SENSING OF IN CHIRAL CARBOXYLATES

Ali Akdeniz

A Dissertation

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

August 2016

Committee:

Pavel Anzenbacher Jr., Advisor

Carol Heckman Graduate Faculty Representative

H. Peter Lu

Andrew T. Torelli

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ABSTRACT

Pavel Anzenbacher Jr., Advisor

Chirality of organic compounds plays a crucial role in human and veterinary medicine.

More specifically, chiral carboxylates are extensively utilized in drug development processes,

and a number of them are commercialized as drugs. Due to distinct pharmacological properties of of chiral drugs, asymmetric synthesis became an essential tool to synthesize enantiopure compounds. While combinatorial libraries enable to screen catalysts, auxiliaries, and conditions rapidly, the enormous amount of resulting samples cannot be tested simultaneously.

In this dissertation, we have reported two optical methods to determine the ee values of the chiral carboxylates in a high-throughput fashion.

In the first method, we have employed cinchona alkaloids, known as Corey-Lygo catalysts, as chemosensors for ee analysis of chiral carboxylates. N-alkylated 9- chloromethylanthracenyl cinchona alkaloids form complexes with carboxylates by means of electrostatic interactions and hydrogen bonds. Using characteristic changes in fluorescence intensities of the chemosensors upon addition of analytes, we were able to perform the successful qualitative and quantitative ee analysis of enantiomers of chiral carboxylates in aqueous media.

Using the sensor array comprising four N-alkylated cinchona alkaloids, we were able to achieve

100 % correct classification of chiral carboxylates, including several enantiomers of non-

steroidal anti-inflammatory drugs (NSAIDs) successfully. Furthermore, quantitative ee analysis of (S)-, (S)-Naproxen, and (S)-Ketoprofen shows prediction errors as low as 3%.

The second method involves fluorescent macrocyclic sensors containing the chiral

BINOL moiety, and H-bond donors in the pocket to form complexes with carboxylates. The iii

intrinsic structure of the pocket of macrocycles displays an enantioselective behavior for chiral

carboxylates, while the substituents at the 3,3’-positions of the BINOL moiety allow for tuning

the shape and size of the cavity to enhance the recognition performance of the macrocycles. The

resulting macrocycles show distinct responses for a number of carboxylates, including the enantiomers of ibuprofen, ketoprofen, 2-phenylpropanoate, mandelate, and phenylalanine. The fingerprint-like responses of the macrocycles, for structurally similar analytes, enabled to perform a qualitative analysis of 12 carboxylates with 100 % correct classification. Quantitative ee analysis of ibuprofen, ketoprofen, and phenylalanine shows that the sensors correctly identify the ee values of the unknown samples with an error of prediction < 3% even in the presence of impurities.

iv

To my beloved wife, Fatmanur, and son, Salih

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ACKNOWLEDGMENTS

First of all, I owe my sincere gratitude to my advisor, Dr. Pavel Anzenbacher, Jr., for his continuous support, guidance, and patience, which allowed me to grow as a scientist. I am very

grateful for his help, professionalism, and most importantly, friendship throughout my graduate

studies.

I would like to thank all my committee members, Dr. Carol Heckman, Dr. H. Peter Lu,

and Dr. Andrew T. Torelli, for their service on the committee.

I would like to thank my colleagues, members of our group, and collaborators from

different universities. Particularly, I want to thank Drs. Mosca, Minami, Caglayan for all the

enlightening discussions, support, and guidance. Without their great help, this dissertation would

be very difficult to finalize. In addition, I am grateful to Prof. Tadashi Ema for his collaboration

and to Vincent M. Lynch from the University of Texas for X-Ray measurements.

I would like to acknowledge all faculty and staff at the Center for Photochemical

Sciences. Especially, I would like to thank Dr. Stephania Messersmith for her guidance, help,

and friendship. I need to express my gratitude and appreciation to Ms. Hilda E. Miranda, Mr.

Doug Martin, Mr. Jadrez Romanowicz, Ms. Alita Frater, and Ms. Nora Cassidy for their help

during these years.

Finally, and most importantly, I would love to express my heartfelt gratitude to my

wife, Fatmanur, for her continuous encouragement and patience throughout our odyssey. Her

love was the biggest support in my life. At the beginning of my Ph.D. adventure, we were

blessed with an adorable son, Salih, who has been my source of motivation and energy. This

accomplishment would not have achieved without them. vi

Financial support from the McMaster Endowment and Center for Photochemical Science is greatly appreciated.

vii

TABLE OF CONTENTS

Page

CHAPTER I. INTRODUCTION ...... 1

1.1. Background ...... 1

1.2. Sensors ...... 2

1.3. Cross-Reactivity of A Sensor...... 3

1.4. Sensitivity and Selectivity of the Sensors ...... 4

1.5. High-throughput Screening (HTS) Methods and Instrumentations ...... 6

1.6. Microplate Arrays ...... 7

1.7. Polymer Chips ...... 9

1.8. Data Analysis ...... 12

1.8.1. Linear Discriminant Analysis (LDA) ...... 12

1.8.2. Support Vector Machine (SVM) ...... 14

1.9. Enantiomeric Excess ...... 15

1.10. Combinatorial Chemistry ...... 16

1.10.1. Split and Pool Method ...... 17

1.10.2. Parallel Synthesis ...... 17

1.10.3. Detection of Enantiomeric Excess (ee) ...... 18

1.10.3.1. HPLC ...... 18

1.10.3.2. HPLC-CD ...... 19

1.10.3.3. Gas Chromatography ...... 20

1.10.3.4. Capillary Electrophoresis ...... 20

1.10.4. Nuclear Magnetic Resonance(NMR) Spectroscopy ...... 21 viii

1.11. References ...... 21

CHAPTER II. SENSING OF ENANTIOMERIC EXCESS IN CHIRAL CARBOXYLIC

ACIDS BY CINCHONA ALKALOIDS ...... 28

2.1. Introduction ...... 28

2.2. Results and Discussions ...... 32

2.3. Conclusion ...... 45

2.4. Experimental Section ...... 46

2.4.1. General ...... 46

2.4.2. Preparation of the Polymer Chips ...... 47

2.4.3. Synthesis ...... 48

2.4.4. Preparation of TBA Salts of Guests ...... 51

2.4.5. Qualitative Analysis ...... 52

2.4.6. Semi-quantitative and Quantitative Analysis of Naproxen ...... 53

2.4.7. Semi-quantitative Analysis of Ibuprofen ...... 55

2.4.8. Semi-quantitative Analysis of Ketoprofen...... 57

2.5. References ...... 58

CHAPTER III. DETERMINATION OF ENANTIOMERIC EXCESS OF

CARBOXYLATES BY FLUORESCENT MACROCYCLIC SENSORS ...... 61

3.1. Introduction ...... 61

3.2. Results and Discussion ...... 63

3.3. Conclusion ...... 78

3.4. Experimental Section ...... 80

3.4.1. Synthesis ...... 82 ix

3.4.1. Qualitative Linear Discriminant Analysis ...... 87

3.4.2. Semi-quantitative Analysis of Phenylalanine ...... 88

3.4.3. Semi-quantitative Analysis of Ibuprofen ...... 89

3.4.4. Semi-quantitative Analysis of Ibuprofen with 5% Acetate ...... 90

3.4.5. Semi-quantitative Analysis of Ibuprofen with 5% Pyrophosphate ...... 91

3.4.6. Semi-quantitative Analysis of Ibuprofen with 0 to 10 % Acetate ...... 92

3.4.7. Semi-quantitative Analysis of Ibuprofen with 0 to 10 % Acetate ...... 93

3.4.8. Semi-quantitative Analysis of Ketoprofen...... 94

3.5. References ...... 95

CHAPTER IV. TRI-SERINE TRI-LACTONE SCAFFOLD FOR QUANTIFICATION OF

CITRATE IN URINE ...... 99

4.1. Introduction ...... 99

4.2. Results and Discussion ...... 101

4.3. Conclusion ...... 114

4.4. Experimental Section ...... 116

4.4.1. Preparation of Polymer Chips ...... 116

4.4.2. Synthesis ...... 117

4.4.3. Job’s plot Experiments ...... 121

4.4.4. Qualitative Analysis ...... 122

4.4.5. Semi-quantitative and Quantitative Analysis-in Water ...... 123

4.4.6. Semi-quantitative Analysis-in Buffer Solution ...... 125

4.4.7. Semi-quantitative Analysis-in Urine ...... 126

4.5. References ...... 127 x

APPENDIX A. LIST OF ABBREVIATIONS, ACRONYMS, AND SYMBOLS ...... 129

APPENDIX B. SUPPLEMENTARY DATA FOR CHAPTER II ...... 132

APPENDIX C. SUPPLEMENTARY DATA FOR CHAPTER III ...... 152

APPENDIX D. SUPPLEMENTARY DATA FOR CHAPTER IV ...... 184

xi

LIST OF FIGURES

Figure Page

1.1. Representation of a chemosensor that utilizes reporter and receptor moieties ...... 3

1.2. A) Representation of a cross-reactive sensor array. B) Response of array upon addition of the analyte of interest. C) Fingerprint-like response of the analyte after subtraction of A. ... 4

1. 3. Relative selectivity of the cross-reactive sensor is the ratio of the association constants of corresponding analytes...... 5

1. 4. A: The sensor has a higher selectivity towards to the analyte A, whereas the response to analyte A is weaker than for analyte B. B: Upon addition of the A and B analytes mixture to the sensor solution, a weak response is observed due to the stronger binding of analyte A ...... 6

1. 5. Example of a microplate (A), BioRAPTR microfluidic dispenser (B), manual syringe dispenser (Hamilton) (C), and CLARIOstar microplate reader (D) ...... 8

1. 6. Example of a custom-made glass micro-well slide...... 10

1. 7. Fabrication process of polymer chips ...... 11

1. 8. Schematic representation of the response matrix ...... 12

1. 9. A) Distribution of two groups of two variables, X1, and X2. B) One-dimensional linear discriminant function Y ...... 13

1. 10. Illustration of mapping the input into a feature space ...... 14

1. 11. Structures of (S)- (left) and (R)-thalidomide (right)...... 16

1. 12. HPLC with parallel columns...... 19

2. 1. Structure of cinchona alkaloids...... 28

2. 2. Four conformations of cinchonine (adapted from Ref. 1)...... 29

2. 3. Structural features of N-alkylated cinchona alkaloids...... 30 xii

2. 4. Structures of the carboxylate guests used in this study...... 31

2. 5. (A) Mass spectrum of the S2-Ibuprofen complex. Inset: Calculated isotopic pattern for

+ C47H50N2O3 . (B) Mass spectrum of the S2-Naproxen complex. Inset: Calculated isotopic pattern

+. for C48H46N2O4 (C) Mass spectrum of the S3-Ketoprofen complex. Inset: Calculated isotopic

+ pattern for C50H46N2O4 ...... 33

2.6. X-ray structure of the S3 complex with (S)-Ibuprofen (left) and (R)-Ibuprofen (right).34

2.7. (Left) Fluorescence titration of S1 by (S)-Ibuprofen. (Right) Fluorescence titration of S4 by (S)-Naproxen...... 35

2.8. Fluorescence titrations of S1 (red) and S3 (blue) with the incremental amounts of (S)-

Ketoprofen, and (R)-Ketoprofen...... 36

2.9. Images of the sensors upon addition of the corresponding analytes ...... 38

2.10. Linear discriminant analysis of ten carboxylates ...... 39

2.11. Linear discriminant analysis plot of ketoprofen. Ten different enantiomeric compositions of ketoprofen were discriminated with 100% correct classification...... 40

2.12. Results of linear regression using support vector machine (SVM) afford quantitative analysis of enantiomeric excess of ketoprofen...... 41

2.13. Results of linear regression using support vector machine (SVM) afford quantitative analysis of enantiomeric excess of ibuprofen (left) and naproxen (right)...... 42

2.14. Linear discriminant analysis (LDA) of ibuprofen mixtures ...... 44

2.15. The canonical scores plot of qualitative analysis of 9 analytes and a control by using S1-

S4 in hydrogel matrix...... 52

2.16. Linear discriminant analysis (LDA) of enantiomeric excess of (S)-Naproxen ...... 53

2.17. The result of the linear regression using support vector machine (SVM) ...... 53 xiii

2.18. The canonical scores plot of Semi-quantitative analysis of (S)-Naproxen ...... 54

2.19. Linear discriminant analysis (LDA) of enantiomeric excess of (S)-Ibuprofen ...... 55

2.20. The result of the linear regression using support vector machine (SVM) ...... 55

2.21. The canonical scores plot of Semi-quantitative analysis of (S)-Ibuprofen ...... 56

2.22. The canonical scores plot of Semi-quantitative analysis of (S)-Ketoprofen ...... 57

3.1. Structures of macrocyclic chemosensors (S1–S4)...... 62

3.2. Synthetic route of macrocyclic chemosensors (S1–S4)...... 64

3.3. A) X-ray crystal structure of macrocyclic chemosensor (S2) with a water molecule in the cavity. B) DFT-optimized structure of S2 at the B3LYP/6-31G* level ...... 65

3.4. UV-VIS absorption and fluorescence spectra of S1 (A), S2 (B), S3 (C), and S4 (D) 66

3.5. ESI-MS spectra of S1+Ibuprofen (A), S2+ Ketoprofen (B), and S3+Mandelate (C) with

the calculated spectra of the complexes...... 67

3.6. Digital photographs of S3 (left) and S4 (right) upon addition of enantiomers of ibuprofen

and a control (in the middle of both panels) ...... 68

3.7. Structures of the carboxylates used in this study...... 70

3.8. Corresponding isotherms of the fluorescence titrations of (A) S1 with PPA, (B) S2 with

IBP, (C) S3 with KTP, and (D) S4 with Phe ...... 71

3.9. Linear discriminant analysis (LDA) plot of twelve analytes results in 100 % correct classification using S1–S4 sensor array ...... 73

3.10. A) Semi-quantitative analysis of ibuprofen. B) Quantitative analysis of ibuprofen using

SVM. C) Semi-quantitative analysis of ketoprofen. D) Quantitative analysis of ketoprofen using

SVM. The analysis was achieved by the S1–S4 array ...... 74 xiv

3.11. A) Linear discriminant analysis (LDA) of the semi-quantitative assay of enantiomeric

composition of ibuprofen in the presence of 5 % acetate. B) Quantitative analysis of the

enantiomeric composition of ibuprofen in the presence of 5 % acetate...... 76

3.12. A) Linear discriminant analysis (LDA) of semi-quantitative assay of enantiomeric

composition of ibuprofen in the presence of increasing composition of acetate from 0–10 % B)

Quantitative analysis of enantiomeric composition of ibuprofen in the presence of increasing

composition of acetate from 0–10%...... 77

3.13. A) Linear discriminant analysis (LDA) of the semi-quantitative assay of enantiomeric

composition of phenylalanine by employing only the S4 array. B) Quantitative analysis of the

enantiomeric composition of phenylalanine ...... 78

3.14. The canonical scores plot of qualitative assay...... 87

3.15. The canonical scores plot of qualitative assay of phenylalanine...... 88

3.16. The canonical scores plot of semi-quantitative assay of ibuprofen...... 89

3.17. The canonical scores plot of semi-quantitative assay of ibuprofen in the presence of 5 % acetate…………...... 90

3.18. The canonical scores plot of semi-quantitative assay of ibuprofen in the presence of 5 % pyrophosphate ...... 91

3.19. The canonical scores plot of semi-quantitative assay of ibuprofen in the presence of 0 to

10 % increasing concentration of acetate...... 92

3.20. The canonical scores plot of semi-quantitative assay of ibuprofen in the presence of 0 to

10 % increasing concentration of pyrophosphate ...... 93

3.21. The canonical scores plot of semi-quantitative assay of ketoprofen ...... 94

4.1. Structure of enterobactin produced by Escherichia coli ...... 100 xv

4.2. Structures of the sensors (S1-S2) based on tri-serine tri-lactone scaffold ...... 101

4.3. Synthetic route of the sensors (S1 and S2)...... 102

4.4. (A) ESI mass spectrum of the complex of [S1+Citrate+Na+MeCN]-. Inset: Calculated

isotope pattern for [S1+Citrate+Na+MeCN]- (B) ESI mass spectrum of the complex of

S2+Citrate+TBA. Inset: Calculated isotope pattern for S2+Citrate+TBA ...... 103

4.5. 1H NMR (500 MHz) titration of S1 (top) and S2 (bottom) upon the addition of citrate as

tri-tetrabutylammonium salts ...... 104

4.6. Left: Fluorescence titration of S1 Right: Fluorescence titrations of S2. Insets:

Corresponding isotherms ...... 106

4.7. Structures of the analytes used in this study ...... 107

4.8. A) Digital fluorescence images of the S1 (top) and S2 (bottom) in the polyurethane

microchips. B) Qualitative analysis of eight carboxylates, including mono, di, and tri

carboxylates ...... 110

4.9. Linear discriminant analysis of semi-quantitative analysis of citrate in buffer ...... 111

4.10. Linear discriminant analysis plot of semi-quantitative analysis of citrate in urine .. 113

4.11. Job’s plot experiments of S1 (left) and S2 (right) with citrate...... 121

4.12. Job’s plot experiments of S1 (left) and S2 (right) with acetate...... 121

4.13. The canonical scores plot of qualitative analysis of 8 analytes and a control ...... 122

4.14. Linear discriminant analysis (LDA) of Citrate-Tri-Na in hydrogel matrix...... 123

4.15. The result of the linear regression using support vector machine (SVM) ...... 123

4.16. The canonical scores plot of Semi-quantitative analysis of citrate tri-sodium salt by using

S1 and S2 in hydrogel matrix...... 124 xvi

4.17. The canonical scores plot of semi-quantitative analysis of citrate tri-sodium salt in buffer solution ...... 125

4.18. The canonical scores plot of semi-quantitative analysis of citrate tri-sodium salt in urine by using S1 and S2 in hydrogel matrix...... 126

xvii LIST OF TABLES

Table Page

2.1. Photochemical Properties of Cinchona Alkaloids ...... 32

2.2. Affinity Constants Obtained by Fluorescence Titration ...... 37

2.3. ANN Analysis of Unknown Ibuprofen Samples ...... 44

2.4. The Jackknifed Classification Matrix of Qualitative Analysis of 9 Analytes and A Control

by Using S1-S4 in Hydrogel Matrix...... 52

2.5. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of (S)-Naproxen by

Using S1-S4 in Hydrogel Matrix...... 54

2.6. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of (S)-Ibuprofen by

Using S1-S4 in Hydrogel Matrix ...... 56

2.7. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of (S)-Ketoprofen

by Using S1-S4 in Hydrogel Matrix...... 57

3.1. Enantiomeric Fluorescence Difference Ratios ...... 69

3.2. Association Constants (Ka, M–1) Determined by Fluorescence Titrations. A...... 72

3.3. The Jackknifed Classification Matrix of Qualitative Assay Using S1-S4...... 87

3.4. The Jackknifed Classification Matrix of Semi-Qualitative Assay of Phenylalanine. 88

3.5. The Jackknifed Classification Matrix of Semi-Qualitative Assay of Ibuprofen...... 89

3.6. The Jackknifed Classification Matrix of Semi-Qualitative Assay in The Presence of 5 %

Acetate Using S1-S4...... 90

3.7. The Jackknifed Classification Matrix of Semi-Qualitative Assay in The Presence of 5 %

Pyrophosphate Using S1-S4...... 91 xviii

3.8. The Jackknifed Classification Matrix of Semi-Qualitative Assay in The Presence of 0 To

10 % Increasing Concentration of Acetate Using S1-S4...... 92

3.9. The Jackknifed Classification Matrix of Semi-Qualitative Assay in The Presence of 0 To

10 % Increasing Concentration of Pyrophosphate Using S1-S4...... 93

3.10. The Jackknifed Classification Matrix of Semi-Qualitative Assay of Ketoprofen .... 94

4.1. Association Constants (Ka, M-1) [A] Of Analytes Used in This Study ...... 108

4.2. The Jackknifed Classification Matrix of Qualitative Analysis of 8 Analytes and A

Control by S1 and S2 in Hydrogel Matrix...... 122

4.3. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of Citrate Tri-

Sodium Salt by Using S1 and S2 in Hydrogel Matrix...... 124

4.4. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of Citrate Tri-

Sodium Salt in Buffer Solution by Using S1 and S2 in Hydrogel Matrix...... 125

4.5. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of Citrate Tri-

Sodium Salt in Urine by Using S1 and S2 in Hydrogel Matrix...... 126

xix

LIST OF EQUATIONS

Equation Page

1.1. Binding constants were calculated by nonlinear least-square methods using the equation

for a 1:1 binding model...... 13

1.2. Equation is used to calculate the enantiomeric excess (ee) of solutions...... 16

2.1. Binding constants were calculated by nonlinear least-square methods using the equation

for a 1:1 binding model...... 47

2.2. The final responses (R) were evaluated as indicated in this equation...... 48

2.3. General route for the synthesis of all the sensors ...... 48

3.1. Binding constants were calculated by nonlinear least-square methods using the equation

for a 1:1 binding model...... 81

3.2. Route for the synthesis of all the sensors ...... 82

4.1. The final responses (R) were evaluated as indicated in this equation...... 117

4.2. The first step of synthesis of the tri-serine tri-lactone scaffold...... 117

4.3. Formation of quaternary ammonium salts of the tri-serine tri-lactone...... 118

4.4. Substitution of reporter moieties of S1 to tri-serine tri-lactone scaffold...... 119

4.5. Substitution of reporter moieties of S2 to tri-serine tri-lactone scaffold...... 120 1

CHAPTER I. INTRODUCTION

1.1. Background

Anions are ubiquitous species that play important roles in many natural processes.

Identification and quantification of anions are desired in a number of fields, such as the

pharmaceutical industry, forensic sciences, pollutant monitoring, and for diagnostic purposes.1

More importantly, reliable analytical methods for determination of anions that are toxic to human

and other species would allow us to be aware of possible complications and find proper solutions

for the resulting consequences.2

A number of instruments and protocols for the detection and quantification of anionic species have been developed over the last century.3 Analytical methods based on the intrinsic

properties of analytes, such as gas chromatography–mass spectrometry (GC-MS) and high-

performance liquid chromatography (HPLC), are intensively used. The majority of such

methods, however, is labor-intensive, requires expensive instrumentation, or may involve pre-

treatment and purification of the product.4 Furthermore, few methods can be utilized for high- throughput analysis and the range of analytes that can be tested is limited. Recently, chemical sensors have attracted great interest due to their remarkable properties, such as being amenable

to high-throughput analysis and real-time acquisition.5 Unlike conventional analytical methods,

chemosensors do not mostly require any sample pre-treatment and are relatively inexpensive.

In the field of supramolecular analytical chemistry, chemical sensors are defined as

synthetic chemical structures that create assemblies which result in signal modulation upon the

addition of analytes.6 A typical qualitative or quantitative analysis of an analyte using

chemosensors usually requires three steps: generation of a signal, detection of the generated 2

signal, and processing of the data collected by a detector. The first step, signal modulation,

usually depends on the chemical interaction and equilibrium of the sensor with the analyte of

interest. Generated signals can be in the form of change in the thermal7, electrical8, mechanical9,

or optical properties10 of the sensors. The second step of the workflow of analysis is signal

detection. Generated signals are collected by a detector, which converts the signals to datasets.

The third step of the analysis, processing, employs chemometric methods to convert output data to final results. For example, in the detection of mercury by an organic field effect transistor, the current (signal) is generated by the interaction of mercury with the transistor and then data are collected by source meter.11 The resulting data matrix is then treated using chemometric

methods. In this dissertation, the term “chemosensors” refers to the fluorescent hosts that display

detectable changes in their fluorescence intensity upon interaction with the analytes of interest

(guests).

1.2. Sensors

Chemosensors, are array based sensors, are designed to utilize the two functional parts:

the receptor and the reporter. It is expected that receptor moieties form a complex with the

analyte of interest, whereas the reporter’s signals are modulated upon complex formation. The conventional chemosensor design mostly relies on the “lock and key” paradigm of molecular recognition proposed by Emil Fischer in 1894 (Figure 1.1).12 This model of recognition was inspired by the enzyme-substrate interaction. The substrate-specific geometry of the active site of an enzyme shows high selectivity for the particular substrate and a few structurally similar compounds. From the lock-and-key model, chemosensors should be selective and show affinity toward a particular analyte. However, the design and synthesis of selective chemosensors with a shape and desired geometry complementary to the analyte can be a difficult task. In addition to 3

geometrical considerations, selective chemosensors come with multiple drawbacks.5 First,

analytes with small structural alterations may result in false-positive signals. Second, the

unknown structure of the sample components will not allow to design the selective chemosensors

in advance. As a final point, selective chemosensors will not be able to analyze complex

mixtures that are recognized by their components.13

Analyte

Reporter: Off Reporter: On Analyte

Receptor Receptor

Figure 1. 1. Representation of a chemosensor that utilizes reporter and receptor moieties. Chemosensors demonstrate a turn-on response upon complex formation with the analyte.

1.3. Cross-Reactivity of A Sensor

As the number of the samples increase, an emerging approach, cross-reactive sensor arrays, are coming to the fore.14 In the mammalian olfaction and gustation systems, cross-

reactive receptors are capable of differentiating thousands of odors and flavors by generating

“fingerprint” response patterns of the analytes.15 Unlike the lock-and-key model of sensing,

recognition is achieved by the differential interaction of cross-reactive receptors with odorants

and flavor molecules. The resulting fingerprint-like responses are then stored in the brain for

future recalls. As given in an earlier example, cross-reactive sensors are not selective; that is,

they display an affinity to multiple analytes to a different extent. Hence, cross-reactive sensor

arrays do not necessarily require high specificity to particular analytes. The resulting fingerprint

responses of the analytes can be evaluated using pattern recognition protocols (Figure.1.2).16 4

A) B) C) Cross-reactive sensors with Finger-print response Cross-reactive sensors Analyte of interest of the analyte

Figure 1. 2. A) Representation of a cross-reactive sensor array. B) Response of the array upon addition of the analyte of interest. C) Fingerprint-like response of the analyte after subtraction of A.

One of the most significant advantages of the cross-reactive sensor array is the ability to

perform qualitative and quantitative analysis of multi-analyte mixtures.17 Following the

introduction of optical cross-reactive sensors by Walt (1996)18 and Suslick (2000)19, sensor

arrays for organic anions20, proteins21, nucleic acids22, metal ions23, and beverages24 have been

reported. Non-optical signal transduction mechanisms, such as cross-reactive sensors based on

radiofrequency25, potentiometry26, conductivity27, and others28, were also studied. Insufficient

discrimination power of chemosensors may require increasing the sensing elements in the

method. For example, piezoelectric sensors, or electronic noses with polymer-coated quartz crystals contain hundreds of sensing elements to perform satisfactory classification. 29

1.4. Sensitivity and Selectivity of the Sensors

A typical chemosensor (selective or cross-reactive) forms a complex with the analyte of

interest and displays signal modulation. In the case of supramolecular chemosensors, the driving

forces of these complex formations are noncovalent interactions, such as hydrogen bonding, π–π stacking, salt bridges, hydrophobic interactions, etc.30 Even though covalent bonds are stronger, 5

noncovalent interactions are preferred for two reasons. First, noncovalent interactions are

reversible processes. Covalent bonds can also be reversible to some extent; however, they mostly

require catalytic support to run reactions faster. Second, noncovalent interactions reach

equilibrium quickly and component exchanges are possible. As mentioned earlier, cross-reactive

chemosensors show a different affinity for each analyte and their different signal modulations.

This property is controlled by supramolecular interactions and depends on two intrinsic

properties of the chemosensors: sensitivity and selectivity.

Sensitivity can be described as the slope of the calibration curve and is the response of

the sensor as a function of analyte concentration.31 The limit of detection (LOD) strongly depends on the slope; thus, the magnitude of change in the response of the sensor. The selectivity of the sensor mainly depends on the receptor moiety. The geometry of the receptor imposes the selectivity among the same type of analytes. The selectivity of a chemosensor depends on the

32 affinity to the analyte of interest, which can be assessed by its association constant (Ka).

Relative selectivity of the sensors can be determined by the ratio of the association constants

(Figure 1.3):

[ ] + = [ ] [ 𝑒𝑒𝑒𝑒] 𝐴𝐴𝐴𝐴 𝑆𝑆𝑆𝑆 = 𝑆𝑆 𝐴𝐴 ⇆ 𝑆𝑆𝑆𝑆 𝐾𝐾 𝑒𝑒𝑒𝑒 𝑒𝑒𝑒𝑒 𝑆𝑆 𝐴𝐴 𝐾𝐾𝑆𝑆𝑆𝑆 [ ] 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑆𝑆𝑆𝑆 + = 𝐾𝐾 [ ] [ ] 𝑆𝑆𝑆𝑆 𝑒𝑒𝑒𝑒 𝑆𝑆 𝐵𝐵 ⇆ 𝑆𝑆𝑆𝑆 𝐾𝐾𝐴𝐴𝐴𝐴 𝑆𝑆 𝑒𝑒𝑒𝑒 𝐵𝐵 𝑒𝑒𝑒𝑒 Figure 1. 3. Relative selectivity of the cross-reactive sensor is the ratio of the association constants of corresponding analytes. 6

The combination of selectivity and sensitivity of a sensor enable to perform qualitative

and quantitative analyses of samples successfully. To illustrate the significance of these

properties, an example of a hypothetical cross-reactive chemosensor and two analytes is given.

