Development of a Selective and Stable Reactive Oxygen Species-Activated Anti-Acute Myeloid Leukemia Agent and Localizing DNA Aptamer

Development of a selective and stable Reactive Oxygen Species-activated anti-Acute Myeloid Leukemia agent and localizing DNA Aptamer

a dissertation submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements of the degree of

DOCTOR OF PHILOSOPHY (Ph.D.)

In the Department of Chemistry of McMicken College of Arts and Sciences by

Kaylin Grace Earnest

Bachelor of Science (B.S.), Chemical Science and Biochemistry, Xavier University, Cincinnati, OH

Dissertation Advisor: Edward J. Merino, PhD

Abstract

Anticancer agents that modify DNA are a mainstay of chemotherapy regimens, but development of new classes of these agents has slowed because of the modifications of DNA in non-cancerous cells. This is what gives rise to serious side effects via poor selectivity. The Merino Lab has developed a pro-drug strategy to achieve specificity by translating the finding that levels of reactive oxygen species (ROS) are elevated in cancers, such as Acute Myeloid Leukemia

(AML). This pro-drug approach allows cellular ROS to oxidize the pro-drug into its active form to achieve selective cytotoxicity. Our current lead agent (A100) is shown to have 10-fold selectivity between AML cells over normal CD34+ blood cells in vitro and showed some efficacy in the in vivo AML mouse model; however, it did not perform as highly as expected.

It was hypothesized that the poor in vivo results were due to poor solubility and susceptibility to metabolic enzymes. This work started with computational analysis to determine what parts of the molecule were a target for metabolic enzymes. The first step taken to improve the molecule was to add polyethylene glycol (PEG) to the free phenol, increasing both its solubility and metabolic stability.

To prove metabolic stability, binding assays against CYP1A2 (Cytochrome P450,

Isoform 1A2) were done, as CYP1A2 is known to attack alcohols. The synthetic addition of the PEG increased stability against CYP1A2 by almost 50%. To prove stability in a more complex matrix, total stability was measured via half-life in pooled human liver microsomes. The PEGylated compound (A100-PEG) showed

i a 7-fold increase in the half-life of A100, as compared to A100 alone. A pharmacokinetic method was developed and optimized to test the stability of A100-

PEG in a mouse model, using only 30μLs of blood. A100 alone was not detectable in mouse blood samples after 15 minutes; however, in all three mouse models,

A100-PEG was detectable even after 100 minutes and was calculated to have a

6-fold increase in half-life, as compared to A100. Though stable, A100-PEG only showed similar efficacy in the in vivo AML mouse model; however, an IV injection was used instead of the previous IP injection.

Having improved its solubility and metabolic stability, the next step was to improve agent delivery. This was done by developing an aptamer to selectively

“shuttle” the molecule to the cancer cells. Aptamers are selective binding nucleic acid macromolecules that are evolved in vitro to bind a specific target, in this case, whole AML cells, through a process called SELEX, systematic evolution of ligands by exponential enrichment. Twelve rounds of SELEX, including two counter selections against fibroblast cells, were completed. Aptamer pools were sequenced, and 3 main candidate sequences were identified. These sequences consisted of two 23 bases primers and a 30-base sequence in between. Binding studies were done using flow cytometry, and the lead sequence was found to have a binding constant of 38+/-2.5nM to AML cells, while having no binding to fibroblast and umbilical cord blood cells at 200nM. A truncation study of the lead sequence was done using 9 shortened sequences, which proved that the 5’ primer was not important for binding. The binding of the lead sequence was tested against 7

ii primary AML patient samples, and 5 of the 7 cell lines showed binding at 200nM. In conclusion, an anti-Acute Myeloid Leukemia agent was metabolically stabilized using PEG and its stability was proven in an in vivo mouse model. Also, a localizing nucleic acid aptamer specific to AML cells was developed, sequenced, and characterized for future drug-aptamer conjugates.

iii

iv Acknowledgements

It is with great humility that I have the chance to say to thank the people who have made this accomplishment possible. I first want to give gratitude to my

God, who gave me the life to pursue this passion and the persistence to complete this dream.

I want to thank my parents, Tom and Anita, for all of their love and support.

So many times throughout this journey I called them wanting to give up, and they always gave me the sane advice I needed to hear. Without them, I don’t see how

I could have done this. I also want to thank my siblings Alicia (and Sean), Matthew

(and Morgan), and Melinda. I had to sacrifice so much time in this endeavor, that I felt I neglected them and the love they showed me. Without their support, both near and far, this would not have been possible. I cannot wait to be able to spend more time with my nephew Walter, and all the nephews and nieces to come. To my extended family – thank you for understanding when I couldn’t come for holidays or spend that extra time with you. I cannot wait to thank you and to make up for the time I lost in the pursuit of this dream, especially my godson Joseph.

In high school, I lost a dear friend to the very disease I ended up researching, acute myeloid leukemia. Tony – thank you for your beautiful life, and

I hope you know that I put the pressure on myself to succeed in this because of you. I want to also pay tribute to my cousin, Jennifer Scoles, who passed too soon from this world because of breast cancer in my first year of graduate school. Jenni

– your passing lit a fire within me that constantly reminded me that the mountain I

v was trying to conquer was well worth the time and energy. To all of the surviors of cancer, whether I know them personally or not, you supplied me with the happiness and drive I needed to carry on.

I owe a great deal of thanks to those who have been a part of my education.

First to the teachers and faculty at Bishop Dwenger High School, you all not only allowed me to grow as a young adult, but you also showed me that eduation and science could be fun. I’d like to specifically note Lara Fairchild, Maryanne Spohn,

Carrie Bleeke, John Bennet, and Lisa Polhamus for their guidance and dedication to my education, especially when I didn’t realize how impactful it would be untll now.

To Xavier University – where do I begin? I owe the Xavier Chemistry

Department so much gratitude – Dr. Barbara Hopkins, Dr. Adam Banage, Dr. Mary

Stroud, Dr. Roger Parker, and Dr. Justin Link (even if he’s technically Physics). If sophomore me could see me today, she wouldn’t recognize me. Thank you Dr.

Dan McLoughlin, my academic advisor, for believing in me, even when I didn’t believe in myself. To Dr. Rick Mullins, my research advisor – who saw more potential in me than I ever thought I had. Thank you allowing me to study under you and sparking my interest in scientific research. You helped me get into graduate school and continued to support me during the last 5 years. To my Xavier friends – thank you understanding my chaotic graduate school schedule and life.

Your support, even when I couldn’t see or hangout with you, helped me through this degree.

vi I also want to thank my advisor Dr. Edward Merino and the University of

Cincinnati Chemistry Department for giving me the opportunity to study the subject that I love. I want to thank my committee: Dr. Patrick Limbach, Dr. Laura, Sagle, and the late Dr. Joe Caruso. Thank you for your support and guidance throughout this journey. I also want to thank my collabortors at CCHMC Dr. Jim Mulloy and

Mark Wunderlich – without you this wouldn’t have been possible. To Dr. Maria

DeRosa, Dr. Erin McConnell, and Dr. Eman Hassan and all my friends at Carleton

University in Ottawa, Ontario Canada – thank you for giving me such a warm home in Ottawa. Your guidance and patience did not go unnoticed. To my lab mates past and present – thank you. Thank you for putting up with my dancing and singing in lab. I will miss you all dearly. To my classmates – this journey has been unforgettable. I’d like to acknowledge my funding sources – the Univeristy of

Cincinnati Chemistry Department, the R.I.T.E. Program, the NIH, the DOD.

Obviously none of this would have been possible without that support.

And lastly I’d like to thank my dog, Bruce. Every presentation or report I had to give – he listened, even though he had no idea what I was talking about. You make me smile every day, which something I definitely took for granted.

I’d like to conclude my acknowledgment by thankfully remembering each and every one who helped me in so many countless ways along this journey.

All my love,

Kaylin Grace

vii

viii Table of Contents

Chapter 1: Introduction to Cancer, Reactive Oxygen Species, A100, and

Aptamers ...... 1

1.1 Reactive Oxygen Species: Production and Depletion ...... 2

1.2 High levels of ROS leads to DNA Damage and Disease ...... 3

1.3 Cancer Statistics ...... 4

1.4. Current Chemotherapeutic Approaches ...... 5

1.5 ROS-Activated Anti-Cancer Agents ...... 6

1.5.1 A100: A ROS-activated Prodrug ...... 8

1.6 Drug Delivery Systems ...... 8

1.6.1 Polyethylene Glycol ...... 9

1.6.2 Aptamer-Drug Conjugates ...... 10

1.7 Research Overview ...... 12

1.7.1 Goals of this Dissertation ...... 12

1.7.2 Overview of Chapter 2 ...... 13

1.7.3 Overview of Chapter 3 ...... 14

1.7.4 Overview of Chapter 4 ...... 15

Chapter 2: Using Polyethylene Glycol derivatives to stabilize a selective anticancer agent ...... 16

2.1 Introduction ...... 17

2.2 Experimental Methods ...... 18

2.2.1 WhichCYP ...... 18

ix 2.2.2 Fast Metabolism ...... 18

2.2.3 Human carboxylesterase 1 (hCE-1) inhibition assay ...... 19

2.2.4 Cytochrome P450 1A2-Isoform (CYP-1A2) Inhibition Luminescence

Assay...... 19

2.2.5 Liver Microsome Stability Assay ...... 20

2.2.6 Percent Recovery for Acetonitrile Crash ...... 21

2.2.7 Pharmacokinetics Mouse Study ...... 22

2.3 Results and Discussion ...... 22

2.3.1 Computational Studies ...... 22

2.3.2 Solubilization and metabolic stabilization of A100 for in vivo application

...... 24

2.3.3 In Vivo Pharmacokinietic Mouse Study ...... 28

2.4 Conclusion ...... 30

Chapter 3: Introduction and Design of a DNA Aptamer through Systematic

Evolution of Ligands by Exponential Enrichment (SELEX) ...... 32

3.1 Introduction ...... 33

3.2 Experiment Methods ...... 35

3.2.1 Cell Culture ...... 35

3.2.2 SELEX library and primers ...... 35

3.2.3 Systematic Evolution of Ligands by Exponential Enrichment (SELEX)

Procedure (in vitro) ...... 36

3.2.4 Quantitative Polymerase Chain Reaction ...... 36

x 3.2.5 Polyacrylamide Gel Electrophoresis (PAGE) Purification ...... 37

3.2.6 Initial Binding Study ...... 38

3.3 Results and Discussion ...... 38

3.3.1 SELEX process ...... 38

3.3.2 PAGE Purification and Enrichment monitoring via qPCR ...... 40

3.3.3 Initial Library 0 vs Round 8 Library Binding Assay ...... 41

3.4 Conclusion ...... 42

Chapter 4: Sequencing, characterization through binding studies, truncation, and primary patient sample screen of the DNA Aptamer ...... 44

