in turn decreased the severity of visual dysfunction.xxiii This body of work, completed by the Palczewski laboratory at Case

Western Reserve University, demonstrates that retinoids hold therapeutic promise in attenuating retinopathies as well AMD.

A question that remains to be answered is the best way to deliver these retinoids.

For orally administered retinoids there is the unknown issue of whether or not there is a long term toxicity associated with accumulation of drug.xxiv For intravenous injections, a significant drawback is that most of the retinoid is eliminated from the bloodstream by the kidneys, rather than stored, and therefore requires a higher dose than other

16 methods.xxv Due to the issue of drug clearance, an intravenous delivery route also requires more frequent administration and could increase the risk of infection.xxiv

Intraocular injections of an inhibitory retinoid, retinylamine, may be successful in reducing the dose of a retinoid, but a potential pitfall is the short therapeutic window of several days to a week.xxvi In order to maintain a therapeutically relevant concentration of drug, intraocular injection therapies usually require supplemental injections that increase the risk of retinal detachment, vitreous hemorrhaging, and cataracts.xxvii

Consequentially, a significant opportunity exists in developing a local delivery technology that can deliver a drug to the site of action at a concentration that avoids system toxicity but maintains long term bioavailability.

Drug Delivery Technology

The goal of local drug delivery is to get a molecule to its site of action at a concentration that is high enough to surpass a therapeutic level but low enough to not exceed a toxic concentration. Drug delivery for a retinoid is therefore concerned with two major issues, administration and transport: how is the drug introduced to the body and once it is in where does it go. Inside the body the drug has an effect on the body

(Pharmacodynamics) and the body has an effect on the drug (Pharmacokinetics).

Pharmacodynamics is a description of a drug’s mechanism of action as it relates to the physiological consequences of its administration together with its side effects, both desired and unwanted. Pharmacokinetics on the other hand includes the liberation, absorption, distribution, metabolism and elimination of the drug. A proposed paradigm for drug delivery device development is highlighted by Figure 4.

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Figure 4. Questions that typically arise during drug delivery platform design.

When a drug delivery platform is involved there are many biological consequences that the platform may have on the body besides the effect elicited by the released drug. The body in turn may change one or several material characteristics of the platform. These effects are traditionally examined in a series of in vitro and in vivo experiments; such work is detailed in Figure 5.xxviii

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Figure 5. In vivo and in vitro experiments are conducted to gain insight into material host interactions. xxviii, cxli

Scientific Enterprise of Drug Delivery

As seen in Figure 4, the drug is centrally important to everything. In terms of drug design, synthetic chemists must work hand in hand with biologists to control the activity, specificity, stability, and cost of a therapeutic agent. Rationalized drug and platform design takes into consideration the assessment of materials early in their developmentxxix, but must also be supported by the regulatory environment, clinical need, and the pharmacoeconomics entwined in an agent’s advancement from bench to bedside.

In an academic environment, this advancement is supported by good translational science. Translational science may be defined as building partnerships with clinical collaborators that extend beyond scientific agreement and understanding, to those that take calculated research risks that translate directly into development stage objectives.

Depending on the stakeholder, such as the patient, practicing physician, research physician, formulation scientist, techology transfer agent, or potential manufacturer, these objectives can vary greatly (Table 2). Present and future work in drug delivery must be

19 attuned to the complexities of designing a drug delivery platform for a novel retinoid.

Biological considerations are one part of the problem, however, one must also be true and concerned with future stakeholders in the technology.

Table 2. Stakeholders in translational science and their motivations/objectives.

Drug Delivery to the Eye

In developing a platform for the delivery of a retinoid to an eye, it is first necessary to provide a broad overview of existing technologies. The current ocular drug delivery market is comprised of mostly anterior segment delivery technologies that are typically administered to the front of the eye. Antibiotics, pressure alleviating, and anti- inflammatory drugs are most commonly formulated in a topical form because they have anterior targets. xxx Even so, these formulations, which are usually eye drop based, require frequent administration because only 1% of the dose is absorbed in the eye; where low bioavailability is due to conjuctival absorption and solution drainage by gravity.xxxi

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Another drawback of topical formulations besides frequent administration is that targets in the posterior of the eye generally do not see a high enough drug concentration to be therapeutically relevant due to a number of physiological constraints. These constraints include solubility limits, conjuctival clearance, and diffusion coefficients of the drug in the tissue.xxxii

In order to overcome these constraints researchers have tried to improve the drug’s topical residence time and permeability.xxxii There are a number of noninvasive gel technologies that seek to increase residence time including polyoxyethylenes, polysaccharide, glycosaminoglycan, and acrylic based materials. Similarly, there has been a lot of work on increasing drug permeation by using pro-drugs, cyclodextrins, and lipid based carriers.xxxii Other non-invasive technologies include nanoparticles administered in a suspension form, iontophoresis, contact lenses, microneedles (non- invasive to an extent), eye misters and microdroplets.xxxii Systemic administration is ineffective in many settings because most drugs cannot pass the blood-aqueous barrier and/or the blood-retinal barrier.xxxiii

Even with all these non-invasive technologies, the critical issue of bioavailability is not the only clinical limitation. Non-specificity to target tissues, systemic side effects, and poor patient compliance are other drawbacks to systemic and topical routes of administration.xxxiv Since the pathophysiology of AMD lends itself to therapeutic targets that are in the posterior segment of the eye, a better route of administration is needed.

Although approved drugs for this disease are traditionally administered parenterally through an intravitreal injection, there are other anatomical compartments that can targeted besides the vitreous. These other invasive targets can be classified in the

21 following routes: intracameral, suprachoroidal, subretinal, intrascleral, episcleral, periocular, subconjuctival, and transcleral.xxx A drug’s bioavailability through these routes may be modeled as biological compartments with elimination and permeation rates through respective tissues.xxxv

Depending on the route of administration there are different delivery technologies that may or may not be investigated due to the anatomy of the compartment and the physiology of the tissue. Microparticles, nanoparticles, liposomes, and simple suspensions, are platforms that can be directly injected.xxx Injectables which form a depot through a solidification or gelation mechanism also hold therapeutic promise for use in the eye. Examples of biodegradable injectable materials include polyesters and their copolymers, alginates, chitosan, polyphosphazenes, and ricinoleic-acid based polymers. These materials are incorporated in thermoplastic pastes, in situ crosslinked, in situ precipitated, and thermally activated systems.xxxvi

There are also a number of biodegradable and non-biodegradable implants that are either being clinically investigated or FDA approved for use in the eye (Table

3).xxxiv,xxxvii A distinguishing characteristic between non-degradable and degradable systems is what happens to the material after it is in the body: does the material fall apart over time or does it remain intact indefinitely. Although degradation profiles may be controlled by synthesis conditions and polymer choice, xxxviii,xxxix release based on a single phase of release, such as transport through a membrane or a matrix, is much easier to model and dose. On the contrary, some non-biodegradable systems require a primary surgery to place inside the eye, and then a secondary follow-up procedure to remove and/or refill (if reservoir based). Based on its release profile, stimulus controlled systems

22 are sometimes considered a third type of drug delivery system. Stimulus controlled systems may improve the efficacy of a drug by triggering its release via an environmental signal such as pH.xl

Table 3. Clinically approved and investigational biodegradable and non-biodegradable ocular drug delivery platforms.

Biodegradable (Trade name) Non-Biodegradable (Trade name) Polylactic Acid Scleral Plug Muscoadhesive Hydrogel-Based Metallic Wire (OpthaCoil) Scleral Discoid Reservoir Type (Restisert, Medidur, Vitrasert) Hyaluronic Acid Plug Micro-Electromechanical Donut-Shaped Minitablet Osmotic Minipump PLGA, HPMC Pellet (Surodex) Micro-Machine Programmable PLGA Rod (Ozurdex) Helical Coil (I-vation) HPC Rod (Lacrisert) Hollow-Fiber (NT-501) Pellet Device with Silicone Shell EVA, Alginic Acid Plug (Ocusert) Silicone Matrix Plug (Lumitect) PVA Rod (Illuvien)

Another defining characteristic of ocular drug delivery systems is the time-scale for the drug’s release (Table 4).xxx In order to treat a chronic condition like AMD one may choose a technology that has a very long delivery period over one which has a much shorter release and duration of action.

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Table 4. Ocular delivery platforms and their estimated duration of action. (adapted from Amo et al.) *theoretically years to lifetime

Delivery/Action Time Technologies Years Non-biodegradable implants Weeks to months Cell microencapsulation Viral gene therapy* Semi-solid polymeric ocular injections Biodegradable implants Weeks Intraocular microparticles Intraocular liposomes Days to weeks Intraocular nanoparticulates Non-viral gene therapy Hours to Days Transcleral iontophoresis Gel-forming topical applications Eye Drops

Cyclodextrins

A proposed hypothesis to improve the longevity of release in matrix-based systems is to incorporate affinity moieties that will change release from having typical first-order kinetics (Eq. 1) to one with near zero-order kinetics (Eq. 2). Zero-order systems release a constant dose of drug over time and are independent of concentration.

First-order systems on the other hand are dependent on concentration. An affinity release, as proposed, aims to be zero-order and therefore independent of drug loading. Zero-order release has a linear cumulative release profile compared to an exponential decay found in first-order (Figure 6).

- k t [A] = [A]0 e (1)

[A] = [A]0 - k t (2)

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Figure 6. Making diffusion based release more linear by retarding a molecule’s path using affinity moieties.

Release delayed by affinity

Examples of affinity moieties include those that exploit Van der Waals forces, hydrophobic interactions, ionic interactions, and hydrogen bonding.xli Currently, there are three main categories for affinity delivery systems: molecular imprint systems, growth- factor delivery, and cyclodextrin-based delivery.xli In particular, cyclodextrin-based systems permit a greater degree of control over drug release.xlii

Cyclodextrins (CD) are well-known cyclic oligosaccharides that have been used extensively in the food and chemical industries as well as in pharmaceuticals. The first reference to cyclodextrins was in 1891 when Villers was seeking to understand how starch was digested by Bacillus amylobacter and ended up with a small yield of a crystalline substance.xliii It was not until 1911 that Schardinger noticed that cyclodextrins formed complexes with various organic compounds. Following this early discovery there was an extensive body of work, completed from 1930s to 1970s, where systematic studies examined the exact makeup of CD to understand how and with what molecules

CD formed inclusion complexes.xliii Since its discovery and subsequent industrialization,

25 the number of publications on cyclodextrins has taken off exponentially and we now have a very complete library on its geometry and properties.

The basic chemical structure of CD consists of at least 6D-(+) glucopyranose units attached by α-(1, 4) glucosidic bonds (Figure 7). There are three main sizes of cyclodextrin consisting of 6,7, or 8 glucopyranose units. Cyclodextrins have a lipophilic inner cavity and a hydrophilic exterior that translates to a truncated cone shape.

Considering this geometry, a difference in the number of subunits changes not only the molecular weight but also the cavity diameter and the way a solvent interacts, thereby influencing its solubility (Table 5xliv). Consequentially, the inner cavity volume varies with the number of subunits, which is important in understanding solvent interactions.xlv

All secondary hydroxyls are on one edge of the ring while primary hydroxyls are on the other side. The secondary hydroxyl side is larger in diameter than the primary hydroxyl side which yields the aforementioned conical shape.xlvi These hydroxyls may also be modified to change a CD’s solubility as well as complexation behavior. Popular substitutions include hydroxyl ethyl, hydroxypropyl, sulfobutylether, methyl, and carboxymethyl modifications.xlvii

Figure 7. Beta-Cyclodextrin cavity has secondary hydroxyls on the outer diameter and primary hydroxyls in the inner diameter.

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Table 5. Common cyclodextrins and several key properties.

Cyclodextrin Number of Solubility in Cavity Molecular Units Water Diameter Weight g/100mL (Ȧ) (RT) Alpha (α) 6 14.5 4.7-5.3 972

Beta (β) 7 1.85 6.0-6.5 1135

Gamma (γ) 8 23.2 7.5-8.3 1297

The lipophilic cavity of CD can act as a host for hydrophobic molecules through noncovalent inclusion interactions. In understanding this interaction many analytical methods have been developed. Generally speaking, complexation behavior can be detected using solid state characterization or solution state characterization. Examples of solid state characterization include thermo-analytical methods, x-ray diffraction and infrared (IR) spectroscopy, and examples of solution state characterization include electrochemistry, conductivity, phase solubility, spectroscopic, NMR, and microcalorimetric methods.xlviii Although many techniques aim to find out a specific complexation ratio (ex: 1:1, 2:1), fluorescent spectroscopy can take this a step further in determining thermodynamic parameters representative of the guest-host binding affinity.xlix

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Figure 8. The hydrophobic cavity of beta-cyclodextrin is able to form reversible affinity complexes with small, hydrophobic molecules. Consequently, the cyclodextrin pockets can load and release hydrophobic drugs.

Though cyclodextrin is used in a number of industries, it has been exceptionally valuable as an excipient in increasing the solubility of pharmaceutical agents (Figure 8).

In functioning as an excipient, it has been incorporated in a wide variety of formulations from manufacturers around the world. Marketed pharmaceutical products containing CD include the following formats: i.v. solutions, oral tablets, parenteral solutions, capsules, chewing tablets, ointments, sublingual tablets, suppository tablets, eye drop solutions, oral solutions, intramuscular solutions, and nasal sprays. CD also enhances the loading of drug, stability of supersaturated drug solutions, and the dissolution rate for drug in a formulation.xlvi

Researchers have explored drug ionization, salt formation, the formation of metal complexes, the addition of acids/bases to form ternary complexes, and the addition of water soluble polymers in order to increase complexation efficiency.l In a separate function, cyclodextrins have been used in drug and gene delivery by exploiting their

28 unique ability to form supramolecular polymers with other water soluble polymers. These include polyrotaxane and polyseudorotaxane materials that take the form of hydrogels, micelles, stimuli responsive, and drug-conjugated systems.li

Cyclodextrin Polymers in Drug Delivery

The abundance of hydroxyls on CD allows it to be readily modified with a wide variety of crosslinking chemistries. Cyclodextrin polymers (CDP), traditionally crosslinked using epichlorohydrin (EPH, Figure 9), were originally developed as a column material for separation chromatography.lii Other commonly used cross-linkers include diepozides, diisocyanates, and anhydrides.lii Similarly, CD has been immobilized to the backbone of other polymers using like chemistries for filtration applications.liii A unique aspect of these CDPs is that when their size is limited to a few CD units, the product is more water soluble than a single CD. For example, water solubility of a single beta cyclodextrin is about 1.85 g/100mLxliv and for a polymer is greater than 25g/100mL

(as was determined experimentally). The molecular weight range for this experiment is from 2000-10000.liv Upon crosslinking past this molecular weight range the polymer becomes an insoluble cross-linked hydrogel network.

An insoluble hydrogel may be further applied in a drug delivery setting. A CD crosslinked with diglycidyl ethers was previously reported as forming a hydrogel capable of loading and releasing therapeutic agents for a period of 10 hours.lv Other cyclodextrin- based device coatings have been prepared by the von Recum group at Case Western

Reserve University in order to extend the release period of a material generated under less harsh conditions. When the researchers further crosslinked EPH-crosslinked cyclodextrin polymer with another crosslinker, hexamethylene diisocyanate, they were

29 able to create an antibiotic delivery material with long term release behavior.lvi Similar work had been completed using 2-isocyanatoethyl 2,6-diisocyanatohexanoate (LTI) as a secondary crosslinker. As a result of using a secondary crosslinker, the researchers from these antibiotic releasing studies were able to deliver drug for a period of greater than 30 days.lvii A unique aspect of these studies is that the release profile was modulated by the hydrophobicity of loaded antibiotic (novobiocin, rifampicin, and vancomycin).lvi,lvii The work also showed that the release of large antibiotics can be slowed even if the drug is only partially incorporated within the pocket.

Likewise, the von Recum group took an EPH crosslinked water soluble CD polymer and further crosslinked it using ethylene glycol diglycidylether for use as an antibiotic releasing surgical mesh coating that can prevent surgical site infections (Figure

10).lviii They tested vancomycin release from a polymer-coated mesh in an surgical site infection, SSI, model. After loading, the mesh was implanted into mice that were inoculated with Staphylococcus aureus. The study compared the homogenated tissue around the mesh in the following experimental groups: a saline flushed wound, a vancomycin flushed wound, and a vancomycin loaded polymer coated mesh. The results showed significant culture values from the flushed groups and absolutely no bacterial growth from the vancomycin loaded polymer coated meshes after four weeks.lviii

In work by other groups, cyclodextrin polymers were used in self-assembling hydrogel networks for the delivery of hydrophobic drugs.lix Moya-Ortega et al. reported on cyclodextrin hydrogels and semi-interpenetrating networks which could release dexamethasone for a period of three days,lx and Alvarez-Lorenzo et al. used ethylene glycol diglycidylether to create CD-agar hydrogels for ciprofloxacin delivery.lxi These

30 studies proved that cyclodextrin and cyclodextrin polymers could be used to modify the release profile of drug through a hydrogel network.

Figure 9. Structure of beta-cyclodextrin crosslinked with epichlorohydrin into a water soluble polymer. *Produced using ChemBioDraw and Jmol chemical drawing software.

31

Figure 10. Structure of beta-cyclodextrin polymer crosslinked with ethylene glycol diglycidyl ether. *Produced using ChemBioDraw and Jmol chemical drawing software.