A B

𝐾𝐾𝐴𝐴 ≫ 𝐾𝐾𝐵𝐵

Figure 1. 4. A: The sensor has a higher selectivity towards to the analyte A, whereas the response to analyte A is weaker than for analyte B. B: Upon addition of the A and B analytes mixture to the sensor solution, a weak response is observed due to the stronger binding of analyte A.

As is illustrated in Figure 1.4.A, the fluorescence modulation of analyte B is stronger than for analyte A. However, it is assumed that the chemosensor shows a higher selectivity toward analyte A. In the case of the addition of an analyte mixture to the sensor solution (Figure

1.4.B), weak signal modulation similar to the analyte A response is observed. This is due to cross-reactive behavior of the chemosensor that prefers to bind analyte A even in the presence of

B; hence, mainly the signal modulation of analyte A is observed.

1.5. High-throughput Screening (HTS) Methods and Instrumentations

Comprehensive analysis techniques, such as methods based on chromatography or NMR, suffer from a chronic weakness: they test one sample at a time. This is a time-consuming 7

approach and in the most cases require trained personnel. High-throughput screening (HTS)

methods enable thousands of chemical and pharmacological tests to be performed rapidly.

Instead of analyzing each individual sample, high-throughput methods are capable of performing

up to 100,000 assays/day.33 High-throughput screening was first introduced in 1970 in the field

of materials research to analyze multi-component systems that are synthesized separately.34

Today, most pharmaceutical research and drug development facilities use HTS systems, and

most of the drug discoveries are at least in part due to advancements in these systems. In addition

to the pharmaceutical industry, there is increasing demand for high-throughput assays in

endeavors such as clinical chemistry, proteomics, biochemistry, and genetics.35

Optical methods have attracted considerable attention due to their easy implementation and potential applications in high-throughput analysis and, including, differential sensing.36

Fluorescent chemosensors based on differential sensing are especially appealing due to their high

sensitivity and the number of possible modes, such as photon counting, polarization, and

lifetime. It is worth to note that even though colorimetric methods (absorbance as a read-out) can

be inexpensive and instrument-free (detected with a naked eye), fluorimetric methods hold their

ground due to their high sensitivity and low signal-to-noise ratio. Thus, fluorimetric methods

tend to provide lower LOD in many cases. In this dissertation, two different platforms for analyte

exposition are used. In the following sections, the details and the procedures of microplates9c and

polymer chips9b,9d will be explained.

1.6. Microplate Arrays

Microplate arrays are static systems that allow sensors and analyte molecules to interact

in miniaturized multi-well plates.37 The introduction of analytes into a chemosensor solution 8

results in the signal modulation of the chemosensor, sometimes with an incubation time. It is

worth noting that microplates and robotic dispensers are required to be compatible with the

solvent used, otherwise expensive chemical-resistant microplates are needed (Figures 1.5 B and

C).

A C

B D

Figure 1. 5. Example of a microplate (A), BioRAPTR microfluidic dispenser (B), manual syringe dispenser (Hamilton) (C), and CLARIOstar microplate reader (D).

Regardless of the way of preparation, multimode plate readers are used to acquire a

protocol based on fluorescence intensity or other modes, such as polarization, lifetime,

absorbance, etc.38 The output signals are recorded in the response matrix and cannot be

interpreted visually. Hence, chemometric methods are mostly used to decrease the

dimensionality of the response matrix, which usually has more than 10 channels (10

dimensions).

In this dissertation, microplate assays were designed in propionitrile solutions due to the

insoluble nature of chemosensors in aqueous media. Solutions of chemosensors and analytes 9

were introduced manually to chemical-resistant multi-well plates (polypropylene) as illustrated in Figure 1.5.A. Resulting signals from the chemosensors were recorded using a CLARIOstar microplate reader equipped with excitation and emission monochromators that allow the

selection of excitation and emission filter bands (Figure 1.5.D). Multiple acquisition channels

were set to record signals as fluorescence photon counts. The resulting datasets were reported as

a matrix in the form of analytes (X) × variables (Y). Variables represent the fluorescence

intensity counts, which are collected using the different sets of filters. To avoid instrumentation

and human errors, ten repetitions of each analyte were pipetted. Evaluation of the response

matrix was then performed by pattern recognition protocols.

1.7. Polymer Chips

The multi-well plate method is advantageous due to the possible modes that can be

acquired.39 However, acquisition time builds up as the number of the channels and wells

increase. Furthermore, evaporation of the samples becomes a concern as the time extends. Unlike

platforms based on multi-well plates, the polymer chip imaging method uses sensitive CDD or

CMOS cameras that can detect several multiple features of the whole platform in significantly shorter time. Although the sensors are insoluble in water, they are supported in a hydrogel matrix by the polymer to enable analysis in aqueous media. Our previous reported work on the behavior of poly (ether-urethane) polymers suggests that the polyethylene oxide part of the PUs absorbs water, thereby preventing hydration of the sensors and analytes, which in turn compete with the binding.40 The custom-made glass slides for the polymer chips were fabricated using ultrasonic

drilling. The typical multi-well slide has 10 × 21 wells with a diameter of 1000 ± 10 μm and a

depth of 250 ± 10 μm (Figure 1.6). 10

Figure 1. 6. Example of a custom-made glass micro-well slide. Each well has a diameter of 1 mm and depth of 0.25 mm. Most of the glass slides used in this dissertation were 10 × 21 micro- wells.

The polymer chip preparation scheme is illustrated in Figure 1.7. First, sensors were dissolved in polymer solution (4% poly(ether-urethane in THF). Next, 200 nL sensor solutions were dispensed into each well in the micro-well slide using a Hamilton dispenser (Figure 1.5.C).

The well bottom was covered with 5 μm of polymer film that converted into a hydrogel upon addition of water (400 nL). Whereas the amount of water absorbed will differ depending on the polymer, Liu et al. characterized the poly(ether-urethane) polymers available to our group.38

Poly (ether-urethane) polymers are designed with three fundamental parts. While the

polyethylene oxide component is hydrophilic, the polybutylene oxide and urethane connector

show hydrophobic properties. The optimum polymer that provides the best condition for our

applications was found to be polyurethane (polyethylene oxide (PEO)/polybutylene oxide (PBO)

= 4.8) polymer, which absorbs up to 60% of water (w/w). 11

Figure 1. 7. Fabrication process of polymer chips.

The controlled water uptake plays an essential role in the recognition process by excluding excessive amount of water, which may inhibit the complex formation.41 Third, 400 nL

of water was pipetted to form a hydrogel matrix after each well was covered with a polymer

solution and fully dried. Finally, 200 nL of aqueous analyte solution was manually cast on each

well and scanned using a 4000 mm KODAK scanner (Figure 1.7) after evaporation of the

excessive water at room temperature. 12

1.8. Data Analysis

Variables

Samples

Figure 1. 8. Schematic representation of the response matrix. Samples are in the rows and features, such as fluorescence intensity, polarization, lifetime, absorbance, etc., are in the columns.

In both the microplate arrays and polymer chips, fluorescence intensity changes were

acquired in the form of a multidimensional response matrix. Figure 1.8 shows a typical response

matrix, which displays the number of samples in rows and a number of variables in columns. As

the number of the channels increase, the response output matrix grows dramatically. Analysis of

such multidimensional data matrix was performed by statistical analysis tools.17 In this

dissertation, linear discriminant analysis (LDA) and support vector machine (SVM) regression analysis were mainly used. 42

1.8.1. Linear Discriminant Analysis (LDA)

Linear discriminant analysis is used to generate a classification model based on known

groups, also called learning or training objects, and to place unknown samples into appropriate

groups in the model. The classification model is obtained by forming linear discriminant function

(LDF) Y, which is a linear combination of all the recorded channels during the experiment.43 13

= + + +

𝑌𝑌 𝑎𝑎1𝑋𝑋1 𝑎𝑎2𝑋𝑋2 ⋯ 𝑎𝑎𝑛𝑛𝑋𝑋𝑛𝑛 Equation 1.1. Linear discriminant function.

As mentioned earlier, n measurements are performed for each sample. Using LDF, n- dimensional response data are reduced to a one-dimensional function of Y. Coefficients of the

LDF are chosen to maximize the separation of groups from each other and minimize the

separation between samples that are in the same group.

Figure 1. 9. A) Distribution of two groups of two variables, X1, and X2. B) One-dimensional linear discriminant function Y results in 100% correct classification into one of two groups (adapted from ref.42).

To illustrate this concept, a sample of spectral data comprises two variables and two groups

are given. Figure 1.9.A shows the distribution of each group on the two-dimensional graph. As

observed, neither variable, X1 nor X2, is enough to separate the clusters completely. However, a

linear combination of variables with coefficient values that maximize the distance between the groups and minimize within-group distances can result in a correct classification (Figure 1.9.B). 14

The performance of the LDF can be tested by two methods.44 The first method, the simplest

method, classifies each sample within the group and reports the percent of the samples correctly

allocated. We did not rely on this method due to over-optimistic results. This is due to the fact that discriminant function Y is formed using all the data in the matrix, including the samples to

be tested. The second method, cross-validation, is always performed in this dissertation to test

the performance of our analysis. Cross-validation, also called the leave-one-out method, forms

LDF by excluding one data point and allocating the excluded data point in the appropriate group.

This procedure is repeated for each data point in the data matrix and performance is evaluated.

The resulting data are reported as a jackknife classification matrix. It is expected that all data

points can be 100% correctly allocated in their groups using both methods for a robust and

reliable analysis.

1.8.2. Support Vector Machine (SVM)

Figure 1. 10. Illustration of mapping the input into a feature space that can be linearly separated. The support vector machine uses kernel functions to form linear support samples while maximizing the distance between support vectors.

The support vector machine is an approach for multivariate clustering and data mining methods. It was first proposed in the 1990s and attracted great attention due to its distinct

properties.42 Unlike LDA, SVM finds the boundaries between classes without employing all the 15 available recorded data. Figure 1.10 illustrates the boundary samples; that is, support vectors of imaginary two classes. Boundary samples were chosen to maximize the space between the groups. Then, unknown samples were classified according to the side of the boundary to which they are allocated. However, multivariable data collected from optical sensors show nonlinear responses and simple linear support vectors cannot be drawn. Here, the way of the boundaries formed needs to be established differently. The SVM handles this problem easily using kernel functions that can be in linear, polynomial, or radial form as the basis function to map nonlinear data into a feature space, where linear support vectors can be drawn. SVM tries to maximize the distance between two classes while the linear trend is being regenerated. Although further explanation goes beyond the scope of this dissertation, in the studies we have performed, SVMs have provided excellent performance in quantitative analysis.

1.9. Enantiomeric Excess

Drug plays a crucial role in pharmacology. Altered positions in space of the four different substituents on a carbon atom result in enantiomers that are non- superimposable mirror images of one another.45 Enantiomers of the same chiral compound may display different pharmacological properties; that is, whereas one of a compound may be an active drug, another enantiomer may not have any pharmacological properties or may even be toxic. One example is a drug, thalidomide, was prescribed widely in Europe for the treatment of morning sickness in pregnant women in the 1960s (Figure 1.11).46 Thalidomide is a chiral molecule and only the (R)-thalidomide was produced as a drug. However, studies showed that (R)-thalidomide racemizes in vivo and (S)-thalidomide, which has a teratogenic effect, is formed. Ten thousand babies suffered severe birth defects as a result of their mothers’ use of this drug. 16

O O O O NH NH N O N O

O O

Figure 1. 11. Structures of (S)-thalidomide (left) and (R)-thalidomide (right).

Due to the different pharmacological activities of enantiomers, the US Food and Drug

Administration (FDA) requires separate testing of the toxicological parameters of both

enantiomers of chiral drugs.47 In the case of the high toxicity of the enantiomeric pair, chiral

drugs need to be sold in their enantiopure forms.

Asymmetric synthesis is a cost-efficient method to obtain enantiopure compounds using a chiral catalyst and auxiliaries.48 To find optimum conditions that lead to an enantiopure product,

screening and testing of a range of catalysts, auxiliaries, and conditions are required. The

efficiency of the asymmetric synthesis is quantified by enantiomeric excess (ee) calculations.

[ ] [ ] = × 100 [ ] [ ] 𝑅𝑅 − 𝑆𝑆 𝑒𝑒𝑒𝑒 � 𝑅𝑅 + 𝑆𝑆 � Equation 1.2. Equation is used to calculate the enantiomeric excess (ee) of solutions.

1.10. Combinatorial Chemistry

Discovery of procedures that yield enantiopure compounds are usually conducted using rational design of the experiments which carry out the experiments one at a time. However, the activity of the catalyst is possibly affected by many factors, including temperature, solvent, substrate, reaction time, etc. Although rational methods are at the forefront of asymmetric synthesis, the testing of many catalysts, auxiliaries, and conditions is time-consuming. In the

1990s, a new method, combinatorial chemistry, was introduced as a tool for the testing catalysts 17

and auxiliaries of a number of conditions and libraries of compounds in a high-throughput

fashion. With advancements in reaction discovery, determination of enantiopurity of each

condition/catalysis becomes a major bottleneck to combinatorial synthesis. Here, two

combinatorial methods are explained briefly as further explanation goes beyond the scope of this

dissertation.

1.10.1. Split and Pool Method

Split and pool techniques are time-efficient methods to synthesize a large number of

compounds in a short time.49 In this method, reagents (A, B, and C) are coupled to polymer resin

beads. Coupled polymer resin beads are combined and then split into n number of groups to react

with n number of reagents. This procedure is repeated with different reagents m times to produce

n number of libraries with nm products. The screening of these products is carried out to reveal

the catalyst/axillaries that yield the desired product. Identification of the product is performed

either by further deconvolution or encoding techniques.50 The split and pool method enables a large number of reactions in a short time.

1.10.2. Parallel Synthesis

Parallel synthesis includes all the advantages of both combinatorial techniques and rational synthetic methods. A smaller number of libraries are tested in a parallel fashion, whereas

each reaction is tested individually. Due to advancements in high-throughput instrumentation,

such as robotic liquid dispensers and micro-well plate readers, the synthesis and screening of a large number of chiral compounds become much easier and faster. In addition to speed and

accuracy, high-throughput instrumentation allows reactions to be performed in a wide range of

conditions (temperature, pH, solvent, etc.).51 Miniaturized reaction settings also reduce the cost

and waste production. 18

Combinatorial synthesis and parallel synthesis are time-efficient approaches to developing catalysts that yields highly enantiopure products.52 However, in addition to their

advantages, these methods are, in part, limited by a lack of methods to screen for ee of the products. Due to the inability to test a large number of products, researchers have to reduce the number of the catalyst/auxiliary to reduce the number of the products that need to be tested.

1.10.3. Detection of Enantiomeric Excess (ee)

As mentioned earlier, the development of practical techniques for ee determination is crucial in drug development processes. The most important methods for analysis of enantiomeric compositions are explained below. These methods include chiral high-performance liquid chromatography (HPLC), HPLC coupled with circular dichroism (HPLC-CD), chiral gas chromatography (GC), capillary electrophoresis (CE), and nuclear magnetic resonance spectroscopy (NMR).53 Other methods, such as liquid crystals54, infrared thermography55,

molecularly imprinted polymers56, and enzymatic and antibody methods57 were reported but not

presented in this dissertation.

1.10.3.1. HPLC

Chiral separation using chromatographic techniques can be classified into two groups: indirect and direct separation. Indirect separation requires to derivatize the enantiomers of the chiral compounds to form which can be separated easily. In contradiction, direct chiral separation includes a chiral stationary phase or mobile phase, which forms diastereomers with enantiomers of the analytes that differ in their physical properties. A reversible complex formation is preferred to allow recovery and reuse of the column. 19

Figure 1. 12. HPLC with parallel columns.

Many types of chiral columns are commercially available.58 However, no chiral column

fits for the ee analysis of all analytes. Ideally, chiral columns should be identified for a particular analyte and the method optimized. In addition to the necessity for analyte-specific chiral columns, HPLC is not compatible with high-throughput analysis. Relatively rapid HPLC analysis takes ten minutes to perform, which is not fast enough to analyze the thousands of samples that can be analyzed using combinatorial techniques. Several approaches have been proposed to reduce the analysis time. For example, parallel columns with individual pumps allow multiple samples to be analyzed simultaneously. Nonetheless, conventional parallel chiral HPLC

can take up to 16 hours to scan 96 samples and uses more than a liter of solvent.59

1.10.3.2. HPLC-CD

HPLC coupled with circular dichroism (CD) is another method proposed for ee analysis.60 Unlike chiral HPLC columns, HPLC-CD involves circularly polarized light to create a chiral environment for detecting enantiomers. HPLC-CD requires a chromophore in intimate contact with the chiral center. However, most analytes do not have a chromophore or 20

chromophore with a strong CD signal. Derivatization can be performed for the analytes that do

not utilize a chromophore.61 Nevertheless, the time-frame of chromatographic methods does not

allow high-throughput analysis.62

1.10.3.3. Gas Chromatography

Gas chromatography uses gasses as a mobile phase. Reversible interaction with chiral

stationary phase (CSP) allows differentiating enantiomers by forming diastereomers.63 The

advantages include high efficiency, sensitivity, and robustness of the analysis while a limited number of analytes can be analyzed due to the thermal stability of the samples. Gas chromatography involves evaporation of the samples at high temperature, which usually results in racemization or degradation of the samples or the column itself.64 Therefore, this technique

can only be used for analytes that are highly volatile and thermally stable.

1.10.3.4. Capillary Electrophoresis

Capillary electrophoresis (CE) is an electrokinetic separation technique performed in a

capillary. It is based on the migration of the charged species through an electrolyte solution

under the influence of an electric field. is achieved by the addition of chiral

selectors, which mostly consist of charged cyclodextrins and polysaccharides.65 Advantages of

CE include high resolution, minimal sample diffusion, small sample size, and, most importantly,

it can be employed for parallel screening in high-throughput analysis.66 However, analysis

requires analytes to be ionizable in the buffer solution and must involve a chromophore or be

derivatized to be detected.67 21

1.10.4. Nuclear Magnetic Resonance(NMR) Spectroscopy

NMR is a spectroscopic method that can be utilized for ee analysis.68 This method involves chiral lanthanide shift reagents (CLSRs)69, or chiral derivatizing reagents (CDAs)65d to form diastereomeric complexes with each enantiomer that may result in different resonance signals. Relative integrals of the corresponding peaks can be easily used to reveal the enantiomeric composition of the samples. CLSRs can coordinate with chiral molecules via their metal centers; however, line broadening is observed due to the paramagnetic nature of lanthanide complexes. On the other hand, compounds must have a basic Lewis lone-pair to coordinate, or further derivatization is needed. The advantages of CDAs over the CLRSs are a better resolution in resonance signals and the resulting diastereomers can be separated by traditional chromatographic methods. However, promising studies of ee analysis using NMR spectroscopy in combinatorial chemistry are limited due to lack of applicability to high-throughput analysis.

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49 Pescarmona, P. P.; Van der Waal, J. C.; Maxwell, I. E.; Maschmeyer, T. Catal. Lett., 1999, 63,

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John Wiley & Sons, Inc., Hoboken, New Jersey. 2011.

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CHAPTER II. SENSING OF ENANTIOMERIC EXCESS IN CHIRAL CARBOXYLIC

ACIDS BY CINCHONA ALKALOIDS

2.1. Introduction

Cinchona alkaloids, namely, quinine, quinidine, cinchonine, and cinchonidine, are organic molecules isolated from the bark of cinchona trees (Figure 2.1). Although medicinal use of cinchona trees dates back to the mid-seventeen century, active compounds were marketed as an antimalarial drug in 1820.1 Besides antimalarial activity, these compounds are also commercialized today as muscle relaxants, cardiac depressants, and food additives. Nowadays,

700 metric tons of cinchona alkaloids are produced annually, and nearly half of production is used as a “bitters” additive (for example, tonic water) in the food and beverage industry.

R 8 9 Quinine OMe R S Quinidine OMe S R Cinchonine H S R Cinchonidine H R S

Figure 2. 1. Structure of cinchona alkaloids.

Cinchona alkaloids were introduced to organic chemistry by Pasteur in 1853.2 Pasteur reported that tartaric acid can be separated from its enantiomers by of their diastereomeric salts with cinchona alkaloids.2 Currently, there is a number of cinchona alkaloids and their derivatives used as resolving agents.3 In addition to the utilization of cinchona alkaloids as resolving reagents, they are also used as enantioselective tools.1 For example, cinchona 29

alkaloids and their derivatives are extensively used as stereoselective catalysts and chiral

auxiliaries for many types of reactions in organic chemistry. 4,5

Figure 2. 2. Four conformations of cinchonine (adapted from Ref. 1).

From the perspective of their structures, cinchona alkaloids have an internal rotation

around the C8-C9 bond that results in four low-energy conformers: anti-closed, anti-open, syn-

closed, and syn-open (Figure 2.2). In addition to their flexible structures, they can be modified

easily by various types of reactions.6 Importantly, N-alkylated cinchona alkaloids display efficient by chiral ion-pairing mechanism.7 In addition to electrostatic interactions of N-alkylated cinchona alkaloids, their structures feature hydroxyl moiety as hydrogen bond donor and aromatic substrates to comprise π -π interactions (Figure 2.3).

30

Figure 2. 3. Structural features of N-alkylated cinchona alkaloids.

In this chapter, N-alkylated cinchona alkaloids with 9-(chloromethyl)anthracene, known

as Corey-Lygo catalyst8, are used as chemosensors for enantiomeric excess analysis of chiral

carboxylates. Upon complex formation between negatively charged chiral carboxylates and

positively charged N-alkylated quinuclidine moiety of cinchona alkaloids these alkaloids display distinctive changes in their fluorescence intensities.9 In addition to electrostatic interaction, the secondary driving force of complex formation, hydrogen bonding and π -π interactions may also contribute to the recognition process as well. Here, we decided to utilize methyl anthracene as a reporter moiety, which also comprises rings that can form additional π-π interactions. We have chosen anthracene as a reporter for the chemosensors because of its known photophysical properties, low energy absorption (> 350 nm) and relatively high fluorescence quantum yield

(~0.25) in organic solvents. Introduction of the reporter moiety was performed by nucleophilic substitution of 9-(chloromethyl)anthracene to the nitrogen of the quinuclidine moiety (N1) (for the synthesis, see experimental section). The reaction results in chloride salts of the N-alkylated cinchona alkaloid chloride salts that did not form any complexes with carboxylates due to the 31

high charge density of the chloride anions. To overcome this complication, we performed counter anion exchange with tetrafluoroborate, which is less nucleophilic than halides. Thus, the

chemosensors (cations) obtained become more reactive toward carboxylates (anions). Once again, in addition to electrostatic interactions, the hydrogen bonding with the 9-hydroxy group and π-π interaction of aromatic substrates favor complex formation, and all these interactions are likely to have an impact on guest-host complexation. Upon complexation, presumably, the fluorescence intensity of the anthracene moiety will be changed and the magnitude of the signal change will be depended on the structural features of the resulting complexes and the possible types of the interactions between the chemosensors and the analytes.

Figure 2. 4. Structures of the carboxylate guests used in this study.

In this study, we selected a group of chiral and achiral carboxylate guests, including the

active and less active (or inactive) enantiomers of nonsteroidal anti-inflammatory drugs

(NSAIDs) commonly used for pain relief and fever reduction (Figure 2.4)10, separately. 32

Several fluorescent chemosensors for the detection of chiral carboxylates are reported, including chiral macrocycles, diacridylnaphthalene11, various BINOL derivatives12, cyclodextrins13,

boronic acids14, fluorescent oligomers15, and others16. Alas, only a few of these receptors and

chemosensors are amenable for high-throughput screening.

2.2. Results and Discussions

We synthesized four cinchona alkaloids alkylated at the quinuclidine nitrogen with

chloromethyl anthracene to obtain N-alkylated (methyl anthracene) cinchona alkaloids, namely,

N-(9-Anthracenylmethyl) quininium tetrafluoroborate (S1), N-(9-Anthracenylmethyl)

quinidinium tetrafluoroborate (S2), N-(9-Anthracenylmethyl) cinchoninium tetrafluoroborate

(S3), and N-(9-Anthracenylmethyl)cinchonidinium tetrafluoroborate (S4). Photochemical

properties, synthetic yields, and melting points of chemosensors S1-S4 are listed in Table 2.1.

Table 2. 1. Photochemical Properties of Cinchona Alkaloids

Sensor R 8 9 Φ τ (ns) MP (ºC) Yield Quinine S1 OMe R S 0.21 3.61 186 0.12

Quinidine S2 OMe S R 0.28 6.59 170 0.68

Cinchonine S3 H S R 0.25 4.89 176 0.59

Cinchonidine S4 H R S 0.20 4.19 130 0.26

Initially, the complex formation ability of N-alkylated cinchona alkaloids S1-S4 with

carboxylates was tested using electrospray ionization mass spectrometry (ESI-MS). Figure 2.5

shows the ESI-mass spectra of the complexes of the S2+Ibuprofen (A), S2+Naproxen (B), and

S3+Ketoprofen (C), with corresponding calculated spectra (insets). The molecular ions and 33

isotopic patterns of the complexes agree with the calculated spectra and isotopic patterns of the complexes with a 1:1 (chemosensor:analyte) stoichiometry.

Figure 2. 5. (A) Mass spectrum of the S2-Ibuprofen complex. Inset: Calculated isotopic pattern + for C47H50N2O3 . (B) Mass spectrum of the S2-Naproxen complex. Inset: Calculated isotopic + pattern for C48H46N2O4 . (C) Mass spectrum of the S3-Ketoprofen complex. Inset: Calculated + isotopic pattern for C50H46N2O4 .

Furthermore, the crystal structures of the S3 with enantiomers of ibuprofen were obtained by the slow diffusion of hexanes into a propionitrile solution of complexes. Figure 2.6 shows the

X-ray crystal structures of S3 with (S)-Ibuprofen (left) and (R)-Ibuprofen (right). Crystal structures of the complexes feature a combination of hydrogen bonding via the hydroxyl group at

C9 position and electrostatic interactions between the positively charged nitrogen of quinuclidine and negatively charged carboxylates of ibuprofen as we expected. Here, one can clearly see that

the orientation of the bulky benzyl group of the carboxylates differs in space, which results in the

vibrational and rotational motions of the fluorophores; which explain the difference in the

response observed for the two ibuprofen enantiomers. It is worth noting that solid-state studies of 34

the complexes may not display all the interactions that contribute to complex formation in a solution environment.

Figure 2.6. X-ray structure of the S3 complex with (S)-Ibuprofen (left) and (R)-Ibuprofen (right).

Lastly, we performed UV-Vis and fluorescence titrations of the chemosensors with

selected carboxylates (see Appendix B). There was no change observed in the absorption spectra

of the chemosensors upon addition of the analytes. Hence, fluorescence titration experiments

were carried out at the excitation wavelength of the absorption maximum of the chemosensors.

Fluorescence intensities of chemosensors S1-S4 were amplified upon addition of the analytes in

most cases. Exceptionally, only naproxen quenched the fluorescence of chemosensors S1-S4,

which was presumably due to a photo-induced electron transfer (PET) mechanism. Notably,

similar to all other carboxylates, excitation wavelength of the sensors did not overlap with the

absorption of naproxen. As an example, Figure 2.7 displays the titration of S1 with (S)-Ibuprofen

(left) and S4 with (S)-Naproxen. Whereas fluorescence intensity of S1 showed 60%

enhancement upon addition of ibuprofen, (S)-Naproxen quenched up to 75% of fluorescence

intensity of S4. Insets of Figure 2.7 show the corresponding isotherms of the titrations, 35

respectively. The fluorescence enhancement of the chemosensors was most likely due to

increased rigidity of the complexes, which limits vibrational and rotational motions of the fluorophore that otherwise would result in non-radiative decay.

1.0 1.0 ) i ) -I 0 0 -I 1.5 i 0.5

)/(I 0.5 -I)/(I

0 0.9 0 (I (I-I

6 -1 6 -1 0.0 Ka = 1.54 x 10 M 0.0 Ka = 2.22 x 10 M -5 -5 -5 -5 0.0 1.0x10 2.0x10 1.0 0.0 1.0x10 2.0x10 0.6

o [(S)-Naproxen] (M) [(S)-Ibuprofen] (M) I/I

0.5 0.3

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

Figure 2.7. (Left) Fluorescence titration of S1 by (S)-Ibuprofen. (Right) Fluorescence titration of S4 by (S)-Naproxen. Titrations were performed in propionitrile and analytes were added as tetrabutylammonium salts ([Sensors] = 10 µM).

The fluorescence titrations of chemosensors S1-S4 indicate their cross-reactivity as well

as their ability to respond to the presence of an analyte by a change in their fluorescent intensity.

More importantly, the chemosensors show different signal responses for the enantiomers of the

chiral analytes. For example, Figure 2.8 displays the fluorescence titrations of S1 (red lines) and

S3 (blue lines) with the enantiomers of ketoprofen. One clearly can see that fluorescence

responses of chemosensors S1 and S3 significantly differ for enantiomers of the same compound.