4.1 Introduction ...... 45

4.2 Experimental Methods ...... 47

4.2.1 Sequencing and Structure Predict ...... 47

4.2.2 Cell Culture ...... 47

4.2.3 Binding Screen and KD Determination using Flow Cytometry ...... 48

4.2.4 MvGJRO Truncation Study ...... 49

4.2.5 Primary Patient Binding Studies using Flow Cytometry ...... 49

4.3 Results and Discussion ...... 50

4.3.1 High throughput sequencing (HTS), bioinformatics analysis, and

Structure Predict ...... 50

4.3.2 Screening for the binding affinity of selected aptamers and KD

determination using flow cytometry ...... 51

4.3.3 Binding specificity of KGE02 to cancer and normal cell lines ...... 53

xi 4.3.4 Truncation of KGE02 and assessment of their binding abilities ...... 53

4.3.5 Binding of KGE02 to primary AML patient cells ...... 57

4.4 Conclusion ...... 59

4.5 Future directions of this project ...... 60

References ...... 62

xii List of Tables and Tables

Chapter 1

Figure 1. 1: ROS production and detoxification. This schematic demonstrates the

major reactions and signaling pathways in ROS production and scavenging

system...... 3

Chapter 2

Figure 2. 1: WhichCYP and FAst MEtabolism Results ...... 23

Figure 2. 2: Effectiveness of A100 and its derivatives in inhibiting human

carboxylesterase ...... 26

Figure 2. 3: A100-PEG construct enhances stability in vitro and confers better

suitability for in vivo studies ...... 27

Figure 2. 4: Compound Percent Recovery from Acetonitrile Crash ...... 29

Figure 2. 5: Metabolic Degradation Curves of A100-PEG from in vivo

Pharmacokinetic Study ...... 30

Chapter 3

Figure 3. 1: Schematic diagram of SELEX method for selection of MLL-AF9 RAS-

specific DNA aptamers with internal counter selection method...... 39

Figure 3. 2: Monitoring enrichment of ssDNA library of MLL-AF9 RAS during

SELEX by qPCR ...... 40

Figure 3. 3: Example PAGE images post amplification ...... 41

Figure 3. 4: Binding screen of Percent Bound for Round 0 Library against Round

8 Library to show enrichment...... 42

xiii Chapter 4

Table 4. 1: Aptamer sequences of MLL-AF9 RAS target and their corresponding

Kd values ...... 51

Figure 4. 1: Binding curves of fluorescein labeled apatmers to MLL-AF9 RAS

(MA9Ras) target cells...... 52

Figure 4. 2: Binding of KGE02 aptamer to MLL-AF9 RAS (MA9Ras), WI-38, and

UCB cells ...... 54

Figure 4. 3: Truncation of MvGJRO aptamer ...... 56

Figure 4. 4: Comparison of secondary structures of KGE02 with full length or

truncated sequence ...... 57

Figure 4. 5: KGE02 binding to primary AML patient samples ...... 58

xiv

Chapter 1: Introduction to Cancer, Reactive Oxygen Species, A100, and

Aptamers

1 1.1 Reactive Oxygen Species: Production and Depletion

Reactive Oxygen Species (ROS) are, just as they sound, reactive forms of oxygen with an unpaired electron in their outermost shell. 1 ROS can be radicals, ions, or molecules and are formed from the metabolism of molecular oxygen. 2

2- Common ROS include superoxide (O ), hydrogen peroxide (H2O2), hydroxyl

1 radical (HO•), singlet oxygen ( O2) and ozone (O3). ROS play an important role in immune defense 3, oxidative biosynthetic reactions, and function as signaling agents. 4 They are naturally produced as the result of normal intracellular metabolism in the mitochondria, peroxisomes, and a number of cytosolic enzyme systems. 5 For example, in the mitochondrial electron transport change, oxygen normally undergoes a four-electron reduction to produce water. Sometimes during this cellular respiration process, electron leak can occur and generate superoxide, via one-electron reduction, as a byproduct of metabolism. 6 As a safeguard, cells have antioxidants that work to counter and deplete levels of ROS. However, there is a delicate balance between the amount of ROS needed for survival and levels that can damage or be lethally toxic to cells. Elevated levels of ROS, due either to increased ROS production or reduced antioxidant defense, can result in ageing and age-related diseases. 7 Under normal physiological conditions, ROS levels are maintained to prevent any cellular damage. Depletion of ROS is accomplished by enzymatic or non-enzymatic molecules. Enzymatic ROS reduction can be initiated by antioxidant enzymes, which specifically scavenge certain kinds of ROS. As an example, superoxide dismutase (SODs) catalyze the reduction of superoxide into

2 hydrogen peroxide. 8 Furthermore, catalase 9 or peroxidase enzymes can breakdown hydrogen peroxide into water and oxygen with the help of glutathione and NADPH as electron donors. 10 Hydrogen peroxide can also be reduced via a

Fenton reaction to form the highly reactive and most dangerous ROS, the hydroxyl radical. 11,12 The continuous production and detoxification of cellular ROS, as depicted in Figure 1.1, leads to a tightly controlled and well-balanced redox status in normal cell.

Figure 1. 1: ROS production and detoxification. This schematic demonstrates the major reactions and signaling pathways in ROS production and scavenging system. 5

1.2 High levels of ROS leads to DNA Damage and Disease

Biological systems have evolved to maintain the delicate balance of ROS production and depletion This is known as redox homeostasis. When significant concentrations of potent radicals, like OH•, are formed and can reach DNA, DNA

3 damage can occur. One of the definitions for the oxidative stress is an imbalance between oxidants and antioxidants with a higher level of the oxidants, potentially leading to damage. 13 DNA damage is a major consequence of oxidative stress.

Exposure of DNA to oxidative conditions can result to lesions including abasic sites, DNA strand breaks, DNA-DNA cross-links, DNA-protein cross-links and a multitude of modifications to the heterocyclic DNA bases. Among DNA bases, guanine is more vulnerable to oxidation due to its lowest redox potential. 14

Oxidative DNA damage can cause the activation of the oncogene or inactivation of the tumor suppressor genes, which are the two required types mutations that can give rise to cancer 15. On the contrary, an oxidative stress status is beneficial for cancer cell proliferation and survival, which has been observed in many types of cancer. 16 This relationship between oxidative stress and cancer has been thoroughly studied. 17,18

1.3 Cancer Statistics

In the United States, one in three women and one in two men will develop cancer in his or her lifetime, and one in four deaths is a result of the disease. 19 An estimated 600,920 Americans will die from cancer in 2017, corresponding to about

1,650 deaths per day. 20 The lifetime probability of being diagnosed with an invasive cancer is 40.8% for men and 37.5% for women 20, which has decreased for men but remained steady for women since 2012. 19 One of many cancers with a low survival rate is acute myeloid leukemia. As of 2016, acute myeloid leukemia

4 (AML) has a 5-year survival rate of only a 26%. 21 In 2017, there was an estimated

21,380 new cases of AML, and an estimated 10,590 deaths from the disease. 20

1.4. Current Chemotherapeutic Approaches

Some current chemotherapeutic approaches include: anthracycline class agents, DNA alkylating agents, camptothecins, antimetabolites, and DNA minor groove binders. 22 Anthracycline class agents, such as doxorubicin, act by DNA intercalation. 23 Unfortunately, their efficacy in treating cancer is limited by a cumulative dose-dependent cardiotoxicity, which can cause irreversible heart failure. 24 DNA alkylating agents are the oldest class of anticancer drugs still commonly used and act by forming bifunctional DNA adducts, 25 despite the adverse side effects caused on bone marrow and other normal tissues. 26

Camptothecin acts by interacting with DNA and Topoisomerase 1, the enzyme responsible for untwisting DNA, to cause double strand breaks. 27 However, camptothecin has major limitations, including poor solubility and inactivity at physiological conditions. However, a series of small molecule camptothecin derivatives have been developed that increase solubility, lactone stability, and bioavailability to some level of success. 28 Antimetabolites, such as 5-fluorouracil

(5-FU) and thio-purines, mimic nucleotides, nucleotide precursors, or cofactors required for nucleic acid biosynthesis and act by depleting cells of dNTPs, inhibiting nucleotide metabolism pathways. They can also inhibit replication by becoming incorporated into the DNA. 29 However, the molecular mechanisms

5 through which anti-metabolites induce cell death are poorly understood. 30 DNA minor groove binders, which induce permanent DNA damage by covalent binding through the action of a reactive electrophilic moiety. 31 At present, relatively little is understood about the mode of action at the molecular level of minor-groove interacting drugs. 32 These classes of cancer therapeutics are riddled with negative side effects, stemming from off-target cytotoxicity because of their lack of selectivity toward cancer cells.

1.5 ROS-Activated Anti-Cancer Agents

Current treatments for cancer include surgery, radiotherapy, and chemotherapy. From the first chemotherapeutic experiments 60 years ago, researchers have worked to develop more effective cancer drugs. 33 Advances in understanding of the molecular and cell biology of cancer has allowed specific targeting of signaling proteins and pathways involved in cancer initiation and progression. 34 However, off-target cytotoxicity is still a major problem in cancer drug design. 35 Anticancer agents that modify DNA are a mainstay of chemotherapy regimens, 36 but development of new classes of these agents has slowed because of the modifications of DNA in non-cancerous cells, causing that off-target cytotoxicity. This gives rise to serious side effects and poor selectivity.

Recent literature studies have shown that cancer cells show elevated levels of

ROS. 37,38 Thus, ROS targeted anti-cancer agents are becoming more attractive.

15 This allows the selective targeting of cancer due to the oxidative stress status of

6 cancers; these targeted drugs promise to develop improved efficacy and minimized toxicity related to anti-cancer therapies.

Current ROS targeted anti-cancer agents can be organized into two categories: those that generate more ROS and those activated by ROS. The first class generates more ROS in the cell one of two ways: either by increasing ROS production or inhibiting antioxidant mechanisms. This increased level of oxidative stress tilts the redox homeostasis and becomes toxic to the cancer cells. 39

Common example agents are Taurolidine and Piperlongumine. In many cancers, increased ROS production was observed with the treatment of taurolidine. 40 ROS- induced apoptosis is to be the major mechanism of action of taurolidine. 41

Piperlongumine induces ROS production in cells by acting as a suicide inhibitor of critical ROS regulator proteins. 42 While this mechanism works, off-target cytotoxicity is still problematic.