The chemical structure of cyclodextrin-based materials leaves researchers with virtually an unlimited library for modifying these polymers. Researchers have combined cyclodextrin into copolymerslxii, functionalized them with biologicslxiii,lxiv, incorporated stimuli responsive moietieslxv, and formed drug-polymer conjugateslxvi. One of the unique advantages of building a material from a functional unit up is the ability to select the shape and form of the drug delivery platform as it would best suit a specific application.

32

Since affinity based release has been shown to be a diffusion based process with interactive moieties that slow release, dimensions remain critically important and one needs to decide going forward whether their platform will take the form of cyclodextrin- based microparticleslxvii, nanoparticleslxviii, filmslxix, coatingslvi, or hydrogelslxx.

Furthermore, the unique ability to completely or partially complex drugs in these platforms may allow for the creation of materials loaded with multiple drugs that can come out at different release rates. This would be useful in treating ocular diseases if several retinoids can be loaded and released to induce separate, or synergistic, inhibitory or stimulatory responses.

Retinoids and Cyclodextrin

For use in a laboratory setting, beta-cyclodextrin immobilized in a column was able to separate retinoids and carotenoids using chromatographic methods.lxxi Alpha- cyclodextrin has also been shown to separate retinoic acid isomers and its degradation products using capillary electrophoresis.lxxii Likewise, a study on the luminescence and absorption of beta-cyclodextrin retinoid complexes proved that different retinoids do indeed complex with CD.lxxiii Muñoz-Botella et al. further investigated this complex using AFM, 1H-NMR and IR spectroscopy.lxxiv Such complexation also increases the aqueous solubility of retinoid in oral formulationslxxv, and photostability of related retinoids.lxxvi

The use of cyclodextrins with retinoids in the clinic dates back to 1982 when

Pitha and Lajos Szente studied the rescue, or potentiation, of retinoid (all-trans retinoic acid) toxicity when administered with and before cyclodextrin administration. The authors found that when administered together, as a complex, the toxicity of retinoic acid

33 was potentiated through the accelerated dissolution and increased solubility of the retinoid. When CD was given after a dose of retinoic acid it attenuated retinoid related toxicity by lowering the amount of free retinoid, thus improving the survival changes of the animal.lxxvii In a similar application, the frequency of hypervitaminosis was reduced in a 2-year-old boy after he was given a CD derivative in a clinical trial.lxxviii In a patented method addressing toxicity, a method has been described for complexing retinoid based polymers with CD.lxxix

With a known complexation behavior in vivo and in vitro, researchers have tried to develop drug delivery formulations that can exploit this interaction. In one such formulation, McCormack and Gregoriadis were able to encapsulate this complex in liposomes which in turn modulated free retinol concentrations.lxxx,lxxxi Also, a topical ointment containing retinoic acid complexed with CD was twice as potent as a formulation without cyclodextrin.lxxxii In a different application related to capturing retinoids, researchers found that CD can bind and remove all-trans retinol in frog rod photoreceptor outer segments.lxxxiii

Conclusion

Retinoids are being investigated as a therapeutic that can improve ocular conditions including AMD. This effect may be enhanced by delivering these molecules for an extended period of time. A safe and efficacious long term delivery route remains an unanswered problem. Currently available delivery options are suboptimal due to dose related toxicities that are associated with poor control over drug release. No published work has considered a retinoid loaded cyclodextrin polymer based delivery platform for use in the eye.

34

Retinoids are known to form inclusion complexes with CD and we aim to exploit these affinity-based interactions in an eluting device that exhibits controlled delivery. We have developed several formulations of insoluble cyclodextrin polymer based hydrogels that may be implanted into the eye in order to treat ocular diseases. Although insoluble and non-degradable, it is believed that these systems may be reloaded with drug in situ, thereby eliminating the need for surgical removal of the device, while also maintaining an ability to control release by exploiting affinity interactions. Furthermore, a delivery period of at least 30 days may be achieved with a primary loading; and with subsequent reloading this delivery window may be extended. A 30 day frequency is also on par with the injection frequency of currently available wet AMD treatments.lxxxiv We believe retinoid delivery from CD will provide an extended clinical benefit with fewer side effects.

35

Chapter 2. Cyclodextrin Polymer Hydrogels, Synthesis and Mechanical Properties

The von Recum group has previously demonstrated that crosslinked cyclodextrin polymer (CDP) hydrogels can release drug with near zero-order release kinetics.lvi,lviii In one study, the CDP based hydrogels were able to control the release of antibiotic drugs for a period of greater than 30 days.lvii The synthesis procedure was conducted at elevated temperatures (70°C).lvii However, in subsequent publications, the authors used a synthesis procedure that used the same composition of reactants but changed the reaction temperature to room temperature (25°C).lvivi The motivation behind the change in reaction conditions was to be able generate a material that could be synthesized at less harsh conditions. These experiments had an unexpected positive outcome: release rate was drastically different between the two experiments. When the reaction temperature was lowered they were able to speed up the in vitro rate of release by an order of magnitude. For example, in the hexamethylene diisocyanate (HDI) crosslinked cyclodextrin (CD) systems, prepared at a molar ratio of 1 CD to 2 HDI, the release of rifampicin increased from 10% at 14 days, conducted at70°C, to 100% at one week, conducted at RT (room temperature). As such, these results show that release rate may be tunable outcome that is a function of the reaction temperature. Although the in vitro release profiles in solution drastically changed, the delivery system maintained its ability to clear a lawn of bacteria (on agar) for a period of 30 days in both protocols.

While it is difficult to determine what is changing the in vitro release kinetics for a gel in these two different environments, more information into the physical properties of the gels could lend insight into how the solvent interacts with the system and further facilitates release. The crosslinking density of the polymer network, the polymer-solvent

36 interaction parameter, and polymerization conditions can all affect the degree of swelling and therefore influence the rate of releaselxxxv and therefore should be explored as clues in understanding this behavior.

In this study the physical properties of cyclodextrin gels prepared at various temperatures are examined in more detail. A dextran material is prepared in parallel, as a control, since it is has a similar chemical composition but is geometrically different.

Dextran is a linear polymer that lacks the lipophilic cavity that defines cyclodextrin.

Mechanical testing was also performed on hydrogel blends consisting of different weight percentages of cyclodextrin and dextran polymers to see the consequence changes in geometry might have on mechanical properties such as swelling, shear modulus, stress recovery and increasing strain. Also, a method for streamlining the synthesis and drug loading steps of a model drug, all-trans retinol (retinol), is examined.

Materials/Experimental

β-cyclodextrin polymer (2-15kDa) was obtained from CTD, Inc. (High Springs, PL).

Dextran (15-20kDa) was purchased from Polysciences, Inc. (Warrington, PA). 1,6- diisocyanatohexane (HDI) and all-trans retinol, was acquired from Sigma Aldrich (St.

Louis, MO). All-trans retinol was protected from light by covering with aluminum foil and stored at -20°C. Dimethylsulfoxide (DMSO) was obtained from Applied Biosystems

(Foster City, CA). All other materials were procured from Fisher Scientific.

Gelation Kinetics/ Oscillating Time Sweep

β-cyclodextrin polymer (BCDP) and dextran were dried under vacuum at 100⁰C for 24 hours. For all samples, 25% w/v polymer was dissolved in dimethylsulfoxide (DMSO).

For drug loaded samples, a 1% w/w (drug/polymer) of 11-all-tran-retinol was dissolved

37 in DMSO before the polymer was added (Table 6). Homogenization of the dissolved solute was assured by using light heating. A TA instruments AR2000(ex) rheometer was then prepped by equilibrating at a fixed temperature for 10 minutes. The geometry of choice was a 2⁰ 40mm composite cone which would control thermal expansion in the cone shaft. A Peltier base attached to a water bath was chosen to promote a constant, well distributed heat profile. The gap distance was set to 54 µm. Before sample loading the temperature of the Peltier plate was allowed to equilibrate for several minutes. After the rheometer was prepped, 1 mL of sample solution was transferred to a new vial.

Hexamethylene diisocyanate, at an amount equal to a 1:0.16 molar ratio (HDI:CD) of respective polymer, was added to the sample that was then stirred for one minute. The mole ratio of polymer was calculated from one glucose unit. 600 μL of sample was placed directly on the Peltier plate. An experimental method of constant strain (0.2%), constant angular frequency (6.283 rad•s-1) was run for four hours at constant temperature

(excluding a two minute equilibration).

Table 6. List of samples for analysis of gelation behavior. (-) indicates no retinol included in reaction mixture and (+) indicates retinol presence.

Cyclodextrin Polymer Dextran Polymer Temperature (°C) Retinol Temperature (°C) Retinol 50 - 50 - 60 - 60 - 70 - 70 - 70 + 70 +

Preparation of Blended Gels

β-cyclodextrin polymer and dextran were dried under vacuum at 100⁰C for 24 hours. The polymers were weighed and combined in various weight percent ratios in 5 mL glass

38 vials. The weight percentages ranged from 100% CDP to 0% CDP with the other part as

0% dextran to 100% dextran respectively (Table 7). The total mass was 20% w/v. 1 mL of DMSO was added to each 5 mL vial. HDI was added to the samples at a mole ratio of

2:1 (HDI:glucose unit). The samples were vortexed and then 1 mL was removed and put into a 10 cm2 Teflon® dish. The solution was allowed to gel for 24 hours at room temperature at which time it was noticed the solutions had not gelled. The Teflon® sample dishes were then placed in a bell jar oven for two hours at 70°C. A small arch punch, 12 mm diameter, was used to punch out individual gels. The punched out gels were placed into separate 20 mL glass vials and 5 mL DMSO was added to each vial to remove unreacted reactants. After 24 hours, DMSO was poured out and replaced by a 5 mL of a 50/50 DMSO/Deionized Water (<18.2 mega-ohm) solution serving as a secondary wash. After another 24 hours, a third water wash of 10mL replaced the secondary wash. 24 hours later the gels were removed and dried in Teflon® dishes at room temperature.

Table 7. List of polymer blends in terms of polymer weight percentage.

Blended Samples Cyclodextrin Polymer Dextran Polymer 100 0 75 25 50 50 25 75 0 100

Equilibrium Water Content

Equilibrium swelling percentage of the blended hydrogels was determined by first weighing the dried polymer disc (WD), followed by a soak in distilled water for 48 hours at 37°C. The gels were then blotted dry and weighed again (WS). Swelling was calculated

39

using Equation 1. Samples were analyzed in triplicate and presented as averages +/-

standard deviation.

(1)

Modulus of Blended Gels and Network Parameters

The AR2000(ex) rheometer was prepped with a 20mm 2° steel cone. The stage

temperature was set to 37°C. An oscillating frequency sweep with a starting angular

frequency of 0.6283 rad•s-1 was programmed to ramp up on a log scale to 628.3 rad/s.

The strain was set to 0.1%. The samples were loaded under a 2N compression force.

After applying the initial compressive force the gels were allowed to relax, equilibrate for

one minute before the run started. To determine the frequency dependence of a gel’s

storage modulus (G’) log G’ (Pa) was plotted against log frequency (rad•s-1 ). The slope

of the line was subsequently extracted (Eq. 2). A plot of Tan Delta was generated to

relate loss modulus (G’’) to storage modulus. Tan delta was determined by equation 3.

( ) ( ) (2)

⁄ (3)

Network parameters were obtained through the following equations:

D0  2  () (4) D1

GE  e .3 .3 (5) RT223 RT

 M c  (6)  e

40

Φ2 is the polymer volume fraction at equilibrium swelling, where D0 is the diameter of the dry hydrogel and D1 is the diameter of the swollen gel. υe is crosslink density, T is the temperature (K), R is the gas constant (m3 PaK-1mol-1), G is the shear modulus at 6.283

-1 rad•s , E is the Young’s modulus, Mc is the molecular weight between crosslinks, and δ is the hydrogel density (kg/m3).lvivi

Strain Sweep of Blended Gels

The AR2000(ex) rheometer was prepped with a 20mm 2° steel cone. The stage temperature was set to 37°C. An angular frequency of 6.283 rad•s-1 was held constant through the sweep. Strain was set at 0.01% and programmed to ramp on a log scale to

20%. The samples were held under a 3N force compression.

Stress Relaxation of Blended Gels

The AR2000(ex) rheometer was prepped with a 20mm 2° steel cone. The stage temperature was set to 37°C. The samples were held under a 2N compressive force. An applied strain of 10% was applied to the samples with a strain rise time of 0.01 seconds.

Shear stress was recorded for 300 seconds.

Statistical Analysis

A correlation value for various properties was determined using a correlation function found in Microsoft Excel’s 2010 data analysis package. ANOVA single factor analysis was used to determine the statistical significance of various relationships based on an alpha value of 0.05.

41

Results/Discussion

Gelation Kinetics: Sol-Gel Transition

The typical synthesis procedure, urethane bond formation, and loading process for drugs into a hydrogel can be found as Figure 11, 12, and 13, respectively. The synthesis reaction involves a polyol (cyclodextrin polymer) and a diisocyanate (HDI). Either end of the isocyanate can react with a single hydroxyl unit of the polyol. A second step between synthesis and loading is a wash of the hydrogel with water to form a carbamic acid that rapidly decomposes to form an amine and carbon dioxide. lxxxvi The instability of the isocyanate in water eliminates the potential for crosslinking to occur after washing and removes unreacted crossliner. The hydrogels that we crosslinked using this method become a porous network that is generally insoluble in water.

At higher crosslink densities short distance attractive forces such as Van der

Waals forces and intermolecular hydrogen bonding play a role in reducing swelling potential. Conversely, thermodynamic expansion is attributed to the condition and character of the swelling solvent.lxxxvii The distribution of charge between the molecule and solvent, and the resultant orientation of solvent molecules may influence swelling behavior. As such, polar solvents including DMSO and water expand the pores while short chain alcohols, such as ethanol, contract the network (not presented). The balance between the attractive and repulsive factors both inherent to the network, and extensive to environmental interaction parameters of the gel system, determine swelling behavior.

It is important to consider that temperature can change the balance between these forces as well as influence network formation. One of the objectives of this work was to understand if similar attractive and repulsive factors affect the gelation and thermosetting

42 behavior of the material during cure under isothermal conditions. In order to explore this question, cyclodextrin-based hydrogels were formed on the stage of a rheometer and gelation kinetics were further analyzed. Even though crosslink density may be controlled by the mole ratio of crosslinker, a disadvantage of this system is the non-specificity of network formation due to the number of chemically active sites on the polyol, hydroxyls, per oligomer subunit. The growth of the network may therefore be asymmetric and inhomogeneous in nature.

In this experiment a forced oscillation mode was chosen which would keep the oscillating force constant, and could measure the displacement of the sample in shear.

The displacement of the sample is considered a time lag and is synonymous with the phase lag, tan delta, between the applied stress and the response in strain. The dynamic modulus G* is the combined modulus (stress over strain) which relates G’ to an imaginary component, G”, by a factor tan delta.lxxxviii Empirically tan delta is calculated using the following mathematically relationship for the difference in phase with respect to constant frequency:

stress  sin t   strain e e sin t o o (7,8) delta G* G’

G’’ Where ω is the period of strain oscillation, t is time, and δ is the phase lag between stress and strain. The delay of strain is used to calculate G”. G’ is a direct response independent of frequency but dependent on constant strain. For thermosetting materials, the sol-gel transition can be identified as an increase in viscosity before the threshold, followed by

43 an increase in modulus after the transition. The frequency dependence of the real and imaginary parts of the complex elastic modulus G*=G’+iG” provides insight into the critical size of the network during cure. A crossover frequency Ѡ* goes to zero when the system does not behave like a liquid or gel.lxxxix

For thermosetting materials, an increase in viscosity is followed by an increase in modulus.lxxxviiiii Because the G’’ crossover point was difficult to discern in our data, the gelation point was therefore estimated to be the point at which tan delta stabilized.xc

tan delta =G’’/G’ = A = Constant (9)

In other words when tanδ is independent of frequency, gelation can be detected. To establish a clearer gel point one could run a similar experiment at multiple frequencies to find the relaxation moduli crossover point. The maximum storage modulus was only determined if the signal had reached a plateau within the four hour cure period. Table 8 represents information extracted from the graphs of the data (Figure 14,15).

44

Figure 11. Typical synthesis procedure for a cyclodextrin hydrogel and its subsequent drug loading. Adapted from Thatiparti et al.

Figure 12. Urethane bond formation during synthesis. Note that as depicted in 7a there are multiple hydroxyl sites that can form a chemical bond with isocyanate.

45

Figure 13. Drug loading process after network formation.

Table 8. Gelation Time and Storage Modulus from isothermal cure data.

β-cyclodextrin Polymer Dextran Polymer

Temperature Gelation G’ Max Temperature Gelation G’ Max (Pa) (+/-Drug) Time(s) (Pa) (+/-Drug) Time(s)

70 (-) 5300 100000 70 (-) 6500 52000

70(+) 4500 60000 70(+) 4600 18000

60(-) 6500 14000 60(-) 11300 No Plateau

50 (-) 12000 13000 50 (-) No Point No Plateau

46

For a CDP based material, higher temperatures resulted in faster gelation and an increase in the overall maximum modulus. A dextran control was used since it does not have the hydrophobic cage like structure of CD but is comprised of similar monomer units. Such a control is useful when comparing the effects of adding drug to the pre- crosslinked sol. For lower temperature dextran samples, 50 and 60⁰C, the material had not reached an equilibrium and no clear modulus could be recorded. At all temperatures, the dextran had slower gelation kinetics than βCDP. A possible explanation for this is that CDP is bulkier (not linear), and perhaps the initial solution viscosity was higher, resulting in lower chain mobility. In order to fully compare dextran to CD one would need to carry the experiment out for a longer period of time at these lower temperatures.