Enantioselective behaviors of the chemosensors were not identical. 36

0.5

0.4 S1 (S)-Ketoprofen 0 S- /I

0 0.3 S1 (R)-Ketoprofen R- I-I S3 (R)-Ketoprofen 0.2 R- S3 (S)-Ketoprofen 0.1 S- 0.0

0.0 1.0x10-5 2.0x10-5 Ketoprofen (M) Figure 2.8. Fluorescence titrations of S1 (red) and S3 (blue) with the incremental amounts of (S)-Ketoprofen, and (R)-Ketoprofen.

The association constants (Kas) of the analytes were calculated based on the change in

fluorescence intensity of the chemosensors at λEm = 424 nm. Table 3 shows the Kas for all carboxylates included in this study. The association constants of the analytes suggest that in the case where enantiomers of the same compound elicit comparable signal changes, differences in the Ka values enable a reliable analysis of corresponding analytes. The significance of the affinity

of the chemosensors was explained in Chapter I (see 1.3. Sensitivity and selectivity of the

sensors). Different binding affinities and response patterns of the chemosensors for each analyte

that we tested encouraged us to perform a qualitative analysis of some of the selected

carboxylates. As chemosensors S1-S4 are not water soluble, we utilized a polymer chip platform17a, which allows us to accommodate the chemosensor array in the aqueous media. In

addition, to provide the mechanical support to the chemosensors, polymer chips also contribute significantly to the recognition process. Details of polymer chip platforms were discussed in

Chapter I (see 1.6. Polymer chips). For the preparation of polymer chips, chemosensors were 37

dissolved in 5% polyurethane polymer solution and were manually cast into multi-well glass

slides.17

-1 [a] Table 2.2. Affinity Constants (Ka, M ) Obtained by Fluorescence Titration

S1 S2 S3 S4 Acetate 3.34 × 106 5.31 × 105 7.53 × 105 > 107

Benzoate 1.44 × 106 7.65 × 105 2.17 × 106 1.08 × 106

(S)-Mandelate 2.33 × 105 4.51 × 104 8.53 × 104 1.13 × 106

(R)-Mandelate 1.40 × 105 3.23 × 104 6.87 × 104 3.17 × 105

(S)-Ibuprofen 2.22 × 106 1.17 × 106 3.41 × 106 > 107

(R)-Ibuprofen 3.29 × 106 3.91 × 106 2.58 × 106 > 107

(S)-Ketoprofen 2.12 × 106 3.00 × 105 ND[b] ND[b]

(R)-Ketoprofen 1.37 × 106 1.42 × 105 2.92 × 105 2.08 × 105

(S)-Naproxen 3.00 × 106 3.85 × 106 2.31 × 106 1.54 × 106

(R)-Naproxen 9.10 × 106 3.41 × 106 2.70 × 106 1.67 × 106

[a] The titrations are recorded in propionitrile and Kas were calculated based on the change in fluorescence intensity change at λEm = 424 nm using 1:1 model. The errors of the curve fitting were < 20%. [b] Ka could not be calculated due to the low magnitude of response.

Then, an aqueous solution of analytes was cast into the polymer chips with embedded chemosensors. The fluorescence images of chemosensors S1-S4 were captured using a UV-Vis scanner. It is worth noting that the polymer matrix forms a semi-solid environment for the chemosensors. Upon addition of the aqueous solution of analytes, water molecules are stripped away from the anions by the hydrophilic part of the polymer enabling the recognition and binding the chemosensors. To eliminate instrumentation errors, ten repetitions of each sample were performed. Resulting multivariate data from the sensor chip array based on S1-S4 were 38 evaluated using linear discriminant analysis (LDA).18 The performance of the LDA was tested using a leave-one-out routine.

Figure 2.9. Images of the sensors upon addition of the corresponding analytes recorded by UV- Vis scanner.

Before performing the qualitative analysis, we carried out a preliminary fluorescence experiment of the enantiomers of ibuprofen, ketoprofen, naproxen, and mandelate. Acetate, benzoate, and water were also added for control purposes. Figure 2.9 shows the fluorescence images of the chemosensors upon addition of the aqueous solutions of analytes. The fluorescence intensities of the chemosensors S1-S4 induced upon addition of aqueous solutions of the analytes show the same behavior as was seen in the organic solvent. More importantly, enantiomers of the similar compounds show significant differences in the fluorescence intensity of the chemosensors. Therefore, the analyte-specific response patterns of chemosensors can be used for qualitative analysis of chiral carboxylates.

39

Control Naproxen Acetate Benzoate S-Mandelate Ibuprofen R-Mandelate 20 Mandelate S-Ibuprofen R-Ibuprofen

) 0 Ketoprofen S-Naproxen

5%

.

3

( R-Naproxen

3 -20 F 20 S-Ketoprofen R-Ketoprofen -40 10

-50 ) % -25 0 .4 6 ( 0 2 F1 F (8 -10 6. 25 2% ) 50 -20 75

Figure 2.10. Linear discriminant analysis of ten carboxylates. The analytes include enantiomers of ibuprofen, ketoprofen, naproxen, and mandelate. Acetate and benzoate are also added for control purposes.

Different responses of the chemosensors to the chiral carboxylates encouraged us to perform a qualitative analysis of selected carboxylates. Figure 2.10 shows the LDA plot of the 11 carboxylates, including enantiomers of ibuprofen, ketoprofen, naproxen, and mandelate. Acetate, benzoate, and water were also added for control purposes. All (100%) correctly classified clusters of chiral carboxylates were significantly separated from each other. More importantly, the enantiomers of the same compounds showed well-resolved clusters. For example, enantiomers of ketoprofen are significantly separated. The outstanding results of this qualitative analysis encouraged us to perform enantiomeric excess (ee) analysis of the chiral carboxylates.

40

-20 Racemic- Ketoprofen 10% ee (S)-Ketoprofen 30% ee (S)-Ketoprofen 0

%) 40% ee (S)-Ketoprofen

5

.

1

( 60% ee (S)-Ketoprofen

3

F 70% ee (S)-Ketoprofen 20 80% ee (S)-Ketoprofen 90% ee (S)-Ketoprofen 20 -30 F Pure (S)-Ketoprofen 2 ( 0 0 8. 2%) ) 30 9.1% 1 (8 -20 F

Figure 2.11. Linear discriminant analysis plot of ketoprofen. Ten different enantiomeric compositions of ketoprofen were discriminated with 100% correct classification.

At first, we performed an analysis of ketoprofen. Ketoprofen is a nonsteroidal anti- inflammatory drug (NSAID) with antipyretic and analgesic effects. It is sold as a in the U.S. and an enantiopure (S)-Ketoprofen, with the name dexketoprofen, in the U.K.

The pharmacological properties of ketoprofen enantiomers are found to be different and enantiopure (S)-Ketoprofen is recommended.19 In this experiment, we prepared an aqueous solution of (S)-Ketoprofen of variable enantiomeric composition (10, 30, 40, 60, 70, 80, 90, and

100% ee) and a racemic mixture of the ketoprofen. Sensor arrays based on the four chemosensors (S1-S4) were able to resolve each cluster of ketoprofen solutions with a wide range of ee values and 100% accuracy (Figure 2.11). The clusters of ketoprofen show a clear trend with increasing ee values of (S)-Ketoprofen. Observing a clear trend in the semi- quantitative analysis is crucial to construct a calibration model for the quantitative analysis. The performance of the chemosensor array was tested by carrying our an additional semi-quantitative ee analysis of analytes, such as ibuprofen and naproxen (see Appendix B). Clear trends in the 41

clusters of both experiments suggest that chemosensor arrays based on S1-S4 can perform

quantitative ee analysis of diverse chiral carboxylates accurately.

100 90 80 Calibration data set Validation data set 70

)-Ketoprofen 60 S (

50 40 30 RMSEC=2.80 20 RMSECV=6.21 10 RMSEP=2.66 Predicted % ee % Predicted 0 0 102030405060708090100 Actual % ee (S )-Ketoprofen

Figure 2.12. Results of linear regression using support vector machine (SVM) afford quantitative analysis of enantiomeric excess of ketoprofen. The plots of actual vs. predicted concentrations show high accuracy of prediction for two enantiomeric compositions of ketoprofen. The root-mean-square errors (RMSEs) of calibration (C), cross-validation (CV), and prediction (P) attest to the quality of the model and prediction. Two unknown samples (red circles ●) were subsequently correctly analyzed.

Positive outcomes from the qualitative and semi-quantitative analyses of chiral carboxylates encouraged us to develop a linear regression model using obtained data from the array and the datapoints corresponding to two different ee values were excluded and used later to validate the regression analysis. The quantitative ee analysis of unknown samples was performed by regression analysis utilizing the support vector machine (SVM) algorithm.20 In the

quantitative ee analysis, 80% of the dataset was used to create a calibration model using SVM

while the remaining 20% of data used to validate the model. The performance of the analysis is

reported as the root-mean-square errors (RMSEs) of calibration (C), cross-validation (CV), and

prediction (P). Figure 2.12 shows the linear regression of the enantiomeric composition of 42

ketoprofen. The chemosensor array based on S1-S4 yielded an accurate quantitative regression

analysis of the enantiomeric composition of ketoprofen. Including the whole range of ee values

of ketoprofen (0-100% ee (S)-Ketoprofen) in the calibration model, ee analyses of two random points (red circles, ●) show very high accuracy, with RMSEP as low as 2.66.

100 Calibration data set 100 Calibration data set 90 Validation data set 90 Validation data set 80 80 70

)-Naproxen 70

)-Ibuprofen S ( S 60 60 (

50 50 40 40 30 30 20 RMSEC=1.12 20 RMSEC=1.86 10 RMSECV=1.70 Predicted % ee % Predicted 10 RMSECV=2.76 Predicted % ee % Predicted RMSEP=1.71 0 0 RMSEP=2.53

0 102030405060708090100 0 102030405060708090100 Actual % ee (S)-Ibuprofen Actual % ee (S)-Naproxen

Figure 2.13. Results of linear regression using support vector machine (SVM) afford quantitative analysis of enantiomeric excess of ibuprofen (left) and naproxen (right). The plots of actual vs. predicted concentrations show high accuracy of prediction for two enantiomeric compositions of ibuprofen and naproxen and the low error of prediction (RMSEP).

In addition to ee analysis of ketoprofen, we performed analyses of ee in ibuprofen and

naproxen samples to test the performance of the chemosensor array for analytes with similar

structures to ketoprofen. It is worth noting that the arrays based on chemosensors S1-S4 were

capable of discriminating enantiomers of the chiral compounds as well as similar structures with

small structural differences. Figure 2.13 plots the regressions of ibuprofen (left) and naproxen

(right). Similar to the ee analysis of ketoprofen, the chemosensor array was able to validate

successfully the unknown ee values of ibuprofen and naproxen. The RMSEPs of ibuprofen and

naproxen were determined as 1.71 and 2.53, respectively. 43

Finally, we successfully performed qualitative and quantitative ee analyses of carboxylates, such as ketoprofen, naproxen, and ibuprofen. However, the enantiomeric excess analysis is not concentration-dependent and the simultaneous determinations of ee and concentration of each enantiomer may be needed.21 Neither the linear discriminant function nor support vector machine algorithm is capable of revealing multiple information simultaneously. A trending method of analysis, artificial neural network (ANN)22, was performed to show the enantiomeric composition of unknown samples along with the actual concentration of each enantiomer. To utilize the ANN, we selected three concentrations of ibuprofen (the total concentration of enantiomers), namely, 0.5, 1, and 2 mM, of varying enantiomeric composition.

To examine the concentration- and ee-dependent trends revealed by the array, we performed a linear discriminant analysis. Figure 2.14 is the LDA plot of various concentrations and ee (100 to

100%) values of ibuprofen. Here, the definite trend in the concentration of ibuprofen was observed along the F1 axis, whereas the ee-dependent trend was seen in the F2 axis. A 100% correctly classified semi-quantitative analysis of ibuprofen (Figure 2.14) suggests that the array is capable of performing the quantitative analysis and ee analysis of ibuprofen. Hence, a network function of ANN was created and trained using all the data (except three data points used for validation). Table 4 shows the results of the ANN analysis of the three unknown ibuprofen solutions excluded from the training stage of analysis. The results of ANN analysis of ibuprofen show high accuracy in their ee values and the concentrations (error < 6%).

44

100% Correct Classification in Water 0.5 mM Pure (R)-Ibuprofen 0.5 mM -80% (S)-Ibuprofen 0.5 mM -40% (S)-Ibuprofen 0.5 mM -20% (S)-Ibuprofen 0.5 mM 20% (S)-Ibuprofen -60 0.5 mM 40% (S)-Ibuprofen 0.5 mM 80% (S)-Ibuprofen -40 0.5 mM Pure (S)-Ibuprofen

-20 1 mM Pure (R)-Ibuprofen ) 1 mM -80% (S)-Ibuprofen 0 2% 1 mM -60% (S)-Ibuprofen (2. 1 mM -40% (S)-Ibuprofen 3 20 F 1 mM -20% (S)-Ibuprofen 1 mM 20% (S)-Ibuprofen 40 1 mM 40% (S)-Ibuprofen 60 1 mM 60% (S)-Ibuprofen 1 mM Pure (S)-Ibuprofen 50 2 mM Pure (R)-Ibuprofen

) 2 mM -80% (S)-Ibuprofen % 2 mM -60% (S)-Ibuprofen 9 . 0 0 2 mM -40% (S)-Ibuprofen 6 ( 2 mM -20% (S)-Ibuprofen -60 1 F -40 2 mM 20% (S)-Ibuprofen -20 2 mM 30% (S)-Ibuprofen F2 0 2 mM 60% (S)-Ibuprofen (20 20 -50 .7% 2 mM Pure (S)-Ibuprofen ) 40 Figure 2.14. Linear discriminant analysis (LDA) of ibuprofen mixtures of various concentrations and ee (-100 to 100%) show perfect separation.

Table 2.3. ANN Analysis of Unknown Ibuprofen Samples[a]

Concentration Enantiomeric Excess

Actual Predicted (ANN) Error Actual Predicted (ANN) Error

0.5 0.50 0.003 20 20.12 0.002

1 0.93 0.064 20 18.61 0.023

2 1.99 0.003 60 62.10 0.105

[a] The analysis is performed using the scores from LDA results. The training set comprised 23 samples (seven repetitions) and three samples (seven repetitions) were used for prediction.

45

2.3. Conclusion

In this chapter, we demonstrated that N- alkylated cinchona alkaloids, a class of phase transfer catalysts, can be used successfully as fluorescent chemosensors for the qualitative and quantitative ee analyses of carboxylates.. First, we synthesized N-alkylated cinchona alkaloids, namely, N-(9-Anthracenylmethyl) quininium tetrafluoroborate (S1), N-(9-Anthracenylmethyl) quinidinium tetrafluoroborate (S2), N-(9-Anthracenylmethyl) cinchoninium tetrafluoroborate

(S3), and N-(9-Anthracenylmethyl)cinchonidinium tetrafluoroborate (S4). The resulting counter anion of chloride was exchanged with a less nucleophilic anion to enhance the affinity of the positively charged chemosensors toward the carboxylates. As a result, the S1-S4 cinchona salts were found capable of binding the carboxylate anions with a strong change in signal in most cases. Fluorescence intensities of the chemosensors S1-S4 were enhanced upon complex formation with carboxylate anions. In designing the chemosensor array, we considered the chiral chemosensors to form diastereomers with chiral carboxylates and the structural features of complexes results in enantiomer specific responses. Due to the formation of the diastereomeric complexes, enantiomers of the same compounds were discriminated. The analyte-specific response patterns of arrays based on S1-S4 were capable of successfully distinguishing and quantifying structurally similar chiral carboxylates and their corresponding enantiomers.

Specifically, we have demonstrated qualitative analysis enantiomers of chiral carboxylates, including several non-steroidal anti-inflammatory drugs (NSAIDs), such as enantiomers of ibuprofen, ketoprofen, and naproxen. Sensor arrays based on four fluorescent sensors (S1-S4) were capable of differentiating between a range of enantiomeric excess analyses of ibuprofen, ketoprofen, and naproxen (RMSEP = 2.66, 1.71, and 2.53, respectively). In addition to ee analysis of chiral carboxylates, we performed ANN experiments to reveal ee 46

values of samples along with their concentrations. Simultaneous determination of ee values and

concentration of ibuprofen shows less than 6% error of prediction.

These results suggest that fluorescent cinchona organocatalysts are well-suited

chemosensors S1-S4 for high-throughput ee analysis of a number of drugs, or drug intermediates, and high-throughput screening probes for the enantiomeric purity of chiral drugs.

2.4. Experimental Section

2.4.1. General

Chemosensors S1-S4 were synthesized using standard laboratory techniques. All starting materials were purchased and used as received. Compounds S1-S4 were prepared according to the literature procedures.23 1H- and 13C-NMR (APT) spectra were recorded using a Bruker®

Avance IITM 500 MHz UltraShieldTM (Bruker Corporation, Mass., USA) spectrometer at 25°C.

Solutions for optical measurements were prepared using freshly distilled propionitrile. Optically

dilute solutions (0.1 A) were used for all photophysical experiments. Fluorescence emission

spectra were acquired using an Edinburgh single photon counting spectrofluorometer (FLSP

920). Fluorescence emission spectra were recorded between 380 nm and 500 nm. The band

passes of both excitation and emission monochromators were set to 1.0 nm. The emission from

probes was scanned in 2 nm steps. The dwell time was adjusted to 0.30 s. Scans were taken

under ambient room conditions. Guest titrations were performed in propionitrile. Titration

isotherms were constructed from changes in the fluorescence maximum at 424 nm. Binding

constants were calculated by nonlinear least-square methods using an equation for a 1:1 binding

model.24 All the calculations were performed using Origin software package.25

47

/ 1 1 4 2

1

Ka: Binding constant [S]t: Concentration of sensor [G]t: Concentration of the guest [G]: Unknown concentration of guest [SG]: Unknown concentration of complex [F]: Guest-dependent fluorescence [F]0: Fluorescence intensity of sensor only [F]i: Saturated fluorescence intensity of sensor

Equation 2.1. Binding constants were calculated by nonlinear least-square methods using the equation for a 1:1 binding model.

Absorption spectra were recorded using a Hitachi U-3010 spectrophotometer. Fluorescence

titrations were performed at room temperature using a quartz cuvette with a path length of 1 cm

at right angle detection and titrations were carried out in propionitrile solutions of sensors by

adding propionitrile solutions of carboxylates as tetrabutylammonium salts. EI-DIP mass spectra

were recorded using a Shimadzu QP5050A. Absolute quantum yields were measured using a

Hamamatsu Quantaurus absolute quantum yield spectrometer QY-C11347.

2.4.2. Preparation of the Polymer Chips

The multi-well 10 x 21 (sub-microliter) glass slides were fabricated by ultrasonic drilling

of microscope slides (well diameter: 1000 ± 10 μm, depth: 250 ± 10 μm). Sensor solutions (2.0

mM) in polymer solution (4% poly(ether-urethane) in THF w/w) were prepared. In a typical

array, 200 nL of sensor-polymer solutions were pipetted into each well of the multi-well glass

slides and dried. Then, water (400 nL) was pipetted into each well and dried to form hydrated gel

matrix. Finally, analytes (200 nL, 5 mM, 1 nmol) were added as aqueous solutions into each well 48

and the chip was dried at room temperature for 1 h.26 Images from the sensor array were

recorded using a Kodak Image Station 440CF (for preliminary experiments) and a Kodak Image

Station 4000MM PRO (for qualitative and quantitative experiments). After acquiring the images,

the integrated (nonzero) gray pixel value (n) was calculated for each well in each channel.

Images of the sensor chip were recorded before (b) and after (a) the addition of an analyte. The

final responses (R) were evaluated as indicated in the following equation 3:

Equation 2.2. The final responses (R) were evaluated as indicated in this equation.

The obtained data for qualitative analysis were then analyzed using Linear Discriminant Analysis

(LDA). The Support Vector Machine (SVM) was used for quantitative assays.

2.4.3. Synthesis

Equation 2.3. General route for the synthesis of all the sensors.

The sensors S1-S4 were prepared according to literature.27 The cinchona alkaloid (500

mg, 1.54 mmol) and 9-(chloromethyl)anthracene (300 mg, 1.32 mmol) were dissolved in toluene

(20 ml) in a 100 ml flask. The reaction mixture was refluxed for 48 hours. After cooling down to

room temperature, diethyl ether (50 ml) was added. The resulting slurry was stirred at room

temperature for 3 hours. The solid was collected by filtration. After chromatography 49

(Silica/CHCl3-MeOH-Et3N 9:0.9:0.1 v/v), pure products were obtained as pale yellow solids.

Counter anion exchange was performed as follows: The sensors were dissolved in methanol-

water solution (0.8/0.2 v/v) and added into saturated solution of NaBF4 in water. The precipitated

final product were filtered and dried in vacuum.

S1 (N-(9-Anthracenylmethyl) quininium tetrafluoroborate): (302 mg, 32%). m.p. : 186 °C

1 (dec.). H NMR (500 MHz, DMSO-d6) δ 8.99 (s, 1H), 8.90 – 8.86 (m, 1H), 8.81 (d, J = 9.0 Hz,

1H), 8.64 (d, J = 8.9 Hz, 1H), 8.28 (d, J = 8.2 Hz, 2H), 8.07 (dd, J = 9.2, 1.3 Hz, 1H), 7.88 (d, J =

3.3 Hz, 1H), 7.83 – 7.72 (m, 2H), 7.71 – 7.61 (m, 3H), 7.54 (d, J = 9.2 Hz, 1H), 7.05 (d, J = 6.5

Hz, 2H), 6.61 (d, J = 14.1 Hz, 1H), 5.75 – 5.65 (m, 1H), 5.61 (d, J = 14.0 Hz, 1H), 4.95 (dd, J =

17.8, 14.3 Hz, 2H), 4.52 (d, J = 6.2 Hz, 1H), 4.42 (t, J = 10.7 Hz, 1H), 4.03 (s, 3H), 3.82 (s, 1H),

3.05 (t, J = 11.3 Hz, 1H), 2.89 – 2.77 (m, 1H), 2.39 (d, J = 7.3 Hz, 1H), 2.27 (d, J = 12.4 Hz, 1H),

2.12 (d, J = 6.0 Hz, 1H), 1.89 (s, 1H), 1.60 (d, J = 10.4 Hz, 1H), 1.45 (t, J = 11.7 Hz, 1H) ppm.

13C NMR (126 MHz, DMSO) δ 157.26, 147.59, 144.22, 143.79, 137.96, 133.16, 132.82, 132.15,

131.39, 131.19, 131.08, 129.80, 129.68, 127.83, 127.65, 125.54, 125.50, 125.36, 124.71, 124.51,

121.81, 120.45, 118.78, 116.68, 102.58, 68.72, 64.20, 59.76, 55.79, 55.31, 51.81, 37.44, 25.32,

+ + 24.70, 20.47 ppm. ESI-MS: m/z 515 ([M-BF4] ), calculated for C35H35N2O2 515.27. Φ = 0.21

%. τ = 3.61 ns.

S2 (N-(9-Anthracenylmethyl) quinidinium tetrafluoroborate): (546 mg, 59%). m.p. : 170

°C (dec.). 1H NMR (500 MHz, DMSO) δ 8.99 (s, 1H), 8.88 (d, J = 4.4 Hz, 1H), 8.75 (d, J = 9.0

Hz, 1H), 8.50 (d, J = 9.1 Hz, 1H), 8.29 (d, J = 8.5 Hz, 2H), 8.06 (d, J = 9.2 Hz, 1H), 7.88 (d, J =

4.4 Hz, 1H), 7.84 (dd, J = 11.1, 4.3 Hz, 1H), 7.80 – 7.75 (m, 1H), 7.71 (d, J = 2.6 Hz, 1H), 7.67

(ddd, J = 8.4, 6.5, 4.7 Hz, 2H), 7.56 (d, J = 2.6 Hz, 1H), 7.54 (d, J = 2.6 Hz, 1H), 7.26 (d, J = 3.0

Hz, 1H), 6.97 (s, 1H), 6.20 (d, J = 14.3 Hz, 1H), 6.01 (ddd, J = 17.5, 10.4, 7.3 Hz, 1H), 5.83 (d, J 50

= 14.2 Hz, 1H), 5.18 (d, J = 10.4 Hz, 1H), 5.07 (d, J = 17.3 Hz, 1H), 4.41 (dd, J = 17.2, 8.2 Hz,

2H), 4.18 (s, 3H), 3.17 (t, J = 11.0 Hz, 1H), 2.68 – 2.59 (m, 1H), 2.39 (ddd, J = 17.4, 16.8, 10.2

Hz, 2H), 1.82 – 1.62 (m, 2H), 1.62 – 1.41 (m, 1H), 1.13 – 1.01 (m, 1H) ppm. 13C NMR (126

MHz, DMSO) δ 157.88, 147.98, 144.24, 144.02, 137.78, 133.36, 133.26, 132.56, 131.81, 131.61,

130.37, 130.20, 128.42, 128.12, 126.00, 125.96, 125.89, 125.10, 124.36, 122.17, 120.93, 120.84,

119.10, 117.52, 103.05, 67.66, 65.89, 56.47, 55.96, 55.74, 55.42, 37.58, 26.00, 24.10, 21.43

+ + ppm. ESI-MS: m/z 515 ([M-BF4] ), calculated for C35H35N2O2 515.27. Φ = 0.28 %. τ = 6.59 ns.

S3 (N-(9-Anthracenylmethyl) cinchoninium tetrafluoroborate): (465 mg, 48%). m.p. :

176 °C (dec.). 1H NMR (500 MHz, DMSO) δ 9.06 (d, J = 4.2 Hz, 1H), 8.99 (s, 1H), 8.93 (d, J =

9.0 Hz, 1H), 8.64 (d, J = 8.1 Hz, 1H), 8.54 (d, J = 8.9 Hz, 1H), 8.29 (d, J = 8.4 Hz, 2H), 8.16 (d, J

= 8.2 Hz, 1H), 8.00 – 7.78 (m, 5H), 7.73 – 7.61 (m, 2H), 7.32 (d, J = 25.4 Hz, 1H), 6.95 (d, J =

11.6 Hz, 1H), 6.25 (d, J = 14.2 Hz, 1H), 6.01 (d, J = 14.0 Hz, 1H), 5.98 – 5.83 (m, 1H), 5.15 (d, J

= 10.4 Hz, 1H), 5.02 (d, J = 17.2 Hz, 1H), 4.41 (dt, J = 19.5, 9.5 Hz, 2H), 4.24 (t, J = 10.6 Hz,

1H), 3.01 (t, J = 11.1 Hz, 1H), 2.73 (dt, J = 23.9, 9.9 Hz, 1H), 2.39 – 2.21 (m, 2H), 1.81 – 1.66

(m, 2H), 1.58 (s, 1H), 1.01 (d, J = 13.3 Hz, 1H) ppm. 13C NMR (126 MHz, DMSO) δ 150.30,

147.73, 145.16, 137.14, 133.21, 132.97, 132.09, 131.24, 131.12, 129.91, 129.83, 129.62, 129.58,

127.85, 127.58, 127.23, 125.56, 125.46, 125.21, 124.54, 124.16, 124.05, 120.25, 119.02, 116.94,

+ 66.75, 65.87, 56.71, 54.64, 54.24, 36.98, 25.48, 23.49, 21.27 ppm. ESI-MS: m/z 485 ([M-BF4] ),

+ calculated for C34H33N2O 485.26. Φ = 0.25 %. τ = 4.89 ns.

S4 (N-(9-Anthracenylmethyl)cinchonidinium tetrafluoroborate): (211 mg, 21.6%). m.p. :

130 °C (dec.). 1H NMR (500 MHz, DMSO) δ 9.05 (d, J = 4.4 Hz, 1H), 8.99 (s, 1H), 8.90 (d, J =

9.1 Hz, 1H), 8.73 (d, J = 9.1 Hz, 1H), 8.60 (t, J = 7.5 Hz, 1H), 8.29 (d, J = 8.5 Hz, 2H), 8.17 (dd,

J = 8.3, 0.9 Hz, 1H), 7.95 – 7.88 (m, 2H), 7.85 (ddd, J = 8.2, 6.9, 1.3 Hz, 1H), 7.82 – 7.75 (m, 51

2H), 7.67 (ddd, J = 8.3, 6.4, 4.4 Hz, 2H), 7.29 (d, J = 4.0 Hz, 1H), 7.04 (s, 1H), 6.48 (d, J = 14.2

Hz, 1H), 5.85 (d, J = 14.1 Hz, 1H), 5.70 (ddd, J = 17.4, 10.5, 7.1 Hz, 1H), 4.99 (dd, J = 26.5,

13.9 Hz, 2H), 4.51 (dt, J = 22.3, 9.7 Hz, 2H), 3.92 – 3.84 (m, 1H), 3.13 – 3.06 (m, 2H), 2.76 (td,

J = 11.2, 5.1 Hz, 1H), 2.45 – 2.37 (m, 1H), 2.25 – 2.18 (m, 1H), 2.05 – 2.01 (m, 1H), 1.88 (d, J =

2.7 Hz, 1H), 1.54 (t, J = 9.8 Hz, 1H), 1.38 (dd, J = 13.1, 10.4 Hz, 1H) ppm. 13C NMR (126 MHz,

DMSO) δ 150.72, 148.22, 146.19, 138.57, 133.61, 133.51, 132.49, 131.61, 131.58, 130.32,

130.11, 130.00, 128.15, 128.06, 127.61, 125.94, 125.93, 125.59, 125.20, 125.00, 124.63, 120.76,

119.58, 117.05, 68.57, 65.17, 60.77, 55.76, 51.76, 37.92, 31.17, 25.71, 25.11, 21.62. Φ = 0.20 %.