The second class is agents that get selectively activated by the slightly higher level of ROS in cancer, which allows it to selectively target cancer. Selective activation has the potential to negate off-target cytotoxicity, as most non-cancerous cells do not have high enough levels of ROS to activate the molecules to make it toxic. Most have deemed this as a prodrug approach. The most common prodrug system is the boronic acid/ester system, where the phenol of a toxic agent is turned into a boron ester. The aryl boron compounds selectively react with H2O2. The agent is non-toxic as a boron ester, but when in high ROS environments, like in

7 43 cancer cells, reformation of the active, toxic phenol is catalyzed by H2O2. This has been used to combat cancers, including leukemia. 44,45

1.5.1 A100: A ROS-activated Prodrug

Previous work in the Merino Lab has led to the development of a reactive oxygen species-activated DNA-binding anti-cancer agent, termed A100. By translating the finding that ROS levels are elevated specifically in Acute Myeloid

Leukemia (AML) cells46, we have been able to achieve selective toxicity, creating a prodrug. Therefore, only upon ROS activation, DNA modification occurs. A100 has an IC50 against AML cells of 750nM. In addition, this compound is exquisitely specific with an order of magnitude lower potency for closely related CD34+ blood stem cells. Without this ROS activation, the compound has little to no reactivity and cytotoxicity at these lower concentrations. 47 More details of the development and the mechanism of action of A100 will be discussed in Chapter 2.

1.6 Drug Delivery Systems

Anticancer agents that are promising in vitro are not always built for in vivo.

Some of the main pharmacological concerns are aqueous solubility, stability, and bioavailability. Many of the pharmacological properties of free anticancer agents can be improved by using drug delivery systems (DDS). These DDS are designed to alter the pharmacokinetics (PK), which is the stability, and biodistribution, which includes solubility and bioavailability. 48 DDS include liposomes and other lipid

8 based carriers (such as micelles), lipid emulsions, lipid-drug complexes, polymer- drug conjugates, polymer microspheres, and various ligand-targeted products such as immunoconjugates. 49–53 The development of an effective drug delivery system could significantly improve cancer patient management with DDS conjugated drugs.

1.6.1 Polyethylene Glycol

Polyethylene glycol (PEG) is the most commonly used polymer and the gold standard for polymers in the emerging field of polymer-based drug delivery. 54 In the 1970s, pioneering research by Davis and colleagues foresaw the potential of the conjugation of PEG to proteins.55 The concept originally started as a method to protect proteins and peptide drugs, which showed promise as therapeutic agents, from degradation by proteolytic enzymes causing a short circulating half- life. Pegylation improves pharmacokinetics by increasing the molecular mass of proteins and peptides and shielding them from proteolytic enzymes.56 PEG is a particularly attractive polymer for conjugation and is widely used as a pharmaceutical excipient. The flexible, highly water-soluble polymer chain extends to give a hydrodynamic radius that is some 5–10 times greater than that of a globular protein of equivalent molecular weight.50

The pharmacokinetics of drugs can change by being guarded by or bonded to PEG, which results in prolonged blood circulation time. Subsequently, this increases the probability that the drug reaches its determine target before being

9 recognized as foreign and cleared from the body. 57 For this reason, PEGylated drugs show a prolonged half-life in the body and, thus, an enhanced bioavailability.

58 Multiple drugs have been stabilized by PEG drug-delivery systems and have received regulatory approval in the US and/or the Europe, as early as 1990. 54

The contribution of PEG to nanotechnology-based DDS, is highly significant. PEGylated nanoparticles affixed with targeting ligands are the latest approach for targeted DDS.59 PEG 1000–5000 is preferable to mask surface charges of nanoparticles and prevent reticuloendothelial system uptake, allowing for increase in the systemic circulation exposure. The main mechanism of prolonged circulation is based on increased hydration and solubility. Sustained release effect of larger PEGS like PEG 3000 to PEG 5000 is seen due to bulking and water retention. Branched PEGs have lower volume of distribution and better covertness with reduced systemic elimination than those with linear PEGs.

Furthermore, a long chain PEG offers much better PEGylation than small units of

PEG chains.60

1.6.2 Aptamer-Drug Conjugates

Aptamers are single-stranded oligonucleotides that are designed to bind specific targets. Targets can be small molecules, proteins, or even whole cells.

Aptamers have quickly emerged as a novel and powerful class of ligands with excellent potential for diagnostic and therapeutic applications. 61 Aptamers possess several advantages over other ligands typically used in drug delivery such

10 as antibodies. Aptamers can be synthesized without relying on biological systems, which makes them easier to produce. Also, they are quite thermally stable and can be denatured and renatured several times without significant loss of activity. 62

Aptamers are also smaller than antibodies, which can lead to better tissue penetration in solid tumors; In addition, lack of ability to induce an immune response is another favorable advantage of aptamers over antibodies. 63

Aptamers are different from antibodies, yet they mimic properties of antibodies in a variety of diagnostic formats. The demand for diagnostic assays to assist in the management of existing and emerging diseases is increasing, and aptamers could potentially fulfill molecular recognition needs in those assays.64 Conjugation of functional groups to aptamers is analogous to nucleic acid chemistry, which means they can be introduced during synthesis. 65

A rising field in chemistry is the use of aptamer-drug conjugates as possible therapeutics. Targeted therapy is a way to reduce adverse side effects, it also aims to increase toxicity to the specific target. Aptamer-drug conjugates are being used in targeted drug delivery in chemotherapy, gene therapy, immunotherapy, photodynamic therapy, and photothermal therapy, primarily of cancer. 66 For example, doxorubicin was conjugated to DNA aptamer sgc8, which selectively targets protein tyrosine kinase 7 (PTK7) overexpressed on many types of cancers.

It was able to deliver doxorubicin selectively to CEM cells, released doxorubicin in endosome, and inhibited cancer cell proliferation. 67,68 Along the same lines, 5- fluorouracil (5-FU), a commonly prescribed drug for many cancers, was linked to

11 a phosphoramidite via a photocleavable linker. The phosphoramidite was then conjugated with aptamers, such as sgc8. Studies were done in cancers again overexpressing PTK7. The sgc8-(5-FU)5, in which one sgc8 aptamer carries 5 copies of 5-FU, selectively delivered 5-FU into HCT116 cells overexpressing PTK, but not to cells that do not have PTK expression. Under light irradiation, the photocleavable linker was cleaved, releasing the tethered 5-FU molecules from the aptamer backbone. 69 Despite all of the attractive features that aptamers bring to the table, the development of aptamer-drug conjugates for targeted therapy is still lacking. Only one aptamer-based drug, Pegaptanib (marketed as Macugen), has been approved by the FDA. Pegaptanib is a PEGylated anti-VEGF aptamer for the treatment of age-related macular degeneration 70. Other than Pegaptanib, one the most promising aptamer drugs for cancer therapy, AS1411, is currently under Phase II investigation.71 Along with AS1411, there are eight other aptamers in different stages of clinical trials.72

1.7 Research Overview

1.7.1 Goals of this Dissertation

This dissertation aims to metaboloically stabilize a research developed, selective ROS-activated novel anti-cancer agent for acute myeloid leukemia. It also aims to develop, sequence, and characterize a whole-cell binding DNA aptamer that is selective for MLL-AF9 leukemia cells.

12 1.7.2 Overview of Chapter 2

The first step taken to improve the molecule was to add differing lengths of polyethylene glycol (PEG) to the free phenol, increasing both its solubility and metabolic stability. Two computational tools were utilized in this design. Fast

Metabolism (FaMe) ranks each individual non-hydrogen molecule for its susceptibility to Cytochrome P450, the main family of oxidases responsible for primary metabolism, and secondary metabolism enzymes. WhichCYP identifies which parts of a molecule are likely susceptible to which of the five major

Cytochrome P450 isoforms. Using a binding assay for Cytochrome P450 1A2, the identified isoform to be protected from by the addition of PEG, it was shown that the PEG increased stability by almost thirty percent. To test total metabolic stability, pooled-human liver microsome and pharmacokinetic studies using a mouse model were completed and showed an increase in the agent’s half-life from

15 minutes (A100) to 62 minutes (A100-PEG2000) in vitro. The most important result is that the A100 formed from the degradation of A100-PEG has a half-life of

116 min, an increase of over 7-fold increase when compared to A100 alone. The pharmacokinetic mouse study showed an average A100-PEG2000 half-life of 49 minutes, with an estimated half-life of A100-formed to be 100.5 minutes. This was an over 6-fold increase from A100 in vitro.

13 1.7.3 Overview of Chapter 3

Aptamers are single-stranded oligonucleotides that are designed to bind specific targets. The purpose of the aptamer is localize and concentrate ROS- activated DNA-modifying Agents to the cancer cells and not to similar normal cells, or random cells passed in the process of journeying to the target cells. This is because if the aptamer delivers the ROS-activated DNA modifying agents to the incorrect location, then the activity of the compounds is actually reduced, as they are nontoxic to unspecific cells, those with lower levels of ROS, at the IC50 dosage.

To enforce specificity, we will run a counter selection and eliminate sequences that bind to healthy CD34+ cord blood cells and fibroblast cells. The evolutionary process, known as systematic evolution of ligands by exponential enrichment, abbreviated SELEX, works as follows. First a selective pressure is enforced on a library of random nucleic acid sequences. In this case, sequences that bind to our model AML cancer cells are separated from sequences that do not bind. Next, those sequences that were bound to AML cells were amplified, via qPCR, to enrich the library for binding. One of the PCR primers is fluorescently labeled with a fluorescein molecule, allowing for easier visualization and purification via polyacrylamide gel electrophoresis. The process was repeated twelve times to develop and identify highly selective sequences, including a counter-selection process against fibroblast tissue cells.

14 1.7.4 Overview of Chapter 4

Once the aptamer was subjected to twelve rounds of selection, six different selection pools were prepped with barcodes for Illumina MiSeq DNA sequencing.

Illumina MiSeq Next Generation Sequencer is an integrated instrument that performs clonal amplification, genomic DNA sequencing, and data analysis with base calling, alignment, variant calling, and reporting in a single run. The pools sequenced were the original library (Pool 0), Pool 6, Pool 8, Pool 11, Pool 12, and counter-Pool 12. Multiple pools are sequenced in order to get data on how well enriched a single sequence is throughout the SELEX process. Sequencing produced four main enriched sequences. These sequenced were then computationally analyzed to predict their three-dimensional structures with folding energy. Three sequences were modified with fluorescein tags, and their binding constants (in the form of a KD) were calculated using single-cell flow cytometry.