Figure 14. Gelation Time and Storage Modulus from isothermal cure data of BCDP

47

Figure 15. Gelation Time and Storage Modulus from isothermal cure data of dextran

The incorporation of a model drug, all-trans retinol during gel synthesis could potentially reduce the process time from 96 hours to 4 hours if one does not have to wash the gel and then load the drug separately. We had hoped the incorporation of the drug into the pre-gel would not interfere with gelation kinetics or the final modulus. A lack of interference may occur if the hydroxyl of the retinol was shielded by the cyclodextrin pocket (Figure 16). Our results show that the drug does interfere with both CDP and dextran samples, indicating that there is some sort of interaction with the crosslinker or cyclodextrin which limited the modulus of the gel. It is proposed that in one scenario the amount of drug in solution is at a concentration which overstresses the capacity of the CD pockets meaning that there is free retinol in solution not shielded by CD. A second option is that the affinity in solution is too weak to hydrophobically encapsulate the retinol in a polar solvent. This is also an option because DMSO itself can complex with

48 cyclodextrin.xci In either scenario the hydroxyl on the retinol may react with 1) isocyanate and itself creating dimer, or 2) react with a CD hydroxyl. A decrease in the crosslinker available for polymer to polymer bonds means that the crosslink density will be lower, resulting in a lower modulus. Future analysis using FTIR and NMR may provide additional insight into which scenario is more plausible.

Figure 16. Hydroxyl of all-trans retinol shielded by cyclodextrin may prevent the drug from interfering with the crosslinking.

Blended Gels Swelling and Volume Fraction

Cyclodextrin and dextran polymers were blended together at varying weight percentages in order to study the effect polymer structure may have on mechanical properties and network parameters. As CDP percentage increased there was a strong negative correlation (0.8064) with swelling percentage at equilibrium and the numbers were proven to be statistically different with a P-value of 0.0013. In other words, as the dextran percentage was increased, the hydrogels experienced more hydrodynamic expansion. For example, 25% CDP gels swelled to 256.5%, nearly twice as much as a

100% CDP, 130.8% (Figure 17). Polymer volume fraction and density were also determined. Density, recorded as kg/m3, and volume fraction, a unit less parameter,

49 showed similar correlation behavior (Table 9). The correlation value between decreasing density and decreasing CDP content was 0.848 and statistically significant P<0.05. The correlation between decreased polymer volume fraction and decreasing CDP content was

0.918 with a P-value of 0.024.

Table 9. Density and polymer volume fraction as a function of CDP content

3 CDP Content (%) Density (kg/m ) Φ2 100 1342.06 0.797 75 1159.87 0.746 50 1280.47 0.723 25 942.59 0.610 0 969.67 0.643

Figure 17. Swelling percentage at equilibrium is greatly influenced by CDP content. A correlation of negative 0.8064 with increasing CDP content was determined.

50

Blended Gels Rheology

A plot of log G’ versus log frequency (rad•s-1 ) showed a frequency dependence that was fairly linear on the log scale (Figure 18). A slope was extracted from the data of this plot to determine if there was any relationship between the cyclodextrin content and frequency dependence. A positive slope for G’ is related to relaxation mechanisms that may come from the flexibility of the long molecules within the gel.xcii The slope may also be related to the liquid or solid-like behavior of the material and is therefore a gauge of its viscoelasticity.xciii A correlation value of 0.94 was obtained for the relationship between increased CDP content and the slope for frequency dependence (Table 10). The

P-value also proved to be statistically significant (0.02). Just as well, all the slopes were less than 0.1 which is characteristic of an elastic solid.xciv

Table 10. Extrapolated parameters from log-log plot of shear modulus versus frequency.

r2, fit CD Content (%) m b parameter

100 0.086 3.5686 0.977

75 0.0655 3.4965 0.9746

50 0.0503 3.4967 0.9675

25 0.0509 3.4361 0.9641

0 0.0407 3.424 0.9434

51

Figure 18. Log-Log plot of shear modulus, G’, versus frequency.

3.8

3.75

3.7

3.65

3.6 100% BCDP 75% BCDP 3.55

50% BCDP Log (Pa) G' Log 3.5 25% BCDP 0% BCDP 3.45

3.4

3.35 0 0.5 1 1.5 2 2.5 Log Frequency (rad•s-1)

A further analysis of tan delta of the CDP/dextran blended gels proved that tan delta was fairly independent of frequency at low frequencies (<100 rad•s-1 ).

Furthermore, the loss modulus, representing the viscous portion and energy dissipated as heat, gained on the storage modulus, the elastic component, as cyclodextrin content increased (Figure 19). It is interesting that swelling percentage is inversely proportional to CDP content even though CDP adds to the viscous nature of the hydrogel. This observation may be related to network formation.

52

Figure 19. Tan delta as a function of frequency.

0.3

0.25

0.2

훿

0.15 Tan Tan

0.1

0.05

0 6.283 62.83 628.3 Frequency (rad/s)

Network parameters were further investigated to understand the internal crosslinking structure of the hydrogel (Table 11). The crosslink densities between the formulations were generally higher in formulations with a higher percentage of CDP, as observed by a somewhat strong correlation value of 0.67 and a statistical difference

(P=0.025). The molecular weight between crosslinks, however, had a very weak correlation value, 0.12. Crosslink densities may have been higher with increasing CDP content if the cyclodextrin is more reactive than the dextran. The reduced gelation time of

CDP compared to dextran, during the rheological cure experiment, also supports this claim. The lack of a relationship between crosslink density and the molecular weight between crosslinks may be related to the dispersion of the polymer during the synthesis; such that one polymer clusters during synthesis into microgel like domains whereas the other polymer maintains a better dispersion. It must be noted that Van der Waals forces

53 and inter-chain hydrogen bonding are limited in the solvent,xcv which may aid in the dispersion of the polymer. Future work will look more closely at the intrinsic viscosity of the solutions before gelation so that dispersion concerns can be concretely ruled out.

Questions surrounding polymer solvent interactions after synthesis may be explored using other quantitative means.

Table 11. Network parameters for polymer blends.

CDP % Φ2 E' (Pa) G' (Pa) Ve Mc (kg/mol) (mol/m^3) 100 0.797386 13287 4429 1.838 730.087 75 0.745721 10176 3392 1.436 807.485 50 0.722749 8835 2945 1.259 1017.160 25 0.61 10494 3498 1.573 599.116 0 0.64346 8511 2837 1.256 772.197

A Flory-Huggins solution theory may be used to understand network formation of these materials. A χ parameter based on the polymer’s interaction with water after crosslinking also provides information on how swelling may be mitigated by polymer choice.xcvi χ is given by a modified Flory-Rehner equationxcvii:

XV[ln1  )     ( .33  .5  ) /  2 (10) 2 2e 12 2 2

3 Where V1 is the molar volume of water, 1.8•10^-5 m /mol. A correlation value of 0.937 shows that χ increases with cyclodextrin content (Figure 20). A linear regression for predicted χ parameters versus calculated values is shown by Figure 20. A higher a χ parameter indicates weaker polymer-water interactions. As cyclodextrin polymer content increases, the solvent interacts less with gel. In terms of drug release, diffusion of a drug through the aqueous phase is affected by this χ parameter since diffusivity is a function of

54 swelling.xcviii Less solvent interaction may in term lower the diffusivity of the drug through the hydrogel matrix.

Figure 20. Linear regression for χ parameter versus based on calculated values.

1.4

1.2

1 0.8 Y

Parameter 0.6 - Predicted Y

Chi 0.4 0.2 0 0 20 40 60 80 100 120 Cyclodextrin Polymer Percentage

Blended Gels- Stress Relaxation and Strain Sweep

Stress relaxation is an important mechanical criterion for hydrogel drug delivery because many times a material will be placed in an environment of constant strain that will in turn affect the release of drugs.xcix Similarly, one can imagine that in an environment of cyclic strain (such as the lungs), the speed and extent at which a gel may recovery is of significance. Just as well, an environment that produces a strain of varying degrees will affect the mechanical properties of the gel. In the eye, for instance, it is commonly known that pressure ranges from 10-20 mmHg (max 2666 Pas). Although there were no statistical trends relating CDP content to strain stain sweep behavior (Figure 21), one can tell that materials perform comparably and don’t see a loss in shear modulus until around

8% strain. The stress relaxation behavior after applying a 10% strain also takes a similar

55 shape regardless of CDP content. The complete dissipation of applied stress occurs mostly within the first 10 seconds (Figure 22).

Figure 21. Strain sweep of cyclodextrin/dextran polymer blended gels.

6000

5000

4000 100% CD

3000 75% CD 50% CD 2000

25% CD G' Modulus Modulus G' (Pa) 1000 0% CD

0 0.01% 0.10% 1.00% 10.00% 100.00% Strain %

Figure 22. Stress relaxation after an applied strain of 10% under a 2N force compression.

500

450

400 350 100% CDP 75% CDP 300 50% CDP 250 25% CDP

200 0% CDP Shear Stress Shear (Pa) Stress 150 100 0.01 0.10 1.00 10.00 100.00 1000.00

Time (s)

56

Conclusions

The present work looked into the mechanical properties of cyclodextrin polymer hydrogels to further understand factors that may influence their prospective use as a drug delivery technology. Compared to a dextran control, CDP formed a more tightly bound network that had a lower swelling equilibrium. Less swelling was further correlated with polymer volume fraction. Polymer volume fraction may be relevant to the drug loading capacity/unit volume of the network. Similarly, gelation kinetics were investigated and it was found that dextran undergoes a slower gelation than cyclodextrin polymers.

Although dextran was previously used as a control for its chemical resemblance, its swelling and network parameters should be carefully monitored to make sure diffusion coefficients of prospective drugs are less reliant on mechanical properties such as swelling and more related to interactions within the gel related to cyclodextrin affinity.

Rheological measurements, and its extracted properties, can further be used to look at reproducibility in chemical synthesis. A method for adjusting these properties may be one which exploits synthesis temperature and cure time. Additionally, a method for incorporating drug prior to gel formation proved that the drug will interact with gelation kinetics and affect the mechanical properties of the gel. Dextran blended CDP gels may allow one to alter the affinity content within the systems volume, but will not change the platforms characteristic response to stress recovery or increased strain at extreme conditions.

57

Chapter 3: Cyclodextrin Polymer Gels: Understanding Drug Loading

As discussed in the introduction, a great deal of work has been completed on understanding cyclodextrin and drug interactions. The knowledge base for water soluble cyclodextrin polymers (CDP) is, however, much more limited. Although many methods exist for characterizing cyclodextrin’s ability to bind lipophilic drugs, it is not clear which methods would work for CDP. One translational component of this problem considers drug loading behavior. For example, if a drug loads at different rates into a material that has affinity complexes compared to one that does not, it may be a predicative tool for understanding future release behavior. In testing out the loading part of this theory, a model molecule was used.

Rhodamine B is a guest molecule that can be used to study CDP because it is very water soluble and has a fluorescent response.c Rhodamine was used in the place of a retinoid due to its low cost, availability, and distinctive visual appearance, bright pink. It further provided a useful starting point to understand mechanistically how a drug might interact with cyclodextrin polymers. Rhodamine was loaded into dextran and cyclodextrin polymer hydrogels that were crosslinked with various amounts of crosslinker. A change in crosslinker concentration in the synthesis served to recognize the effect mole ratio of crosslinker to polymer might have on diffusion behavior. Diffusion was tracked using optical photography and subsequent processing. The diffusion behavior of a Rhodamine guest into CDP and dextran based hydrogels provides important information about how a drug might load into these gels.

Materials β-cyclodextrin polymer (2-15kDa) was obtained from CTD, Inc. (High Springs, PL).

Dextran (15-20kDa) was purchased from Polysciences, Inc. (Warrington, PA). 1,6-

58 diisocyanatohexane (HDI) and Rhodamine B was acquired from Sigma Aldrich (St.

Louis, MO). Rhodamine B was refrigerated at 2-8°C. Dimethylsulfoxide (DMSO) was obtained from Applied Biosystems (Foster City, CA). All other materials were procured from Fisher Scientific.

Methods Preparation of Traditional Dextran and Cyclodextrin Polymer Gels

CDP and dextran were dried under vacuum at 100⁰C for 24 hours. First, 200 mg of each polymer was dissolved separately in 1 mL DMSO in a 5 mL glass vial (Table 12). After the polymer was completely dissolved, various mole ratios of hexamethylene diisocyanate were added to respective vials that were then mixed. Molar ratio of crosslinker was calculated as the number of HDI molecules per a molecule of glucose.

0.5 mL was removed using a positive displacement pipet and pipetted into a standard polystyrene cuvettes serving as the reaction vessel. Observations were recorded between

24 and 120 hours of synthesis. Afterwards, a 1 mL wash of DMSO was added to each sample. The wash was replaced 48 hours later and repeated. This wash was then dumped out, leaving just the gel in the cuvette.

Table 12. Rhodamine Loading Study Sample List

Prepared Cuvettes Cyclodextrin Polymer Dextran Polymer Sample Number, HDI ratio Sample Number, HDI ratio #1, 0.16 #4, 0.16 #2, 0.32 #5, 0.32 #3, 0.48 #6, 0.48

59

Rhodamine Loading, Image Acquisition and Processing.

As a preloading reference, a picture was taken with a 12 megapixel Nikon P90 Camera. 1 mg/mL Rhodamine B was prepared in DMSO. 0.5 mL of this the dye solution was added to each cuvette. Pictures were taken in a controlled lighting environment at 3 min, 15 min, 45 min, 1.5 hr, 3 hr, 5 hr, 9 hr, 24 hr, 48 hr, and 2 week time points. Between time points the samples were covered with Parafilm® to make sure solvent did not evaporate.

The samples were also not moved during this time. For a visual reference, a ruler was set next to the cuvettes before the pictures were taken. Images were imported into Google

Picasa and if necessary the photos were straightened. The pictures were cropped to the dimension of the cuvettes to eliminate background. A black and white filter was then applied and the contrast of each photo was adjusted similarly. The images were saved and opened in Image J software (NIH version 1.43). Using the ruler as a reference, a rectangle was of 1.75cm length was created and moved over the samples so that the bottom of the rectangle was nearly touching the edge of the plastic cuvette. This was done for every cuvette. A lane was selected inside the rectangle from the top of the rectangle to the bottom. A profile was then plotted for each lane. The resultant profile was saved to

Microsoft Excel 2010 and then the pixels were normalized by the length of the ruler so that each profile could be plotted against distance rather than pixels.

Results/Discussion

Rhodamine Diffusion Through Traditional Gels

Cyclodextrin and dextran polymer were prepared in cuvettes in order to provide a single surface for a dye front to interface with. Rhodamine B, prepared at 1mg/mL was an ideal dye front to track due to its obvious visual appearance. In synthesizing the gels at RT, one

60 could observe that at time points up to 24 hours, none of the samples looked completely gelled. After 48 hours, all samples except those with the lowest molar ratio of crosslinker

(0.16) had formed stable gels. In subsequent analysis, the 1st and 4th lanes (0.16) are not examined. The dye front was tracked for a period of 2 weeks (Figure 23).

Figure 23. Rhodamine Dye Front Pictures. Lane 1 left to lane 6, far right.

Figure 24. Dye front after applying black and white filter.

61

The gray level units (GLUs) that were extracted from ImageJ were further analyzed as a function of distance along the cuvette in the vertical axis. An increasing gray level shows the front of the dye diffusing into the gel in the direction right to left on

Figure 25, representing a top to bottom direction Figure 24. A comparison of the 24 hour dye front to the 3 minute dye front was made by subtracting the gray levels at a given location of one profile from another (Figure 26). One can notice that a positive difference in gray level means that the gel is taking on the color of the dye. Intensity differences could be seen as an increased area under the curve, yet due to static in the signal from the white balance, a quantitative number is not discussed for loading. Qualitatively, it looks as if the dextran samples absorbed more dye in the first 24 hours than cyclodextrin polymer gels. This may be attributed to the fact that dextran gels swell more than cyclodextrin gels as was discussed in Chapter 1.

62

Figure 25. Gray level as a function of distance along the cuvette at 24 hours (lines shown are 10pt averages) 300

250

200

150 CDP (.32 HDI)

Gray Level Gray CDP (.48 HDI) 100 Dextran (.32 HDI) Dextran (.48 HDI) 50

0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Distance (cm)

Figure 26. Gray level as a function of distance along the cuvette. The level plotted is the 24 hour value minus the value at 3 minutes. A positive value correlates to increased dye presence. Distance is from top to bottom of cuvette (lines shown are 10pt averages). 100

80

60 CDP (.32 HDI) CDP (.48 HDI) Dextran (.32 HDI) 40 Dextran (.48 HDI)

Gray Level Gray 20

0

-20

0 0.5 1 1.5 2 -40 Distance (cm) 63

In order to analyze the dye front in more detail, limits were choosen for a gray level transition regime that could represent the interface between the dye sink and the less saturated gel. As noticed by the 2 week time point, the dye did not appear to reach a complete equilibrium. This means that for drug loading experiments one may have to load for an extended period of time, at a very high concentration to make sure that a concentration gradient is always present. By taking the slope of this transition region, one sought to correlate the polymer that the gel was made from with diffusion behavior.