τ = 4.19 ns.

2.4.4. Preparation of TBA Salts of Guests

Chiral carboxylate analytes were dispersed in water and aqueous solution of tetrabutylammonium hydroxide (0.5M) was added slowly until pH 7 was reached. While adding

TBA-OH, carboxylate mixture started to dissolve as TBA salts formed. Then, the water was removed by lyophilization.

52

2.4.5. Qualitative Analysis

Table 2.4. The Jackknifed Classification Matrix of Qualitative Analysis of 9 Analytes and A Control by Using S1-S4 in Hydrogel Matrix.

Jackknifed classification matrix

-Ibuprofen -Mandelate -Naproxen Acetate Benzoate Control Ketoprofen R R R S -Ibuprofen S - Ketoprofen S -Mandelate S -Naproxen % correct Acetate 8 0 0 0 0 0 0 0 0 0 0 100 Benzoate 0 8 0 0 0 0 0 0 0 0 0 100 Control 0 0 8 0 0 0 0 0 0 0 0 100 Ketoprofen 0 0 0 8 0 0 0 0 0 0 0 100 R-Ibuprofen 0 0 0 0 8 0 0 0 0 0 0 100 R-Mandelate 0 0 0 0 0 8 0 0 0 0 0 100 R-Naproxen 0 0 0 0 0 0 8 0 0 0 0 100 S-Ibuprofen 0 0 0 0 0 0 0 8 0 0 0 100 S-Ketoprofen 0 0 0 0 0 0 0 0 8 0 0 100 S-Mandelate 0 0 0 0 0 0 0 0 0 8 0 100 S-Naproxen 0 0 0 0 0 0 0 0 0 0 8 100 Total 8 8 8 8 8 8 8 8 8 8 8 100

Figure 2.15. The canonical scores plot of qualitative analysis of 9 analytes and a control by using S1-S4 in hydrogel matrix.

53

2.4.6. Semi-quantitative and Quantitative Analysis of Naproxen

100% Correct Classification in Water 40

Pure (S)-Naproxen 20 80% ee (S)-Naproxen 60% ee (S)-Naproxen 40% ee (S)-Naproxen ) 20% ee (S)-Naproxen

%

8 0 . -20% ee (S)-Naproxen 1

(

-40% ee(S)-Naproxen

3 -30 F -60% ee (S)-Naproxen -80% ee (S)-Naproxen -20 ) -20 Pure (R)-Naproxen

% -10

6

. 6

1 0

(

2

F 10 -40 -40 20 0 -20 40 20 F1 (78.6%)

Figure 2.16. Linear discriminant analysis (LDA) of enantiomeric excess of (S)-Naproxen in hydrogel matrix. LDA shows the trend depending on the enantiomeric composition of Naproxen.

100 Calibration data set Validation data set 50

0

-50 RMSEC=3.72 RMSECV=5.52 RMSEP=5.06 Predicted % ee of (S)-Naproxen of ee % Predicted -100 -100 -50 0 50 100 Actual % ee of (S)-Naproxen

Figure 2.17. The result of the linear regression using support vector machine (SVM) affords quantitative analysis of enantiomeric excess in the mixtures. The plots of actual vs. predicted concentration show high accuracy of prediction for multiple guest concentrations. Two unknown samples (red squares ■) were simultaneously correctly analyzed.

54

Table 2.5. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of (S)- Naproxen by Using S1-S4 in Hydrogel Matrix.

Jackknifed classification matrix

)-Naproxen R S )-Naproxen S )-Naproxen S )-Naproxen S )-Naproxen S )-Naproxen S )-Naproxen S )-Naproxen S )-Naproxen S )-Naproxen 10% ( 20% ( 30% ( 40% ( 60% ( 70% ( 80% ( 90% ( Pure ( Pure ( % correct 10 % (S)-Naproxen 8 0 0 0 0 0 0 0 0 0 100 20 % (S)-Naproxen 0 8 0 0 0 0 0 0 0 0 100 30 % (S)-Naproxen 0 0 8 0 0 0 0 0 0 0 100 40 % (S)-Naproxen 0 0 0 8 0 0 0 0 0 0 100 60 % (S)-Naproxen 0 0 0 0 8 0 0 0 0 0 100 70 % (S)-Naproxen 0 0 0 0 0 8 0 0 0 0 100 80 % (S)-Naproxen 0 0 0 0 0 0 8 0 0 0 100 90 % (S)-Naproxen 0 0 0 0 0 0 0 8 0 0 100 Pure (R)-Naproxen 0 0 0 0 0 0 0 0 8 0 100 Pure (S)-Naproxen 0 0 0 0 0 0 0 0 0 8 100 Total 8 8 8 8 8 8 8 8 8 8 100 Canonical Scores Plot

FACTOR(1) FACTOR(2) FAC TOR(3) FACTOR(4) FACT OR(5) FACTOR(1) FACTOR(1)

FACTOR(2) VAR_1$ 10 % (S)-Naproxen FACTOR(2)

FACTOR(3) 20 % (S)-Naproxen 30 % (S)-Naproxen

FACTOR(3) 40 % (S)-Naproxen

FACTOR(4) 60 % (S)-Naproxen 70 % (S)-Naproxen

FACTOR(4) 80 % (S)-Naproxen

FACTOR(5) 90 % (S)-Naproxen Pure (R)-Naproxen

FACTOR(5) Pure (S)-Naproxen FACTOR(1) FACTOR(2) FAC TOR(3) FACTOR(4) FACT OR(5)

Figure 2.18. The canonical scores plot of Semi-quantitative analysis of (S)-Naproxen by using S1-S4 in hydrogel matrix. 55

2.4.7. Semi-quantitative Analysis of Ibuprofen

Pure (S)-ibuprofen 80 % ee (S)-ibuprofen 60 % ee (S)-ibuprofen 40 % ee (S)-ibuprofen 20 % ee (S)-ibuprofen 20 % ee (R)-ibuprofen 40 % ee (R)-ibuprofen 30 60 % ee (R)-ibuprofen 80 % ee (R)-ibuprofen Pure (R)-ibuprofen

) 15

%

3

.

1 (

0

3 F -15

-30 -30 -20 40 -10 F1 ( 0 ) 71 20 % .7 .1 %) 10 6 (2 2 0 F 20 -20 30 Figure 2.19. Linear discriminant analysis (LDA) of enantiomeric excess of (S)-Ibuprofen in hydrogel matrix. LDA shows the trend depending on the enantiomeric composition of ibuprofen.

100 80 60 Calibration data set 40 Validation data set )-Ibuprofen

S 20 0

ee % ( -20 -40 -60 RMSEC= 2.24 RMSECV= 3.40 Predicted -80 RMSEP=3.41 -100 -100-80 -60 -40 -20 0 20 40 60 80 100 Actual % ee ( S)-Ibuprofen

Figure 2.20. The result of the linear regression using support vector machine (SVM) affords quantitative analysis of enantiomeric excess in the mixtures. The plots of actual vs. predicted concentration show high accuracy of prediction for multiple guest concentrations. Two unknown samples (red squares ■) were simultaneously correctly analyzed. 56

Table 2.6. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of (S)- Ibuprofen by Using S1-S4 in Hydrogel Matrix

Jackknifed classification matrix

)-Ibuprofen )-Ibuprofen R S )-Ibuprofen S )-Ibuprofen S )-Ibuprofen S )-Ibuprofen S )-Ibuprofen S )-Ibuprofen S )-Ibuprofen S )-Ibuprofen S )-Ibuprofen 10% ( 20% ( 30% ( 40% ( 60% ( 70% ( 80% ( 90% ( Pure ( Pure ( % correct 10 % (S)-Ibuprofen 8 0 0 0 0 0 0 0 0 0 100 20 % (S)-Ibuprofen 0 8 0 0 0 0 0 0 0 0 100 30 % (S)-Ibuprofen 0 0 8 0 0 0 0 0 0 0 100 40 % (S)-Ibuprofen 0 0 0 8 0 0 0 0 0 0 100 60 % (S)-Ibuprofen 0 0 0 0 8 0 0 0 0 0 100 70 % (S)-Ibuprofen 0 0 0 0 0 8 0 0 0 0 100 80 % (S)-Ibuprofen 0 0 0 0 0 0 8 0 0 0 100 90 % (S)-Ibuprofen 0 0 0 0 0 0 0 8 0 0 100 Pure (R)-Ibuprofen 0 0 0 0 0 0 0 0 8 0 100 Pure (S)-Ibuprofen 0 0 0 0 0 0 0 0 0 8 100 Total 8 8 8 8 8 8 8 8 8 8 100

Canonical Scores Plot

FACT OR(1) FACTOR(2) FAC TOR(3) FACTOR(4) FACTOR(5) FACTOR(1) FACTOR(1)

FACTOR(2) VAR_1$ 10% S-Ibuprofen FACTOR(2)

FACTOR(3) 20% S-Ibuprofen 30% S-Ibuprofen

FACTOR(3) 40% S-Ibuprofen

FACTOR(4) 60% S-Ibuprofen 70% S-Ibuprofen

FACTOR(4) 80% S-Ibuprofen

FACTOR(5) 90% S-Ibuprofen Pure R-Ibuprofen

FACTOR(5) Pure S-Ibuprofen FACT OR(1) FACTOR(2) FAC TOR(3) FACTOR(4) FACTOR(5)

Figure 2.21. The canonical scores plot of Semi-quantitative analysis of (S)-Ibuprofen by using S1-S4 in hydrogel matrix. 57

2.4.8. Semi-quantitative Analysis of Ketoprofen

Table 2.7. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of (S)- Ketoprofen by Using S1-S4 in Hydrogel Matrix.

Jackknifed classification matrix

S )-Ketoprofen S )-Ketoprofen S )-Ketoprofen S )-Ketoprofen S )-Ketoprofen S )-Ketoprofen S )-Ketoprofen S )-Ketoprofen S )-Ketoprofen -Ketoprofen 55% ( 60% ( 65% ( 75% ( 80% ( 85% ( 90% ( 95% ( Pure ( Rac % correct 55% (S)-Ketoprofen 8 0 0 0 0 0 0 0 0 0 100 60% (S)-Ketoprofen 0 8 0 0 0 0 0 0 0 0 100 65% (S)-Ketoprofen 0 0 8 0 0 0 0 0 0 0 100 70% (S)-Ketoprofen 0 0 0 8 0 0 0 0 0 0 100 80% (S)-Ketoprofen 0 0 0 0 8 0 0 0 0 0 100 85% (S)-Ketoprofen 0 0 0 0 0 8 0 0 0 0 100 90% (S)-Ketoprofen 0 0 0 0 0 0 8 0 0 0 100 95% (S)-Ketoprofen 0 0 0 0 0 0 0 8 0 0 100 Pure (S)-Ketoprofen 0 0 0 0 0 0 0 0 8 0 100 Rac-Ketoprofen 0 0 0 0 0 0 0 0 0 8 100 Total 8 8 8 8 8 8 8 8 8 8 100

Figure 2.22. The canonical scores plot of Semi-quantitative analysis of (S)-Ketoprofen by using S1-S4 in hydrogel matrix. 58

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Puccioni, S. Chem. Commun., 2012, 48, 10428.

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CHAPTER III. DETERMINATION OF ENANTIOMERIC EXCESS OF

CARBOXYLATES BY FLUORESCENT MACROCYCLIC SENSORS

3.1. Introduction

Chiral carboxylates are important anions that are involved in a number of natural processes. Amino acids, enzymes, antibodies, and other natural products comprise the carboxylate entities that account for the biochemical behavior of these structures.1 In addition to

their significance in nature, chiral carboxylates are used extensively in the pharmaceutical

industry and as intermediates in natural product synthesis.2 This particular interest in chiral

carboxylates is mainly due to their significant roles in asymmetric synthesis and chiral drug

development processes.3 For example, enantiomeric excess (ee) screening of chiral products is

crucial for developing methodologies for catalytic asymmetric synthesis. A number of studies

have been devoted to establishing procedures for the determination of enantiomeric purity of

chiral products.4 Despite all these efforts, there are still multiple drawbacks that need to be

addressed. The current methods and their weaknesses were discussed in Chapter 1 (see 1.8.

Enantiomeric excess).

In this chapter, we studied optical chemosensors that have attracted great attention due to

their easy implementation and potential application in high-throughput analysis.5 In this regard,

several chiral chemosensors for analytes, including carboxylic acids, alcohols, ketones, and

amines, have been reported.6 Recently, Prof. T. Ema et al. proposed that the macrocyclic ligand,

chirabite-AR, exhibits the ability to form strong complexes with anions (Figure 3.1).7 The

structure of the macrocycle features a chiral naphthalene auxiliary8 and H-bonding sites in the

cavity to form complexes with anions, especially carboxylates.9 The intrinsic cavity of the

macrocycle plays a major role in complex formation while the substituents at the naphthalene 62

serve for tuning the shape and size of the cavity to enhance the enantioselective recognition

performance of the macrocycles.10

Figure 3.1. Structures of macrocyclic chemosensors (S1–S4).

The study in this chapter is collaborated with Prof. T. Ema and he has proposed that

increasing the ability of macrocycles to perform ee analysis requires significant changes in the structure of the chiral cavity.11 Therefore, Prof. T. Ema et al. have designed and synthesized four

macrocycles (S1–S4) by substituting at the 3,3’-positions of the binapthalene moiety of

chirabite-AR to tune the size and shape of the cavity, whereas we have performed the method

optimization and analysis.15 Improved structures of chirabite-AR would enhance chiral

selectivity of the macrocycles by limiting the access of chiral guests to the chiral cavity. In

addition to spatial considerations, we have included several fluorophore structures in a manner that chemosensors would display multivariate responses for the same analyte.

To investigate the performance of the macrocycles, we studied several non-steroidal anti- inflammatory drugs (NSAIDs) commonly prescribed for more than 70 million patients, with 63

more than 30 billion doses consumed from over-the-counter sales in the United States annually.12

NSAIDs inhibit cyclooxygenase enzymes and, consequently, prostaglandin biosynthesis.13

Although the inhibition performances of NSAID enantiomers are considerably different, most are sold as racemic due in part also difficulties with enantiomeric resolution. For example, (S)-

Ibuprofen was found to be 160 times more effective as an inhibitor than (R)-Ibuprofen in in vitro studies.14 Further drawbacks of racemic drugs and their regulations were explained in Chapter I

(see 1.8. Enantiomeric excess).

Here, we report four fluorescent macrocyclic chemosensors (S1–S4) capable of forming

complexes with chiral carboxylates. The macrocycles S1–S4 show differentiated affinities for the

enantiomers of the same compounds and, consequently, distinctive signal fluorescence response.

The enantioselective macrocycles are capable for generating response that allow for qualitative

analysis identification. and accurate determination of enantiomeric composition of the

carboxylates even in the presence of interferents, such as acetate, and pyrophosphate.

3.2. Results and Discussion

The synthetic route of macrocycles is developed by Prof. T. Ema from Okayama

University, Japan and shown in Figure 3.2.15 Whereas S1–S3 were obtained by Sonogashira

cross-coupling reaction of a single precursor, (R)-1 with aryl acetylenes, S4 was obtained by

Suzuki–Miyaura cross-coupling reactions of (R)-1 with aryl boronic acids. Modifications at the

3,3’-positions of the binaphthyl moiety were enabled to tune the size and shape of the chiral

pocket of the macrocycles. 64

Figure 3.2. Synthetic route of macrocyclic chemosensors (S1–S4).

At BGSU, we studied the effect of incorporation of the substituents to the chirabite structure and achieve chiral recognition by changing the size and shape of the cavity of the macrocycles. First, Prof. Ema et al. have explored the structural features of the chiral cavity of the macrocycles by X-ray crystallography. The crystal structure of macrocycle S2, is shown in

Figure 3.3A. Substituted arylethynyl moiety at the 3,3’-positions of the binaphthyl are allocated at 90° toward the lower segment of the binding pocket. For the access of the analyte, the proximity of the arylethynyl moiety to the binding pocket can enhance the chiral selectivity due to spatial limitations. In addition to spatial limitations of chiral cavity, fluorescence of the macrocycles is sensitive to analyte binding due to the vicinity of the arylethynyl moiety, which together with the binapthalene constitutes the fluorophore (binaphthyl) of the macrocycles. In the crystal structure (Figure 3.3A), one of the 4-methoxyphenyl group on the right shows different orientation due to π–π interaction (not shown) of substituents with the adjacent molecule and the one on the left shows an orthogonal direction to naphthalene moiety. While the solid phase 65

studies can provide the absolute structure of the compound, the conformation and binding features of macrocycles in the solution may be different. The asymmetrical crystal structure of

macrocycle S2 was optimized by DFT calculations at the B3LYP/6-31G* level (Figure 3.3B). As

one can see, the symmetric structure of macrocycle S2 shows the similar dihedral angle of the

binaphthyl moiety (−99°) compared with chirabite-AR (−101°).7 1H and 13C NMR studies of

macrocycles also confirm the symmetrical structures of macrocycles S1–S4, with free rotation of

conjugated moieties (arylethynyl or aryl substituents) in solution at room temperature (see

Appendix C).

Figure 3.3. A) X-ray crystal structure of macrocyclic chemosensor (S2) with a water molecule in the cavity. B) DFT-optimized structure of S2 at the B3LYP/6-31G* level. Ø indicates the dihedral angle between the 4-methoxyphenyl group and the naphthyl group.

66

A 1.0 1.0 C 1.0 1.0

0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4

Normalized absorbance 0.2 0.2 Normalized absorbance 0.2 0.2 Normalized fluorescence intensity Normalized fluorescence intensity 0.0 0.0 0.0 0.0 250 300 350 400 450 500 250 300 350 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

B 1.0 1.0 D 1.0 1.0

0.8 0.8 0.8 0.8

0.6 0.6 0.6 0.6

0.4 0.4 0.4 0.4 Normalized absorbance 0.2 0.2 Normalized absorbance 0.2 0.2 Normalized fluorescence intensity Normalized fluorescence intensity fluorescence Normalized 0.0 0.0 0.0 0.0 250 300 350 400 450 500 250 300 350 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

Figure 3.4. UV-VIS absorption and fluorescence spectra of S1 (A), S2 (B), S3 (C), and S4 (D) in propionitrile.

First, we investigated the photophysical properties of macrocycles S1–S4. Absorption and fluorescence spectra of S1–S4 are shown in Figure 3.4. Quantum yields (Φ) of the macrocycles S1–S4 were found as 3.5%, 3.2%, 1.0%, and 0.7%, respectively. Notably, macrocycles S3 and S4 display red-shifted emission spectra and lower quantum yields (< 1%) compared with the other two macrocycles, S1 and S2. This significant change in quantum yields of macrocycles is presumably due to the energy gap law that expects the increase in the non- radiative decay as the fluorescence is red-shifted.16 To carry out fluorescence titration experiments, we performed excitation scan experiments to reveal the radiative absorption bands of the macrocycles (see Appendix C). In this experiments, we found that the absorbance spectra 67

of modified binaphthyl moieties of macrocycles overlap with the absorption spectra of the conjugated lower segment of the macrocycles. As one can see, the emissive absorption bands of the macrocycles S1–S4 appear as a small shoulder on the very right of the absorption spectra and

have absorption maxima at 330 nm, 335 nm, 375 nm, and 330 nm, respectively.

Figure 3.5. ESI-MS spectra of S1+Ibuprofen (A), S2+ Ketoprofen (B), and S3+Mandelate (C) with the calculated spectra of the complexes.

Second, complex formation ability of the macrocycles S1–S4 with carboxylates were

tested using electrospray ionization mass spectrometry (ESI-MS). The presence of the complexes

attests the strong affinity of the macrocycles for the particular analytes. Here, Figure 3.5 shows

the ESI-MS spectra of S1+Ibuprofen (A), S2+Ketoprofen (B), and S3+Mandelate (C) with the

calculated spectra of corresponding complexes (insets). The molecular ions and isotopic patterns

of the complexes agree with the calculated spectra and isotopic patterns of the complexes with a

1:1 (chemosensor: analyte) stoichiometry. 68

Figure 3.6. Digital photographs of S3 (left) and S4 (right) upon addition of enantiomers of ibuprofen and a control (in the middle of both panels) enable a naked-eye observation of the change in the fluorescence induced by chiral carboxylates.

Analyte-induced responses of the macrocyclic chemosensors are used for qualitative and quantitative ee analysis of chiral carboxylates. Differentiated response patterns generated by the analytes play a crucial role in a successful analysis. For example, the fluorescence responses of macrocycles S3 and S4 upon addition of enantiomers of ibuprofen were recorded (Figure 3.6). In this experiment, three vials were prepared. The vials in the middle represent macrocycle solutions without any analytes (control). Vials on the left represent fluorescence intensities of the macrocycle upon addition of (S)-Ibuprofen while ones on the right represent the addition of (R)-

Ibuprofen. As one can see, both macrocycles (S3 and S4) show significantly higher fluorescence enhancement upon addition of (S)-Ibuprofen compared with the (R)-Ibuprofen. This enantioselective behavior of the macrocycles is due to the chiral cavity that enables binding enantiomers of the same compounds with different affinity. The values of enantiomeric fluorescence difference ratios (ef) were calculated (Table 3.1). The ef values of chiral carboxylates suggest that fluorescence intensity induced by the enantiomers of the same compounds are considerably different and may be used in the quantitative ee analysis of chiral carboxylates. One can clearly see that fluorescence responses of chemosensors S3 and S4 69 significantly differ for enantiomers of the same compound. Enantioselective behaviors of the all chemosensors were not identical as well. For example, ef values of ketoprofen for S1 (1.02) and

S4 (6.66) were significantly different from each other (Table 3.1). On the other hand, chirabite-

AR (Figure 3.1) , the unsubstituted macrocyclic sensor, did not show any enantioselectivity and ef values were not significant (see an example of the fluorescent titrations of chirabite-AR in

Appendix C).

Table 3.1. Enantiomeric Fluorescence Difference Ratios (efa)

ef values Guest S1 S2 S3 S4 Ibuprofen 1.15 1.53 1.19* 1.96* Ketoprofen 1.02 1.00 1.21* 6.66* Mandelic acid 1.29 1.39 1.40 1.29 Phenylalanine 1.04* 1.06 1.02 1.80* Phenylpropionic acid 1.25 1.23 1.71* 1.26*

(a) The value of enantiomeric fluorescence ratio is calculated by: ef = (IS-I0)/ (IR-I0) and ef *= 17 (IR-I0)/ (IS-I0). IS, IR, I0 refers to fluorescence intensity of the sensors upon addition of S- enantiomer, R-enantiomer, and sensor only respectively.

In the experimental design, we included four macrocycles (S1–S4) that all show differentiated responses for the enantiomers of chiral carboxylates as well as carboxylates with similar structures. S1 and S2 showed fluorescence quenching upon complex formation with carboxylates. The quenching of the fluorescence is most probably due to the photo-induced electron transfer (PET) mechanism which suggests that upon complex formation, the reduction potential of the amines in the cavity is enhanced, hence the electrons from the receptor quenched the fluorescence of the binaphthyl moiety more efficiently.18 In contrast, macrocycles S3 and S4 70

displayed fluorescence enhancement upon complex formation with carboxylates. The

fluorescence enhancement of macrocycles is presumably due to the formation of rigid complexes

with carboxylates that limit the vibrational and rotational motion of the fluorophore, which

would otherwise have decayed non-radiatively.

Figure 3.7. Structures of the carboxylates used in this study.

To reveal the enantioselectivity and quantify the binding affinity of the macrocycles, we

performed a series of fluorescence titrations of selected carboxylates including enantiomers of

ibuprofen (IBP), ketoprofen (KTP), 2-phenylpropanoate (PPA), mandelate (MA), and phenylalanine (Phe) (Figure 3.7). Acetate and benzoate (BA) were included for reference

purposes (see Appendix C, fluorescence titrations). In these experiments, fluorescence spectra of

macrocycles were recorded upon the incremental addition of analytes. The binding isotherms

were plotted from fluorescence changes as a function of analyte concentration (Figure 3.8). 71

Figure 3.8. Corresponding isotherms of the fluorescence titrations of (A) S1 with PPA, (B) S2 with IBP, (C) S3 with KTP, and (D) S4 with Phe ([S1–S3] = 20 µM; [S4] = 40 µM).

Figure 3.8 shows the fluorescence responses of macrocycles are significantly different for the enantiomers of the same compounds. Association constants (Kas) of the analytes were obtained from the fluorescence titrations and listed in Table 3.1. In most cases, association constants of the enantiomers of the same compounds are slightly different. These differences in association constants results analyte specific fluorescence responses for the enantiomers of the same analytes.

The combination of differentiated responses and association constants of the analytes suggests that macrocyclic sensors are cross-reactive, and form fingerprint-like patterns of the 72

analytes that can be utilized successfully in the qualitative analysis of carboxylates. Notably, the

individual enantiomers of the same compounds show slight differences in their association constants, but the small differences would still be used in a pattern-recognition based qualitative

analysis.

–1 a Table 3.2. Association Constants (Ka, M ) Determined by Fluorescence Titrations.

Guest S1 S2 S3 S4 (R)-IBP 6.7 × 104 1.6 × 104 3.0 × 104 3.4 × 105 (S)-IBP 4.8 × 104 6.2 × 104 3.1 × 104 5.1 × 106 (R)-KTP 1.4 × 105 8.2 × 104 5.7 × 104 NDb (S)-KTP 6.2 × 104 6.2 × 104 4.1 × 104 6.3 × 104 (R)-PPA 3.1 × 104 2.6 × 104 3.9 × 104 4.0 × 106 (S)-PPA 4.5 × 104 2.7 × 104 8.0 × 105 2.0 × 105 (R)-MA 8.4 × 103 1.9 × 104 2.9 × 103 6.8 × 104 (S)-MA 1.4 × 104 2.7 × 104 3.0 × 103 5.7 × 104 (R)-Phe 6.8 × 104 4.6 × 104 2.7 × 105 3.8 × 105 (S)-Phe 7.0 × 104 4.3 × 104 1.6 × 105 3.9 × 105 BA 2.0 × 104 1.2 × 105 4.0 × 104 1.1 × 105 Acetate 2.5 × 104 3.3 × 104 3.5 × 104 1.0 × 105

a Fluorescence titrations were performed in propionitrile at 22°C. All guests were added as tetrabutylammonium salts. Association constants were calculated by the nonlinear least-squares method. The errors of the curve fitting < 15%. b Association constant could not be calculated due to small changes in fluorescence response.

To test this hypothesis, we performed a qualitative analysis of 12 carboxylates. In this experiment, a propionitrile solution of macrocycles S1–S4 was manually dispensed into conventional multi-well plates. Then, tetrabutylammonium salts of the carboxylates were added as propionitrile solutions (1:2 ratios, macrocycle: carboxylate), in 10 repetitions. The resulting chemosensor array was scanned using the multi-well plate reader using excitation at UV for all sensors and emission at 370 nm, 390 nm, 410 nm, and 430 nm for macrocycles of S1–S2, and 73

480 nm, 490 nm, 500 nm, and 520 nm for macrocycles of S3–S4 (for more information about the microplate platforms, see 1.5. Microplate array). Response data were analyzed using a pattern recognition method, linear discriminant analysis (LDA). The fundamentals of LDA were discussed in chapter 1.7.1. Linear Discriminant Analysis. The performance of the LDA were tested using the leave-one-out routine.

Acetate 60 100 % Correct Classification BA (R)-MA (S)-MA

30 (R)-IBP ) (S)-IBP

% (R)-2-PPA 5 .

5 0

( (S)-2-PPA

3

F (R)-KTP (S)-KTP

-30 30 F

2

(R)-Phe (

0 2

0 (S)-Phe

. 5

-60 -30 % Control

-80 ) 0 F1 ( 80 -60 69.2%)

Figure 3.9. Linear discriminant analysis (LDA) plot of twelve analytes results in 100% correct classification using S1–S4 sensor array ([S1–S3] = 20 µM, [S4] = 40 µM, [analyte] = 100 µM).

Figure 3.9 shows the LDA plot of the carboxylate s including the enantiomers of ibuprofen

(IBP), ketoprofen (KTP), 2-phenylpropanoate (PPA), mandelate (MA), and phenylalanine (Phe).

Acetate, benzoate (BA), and water were included for reference purposes. The enantiomers of the same compounds were linked with dashed lines in the LDA plot. The chemosensor arrays based on the four macrocycles (S1–S4) were capable of discriminating among twelve carboxylates with

100% accuracy. Notably, clusters of the enantiomers of the same compounds were separated 74 significantly, which suggested that semi-quantitative ee analysis of chiral carboxylates can also be achieved successfully.