The top candidate, KGE02, had a KD of 37.5nM and hill coefficient of 2 to AML cells, while having no binding to fibroblast and umbilical cord blood cells at 200nM.

Truncation studies were done on KGE02 to determine what section of the 76-base sequence was responsible for the most binding. The truncation study of the lead sequence was done using 9 shortened sequences, which proved that the 5’ primer was not important for binding. The binding of the lead sequence was tested against

7 primary AML patient samples, and 5 of the 7 cell lines showed binding at 200nM.

Chapter 2: Using Polyethylene Glycol derivatives to stabilize a selective anticancer agent

16 2.1 Introduction

Derivation of cytotoxic agents to improve overall stability is a common practice in medicinal chemistry. The lead agent, A100, is known to be 7-fold more selectively toxic to acute myeloid leukemia cells than to non-cancerous human

CD34+ blood stem/progenitor cells (UCB) 47. This selectivity is a result of its prodrug design, requiring activation by elevated levels of reactive oxygen species to be cytotoxic 73. Once activated by oxidation, the quinone entity of the molecule binds to DNA, specifically guanine bases, during replication in the rapidly proliferating cell 74. While A100 has great in vitro qualities, its low aqueous solubility and liver microsome stability need to be improved to move to in vivo studies.

The first way to improve in vivo properties is to identify molecules that are better suited for the harsh environment inside a mouse/person. Within this environment, compounds are degraded by Cytochrome P450 enzymes (CYP) in the liver. This process is termed phase 1 metabolism, and 5 key enzymes are

CYP1A2, CYP2C9, CYP2C19, CYP2D6, or CYP3A4 75. Non-carbon atoms like nitrogen and oxygen are target atoms for CYP enzymes, and free hydroxyls are known to be highly susceptible to metabolism 76,77. To decrease the CYP binding affinity, it is necessary to protect nitrogens and oxygens with functional groups that need to be removed prior to phase 1 metabolism. Literature has shown that using polyethylene glycol (PEG) to protect hydroxyls increases both solubility and stability, creating a prodrug delivery system 7879. Other effective drugs with low

17 solubility have been synthesized as prodrugs that increase solubility, while adding an additional metabolic reaction 80–82.

The addition of PEG to A100 would increase both its aqueous solubility and its metabolic stability. The addition of the ester would cause the need for cleavage by esterase enzymes, allowing for longer circulation in vivo. Once cleaved from

PEG, A100 would then be free for oxidative activation in cells with elevated reactive oxygen species. The addition of the PEG would give it the deserved in vivo characteristics without too much loss in cytotoxicity. This chapter highlights the studies done to increase solubility and prove the metabolic protection of A100 using PEG through computational studies, direct enzyme assay, liver microsome assay, and an in vivo pharmacokinetic study.

2.2 Experimental Methods

2.2.1 WhichCYP

Structures were drawn and run through the online database available at http://www.farma.ku.dk/whichcyp/ 83.

2.2.2 Fast Metabolism

Structures were drawn in ChemDraw and converted to sdf files. License to run FAst MEtabolism (FAME) was acquired and structures were ran through database 84.

18 2.2.3 Human carboxylesterase 1 (hCE-1) inhibition assay

Acetylesterase activity was measured in the absence of inhibitor by incubating 70 ng of enzyme with 0-1.5 μM of p-nitrophenylacetate (PNP-Ac) in

0.1mM Phosphate buffer, pH 7.4 at 37 °C for 60 min. Product formation was observed at 410 nm, and rates of ester hydrolysis were calculated by linear regression of absorbance versus time. Lineweaver-Burke analysis was used to determine the Michaelis constant. For agent inhibitor 177 activity, 50μM of agent

(A100 or A100-PEG) was incubated with 70 ng of enzyme with 0-1.5 μM of p- nitrophenylacetate (PNP-Ac) in 0.1mM phosphate buffer, pH 7.4 at 37 °C for 1 h.

Product formation was observed at 410 nm, and rates of ester hydrolysis were calculated by linear regression of absorbance versus time. The Lineweaver-Burke slopes of the agents were then converted to inhibitor constants (Ki) by the derived kinetic equation Ki=0.05mM/[(SlopeInhibitor)/SlopePNP-Ac)-1].

2.2.4 Cytochrome P450 1A2-Isoform (CYP-1A2) Inhibition Luminescence

Assay

The following experiments were all performed simultaneously in triplicates on 96-well plate at room temperature. No inhibitor curve was produced by adding

4X Luciferin-1A2 (0-50μM, final concentration), 400mM phosphate buffer (pH 7.4), and NADPH Regeneration System (NADPH and 1μg/reaction Liver Microsome), total volume 50μL. Reactions were quenched using 50μL of reconstituted Luciferin

Detection Reagent (D-cysteine and Beetle Luciferase Enzyme), total reaction

19 volume 100μL. Agent curves (A100 and A100-PEG) were produced by adding 4X

Luciferin-1A2 (0-50μM, final concentration), 4X Agent (100μM, final concentration) in 400mM phosphate buffer (pH 7.4), and NADPH Regeneration System (NADPH and 1μg/reaction Liver Microsome), total volume 50μL. Reactions were quenched using 50μL of reconstituted Luciferin Detection Reagent (D-Cysteine and Beetle

Luciferase Enzyme), total reaction volume 100μL. Luciferin standard curve was produced by adding varying concentrations of 4X Beetle Luciferin (0-16μM, final concentration), 400mM Phosphate buffer (pH 7.4), and 2X NADPH Regeneration

System (NADPH and 20μg/μL BSA), total volume 50μL. Standards were quenched using 50μL of reconstituted Luciferin Detection Reagent (D-Cysteine and Beetle Luciferase Enzyme), total volume 100μL. Concentrations of D-Luciferin produced per reaction were calculated by using the standard Luciferin curve.

Percent bound was determined by taking the average amount of D-Luciferin produced via given reaction divided by the average amount of D-Luciferin produced by the No Inhibitor reaction multiplied by 100.

2.2.5 Liver Microsome Stability Assay

From 500mM stock solution of agent (A100 or A100-PEG), 150.0μL of 5mM solution was prepared in deionized H2O. 1X buffer solution was prepared by mixing 1.0mL 500mM phosphate buffer (pH 7.4), 1.0mL 10mM EDTA (pH 8),

1.0mL 30mM MgCl2, and 7.0mL deionized H2O. 10mM NADPH was prepared by dissolving 7.44mg NADPH in 1.0mL of deionized H2O. To a 1.7mL eppendorf tube,

20 1308.75μL buffer, 37.5μL 10mM NADPH, and 150μL 5mM agent were added.

Then, 3.75μL human liver microsomes (0.5μg/μL) were added. The solution was briefly vortexed. Immediately, a 150μL aliquot of this solution was thoroughly mixed with 150μL of acetonitrile in a plastic HPLC vial and then sealed. Aliquots were taken at different time points over a 6-hour period. Samples were quantified using high performance liquid chromatography (For A100, Stationary: 95% ACN,

5% H2O, Mobile: 95% H2O, 5% ACN, 0.1% Formic Acid; For A100-PEG,

Stationary: 95% ACN, 5% H2O, Mobile: 95% H2O, 5% ACN) with a C18 reverse phase column.

2.2.6 Percent Recovery for Acetonitrile Crash

Mouse blood was acquired from the Mulloy Lab at CCHMC. 2mM samples of A100-HCl, A100-PEG500, and A100-PEG2000 were made in water. Samples were mixed as follows: 15µL 2mM compound, 15µL mouse blood, 30µL 3% Formic

Acid (HPLC grade), and 75µL Acetonitrile (HPLC grade). Samples were placed on ice for 20 minutes and then centrifuged at 3000rpm for 10 minutes. The supernatant was recovered and the volume measured (for percent recovery calculation). Samples were then quantified using high performance liquid chromatography (Stationary: 95% ACN, 5% H2O, Mobile: 95% H2O, 5% ACN) with a small pore C18 reverse phase column. Samples were done in triplicate.

21 2.2.7 Pharmacokinetics Mouse Study

A 30mg/mL stock solution of A100-PEG2000 was made in 1xPBS. Mouse studies were completed in the Mulloy Lab at CCHMC. Three mice were dosed at

300mg/kg and 10µL/g via IV tail injection. 30µL of blood was drawn from the tail before injection, 30 minutes after injection, 60 minutes, 120 minutes, and 240 minutes. Blood samples were mixed with 1 equivalent 3% formic acid and 2.5 equivalents of acetonitrile. Samples were placed on ice for 20 minutes and then centrifuged at 3000rpm for 10 minutes. The supernatant was recovered. Samples were then quantified using high performance liquid chromatography (Stationary:

95% ACN, 5% H2O, Mobile: 95% H2O, 5% ACN) with a small pore C18 reverse phase column.

2.3 Results and Discussion

2.3.1 Computational Studies

The first computational study done used WhichCYP, which was developed at the University of Copenhagen and is a method for predicting which Cytochrome

P450 isoforms are likely to bind and metabolize a drug-like small molecule; therefore, it predicts if a molecule is likely to inhibit any of the five cytochromes isoforms 1A2, 2C9, 2C19, 2D6, or 3A4. A good anti-cancer agent will be shown as a poor inhibitor, because the enzyme is unable to recognize it (Rostkowski, M,

Bioinformatics 2013, 29 (16), 2051-2052). A100 and A100-PEG were run through the program and results are shown in Figure 2.1A and B. A100 was shown to be

22 a target of three of the major isoforms, with two of those isoforms attacking the quinone portion of the molecule, which is the entity responsible for cytotoxicity.

Because that portion of the molecule cannot be altered without losing toxicity, protection of the alcohol is the best way to deter metabolism. According to

WhichCYP, the addition of the polyethylene glycol protects A100 from two isoforms, 2C19 and 1A2.

Figure 2. 1: WhichCYP and FAst MEtabolism Results. (A-B) WhichCYP results for A100 and A100-PEG, respectively. Addition of PEG eliminates CYP1A2 and CYP2C19. (C-D) FAst MEtabolism results for A100 and A100-PEG, respectively.

The second program is FAst Metabolism (FAME), which is a predictor of sites of metabolism (SoM). It is based on a collection of models trained on diverse data sets of more than 20,000 molecules with experimentally determined sites of metabolism. It is not limited to a specific enzyme family and is able to predict phase

I and II metabolism together or independently (Kirchmair, J., J. Chem. Inf. Model.