Figure 27 (a-b) is an example of this transition regime and Table 13 documents the slopes. At 24 hours, the diffusion of the dye appears only regulated by the crosslink density and not the structure of the polymer that was used to make the gel (CDP vs dextran). At 24 hours transition slopes of 260 GLUs/cm are seen for both the dextran sample and CDP sample crosslinked with a 0.32 HDI:CD ratio. Similarly, at 0.48 HDI slopes are 340 GLUs/cm for both samples. A higher slope corresponds with a sharper transition denoting that the dye is diffusing slower due to a higher crosslink density.

Table 13. Slope of gray level transition regime at 24 and 48 hours.

Polymer (Crosslinker) 24 Hour 48 Hours Transition Slope Transition Slope BCDP (0.32 HDI) 262.23 200.19 BCDP (0.48 HDI) 340.52 278.03

Dextran (0.32 HDI) 260.84 250.71 Dextran (0.48 HDI) 339.84 333.65

64

Figure 27a. An example of the gray level transition regime and boundaries at 24 hours. Legend is the same as in Figure 25.

250 100-190 Gray Level Transition

200

150

100 Gray Level Gray

50

0 0 0.2 0.4 0.6 0.8 1 Distance (cm)

Figure 27b. An example of a gray level transition regime and the set boundaries at 48 hours. Legend is the same as in Figure 25.

250

200 100-190 Gray Level Transition

150

100 Gray Level Gray

50

0 0 0.2 0.4 0.6 0.8 1 Distance (cm)

65

At 48 hours the transition region is broader in both CDP samples compared to the dextran gels. A possible explanation for this is that the cyclodextrin pockets inside the gel at longer time points are driving the dye front into the gel faster than what is seen with the dextran samples. A full time course for a single sample, BCDP crosslinked with HDI at a 0.32 molar ratio (Figure 28) is also presented. With time the slope becomes broader reflecting an equilibration of the dye concentration gradient. The dye found in the top of the cuvette, or loading solution, also is shifted down. This change in intensity is from less dye being present in solution and more in the gel. Furthermore, the steepness of the transition is sharpest at 3 minutes and softens as time progresses, which is consistent with previous observations. In experiments where a gel is not loaded for very long time there is consequentially a strong possibility that only the surface of the gel is loaded with drug.

Figure 28. Full time course, 3 min- 2 weeks, of loading data for BCDP crosslinked with HDI at 0.32 molar ratio.

300

250

200

150 Gray Level Gray 100 48ln248hr Hours ln224hr 50 24 Hours 3ln23min Minutes ln22Weeks 0 2 Weeks 0 0.5 1 1.5 2 Distance (cm)

66

Conclusions

A method has been outlined to quantify the diffusion of a dye front through a gel. At shorter time points, up to 24 hours, loading appears to be controlled only by crosslink density, whereas at later time points affinity moieties may play some role in facilitating the loading of the dye. The implications of this work include a method for testing the loading time of a drug into a CDP gel. In the future, other parameters that may influence loading can be analyzed including the effect of swelling, the role of drug concentration gradient, and drug solubility.

67

Chapter 4: Cyclodextrin Polymers:Understanding and Predicting Drug Interactions

In order to understand the process behind retinoid loading and release from a cyclodextrin-based platform, one must first answer fundamental questions behind retinoid and polymer interactions. In this chapter simulation and spectroscopic methods are developed in order to understand the complexation behavior of retinol to various cyclodextrin polymers. Spectroscopic methods were able to solve for thermodynamic parameters of a retinoid in an aqueous environment; these experimental parameters were then compared to simulation values. Docking software of a ligand to cyclodextrin was used to justify the use of a cheaper surrogate molecule, all-trans retinol, in place of other more clinically relevant retinoids. The simulation values were then used in a custom

MATLAB drug release program in order to predict release behavior of hydrogels with affinity interactions.

Materials:

β-cyclodextrin polymer (BCDP), α-cyclodextrin polymer (ACDP), and γ-cyclodextrin

(GCDP) polymer were obtained from CTD, Inc. (High Springs, PL). Dextran (15-20kDa) was purchased from Polysciences, Inc. (Warrington, PA). 1,6-diisocyanatohexane (HDI) and Bovine Serum Albumin (BSA) Fraction V was acquired from Sigma Aldrich (St.

Louis, MO). BSA solutions were purified before use with PALL 0.2 µm Acrodisc

Syringe Filters. All-trans retinol, also bought from Sigma, was protected from light by covering with aluminum foil and stored at 2-8°C. Dimethylsulfoxide (DMSO) was obtained from Applied Biosystems (Foster City, CA). Hyclone PBS (pH 7.4) from Fisher was diluted with DI water (<18.2 mega-ohm) from 10x to 1x and then autoclaved before use. All other materials were procured from Fisher Scientific.

68

Methods:

UV-Vis Retinol Fit in Different Polymers

250mg of each polymer (ACDP, BCDP, GCDP, dextran) was dissolved into 1 mL of

DMSO in separate vials. 500µL of each polymer solution was combined with 10µL of a

1mg/mL all-trans retinol in 100% ethanol solution. The samples were incubated for 30 minutes at room temperature. 100uL of each sample was added to a 96 well flat bottom

Costar plate. A Tecan SAF II plate reader was programmed to excite at 330 nm and then the emissions wavelength was read from 350 nm to 650 nm with a bandwidth of 5 nm.

The Z-position was manually optimized using an excitation wavelength of 330 nm and emissions of 510 nm. After an emission maximum was determined, the wells were reanalyzed over a time course of 126 minutes. The temperature was raised several degrees every 10 minutes from RT to 40°C. After 40°C the temperature was lowered back down using similar steps to 27.5 °C. The temperature was controlled using the incubator function on the plate reader. Before the temperature was set, the plate was read and resultant emission value was recorded. Dextran polymer and DMSO with no polymer but with retinol were used as controls.

Docking Software Simulations

Docking simulations were performed on various retinoids using cyclodextrin.net software for drawing and predicting interactions with various cyclodextrins. The simulation abstract is provided below with an example molecule “Retinol” interacting with beta- cyclodextrin. Method from cyclodextrin.net:

“Docking calculations were carried out using CycloPredict. The Dreiding force field (Mayo, 1990) was used for energy minimization of ligand

69

molecule Retinol using built-in Chemaxon tools in CycloPredict. PM6 semiempirical charges calcuted by MOPAC2007 (J. P. Stewart, Computer code MOPAC2007, Stewart Computational Chemistry, 2007) were added to the ligand atoms. Non-polar hydrogen atoms were merged, and rotatable bonds were defined. Docking calculations were carried out on β-Cyclodextrin, BCD model. Essential hydrogen atoms, Kollman united atom type charges, and solvation parameters were added with the aid of AutoDock tools (Morris, Goodsell et al., 1998). Affinity (grid) maps of 20×20×20 Å grid points and 0.375 Å spacing were generated using the Autogrid program (Morris, Goodsell et al., 1998). AutoDock parameter set- and distance-dependent dielectric functions were used in the calculation of the van der Waals and the electrostatic terms, respectively. Docking simulations were performed using the Lamarckian genetic algorithm (LGA) and the Solis & Wets local search method (Solis and Wets, 1981). Initial position, orientation, and torsions of the ligand molecules were set randomly. Each docking experiment was derived from 10 different runs that were set to terminate after a maximum of 2500000 energy evaluations. The population size was set to 150. During the search, a translational step of 0.2 Å, and quaternion and torsion steps of 5 were applied.” The retinoid molecules were then compared to experimental values found in

Muñoz-Botella et al. ci All-trans retinol as a ligand was also simulated with

ACDP, BCDP, and GCDP. An ANOVA linear regression that sought to compare simulation values to experimental values was performed with Microsoft Excel

2010.

Thermodynamic Value Determination A 0.25 mg/mL retinol in DMSO solution was prepared and diluted 1:1 with 1x PBS (pH

7.4). The solution was vortexed and added to 5 mL vials with different amounts of pre- weighed BCDP inside. The effect of polymer concentration on emission maxima was determined by first preparing beta-cyclodextrin polymer in DMSO (2.5, 10, and 25 mg/mL) with .125mg/mL retinol. These samples were excited at 385 nm and scanned from 400 nm to 700 nm. The bandwidth was 5 nm and the number of flashes was 3 with a time between reads of 100 ms. Subsequently, new samples of beta-cyclodextrin polymer were pre-weighed at 10, 15, 20, 25, 50, 75, and 0 mg making the effective concentrations

5, 7.5, 12.5, 25, 37.5, and 0 mg/mL respectively after being added to a 2 mL retinol

70 solution. A DMSO control with no polymer was also prepared. 185 µL of each sample was added in triplicate to separate wells in a Costar white transparent bottom 96 well plates. The plate was read using an excitation wavelength of 385 nm and an emission wavelength of 510 nm. The gain was set to 100, and the time between move and flash was 100 ms, where the number of flashes was set to 5. After reading all the samples, a new temperature was set and allowed to incubate for 20 minutes before the next reading.

This temperature range was from 24.4 to 42.1°C with a step of 3° per a measurement.

Results

UV-Vis Retinol Fit in Different Polymers

An emissions spectrum indicated that each of the polymers had slightly different emission maximums. The emissions max for polymers with retinol were 502 nm, 509 nm,

514 nm, 507 nm, and 504 nm for DMSO, dextran, ACDP, GCDP, and BCDP respectively (Figure 29). A change in emission maximum is also known as a Stokes shift; in previous reports a dye can change its Stoke’s shift in the presence of a dopant of varying size, polarity, and relaxation time.cii Accounting for the bandwidth of the detector in this run, 5 nm, the differences are near negligible, therefore the shift was not determined to be a major experimental determinant.

71

Figure 29. 330 nm excitation and emissions scan for various polymers doped with retinol at 25°C.

2500 DMSO+ Retinol

Dextran + Retinol 2000

ACDP + Retinol

GCDP + Retinol 1500 BCDP + Retinol

1000 Emmisions (AU) Emmisions

500

0 300 400 500 600 700

Wavelength (nm)

As temperature was increased all of the samples had a decrease in their emissions signal (Figure 30). This decrease plateaued when the temperature reached a maximum at

40°C. Other researchers have stated that with an increase in temperature there is more collisional deactivation that then produces a lower fluorescent quantum yield and a decreased decay time.ciii Conversely, with a decrease in temperature there should be an increase in quantum yield and increased decay time, which was also seen. However, in the case of DMSO and dextran, this did not happen, as can be seen by the yield remaining unchanged when temperature was once again lowered. It is possible that when the retinol was initially dissolved in these two solutions it had not yet reached a solid-solute phase

72 equilibriumciv. This equilibrium, or complete dissolution, was a side effect of additional heating in the DMSO and dextran samples. The similar behavior between DMSO and dextran samples is also viewed as preliminary evidence that there is no complexation behavior in these controls.

Cyclodextrins have previously been shown to increases the fluorescence of complexed molecules in solution due to factors such as enhanced solubility. The enhanced solubility may result in better separation of fluorophores, which would then produce a higher fluorescent yield.cv For cyclodextrin polymers, a decrease in temperature resulted in an increased emissions signal. There was also enhanced separation between the different types of polymers. To further analyze this separation, a ratio was calculated based on the signal from a sample divided by the signal of a DMSO sample control (no polymer) at a given temperature (Table 14). The quantum yield of retinol is usually determined on a relative basis,cvi which is why a ratio and not absolute yield value was determined. As defined, GCDP had an increase of 2.16, followed by

BCDP and ACDP with 1.82 and 1.44 respectively at the end of the experiment (27.5°C).

This experiment shows that not only do cyclodextrin polymers behave in the same way as single cyclodextrins, but some polymer species are more effective at complexing retinoids than others.

Table 14. Change in emission signal compared to DMSO control at 40°C and end of experiment.

Ratio Ratio Sample 40°C 27.5°C 40°C 27.5°C DMSO 1051 1.00 1009 1.00 Dextran 1065 1.01 1049 1.04 ACDP 1134 1.08 1456 1.44 GCDP 1372 1.31 2178 2.16 BCDP 1199 1.14 1833 1.82

73

Figure 30. Retinol emissions from solutions containing different types of cyclodextrin polymers.

2500 45 40

2000 35

30 DMSO 1500 25 Dextran 20 ACDP 1000 15 GCDP Emissions (AU) Emissions BCDP 10 500 Temperature 5 0 0 0 50 100 150 Time (minutes)

All-trans retinol Simulation

To further understand the complexation behavior of all-trans retinol to various cyclodextrin polymers, all-trans retinol was a docking simulation was performed on cyclodextrin.net. The software predicted binding energies of -4.83 kcal, -4.78 kcal, and -

4.14 kcal for GCDP, BCDP, and ACDP respectively. The order of these values is similar to those found in the prior experiment that was completed under a temperatures scale.

Using retinoids with documented experimental association constants (Muñoz-Botella et al. paper), we then compared the Gibbs free energy of binding from the model to verify that the model was accurate (Table 15). If the model proved to be precise we could run a simulation on all-trans retinol to see if it could be used as a surrogate molecule for a novel therapeutic retinoid, retinyl acetate or other. An ANOVA linear regression was performed on this data set and is plotted as Figure 31. The regression shows that the

74 simulation data set does relate well to the experimental data. The relationship between association constant and free energy of binding is discussed later. The t-stat value of 4.81 does indicate considerable deviation from the predicted means, however, a P-value of less than 0.05 for both the intercept and the x-variable denotes a reasonable fit. Comparing the free energy of binding of all-trans retinol to 9-cis retinyl acetate one comes up with a nearly identical value, -4.78 to -4.82 respectively (Table 16). These similar values illustrate that all-trans retinol is a valid surrogate retinoid for other more relevant therapeutic agents.

Table 15. Free energy of binding from simulation and experimental association constants based on Muñoz-Botella et al. data.ci

Retinoid Name Gibbs Free Energy of Binding (kcal) Log Ka (Paper) All-trans retinal to BCD -4.96 5.99

9-cis-retinal BCD -4.65 5.9

13-cis-retinal BCD -4.6 5.8

All-trans retinoic acid BCD -4.77 5.76

13-cis-retinal HPBCD -5.3 5.14

All-trans-retinyl acetate BCD -5.02 4.81

All-trans-retinal HPBCD -5.19 3.91

All-trans-retinyl acetate HPBCD -5.18 3.45

All-trans retinoic acid HPBCD -5.47 2.82

75

Figure 31. A linear regression plot for experimental values against simulated values from docking experiments. Regression statistics including coefficients, standard error, t-stat, and P- value are also presented.

X Variable 1 Line Fit Plot 7

6

5

4 Ka Y

Log Log 3 Predicted Y 2

1

0 -5.6 -5.4 -5.2 -5 -4.8 -4.6 -4.4 Est. Free Energy of Binding (kcal)

Table 16. Free energy of binding from docking simulations for all-trans retinol and retinyl acetate to beta-cyclodextrin. Picture of BCD and retinol inclusion shown on the right.

Est. Free Energy of Complex Binding (kcal) 9-cis-retinyl acetate to β- -4.82 Cyclodextrin, BCD

Retinol to β-Cyclodextrin, -4.78 BCD

76

BCDP Concentration and Emissions Yield Previous researchers have established methods for determining thermodynamic parameters, including entropy, enthalpy, and Gibbs free energy, for cyclodextrin inclusion complexes.cvii Using similar methods we aimed to understand the complexation behavior of cyclodextrin polymers to all-trans retinol. The intent of which was to provide a secondary validation method to the simulation work already discussed. Due to the similarities in the expected interactions between retinol and GCDP or BCDP, we chose

BCDP for this work because it was readily available and lower in cost. From our prior work we knew that complexation behavior is very dependent on temperature. The following experiments thereby exploit both temperature and concentration of polymer as variables in order to discriminate thermodynamic parameters. Before exploiting these variables, a primary emissions scan of retinol in three different concentrations of BCDP

(Figure 32) was completed to make sure that concentration would indeed have an effect on response similar to that found with cyclodextrin oligomers by Catena et al.cvii As cyclodextrin polymer concentration was increased, the fluorescent response also increased. This effect may be attributed to enhanced solubility of the retinoid in solution.

Further separation of the fluorophore would yield a greater fluorescence yield, consistent with previously stated findings comparing CDP to dextran.

These initial findings led to a more complete study of retinol’s fluorescence at different concentrations and temperatures (Figure 33). With increasing temperature there was a decreased response across all concentrations of BCDP except in the control where there was no change. The decrease in response also had a very linear relationship with temperature.

77

Figure 32. 0.125mg/mL retinol in DMSO with various concentrations of BCDP.

9000

8000

7000

6000

5000 2.5mg/mL 4000 10 mg/mL 3000 Emission (AU) Emission 25mg/mL 2000

1000

0 400 450 500 550 Wavelength (nm)

Figure 33. 0.125mg/mL retinol in DMSO with various concentrations of BCDP at different temperatures. Excitation at 385 nm, Emissions at 510 nm.