A 60 C 60

40 40 20 20

0

0

F1 (96.2%) F1 -20 -20 (98.2%) F1 -40 -40 100 % Correct Classification 100 % Correct Classification -60 -60 0 102030405060708090100 0 102030405060708090100 % ee value of (R)-IBP % ee value of (S)-KTP

B 100 Calibration data set D 100 Calibration data set -KTP )-IBP Prediction data set ) Prediction data set R S 80 ( 80

60 60

40 40 RMSEC = 0.69 RMSEC = 1.04 20 20 RMSECV = 1.21 RMSECV = 1.31 RMSEP = 1.61 0 RMSEP = 1.43 0 Predicted % ee values of ( of %Predictedvalues ee Predicted % ee values of of values ee % Predicted 0 20406080100 0 20406080100 Actual % ee values of (R)-IBP Actual % ee values of (S)-KTP

Figure 3.10. A) Semi-quantitative analysis of ibuprofen. B) Quantitative analysis of ibuprofen using SVM. C) Semi-quantitative analysis of ketoprofen. D) Quantitative analysis of ketoprofen using SVM. The analysis was achieved by the S1–S4 array ([S1] = [S2] = [S3] = 20 µM, [S4] = 40 µM, [(R) + (S) analyte] = 100 µM).

The changes in the fluorescence of the macrocyclic chromophores provide information- rich responses that is useful in discriminating the carboxylates with similar structures. The enantioselective chiral cavity of the macrocycles enables us performing quantitative ee analysis of chiral carboxylates. After successful qualitative analysis, we were encouraged to elucidate the enantiomeric composition of chiral carboxylates in a semi-quantitative fashion. Thus, we 75

prepared a series of mixtures of chiral samples with varying the enantiomeric compositions of

samples (Pure (R)-enantiomer to Pure (S)-enantiomer). In the course of variation of enantiomeric

composition, we have kept the total concentration of enantiomers constant for all the samples in

the analysis.

Toward this end, we performed semi-quantitative ee analyses of ibuprofen and

ketoprofen using S1–S4 macrocycles in the array. Figure 3.10 (panel A and C) shows the first

canonical factor (> 98%) of semi-quantitative analysis of ibuprofen (Figure 3.10A) and

ketoprofen (Figure 3.10B) as a function of their corresponding ee values. In both cases, 13

different ee values of the samples were discriminated 100% correctly. As one can see, trends in

the canonical factor (F1) of both analyses show direct correlation with the ee values of the samples. This clear trend in the semi-quantitative ee analysis of chiral carboxylates enabled us to

carry out quantitative analyses of samples with unknown ee values using the support vector

machine (SVM) algorithm.19 In this procedure, 11 out of 13 data points were used to construct a

calibration curve (SVM) and remaining data points (2 out of 13) were used for validation. Figure

3.10 (bottom two) shows the quantitative ee analyses of ibuprofen (Figure 3.10B) and ketoprofen

(Figure 3.10D). Both analyses show very accurate results, with RMSEP as low as < 1.61%. The root-mean-square error of calibration (RMSEC), cross-validation (RMSECV), and prediction

(RMSEP) in the graphs attest to the quality of the prediction.

The chemosensor array that can operate in a competitive media is desired in real-world samples. The robustness of the array is an essential property that needs to be investigated. Here, we performed quantitative ee analyses of samples in the presence of potential impurities. These experiments have revealed the outstanding performance of chemosensor arrays based on macrocycles S1–S4 in a competitive media. First, we performed ee analysis of chiral 76 carboxylates in the presence of 5% acetate. In the first set of experiments, all impurity concentrations were kept constant.

A 100 % Correct Classification in B 100 In the presence of 5 % Acetate 40 presence of 5 %Acetate 80 Calibration data set 20 Prediction data set

) 60

%

4 .

1 0 40

(9

1

F 20 -20 Predicted % S-Ibuprofen

0 RMSEP=1.98

-40 0 20406080100 0 102030405060708090100 Actual % S-Ibuprofen % of (S)- Ibuprofen

Figure 3.11. A) Linear discriminant analysis (LDA) of the semi-quantitative assay of enantiomeric composition of ibuprofen in the presence of 5 % acetate by employing the S1–S4 array. B) Quantitative analysis of the enantiomeric composition of ibuprofen in the presence of 5 % acetate using the support vector machine (SVM). The analysis was achieved by the S1–S4 array. Root-mean-square error of prediction (RMSEP) of 1.98% relates to the error with which the ee was determined in this assay.

Figure 3.11A shows the semi-quantitative analysis of ibuprofen in the presence of 5% acetate with absolute accuracy (100% correct classification). The well-resolved clusters of ibuprofen with variable ee values enabled us to construct a calibration model capable of validating the ee in unknown samples. The chemosensor array was able to detect unknown solutions of ibuprofen in the presence of an impurity, with high accuracy (RMSEP = 2.97). In the second part of the experiment, we investigated the ee analysis of ibuprofen with an increasing acetate concentration from 0–10%. The results of the ee analysis of ibuprofen with an increasing concentration of the impurity, acetate, shows accurate results similar to the previous qualitative analysis (Figure 3.12). Semi-quantitative analysis of ibuprofen shows 100% correct classification 77

(Figure 3.12A) and the ee of unknown samples were predicted with a high accuracy (RMSEP =

2.97). In addition to the analysis of ibuprofen in the presence of acetate, the same set of experiments was carried out for pyrophosphate (see Appendix C). Hence, the presence of impurities (carboxylates or phosphates) in the samples do not have a detrimental effect on the quantitative ee analysis of chiral carboxylates and all display low RMSEP values.

A 100 % Correct Classification in B 100 In the presence of 0 to 10 % Acetate presence of 0 to 10 % Acetate 20 80 Calibration data set

Prediction data set )

% 60 6

. 0

3 9

( 40

1 F -20 20 Predicted % S-Ibuprofen % Predicted

0 RMSEP=2.97

0 102030405060708090100 0 20 40 60 80 100 % of (S)- Ibuprofen Actual % S-Ibuprofen

Figure 3.12. A) Linear discriminant analysis (LDA) of semi-quantitative assay of enantiomeric composition of ibuprofen in the presence of increasing composition of acetate from 0–10 % by employing the S1–S4 array. B) Quantitative analysis of enantiomeric composition of ibuprofen in the presence of increasing composition of acetate from 0–10 % using the support vector machine (SVM). The analysis was achieved using the S1–S4 array. Root-mean-square error for prediction (RMSEP) of 2.97% relates to the error of the assay.

To this point, we demonstrated analysis of carboxylates using the chemosensor array based on macrocycles S1–S4. However, among these macrocycles, S4 has exceptional discrimination power. For example, fluorescence enhancement of macrocycle S4 upon addition of (S)-Phenylalanine was significantly different than (R)-Phenylalanine (Figure 3.8D). This remarkable difference in the responses of S4 for the enantiomers of phenylalanine could be sufficient to perform ee analysis of phenylalanine alone. To test this hypothesis, we performed 78 semi-quantitative and quantitative ee analyses of phenylalanine using the macrocycle S4 (Figure

3.13). Here, again, the LDA plot of the semi-quantitative ee analysis of phenylalanine showed a clear trend of the cluster with 100% correct classification (Figure 3.13A). This linear trend was used to construct a calibration model and thee ee of the unknown phenylalanine samples were successfully predicted (Figure 3.13B). (RMSEP = 1.46).

60 A B 100 Calibration data set )-Phe 40 Prediction data set 80 20 60

0

F1 (99.6%) 40 -20 20 RMSEC = 1.30 -40 RMSECV = 1.55 100 % Correct Classification 0 RMSEP = 1.46 -60 Predicted % ee values of ( S of values ee % Predicted 0 102030405060708090100 0 20406080100 % ee value of (S)-Phe Actual % ee values of (S)-Phe

Figure 3.13. A) Linear discriminant analysis (LDA) of the semi-quantitative assay of enantiomeric composition of phenylalanine by employing only the S4 array. B) Quantitative analysis of the enantiomeric composition of phenylalanine using the support vector machine (SVM). The analysis was achieved by only the S4 array. Root-mean-square error of prediction (RMSEP) of 1.46% relates to the error with which the ee was determined in this assay.

3.3. Conclusion

In this chapter, four fluorescent macrocycles S1–S4 (derivatives of chirabite-AR) were reported. The macrocyclic chemosensors utilized a binapthalene modified in the

3,3’-positions with different electron-rich conjugated moieties to form a chiral cavity for selective binding. These macrocyclic chemosensors were found to bind carboxylates using ESI-

MS, X-ray crystallography, and fluorescence titrations with a 1:1 stoichiometry

(macrocycle:analyte). Fluorescence titration experiments of macrocycles revealed the binding 79

affinities of a number of carboxylates and the change in the fluorescence intensity induced by the

analytes. Macrocycles have displayed different fluorescence responses and affinity constants for

chiral carboxylates. Fingerprint-like responses of macrocycles for the individual analytes provided an information-rich data matrix for the qualitative and quantitative analyses of analytes, including enantiomers of mandelic acid (MA), ibuprofen (IBP), ketoprofen (KTP), 2- phenylpropionic acid (PPA), and phenylalanine (Phe). Water, acetate, and benzoate (BA) are also added for control purposes (Figure 3.7). Cross-reactive fluorescent macrocycles were capable of performing qualitative analysis of 13 analytes with 100% correct classification.

Furthermore, semi-quantitative and quantitative analyses of chiral carboxylates were performed employing chemosensor arrays based on four macrocycles (S1–S4). Semi-quantitative analysis was successful and revealed a linear trend in the fluorescence response pattern with the increasing ee values of the samples which enabled performing also a quantitative ee analysis of chiral carboxylates. The performance of the chemosensor array in the presence of the potential impurities was tested. The results showed the arrays were able to operate in the presence of 5% acetate and pyrophosphate. The possible fluctuation of the concentration of the impurities was also tested and there was no impact of the impurity concentrations (acetate or phosphate). At last, the outstanding performance of macrocycle S4 for particular analytes was tested. The chemosensor array based on only macrocycle S4 successfully carried out the semi-quantitative and quantitative ee analyses of phenylalanine.

To sum up, chemosensor arrays based on macrocycles S1–S4 have excellent

discrimination power and can be used for qualitative and quantitative ee analyses for chiral

carboxylates. Cost effective, HTS-amenable optical methods may be able to replace the existing 80

methods for ee analysis of carboxylates and described macrocycles S1–S4 could find a broad

range of applications in the field of asymmetric synthesis and drug development.

3.4. Experimental Section

Mass spectrometry study with complexes was performed using a Shimadzu LCMS-2010A

spectrometer (ESI). Solutions for optical measurements were prepared using freshly distilled

propionitrile. Photophysical experiments were carried out by using optically dilute solutions.

Fluorescence emission spectra were acquired using the Edinburgh single photon counting

spectrofluorimeter (FLSP 920). Fluorescence emission spectra were recorded between 350 nm

and 500 nm for S1 and S2, 400 nm and 600 nm for S3 and S4. The emission from probes was

scanned in 2 nm steps. The dwell time was 0.30 sec. Scans were taken under ambient room

conditions. Guest titrations were performed in propionitrile (Host concentrations: [S1], [S2], [S3]

= 20 μM, [S4] = 40 μM). Sensor solutions were excited at 330 nm for S1, 335 nm for S2, 375 nm

for S3, 330 nm for S4, respectively. Titration isotherms were constructed from changes in the

fluorescence maximum at 372 nm for S1, 380 nm for S2, 492 nm for S3, 470 nm for S4,

respectively. Binding constants were calculated by nonlinear least-square methods using an

equation for 1:1 binding model.20 All the calculations were performed using Origin.21

Quantum yield measurements were acquired using a Hamamatsu absolute quantum yield instrument Quantaurus C11347. The array experiments were performed in 384-well plates. Each experiment was performed in 10 repetitions. Each well received 35 µL sensor-guest solution in propionitrile after 10 min incubation. The plates were measured immediately after pipetting using BMG CLARIOstar. The coefficient of variability (CV) among the data within the class of

12 repetitions was lower than 1%. Thus, obtained data for quantitative and semi-quantitative

analysis were then analyzed using linear discriminant analysis (LDA) without any further 81 pretreatment. A support vector machine (SVM) with preprocessing of LDA and a log decay scaling (scale = 0.1450) was used.

/ 1 1 4 2

1

Ka: Binding constant

[S]t: Concentration of sensor

[G]t: Concentration of the guest

[G]: Unknown concentration of Guest

[SG]: Unknown concentration of complex

[F]: Guest-dependent fluorescence

[F]0: Fluorescence intensity of sensor only

[F]i: Saturated fluorescence intensity of sensor

Equation 3.1. Binding constants were calculated by nonlinear least-square methods using the equation for a 1:1 binding model.

82

1.4.1. Synthesis

Equation 3.2. Route for the synthesis of all the sensors.

(R)-2,2’-Bis[(tert-butoxycarbonyl)methoxy]-3,3’-diiodo-1,1’-binaphthyl ((R)-3). A mixture of

22 (R)-2 (7.19 g, 13.4 mmol), tert-butyl bromoacetate (4.11 mL, 28.0 mmol), and K2CO3 (4.06 g,

29.4 mmol) in acetone (80 mL) was heated at reflux for 13 h under N2. The mixture was filtered

and concentrated. Purification by silica gel column chromatography (hexane/EtOAc (5:1)) gave

28 1 (R)-3 as a white solid (9.37 g, 92%): [α] D –22.0 (c 1.00, CHCl3); H NMR (d6-acetone, 400 83

MHz) δ 1.23 (s, 18H), 4.16 (d, J = 15.4 Hz, 2H), 4.59 (d, J = 15.5 Hz, 2H), 7.01 (dd, J = 1.0, 8.5

Hz, 2H), 7.35 (dt, J = 1.4, 7.7 Hz, 2H), 7.50 (dt, J = 1.2, 7.6 Hz, 2H), 7.97 (d, J = 8.2 Hz, 2H),

13 8.67 (s, 2H); C NMR (d6-acetone, 100 MHz) δ 28.1, 70.9, 81.6, 92.2, 126.3, 126.4, 126.9,

128.1, 128.3, 133.5, 134.5, 141.0, 154.2, 167.2; IR (KBr) 3067, 2978, 2911, 1753, 1564, 1412,

1393, 1368, 1348, 1090, 1070, 1051, 1022, 1001, 889, 881, 851, 802, 748, 613 cm–1; Anal. Calcd

for C32H32I2O6: C, 50.15; H, 4.21. Found: C, 50.25; H, 4.29; HRMS (FAB) calcd for C32H33I2O6

767.0366, found 767.0372 ([M + H]+).

Chiral Macrocycle (R)-1. To a solution of diester (R)-3 (12.0 g, 15.6 mmol) in CH2Cl2 (80 mL) was added CF3CO2H (28 mL), and the mixture was stirred at room temperature for 18 h. The solution was concentrated and dried in vacuo to give the corresponding diacid as a yellow solid.

To a suspension of the diacid (5.11 g, 7.81 mmol) in dry CH2Cl2 (145 mL) were added (COCl)2

(7.3 mL, 86 mmol) and DMF (2 drop). The mixture was stirred at room temperature for 14 h.

The volatiles were removed by rotary evaporation, and the residue was dried in vacuo for 3 h.

The obtained acid chloride was used without further purification. A solution of the acid chloride

(5.40 g, 7.81 mmol) in dry THF (250 mL) and a solution of diamine 423 (3.69 g, 9.37 mmol) and

Et3N (2.2 mL, 16 mmol) in dry THF (250 mL) were added dropwise simultaneously to dry THF

(100 mL) at room temperature with vigorous stirring over 6 h. The mixture was stirred for an

additional 13 h, and the volatiles were removed by rotary evaporation. The solid residue was

dissolved in CH2Cl2, and the solution was washed with brine, dried over Na2SO4, and

concentrated. Purification by silica gel column chromatography (CH2Cl2/THF (30:1)) gave (R)-1

27 1 as a yellow solid (4.13 g, 52%): [α] D +170 (c 1.00, CHCl3); H NMR (d6-acetone, 400 MHz) δ

3.75 (d, J = 15.1 Hz, 2H), 4.40 (d, J = 15.1 Hz, 2H), 7.29 (dd, J = 0.9, 8.5 Hz, 2H), 7.51 (dt, J =

1.3, 7.7 Hz, 2H), 7.63 (dt, J = 1.3, 7.6 Hz, 2H), 7.85 (dd, J = 0.9, 8.0 Hz, 2H), 7.91 (t, J = 8.0 Hz, 84

2H), 8.08 (d, J = 8.2 Hz, 2H), 8.13 (dd, J = 0.9, 7.8 Hz, 2H), 8.77 (s, 2H), 8.83 (s, 2H), 8.98 (d, J

13 = 1.5 Hz, 2H), 9.64 (t, J = 1.4 Hz, 1H), 10.15 (s, 2H); C NMR (CDCl3, 100 MHz) δ 71.7, 90.7,

109.8, 110.3, 125.3, 125.7, 126.9, 127.0, 127.5, 128.3, 128.8, 132.6, 133.1, 135.7, 140.9, 141.4,

148.5, 149.0, 149.9, 151.5, 161.0, 166.0; IR (KBr) 3393, 3059, 2914, 1697, 1585, 1456, 1395,

1342, 1315, 1244, 1221, 1150, 1080, 1051, 1022, 889, 802, 752, 737, 719 cm–1; HRMS (FAB)

+ calcd for C42H28I2N7O8 1012.0089, found 1012.0082 ([M + H] ).

Chiral Macrocycle S1. A mixture of (R)-1 (253 mg, 0.250 mmol), p-tolylacetylene (152 μL,

1.20 mmol), PdCl2(PPh3)2 (8.80 mg, 12.5 μmol), and CuI (2.46 mg, 12.9 μmol) in degassed THF

(5 mL) and Et3N (0.75 mL) was heated at 65 °C for 5 h under N2. The reaction mixture was

filtered, and the volatiles were removed by rotary evaporation. Purification by silica gel column

chromatography (CH2Cl2/THF (100:1)) gave S1 as a yellow solid (179 mg, 73%): mp 271 °C;

28 1 [α] D +187 (c 1.00, CHCl3); H NMR (CDCl3, 400 MHz) δ 2.23 (s, 6H), 4.03 (d, J = 15.4 Hz,

2H), 4.64 (d, J = 15.2 Hz, 2H), 6.87 (d, J = 7.8 Hz, 4H), 7.17 (d, J = 7.6 Hz, 4H), 7.25 (d, J =

10.3 Hz, 2H), 7.40 (t, J = 7.6 Hz, 2H), 7.51 (t, J = 7.5 Hz, 2H), 7.73 (t, J = 8.0 Hz, 2H), 7.88 (d,

J = 7.5 Hz, 2H), 7.90 (d, J = 7.3 Hz, 2H), 8.01 (d, J = 8.0 Hz, 2H), 8.26 (s, 2H), 8.47 (s, 1H),

13 8.70 (s, 2H), 9.07 (s, 2H), 9.11 (s, 2H); C NMR (CDCl3, 100 MHz) δ 21.5, 72.7, 84.2, 95.6,

109.6, 110.4, 116.8, 119.0, 125.3, 125.4, 126.6, 127.2, 127.8, 128.1, 128.4, 129.1, 131.0, 131.3,

133.3, 134.8, 135.9, 139.2, 141.2, 148.89, 148.94, 150.2, 153.3, 160.8, 167.2; IR (KBr) 3391,

3080, 3065, 3028, 2916, 2872, 2210, 1699, 1585, 1539, 1512, 1452, 1423, 1346, 1306, 1244,

1206, 1150, 1086, 1040, 1022, 993, 910, 897, 816, 802, 746, 731, 719 cm–1; HRMS (FAB) calcd

+ for C60H42N7O8 988.3095, found 988.3094 ([M + H] ).

Chiral Macrocycle S2. A mixture of (R)-1 (509 mg, 0.503 mmol), 4-ethynylanisole (0.30 mL,

2.3 mmol), PdCl2(PPh3)2 (17.8 mg, 25.4 μmol), and CuI (4.72 mg, 24.8 μmol) in degassed THF 85

(10 mL) and Et3N (1.5 mL) was heated at 50 °C for 5 h under Ar. The reaction mixture was

filtered, and the volatiles were removed by rotary evaporation. Purification by silica gel column

chromatography (CH2Cl2/THF (30:1)) gave S2 as a yellow solid (471 mg, 92%): mp 274 °C

27 1 (dec); [α] D +153 (c 1.00, CHCl3); H NMR (CDCl3, 400 MHz) δ 3.70 (s, 6H), 4.02 (d, J = 15.4

Hz, 2H), 4.63 (d, J = 15.4 Hz, 2H), 6.58 (d, J = 8.9 Hz, 4H), 7.21 (d, J = 8.9 Hz, 4H), 7.24 (d, J =

9.0 Hz, 2H), 7.40 (dt, J = 1.3, 7.8 Hz, 2H), 7.50 (dt, J = 1.1, 7.5 Hz, 2H), 7.72 (t, J = 8.1 Hz, 2H),

7.87 (d, J = 7.6 Hz, 2H), 7.89 (d, J = 7.9 Hz, 2H), 8.01 (d, J = 7.5 Hz, 2H), 8.23 (s, 2H), 8.61 (s,

13 1H), 8.87 (s, 2H), 9.02 (s, 2H), 9.09 (d, J = 1.3 Hz, 2H); C NMR (CDCl3, 100 MHz) δ 55.3,

72.6, 83.6, 95.6, 109.7, 110.5, 114.0, 114.1, 116.9, 125.3, 125.5, 126.7, 127.3, 128.0, 128.2,

128.4, 131.0, 133.0, 133.2, 134.7, 135.9, 141.4, 148.9, 149.1, 150.1, 153.2, 160.0, 161.1, 167.2;

IR (KBr) 3391, 3086, 3057, 2955, 2916, 2835, 2207, 1697, 1605, 1585, 1539, 1510, 1452, 1425,

1346, 1306, 1248, 1207, 1173, 1150, 1105, 1086, 1024, 993, 953, 893, 831, 802, 748, 729, 719,

–1 + 692 cm ; HRMS (FAB) calcd for C60H42N7O10 1020.2993, found 1020.2972 ([M + H] ).

Chiral Macrocycle S3. A mixture of (R)-1 (253 mg, 0.250 mmol), 4-ethynyl-N,N- dimethylaniline (175 mg, 1.20 mmol), PdCl2(PPh3)2 (8.81 mg, 12.5 μmol), and CuI (2.39 mg,

12.5 μmol) in degassed THF (5 mL) and Et3N (0.75 mL) was heated at 65 °C for 5 h under N2.

The reaction mixture was filtered, and the volatiles were removed by rotary evaporation.

Purification by silica gel column chromatography (CH2Cl2/THF (50:1)) gave S3 as a brown solid

27 1 (189 mg, 72%): mp 221–224 °C (dec); [α] D +54.2 (c 1.02, CHCl3); H NMR (CDCl3, 400

MHz) δ 2.86 (s, 12H), 4.00 (d, J = 15.4 Hz, 2H), 4.66 (d, J = 15.4 Hz, 2H), 6.36 (d, J = 8.5 Hz,

4H), 7.14 (d, J = 8.8 Hz, 4H), 7.23 (d, J = 8.4 Hz, 2H), 7.37 (t, J = 7.6 Hz, 2H), 7.49 (t, J = 7.5

Hz, 2H), 7.73 (t, J = 8.1 Hz, 2H), 7.88 (d, J = 7.6 Hz, 2H), 7.90 (d, J = 8.0 Hz, 2H), 8.02 (d, J =

13 8.0 Hz, 2H), 8.20 (s, 2H), 8.59 (s, 1H), 8.95 (s, 2H), 9.06 (s, 2H), 9.11 (s, 2H); C NMR (CDCl3, 86

100 MHz) δ 40.0, 72.5, 83.1, 97.2, 108.5, 109.6, 110.4, 111.6, 117.5, 125.2, 125.5, 126.4, 127.2,

127.7, 128.1, 128.3, 131.1, 132.7, 132.9, 134.3, 136.1, 141.1, 149.0, 149.1, 150.16, 150.23,

153.1, 161.1, 167.3; IR (KBr) 3385, 3090, 3057, 2913, 2884, 2860, 2803, 2197, 1697, 1653,

1609, 1585, 1558, 1522, 1452, 1425, 1362, 1348, 1314, 1244, 1219, 1190, 1165, 1150, 1084,

–1 1040, 1022, 993, 945, 912, 893, 816, 802, 746, 719 cm ; HRMS (FAB) calcd for C62H48N9O8

1046.3626, found 1046.3628 ([M + H]+).

Chiral Macrocycle S4. A mixture of (R)-1 (102 mg, 0.101 mmol), 4-

(dimethylamino)phenylboronic acid (52.7 mg, 0.303 mmol), K3PO4 (65.6 mg, 0.309 mmol), and

Pd(PPh3)4 (11.4 mg, 9.86 μmol) in degassed THF/H2O (3:1) (1.2 mL) was heated at 65 °C for 6 h

under Ar. The volatiles were removed by rotary evaporation, and the solid residue was dissolved in CH2Cl2. The solution was washed with brine, dried over Na2SO4, and concentrated.

Purification by silica gel column chromatography (CH2Cl2/THF (30:1)) gave S4 as a dark orange

23 1 solid (95.9 mg, 95%): [α] D –81.6 (c 1.01, CHCl3); H NMR (CDCl3, 400 MHz) δ 2.72 (s, 12H),

3.72 (d, J = 15.4 Hz, 2H), 3.86 (d, J = 15.4 Hz, 2H), 6.69 (s, 4H), 7.21 (d, J = 8.5 Hz, 2H), 7.33

(dt, J = 1.1, 7.7 Hz, 2H), 7.46–7.52 (m, 6H), 7.74 (d, J = 4.2 Hz, 4H), 7.92 (d, J = 8.2 Hz, 2H),

7.95 (s, 2H), 8.06 (t, J = 4.4 Hz, 2H), 8.49 (s, 2H), 8.90 (s, 1H), 9.17 (d, J = 1.1 Hz, 2H), 9.26 (s,

13 2H); C NMR (CDCl3, 100 MHz) δ 40.3, 71.6, 109.6, 110.4, 112.5, 125.4, 125.6, 125.9, 126.8,

127.2, 128.2, 128.8, 130.0, 130.8, 131.4, 132.7, 134.7, 136.0, 141.3, 148.7, 149.0, 149.8, 150.0,

152.1, 161.3, 166.7; IR (KBr) 3375, 3086, 2911, 1701, 1611, 1585, 1526, 1452, 1418, 1346,

1314, 1246, 1223, 1200, 1150, 1080, 1053, 1022, 820, 802, 750, 735, 719 cm–1; Anal. Calcd for

C58H47N9O8: C, 69.80; H, 4.75; N, 12.63. Found: C, 69.55; H, 4.77; N, 12.33; HRMS (FAB)

+ calcd for C58H48N9O8 998.3626, found 998.3600 ([M + H] ).

87

3.4.1. Qualitative Linear Discriminant Analysis

Table 3.3. The Jackknifed Classification Matrix of Qualitative Assay Using S1-S4.

Jackknifed Classification Matrix

Acetate R-Ketoprofen D-PhenylAlanine L-PhenylAlanine Control Benzoate S-Mandelate R-Mandelate S-Ibuprofen R-Ibuprofen S-PhenylPropanoate Propanoate R-Phenyl S-Ketoprofen %correct

Acetate 12 0 0 0 0 0 0 0 0 0 0 0 0 100 R-Ketoprofen 0 12 0 0 0 0 0 0 0 0 0 0 0 100 D-Phenyl Alanine 0 0 12 0 0 0 0 0 0 0 0 0 0 100 L-Phenyl Alanine 0 0 0 12 0 0 0 0 0 0 0 0 0 100 Control 0 0 0 0 12 0 0 0 0 0 0 0 0 100 Benzoate 0 0 0 0 0 12 0 0 0 0 0 0 0 100 S-Mandelate 0 0 0 0 0 0 12 0 0 0 0 0 0 100 R-Mandelate 0 0 0 0 0 0 0 12 0 0 0 0 0 100 S-Ibuprofen 0 0 0 0 0 0 0 0 12 0 0 0 0 100 R-Ibuprofen 0 0 0 0 0 0 0 0 0 12 0 0 0 100 S-Phenyl Propanoate 0 0 0 0 0 0 0 0 0 0 12 0 0 100 R-Phenyl Propanoate 0 0 0 0 0 0 0 0 0 0 0 12 0 100 S-Ketoprofen 0 0 0 0 0 0 0 0 0 0 0 0 12 100 Total 12 12 12 12 12 12 12 12 12 12 12 12 12 100

Figure 3.14. The canonical scores plot of qualitative assay. 88

3.4.2. Semi-quantitative Analysis of Phenylalanine

Table 3.4. The Jackknifed Classification Matrix of Semi-Qualitative Assay of

Phenylalanine. A %D-Phenyl10 A %D-Phenyl20 A %D-Phenyl30 A 4 A %D-Phenyl5 A %D-Phenyl50 A %D-Phenyl60 0 %D-Phenyl0 lanine lanine lanine lanine lanine lanine lanine Alanine %70 D-Phenyl Alanine %80 D-Phenyl Alanine %90 D-Phenyl Alanine %95 D-Phenyl Alanine D-PhenylPure Alanine L-PhenylPure %correct

10 % D-Phenyl Alanine 12 0 0 0 0 0 0 0 0 0 0 0 0 100 20 % D-Phenyl Alanine 0 12 0 0 0 0 0 0 0 0 0 0 0 100 30 % D-Phenyl Alanine 0 0 12 0 0 0 0 0 0 0 0 0 0 100 40 % D-Phenyl Alanine 0 0 0 12 0 0 0 0 0 0 0 0 0 100 5 % D-Phenyl Alanine 0 0 0 0 12 0 0 0 0 0 0 0 0 100 50 % D-Phenyl Alanine 0 0 0 0 0 12 0 0 0 0 0 0 0 100 60 % D-Phenyl Alanine 0 0 0 0 0 0 12 0 0 0 0 0 0 100 70 % D-Phenyl Alanine 0 0 0 0 0 0 0 12 0 0 0 0 0 100 80 % D-Phenyl Alanine 0 0 0 0 0 0 0 0 12 0 0 0 0 100 90 % D-Phenyl Alanine 0 0 0 0 0 0 0 0 0 12 0 0 0 100 95 % D-Phenyl Alanine 0 0 0 0 0 0 0 0 0 0 12 0 0 100 Pure D-Phenyl Alanine 0 0 0 0 0 0 0 0 0 0 0 12 0 100 Pure L-Phenyl Alanine 0 0 0 0 0 0 0 0 0 0 0 0 12 100 Total 12 12 12 12 12 12 12 12 12 12 12 12 12 100

Figure 3.15. The canonical scores plot of qualitative assay of phenylalanine.