2013, 53, 2896−2907). A SoM is defined as the atom where a metabolic reaction

23 is initiated. FAME is likely to flag one or two neighboring atoms of potential SoMs, and this contextually richer signal can provide valuable hints about which metabolic reaction may be taking place. For each heavy atom (non-hydrogen in the case of these molecules), the number reported is the probability of each atom being a

SoM. Therefore, the closer the number is to one, the more likely that atom is a site of metabolism. These results are shown in Figure 2.1C and D. The hydroxyl of

A100 has the highest number in the molecule, 0.4, making it the most vulnerable atom for attack. The addition of the polyethylene glycol is able to shit that focus from the alcohol alone, to the entire polyethylene glycol portion of the molecule, with numbers greater than 0.4. Combining this with WhichCYP allows us to determine which CYP enzymes are likely to bind and where in the molecule they are likely to bind.

2.3.2 Solubilization and metabolic stabilization of A100 for in vivo application

A100 is soluble in the polar organic solvent dimethyl sulfoxide (DMSO) but has very poor solubility in aqueous solutions. Because of its electron rich structure,

A100 displays suitable characteristics for the use in in vitro experiments. However, to proceed to in vivo studies, we made further modifications to A100 (Fig. 2.2A) to increase its solubility in water. Polyethylene glycol (PEG), a polyether compound that is very soluble in water, was added to A100 via replacement of the free phenol

(Fig. 2.2A). To make sure that the activated A100 could still be released intracellularly, we compared the effectiveness of A100-acetate and A100-PEG in

24 their ability to inhibit human carboxylesterase-1 (hCE-1), a hydrolytic serine esterase enzyme involved with drug metabolism. This enzyme catalyzes the conversion of lipophilic ester substrates to a more water-soluble carboxylic acid and other alcohol products, thus facilitating their elimination. The hCE-1 substrate p-nitrophenylacetate (PNP-Ac) was used in this assay. Inhibition of the enzyme by our compound demonstrated that the ester was cleaved off to produce the oxidized

A100, the active agent. A100-acetate served as a control molecule for the assay since A100 is not a target of hCE-1. Our results demonstrated that inhibition of hCE-1 (Fig. 2.2B and C) was higher with A100-PEG than with A100-acetate, with

Ki of 93+10 µM and 17+3 µM respectively, indicating that the compound could be cleaved by esterases. These results demonstrate that the addition of the cleavable

PEG polymer was able to increase the water solubility of A100, while maintaining its activity.

We also determined that the addition of PEG improved A100 metabolic stability. Free alcohols are notoriously major targets of metabolism via the cytochrome P450 enzyme family. To test the stability of A100-PEG, a cytochrome

P450, family 1, subfamily A, polypeptide 2 (CYP-1A2) inhibition assay was conducted using 2-cyano-6-methoxybenzothiazole as a substrate, which produces luciferin whose concentration was then measured by luminescence. Fig. 2.3A shows the percent of luciferin bound. The lower the percent of substrate bound means that more CYP-1A2 is bound to the agent, leading to its metabolic elimination. Therefore, percentages closer to the no inhibitor maximum value

25 (representing totally bound substrate) is preferable. As shown, the addition of PEG polymer was able to stabilize A100 in the presence of CYP-1A2 (Fig. 2.3A).

Figure 2. 2: Effectiveness of A100 and its derivatives in inhibiting human carboxylesterase. (A) Structure of A100 and ester derivatives A100-Ac and A100- PEG. (B-C) Quantification of inhibition of hCE-1 by A100 derivatives via Michaelis- Menten and Lineweaver-Burke via competitive inhibition absorbance assay. The substrate p-nitrophenylacetate was used for hCE-1, which produces PNP that maximally absorbs at 454 410 nm. The rate of production of PNP was measured over time by UV-Vis spectroscopy using 50 μM inhibitor and 0-1.5 mM PNP-Ac. No inhibitor (triangle, traced line), A100-Ac (diamond, solid line), and A100-PEG (square, dotted-traced line).

Next, in order to test the total metabolic stability, we conducted a liver microsome assay. A100 and A100-PEG were incubated in a buffer containing pooled human

26 liver microsomes in the presence of NADPH, and the concentration of agent remaining was measured via high performance liquid chromatography (HPLC).

Figure 2. 3: A100-PEG construct enhances stability in vitro and confers better suitability for in vivo studies. (A) Competitive inhibition assay of compounds against CYP1A2 using 2-Cyano-6-methoxybenzothiazole as a substrate and quenching with D-cysteine and Beetle Luciferase to produce Luciferin. Substrate bound was measured via luminescence (N=1,triplicate). (B) HPLC chromatogram of degradation of A100-PEG which is cleaved by esterases in human liver microsome cells over time (peak ~18 minutes) to produce A100 (peak ~16 minutes) (N=1). (C) Kinetic analysis of the degradation of A100-PEG (diamond, black line) into A100 (square, gray line) over time with derived fit lines. The half-life of A100-PEG was determined to be 62 minutes, and the half-life of A100 formed from the A100-PEG was calculated to be 116 minutes (N=1, triplicate). (D) Kinetic analysis of the degradation of A100 with an estimated half- life of 15 min (N=1, triplicate).

27 Fig. 2.3B shows the HPLC chromatogram of three time points during the assay, highlighting the degradation of A100-PEG, the appearance of A100 after ester cleavage, and the degradation of A100. Best-fit curves for both A100-PEG and

A100 formed from A100-PEG are shown in Fig. 2.3C. The half-life of A100-PEG was calculated to be 62 min, while the half-life of A100 was estimated to be 15 min as showed in Fig. 2.3D. The most important result is that the A100 formed from the degradation of A100-PEG has a half-life of 116 min, an increase of over 7-fold increase when compared to A100 alone. Therefore, the addition of the PEG polymer not only increased the water solubility, but also made A100 more metabolically stable and suitable for the in vivo study.

2.3.3 In Vivo Pharmacokinietic Mouse Study

Having improved the solubility and stability in vitro, it was necessary to test the in vivo properties. A method of detection of the compounds in blood had to be developed. There are multiple ways to precipitate out proteins 85. Because are compounds are soluble in organic solvent, acetonitrile protein precipitation was chosen. Percent recovery of compounds in a spiked blood sample was done

(Figure 2.4). All compounds had a percent recovery higher than 75% with 79.1% for A100-PEG500, 75.4% for A100-PEG2000, and 92.7% for A100. A100 had the best recovery because it is the compound that is most soluble in organic solvents.

28 A100-PEG500 79.1

A100-PEG2000 75.4

A100 92.7

0 20 40 60 80 100 120 Percent Recovered

Figure 2. 4: Compound Percent Recovery from Acetonitrile Crash. A100 and two of the pegylated derivatives were subjected to the ACN crash protocol to determine the percent recovery in blood.

Next, a healthy mouse pharmacokinetic study was done. Three mice were dosed with 300mg/kg A100-PEG2000 with 10µL/g injections. Because the PEG addition increased the aqueous solubility, this was done in 1X PBS with a tail IV injection. No DMSO was necessary to dissolve the compound. Blood samples

(30µL) were drawn before injections and after 30, 60, 120, 240 minutes. Samples were worked up using the acetonitrile crash method and analyzed using high performance liquid chromatography. Samples were converted to concentration via a calibration curve and plotted against time. Degradation curves were derived, and half-life was calculated using first-order kinetics. These curves are shown in Figure

2.5. For A100-PEG2000, Mouse #1 (body weight of 33.3g) had a half-life of 47.8 minutes, Mouse #3 (30.9g) had a half-life of 77.0 minutes, and Mouse #4 (34.0g) had a half-life of 23.1 minutes. This gave an average half-life of 49.3 minutes in

29 vivo. There was a correlation of A100-PEG200 half-life to mouse body weight. The heavier the mouse, the faster the compound was degraded.

450

375 Mouse #1 300 Mouse #1 fit Mouse #3 225 Mouse #3 fit Mouse #4 Mouse #4 fit PEG2000 PEG2000 (µM)

- 150

A100 75

0 0 100 200 300 400 500 600 Time (minutes)

Figure 2. 5: Metabolic Degradation Curves of A100-PEG from in vivo Pharmacokinetic Study. 3 mice were injected with 300mg/kg (10µL/g) A100- PEG2000 in 1X PBS. Samples were worked up and plotted against time and fit to kinetic degradation curves, resulting in an average half-life of 49.3 minutes. Mouse #1 = 47.8 minutes; Mouse #3 = 77.0 minutes; Mouse #4 = 23.1 minutes.

2.4 Conclusion

I hypothesized that the poor in vivo results were due to poor solubility and susceptibility to metabolic enzymes. My work started with computational analysis to determine what parts of the molecule were a target for metabolic enzymes. The

30 first step taken to improve the molecule was to add polyethylene glycol (PEG) to the free phenol, increasing both its solubility and metabolic stability. To prove metabolic stability, binding assays against CYP1A2 (Cytochrome P450, Isoform

1A2) were done, as CYP1A2 is known to attack alcohols. The synthetic addition of the PEG was able to increase stability against CYP1A2 by almost 50%. To prove stability in a more complex matrix, total stability was measured via half-life in pooled human liver microsomes. The PEGylated compound (A100-PEG2000) showed a 7-fold increase in the half-life of A100, as compared to A100 alone. A pharmacokinetic method was optimized to test the stability of A100-PEG2000 in a mouse model. A100 alone was not detectable in mouse blood samples after 15 minutes; however, in all three mouse models, A100-PEG2000 was detectable even after 100 minutes and was calculated to have a 6-fold increase in half-life, as compared to A100. Though stable, A100-PEG2000 only showed similar efficacy in the in vivo AML mouse model; however, we were able to use IV injection instead of IP injection.

Chapter 3: Introduction and Design of a DNA Aptamer through Systematic

Evolution of Ligands by Exponential Enrichment (SELEX)

32 3.1 Introduction

Aptamers are small single-stranded oligonucleotides that fold into a defined three-dimensional structure. They are designed to bind specific targets and show a high affinity and specificity for their target molecules. Targets can be small molecules, proteins, or even whole cells. Aptamers can be synthesized by chemical or enzymatic procedures, or a combination of the two, making them both chemical and biological substances. 86 Aptamers are designed through a process known as systematic evolution of ligands by exponential enrichment (SELEX). A random library of DNA or RNA ligands subjected to sequential binding experiments, so that after a series of experiments, a single ligand will emerge.87

SELEX relies on repeating positive and negative selection processes that eliminate weakly binding and non- binding sequences. Aptamers can retain their binding behavior after immobilization on a carrier material88 or after delivery into animals,89 and can be labeled with various functional groups.90 These properties of aptamers have led to their application in many areas of biomedical sciences such as purification processes, target validation, drug development, diagnostics, MRI- based cell tracking, and even therapy.91 A modification of the traditional SELEX process that uses living whole cells as targets has been named cell-SELEX.