16000

14000

12000 5 mg/mL+ R 10000 7.5 mg/mL+ R 8000 10 mg/mL+ R

6000 12.5 mg/mL+ R

4000 25 mg/mL+ R

2000 37.5 mg/mL+R Excitation at 510nm (AU) 510nm at Excitation 0 0 mg/mL+R 20 25 30 35 40 45 Temperature (°C)

78

Using the temperature sweep data we aimed to find an equilibrium constant that was related to the concentration of BCDP. To do this we had to first convert the concentration to a molar unit, which required a number average molecular weight. Gel permeation chromatography (not shown) determined a number average molecular weight for BCDP of 7864 Da. A double reciprocal plot of 1/intensity versus 1/concentration was used to find the equilibrium constant at each temperature. The constant is related to this plot by the Michaelis-Menten equation.cvii

1K 1 1 ()m (1) v Vmax[] S V max

v is the fluorescent intensity, Vmax, is the intensity of the lowest concentration, S is the concentration of polymer, and Km is the equilibrium constant (Figure 34). A linear regression therefore produces an intercept as well as a slope. The intercept divided by the slope is the equilibrium constant. Other researchers use a similar Benesi-Hildebrand plot to extract binding constants, though there are no discernable differences except perhaps more accuracy at low concentrations.cviii The R2 value of 0.9984 indicates not only a good fit but is also proof of a 1:1 complexation ratio between the guest and the host.cvii

79

Figure 34. A double reciprocal plot can be used to determine an equilibrium constant for of retinol complexed with various concentrations of BCDP at a single temperature (24.4°C shown).

1.4E-04

1.2E-04

1.0E-04

8.0E-05

6.0E-05 y = 4E-08x + 6E-05

1/Intensity (AU) 1/Intensity 4.0E-05 R² = 0.9984

2.0E-05

0.0E+00 0 500 1000 1500 2000 1/Concentration (M)

An Arrhenius plot across all temperatures further represents the relationship between ln(K) and 1/(°K). The Van’t Hoff Equation can then be used to determine thermodynamic parameters.

HS ln K    (2) RT R K is the equilibrium constant, H is enthalpy, S is entropy, R is the universal gas constant

(8.3145 JK-1mol-1), and T is temperature (°K). From this equation one can find the enthalpy as Slope•R•-1 (kJ mol-1) and entropy (kJ/K•mol). This plot across the range of temperatures (Figure 35) resulted in an enthalpy of -6.97 and entropy of 37.07. Using a standard equation for Gibbs free energy we were able to find the free energy of binding,

-4.415 kcal.

GHTS     (3)

80

Figure 35. Arrhenius plot of ln(K) versus 1/T is used to find thermodynamic properties.

7.3 7.28 7.26 7.24 7.22

7.2

ln(K) 7.18 y = 839x + 4.4581 7.16 R² = 0.9563 7.14 7.12 7.1 7.08 0.00315 0.0032 0.00325 0.0033 0.00335 0.0034 1/T (°K)

Our first impression was that this experimental value was indistinguishable from the simulation values of -4.78 kcal, however, we were uncertain the role a difference of .38 kcal would play in drug release. To appreciate this difference a drug release model was coded using MATLAB R2010b.

Drug Release Modeling

Gibbs free energy of binding, as it pertains to temperature dependent entropy values, may be influenced by the size of the cavity as well as the shape of the ligand. We aimed to hold these variables constant in developing a drug release model. Because there were slight differences in the free energy of binding between our experimental number and those found in simulation, we were interested in how release might be influenced by such variance. Before we could model drug release we first had to define the boundaries of our drug delivery system. We chose a rectangular system based on matrix diffusion that could

81 be compared to a non-degradable implant (Figure 36). The simple diffusion equation we used was based on Fick’s second law where D is the diffusion coefficient (Eq 4).

(4)

Figure 36. Sample geometry and boundary conditions. x=L

x=0

I.C.: C(x,t) = C0, t=0, 0

B.C.: C(x,t) = 0, t>0, x=L (and/or –L)

( ) , t>0, x=0

Finite differences/method of lines were used to solve the partial differential equation.

This was completed in MATLAB R2010b using a user written function (see below). function dCdt = dC(t,C,C0,dx,N,D,Kb) % Method of Lines + boundary conditions dCdt = zeros(N,1); % dC/dt[0] = 0 dCdt(1)=2*D/(Kb*C0/C(1)+1)*(C(2)-C(1))/dx^2; for i=2:N-2 dCdt(i)=D/(Kb*C0/C(i)+1)*(C(i+1)-2*C(i)+C(i-1))/dx^2; end % C[N] = 0 dCdt(N-1)=D/(Kb*C0/C(N-1)+1)*(-2*C(N-1)+C(N-2))/dx^2;

The diffusion coefficient was set at a constant value representing the diffusion of some small molecule through a dextran control. In the literature we found that chemically similar dextran hydrogel used to release indomethacin had a diffusivity of 1•10-7.cix A relationship between diffusivity in a system that had immobilized affinity elements and one which did not was further determined. A diffusion coefficient may be lowered by a

82 variable (Kb) representative of the interaction between the cyclodextrin polymer and the ligand through the following relationshipcx:

Dcyclodextrin=Dcextran/(Kb+1) (5)

Dcyclodextrin is the diffusion coefficient of cyclodextrin and Ddextran is the coefficient of dextran. Kb relates information of the equilibrium association Ka or dissociation Kd for receptor-ligand pairs.cx

kf LRRL  kr

0kfr [ L ][ R ] k [ L R ] ; at equilibrium

1 [LR ][ ] kr Kd    Kaf[] L R k (6)

L is the drug and R is the receptor, cyclodextrin. If [L] = cA and the complex [L•R]=cB when the receptor concentration is constant, as is the case in a hydrogel, the constant Kb is cx:

cB []R Kb  cKAd (7)

Gibbs free energy as the free energy of binding (kcal), based on a 1:1 binding interaction, was related to Kb through the previous equations using a Ka based on a thermodynamic relationship:

⍙G = –RTlnKa (8)

In this manner one could use the free energy, and solve the previous equations backwards to extract Kb. In our model, the ratio of drug to cyclodextrin was initial set at 1000:1, therefore, the binding affinity distributed over each drug molecule is 1/1000 of Ka.

[R]= .001•(Co/Ct) (9)

83

Other parameters that were chosen were width of the gel (0.2 cm) and an initial concentration (60ng/L). Ode15s, a MATLAB differential equation solver, based on a set time interval and an initial concentration, could pass values on to dCdt (method of lines function). Because we were comparing a simulation value (-4.78 kcal) to an experimental value (-4.41 kcal), a value in the middle, -4.59 kcal, was chosen as a combined reference.

Plots for concentration vs. space and time as well as plots of cumulative release versus time were generated for these values (dextran- Figure 37, CDP -38).

From these plots there are only slight differences in cumulative release between our experimental value and the Gibbs free energy value found in docking simulations. It appears, however, that at shorter time frames the difference in cumulative release is greater than that at longer times. Half-lives and other parameters could be extracted but it is the researchers’ belief that the model is not sensitive enough to environmental factors that were simplified. Because we modeled this system as diffusion with reversible binding to immobilized elements, the relationship between the number of drug molecules and the number of receptors greatly influences release properties. The simplification that we used is of a distributed affinity so that one molecule has an increasing probability of interacting with a receptor as less drug is present in the system. It is likely that this relationship is not linear since the presence of too much drug, such as a system that has way more drug than cyclodextrin pockets, can affect the binding behavior of bound drug.

In other words, when there is too much drug it is possible for the complexation ratio to change from being 1:1 to 2:1, which would in turn affect the affinity of a given molecule to a cyclodextrin. Future models may consider a better way to distribute this interaction.

84

Figure 37. Dextran drug release simulation with no affinity term, pure diffusion. Plots of space and time (left) as well as cumulative release (right).

Figure 38. Experimental (top), simulation (bottom) and in-between (middle) free energy values through a CDP hydrogel were used to model concentration versus space and time (left) as well as cumulative release (right).

85

Conclusion

In this chapter experimental and computational based methods were used to characterize the complexation behavior of retinoids with cyclodextrin oligomers and cyclodextrin polymers. We first found that guest-host interactions are influenced by temperature, thus signifying that this complexation relationship is entropy driven at low temperatures. We also compared three different types of cyclodextrin polymers and saw that GCDP forms the best complex with cyclodextrin, followed by BCDP and then

ACDP. Docking simulations were performed on various retinoids and were then compared to experimental data to verify the validity of this simulation. Successfully, the simulations generated free energies of binding for a single cyclodextrin unit that had the same order of binding strengths (greatest to least) to that which was found experimentally with cyclodextrin polymers. Similarly, also using an experimental method, exact thermodynamic parameters for all-trans retinol’s complexation with cyclodextrin polymer was found to have closely related Gibbs free energies (kcal) compared to simulation.

These results provided proof that all-trans retinol may be used as a surrogate molecule for the release of other more therapeutically relevant retinoids. BCDP is used in later work because of its confirmed ability to form complexes with retinoids and due to its lower price compared to GCDP. Using the Gibbs free energy of binding we built a drug release model to predict the diffusion of retinol through a hydrogel. The model illustrated the significance of the free energy of binding in lowering the diffusion coefficient. Compared to a dextran control, a cyclodextrin-based material has affinity moieties that lower the diffusion coefficient and move the cumulative release curve down and to the right. Although this shift makes the release look slightly more linear and zero-

86 order, concentration still has a strong dependence on release. Such behavior is used as a point of reference in later work.

87

Chapter 5- Retinoid Delivery with 1st Generation Cyclodextrin Hydrogel

Retinol-cyclodextrin guest-host interactions were previously investigated through experimental and computational methods. A confirmed interaction of retinol with both cyclodextrin and cyclodextrin polymers allowed us to consider the use of a cyclodextrin polymer based hydrogel for use as a drug delivery technology. The low solubility of retinol in water was an additional consideration that we believed would increase the probability of success for this platform.

A drug delivery model predicted retinol release for a period of at least 15 days.

We aimed to exceed this model’s prediction by delivering drug for a period of 30 days or greater. A first generation cyclodextrin-based delivery system was designed with this goal in mind. A loading protocol and quantification method were developed to test a cyclodextrin polymer hydrogel prototype against a chemically similar dextran hydrogel.

Drug release was further analyzed using a two phase release equation that related percent of drug released to a short burst phase and a slow secondary phase release. Limitations of this analysis and the platform in general are also elaborated on. The primary objective, however, was to develop a proof of concept material that could be modified and optimized in later studies.

Materials

β-cyclodextrin polymer (2-15kDa) was obtained from CTD, Inc. (High Springs, PL).

Dextran (15-20kDa) was purchased from Polysciences, Inc. (Warrington, PA). 1,6- diisocyanatohexane (HDI) and all-trans retinol, was acquired from Sigma Aldrich (St.

Louis, MO). All-trans retinol was protected from light by covering with aluminum foil and stored at -20°C. Hyclone PBS (pH 7.4) from Fisher was diluted with DI water (<18.2

88 mega-ohm) from 10x to 1x and then autoclaved before use. Bovine Serum Albumin,

Fraction V, was obtained from Fisher and stored at 2-8°C before use. Dimethylsulfoxide

(DMSO) was obtained from Applied Biosystems (Foster City, CA). All other materials were procured from Fisher Scientific.

Methods

Preparation of 1st Generation Gels

β-cyclodextrin polymer (BCDP) and dextran were dried under vacuum at 100⁰C for 24 hours. 1 gram BCDP was dissolved in 4 mL DMSO in a 5 mL glass vial. HDI was added at a ratio equivalent to 1 mole HDI/ 1 mole of glucose unit, or 1 mole HDI to 0.16 mole

CD. The gel was prepared in a Teflon® dish that was heated for 2.25 hours at 70°C. The gel was further air dried for 24 hours at RT. 12 mm diameter gels were punched out using a small arch punch. The gels were placed in a 20 mL scintillation vial and 15 mL DI water was added. The gels were washed in water for 24 hours. A subsequent wash in excess DMSO was conducted for 24 hours followed by two additional water washes for

24 hours each. Enough water was added to cover the samples and then the samples were frozen with liquid nitrogen. The gels were subsequently lyophilized using a Virtis

Freezemobile Freeze Dryer. The air temperature was set to approximately -70°C and water vapor was removed under vacuum. 48 hours later the gels were removed and stored at RT prior to loading.

Drug Loading 1st Generation

All-trans retinol was dissolved in 100% ethanol at 7.5 mg/mL. This solution was diluted with 3 parts DI water. The effective loading sink concentration was therefore approximately 1.9 mg/mL. 1 mL of this solution was added to individual samples of

89 dextran and CD (n=3) and incubated at 37°C for 96 hours. Every 24 hours, observations of the loading sink were made.

Release Study of 1st generation Gels

In preparing the release sink, 1% w/v bovine serum albumin was dissolved in sterile PBS.

1 mL of sink solution was placed in 5 mL glass vials. At given time points 1 mL of sample was removed and replaced by fresh release sink. The release study was conducted for 32 days.

Quantification

A calibration curve of retinol in a 1% BSA solution was generated using a Tecan Safire I plate reader. Different amounts of retinol, dissolved in ethanol, were added to the BSA solution in order to generate a calibration curve. Retinol presence was detected as a function of the quenched emission signal from BSA due to incorporation of retinol in the proteins hydrophobic core. 200 uL calibration samples were aliquoted in triplicate in a

Costar 96 well plate. Samples were excited at 280 nm and their emissions spectrums were recorded from 300 nm to 450 nm with a step of 2 nm. Release samples from the various time points were also collected and quantified based on the calibration curve at their 350 nm emissions value.

Results

Drug Loading 1st Generation

The loading of the gels was completed in a hypersaturated sink, such that a fraction of the retinol that was not soluble in the liquid phase was free-floating in suspension. The loading solution was in 75% Water/ 25% Ethanol with retinol dissolved at 1.9mg/mL. The suspension initially took the appearance of a turbid and cloudy

90 solution. After 24 hours of gel loading, the solutions were still cloudy, yet after 48 hours the loading sink took on a pale yellow and transparent appearance. Since the gel does not swell in ethanol (not shown), the swelling in this sink is solely due to the water. Because of retinols poor solubility in water, 0.06µMcxi, the retinol was initially dissolved in the ethanol, which has solubilizing capacity of at least 0.035M based on initial observations.

The co-solvent drastically reduces the solubility of retinol which is why a suspension is seen. The water is needed because the gel loads through a hydrodynamic welling mechanism and these forces are desensitized by ethanol. It was also hypothesized that less swelling would lower drug loading. The change in turbidity is due to less retinol in the sink related to the hydrophobic incorporation/loading of retinol inside the gel.

Future work will aim to quantify the change of concentration of retinol in the loading sink during loading. One can further postulate the relationship between the hydrophobic character of the gel and the speed at which loading occurs. An optimum loading time can also be established by relating the concentration of retinol removed from the sink vs. time. Maximum loading of retinol may therefore dependent on time, the concentration gradient across the gel (perhaps a function of swelling percentage), and the ability to maintain retinol in suspension. For the first generation cyclodextrin hydrogels the exact drug loading was not determined. As such, release profiles presented later are cumulative in terms of mass of retinol released and not as a percentage unless otherwise stated.

Quantification Protocol

In the blood stream retinoids are transported by retinoid binding proteins (RBP).

These proteins are able to shuttle retinoids to the blood-retina barrier where they are

91 picked up by RPE cells that will further process them for use in the visual phototransduction cascade.cxii,cxiii In designing a release sink we wanted to have a protein in solution that could increase retinol’s solubility but remain biologically relevant. Due to the high cost of acquiring RBP this work used a different protein, bovine serum albumin, as the release sink receptor for retinol.

Das et al. has previously described the complexation behavior of a pyrylretinol, a different retinoid, with BSA. cxiv In this work BSA’s fluorescence decreased proportionally to the amount of retinoid in solution. Other work by N'soukpoe-Kossi et al. also documented similar behavior with human serum albumin using FTIR, UV-vis,

CD and fluorescence spectroscopic methods.cxv These proteins are able to bind retinol due to their lipophilic core (Figure 39) and when retinol is present in high concentration it stabilizes the protein’s secondary structure cxv. We sought to exploit this behavior with all-trans retinol. Using an excitation wavelength of 280 nm and an emissions spectrum of

300-450 nm the fluorescence of BSA in the presence of difference concentrations of retinol was recorded (Figure 40).

Figure 39. Visual Molecular Dynamics (University of Illinios) illustration of BSA and its hydrophobic domains labeled as beads.

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Figure 40. BSA fluorescence quenched by increasing concentration of retinol. Down arrow represents increasing concentration.

40000

35000

30000

25000

20000

15000 Emission (AU) Emission 10000

5000

0 300 350 400 450 500 Wavelength (nm)

The maximum emission was recorded at 350 nm. In order to relate the intensity of

BSA’s fluorescence to the presence of retinol a Stern-Volmer equation was used.

F 0 1KQ [ ] (1) F SV

where F0 is the fluorescence without a quenching agent, and F is the signal after a quencher is added. The quencher in this example is retinol. KSV is the quenching constant, and [Q] is the concentrations of retinol. KSV can be determined from plotting difference concentrations of quencher versus concentration.cxvi The linear concentration range plotted in Figure 41 is from 17 µM to 830.7 µM (0.005 mg/mL to 0.238 mg/mL).

This range provides approximately 1.5 orders of magnitude detection sensitivity. The

2 constant was found to be 9.091 AU/mg. The linear fit, described by R , was 0.996 across

93 this range. This exceptional fit confirmed that the quenching behavior of retinol in a BSA sink can be used as a calibration curve for future release studies.

Figure 41. Stern-Volmer plot for retinol quenching BSA fluorescence across a concentration range.