89

3.4.3. Semi-quantitative Analysis of Ibuprofen

Table 3.5. The Jackknifed Classification Matrix of Semi-Qualitative Assay of Ibuprofen Using S1-S4.

Jackknifed Classification Matrix Jackknifed Classification Matrix (contd...) 10% 10% S-Ibuprofen 20% S-Ibuprofen 30% S-Ibuprofen 40% S-Ibuprofen S-Ibuprofen 5% 50% S-Ibuprofen 60% S-Ibuprofen 70% S-Ibuprofen 80% S-Ibuprofen 90% S-Ibuprofen 95% S-Ibuprofen R-Ibuprofen Pure S-Ibuprofen Pure %correct

10% S-Ibuprofen 12 0 0 0 0 0 10% S-Ibuprofen 0 0 0 0 0 0 0 100 20% S-Ibuprofen 0 12 0 0 0 0 20% S-Ibuprofen 0 0 0 0 0 0 0 100 30% S-Ibuprofen 0 0 12 0 0 0 30% S-Ibuprofen 0 0 0 0 0 0 0 100 40% S-Ibuprofen 0 0 0 12 0 0 40% S-Ibuprofen 0 0 0 0 0 0 0 100 5% S-Ibuprofen 0 0 0 0 12 0 5% S-Ibuprofen 0 0 0 0 0 0 0 100 50% S-Ibuprofen 0 0 0 0 0 12 50% S-Ibuprofen 0 0 0 0 0 0 0 100 60% S-Ibuprofen 0 0 0 0 0 0 60% S-Ibuprofen 12 0 0 0 0 0 0 100 70% S-Ibuprofen 0 0 0 0 0 0 70% S-Ibuprofen 0 12 0 0 0 0 0 100 80% S-Ibuprofen 0 0 0 0 0 0 80% S-Ibuprofen 0 0 12 0 0 0 0 100 90% S-Ibuprofen 0 0 0 0 0 0 90% S-Ibuprofen 0 0 0 12 0 0 0 100 95% S-Ibuprofen 0 0 0 0 0 0 95% S-Ibuprofen 0 0 0 0 12 0 0 100 Pure R-Ibuprofen 0 0 0 0 0 0 Pure R-Ibuprofen 0 0 0 0 0 12 0 100 Pure S-Ibuprofen 0 0 0 0 0 0 Pure S-Ibuprofen 0 0 0 0 0 0 12 100 Total 12 12 12 12 12 12 Total 12 12 12 12 12 12 12 100

Canonical Scores Plot

FACTOR(1) FACT OR(2) FACTOR(3) FACTOR(4) FACT OR(5) FACTOR(1) VAR$ 10% S-Ibuprofen FACTOR(1) 20% S-Ibuprofen FACTOR(2) 30% S-Ibuprofen 40% S-Ibuprofen FACTOR(2)

FACTOR(3) 5% S-Ibuprofen 50% S-Ibuprofen

FACTOR(3) 60% S-Ibuprofen

FACTOR(4) 70% S-Ibuprofen 80% S-Ibuprofen

FACTOR(4) 90% S-Ibuprofen

FACTOR(5) 95% S-Ibuprofen Pure R-Ibuprofen

FACTOR(5) Pure S-Ibuprofen FACTOR(1) FACT OR(2) FACTOR(3) FACTOR(4) FACT OR(5)

Figure 3.16. The canonical scores plot of semi-quantitative assay of ibuprofen.

90

3.4.4. Semi-quantitative Analysis of Ibuprofen with 5% Acetate

Table 3.6. The Jackknifed Classification Matrix of Semi-Qualitative Assay in The Presence of 5 % Acetate Using S1-S4.

Jackknifed Classification Matrix

S-Ibuprofen 10 % S-Ibuprofen 20 % S-Ibuprofen 30 % S-Ibuprofen 40 % Ibuprofen 5 % S- S-Ibuprofen 50 % S-Ibuprofen 60 % S-Ibuprofen 70 % S-Ibuprofen 80 % S-Ibuprofen 90 % S-Ibuprofen 95 % R-Ibuprofen Pure S-ibuprofen Pure %correct

10 % S-Ibuprofen 12 0 0 0 0 0 0 0 0 0 0 0 0 100 20 % S-Ibuprofen 0 12 0 0 0 0 0 0 0 0 0 0 0 100 30 % S-Ibuprofen 0 0 12 0 0 0 0 0 0 0 0 0 0 100 40 % S-Ibuprofen 0 0 0 12 0 0 0 0 0 0 0 0 0 100 5 % S-Ibuprofen 0 0 0 0 12 0 0 0 0 0 0 0 0 100 50 % S-Ibuprofen 0 0 0 0 0 12 0 0 0 0 0 0 0 100 60 % S-Ibuprofen 0 0 0 0 0 0 12 0 0 0 0 0 0 100 70 % S-Ibuprofen 0 0 0 0 0 0 0 12 0 0 0 0 0 100 80 % S-Ibuprofen 0 0 0 0 0 0 0 0 12 0 0 0 0 100 90 % S-Ibuprofen 0 0 0 0 0 0 0 0 0 12 0 0 0 100 95 % S-Ibuprofen 0 0 0 0 0 0 0 0 0 0 12 0 0 100 Pure R-Ibuprofen 0 0 0 0 0 0 0 0 0 0 0 12 0 100 Pure S-ibuprofen 0 0 0 0 0 0 0 0 0 0 0 0 12 100 Total 12 12 12 12 12 12 12 12 12 12 12 12 12 100

Figure 3.17. The canonical scores plot of semi-quantitative assay of ibuprofen in the presence of 5 % acetate.

91

3.4.5. Semi-quantitative Analysis of Ibuprofen with 5% Pyrophosphate

Table 3.7. The Jackknifed Classification Matrix of Semi-Qualitative Assay in The Presence of 5 % Pyrophosphate Using S1-S4.

Jackknifed Classification Matrix

10 % 10 S-Ibuprofen % 20 S-Ibuprofen % 30 S-Ibuprofen % 40 S-Ibuprofen S- % 5 Ib profen % 50 S-Ibuprofen % 60 S-Ibuprofen % 70 S-Ibuprofen % 80 S-Ibuprofen % 90 S-Ibuprofen % 95 S-Ibuprofen Pure R-Ibuprofen Pure S-ibuprofen %correct

10 % S-Ibuprofen 12 0 0 0 0 0 0 0 0 0 0 0 0 100 20 % S-Ibuprofen 0 12 0 0 0 0 0 0 0 0 0 0 0 100 30 % S-Ibuprofen 0 0 12 0 0 0 0 0 0 0 0 0 0 100 40 % S-Ibuprofen 0 0 0 12 0 0 0 0 0 0 0 0 0 100 5 % S-Ibuprofen 0 0 0 0 12 0 0 0 0 0 0 0 0 100 50 % S-Ibuprofen 0 0 0 0 0 12 0 0 0 0 0 0 0 100 60 % S-Ibuprofen 0 0 0 0 0 0 12 0 0 0 0 0 0 100 70 % S-Ibuprofen 0 0 0 0 0 0 0 12 0 0 0 0 0 100 80 % S-Ibuprofen 0 0 0 0 0 0 0 0 12 0 0 0 0 100 90 % S-Ibuprofen 0 0 0 0 0 0 0 0 0 12 0 0 0 100 95 % S-Ibuprofen 0 0 0 0 0 0 0 0 0 0 12 0 0 100 Pure R-Ibuprofen 0 0 0 0 0 0 0 0 0 0 0 12 0 100 Pure S-ibuprofen 0 0 0 0 0 0 0 0 0 0 0 0 12 100 Total 12 12 12 12 12 12 12 12 12 12 12 12 12 100

Figure 3.18. The canonical scores plot of semi-quantitative assay of ibuprofen in the presence of 5 % pyrophosphate.

92

3.4.6. Semi-quantitative Analysis of Ibuprofen with 0 to 10 % Acetate

Table 3.8. The Jackknifed Classification Matrix of Semi-Qualitative Assay in The Presence of 0 To 10 % Increasing Concentration of Acetate Using S1-S4.

Jackknifed Classification Matrix

%correct 10 % 10 S-Ibuprofen % 20 S-Ibuprofen % 30 S-Ibuprofen % 40 S-Ibuprofen S- % 5 Ib% 50 S-Ibuprofen f % 60 S-Ibuprofen % 70 S-Ibuprofen % 80 S-Ibuprofen % 90 S-Ibuprofen % 95 S-Ibuprofen Pure R-Ibuprofen Pure S-ibuprofen

10 % S-Ibuprofen 12 0 0 0 0 0 0 0 0 0 0 0 0 100 20 % S-Ibuprofen 0 12 0 0 0 0 0 0 0 0 0 0 0 100 30 % S-Ibuprofen 0 0 12 0 0 0 0 0 0 0 0 0 0 100 40 % S-Ibuprofen 0 0 0 11 0 0 0 0 0 0 0 0 0 100 5 % S-Ibuprofen 0 0 0 0 12 0 0 0 0 0 0 0 0 100 50 % S-Ibuprofen 0 0 0 0 0 12 0 0 0 0 0 0 0 100 60 % S-Ibuprofen 0 0 0 0 0 0 12 0 0 0 0 0 0 100 70 % S-Ibuprofen 0 0 0 0 0 0 0 12 0 0 0 0 0 100 80 % S-Ibuprofen 0 0 0 0 0 0 0 0 12 0 0 0 0 100 90 % S-Ibuprofen 0 0 0 0 0 0 0 0 0 12 0 0 0 100 95 % S-Ibuprofen 0 0 0 0 0 0 0 0 0 0 12 0 0 100 Pure R-Ibuprofen 0 0 0 0 0 0 0 0 0 0 0 12 0 100 Pure S-ibuprofen 0 0 0 0 0 0 0 0 0 0 0 0 12 100 Total 12 12 12 11 12 12 12 12 12 12 12 12 12 100

Figure 3.19. The canonical scores plot of semi-quantitative assay of ibuprofen in the presence of 0 to 10 % increasing concentration of acetate.

93

3.4.7. Semi-quantitative Analysis of Ibuprofen with 0 to 10 % Acetate

Table 3.9. The Jackknifed Classification Matrix of Semi-Qualitative Assay in The Presence of 0 To 10 % Increasing Concentration of Pyrophosphate Using S1-S4.

Jackknifed Classification Matrix 10 % 20 % 30 % 40 % 5 % S- 50 % 60 % 70 % 80 % 90 % 95 % Pure Pure %corre S- S- S- S- Ibuprofen S- S- S- S- S- S- R- S- ct Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen ibuprofen 10 % S-Ibuprofen 12 0 0 0 0 0 0 0 0 0 0 0 0 100 20 % S-Ibuprofen 0 12 0 0 0 0 0 0 0 0 0 0 0 100 30 % S-Ibuprofen 0 0 12 0 0 0 0 0 0 0 0 0 0 100 40 % S-Ibuprofen 0 0 0 12 0 0 0 0 0 0 0 0 0 100 5 % S-Ibuprofen 0 0 0 0 12 0 0 0 0 0 0 0 0 100 50 % S-Ibuprofen 0 0 0 0 0 12 0 0 0 0 0 0 0 100 60 % S-Ibuprofen 0 0 0 0 0 0 12 0 0 0 0 0 0 100 70 % S-Ibuprofen 0 0 0 0 0 0 0 12 0 0 0 0 0 100 80 % S-Ibuprofen 0 0 0 0 0 0 0 0 12 0 0 0 0 100 90 % S-Ibuprofen 0 0 0 0 0 0 0 0 0 12 0 0 0 100 95 % S-Ibuprofen 0 0 0 0 0 0 0 0 0 0 12 0 0 100 Pure R-Ibuprofen 0 0 0 0 0 0 0 0 0 0 0 12 0 100 Pure S-ibuprofen 0 0 0 0 0 0 0 0 0 0 0 0 12 100 Total 12 12 12 12 12 12 12 12 12 12 12 12 12 100

Figure 3.20. The canonical scores plot of semi-quantitative assay of ibuprofen in the presence of 0 to 10 % increasing concentration of pyrophosphate.

94

3.4.8. Semi-quantitative Analysis of Ketoprofen

Table 3.10. The Jackknifed Classification Matrix of Semi-Qualitative Assay of Ketoprofen Using S1-S4. Jackknifed Classification Matrix S-Ketoprofen 10 % S-Ketoprofen 20 % S-Ketoprofen 30 % S-Ketoprofen 40 % S-Ketoprofen 5 % S-Ketoprofen 50 % S-Ketoprofen 60 % S-Ketoprofen 70 % S-Ketoprofen 80 % S-Ketoprofen 90 % S-Ketoprofen 95 % R-Ketoprofen Pure S-Ketoprofen Pure %correct

10 % S-Ketoprofen 12 0 0 0 0 0 0 0 0 0 0 0 0 100 20 % S-Ketoprofen 0 12 0 0 0 0 0 0 0 0 0 0 0 100 30 % S-Ketoprofen 0 0 12 0 0 0 0 0 0 0 0 0 0 100 40 % S-Ketoprofen 0 0 0 12 0 0 0 0 0 0 0 0 0 100 5 % S-Ketoprofen 0 0 0 0 12 0 0 0 0 0 0 0 0 100 50 % S-Ketoprofen 0 0 0 0 0 12 0 0 0 0 0 0 0 100 60 % S-Ketoprofen 0 0 0 0 0 0 12 0 0 0 0 0 0 100 70 % S-Ketoprofen 0 0 0 0 0 0 0 12 0 0 0 0 0 100 80 % S-Ketoprofen 0 0 0 0 0 0 0 0 12 0 0 0 0 100 90 % S-Ketoprofen 0 0 0 0 0 0 0 0 0 12 0 0 0 100 95 % S-Ketoprofen 0 0 0 0 0 0 0 0 0 0 12 0 0 100 Pure R-Ketoprofen 0 0 0 0 0 0 0 0 0 0 0 12 0 100 Pure S-Ketoprofen 0 0 0 0 0 0 0 0 0 0 0 0 12 100 Total 12 12 12 12 12 12 12 12 12 12 12 12 12 100

Figure 3.21. The canonical scores plot of semi-quantitative assay of ketoprofen.

95

3.5. References

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99

CHAPTER IV. TRI-SERINE TRI-LACTONE SCAFFOLD FOR QUANTIFICATION OF

CITRATE IN URINE

4.1. Introduction

Citrate is an organic tri-carboxylate anion that plays a crucial role in the Krebs cycle as an important intermediate and is an essential constituent of many biological processes, such as fatty acid synthesis, photorespiration, nitrogen metabolism, and glyoxylate cycle.1 In addition, citrate is a strong chelating agent that supports heavy-metal elimination in bacteria and is used as

an anticoagulant (acts by chelating calcium anions) as an alternative to heparin in intensive care

unit practice.2 Moreover, citrate is consumed extensively as an additive in the food, beverage and

pharmaceutical industries.3

Detection and monitoring of carboxylates are critical in clinical, biochemical, and

environmental chemistry.4 Particularly in clinical chemistry, a low level of urinary citrate,

excretion has been linked to various diseases, such as urolithiasis, pathological dysfunction, and

glycogenosis.5 Moreover, significant changes in citrate levels in malignant prostate cancer

tissues are strongly correlated to the stages of cancer.6 Hence, simple methods for quantification

of citrate in biological fluids, such as urine, are highly desired for preventive and diagnostic

purposes. Currently, analytical detection of urinary citrate is performed using enzymatic arrays7,

HPLC8, ion chromatography9, or colorimetry10. NMR spectroscopy was also used for detection of citrate; however, the low sensitivity of NMR limits its application in real-world samples.

Nevertheless, most of all these methods require trained personnel, sample pre-treatment, and

expensive instrumentation in some cases. Simple and cost-effective optical methods for the

detection of citrate are coming to the fore due to their easy implementation in high-throughput

screening.11 There are several optical sensors reported for citrate quantification12; however, 100 direct turn-on sensors that can be used in a complex biological milieu, such as urine, have not been reported up to now.

Figure 4.1. Structure of enterobactin produced by Escherichia coli.

In this chapter, we study the recognition of citrate in urine due to its diagnostic value.

Citrate, a tri-carboxylate, is a highly charged hydrophilic moiety at neutral pH. The unique structure of citrate suggests that the binding pocket of the sensor should reflects the size and shape of the citrate. The binding pocket should display complementary symmetry and multiple hydrogen binding sites to achieve strong and selective binding in an aqueous environment. Here, we have introduced two citrate sensors by mimicking the natural product enterobactin, which is produced by Escherichia coli, to bind Fe3+ (Figure 4.1). The Fe3+ ion predominately exists in the form of Fe(OH)3, which is not soluble in an aerobic environment. To overcome this challenge in the bioavailability of Fe3+ ion, enterobactin is secreted by the bacteria into extracellular environment to form a complex with ferric ions that can then diffuse into the bacterial periplasm.13 Notably, enterobactin comprises a tri-serine tri-lactone scaffold to pre-organize the 101

catecholate groups, thereby achieving the highest affinity toward ferric ion known so far (Kaffinity

= 1049 M-1).

4.2. Results and Discussion

Here, we report two tripodal fluorescent sensors comprising the tri-serine tri-lactone

scaffold that pre-organizes the binding sites of the sensors to form complexes with carboxylates, especially citrate. In the sensor design, we have included N-H fragments (thiourea in S1,

sulfonamide in S2) to establish directional hydrogen bond interactions with the carboxylates. The pre-organized structure of tri-serine tri-lactone scaffold satisfies the geometry of the binding site to bind citrate while different types of N-H fragments in the sensors will result in strong complexation of citrate.

Figure 4.2. Structures of the sensors (S1-S2) based on tri-serine tri-lactone scaffold.

Both sensors comprise the dimethylamino naphthyl moieties as reporters, which are

covalently connected to the binding site of the receptor. The synthesis was carried out using

standard laboratory techniques and the synthetic route to the sensors (S1 and S2) is shown in

Scheme 1. 14 The synthesis of sensors includes trimerization of the N-trityl-L-serine methyl ester 102

into trityl-protected tri-serine tri-lactone. Subsequently, the deprotection of amines using ethanolic HCl yielded ammonium salts of tri-serine tri-lactone trimer. Coupling of quaternary

ammonium salts of tri-serine tri-lactone amines with 4-dimethylamino-1-naphthyl isothiocyanate

and 5- dimethylamino naphthalene-1-sulfonyl chloride generated S1 and S2, respectively.

Gutierrez et al. reported that carbamate tri-serine tri-lactone scaffold favors enterobactin of the

pseudoaxial conformation in aqueous and organic solvents.15 Likewise, the 1H NMR studies of

S1 and S2 suggest that the tri-serine tri-lactone scaffold of the sensors also favors the pre- organized pseudoaxial conformation with C3 symmetry. For more details about the synthesis and characterization, see the following experimental section.

Figure 4.3. Synthetic route of the sensors (S1 and S2).

The sensors based on tri-serine tri-lactone scaffold presumably have the ideal orientation

of the hydrogen bond donor moieties for the citrate and fluorescence of the aromatic 103

dimethylamine naphthyl moieties is presumed to change upon complex formation with citrate.

To test this hypothesis, we first tested the complex formation ability of the sensors (S1-S2) with

citrate using electrospray ionization mass spectrometry (ESI-MS). Figure 4.4 shows that both

sensors form strong complexes with citrate with a 1:1 stoichiometry. The binding stoichiometry

of the sensor–citrate (1:1) complexes was also confirmed by Job’s plot experiments. In addition

to citrate, we have performed Job’s plot experiments for a mono-carboxylate anion (acetate),

which displayed a binding stoichiometry of 1:3 ratio (sensor:acetate) (see experimental section).

These findings suggest that three acetate anions can fit in the cavity of the sensors, whereas only

one citrate anion binds to all the hydrofen bond donors of the sensors with a perfect match.

Figure 4.4. (A) ESI mass spectrum of the complex of [S1+Citrate+Na+MeCN]-. Inset: Calculated isotope pattern for [S1+Citrate+Na+MeCN]- (B) ESI mass spectrum of the complex of S2+Citrate+TBA. Inset: Calculated isotope pattern for S2+Citrate+TBA.

104

Figure 4.5. 1H NMR (500 MHz) titration of S1 (top) and S2 (bottom) upon the addition of citrate as tri-tetrabutylammonium salts. [Sensors] = 2 mM. Titrations were performed in deuterated acetonitrile solution at room temperature. 105

Furthermore, we have investigated the contribution of the binding site to complex formation by NMR spectroscopy. In these experiments, we prepared solutions of the sensors and titrated with incremental amounts of citrate-TBA in deuterated acetonitrile solutions (Figure 4.5).

In essence, the thiourea protons of S1 showed that the first set of thiourea NH protons ( ) shifted from 6.33 ppm to 9.55 ppm and the second set of thiourea NHs ( ) is shifted from 8.36 ppm to

10.89 ppm. These concerted downfield shifts suggest hydrogen bonding with the citrate. At the same time, the signal of the aromatic protons corresponding to the diamino methylnaphthalene moieties become more complicated, presumably due to the fact that the relatively tight complex cannot accommodate well all three diamino methylnaphthalene moieties in a symmetrical fashion. The case of S2 is different due to the higher acidity of the sulfonamide NH ( )

(resonance at 6.34 ppm). This suggests a rapid change over several different conformations in the sensor upon binding the citrate or deprotonation of the sulfonamide moiety. We believed that this might account for the different response in the fluorescence titrations. However, in the polymer chip assay, the citrate was added as a sodium salt in water (in 25 mM HEPES buffer pH 7.4).

Thus, at least in HEPES, we discounted the deprotonation hypothesis. Needless to say, the differential fluorescence signals were found the same in acetonitrile and water (HEPES).

So far, we have demonstrated the complex formation ability of the sensors with carboxylates. In this study, we utilized the fluorescence changes of the sensors upon complexation with carboxylates to perform qualitative and quantitative analyses. To investigate the magnitudes of the change in the color and fluorescence intensity of the sensors upon complex formation, we performed series of colorimetric and fluorescence titrations with carboxylates, including mono, di, and tri-carboxylate anions with similar structures. In the UV-Vis absorbance titrations, S1 showed red-shifting upon complexation with carboxylates; conversely, S2 resulted 106

in blue-shifting, with small changes depending on the analytes in both sensors. (For the UV-Vis

titrations, see Appendix D.) Fluorescence titrations of sensors were carried out with an excitation

wavelength of isosbestic points in the absorption spectra of the sensors. For example, Figure 4.6

shows the fluorescence titrations of sensors S1 (left) and S2 (right) with citrate, with the

corresponding isotherms in the insets.

5 1.0 1.0 1.2 ) 0 ) -I 0 i -I i /(I ) /(I 0 4 ) 0.5 0.5 0 (I-I (I-I

0.0 0.0 0.8 3 0 102030 0 50 100 150 I/I

o  Citrate ( M) Citrate (M) o I/I

2 0.4 1

0 0.0 350 400 450 500 550 600 400 450 500 550 600 650 Wavelength (nm) Wavelength (nm)

Figure 4.6. Left: Fluorescence titration of S1 shows fluorescence enhancement up to three times upon addition of citrate. Right: Fluorescence titrations of S2 shows complete quenching upon addition of citrate. Insets: Corresponding isotherms. [S1] = [S2] = 20 µM. Titrations were performed in acetonitrile and citrate were added as tris-tetrabutylammonium salts.

As one can see, the fluorescence intensity of S1 enhanced up to three times, whereas S2

goes to complete quenching upon addition of an incremental amount of citrate. Notably, S1 has

significantly low fluorescence quantum yield (Φ < 1%) in resting state and displays fluorescence

enhancement upon complex formation with citrate. In contradistinction, S2 has considerably

higher fluorescence quantum yield (Φ < 41%) and goes to complete quenching upon

complexation. Fluorescence enhancement of S1 upon complexation is presumably due to 107

limiting the vibrational and rotational motion of the fluorophore, hence, diminishing the non-

radiative decay. The fluorescence quenching of S2 upon complex formation is more likely due to

photo-induced electron transfer (PET) mechanism. We have carried out a series of fluorescence

titrations of sensors with a number of carboxylates, such as mono-anions (benzoate and acetate),

di-anions (glutarate, oxalate, maleate, and malonate), and tri-anions (tricarballylate and citrate).

In fluorescence titration experiments, analyte-induced fluorescence responses of sensors suggest

that both sensors are cross-reactive but can discriminate structurally similar anions successfully.

Figure 4.7. Structures of the analytes used in this study.

The combination of different fluorescence responses and binding affinities of analytes

can result in fingerprint-like patterns that can be used for qualitative and quantitative analyses in a competitive media. Differentiated fluorescence responses of sensors for the structurally similar anions were revealed by fluorescence titrations. Here, using the corresponding fluorescence titration isotherms, binding affinities of the sensors for the selected carboxylates were quantified by a nonlinear least-square method and 1:1 binding model.16 Table 4.1 shows the binding affinities of sensors for the analytes used in this study. As expected, the binding affinities of the 108

S1 decrease as the backbone of the analytes becomes shorter (glutarate > maleate > malonate > oxalate). However, the similar structures of citrate and tricarballylate do not follow this trend.

This behavior is presumably due to higher desolvation energy of tri-anions (citrate, tricarballylate) compared with di-anions (glutarate, maleate, malonoate, and oxalate). Binding affinities of S1 for the acetate and benzoate could not be calculated due to complex isotherm (1:2 or 1:3) binding. In the case of S2, most of the analytes showed mixed binding modes that could not be calculated. Notably, chloride, the most common impurity, did not form any complexes with either sensor suggesting that an array based on S1 and S2 can be carried out in the presence of chloride.

-1 [a] Table 4.1. Association Constants (Ka, M ) of Analytes Used in This Study

Guest S1 S2 A1 Benzoate (A1) ND[b] No Response [c] A2 Acetate (A2) ND[b] No Response [c] A3 Oxalate (A3) 2.08 × 105 ND[b] A4 Maleate (A4) 2.90 × 105 ND[b] A5 Tricarballylate (A5) 7.44 × 104 4.29 × 104 A6 Malonoate (A6) 2.25 × 105 ND[b] A7 Glutarate (A7) 3.72 × 105 2.08 × 104 A8 Citrate (A8) 8.32 × 104 8.28 × 103 Chloride No Response[c] No Response [c] a The titrations recorded in acetonitrile and Kas are calculated based on the change in fluorescence intensity upon addition of each guest. The errors of the curve fitting were < 10%. b Ka could not be calculated due to 1:2 or 1:3 binding isotherm. c No significant response observed.

109

In the previous two chapters, we described that cross-reactive sensors were able to carry out qualitative and quantitative analyses of carboxylates successfully.17 Here, we seek to take

this concept further and perform qualitative and quantitative analyses of carboxylates in a highly

complex milieu: urine.

First, to demonstrate the recognition ability of the cross-reactive sensors S1 and S2 for

the structurally similar anions, we performed a preliminary qualitative test of the analytes A3-

A8) and potential interferents (A1-A2 and chloride) in water. S1 and S2 are not water soluble

and most of the applications of citrate sensing are carried out in an aqueous environment. To

perform analysis in an aqueous environment, we have utilized the polymer chip platform in our

studies. In this platform, sensors are embedded into a polyurethane matrix and analytes added as

aqueous solutions.18 Detailed procedure was explained in Chapter I. However, it is worthy to

note that the polyurethane matrix has two important roles in polymer chip platforms. First, the

polyurethane matrix keeps the sensors in place and allows us to perform the analysis in aqueous

media. Second, the hydrophilic part of polyurethane strips away the solvated water molecules

from anions, which allows preferential binding to the sensors.19 Upon addition of aqueous

solutions of analytes into the array, fluorescence images of polymer chips were recorded using

multiple channels using excitation at UV and emission filters of 440 nm 535 nm and 570 nm.