Aptamers with high affinity and specificity for cells have been produced successfully, demonstrating that complex targets, including tumor cells and tissues, are compatible with the SELEX process.92

33 Aptamer-related research is currently a fast-growing field. Aptamers have utility for specific biomarkers for cancers and other diseases.93 Specific aptamers have been developed by researches for proteins, such as thrombin, and vascular endothelial growth factor (VEGF).94 Cell-SELEX can select high affinity aptamers that can specifically bind to these individual extracellular features.95 For example, researchers were able to for select several aptamers that exclusively recognized acute lymphoblastic leukemia cell (CCRF-CEM) with not binding a Burkitt's lymphoma cell line (Ramos). 96 Moreover, some researchers are using specific biomarkers to selectively target cells. A single-strand DNA aptamer specific for the biomarker CD117 was developed to target acute myeloid leukemia (AML) cells, because CD117 is highly expressed on AML cells.97 Other researchers have used live AML cells to select a group of DNA aptamers, which can recognize AML cells with dissociation constants (Kd’s) in the nanomolar range. In the study, one aptamer (KH1C12) compared with two control cell lines (K562 and NB4) showed significant selectivity to the target AML cell line (HL60) and recognized the target cells within a complex mixture of normal bone marrow aspirates.98 The cell-based aptamer selection holds a great promise in developing specific molecular probes for cancer diagnosis and cancer biomarker discovery. In addition, aptamers are isolated based on their localization, permitting a built in negative selection, directly eliminating those sequences that disperse to organs or tissues not of interest. This chapter discusses the development of an aptamer designed to specifically target

MLL-AF9 acute myeloid leukemia cells using whole cell-SELEX.

3.2 Experiment Methods

3.2.1 Cell Culture

CD34+ selected Human umbilical cord blood (UCB), MA9.3RAS cells were obtained from Mulloy Lab at CCHMC. MA9.3RAS AML cells were cultured in IMDM

20% bovine calf serum. Fibroblast cells were cultured in 5% Fetal Bovine Serum,

1X Antibiotic/Antimycotic Solution, 1mM Sodium Pyruvate, 2mM Glutamax,

10ng/mL Epidermal Growth Factor, 5µg/mL Insulin, 0.5µg/mL Hydrocortisone in

DMEM.

3.2.2 SELEX library and primers

The ssDNA library consisted of a central stretch of 30 base randomized sequences flanked by 23 base PCR primer sequences (5’-

TAGGGAAGAGAAGGACATATGAT-N30-TTGACTAGTACATGACCACTTGA-3’).

Fluorescein labeled 5’ primer and poly-A 3’ primer were used during PCR. The ssDNA library was purchased from TriLink Biotechnologies, and the primers were purchased from Eurofins Genomics. The synthesized dsDNA was purified using

8% polyacrylamide gel electrophoresis.

35 3.2.3 Systematic Evolution of Ligands by Exponential Enrichment (SELEX)

Procedure (in vitro)

Positive selection: DNA pool (20-30µL) is heated at 95°C for 5 minutes.

Remove from heat and add Binding Buffer (10mM Tris-HCl pH 7.5, 2mM MgCl2,

140mM NaCl) to 500µL total volume and let cool to room temperature. Count and pellet one million target cells, washing twice with Binding Buffer. Resuspend in the

500µL solution containing DNA pool. Incubate at 37°C for 30 minutes, shaking.

Pellet and wash twice with Binding Buffer. Resuspend in 500µL of sterile water.

Heat at 95°C for 15 minutes to lyse. Remove the supernatant and use as next round DNA pool. For negative selection: DNA pool (20-30µL) is heated at 95°C for

5 minutes. Remove from heat and add Binding Buffer to 500µL total volume and let cool to room temperature. Count and pellet one million counter cells, washing twice with Binding Buffer. Resuspend in the 500µL solution containing DNA pool.

Incubate at 37°C for 30 minutes, shaking. Pellet and remove supernatant. Count and pellet one million target cells, washing twice with Binding Buffer. Add supernatant to target cells and finish with above protocol.

3.2.4 Quantitative Polymerase Chain Reaction

Template Concentration Optimization: Each Round 1:10, 1:10, 1:1000 dilutions were made with sterile water. 25µL reactions were done with varying template volume (1-7µL) of each dilution. Template concentration with the lowest measurable quantification cycle was used in round amplification. Round

36 Amplification: reactions were done on a 24-well PCR plate using a Thermo

Scientific™ PikoReal™ Real-Time PCR System. Quantities listed are per reaction well: Maxima SYBR Green/ROX qPCR Master Mix 2X (Thermo Scientific) 25µL and 2.5µL of 10µM (Final 500nM) primer. Template concentration and sterile water volume varied to total 50µL per reaction well. 23 wells were complete reaction, 1 well was a no polymerase control. 40 cycles: Denaturation of DNA at 95°C for 30 seconds, Annealing at 58°C for 30 seconds, Elongation at 72°C for 45 seconds.

Then a final Elongation at 72°C for 5 minutes. Quantification cycle was measured and obtained from the PikoReal™ software. Reactions were combined in an

Eppendorf tube and dried with a speed vacuum overnight.

3.2.5 Polyacrylamide Gel Electrophoresis (PAGE) Purification

Amplified SELEX rounds were purified via 8% PAGE. Gels were mixed as follows: 12mL of 40% Acrylamide/Bisacrylamide, 6mL 10X TBE, 42mL deionized water, 500µL 10% Ammonium Persulfate (APS), and 50µL

Tetramethylethylenediamine (TEMED). Gels were pre-run for 10 minutes at 12W in 0.5X TBE. Sample was mixed 1:1 with denaturing loading dye (95% Formamide in 10X TBE with Bromophenyl Blue and heated at 95°C for 10 minutes before loading. Gel was imaged, and fluorescent bands (5’-Fluorescein primer) were cut and suspended in water overnight, shaking. Gel was then filtered, and DNA was filtered through 3k Molecular Weight Amicon Ultra-0.5 mL Centrifugal Filters. Add

100µL of 10mM Tris-HCl pH 7.5 to remaining DNA volume.

3.2.6 Initial Binding Study

Fluorescent Round 0 Library was made via qPCR protocol (1.5ng template/reaction) and purified using PAGE. Per replicate: Count and pellet one million target cells. Wash in Hank’s Balance Salt Solution (HBSS, HyClone purchased from Fisher Scientific). DNA pool solution: 20µL fluorescein tagged

DNA pool (Round 0 or Round 8) in 2mM MgCl2-HBSS (200µL total volume).

Resuspend cells in DNA pool solution. Incubate at room temperature, shaking.

Pellet cells, remove and save supernatant. Wash in 2mM MgCl2-100µg/mL BSA-

HBSS and save wash. Resuspend in 200µL of 2mM MgCl2-HBSS. Analyzed using

*fluorimeter with a plate reader by measuring fluorescence of all saved solution.

Percent bound was calculated by taking sample fluorescence over total fluorescence in all solutions.

3.3 Results and Discussion

3.3.1 SELEX process

Whole cell-based SELEX was effectively used for selection, using MLL-AF9

RAS (MA9Ras) cells as the positive selection target and fibroblast connective tissue cells as the counter selection. Using a connective tissue as a counter was done as a preventative tactic to have the aptamer remain in the blood stream to better find its target cells upon application in an animal model. No target was specified on the surface of the cells, so whole cell SELEX was used. The reaction

38 schematic is shown in Figure 3.1. A random library was subjected to consecutive binding and elution to enrich DNA sequences specific to the target cell. Selection was done by heating the DNA pool to 95°C in Tris-HCl pH 7.5 buffer with MgCl2 and NaCl. Once cooled to room temperature, one million cells were suspended in the DNA pool-buffer solution and incubated for 30 minutes at 37°C. After incubation, cells were pelleted, and the supernatant was removed and discarded.

Cells were resuspended in sterile water and lysed with heat at 95°C for 15 minutes.

Samples were centrifuged and the supernatant was saved as next round enriched

DNA pool. Counter selection was done by incubating an enriched pool with fibroblast cells, pelleting, and taking the supernatant and directly incubating it with

MA9Ras cells. Two counter selection rounds were done to eliminate off-target, non-specific binding.

Figure 3. 1: Schematic diagram of SELEX method for selection of MLL-AF9 RAS-specific DNA aptamers with internal counter selection method.

3.3.2 PAGE Purification and Enrichment monitoring via qPCR

DNA pools collected after each round of selection were amplified and monitored by SYBR green qPCR for enrichment. The cycle in which fluorescence can be detected is termed quantitation cycle (Cq for short) and is the basic result of qPCR. A lower Cq value reflects higher initial copy numbers of the target, which is indicative of more binding sequences being recovered from the cell sample. The

Cq values for each selection round are reported in Figure 3.2. Increased library binding was indicated by an observed decrease in Cq number: the first round of selection yielded a Cq of 18.7 whereas in the final round of selection the Cq had decreased to 13.8. The consistency of the Cq value between thirteen and fourteen in the last three selections was evidence enough that the pool had reached a maximum enrichment.

Figure 3. 2: Monitoring enrichment of ssDNA library of MLL-AF9 RAS during SELEX by qPCR. Each round was amplified via quantitative PCR. The relative fluorescence of the full plate was averaged, and the quantitation cycle (Cq) was determined by the software.

40 After amplification, each round was purified using 8% polyacrylamide gel electrophoresis. Example of imaged gels can be seen in Figure 3.3. The top fluorescent band was cut from the gel and purified for use in the next round of

SELEX.

Figure 3. 3: Example PAGE images post amplification of rounds 7, 8, 9 and 11.

3.3.3 Initial Library 0 vs Round 8 Library Binding Assay

While enrichment was being monitored by qPCR, it was also necessary to screen the binding ability of later rounds against the initial random library. This was done with Round 8 because of the quantity of DNA acquired from the selection process (quantity based on amount of template required for qPCR). To screen against Round 0, Round 0 had to be amplified using the fluorescein tagged primer.

Since both Round were then fluorescently tagged, a binding assay was done and

41 analyzed using *multi-purpose spectrophotometer with a plate reader. Percent bound was calculated by taking the fluorescence of the suspended sample over the total amount of fluorescence measure (sample, wash, supernatant) and is listed in Figure 3.4. Round 8 Library showed almost double the percent bound compared to the Round 0 Library. This showed the enrichment in binding via successful SELEX.