2.5

2

1.5

1 Fo/F (AU) Fo/F 0.5

0 0 0.05 0.1 0.15 0.2 0.25

-0.5 Concentration (mg/mL)

Release Profile A release profile for cyclodextrin polymer based hydrogels and dextran based gels was generated by sampling the release sink at various intervals up to 32 days. The all- trans retinol concentration in solution is presented as a cumulative mass (g). As one can tell from the release (Figure 42), the cumulative release was under 0.2 mg for both sets of samples. The dextran gels had a sharp burst release followed by a secondary release at 5 days. CDP gels displayed a more linear profile. Similarly, the BCDP gels appear to not have finished releasing whereas the dextran gels have a profile that indicates complete release. The low cumulative release is a result of poor loading of the gels. Since the gels were loaded at 1.9 mg/mL it is assumed that the loading capacity was not fully reached.

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Figure 42. Retinol release from BCDP and dextran hydrogels was recorded for a period of 32 days.

It is believed that the CDP gel was able to load more drug in similar conditions due to the increased solubility of retinoid in the presence of CD cavities. The chemically similar but structurally dissimilar dextran was not able to complex and load retinoid to the same degree. In later work swelling equilibrium was examined as an additional explanation for this phenomenon. The results of other work (not shown) with lyophilized gels showed that dextran gels actually swell almost two times more than CDP gels synthesized in a similar fashion. The hypothesis that swelling alone will result in higher loading is thereby discredited.

The kinetics of the presented release was analyzed using a biphasic release equation:

95

 k1at  k1bt (2) % Diss (t)  100  ( f a  e  (1  f a )  e ) 100 where k1a is the rate constant for the first phase of release and k1b is the rate constant of the second phase of release. fa represents some fraction of the dose and % Diss is the amount released. The first phase represents a burst release while the second phase represents a slow release. These terms can be better represented by the following diagram

(Figure 43):

Figure 43. Release may be described as a biphasic process (first phase red, second phase purple line).

Release %

Log Time

Using a nonlinear least squares fitter from statpages.org, we were able to find the parameters of best fit for this series of data. Eq. 2 represents a percent released so in our fitting we set the cumulative concentration at Day 32 to 100% (Figure 44). The fitted parameters (Table 17) indicate that the burst phase release is responsible for a much greater fraction of the release in dextran materials compared to CDP materials. The estimates also indicate that the burst rate was faster in dextran gels compared to cyclodextrin gels, which is consistent with our hypothesis that CD will retard release. The second phase of release has very similar rate constants 0.101543 and 0.10135, for dextran and CDP respectively. This is concerning because it means that in later stages of release the affinity complexes are not doing much to slow release.

96

Table 17. Summary of fit parameters estimated error and associated P-value.

Figure 44. Fitted values based on biphasic model. Error bars represent data point averages and standard error. Purple squares are fitted values for dextran and blue squares are fitted values for BCDP.

120

100

80

Release (%) Release 60

40

20 Cumulative Cumulative 0 0 10 20 30 40

Time (Days)

The fitted parameters can also be used to determine relevant pharmacokinetic benchmarks such as t1/2 and t8/10 (Table 18). The t1/2 for the slow release part of the fit only differs by a day yet at t8/10 this difference is approximately 8 days. The physiological relevance of release rate in terms of pmol•hr-1 is discussed in the final chapter of this work.

97

Table 18. Summary of pharmacokinetic parameters including t1/2 of both fast and slow release from BCDP and dextran hydrogels.

Parameter Units BCDP Dextran

tlast day 32.00 32.00

%Diss(0-tlast) % 100

fa 0.21 0.64 -1 k1a d 10.68 12.2 -1 k1b d 0.10 0.1

t½_1a day 0.08 0.02 t½_ day 4.57 3.29 1b t80_1b day 13.73 5.88

Conclusions

Loading, quantification, and cumulative release methods were presented for a 1st generation cyclodextrin hydrogel material. A mathematical equation based on a two- phase release profile proved to fit the data well. Extracted parameters showed that cyclodextrin slowed release at early time points but did drastically change release kinetics at longer times. In future work we will aim to optimize the loading of retinol into the gels and will also express cumulative release as a percentage of the amount initially loaded. A percentage based profile will normalize release profiles and a better comparison of release parameters can be completed.

A higher loading percentage may or may not exaggerate the effect cyclodextrin has on release. It is possible that the affinity binding moieties only influence release at very low levels of drug, such that the drug has many open hydrophobic pockets to interact with and the probability for interacting with these cavities increases with time as

98 was suggested by Equation 9 in Chapter 4. In other words, binding interactions are likely to be inversely proportional to initial drug concentration. In designing an affinity drug delivery platform one therefore has to balance loading with the ability to control release.

Additionally, in an implant that will deliver drug to the eye, a higher degree of loading may be necessary to meet minimum therapeutic concentrations.

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Chapter 6- Retinoid Delivery with Cyclodextrin/Dextran Hydrogel Blends

Typical hydrogel drug delivery systems result in first-order release kinetics.cxvii

Release in such systems can be been modulated by controlling the crosslinking density, the extent of water swelling, the hydrated state before release, and the distribution of drug in the hydrogel.cxvii A first-order process is highly undesirable for treating chronic conditions because the dose of released drug is exponentially decaying with time. In the treatment of AMD, a chronic condition that develops over years, one might prefer an extended release profile that is independent of concentration in this device. In the previous chapter we provided initial evidence that burst phase release was reduced in a cyclodextrin-based hydrogel approaching near zero-order release. We here aim to better understand the influence of cyclodextrin on release. To this end, copolymer mixtures of cyclodextrin polymer and dextran were prepared to see if release would transition from a first-order process to a near zero-order process. In other words, we would like to be able to tune release using cyclodextrin polymer content. Graphically this represents the area between the two curves presented in Figure 6. The interaction of cyclodextrin and retinoid was also investigated using DSC and TGA.

Materials

β-cyclodextrin polymer (2-15kDa) was obtained from CTD, Inc. (High Springs, PL).

Dextran (15-20kDa) was purchased from Polysciences, Inc. (Warrington, PA). 1,6- diisocyanatohexane (HDI) and Bovine Serum Albumin (BSA) Fraction V was acquired from Sigma Aldrich (St. Louis, MO). BSA solutions were purified before use PALL

.2µm Acrodisc Syringe Filters. All-trans retinol, also bought from Sigma, was protected from light by covering with aluminum foil and stored at -20°C. Dimethylsulfoxide

100

(DMSO) was obtained from Applied Biosystems (Foster City, CA). Hyclone PBS (pH

7.4) from Fisher was diluted with DI water (<18.2 mega-ohm) from 10x to 1x and then autoclaved before use. A (4-15%) Ready Gel was purchased from Bio Rad and stored at

2-8°C. A wide range protein ladder 12-225kDa was purchased from Invitrogen. Simply

Blue Safe Stain was also acquired from Invitrogen. All other materials were procured from Fisher Scientific.

Methods

Preparation of 2nd Generation Blended Gels

β-cyclodextrin polymer and dextran were dried under vacuum at 100⁰C for 24 hours. The polymers were weighed and combined in various weight percent ratios in 5 mL glass vials. The total mass was 20% w/v. The weight percentages ranged from 100% CDP to

0% CDP with the other part as 0% Dex to 100% Dex respectively (Table 18). 1 mL of

DMSO was added to each 5 mL vial. HDI was added to the samples at a mole ratio of 2:1

(crosslinker:glucose unit). The samples were vortexed and then 1 mL was removed and put into a 10 cm2 Teflon® dish. The solution was allowed to gel for 24 hours at room temperature at which time it was noticed the solutions had not gelled. The Teflon® sample dishes were then placed in 70°C bell jar oven for two hours. A small arch punch,

12 mm diameter, was used to punch out gels. The punched out gels were placed into separate 20 mL glass vials and 5 mL DMSO was added to each vial to remove unreacted reactants. After 24 hours, DMSO was poured out and replaced by a 5 mL of a 50/50

DMSO/DI Water (<18.2 mega-ohm) solution serving as a secondary wash. After another

24 hours, a third water wash of 10mL replaced the secondary wash. 24 hours later, the gels were removed and dried in Teflon® dishes at room temperature.

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Table 19. Percentages of CDP or dextran for use in synthesizing copolymer gels

Blended Samples Cyclodextrin Polymer Dextran Polymer 100 0 75 25 50 50 25 75 0 100

Loading/ Loading Percentage

The gels were swelled in a 10 mg/mL, retinol in DMSO solution, for 72 hours (1 mL volume). After loading the gels were dried at 70°C for 48 hours to remove DMSO.

Total drug loaded was determined using the following formula:

CCCL NR R (1)

where CL(mg) is the amount loaded, CR is the cumulative amount released (mg), and CNR is the amount remaining inside the gel at the final time point (mg). Loading percent can be calculated from the samples initial mass (mg).

CCW%  (Ls / )*100 (2)

where C% is the percent loaded and Ws is the weight of the sample.

The amount of drug remaining inside the gel was determined by washing the gels with

DMSO after the final release time point. The DMSO extract was then diluted 1:1 with DI water and the samples were read using a Tecan Safire I UV-Vis plate reader. The extract samples were excited at 385 nm and emissions was read at 510 nm. Concentrations were determined by using a calibration curve based on known concentrations of retinol in

DMSO/Water.

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An additional loading calculation is based on the dried weight of the gel after loading

compared to the initial weight. WD+G is the weight after loading and WG is the weight before loading.

WWDGG  C%  ( )*100 (3) WG

Release

A release sink of 1% w/v BSA in PBS plus 0.1% w/v sodium azide was prepared. The release medium was sterile filtered through a 0.2 µm Pall sterile filter before being added to the samples. 5 mL of release medium was added to individual 20 mL glass vials. The sample size was 3. In addition, a control of only release medium and no sample was used as an internal standard. At various time points 1 mL of sample was removed and replaced by 1 mL fresh medium. The samples were stored at -20°C until the release was completed. The release was conducted for 48 days.

Quantification

A calibration curve of retinol in a 1% BSA solution was generated using a Tecan Safire I plate reader as described in the previous chapter. Release samples from the various time points were excited at 280 nm and quantified based on a calibration curve at their 350 nm emissions value. Cumulative release was determined by the following equation:

n %Diss ( Cn (1/ 5) C n1 ) / C L (4) i1 where n is the concentration of a sample at a given time point that is added to a summation of the amount removed in previous samplings. A ratio of 1:5 represents the 1

103 mL removed from the sink of 5 mL. %Diss represent the cumulative release percentage and CL is the amount of drug inside the gel to begin with.

SDS-PAGE : BSA Stability

SDS-PAGE was completed on end point release samples to see if BSA stability had changed during the release. 1 µg/uL protein solution was prepared by diluting the release medium with water. A 1:1 ratio of Laemmeli buffer was added to the sample volume (4%

SDS, 0.004% bromophenol Blue, .125 M Tris HCl, 20% glycerol, 10% B- mercaptoethanol). The samples were denatured by heating at 99°C for three minutes. A running buffer of 25 µM Tris, 90 µM glycine, 150 mM NaCl, 0.05% Tween 20 was prepared. A mini-Protean 3 Cell system was loaded with a Bio-Rad Ready Gel (4-15%).

The samples were run alongside a freshly prepared sample of release medium and a protein ladder. A power supply was connected at 60 V until the wells had emptied, after which the voltage was increased to 105 V and left running for one hour. Under shaking, a

Simply Blue Safe Stain solution was used to stain the gel for 1.5 hours. DI water was used to wash off excess dye.

104

DSC

A DSC Q2000 instrument was used to analyze dried drug loaded and non-loaded dextran and cyclodextrin polymer hydrogels. The DSC method followed a heat cool heat method

(Table 20). DSC was also used to analyzing bulk retinol. In analyzing bulk drug the method was a simple heat ramp (20°Cmin) from 15-175°C. Respective thermograms are reported.

Table 20. DSC method for dextran and cyclodextrin gels.

Step #: Path 1: Equilibrate at -90.00 °C

2: Isothermal for 2.00 min 3: Mark end of cycle 1 4: Ramp 10.00 °C/min to 200.00 °C 5: Isothermal for 2.00 min 6: Mark end of cycle 2

7: Ramp 10.00 °C/min to -90.00 °C 8: Isothermal for 2.00 min 9: Mark end of cycle 2 10: Ramp 10.00 °C/min to 200.00 °C

TGA

TGA was performed on dried drug loaded and non-loaded dextran and cyclodextrin polymer hydrogels. A Q500 TGA was programmed to heat samples at 10°C/min to

800°C. Sample weight was first recorded and inputted into the software. Data is presented as a fraction of mass remaining versus temperature.

Statistical analysis Statistical analysis was completed using several software packages. SAS JMP 8 software was used to plot the normal distribution of BSA-retinol ratios at various release time points. Other statistics may be expressed as means + standard deviation. ANOVA single

105 factor analysis, based on an alpha of 0.05, and correlation values were solved using data analysis packages found in Microsoft Excel 2010.

Results

Loading

The concentration of the drug loading sink is an important variable in influencing drug loading percentage. In related work we were able show that similar cyclodextrin hydrogels took on more color with higher retinol concentrations (Figure 45).

Figure 45. Increasing the concentration of loading sink results in a higher loading percent as seen by the darker color (concentration increase from left to right).

20 10 5 2.5 1 (mg/mL)

In this study we used DMSO to load the cyclodextrin and dextran based hydrogels because the retinol was highly soluble in this solvent. Qualitatively, a 10 mg/mL loading resulted in a gel that was dark orange compared to a first generation gel that was light yellow. Contrary to our initial hypothesis, as cyclodextrin polymer content was increased in dextran blended gels, the samples appeared to load less drug (Figure 46). A drug loading percentage was expressed from the weight after loading compared to the gel’s mass before loading (Figure 47). As cyclodextrin polymer content increased, loaded percentage was reduced. A correlation value of -0.94 confirmed this effect. This surprising relationship led us to an alternative method to quantifying drug loading based on the amount released and the amount that could be extracted from the gel after release

106

(Eq. 2). This method produced loading values almost an order of magnitude lower than those obtained from the original method (Figure 48). Using this method there was no correlation between cyclodextrin and loading.

Figure 46. Cyclodextrin and dextran blended gels after loading.

100% CDP 75%CDP 50%CDP 25%CDP 0%CDP

Figure 47. Drug loading percent based on mass after loading and mass before.

107

Figure 48. Drug loading percent based on mass of drug released and mass remaining in gel after release.

4.5

4 3.5 3

2.5 2 1.5

PercentLoaded (w/w) 1

0.5 0 100 75 50 25 0 Percentange of Cyclodextrin Prepolymer in Synthesis

Characterization

Due to the inconsistency between the two methods of drug loading, we were interested in finding out if something was falsely offsetting the dry weight of the gel. A

TGA method was executed to see if remaining solvent was in the gels. Our hypothesis was that solvent that could not be evaporated from our drying cycle and this would in turn artificially inflate drug loading percent. TGA was performed on both drug loaded and unloaded samples of either 100% CDP (Figure 49) or 100% dextran samples (Figure

50).

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Figure 49. TGA curves for dried CDP hydrogel with or without retinol.

Figure 50. TGA curves for dried dextran hydrogel with or without retinol.

109

The TGA curves of dried polymer hydrogels displayed a 15-20% mass loss at temperatures prior to the bulk degradation of the sample ~230°C. This mass loss was seen across all samples. The mass loss is believed to be remaining DMSO left in the samples. DMSO has a boiling point at 190°C thus providing enough time for significant evaporation.cxviii The weight loss of dextran has a steeper decline than that which was seen in CDP hydrogel for this temperature range. A greater mass loss due to solvent is reflective of the amount of solvent left in the samples. In this experiment dextran samples had more solvent than CDP samples. This difference would further explain the correlation between the unusually high drug loading percentage related to dextran content such that less solvent is withheld as the cyclodextrin content is increased. The derivative weight loss on the retinol loaded dextran sample had a shifted peak at 120°C compared to the non-loaded dextran. It is believed that a thermal transition of retinol in the sample facilitated, or lowered, the required heat of vaporization of the DMSO in the gels. The cyclodextrin samples did not have this shifted peak, which we attribute to the complexation of retinol in the CD cavities. To investigate this possible transition, a sample of bulk retinol was run on a DSC and a thermogram is presented as evidence of thermal properties (Figure 51).

An endothermic peak at 55°C corresponds to the melting temperature of retinol.

This temperature is off from published value of 61-63°C.cxix A second endothermic peak at 147°C corresponds to the boiling point of retinol, which is significantly higher than published values of 125°C.cxx An explanation for this discrepancy is due to the high heating rate of the sample pan. The 120°C peak on the TGA curve may correspond with this boiling point. Work by others have shown that when retinol is complexed with

110 cyclodextrin the endothermic peak of melting is masked.cxxiHowever, due to retinol’s low loading content in our CDP and dextran hydrogels, a DSC thermogram of the bulk polymer did not show any noticeably different transition regions (Figure 52,53).

Figure 51. DSC thermogram of bulk all-trans retinol

Endothermic Retinol

0 50 100 150 200 Temperature (°C)

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Figure 52. DSC thermogram of retinol loaded and unloaded CDP hydrogel

Figure 53. DSC thermogram of retinol loaded and unloaded dextran hydrogel

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Release

Release was carried out for 42 days in a PBS sink with 1% BSA and 0.1% sodium azide. Cumulative release is graphed in Figure 54. Even though this experiment was run in triplicate, the error bars are not shown because they would occlude the figure. We were not able to fit the data to the equation specified in the previous chapter. However, a simple logarithmic fit of the form y= A•ln(x)+B shows that nearly all the releases curves have a near identical shape, except for the B value. To better illustrate this fit, Figure 55 is plotted on a log time scale.

Figure 54. Cumulative release from CDP and dextran blended hydrogels. Error bars not shown but n=3.