The cross-reactive sensors (S1-S2) were able to discriminate the structurally similar anions in

polymer chip platform. Here, we aimed to perform a qualitative analysis of carboxylates,

including citrate, glutarate, maleate, malonate, oxalate, and tricarballylate and potential

interferents such as acetate and benzoate. Sensor-embedded polymer chips were prepared and an

aqueous solution of analytes added, in ten repetitions. Resulting fluorescence responses were

recorded using a UV-Vis scanner with multiple channels. The resulting high- information density 110

response matrix was analyzed using linear discriminant analysis (LDA).20 Figure 4.8A shows the digital fluorescence images of the S1 (top) and S2 (bottom) in the polyurethane microchips.

Here, S1 showed a cross-reactive behavior, with a strong fluorescence enhancement for acetate and citrate. In contradiction, S2 showed complete quenching upon addition of citrate. Neither sensor responded to chloride, whereas other analytes resulted in significant differences in their fluorescence responses. Notably, all analytes were added as sodium salts in aqueous solution and the analyte-specific behaviors of the sensors were comparable with their behaviors in the acetonitrile (fluorescence titrations).

Figure 4.8. A) Digital fluorescence images of the S1 (top) and S2 (bottom) in the polyurethane microchips. B) Qualitative analysis of eight carboxylates, including mono, di, and tri carboxylates. [S1] = 100 µM, [S2] = 200 µM, [analyte] = 100 µM. 111

LDA is a frequently used supervised pattern recognition method that generates a

classification model based on known groups, also called learning or training objects, and to place unknown samples into appropriate groups in the model. The performance of the LDA was tested using one of the cross-validation methods, the leave-one-out routine. The fundamentals of the

LDA were explained in Chapter I. Figure 4.8B shows combined responses of the sensors in the response space defined canonical factors F1-F2 of the LDA analysis. The outstanding cross-

reactive behavior of the sensors (S1-S2) showed high variance in their responses for eight

carboxylates and 100% correct classification. As one can see, clusters of structurally similar

eight carboxylates were separated from each other by significant distances. Especially, citrate

cluster was placed separately from all the analytes due to its unique response pattern in the array.

30 100 % Correct Classification in 25 mM HEPES, pH: 7.4

0

100 Control ) -30 Calibration data set 10 M Citrate

%

M) 80 Validation data set 7  . 20 M Citrate

4

( 60 30 M Citrate

2

F -60 

40 M Citrate 40 50 M Citrate 20 RMSEC=0.30 60 M Citrate RMSECV=1.72 -90  Predicted [Citrate] ( [Citrate] Predicted 0 RMSEP=1.74 70 M Citrate  0 20406080100 80 M Citrate Actual [Citrate] (M) 90 M Citrate

-40 -30 -20 -10 0 10 20 30 40 F1 (92.2%)

Figure 4.9. Linear discriminant analysis plot of semi-quantitative analysis of citrate in buffer solution (25 mM HEPES, pH 7.4). [S1] = 100 µM, [S2] = 200 µM, [analyte] = 0 - 90 µM. Inset: regression analysis of citrate using the support vector machine (SVM) algorithm. The root-mean- square error of calibration (RMSEC), cross-validation (RMSECV), and prediction (RMSEP) attest to the quality of the prediction. 112

Second, the successful qualitative analysis of structurally similar carboxylates using a sensor array based on two sensors (S1-S2) encouraged us to perform a semi-quantitative analysis

of citrate. In this manner, we have carried out three semi-quantitative analyses of citrate to reveal

concentration-dependent trends in different environments, including water (see experimental

section), buffer (Figure 4.9), and diluted urine with buffer solution (Figure 4.10).

Figure 4.9 displays the LDA plot of the semi-quantitative analysis of incremental

amounts of citrate from 10 µM to 90 µM in 25 mM HEPES, pH at 7.4. The concentration-

dependent linear trend of the citrate clusters in the semi-quantitative analysis enabled us to carry

out a regression analysis of unknown citrate concentration (Figure 4.7 inset). In the regression

analysis, the multivariate data matrix was divided into two parts. The first part was used to

construct calibration model using support vector machine (SVM) algorithm and the second part

was used for validation purposes.21 Figure 4.9 inset shows the predicted concentration as a

function of the actual concentration of citrate. Sensor arrays based on sensors S1, and S2 were

able to correctly predict unknown citrate concentrations (red circles) in a buffer solution with a

high accuracy (RMSEP = 1.74).

As mentioned earlier, monitoring the urinary citrate excretion levels is an important tool

for the diagnosis of certain diseases.3 However, citrate analysis in such a competitive media is a

difficult task due to the complex nature of urine, which includes a number of electrolytes, small molecules, and proteins. Hence, methods that do not require any sample pre-treatment are very rare and needed.22 In general, daily urinary citrate excretion level is 320–1200 mg citrate and,

due to the fluctuation of instant citrate excretion in urine, 24 h specimen collection is needed for

reliable results.23 Here, we have performed citrate analysis in urine donated by a healthy

volunteer (Figure 4.10). The analysis of actual citrate concentration was performed using a 113

colorimetric method, which gives reliable results at high citrate concentration.24 The colorimetric

analysis of urinary citrate involves a yellow complex formed by ferric chloride and urinary

citrate, which is then measured by absorption at 390 nm. Even though colorimetric methods are

not recommended for diagnosis of low citrate levels, we were able to utilize this method due to

the high citrate levels of the sample (healthy citrate excretion), which was found as 1.95 mM.

The urine with a mid-range citrate concentration was diluted 100× and used to construct a

calibration curve by adding incremental amounts of known citrate concentration to cover the

range of hypocitraturia and daily citrate excretion level of a healthy person after 10× dilution.

100 % Correct Classification in Urine

0

199.5 Citrate 200 Calibration data set  M) 180 Validation data set 139.5 Citrate  160 )  -40 119.5 Citrate

% 140

4

. 120 99.5 Citrate

6

( 100 96.8  lower level 2 80

F % RMSEC=1.42 60 79.5 Citrate 40 % RMSECV=3.72

Predicted [Citrate] ( [Citrate] Predicted % RMSEP=2.42 59.5 Citrate -80 20 20 40 60 80 100 120 140 160 180 200 19.5 Citrate Actual [Citrate] (M)

-20 -10 0 10 20 F1 (85.3%)

Figure 4.10. Linear discriminant analysis plot of semi-quantitative analysis of citrate in urine. [S1] = 200 µM, [S2] = 400 µM. Inset: Regression analysis of citrate using the support vector machine (SVM) algorithm. The root-mean-square error of calibration (RMSEC), cross-validation (RMSECV), and prediction (RMSEP 2.42%) attest to the quality of the prediction.

114

In this experiment, we have included a several citrate concentrations lower than the cut-

off level for hypocitraturia (red line, 96.8 µM) to reveal the sensor array’s ability to detect low

citrate levels. The semi-quantitative analysis of citrate in urine displays well-resolved clusters

with 100% correct classification. The array response showed a clear dependence on citrate concentration suggesting the potential for quantitative analysis of citrate concentration in urine.

Similar to citrate analysis in buffer solution above, linear regression analysis allowed for successful quantitative analysis of citrate in urine (Figure 4.10 inset). The inset in Figure 4.10 shows the prediction of unknown citrate concentration using the calibration curve constructed using the SVM algorithm. The array based on two sensors, S1, and S2, was able to predict unknown citrate concentrations (red circles) in urine with a high accuracy (RMSEP = 2.42).

Notably, the array was able to perform quantitative analysis of the low urinary citrate concentration, which could not be determined using the colorimetric method. Limit of detection

(LOD) of the present fluorescence-based assay was calculated as 1.4 µM citrate. Thus, the proposed assay displays about 100 times lower LOD than the colorimetric method and still can be used to analyze samples of normal physiological citrate concentration. These results suggest that the array based on two fluorescent sensors is capable of succesfully performing quantitative analysis of citrate in water, buffer solution, and urine.8

4.3. Conclusion

We have synthesized two fluorescent sensors (S1-S2) comprising tri-serine tri-lactone

scaffold for the detection and quantification of citrate in water, buffer solution, and urine. The

two sensors differ in their recognition moieties which are also connector units between the fluorophores and the scaffold were used to assess the binding of carboxylates by the two sensors.

The sensor with thiourea (S1) as a recognition moiety showed fluorescence enhancement upon 115

complexation with carboxylates; conversely, the sensor with sulfonamide (S2) showed

quenching upon complexation. The binding affinity of the carboxylates, including mono-anions

(benzoate and acetate), di-anions (glutarate, oxalate, maleate, and malonate), and tri-anions

(tricarballylate and citrate) were calculated. Both sensors showed the different binding affinity

and response for the carboxylates of similar structures. Fingerprint-like responses of the sensors

for the carboxylates were used to perform qualitative and quantitative analysis of carboxylates,

especially citrate. To perform analysis in aqueous solutions, we have included the polymer chip

platform which utilizes the polyurethane matrix that supports the sensors and enhances the

recognition ability in aqueous media. Analyte-induced fluorescence responses of cross-reactive

sensors enable to perform a qualitative analysis of several carboxylates. It is important to note

that the structurally very similar carboxylates, including mono-, di-, and tri-carboxylates, were

discriminated with 100% correct classification. Furthermore, semi-quantitative analyses of

citrate in water, buffer solution, and urine revealed the concentration-dependent trends of the

citrate. The well-resolved clusters of the array enable a regression analysis of citrate using the

support vector machine algorithm. Successful regression analysis of citrate in aqueous solutions,

including water, buffer solution, and urine allowed predicting the citrate concentrations with a

high accuracy. Notably, the sensor array based on two sensors was able to detect and quantify

significantly low concentrations of citrate in urine accurately.

The successful results of the sensor array suggest that fluorescent sensors utilizing the tri- serine tri-lactone scaffold could potentially be used in diagnostics for certain diseases. This simple, inexpensive, and high-throughput-amenable fluorometric method can replace existing methods for citrate analysis.

116

4.4. Experimental Section

Standard laboratory techniques were performed in all the synthesis. Starting materials are used without further purification. 1H- and 13C-NMR (APT) spectra were recorded using a

Bruker® Avance IITM 500 MHz UltraShieldTM (Bruker Corporation, Mass., USA)

Spectrometer at 25 °C. Mass spectrometry studies were performed using Shimadzu LCMS-8030 liquid chromatograph mass spectrometer (ESI) or Shimadzu Axima Performance MALDI-TOF mass spectrometer. All the optical measurements were performed in acetonitrile. Acetonitrile is dried over 4Å molecular sieves. Optically dilute solutions (0.1 A) were used for all photophysical experiments. Fluorescence emission spectra were acquired using an Edinburgh single photon counting spectrofluorimeter (FLSP 920). Fluorescence titrations isotherms were calculated according to previously published methods.

Isotherms were constructed using emission maxima of each titration. Absorption spectra were recorded using a Hitachi U-3010 spectrophotometer. All the optical measurements were performed using a quartz cuvette with a path length of 1 cm at room temperature. The absolute quantum yields were measured using a Hamamatsu Quantaurus absolute quantum yield spectrometer QY-C11347.

4.4.1. Preparation of Polymer Chips25

The multi-well 10 x 21 (sub-microliter) glass slides were fabricated25 by ultrasonic drilling of microscope slides (well diameter: 1000 ± 10 μm, depth: 250 ± 10 μm). Sensor solutions in polymer solution (4% poly (ether-urethane) in THF w/w) were prepared. In a typical array, 200 nL of sensor-polymer solutions were pipetted into each well of the multi-well glass slides and dried at room temperature for 30 min. Then, water (400 nL) was pipetted into each 117 well and dried to form hydrated gel matrix. Finally, analytes were added as aqueous solutions into each well and the chip was dried at room temperature for 1 hr. Images from the sensor array were recorded using a Kodak Image Station 440CF (for preliminary experiments) and a Kodak

Image Station 4000MM PRO (for qualitative and quantitative experiments). After acquiring the images, the integrated (nonzero) gray pixel value (n) is calculated for each well in each channel.

Images of the sensor chip were recorded before (b) and after (a) the addition of an analyte. The final responses (R) were evaluated as indicated in the following equation:

Equation 4.1. The final responses (R) were evaluated as indicated in this equation.

Thus obtained data for qualitative analysis were then analyzed using Linear Discriminant

Analysis (LDA). Qualitative analysis was performed using Support Vector Machine (SVM) algorithm.

4.4.2. Synthesis

(3S, 7S, 11S)-3,7,11-tris(tritylamino)-1,5,9-trioxacyclododecane-2,6,10-trione (1)26

Equation 4.2. The first step of synthesis of the tri-serine tri-lactone scaffold.

118

(2S)-methyl 3-hydroxy-2-(tritylamino)propanoate (10 g; 27.7 mmol) and 2,2-dibutyl-1,3,2- dioxastannolane (0.18 g; 0.61 mmol) are dissolved in toluene (200 mL) and refluxed for 3 days under N2 equipped with a Dean and Stark device loaded 4Å sieves . All the solvent is removed.

The crude product is dissolved in CH2Cl2 and purification is achieved by column

chromatography (SiO2; CH2Cl2 only). Compound 1 is obtained as a white powder (6.085 g; 6.1 mmol; 66%).

1 M. P. > 250 °C. H NMR (500 MHz, CDCl3) δ 7.57 – 7.04 (m, 45H), 4.09 (t, J = 10.9 Hz, 3H),

13 3.59 – 3.38 (m, 6H), 2.66 (d, J = 9.0 Hz, 3H). C- NMR (126 MHz, CDCl3) δ 172.39, 145.29,

128.67, 128.06, 126.66, 71.36, 66.71, 54.40 ppm. MS (ESI) m/z: 1010.74 [M+Na]+.

(3S,7S,11S)-3,7,11-triamino-1,5,9-trioxa-cyclododecane-2,6,10-trione, trishydrochloride

(2)26

Equation 4.3. Formation of quaternary ammonium salts of the tri-serine tri-lactone.

Tri-serine tri-lactone (1) (4.85 g; 4.9 mM) is suspended in dry ethanol (80 mL). 2 mL of Acetyl chloride is added slowly into 10 mL of ethanol over 10 min (exothermic). Acid solution is added into slurry solution of compound 1 over 5 min. Reaction mixture is refluxed for 2 h. Resulting solution is concentrated to 25 mL under vacuum. Precipitates are collected by vacuum filtration 119

and washed with CHCl3 (50 mL), EtOH (50 mL), and Et2O (50 mL). Product is a white powder.

(1.75 g; 4.7 mmol; 96%). M. P. > 250 °C. 1H NMR (500 MHz, DMSO) δ 9.36 (s, 9H), 5.10 (dd,

J = 12.4, 1.8 Hz, 3H), 4.59 (s, 3H), 4.31 (dd, J = 12.4, 2.6 Hz, 3H). 13C-ATP NMR (126 MHz,

DMSO) δ 165.40, 63.20, 53.01 ppm. MS (ESI) m/z: 262.03 [M+H -3Cl]+.

(3S,7S,11S)-2,6,10-trioxo-1,5,9-trioxacyclododecane-3,7,11-triyl)tris(3-(4-

dimethylamino)naphthalen-1-yl)thiourea (3)

Equation 4.4. Substitution of reporter moieties of S1 to tri-serine tri-lactone scaffold.

(3S,7S,11S)-3,7,11-triamino-1,5,9-trioxa-cyclododecane-2,6,10-trione, trishydrochloride (2) (50 mg; 0.13 mmol) is suspended in CH2Cl2 with 4Å sieves. 120 mg of 4-Dimethylamino-1-naphthyl

isothiocyanate is added as CH2Cl2 solution. Reaction flask is stirred for 3 h at R.T. Resulting

solution is evaporated to dryness. Purification is achieved by column chromatography. (SiO2;

CH2Cl2 : MeOH : Acetonitirile (10 : 0.25 : 1)). Compound 1 is obtained as a white powder (35

1 mg; 0.04 mmol; 30.7 %). M.P. = 153 °C. H NMR (500 MHz, CD3CN) δ 8.41 (s, 1H), 8.18 (d, J 120

= 8.3 Hz, 1H), 7.79 (d, J = 7.8 Hz, 1H), 7.54 – 7.45 (m, 2H), 7.13 (d, J = 7.3 Hz, 1H), 6.97 (d, J

= 7.8 Hz, 1H), 6.35 (s, 1H), 5.25 (d, J = 4.8 Hz, 1H), 4.22 (d, J = 9.0 Hz, 1H), 4.15 – 4.05 (m,

13 1H), 2.78 (s, 7H). C NMR (126 MHz, CD3CN) δ 168.99, 151.51, 131.21, 129.28, 126.86,

126.00, 125.80, 124.69, 122.91, 117.35, 113.63, 64.94, 56.42, 44.37, 26.61. MS (ESI) m/z:

946.38 [M]+.

(3S,7S,11S)-2,6,10-trioxo-1,5,9-trioxacyclododecane-3,7,11-triyl)tris(5-

(dimethylamino)naphthalene-1-sulfonamide (4)

Equation 4.5. Substitution of reporter moieties of S2 to tri-serine tri-lactone scaffold.

(3S,7S,11S)-3,7,11-triamino-1,5,9-trioxa-cyclododecane-2,6,10-trione, trishydrochloride (2) (50 mg; 0.13 mmol) is suspended in CH2Cl2 with 4Å sieves. It is dissolved as 0.2 mL of

trimethylamine is added. 120 mg of 5-(Dimethylamino)naphthalene-1-sulfonyl chloride is added 121

as CH2Cl2 solution. Reaction flask is stirred for 3 h at R.T. Resulting solution is evaporated to

dryness. Purification is achieved by column chromatography. (SiO2; CH2Cl2 : MeOH (10 : 0.6 )).

1 M.P. = 155 °C H NMR (500 MHz, CDCl3) δ 8.56 (d, J = 8.3 Hz, 1H), 8.35 (d, J = 5.8 Hz, 1H),

8.24 (d, J = 6.9 Hz, 1H), 7.48 (dd, J = 10.8, 4.7 Hz, 2H), 7.12 (d, J = 7.3 Hz, 1H), 6.90 (s, 1H),

13 4.44 (dd, J1 = 11.3, J2 = 3.0 Hz, 1H), 4.13 (d, J = 9.6 Hz, 1H), 3.48 (s, 1H), 2.89 (s, 6H). C

NMR (126 MHz, CDCl3) δ 166.94, 133.97, 131.24, 130.06, 129.75, 129.38, 128.86, 123.24,

115.49, 77.29, 77.04, 76.78, 66.01, 55.46, 45.47, 26.92. MS (ESI) m/z: 961.31 [M]+.

4.4.3. Job’s plot Experiments

4 5 4.0x10 1.0x10

3.5x104 8.0x104 3.0x104

2.5x104 6.0x104 2.0x104

4

4 Fluorescence

1.5x10  4.0x10

Fluorescence 

4

1.0x10 citrate

 4 citrate 2.0x10  5.0x103

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0  = [Citrate]/([Citrate]+[ ])  Citrate S1 Citrate = [Citrate]/([Citrate]+[S2]) Figure 4.11. Job’s plot experiments of S1 (left) and S2 (right) with citrate.

4 2.0x10 1.2x105

1.0x105 1.5x104

8.0x104

1.0x104 6.0x104

Fluorescence Fluorescence 4 Fluorescence 

 4.0x10 5.0x103 Acetate Acetate  4  2.0x10

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Acetate = [Acetate]/([Acetate]+[S2]) Acetate = [Acetate]/([Acetate]+[S1])

Figure 4.12. Job’s plot experiments of S1 (left) and S2 (right) with acetate. 122

4.4.4. Qualitative Analysis

Table 4.2. The Jackknifed Classification Matrix of Qualitative Analysis of 8 Analytes and A Control by S1 and S2 in Hydrogel Matrix.

Figure 4.13. The canonical scores plot of qualitative analysis of 8 analytes and a control by using S1 and S2 in hydrogel matrix.

123

4.4.5. Semi-quantitative and Quantitative Analysis-in Water

40

100 % Correct Classification in Water Control

20 10 M Citrate 20 M Citrate

%)

7

. 30 M Citrate

3

1

(  0 40 M Citrate

2

F 50 M Citrate 60 M Citrate  -20 70 M Citrate 80 M Citrate 90 M Citrate

-40 -20 0 20 40 F1 (73.5%)

Figure 4.14. Linear discriminant analysis (LDA) of Citrate-Tri-Na in hydrogel matrix. LDA shows the trend depending on increasing concentration of citrate.

Prediction of Citrate in Water 100

80 Calibration data set Validation data set M)  60

40

20 RMSEC=1.26 Predicted [Citrate] ( [Citrate] Predicted RMSECV=1.84 0 RMSEP=2.14

0 20406080100 Actual [Citrate] (M)

Figure 4.15. The result of the linear regression using support vector machine (SVM) affords quantitative analysis of various concentration of citrate. The plots of actual vs. predicted concentration show high accuracy of prediction for multiple analyte concentrations. Two unknown samples (red squares ■) were simultaneously correctly analyzed. 124

Table 4.3. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of Citrate Tri-Sodium Salt by Using S1 and S2 in Hydrogel Matrix.

Figure 4.16. The canonical scores plot of Semi-quantitative analysis of citrate tri-sodium salt by using S1 and S2 in hydrogel matrix.

125

4.4.6. Semi-quantitative Analysis-in Buffer Solution

Table 4.4. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of Citrate Tri-Sodium Salt in Buffer Solution by Using S1 and S2 in Hydrogel Matrix.

Figure 4.17. The canonical scores plot of semi-quantitative analysis of citrate tri-sodium salt in buffer solution (25 mM HEPES, pH at 7.4) by using S1 and S2 in hydrogel matrix. 126

4.4.7. Semi-quantitative Analysis-in Urine

Table 4.5. The Jackknifed Classification Matrix of Semi-Quantitative Analysis of Citrate Tri-Sodium Salt in Urine by Using S1 and S2 in Hydrogel Matrix.

Jackknifed Classification Matrix 199.5 139.5 119.5 99.5 79.5 59.5 19.5 %correct 199.5 10 0 0 0 0 0 0 100 139.5 0 10 0 0 0 0 0 100 119.5 0 0 10 0 0 0 0 100 99.5 0 0 0 10 0 0 0 100 79.5 0 0 0 0 10 0 0 100 59.5 0 0 0 0 0 10 0 100 19.5 0 0 0 0 0 0 10 100 Total 10 10 10 10 10 10 10 100

Figure 4.18. The canonical scores plot of semi-quantitative analysis of citrate tri-sodium salt in urine by using S1 and S2 in hydrogel matrix.

127

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26 Ramirez, R. J. A.; Karamanukyan, L.; Ortiz, S.; Gutierrez, C. G. Tetrahedron Lett. 1997, 38,

749. 129

APPENDIX A. LIST OF ABBREVIATIONS, ACRONYMS, AND SYMBOLS

ANN Artificial neural network

BA Benzoate

CD Circular dichroism

CDA Chiral derivatizing reagents

CE Capillary electrophoresis

CLSR Chiral lanthanide shift reagent

cm Centimeter

CSP Chiral stationary phase

CV Coefficient of variability

DMSO Dimethyl sulfoxide

ee Enantiomeric excess

ef the Values of enantiomeric fluorescence difference ratios

ESI-MS Electrospray ionization mass spectrometry

EtOH Ethanol

FDA the US Food and Drug Administration

FLSP Single photon counting spectrofluorometer

GC Gas chromatography 130

GC-MS Gas chromatography–mass spectrometry

HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

HPLC High-performance liquid chromatography

HTS High-throughput screening

IBP Ibuprofen

Ka Association constant

KTP Ketoprofen

LDA Linear discriminant analysis

LDF Linear discriminant function

LOD Limit of detection

M Molar (mol/lt)

MA Mandelate

MeCN Acetonitrile

MeOH Methanol nL Nano liter nm Nanometer

NMR Nuclear magnetic resonance spectroscopy ns Nanosecond 131

NSAID Nonsteroidal anti-inflammatory drug

PBO Polybutylene oxide

PEO Polyethylene oxide

PET Photo-induced electron transfer

Phe Phenylalanine pKa -log of the acidity constant

PPA 2-Phenylpropanoate ppb Parts per billion

PU Poly urethanes

RMSEC the Root-mean-square error of calibration

RMSECV the Root-mean-square error of cross-validation

RMSEP the Root-mean-square error of prediction

SVM Support vector machine

THF Tetrahydrofuran

UV-vis Ultraviolet-visible

μM Micromolar (10-6 M)

μm Micron (10-6 m)

Φ Quantum yield 132

APPENDIX B. SUPPLEMENTARY DATA FOR CHAPTER II

Examples of UV-Vis absorption titration experiments

UV-Vis titrations were performed by addition of tetrabutylammonium salts of analytes into sensor solution in propionitrile. The absorption spectra were collected at 1:0.5, 1:1, and 1:4

(hosts:guest) ratios.

UV-Vis titration of S1

0.30 0.5 Acetate Benzoate 0.25 0.4 0.20 0.3 0.15

0.2 Absorbance Absorbance 0.10

0.05 0.1

0.00 0.0 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.25 0.25 (R)-Ibuprofen (S)-Ibuprofen

0.20 0.20

0.15 0.15 0 0

I / I / 0.10 0.10

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm) 133

0.25 0.25 (S)-Mandelate (R)-Mandelate

0.20 0.20

0.15 0.15 0 0

I / I / 0.10 0.10

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.25 0.25 (S)-Naproxen (R)-Naproxen

0.20 0.20

0.15 0.15 0 0

I / I / 0.10 0.10

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.25 0.25 (S)-Ketoprofen Ketoprofen

0.20 0.20

0.15 0.15 0 0

I / I / 0.10 0.10

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

134

UV-Vis titration of S2

0.25 Acetate 0.4 Benzoate 0.20 0.3 0.15

0.2 0.10 Absorbance Absorbance 0.1 0.05

0.00 0.0 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.20 (S)-Ibuprofen (R)-Ibuprofen 0.15 0.15

0.10 0 0 0.10

I / I /

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.20 (S)-Mandelate (R)-Mandelate 0.15

0.15

0.10 0 0.10 0

I / I /

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm) 135

0.20 (S)-Naproxen (R)-Naproxen 0.15 0.15

0.10 0 0 0.10

I / I /

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.20 (S)-Ketoprofen Ketoprofen 0.15 0.15

0.10 0 0 0.10

I / I /

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

136

UV-Vis titration of S3

0.5 0.25 Benzoate Acetate 0.4 0.20

0.3 0.15

0.2 0.10 Absorbance Absorbance 0.1 0.05

0.0 0.00

300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.25 0.25 (S)-Ibuprofen (R)-Ibuprofen

0.20 0.20

0.15 0.15 0 0

I / I / 0.10 0.10

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.25 0.25 (S)-Mandelate (R)-Mandelate

0.20 0.20

0.15 0.15 0 0

I / I / 0.10 0.10

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm) 137

0.25 0.20 (S)-Naproxen (R)-Naproxen

0.20 0.15

0.15 0 0 0.10

I / I / 0.10

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.25 (S)-Ketoprofen Ketoprofen 0.20 0.20

0.15 0.15 0 0

I / I / 0.10 0.10

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

138

UV-Vis titration of S4

0.40 Benzoate 0.25 Acetate 0.35 0.30 0.20

0.25 0.15

0.20 0.10 Absorbance 0.15 Absorbance 0.10 0.05 0.05 0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.14 (S)-Ibuprofen 0.14 (R)-Ibuprofen

0.12 0.12

0.10 0.10 0 0.08 0 0.08

I / 0.06 I / 0.06 0.04 0.04 0.02 0.02 0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.20 0.20 (S)-Mandelate (R)-Mandelate

0.15 0.15 0 0.10 0 0.10

I / I /

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm) 139

0.25 0.25 (S)-Naproxen (R)-naproxen 0.20 0.20

0.15 0.15 0 0

I / I / 0.10 0.10

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

0.20 0.20 Ketoprofen (S)-ketoprofen

0.15 0.15 0 0 0.10 0.10

I / I /

0.05 0.05

0.00 0.00 300 320 340 360 380 400 420 440 300 320 340 360 380 400 420 440 Wavelength (nm) Wavelength (nm)

140

Fluorescence Titrations

Fluorescence titrations were recorded upon addition of tetrabutylammonium salt of analytes into sensor solution in propionitrile.

-1 [a] Table S1. The affinity constants (Ka, M ) obtained from fluorescence titration.

S1 Error % S2 Error % S3 Error % S4 Error %

Acetate 3.34 × 16.4 5.31 × 10.9 7.53 × 13.3 > 107 NA

Benzoate 1.44 × 4.7 7.65 × 7.71 2.17 × 14.7 1.08 × 12.5

(S)- 2.33 × 16.3 4.51 × 16.5 8.53 × 9.8 1.13 × 19.0

(R)- 1.40 × 5.6 3.23 × 10.3 6.87 × 7.2 3.17 × 2.4

7 (S)- 2.22 × 16.6 1.17 × 16.6 3.41 × 16.6 > 10 NA

7 (R)- 3.29 × 18.4 3.91 × 17.1 2.58 × 15.2 > 10 NA

[b] [b] (S)- 2.12 × 9.7 3.00 × 8.5 ND ND ND ND

(R)- 1.37 × 16.9 1.42 × 14.5 2.92 × 8.7 2.08 × 6.5

(S)- 3.00 × 7.5 3.85 × 14.4 2.31 × 11.2 1.54 × 9.7

(R)- 9.10 × 16.1 3.41 × 18.9 2.70 × 16.9 1.67 × 16.8

[a] The titrations are recorded in propionitrile and Kas were calculated based on the change in fluorescence intensity change at λEm= 424 nm. The errors of the curve fitting were < 20%. [b] Ka could not be calculated due to the low magnitude of response.