14.0 12.0 10.0 8.0 6.0 10.4 4.0 PercentBound 5.3 2.0 0.0 Round 0 Round 8 Aptamer Library

Figure 3. 4: Binding screen of Percent Bound for Round 0 Library against Round 8 Library to show enrichment.

3.4 Conclusion

Using SELEX to produce aptamers that bind whole cells has been used in literature for a variety of targets. However, there is a lack of published aptamers that bind acute myeloid leukemia cells without using specific markers. My technique and approach allowed for the development of a DNA library pool that

42 binds MLL-AF9 RAS cells at twice the binding ability of the initial library, or more.

This proves that the whole-cell SELEX process was successful, and the Round 12

Library is ready to be sequenced for further development of a single, specific aptamer.

Chapter 4: Sequencing, characterization through binding studies, truncation, and primary patient sample screen of the DNA Aptamer

44 4.1 Introduction

Following successive rounds of SELEX, the next step in design of an aptamer is to sequence. Most aptamer pools are sequenced using high-throughput sequencing (HTS) technologies.99 Though originally developed for taking on the complexity of whole genome sequencing. HTS technologies have evolved and changed many fields of biomedical research.100 They allow the analysis of millions of sequences found in each round of aptamer selection, and open a new possibility for identification and optimization of aptamers.101 Sequencing multiple selection pools allows for a more in-depth analysis. HTS data obtained from multiple rounds of the selection can be used to monitor the dynamic sequence changes from round to round of aptamer selection to identify the best-performing sequences even in early rounds.102

Screening and characterization of aptamers is difficult, and there are no unanimously accepted guidelines. This makes it challenging to choose the right assay for accurate affinity measurements. For example, in the case of small molecule binding aptamers, aptamers are considerably larger than small molecules, which leads to high signal/noise ratio in size-based measurements; therefore, only sensitive assays will be able to detect their interaction.103 Current assays which have been demonstrated to effectively measure aptamer-target interactions include surface plasmon resonance (SPR)104, isothermal titration calorimetry (ITC)105, fluorescence polarization/anisotropy (FA/FP)106, and capillary

45 electrophoresis (CE)107, however each assay has its own set of limitations and not all are suitable depending on the downstream application.

While small molecule characterization techniques have been widely studied108, cell-aptamer characterization is still a developing field. The most common characterization technique for cell-based aptamers is flow cytometry.109

McKeague et al. designed a workflow which sets out suggestions for appropriate combinations of assays for use in (1) the initial screening of aptamer sequenced,

110 (2) truncation of aptamers, (3) KD characterization, and (4) functional validation.

Once selected, aptamers may be optimized for in vivo applicability in order to reduce their length and improve their properties in terms of stability and clearance.72 Long sequences (greater than 60-70 bases) remain difficult to synthesize and have high costs of manufacturing, while shorter libraries (less than

50 bases) reduces the complexity, limiting the effectiveness of the selection.111

Longer aptamers are generally selected and then reduced in their length to minimal functional sequences. The most common approach to achieve a short sequence is to truncate the aptamer guided by its structural prediction.112 This chapter discusses characterization of the AML DNA aptamer from the sequencing, KD determination, specificity screening, and affinity screening to AML subtypes.

4.2 Experimental Methods

4.2.1 Sequencing and Structure Predict

High throughput sequencing (HTS) was performed using Illuminia sequencing. The enriched ssDNA pool from selection rounds 0, 6, 8, 11, 12+, and

12- SELEX were PCR amplified using Illuminia special adapters TruSeq primers) to a minimum number of cycles (20 cycles). PCR products were purified using 8%

PAGE and the DNA quantified using a Nanodrop spectrophotometer (Thermo

Fisher, Canada). All amplified pools were combined to provide a total of 75ng of

DNA in each pool. Amplified pools were then sequenced at Carleton University using Illumina MiSeq sequencing platform. AptaCLuster was used as the software to analyze the sequencing data and RNAstructure software (Mathews Lab

RNAstructure) was used to predict the secondary structure of the candidate sequences.

4.2.2 Cell Culture

CD34+ selected Human umbilical cord blood (UCB), MA9.3RAS, and primary patient AML cells were obtained from the Mulloy Lab at CCHMC.

MA9.3RAS AML cells were cultured in IMDM 20% bovine calf serum. Human umbilical cord blood (UCB) cells and primary patient cells were cultured in IMDM

20% bovine calf serum and supplemented with SCF, IL-3, IL-6, Flt-3L and TPO.

Fibroblast cells were cultured in 5% Fetal Bovine Serum, 1X Antibiotic/Antimycotic

47 Solution, 1mM Sodium Pyruvate, 2mM Glutamax, 10ng/mL Epidermal Growth

Factor, 5µg/mL Insulin, 0.5µg/mL Hydrocortisone in DMEM.

4.2.3 Binding Screen and KD Determination using Flow Cytometry

Binding screens were done with 200nM 5’-fluorescein tagged aptamer.

Reactions contained 250,000 cells (MA9Ras or Fibroblast) in 250µL of Binding

Buffer (2mMMgCl2-HBSS). Aptamer was heated at 95°C in Binding Buffer for 5 minutes and cooled to room temperature before addition. Reactions were incubated at 37°C for 30 minutes, shaking. Cells were pelleted and washed in 2mM

MgCl2-100µg/mL BSA-HBSS. Samples were resuspended in 250µL Binding Buffer and analyzed via flow cytometry. Flow studies were done on a BD FACSCanto at

RFCC at Cincinnati Children’s Hospital. Samples were done in triplicate.

KD binding studies were done using 200nM, 100nM, 50nM, 25nM, 12.5nM, and 0nM 5’-fluorescein tagged aptamer. Each reaction contained 250,000 cells

(MA9Ras) in 250µL of Binding buffer (2mM MgCl2-HBSS). Aptamer was heated at

95°C in Binding Buffer for 5 minutes and cooled to room temperature before addition. Reactions were incubated at 37°C for 30 minutes, shaking. Cells were pelleted and washed in 2mM MgCl2-100µg/mL BSA-HBSS. Samples were resuspended in 250µL Binding Buffer and analyzed via flow cytometry. Flow studies were done on a BD FACSCanto at RFCC at Cincinnati Children’s Hospital.

Samples were done in triplicate.

48 4.2.4 MvGJRO Truncation Study

Truncation studies were done as a fluorescence blocking assay. Reactions contained 500,000 cells (MA9Ras) in 250µL of Binding Buffer (2mMMgCl2-HBSS).

All aptamer sequences were heated at 95°C in Binding Buffer for 5 minutes and cooled to room temperature before addition. Incubation 1 was done with 1µM of non-fluorescent blocking sequence or no aptamer at room temperature for 60 minutes, shaking. After 60 minutes, 200nM 5’-fluorescein tagged aptamer was added directly to all samples for incubation 2 at room temperature for 60 minutes, shaking. Cells were pelleted and washed in 2mM MgCl2-100µg/mL BSA-HBSS.

Samples were resuspended in 250µL Binding Buffer and analyzed by *fluorimeter with a plate reader. Samples were done in triplicate. P values were calculated using Kaleidagraph.

4.2.5 Primary Patient Binding Studies using Flow Cytometry

Binding screens were done with 200nM 5’-fluorescein tagged aptamer.

Reactions contained 250,000 cells (7 primary patient samples acquired from the

Mulloy Lab at CCHMC) in 250µL of Binding Buffer (2mMMgCl2-HBSS). Aptamer was heated at 95°C in Binding Buffer for 5 minutes and cooled to room temperature before addition. Reactions were incubated at 37°C for 30 minutes, shaking. Cells were pelleted and washed in 2mM MgCl2-100µg/mL BSA-HBSS. Samples were resuspended in 250µL Binding Buffer and analyzed via flow cytometry. Flow studies were done on a BD FACSCanto at RFCC at Cincinnati Children’s Hospital.

4.3 Results and Discussion

4.3.1 High throughput sequencing (HTS), bioinformatics analysis, and

Structure Predict

After twelve rounds of SELEX, the resulting ssDNA library was sequenced using high throughput MiSeq Illuminia sequencing. The following selection rounds were sequenced: 0 (original random library), 6, 8, 11, 12, and 12 counter. The high throughput datasets were grouped into clusters using AptaCluster software, according to their similarity. Clusters are groups of aptamers in all sequenced rounds of selection that have sequence similarities and only vary by a few bases.

Each cluster contained sequences that were arranged according to their enrichment and count. The count is the frequency of that sequence within a sequenced round, whereas the enrichment is the ratio of the count from one sequenced round to the next. A high enrichment means that sequence appeared more often in the later round. Comparing enrichment of a sequence, rather than just copy number, from earlier to later selection rounds, allows for the choice of sequences with improved aptamer-target binding. Three aptamer sequences were chosen from the final pools for further study, based on their enrichment in later selection rounds compared to Round 0. Full sequences of the three chosen aptamers can be found in Table 4.1. The secondary structure of all candidate aptamers was predicted using RNA structure software. All three candidates have relatively simple structures, with KGE01 and KGE02 having similar structures

50 (Figure 4.1); however, the structure may be more complex when interacting with their target; therefore, it is necessary to determine the binding constants to coinside with the predicted structures.

Table 4. 1: Aptamer sequences of MLL-AF9 RAS target and their corresponding Kd values.

4.3.2 Screening for the binding affinity of selected aptamers and KD determination using flow cytometry

To evaluate the binding affinity, the three selected aptamers were modified with a 5’ fluorescein tag and were subjected to flow cytometry analysis against the target MA9Ras cell line to determine their KD binding values. All KD values obtained were in the nanomolar range, which is typical for aptamers developed against cancer cells using cell-SELEX. 113 Two of the selected aptamers, KGE03 and

KGE02, showed KD values in the low nanomolar range (12+/-2.2nM and 38+/-

2.5nM, respectively) while the third, KGE01, showed a higher value (154+/-98nM)

(Figure 4.1). KGE02 was chosen as the lead aptamer because of three qualities: the high enrichment between rounds, the nanomolar binding constant to the target, and the double hairpin secondary structure, which allows for possible truncation.

However, it is necessary to first check the specificity of this particular sequence.