90.00%

80.00%

70.00%

60.00%

50.00% 100% CDP 75% CDP 40.00% 50% CDP

30.00% 25% CDP

Cumulative Release (%) Release Cumulative 0% CDP 20.00%

10.00%

0.00% 0 200 400 600 800 1000 1200 Time (Hours)

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Figure 55. Cumulative release from CDP and dextran blended hydrogels with logarithmic fits. Sample labels are the same as Figure 56. 90%

80%

70% 60%

50% y = 0.0975ln(x) + 0.045 R² = 0.9414 40% y = 0.0949ln(x) + 0.0729 30% R² = 0.964 y = 0.0951ln(x) + 0.0898 20% R² = 0.9621

y = 0.0942ln(x) + 0.1677 Cummulative (%) Release Cummulative 10% R² = 0.9759 y = 0.0975ln(x) + 0.1253 R² = 0.9738 0% 1.00 10.00 100.00 1000.00 10000.00 Time (Hours)

An ANOVA single factor analysis showed that there was no statistical difference between the coefficient, A, as the P-value was 0.48. For the intercept, B, there were statistical differences as illustrated by a P-value of 0.02. A strong negative correlation of

0.84 was found for the intercept, which means that increasing cyclodextrin content shifted the total release down and to the right but did not change drastically change shape of the release curve. The shape of the release curve may be related to the order of the release, where all samples appear to have first-order profiles and cyclodextrin content does not significantly bring the release profiles any closer to zero-order kinetics.

An alternative inclination was that the sink conditions regulated release. To look into this potential problem we recalculated all the data points in terms of a stoichiometric

114 ratio between BSA and retinol. To facilitate this conversion the calibration curve was plotted as molar ratio versus fluorescence signal (Figure 56). The molar ratios of all 15 samples were imported into JMP 8 statistical software and a normal quantile plot was generated. The data (Figure 57) shows that there is a strong tendency of retinol to bind to

BSA at a 1:1 ratio, therefore, release may have been hindered by the capacity of BSA and not diffusicity. To overcome the obstacle of sink saturation the release was redone at a higher concentration of BSA, 10%, with smaller gels (~10 mg versus 30 mg used in presented work). A shortcoming of this follow-up experiment was that the amount released was not within the sensitivity of the detection method after the first 24 hours of release, therefore, future work will focus on optimizing these sink conditions.

Figure 56. Calibration curve based on stoichiometric ratio of retinol to BSA.

2.5

2

1.5

1 y = 0.3938x - 0.004 R² = 0.9955 0.5 Fo/F Value, Emission 350nm

0 0 1 2 3 4 5 6

Mole Ratio (Retinol:BSA)

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Figure 57. Normal quantile distribution of stoichiometric ratios of retinol to BSA.

An additional concern we had was of the stability of BSA in the release medium during the course of the release. Because not all the medium was removed at every sampling, there was a potential for the BSA to denature, degrade via hydrolysis, or aggregate. SDS-PAGE of one sample from each group compared to a freshly prepared

BSA control showed no differences in the protein size. In all groups, the release sink proteins had similar bands 67 kDa, 140 kDa, and 215 kDa (Figure 58). These numbers correspond to the protein’s MW 68 kDacxxii, and respective dimers and trimers. The color below 67 kDa is believed to be residual dye and does not correspond to a degraded protein product. Because this assay evaluates only protein size, future work may use other assays to examine protein activity and other conformational changes in order verify sink stability.

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Figure 58. SDS-PAGE of release end-point samples to look into protein stability. Freshly prepared BSA is directly left of the protein ladder.

Conclusion

A success of these second generation materials, compared to the first generation, is that prolonged release with a higher concentration of drug was achieved.

Unfortunately, no strong conclusion can be made on the ability of cyclodextrin to change the shape of the release curve. In other words, the release curves are generally not more linear in the presence of cyclodextrin binding moieties and do not change the order of release kinetics (zero-order vs first-order).

Furthermore, the only correlation between cyclodextrin content and release behavior was the intercept of the fit that relates the orientation of the curve within the release space. Cyclodextrin content was found to shift cumulative release curve down and to the right. This is consistent with equation 5 from chapter three where the diffusion

117 coefficient is lowered by some factor, Kb, which was found to shift the simulated release curves down and to the right rather than drastically changing the order of release kinetics.

Dcyclodextrin=Ddextran/(Kb+1) (Eq. 5, Chapter 4)

One may therefore conclude that the diffusion coefficient is affected by cyclodextrin content but the order of release is not significantly altered. As stated in the previous chapter, it is possible that cyclodextrin modulates release only at very low drug loading concentrations since the probability for affinity complexation increases with a decrease of drug presence.

Also, a difference in concentration at earlier time points may be related to the solvent exchange between remaining DMSO in the gels and the surrounding water. As such, the higher DMSO content in the dextran gels resulted in more retinol being driven out during the solvent exchange at early time points. It is also suspected that partitioning plays an important role in this release system. There is a strong possibility that as water enters the gel the partitioning coefficient, Kow, increases. Kow is the coefficient between the water (the release medium or aqueous phase) and the oil phase (hydrophobic matrix components). A stronger partition will lead to a lower concentration of the drug in the aqueous phase and a slower and more sustained release.cxxiii Future work should investigate this more thoroughly since if the bulk composition of the matrix results in sustained release, a dextran based system would be a much less expensive drug delivery platform. Release rates in this scenario may be changed by simply crosslinking the system more or less to try to emulate the effect cyclodextrin may have had.

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Chapter 7: Retinoid Delivery with BSA/Cyclodextrin Hybrid Hydrogels

BSA’s hydrophobic core was previously established as a sink for retinol. BSA’s lipophilic cavity for solubilizing retinol may function similar to that of cyclodextrin in its ability to bind drug through affinity interactions. The following body of work explored these interactions as a means to create a dual affinity drug delivery platform. This is particularly novel because one affinity is biologically based and another is of small molecule origin. Insight is provided into the competitive binding of retinol to free BSA or

CD in solution. Based on positive results, CDP and BSA were combined into a single platform to further modulate release. This combination has been previously published in work by Dai et al. who aimed to derivatize BSA with BCD for use as a chiral selector in pressured capillary electrochromotography.cxxiv In their application they conjugated a single CD to the amine end of BSA, and then immobilized the derivatized BSA to the stationary phase of a capillary in order to create an active separation surface.

Compared to a Dai’s work of a protein with cyclodextrin grafts, we suggest an alternative application where these components are synthesized into an insoluble crosslinked hydrogel made up entirely of BSA, crosslinker and CDP. The application is therefore not a separations technology but a depot for the controlled delivery of drug. The synthesis procedure involves a difunctional crosslinker, ethylene glycol diglycidyl ether, in a basic environment. Swelling data is presented for various compositions and then release is tested with a dye that has known affinity for BSA, Evan’s Blue.cxxv Evan’s Blue affinity toward CD is investigated using online docking software, thus proving a potential for dual affinity. Retinol loaded hybrid gels of BSA/BCDP are then compared in an in vitro release against a 100% BSA gel. Cumulative release is presented for both scenarios.

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Materials

β-cyclodextrin polymer, CDP, (2-15kDa) was obtained from CTD, Inc. (High Springs,

PL). Ethylene glycol diglycidyl ether (EGDE) was obtained from PolySciences Inc.

Bovine Serum Albumin (BSA) Fraction V and Evan’s Blue were acquired from Sigma

Aldrich (St. Louis, MO). BSA and EGDE were stored at 2-8°C. BSA solutions were purified before use PALL 0.2 µm Acrodisc Syringe Filters. All-trans retinol, also bought from Sigma, was protected from light by covering with aluminum foil and stored at -

20°C. Dimethylsulfoxide (DMSO) was obtained from Applied Biosystems (Foster City,

CA). Hyclone PBS (pH 7.4) from Fisher was diluted with DI water (<18.2 mega-ohm) from 10x to 1x and then autoclaved before use.

Methods

Gel Synthesis for Evan’s Blue Release

BSA was combined with CDP at various ratios and dissolved in 2 mL of a .1 M Sodium

Bicarbonate solution (Table 21). 150 µL of EGDE was added to the solution and mixed well. Solutions were added to individual Teflon® dishes and covered with Parafilm®.

The samples were cured at RT for 24 hours which was followed by a 40°C cure for 120 hours. 12 mm gels were punched out and each group was placed into individual wells of a 6 well plate. The wells were then stored at RT (not washed).

Table 21. Various compositions of BSA/CDP hybrid gels

BSA CDP Weight Percent CDP w/v in Solution Sample (mg) (mg) (%) (%) Name 200 100 33 15 CB1 200 200 50 20 CB2 300 200 40 25 CB3

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Swelling of Gels for Evan’s Blue Release

The dry weight of the samples (n=3) were recorded. The samples were then placed in individual wells of a 12 well culture plate and 4mL of PBS was added. Wet weights were taken at various time points, between time points the samples were dried on Teflon® plates at RT. Pictures were taken before drying the sample. Swelling percentage was calculated as:

(1)

Evan’s Blue and Cyclodextrin Simulation

The interaction between the dye and CD was simulated using methods described in previous chapters using cyclodextrin.net docking software.

Evan’s Blue Loading and Release

Evan’s Blue was dissolved in PBS at 1mg/mL. Gel fragments of the 33 wt% CDP (CB1) were placed in 1 mL of the loading solution for two hours. Release was completed in

10mL of PBS for 190 hours. 1 mL of release medium was removed at each sampling and replaced by fresh medium. After 92 hours, the same gels were transplanted into a sink of

10% BSA in PBS and release was analyzed for a period of 146 hours using a similar sampling protocol. Release was quantified using two separate calibration curves, Evan’s

Blue in PBS, and Evan’s Blue in 10% BSA, using an absorbance reading at 620 nm with a Tecan Safire I plate reader. Cumulative release is presented as a total mass released and not as a percentage.

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Competitive Binding Between CD and BSA

1% BSA + 0.1% sodium azide in PBS was first prepared. Retinol was then added to separate vials of this solution at 0:1, 1:1, and 3:1 molar ratios of retinol to BSA. Various concentrations of CDP were dissolved in separate aliquots of these solutions (0, 7.5, 10,

12.5, 25 mg/mL). Samples are incubated for 14 hours at RT and then read using at 350 nm after a 280 nm excitation. Readings were taken at 27, 32, and 37°C.

Gel Synthesis for Retinoid Release

200 mg of cyclodextrin polymer and 200 mg of BSA were dissolved in 2 mL of 0.1 M

NaOH. 150 µL of EGDE was added to vial and mixed well. Using a positive displacement pipet the solution was transferred to a Teflon® dish. The samples were cured at RT for 24 hours, followed by a 40°C cure for 72 hours, and an additional 24 hours at 55°C. The samples were then punched out with a 6 mm punch and transferred to a DI water wash for 72 hours. This was repeated for a gel made of 400 mg of BSA.

Retinoid Loading and Release

Retinol was dissolved in ethanol at 10 mg/mL and then diluted 1:1 with DI water. Three samples of each type of gel were placed in the loading sink for 7 days. Pictures were taken of both the gels and the loading solutions before release. Release was conducted in a 2.5% w/v BSA + 0.1% sodium azide in PBS (pH 7.4). Samples were placed in individual 20 mL glass vials and 5 mL of release volume was added. At given time points

1 mL of release volume was replaced with 1 mL of fresh release medium. Release was conducted at 37°C and extracted samples were covered with aluminum foil and stored at

RT. Quantification of retinol concentration was completed using a method previously described with the exception of using a 2.5% w/v BSA solution for the calibration curve.

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In brief, BSA is excited at 280 nm and 350 nm emissions are recorded, where a reduction in signal is due to retinol quenching BSA’s fluorescence. A Molecular Dynamics

SpectraMax M3 plate reader was used for this release.

Fitting of Retinoid Release

Release data was fitted using a 2D double exponent curve fitter from zunzun.com.

Results

BSA/CDP Hybrid Gels

Ethylene glycol diglycidyl ether, EGDE, can react with carboxyl, amine, and hydroxyls substituent groups through an ether ring opening reaction.cxxvi Cyclodextrins polymers have been previously crosslinked with EGDE into hydrogels for use as an antibiotic delivery platform.lviii By exploiting the non-specific reaction of EGDE, and its known ability to crosslink cyclodextrin, we aimed to create hybrid gels of CDP and bovine serum albumin (Figure 59).

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Figure 59. Schematic of crosslinked CDP/BSA gels where the yellow triangles are EGDE, the blue circles are BSA protein, and the red rods are CDP. Under basic conditions a SN2 reaction is favored where the reactive nucleophiles attack the less substituted end of the epoxide.

Our initial attempts were aimed at optimizing the synthesis of these materials.

When using a bicarbonate buffer (pH 8.5) the reaction proceeded very slowly. A slow reaction was preferred for fear of a faster reaction forming a non-homogenous network with separate protein domains and cyclodextrin domains. Several of the formulations (not mentioned) failed before we were able to generate materials that did would not fall apart.

Very viscous, highly concentrated solutions, formed brittle gels whereas dilute solutions would not gel. Qualitatively a successful blend of these materials, along with the right amount of crosslinker, yielded a material that exhibited a fair amount of elasticity when they were not hydrated. In fact, the materials could be stretched nearly 2x their length before fracturing or not recovering their shape. Such mechanical properties, although not

124 officially investigated here, may lend themselves to applications that require structural flexibility. A picture of the swollen gels at equilibrium can be found as Figure 60. We found that swelling percentage was more of a function of protein content than cyclodextrin polymer content. Equilibrium swelling percentages were 519.81 ± 44.76,

415.52 ± 13.19, and 231.3 ± 12.45 percent for CB1, CB2, and CB3 respectively. The high degree of swelling is indicative of strong repulsive forces within the gel in the presence of water. The second generation of CDP/BSA gels, those used in the retinol release, were crosslinked under slightly more basic conditions and at a higher temperature to increase strength.

Figure 60. CDP/BSA hybrid gels at equilibrium swelling. CB1 left, CB2 middle, CB3 right.

Evan’s Blue and Hybrid Gels

Although it is widely known that Evan’s Blue has a strong affinity to BSA, we were unsure if it would have an affinity to BCD. The affinity of Evan’s Blue was examined using docking software and we found the estimated free energy of binding to be -3.29 kcal (Pictured as Figure 61).

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Figure 61. Image of Evan’s Blue docking with BCD.

A release experiment of two samples of CB1 in PBS displayed a typical diffusion based release profile that stopped releasing after 48 hours in solution (Figure 62-calibration, 63- release). The differences in the cumulative mass released is likely due to differences in the gel fragments’ initial weight. It was interesting that even though Evan’s Blue stopped releasing the gels were still dark blue. We therefore believed that much of the dye was still in the gel, and held inside through affinity interactions. Likewise, due to the high solubility of the dye in water, we did not believe that partitioning was responsible for this retention.

The same samples that had stopped releasing in PBS were put in a 10% BSA sink and the dye immediately started diffusing out of the gel (Figure 64).

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Figure 62. Calibration of Evan’s Blue in PBS using 620 nm absorbance.

3.5000 y = 20.272x + 0.0047 R² = 0.9998 3.0000

2.5000

2.0000

1.5000

Absorbance 1.0000

0.5000

0.0000 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 Concentration (mg/mL)

Figure 63. Evan’s Blue release in PBS from two CB1 gel fragments based on calibration curve.

0.6

0.5

0.4

0.3

0.2

0.1 Cumulative Release (mg) Release Cumulative 0 0 20 40 60 80 100 Time (Hours)

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Figure 64. Evan’s Blue when transferred to a 10% BSA sink immediately starts to diffuse out.

Subsequently, a release in 10% BSA was performed and cumulative release was quantified using an independent calibration curve (Figure 65,66). The results looked more linear than other generations of hydrogels reported in this thesis. These results reinforce the idea that release in affinity based delivery is based on competition between the gel and its surroundings since release was promoted by a change in sink conditions. In the release of Evan’s Blue, it is likely that unbound dye, those molecules not bound by affinity, underwent diffusion based on first-order kinetics.

It is also plausible that the one type of affinity was stronger than another in this platform; such that dye molecules are complexed more tightly to BSA than to cyclodextrin. When the sink conditions were changed to include BSA, the balance of binding energies shifted or reached equilibrium outside the hydrogel so that a concentration gradient could reform and promote release. Due to the sophisticated relationship of having multiple affinities in a single platform we sought to understand the competitive binding forces for retinol in the presence of BSA and CDP.

The competitive binding forces were investigated by generating a solution that had both components dissolved and floating freely. In this experiment the amount of BSA was kept constant and CDP concentration was varied to generate different mock sinks.

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Similarly, by changing the concentration of retinol in these solutions more information could be learned about what happens when BSA is overloaded, not loaded, or loaded 1:1 with drug.

Figure 65. Calibration of Evan’s Blue in 10% BSA using 620 nm absorbance.

2.5000

2.0000 y = 12.662x + 0.1497 R² = 0.9998 1.5000

1.0000 Absorbance (AU) Absorbance 0.5000

0.0000 0 0.05 0.1 0.15 0.2 Concentration (mg/mL)

Figure 66. Evan’s Blue release in a 10% BSA sink from two samples of CB1, following PBS release.