Figure 4. 1: Binding curves of fluorescein labeled apatmers to MLL-AF9 RAS (MA9Ras) target cells. (A) KGE02 Kd curve: MA9Ras cells were incubated with increasing concentrations (nM) of KGE02 then were evaluated by flow cytometry. (B) The predicted secondary structure of KGE02 aptamer using RNAstructure software. (C) KGE01 Kd curve: MA9Ras cells were incubated with increasing concentrations (nM) of KGE01 then were evaluated by flow cytometry. (D) The predicted secondary structure of KGE01 aptamer using RNAstructure software. (E) KGE03 Kd curve: MA9Ras cells were incubated with increasing concentrations (nM) of KGE03 then were evaluated by flow cytometry. (F) The predicted secondary structure of KGE03 aptamer using RNAstructure software. To calculate the apparent Kd, the mean fluorescence intensity of the aptamer-cell dissociation vs. concentration was fit to the equation Y=Bmax X/(Kd+X)..The Kd graphs are the average of three trials. Values are shown as means ± S.E.M.

52 4.3.3 Binding specificity of KGE02 to cancer and normal cell lines

The binding specificity of KGE02 was further tested for normal and cancer cell lines via flow cytometry because of its predicted three-dimensional structure and low KD. The predicted structure had two central short hairpin loops flanked with 9 or more bases on either end, which would allow for potential truncation. As expected, KGE02 bound with a high affinity, shown by the shift in fluorescence, to the target MA9Ras cells (red line), but showed no affinity for WI-38 (blue line)

(Figure 4.2A). To further prove its specificity, KGE02 was incubated with two different lines of CD34+ human umbilical blood cord cell lines. Results showed it had no affinity to either cell line (Figure 4.2B). It is worth noting that the MA9Ras cell line was created by expressing the MLL-AF9 RAS gene in CD34+ cells. Having determined that it is still specific to the target, the next step would be trying to truncate to a shorter sequence.

4.3.4 Truncation of KGE02 and assessment of their binding abilities

Once KGE02 was proven to be specific, the next step was to truncate the

76-base sequence to create a smaller binding sequence. Truncated sequences were created based on the predicted three-dimensional structure (Figure 4.1B), which shows two hairpin loops formed between bases 20-70. Truncation from the

5’ prime end produced three sequences, in which either a portion (10 or 20 bases), or the entire 5’ prime primer (23 bases) were removed. Truncation from the 3’ prime end produced two sequences, in which either 20 bases (interrupting the second

53 hairpin loop) or 42 bases (interrupting the first hairpin loop) were removed. Then to determine if one or both hairpin loops were important to binding, three more sequences were produced. These sequences included both loops (20-70), just the first loop (20-46), and just the second loop (42-70). The final truncated sequence was the random N30 middle sequence lacking both primers (24-54). Sequence lengths are listed in Figure 4.3 (left side).

Figure 4. 2: Binding of KGE02 aptamer to MLL-AF9 RAS (MA9Ras), WI-38, and UCB cells. (A) Mean of fluorescence intensity of MA9Ras (left) without (black) and with 200nM KGE02 aptamer (pink); and mean of fluorescence intensity of WI- 38 fibroblast (right) without (black) and with 200nM KGE02 aptamber (blue). Cells were incubated and 10000 events were counted by flow cytometry. Then data was analyzed using flow cytometry C6 sampler software. (B) Mean of fluorescence intensity of two UCB cell lines without (black) and with 200nM KGE02 aptamer (red). Cells were incubated and 10000 events were counted by flow cytometry. Then data was analyzed using flow cytometry FCS Express software.

54 To determine binding ability, a blocking assay was performed. The premise of the assay was that if truncated sequences bound better than the 5’-fluorescein full length KGE02, fluorescence would be blocked, or decreased, based on competitive displacement. The larger the decrease in fluorescence, the greater the binding ability of the truncated sequence. MA9Ras cells were first incubated with

1µM of the truncated sequence (non-fluorescent) for 60 minutes. Then, 200nM of

5’-fluorescein tagged KGE02 (Fl-Apt) was directly added to the reaction and incubated for 60 minutes. Reactions were then pelleted, resuspended in fresh buffer, and the total fluorescence of the solution was measured. As a negative control, MA9Ras cells were incubated with just 200nM of 5’-fluorescein tagged

KGE02; this gave the maximum fluorescence. As a positive control, MA9Ras cells were incubated first with 1µM of full length non-fluorescent KGE02, followed by

200nM of Fl-Apt; this gave the minimum fluorescence (maximum blocked). Results are quantified in Figure 4.3.

Based on truncation results, it can be assumed that both hairpin loops are important in aptamer binding. Sequences that interrupted either loop (20-46, 42-

70, 3’ -20, and 3’ -42) all showed little to no decrease in fluorescence. While the random region (N30) showed a decrease in fluorescence, it was not statistically significant (p > 0.05) compared to the fluorescence from just Fl-Apt alone.

Sequence that truncated from the 5’ prime end showed the greatest blocking ability. Sequences 5’ -10, 5’ -20, and 5’ -23 all showed significant decrease in fluorescence from just Fl-Apt alone, and encroached toward the binding ability of

55 the positive control. It can be concluded that both hairpin loops are necessary for aptamer binding and the 5’ primer is not important to the binding ability of the aptamer. To check that the removal of the 5’ primer did not change the predicted secondary structure, the structure of the shortened 53-base sequence was predicted using RNA structure software. The double hairpin structure in the full sequence remains uninterrupted in the shortened sequence, which can be seen in

Figure 4.4.

Figure 4. 3: Truncation of MvGJRO aptamer. Relative sequence length (left) and average fluorescence (right) of fluorescein -labeled full length MvGJRO, unlabeled full length MvGJRO, and nine unlabeled truncated sequences. Samples were incubated with truncated sequence (blocking sequence) for one hour then 200nM fluorescein labeled MvGJRO was added and samples were incubated for an additional hour. Reported p values are the truncated sequence fluorescence against the fluorescence of no truncated blocking sequence (Fl-Apt) Fluorescence was measured using a plate reader fluorimeter.

Figure 4. 4: Comparison of secondary structures of KGE02 with full length or truncated sequence. (A) The predicted secondary structure of the full length KGE02 aptamer using RNAstructure software. (B) The predicted secondary structure of the KGE02 aptamer lacking the 23-base 5’ primer using RNAstructure software. The bases highlighted in yellow are the 30 sequenced bases from the random N30 region.

4.3.5 Binding of KGE02 to primary AML patient cells

5’-fluorescein labeled KGE02 aptamer was tested against seven primary

AML patient cell samples for binding ability via flow cytometry analysis. The samples were from relapsed patients with a variety of different karyotypes. The binding screens were done at 200nM, and the resulting histograms and karyotypes are shown in Figure 4.5. Five of the seven samples showed binding (Samples 94,

14, 97, 7, and 1), evidenced by a shift in fluorescence. The black line is the sample with no aptamer, and the red line is the sample incubated with aptamer. Samples

63 and 35 showed no shift in fluorescence.

Figure 4. 5: KGE02 binding to primary AML patient samples. (A) Mean of fluorescence intensity histograms of different AML primary patient samples (black) incubated with 200nM KGE02 aptamer (red). Samples were incubated for an hour and 10000 events were counted by flow cytometry. Data as analyzed using FCS Express software. (B) Table of the specific karyotype mutation of the primary patient samples.

There is a mix of karyotypes among the samples that show binding, and the two samples that do not show binding are not similar in karyotype. Samples 35 and 97 could be considered similar subtypes, called RAM immunophenotype, which was identified a couple years ago as a very aggressive type of AML;114 one

RAM sample binds and the other one does not. This shows that there is no correlation to karyotype, or subtype of AML, and aptamer binding. However, it does show that the aptamer has been designed to bind a biomarker target on many AML subtypes, but the biomarker target does not appear on healthy umbilical cord blood cells that lack binding.

58 4.4 Conclusion

Characterization of cell-based aptamers relies strongly on flow cytometry, where one end of the aptamer is labeled with a fluorophore. High throughput sequencing of multiple selection rounds allows for the ability to compare enrichment and count between sequence clusters. This allowed for the selection of three aptamers with different and unique sequence that did not belong to the same cluster family. The binding affinity of the three chosen aptamers was done via flow cytometry using a fluorescein tag, which is a common fluorophore and is commercially available. All three aptamers had binding constants in the nanomolar range, so the secondary structures had to be considered. While KGE03 had the lowest binding constant, its secondary structure was complex, with multiple loops and junctures. KGE01 had a simpler structure but had the highest binding constant. KGE02 had the desired low nanomolar binding constant 38+/-2.5nM, and the secondary structure was not only simple (containing two hairpin loops that formed almost a key-like structure) but also had room on both the 5’-prime and 3’- prime ends for truncation.

Prior to truncation KGE02 was subjected to binding affinity studies. The counter selection in the SELEX process was fibroblast cells; therefore, it was necessary to test whether it showed any affinity. Fibroblast cells were used because of the desire to directly eliminate those sequences that disperse to organs or tissues not of interest in future clinical applications. While there was a slight shift in fluorescence, equating to some binding to fibroblast cells, the shift was not as

59 evident as the one seen when the aptamer was incubated with the target MA9Ras cells. Even though the target cells were made by expressing leukemic oncogenes in a healthy CD34+ human umbilical blood cord cell lines, KGE02 showed no apparent shift in binding, which means the biomarker responsible for binding is only expressed on AML cells.

Because the full-length aptamer was 76 bases long, a truncation study was done to try and make the sequence shorter, making it more cost effect and easier to manufacture. The secondary structure showed two hairpin loops, so truncation sequences consisted of sequences that cut the primers and the hairpin loops.

Truncation of the 3’-prime end very much affected the ability to bind, while loss of the 5’ primer still allowed the aptamer to bind.

Because the goal is to be able to use the aptamer clinically, the affinity of

KGE02 towards other subtypes of AML was tested. The patient samples came from a variety of karyotypes as well as years, but all samples were from relapsed patients. Five of the seven samples showed some binding affinity, and there was no real correlation between binding and cell karyotype. However, binding toward any patient samples shows promise of future treatment with clinical drug conjugates.

4.5 Future directions of this project

While a DNA cell specific binding aptamer was successfully developed and characterized, there is still work to be done. To confirm the truncated sequence

60 could be used moving forward, the sequence lacking 23 bases on the 5’ end should be synthesized with a fluorescein tag, and binding constant studies should be repeated with the shorter fluorescent sequence. This would allow for the quantification of the truncated sequence’s binding ability to compare to the full- length sequence. Once the proper length of the sequence is determined, it can be conjugated to any sort of anti-AML agent. Having completed the work in chapter

2, I would like to see the A100-PEG bound to the aptamer, as one of the concerns in vivo was the ability to circulate long enough to find the target cells. However, I do think the aptamer should be linked to polyethylene glycol, regardless of the agent attached, as it is cleavable in vivo, unless other suitable linkers are researched. The aptamer’s binding specificity to multiple subtypes of AML but not to the healthy human umbilical blood cord cells does show promise for future clinical applications.

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