1.4

1.2

1

0.8 Release (mg) Release 0.6

0.4

Cumulative Cumulative 0.2

0 0 50 100 150 200 Time (Hours)

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Retinol Competitive Binding

To understand how retinol might be incorporated in a BCDP/BSA gel we investigated a setting where retinol concentration could be varied in the presence of BSA and CDP. The effect of temperature was also examined. As discussed in previous chapters, it is believed that retinol binds to BSA in a 1:1 ratio. If there is more retinol in the sink than a 1:1 ratio provides, then the solution is artificially oversaturated. A 3:1 retinol to BSA ratio was used for this purpose and a 0:1 stoichiometric ratio was used as a control. Various amounts of cyclodextrin polymer were then added to these solutions

(after retinol was added) to see if cyclodextrin drives the retinol away from the BSA and into the CD pockets. The experimental method therefore considered a reduced fluorescent signal of BSA, or less quenching, as retinol was removed from the BSA and entrapped by the CD. This experiment was repeated at three temperatures to see if increasing temperature would amplify this effect (Figure 67).

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Figure 67. BSA quenching recorded as a function of CDP content and concentration of retinol. Retinol : BSA ratio is found from the fluorescent signal (calibration curve). Note that even though the solution prepared was meant to be 3:1, the initial value of 3.5:1 may be due to unaccounted for noise as can be seen by the slightly positive signal of the 0:1 control.

We found that in an overload scenario, retinol is indeed pulled away from BSA by

CDP. In a 1:1 scenario, the signal is not diminished much indicating that BSA tightly binds the retinol and CDP cannot release retinol’s quenching ability. However, when the sink is oversaturated, the BSA has incorporated more retinol than is favored, therefore the molecules of retinol are less tightly bound and can be pulled away by BSA. The effect of temperature is only an increased fluorescent signal; it does not change the aforementioned behavior. As it relates to previous release experiments, it is unlikely that retinol released into the sink is given up by BSA once its outside of the gel (discounting transport between BSA molecules). The driving force outside of the gel may not be much stronger than affinity forces inside the gel, which would in turn inhibit release. In a hybrid BSA/CDP gel it is fathomable that the retinol exchanges couriers inside the gel

131 going from BSA to CD back to BSA, almost like a molecular hopscotch with different size boxes. In order to load the CD pockets of such a hybrid gel with retinol, we found it is necessary to oversaturate the BSA inside the gel.

Retinol Loaded BSA/CDP Gels

With a deeper understand of competition in a dual affinity sink, BSA/CDP Gels of a 50/50 weight percentage and 100% BSA gels were prepared (weight percent excludes crosslinker and refers only to the amount of bulk solids relative to each other). As suggested, the gels were loaded with retinol in a supersaturated sink meaning that retinol was in suspension due to exceeding its solubility limit. After several days of loading, the

100% BSA hydrogels appeared to absorb more retinol than the hybrid gels. As can be seen in Figure 68, the 100% BSA gel loading solution appears clearer than its hybrid counterpart. It is believed this is because more retinol was removed from the sink thereby lowering the turbidity of the solution.

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Figure 68. Hybrid gels (left) and 100%BSA gels (right) after crosslinking (top) and in loading sink (bottom).

The 100% BSA gels took on a deeper yellow tone after loading, which may also indicate greater loading (Figure 69). Concentration was determined using a calibration curve of retinol with 2.5% w/v BSA in solution (Figure 70). Further evidence of a difference in loading can be seen from the cumulative release profiles in Figure 71.

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Figure 69. Hybrid gels (left) and 100%BSA gels (right) before in vitro release (top) and after 20 days of release (bottom).

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Figure 70 . Calibration curve of retinol quenching BSA’s fluorescence in a 2.5% BSA sink with 0.1% sodium azide.

1.6

1.4

1.2

1

0.8

0.6 y = 5.7123x + 0.0371 Fo/F (AU) Fo/F R² = 0.9963 0.4

0.2

0 0 0.05 0.1 0.15 0.2 0.25 Concentration (mg/mL)

Figure 71. Retinol release in CDP/BSA hybrid gels as well as 100%BSA gels (n=3).

0.70

0.60

0.50

0.40

0.30

Hybrid Hydrogel 0.20

100% BSA Hydrogel Cumulative Release (mg) Release Cumulative 0.10

0.00 0 100 200 300 400 500 600 Time (hours)

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The hybrid gels appear to have stopped releasing retinol at 20 days, whereas the 100%

BSA gels, those that maintained a stronger yellowish hue, were still releasing at this time point. Since an extraction method for retinol from BSA gels has yet to be developed, an absolute loading figure cannot be presented. If one takes the day 20 time point as 100% release one may better visual the release behavior (Figure 72). From Figure 72 one can see that the release has two exponential components, possibly associated with a burst phase followed by a slow release phase. Because we do not have an absolute loading value to determine % Diss, an alternative 2D double exponential fit equation was used to analyze the cumulative release data from Figure 73: y a** ebx c e dx (1)

Figure 72. Logarithmic plot of retinol release from CDP/BSA hybrid gels as well as 100%BSA gels (n=3).

120.00%

100.00%

80.00%

60.00%

Hybrid Hydrogel 40.00% 100% BSA Hydrogel

Cumulative Release (%) Release Cumulative 20.00%

0.00% 1 10 100 1000 Time (hours)

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Table 22 compares the fit parameters for the two types of gels. As one can see, there is not much difference between the first exponential term, a and b, of the fits. However, for the second exponential term, c and d, there is a decay in the release of the hybrid gels and a slight growth in the 100% BSA gels. An extrapolated 45 day release value results in virtually no change in the hybrid system; whereas in the 100% BSA gel an additional

0.17mg of retinol is released to reach a value of 0.747 mg (fit illustrated in Figure 73).

Table 22. Fit parameters for hybrid and 100% BSA gels based on Equation 1. Extrapolated to 45 days of cumulative release value also included.

Parameter Hybrid Values 100% BSA Values A -2.98 E-01 -2.78E-01 B -1.18E-02 -1.95E-02 C 3.86E-01 4.76E-01 D -2.34E-05 4.17E-04 R-squared .987 .989 45 Day Release (mg) .37655 .747

Figure 73. Fitted curves using 2D double exponential fit for hybrid gels (left) and 100%BSA gels right. Y data is cumulative release in mg, and x data is in hours.

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Conclusions A dual affinity platform for Evan’s Blue dye is presented in the form of a hybrid

BSA/CDP hydrogel. By changing release environment one may be able to stop or promote release of these or other molecules based on competitive forces in the release sink. Consequentially, the sink choice yields two distinct release profiles, one that is first- order, and one that is closer to zero-order release kinetics. The importance of this phenomenon may be useful in loading and releasing multiple drugs that have distinct affinities for either or both components. For example, a molecule that binds to BSA may not have affinity for CD and a molecule that can complex with CD may not have an affinity for BSA. As such, two distinct release profiles may result. In another application, the BSA/CD hybrid platform could deliver a single molecule, all-trans retinol, for a period of 14 days. Although loading in the hybrid platform is lower than what was found in a 100% BSA gel, it provides proof that a dual affinity platform may be considered as a release system for a retinoid that can treat AMD.

Future work must better characterize these materials. It is necessary to understand the composition of the gels to ensure that the components are homogenously distributed, otherwise there would be regions of rich or poor drug loading. The effect of temperature, pH, and salt content on swelling potential would also be of interest in developing a material that is sensitive/responsive to environment. Due to an unmodified cyclodextrin’s inert chemical structure, it may not be responsive to these variables. BSA on the other hand is a protein and it is likely responsive to these conditions. As such, one has a release component that is independent of environment and one that is dependent on it. The development of better or other suitable proteins for use in a hybrid gel is also an attractive research question. A hybrid gel may also be prone to degradation by enzymes.

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Chapter 8: Conclusions/Future Direction

All-trans retinol was used in this work as a surrogate for novel retinoids that may be used to treat various ocular conditions including AMD. Cyclodextrin polymers were investigated as an affinity host for all-trans retinol. The guest-host interaction for retinol- cyclodextrin was explored using spectroscopic methods as well as simulation. Three generations of cyclodextrin polymer based hydrogels were synthesized, loaded with retinol, and then release was performed. These platforms are all insoluble crosslinked hydrogel systems that could potentially be implanted into the vitreous of the eye for controlled release of retinoid for a period of between 14-30+ days.

The first generation cyclodextrin polymer hydrogel was compared against a chemically similar dextran hydrogel. The loading of this generation was low, but differences in release profiles promted further investigation. Release behavior took the form of a biphasic release with a burst phase followed by a slower secondary phase. The loading behavior of cyclodextrin polymer gels, crosslinked with different amounts of crosslinker, was analyzed using digital photography and image analysis software. It was found that loading is controlled at shorter time frames by the crosslink density of the network, whereas at longer time periods loading may be influenced by affinity interactions.

A second generation cyclodextrin polymer based hydrogel was synthesized in the presence of different weight percentages of dextran polymer effectively creating a blended hydrogel network. By using different weight percentages of dextran to cyclodextrin we aimed to tailor the release kinetics based on the degree of affinity binding moieties in the gel. Although in this generation we successfully increased loading

139 and were able to release drug for the targeted time period, the shape of the release did not statistically differ between the formulations. It is therefore difficult to conclude, in a retinoid loaded system, the ability of affinity interactions to transform release from a first-order process to one that is zero-order. Cyclodextrin content did change the diffusivity of the gels, resulting in a curve that was shifted down and to the right. It is possible that a more hydrophilic or amphiphilic molecule would have a release profile more closely related to previously published works. Similarly, the effect cyclodextrin content has on release may be more pronounced materials with a lesser degree of drug loading, as was seen in the first generation of gels. The issue of release medium and optimizing its sink-like properties needs to be better understood before one can concretely reject our initial hypothesis. For example, the incorporation of other solubilizing molecules such as surfactants is likely to alter to release. As an alternative, a proposed experiment would use soluble cyclodextrin polymer in the sink so that the sink has affinity moieties that are just as competitive compared to those found in the hydrogel.

The mechanical properties of blended hydrogels were examined using rheological methods. It was found that in general the gels respond similarly to high strain and have a comparable stress recovery profile. An oscillation sweep did show differences between the elastic and viscous properties of the gels related to their composition. Chemical characterization methods comparing a drug loaded material to a non-drug loaded material sought to understand more about solid state complexation behavior, however no significant differences were observed. It is believed the low loading percent (2-3%) and poor distribution of the drug are responsible for this discontinuity.

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The release rate of all-trans retinol may be compared against a biologically relevant level of drug. In a paper by Del Priore et al., researchers were able to follow the disease progression of AMD by tracking the RPE cell density with age.cxxvii A linear relationship was found between age and number of remaining RPE cells. Mata et al. was able to determine the isomerization rate potential of RPE cells based on visual pigment regeneration studies in chickens.cxxviii Although using cross-species data has obvious consequences, we correlated RPE cell loss with the regeneration potential of visual pigment based on the assumption that isomerization potential was directly proportional to the number of RPE cells (Figure 74). Using this comparison one can see that the average person loses about 25% of their ability to generate visual pigments over their lifetime.

When we compare the release rate of retinol to isomerization rate loss (pmol/hr) we can see that average release rate over 2-14 days of release for these hydrogels is biologically relevant (Figure 75).

Figure 74. Regeneration of visual pigment, isomerization rate potential, versus age.

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Figure 75. Isomerization rate loss (blue line) compared to average release rate (green line) over 2-14 days of release in an aging population.

A third generation hydrogel material was developed to investigate the potential of incorporating two distinct affinities into one platform. Evan’s Blue dye as well as retinoid was released using this system. In the future a dual affinity platform may be capable of binding two different drugs through two different types of interactions. For example, imagine a porous hybrid gel that is loaded with chemoattractants that only have an affinity for cyclodextrin, and another chemotherapeutic agent hat has a strong affinity for protein in the gel. The cyclodextrin may first release the chemoattactant in a controlled manner that will in turn lure cancerous cells inside the pores. Upon entering the pores the cells interact with protein component of the hybrid gel (perhaps an enzyme or receptor).

The chemotherapeutic, incorporated via an affinity interaction with the protein phase, can then be directly delivered to the cells of interest. In this example the exact opposite

142 scenario may be possible where cyclodextrin carries the active ingredient and the protein releases the signal.

Other Cyclodextrin-based Materials Several preliminary studies were completed on alternative drug delivery platforms based on cyclodextrin. One such study sought to develop an injectable platform as an alternative to the implantable technologies discussed in this document. One family of materials that have been extensively studied for their thermally reversible behavior and injectability is poloxamers, or Pluronics®.

Pluronics® are amphiphilic triblock copolymers made up of ethylene oxide and propylene oxide blocks of varying length. In literature, pluronics® have been used as a drug delivery vehiclescxxix and cell scaffoldscxxx. Relating to cyclodextrin, researchers have developed pluornic based materials which can self-assemble in the presence of beta- cyclodextrin into a supramolecular network.cxxxi The attractiveness of using pluronics® is that the range in which they can transition from a sol-gel (25-37°C).cxxxii The aim of this work was to generate a beta-cyclodextrin polymer based material that could undergo a sol-gel transition at physiological conditions.

Although the details of synthesis are not discussed here, cyclodextrin polymer was crosslinked in the presence of a very low amount of crosslinker to completion. A certain amount of Pluronic® F-127 (donated from BASF Corp.) was added to mixture and Rhodamine B was also added as the guest molecule. Our formulation formed a solid gel at room temperature and became a sol at 37°C (Figure 76).

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Figure 76. Pluronic®-Cyclodextrin material is a gel at RT (left) and a sol at 37°C (right).

Rhodamine B in this setting was used as a fluorescent probe to track gelation. The fluorescence of Rhodamine B was followed from a liquid state (preheating) to a gelled state (holding at RT), and then back to a liquid state (increasing the temperature) using a plate reader. The latter two steps were conducted in the plate reader using the incubator temperature control (Figure 77). As the material underwent gelation, the fluorescent response increased and then subsequently decreased as the material was again heated to a sol state. Although in the CDP control, without Pluronic®, the fluorescence also increased as the material cooled (discussed in Chapter 4), the behavior is exaggerated in the formulation with Pluronic®. The experiment provides preliminary evidence for a

BCDP material that can complex a guest and is also stimuli responsive. Upon strong mechanical agitation the gel transitioned to a sol. Potential future applications of this technology may not be limited to eye. For example, wounds may be treated with a gel that can turn into a liquid when in contact with the body and an active molecule may be released. The release of the molecule may be inhibited due to the cyclodextrin polymer content in the formulation. Methods, not described here, were also developed for testing

144 the injection force of such gels through a syringe. Future work may further develop this material into a platform that can be injected through a syringe needle; shear forces may dissociate the gel into a viscous liquid that can reform into a gel post-injection.

Figure 77. Gelation is tracked using Rhodamine B as a fluorescent probe. Samples are preheated to a liquid and then allowed to gel in a plate reader, and then subsequently heated again. Note that the reader maxed out at 65000 AU.

70000 45

40 60000 BCDP+Pluornic+Dye

BCDP+Dye 35

50000 BCDP+Pluronic BCDP 30 40000 Temperature 25

Emission (AU) 30000 20 15 Temperature (C) 20000 10 10000 5

0 0 0 200 400 600 800 1000 1200 Time Elapsed (min)

Outside the realm of an injectable formulation, implants that are non-degradable, such as the ones developed in this body of work, may require surgical intervention to remove after the disease condition is improved. Although it has yet to be officially investigated, the solubilizing potential of drug in this platform may yield a device that can be reloaded in situ during the course of treatment. In situ loading is advantageous because if drug is driven into the platform, its subsequent release will also be controlled.

In the future the platform might also take the form of cyclodextrin microparticles that can be injected through a small needle, yet maintain their ability to control release.

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A reloadable reservoir type device could use crosslinked polymers hydrogels to control diffusion (Figure 78). A proposed future activity is to test this device in a full release study. A brief method to study loading behavior using solid state fluorescence is shown in Figure 81. Future work might also consider studying diffusion through this technique as an alternative to the photographic method discussed in Chapter 3.

Figure 78. Prototype reloadable device assembly (left), loaded with Rhodamine B using a 25G needle in PBS (right).

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Figure 79. Loading analysis of the reloadable device after 24 hours in solution. Gel is taken apart and solid state fluorescence is measured by exciting the surface at 510nm and recording emissions at 584 nm using a grid pattern. Grid pattern data is normalized and plotted using an a array plot in Wolfram Mathematica.

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Retinoids and Lasting Impact

In the setting of ocular diseases, particularly AMD, the next logical step for these materials is to implant them in an eye in vivo model so that efficacy, biocompatibility, and pharmacokinetics can be evaluated. Retinoid delivery technologies have a broader impact than just being able to treat ocular conditions. Retinoid therapies are currently used on a wide variety of health problems ranging from poor diet to acne to a type of leukemia (Table 23). A controlled release platform for the delivery of retinoids therefore has broader meaning. In our effort we were able to successfully deliver drug from a variety of cyclodextrin-based platforms. These platforms had varying degrees of control over release behavior and unique properties related to their synthesis and processing. A family of delivery technologies related to cyclodextrin has a far reaching impact since clinical need can be met by choosing a technology that has the best properties for a given application. I hope that in the future, derivatives of this work, and scientific questions answered in its evolution, may someday benefit the human condition.

Table 23. Retinoids are used in a number of clinical applications.

Name of Retinoid Clinical Use Retinol Malnourishmentcxxxiii All-trans retinoic acid Acnecxxxiv, acute promyelocytic leukemiacxxxv 9-cis retinoic acid Kaposi’s sarcomacxxxvi, chronic hand eczemacxxxvii 13-cis retinoic acid Neuroblastomacxxxviii, Fibrodysplasia ossificans progressivacxxxix Retinyl palmitate Cosmetic skin productscxl

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