141

S1 Titrations

2.0 Acetate 2.0 Benzoate

1.5 1.5 0 0 I / I /

1.0 1.0

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

(S)-Ketoprofen (R)-ketoprofen 1.5 1.5 0 0 1.0 1.0 I / I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

2.0 2.0 (S)-Ibuprofen (R)-Ibuprofen

1.5 1.5 0 0

I / 1.0 I / 1.0

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm) 142

(S)-Naproxen (R)-Naproxen

1.0 1.0 0 0 I / I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

(R)-Mandelate (S)-Mandelate 1.5 1.5

0 1.0 0

1.0 I /

I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

143

S2 Titrations

2.0 2.0 Acetate Benzoate

1.5 1.5 0 0 I / I /

1.0 1.0

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

(S)-Ketoprofen (R)-Ketoprofen 1.5 1.5 0 0 1.0 1.0 I / I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

2.0 2.0 (S)-Ibuprofen (R)-Ibuprofen

1.5 1.5 0 0 I / I / 1.0 1.0

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

144

1.5 (S)-Naproxen (R)-Naproxen ) i ) i -I 0 -I 0 )/(I )/(I -I 0 -I 0 (I 1.0 (I 1.0 0 0 I / I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

(S)-Mandelate (R)-Mandelate 1.5 1.5 0 1.0 0 1.0 I / I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

145

S3 Titrations

2.5 2.0 Acetate Benzoate 2.0 1.5

1.5 0 0

I / I / 1.0

1.0

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

(S)-Ketoprofen (R)-Ketoprofen 1.5 1.0 0

0 1.0 I /

I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.5 (S)-Ibuprofen 1.5 (R)-Ibuprofen

1.0 1.0 0 0 I / I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

146

1.2 (S)-Naproxen 1.2 (R)-Naproxen ) i ) i -I -I 0 0 )/(I

1.0 )/(I

1.0 -I -I 0 0 (I (I 0.8 0.8 0 0 I / I /

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.5 (S)-Mandelate 1.5 (R)-Mandelate

1.0 1.0 0 0 I / I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

147

S4 Titrations

2.0 2.0 Acetate Benzoate

1.5 1.5 0 0 I / I / 1.0

1.0

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.5 (S)-Ketoprofen (R)-Ketoprofen

1.0 1.0 0 0 I / I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.5 (R)-ibuprofen 1.5 (S)-Ibuprofen

1.0 1.0 0 0 I /

I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm) 148

1.4 (S)-Naproxen 1.2 (R)-Naproxen 1.2 ) i ) i -I -I 0 0 )/(I )/(I

-I 1.0 -I 0 0 (I 1.0 (I 0.8

0 0.8 0 I / I /

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.5 (S)-Mandelate 1.0 (R)-Mandelate

1.0 0 0 I / I /

0.5 0.5

0.0 0.0 380 400 420 440 460 480 500 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

X-ray structural analysis

The data were collected on a Nonius Kappa CCD diffractometer using a graphite monochromatized Mo-Kα radiation using an Oxford Cryostream low temperature device. Data reduction was performed using DENZO-SMN.1 The structure was solved by direct methods using SIR972 and refined by full-matrix least-squares on F2 with anisotropic displacement parameters for the non-H atoms using SHELXL-97.3 Crystals of S3 with (S)-ibuprofen and S3 with (R)-ibuprofen grew as colorless crystals by diffusion of hexanes into propionitrile solution of the complexes.

149

Table S2. Crystal data and structure refinement for S3 with (S)-ibuprofen.

Bond precision: C-C = 0.0062 A Wavelength=1.54180 Cell: a=12.9055(4) b=25.5214(7) c=27.4203(10) α=90° β=90° γ=90°

Temperature: 100 K

Calculated Reported

Volume 9031.3(5) 9031.3(5) Space group P212121 P212121 2(C H N O), 2(C H O ), 2(C H N O), 2(C H O ), C H Moiety formula 34 33 2 13 17 2 34 33 2 17 13 2 3 5 0.5(C6 H14), C3 H5 N N, 1/2 C6 H14 Sum formula C100 H112 N5 O6 C100 H112 N5 O6 Mr 1479.95 1479.94 D,g cm-3 1.089 1.088 Z 4 4 μ (mm-1) 0.521 0.521 F000 3180.0 3180.0 F000' 3188.58 h,k,lmax 15, 31, 33 15, 31, 33 Nref 17126 [9348] 16758 Tmin,Tmax 0.957, 0.969 0.809, 1.000 T 0.732 min' Correction method= MULTI-SCAN

Data completeness= 1.79 / 0.98 Theta(max)= 69.992 R(reflections)= 0.0624 (14677) wR2(reflections)= 0.1903 (16758)

S = 1.040 Npar= 1003

150

Table S3. Crystal data and structure refinement for S3 with (R)-ibuprofen.

Bond precision: C-C = 0.0067 A Wavelength=1.54180 Cell: a=26.3802(8) b=13.2726(4) c=48.5657(15) α=90° β=102.432(4)° γ=90°

Temperature: 133 K

Calculated Reported

Volume 16605.8(9) 16605.8(9) Space group I 2 I 2 8(C H N O), 8(C H O ), 2(C 8(C H N O), 8(C H O ), 5(C Moiety formula 34 33 2 13 17 2 6 34 33 2 13 17 2 3 H14), 5(C3 H5 N) H5 N), 2(C6 H14) Sum formula C403 H453 N21 O24 C403 H453 N21 O24 Mr 5974.86 5974.86 Dx,g cm-3 1.195 1.195 Z 2 2 Mu (mm-1) 0.572 0.572 F000 6420.0 6420.0 F000' 6437.32 h,k,lmax 32, 16, 60 32, 16, 60 Nref 33457 [17487] 27629 Tmin,Tmax 0.921, 0.972 0.844, 1.000 T 0.814 min' Correction method= MULTI-SCAN

Data completeness= 1.58 / 0.83 Theta(max)= 73.373 R(reflections)= 0.0566 (24714) wR2(reflections)= 0.1663 (27629)

S = 1.024 Npar= 2053

151

References

1 Otwinowski, Z.; Minor, W. Denzo-SMN. In Methods in Enzymology, Macromolecular

Crystallography, part A; Carter, Jr., C. W.; Sweets, R. M., Eds.; Academic Press: 1997; Vol.

276, pp 307–326.

2 Altomare, A.; Burla, M. C.; Camalli, M.; Cascarano, G. L.; Giacovazzo, C.; Guagliardi, A.;

Moliterni, A. G. G.; Polidori, G.; Spagna, R. J. Appl. Cryst. 1999, 32, 115.

3 Sheldrick, G. M. SHELXL97. Program for the Refinement of Crystal Structures, University of

Gottingen, Germany. 1994.

152

APPENDIX C. SUPPLEMENTARY DATA FOR CHAPTER III

1H and 13C NMR Spectra

1 400 MHz H NMR spectrum of S1 in CDCl3.

153

13 100 MHz C NMR spectrum of S1 in CDCl3.

154

1 400 MHz H NMR spectrum of S2 in CDCl3.

155

13 100 MHz C NMR spectrum of S2 in CDCl3.

156

1 400 MHz H NMR spectrum of S3 in CDCl3.

157

13 100 MHz C NMR spectrum of S3 in CDCl3.

158

1 400 MHz H NMR spectrum of S4 in CDCl3.

159

13 100 MHz C NMR spectrum of S4 in CDCl3.

160

1 400 MHz H NMR spectrum of (R)-1 in d6-acetone.

161

13 100 MHz C NMR spectrum of (R)-1 in CDCl3.

162

1 400 MHz H NMR spectrum of (R)-3 in d6-acetone.

163

13 100 MHz C NMR spectrum of (R)-3 in d6-acetone.

164

Fluorescence titrations (S1)

1.1 1.1 1.0 S1 + (S)-Ibuprofen 1.0 S1 + (R)-Ibuprofen 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0 0

I/I 0.5 I/I 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.1 1.1 S1 + (S)-2-Phenylpropanoate S1 + (R)-2-phenylpropanoate 1.0 1.0 0.9 0.9 0.8 0.8 0.7 0.7 0 0 0.6 I/I 0.6

I/I 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.1 1.1 + (S)-Mandelate 1.0 S1 1.0 S1 + (R)-Mandelate 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0 0

I/I 0.5 I/I 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

165

1.1 1.0 S1 + (S)-Ketoprofen 1.0 S1 + (R)-Ketoprofen 0.9 0.8 0.8 0.7 0.6

0 0.6 0

I/I 0.5 I/I 0.4 0.4 0.3 0.2 0.2 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.0 S1 + (S)-Phenylalanine 1.0 S1 + (R)-Phenylalanine

0.8 0.8

0.6 0 0.6 I/I 0

I/I 0.4 0.4

0.2 0.2

0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.1 1.1 1.0 S1 + Acetate 1.0 S1 + Benzoate 0.9 0.9 0.8 0.8 0.7 0.7

0 0.6 0.6 0 I/I

0.5 I/I 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

166

Binding isotherms (S1)

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i i 0.6 0.6 / (I / (I ) )

0 0

I-I 0.4 I-I 0.4 ( (

0.2 0.2

4 -1 0.0 4 -1 0.0 K = 6.70 x 10 M Ka = 4.79 x 10 M a 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 (SS-Ibuprofen)-Ibuprofen [M] [M] R-Ibuprofen [M] (R)-Ibuprofen [M]

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i i 0.6 0.6 /(I /(I ) )

0

0

I-I 0.4 I-I

0.4 ( (

0.2 0.2 4 -1 K = 3.13 x 104 M-1 0.0 Ka = 4.49 x 10 M 0.0 a

-4 -4 0.0 1.0x10-4 2.0x10-4 0.0 1.0x10 2.0x10 (SS-Phenyl)-2-Phenylpropanoate Propanoate [M] [M] (R-PhenylR)-2-Phenylpropanoate Propanoate [M] [M]

1.0 1.0

0.8 0.8 ) ) 0 0 -I i -I 0.6 i 0.6 / (I / (I ) )

0

0

I-I 0.4

I-I 0.4 ( (

0.2 0.2 4 -1 K = 8.37 x 103 M-1 0.0 Ka = 1.42 x 10 M 0.0 a 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Mandelate [M] R-Mandelate [M] (S)-Mandelate [M] (R)-Mandelate [M]

167

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i i 0.6 0.6 /(I / (I ) )

0 0

I-I 0.4 I-I 0.4 ( (

0.2 0.2 K = 6.22 x 104 M-1 5 -1 0.0 a 0.0 Ka = 1.42 x 10 M

-4 -4 0.0 1.0x10 2.0x10 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Ketoprofen [M] R- Ket opr ofen [M] (S)-Ketoprofen [M] (R)-Ketoprofen [M]

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i i 0.6 0.6 /(I /(I ) )

0

0 I-I

I-I 0.4

0.4 ( (

0.2 0.2 4 -1 K = 6.95 x 10 M K = 6.84 x 104 M-1 0.0 a 0.0 a

0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 (SS-Phenylalanine)-Phenylalanine [M] [M] R-Phenylalanine [M] (R)-Phenylalanine [M]

1.0 1.0

0.8 ) ) 0 0 -I -I i i 0.6 /(I /(I ) )

0 0 0.5 I-I I-I 0.4 ( (

0.2 K = 2.49 x 104 M-1 4 -1 a 0.0 Ka = 2.03 x 10 M 0.0 0.0 1.0x10-4 2.0x10-4 0.0 1.0x10-4 2.0x10-4 [Acetate] (M) Benzoate [M] Acetate [M] Benzoate [M]

168

Fluorescence titrations (S2)

1.1 1.1 1.0 S2 + (S)-Ibuprofen 1.0 S2 + (R)-Ibuprofen 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0 0

I/I 0.5 I/I 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.1 1.1 + (S)-2-Phenylpropanoate 1.0 S2 1.0 S2 + (R)-2-phenylpropanoate 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0 0

I/I 0.5 I/I 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.1 1.1 1.0 S2 + (S)-Mandelate 1.0 S2 + (R)-Mandelate 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0 0

I/I

0.5 I/I 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

169

1.0 S2 + (S)-Ketoprofen 1.0 S2 + (R)-Ketoprofen

0.8 0.8

0.6 0.6 0 0

I/I I/I 0.4 0.4

0.2 0.2

0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.1 1.0 S2 + (S)-Phenylalanine 1.0 S2 + (R)-Phenylalanine 0.9 0.8 0.8 0.7 0.6 0.6 0 0

I/I I/I 0.5 0.4 0.4 0.3 0.2 0.2 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

1.1 1.1 S2 + Acetate 1.0 1.0 S2 + Benzoate 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0 0

I/I I/I 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 360 380 400 420 440 460 480 500 360 380 400 420 440 460 480 500 Wavelength (nm) Wavelength (nm)

170

Binding isotherms (S2)

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i 0.6 i 0.6 /(I /(I ) )

0 0

I-I 0.4 I-I 0.4 ( (

0.2 0.2 4 -1 4 -1 0.0 Ka = 6.15 x 10 M 0.0 Ka = 1.57 x 10 M

0.0 1.0x10-4 2.0x10-4 0.0 1.0x10-4 2.0x10-4 [S-Ibuprofen] (M) [R-Ibuprofen] (M) (S)-Ibuprofen [M] (R)-Ibuprofen [M]

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i 0.6 i 0.6 / (I / (I ) )

0 0

I-I 0.4 I-I 0.4 ( (

0.2 0.2

4 -1 0.0 4 -1 0.0 Ka = 2.66 x 10 M Ka = 2.59 x 10 M 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Phenyl Propanoate[M] R-Phenyl Propanoate [M] S(-S2)--Phenylpropanoate2-Phenylpropanoate [M] [M] (RR-)2-2-Phenylpropanoate-Phenylpropanoate [M] [M]

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i 0.6 i 0.6 /(I / (I ) )

0

0

I-I 0.4

I-I 0.4 ( (

0.2 0.2

4 -1 0.0 K = 2.73 x 10 M 0.0 4 -1 a Ka = 1.88 x 10 M 0.0 1.0x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Mandelate [M] R-Mandelate [M] (S)-Mandelate [M] (R)-Mandelate [M] 171

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i 0.6 i 0.6 / (I / (I ) )

0 0 I-I 0.4 I-I 0.4 ( (

0.2 0.2

4 -1 4 -1 0.0 Ka = 6.19 x 10 M 0.0 Ka = 8.19 x 10 M 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 (S)-KetoprofenS- Ket opr ofen [M] [M] (RR-) Ket-Ketoprofen opr ofen [M] [M]

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i 0.6 i 0.6 /(I /(I ) )

0

0 I-I 0.4 I-I 0.4 ( (

0.2 0.2 K = 4.25 x 104 M-1 4 -1 0.0 a 0.0 Ka = 4.60 x 10 M 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Phenylalanine [M] R-Phenylalanine [M] (S)-Phenylalanine [M] (R)-Phenylalanine [M]

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i i 0.6 0.6 /(I / (I ) )

0

0

I-I 0.4 I-I

0.4 ( (

0.2 0.2 5 -1 4 -1 0.0 Ka = 1.15 x 10 M 0.0 Ka = 3.25 x 10 M -4 -4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 1.0x10 2.0x10 Benzoate [M] AcetateAcetate [M] [M] Benzoate [M]

172

Fluorescence titrations (S3)

S3 + (S)-Ibuprofen S3 + (R)-Ibuprofen 2.5 2.5

2.0 2.0

1.5 1.5 0 0

I/I I/I 1.0 1.0

0.5 0.5

0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

2.5 S3 (S)-2-Phenylpropanoate 2.5 S3 + (R)-2-Phenylpropanoate

2.0 2.0

0 1.5 1.5 0

I/I I/I 1.0 1.0

0.5 0.5

0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

1.8 1.8 S3 + (S)-Mandelate S3 + (R)-Mandelate 1.6 1.6 1.4 1.4 1.2 1.2 1.0 1.0 0 0

I/I 0.8 I/I 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

173

1.8 S3 + (S)-Ketoprofen 1.8 S3 + (R)-Ketoprofen 1.6 1.6 1.4 1.4 1.2 1.2

0 1.0

0 1.0

I/I 0.8 I/I 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

1.8 1.8 S3 + (S)-Phenylalanine S3 + (R)-Phenylalanine 1.6 1.6 1.4 1.4 1.2 1.2 1.0 1.0 0 0

I/I I/I 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

1.8 S3 + Acetate 1.4 S3 + Benzoate 1.6 1.4 1.2 1.2 1.0 1.0 0 0.8 0

I/I 0.8 I/I 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

174

Binding isotherms (S3)

1.0 1.0

) 0.8 ) 0.8 0 0 -I -I i i I I ( (

/ 0.6 / 0.6 ) ) 0 0

I-I I-I

( 0.4 ( 0.4

0.2 0.2

4 -1 4 -1 0.0 0.0 K = 3.04 x 10 M Ka = 3.12 x 10 M a 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Ibuprofen [M] R-Ibuprofen [M] (S)-Ibuprofen [M] (R)-Ibuprofen [M]

1.0 1.0

) 0.8

) 0.8 0 0 -I -I i i I I ( ( / 0.6

/ 0.6 ) ) 0

0

I-I I-I ( ( 0.4 0.4

0.2 0.2 K = 8.01 x 105 M-1 K = 3.88 x 104 M-1 0.0 a 0.0 a

0.0 1.0x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Phenyl Propanoate [M] R-Phenyl Propanoate [M] S-2-Phenylpropanoate [M] (S)-2-Phenylpropanoate [M] R(-R2-)Phenylpropanoate-2-Phenylpropanoate [M] [M]

1.0 1.0 ) ) 0.8 0.8 0 0 -I -I i i I I ( ( / / 0.6 0.6 ) ) 0 0

I-I I-I ( ( 0.4 0.4

0.2 0.2 K = 2.96 x 103 M-1 K = 2.91 x 103 M-1 0.0 a 0.0 a

0.0 1.0x10-4 2.0x10-4 0.0 1.0x10-4 2.0x10-4 S-Mandelate [M] R-Mandelate [M] (R)-Mandelate [M] (S)-Mandelate [M]

175

1.0 1.0 ) ) 0.8 0.8 0 0 -I -I i i I I ( ( / / 0.6 0.6 ) ) 0 0

I-I I-I ( ( 0.4 0.4

0.2 0.2 K = 1.55 x 105 M-1 K = 2.66 x 105 M-1 0.0 a 0.0 a

0.0 1.0x10-4 2.0x10-4 0.0 1.0x10-4 2.0x10-4 S-Phenylalanine [M] R-Phenylalanine [M] (S)-Phenylalanine [M] (R)-Phenylalanine [M]

1.0 1.0 ) 0.8 ) 0.8 0 0 -I -I i i I I ( ( / 0.6 / 0.6 ) ) 0 0

I-I I-I ( 0.4 ( 0.4

0.2 0.2 4 -1 K = 5.68 x 104 M-1 0.0 Ka = 4.17 x 10 M 0.0 a 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Ketoprofen [M] R-Ketoprofen [M] (S)-Ketoprofen [M] (R)-Ketoprofen [M]

1.0 1.0 ) 0.8 ) 0.8 0 0 -I -I i i I I ( ( / 0.6 / 0.6 ) ) 0 0

I-I I-I ( 0.4 ( 0.4

0.2 0.2 4 -1 4 -1 Ka = 3.51 x 10 M 0.0 0.0 Ka = 4.03 x 10 M 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 Acetate [M] Benzoate [M] Acetate [M] Benzoate [M]

176

Fluorescence titrations (S4)

2.5 2.5 S4 + (S)-Ibuprofen S4 + (R)-Ibuprofen 2.0 2.0

1.5 1.5 0 0

I/I I/I 1.0 1.0

0.5 0.5

0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

S4 + (S)-2-Phenylpropanoate S4 + (R)-2-Phenylpropanoate 2.0 2.0

1.5 1.5 0 0

I/I I/I 1.0 1.0

0.5 0.5

0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

+ (S)-Mandelate S4 + (R)-Mandelate 2.0 S4 2.0

1.5 1.5 0 0

I/I I/I 1.0 1.0

0.5 0.5

0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

177

1.2 S4 + (S)-Ketoprofen 1.2 S4 + (R)-Ketoprofen

1.0 1.0

0.8 0.8 0 0

0.6 I/I I/I 0.6

0.4 0.4 0.2 0.2 0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

S4- (S)-Phenylalanine 2.0 S4- (R)-Phenylalanine

2 1.5 0 0 I / I /

1.0 1

0.5

0 0.0 400 440 480 520 560 600 400 440 480 520 560 600 Wavelength (nm) Wavelength (nm)

1.4 S4 + Acetate 1.6 S4 + Benzoate 1.4 1.2 1.2 1.0 1.0

0.8 0

0

I/I 0.8 I/I 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 400 450 500 550 600 400 450 500 550 600 Wavelength (nm) Wavelength (nm)

178

Binding isotherms (S4)

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i 0.6 i 0.6 /(I / (I ) )

0

0

I-I 0.4

I-I 0.4 ( (

0.2 0.2 6 -1 5 -1 Ka = 5.14 x 10 M 0.0 0.0 Ka = 3.39 x 10 M -4 -4 0.0 1.0x10 2.0x10 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Ibuprofen [M] R-Ibuprofen [M] (S)-Ibuprofen [M] (R)-Ibuprofen [M]

1.0 1.0

0.8

0.8 ) ) 0 0 -I i -I

i 0.6

0.6 /(I ) / (I ) 0

0

I-I 0.4 (

I-I 0.4 (

0.2 0.2 6 -1 5 -1 0.0 Ka = 4.04 x 10 M 0.0 Ka = 1.95 x 10 M -4 -4 -5 -4 -4 -4 0.0 0.0 5.0x10 1.0x10 1.5x10 2.0x10 1.0x10 2.0x10 S-Phenyl Propanoate [M] R-Phenyl(R)-2-Phenylpropanoate Propanoate [M] [M] (SS-)2--2Phenylpropanoate-Phenylpropanoate [M] [M] R-2-Phenylpropanoate [M]

1.2 1.0 1.0 0.8 ) ) 0 0.8 0 -I -I i i 0.6 / (I / (I ) 0.6 )

0 0 I-I

I-I 0.4 ( 0.4 (

0.2 0.2 4 -1 4 -1 0.0 0.0 Ka = 5.74 x 10 M Ka = 6.80 x 10 M 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S-Mandelate [M] R-Mandelate [M] (S)-Mandelate [M] (R)-Mandelate [M]

179

1.0

0.8 ) 0 -I i 0.6 / (I )

0

I-I 0.4 (

0.2

4 -1 0.0 Ka = 6.31 x 10 M 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 S- Ket opr ofen [M] (S)-Ketoprofen [M]

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i i 0.6 0.6 /(I /(I ) )

0

0

I-I 0.4 I-I

0.4 ( (

0.2 0.2 5 -1 5 -1 0.0 K = 3.80 x 10 M 0.0 Ka = 3.85 x 10 M a -5 -4 -4 -4 0.0 1.0x10-4 2.0x10-4 0.0 5.0x10 1.0x10 1.5x10 2.0x10 [(S)-Phenylalanine] (M) (R[(R)-Phenylalanine)-Phenylalanine [M]] (M) (S)-Phenylalanine [M]

1.0 1.0

0.8 0.8 ) ) 0 0 -I -I i 0.6 i 0.6 / (I / (I ) )

0 0

I-I 0.4 I-I 0.4 ( (

0.2 0.2

4 -1 0.0 K = 9.99 x 10 M 0.0 5 -1 a Ka = 1.09 x 10 M 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 0.0 5.0x10-5 1.0x10-4 1.5x10-4 2.0x10-4 Acetate [M] Benzoate [M] Acetate [M] Benzoate [M]

180

Mass spectrometric study of sensor and guest complexes

The ESI mass spectrum of the complex of S1 and ibuprofen.

The ESI mass spectrum of the complex of S2 and ketoprofen.

The ESI mass spectrum of the complex of S3 and mandelate.

181

DFT calculations

Computational calculations were performed at the B3LYP/6-31G* level using Spartan’08 (Wavefunction, Inc.). The optimized structure of macrocycle S2 is shown below.

The coordinates for the optimized structure of S2. O 0.0141823 -3.08537 -0.145549 O -3.15485 -4.73098 0.127977 O -6.62712 2.53816 -0.193541 O 2.85923 -0.636566 0.671278 O 5.23912 1.90515 -0.158489 O -1.02648 6.86818 -1.32694 O -7.06529 7.32716 0.471553 O -5.37122 8.6653 0.159367 N -2.56295 -2.50247 0.0329258 N -3.69286 -0.517883 -0.0601322 N -4.53831 1.58837 -0.163926 N 2.93299 1.87532 -0.105796 N 1.27846 3.41327 -0.430639 N -0.564262 4.72118 -0.647126 N -5.88614 7.5505 0.208105 C 2.392 -3.24456 -0.204746 C 3.60104 -3.46982 -0.941698 C 4.88046 -3.48168 -0.32319 C 6.02386 -3.70847 -1.05927 C 5.94867 -3.93471 -2.45395 C 4.72472 -3.94305 -3.08226 C 3.52587 -3.72167 -2.35087 C 2.26145 -3.74996 -2.98287 C 1.08701 -3.56305 -2.26953 C 1.17638 -3.31773 -0.857863 C -0.833037 -4.22291 0.0633297 C -2.31037 -3.84419 0.0706586 C -3.80221 -1.84733 0.0320306 C -5.03488 -2.51118 0.121619 182

C -6.18044 -1.72082 0.0966153 C -6.10015 -0.33331 -0.00964399 C -4.81412 0.211937 -0.078538 C -5.40776 2.64957 -0.21921 C 2.42911 -2.90758 1.25344 C 2.1999 -3.90785 2.25433 C 2.00529 -5.27862 1.93347 C 1.78614 -6.21567 2.92078 C 1.74955 -5.83119 4.28199 C 1.94949 -4.51492 4.62858 C 2.1873 -3.52512 3.63599 C 2.42253 -2.1751 3.98426 C 2.67883 -1.20996 3.0218 C 2.67709 -1.60471 1.64164 C 4.18704 -0.118497 0.52203 C 4.17639 1.33099 0.0465938 C 2.5887 3.16591 -0.527597 C 3.51166 4.10195 -1.01783 C 3.00601 5.33662 -1.41616 C 1.64504 5.6256 -1.32485 C 0.826952 4.61159 -0.816915 C -1.39995 5.78021 -0.908839 C -5.01756 6.38674 -0.0699141 C -5.57278 5.11304 -0.0254139 C -4.75497 4.00974 -0.287479 C -3.40549 4.2212 -0.595911 C -2.85397 5.50835 -0.613084 C -3.67645 6.60953 -0.360673 H -3.54371 1.76366 -0.126373 H -0.593898 -4.68675 1.02796 H -0.69757 -4.97157 -0.721669 H -0.943182 3.90258 -0.190219 H 4.73785 -0.712838 -0.215233 H 4.73363 -0.146938 1.46921 H -1.75573 -1.89512 -0.0654517 H 4.94824 -3.32018 0.747864 H 6.99098 -3.7168 -0.564128 H 6.85765 -4.10658 -3.02343 H 4.65305 -4.12454 -4.15186 H 2.20489 -3.92866 -4.05266 H -5.06774 -3.58726 0.208064 H -7.1563 -2.19406 0.163562 H -6.96997 0.306694 -0.0304547 H 2.15047 1.29073 0.169895 H 1.64343 -7.2587 2.65194 H 1.57055 -6.57822 5.05003 183

H 1.93389 -4.20924 5.67191 H 2.41124 -1.88576 5.03107 H 4.5615 3.85352 -1.07486 H 3.68528 6.09018 -1.80543 H 1.22525 6.5761 -1.62082 H -6.62137 4.9678 0.201511 H -2.77883 3.3825 -0.884821 H -3.27168 7.61324 -0.393537 H 2.04409 -5.58679 0.893297 C -0.178011 -3.59157 -2.92077 C -1.25352 -3.64489 -3.48892 C -2.50476 -3.70528 -4.16657 C -4.97367 -3.82264 -5.5161 C -2.61248 -3.29762 -5.51483 C -3.6603 -4.17426 -3.51626 C -4.88392 -4.23377 -4.17964 C -3.82642 -3.35415 -6.17808 H -1.72956 -2.93283 -6.03066 H -3.59972 -4.5019 -2.48296 H -5.75371 -4.60119 -3.64777 H -3.91763 -3.04134 -7.21343 C 2.93089 0.14244 3.38415 C 3.18893 1.28965 3.7007 C 3.50178 2.62404 4.08555 C 4.13948 5.25383 4.86875 C 3.07681 3.13134 5.33377 C 4.25183 3.46577 3.2443 C 4.56891 4.76693 3.62687 C 3.38968 4.42378 5.71885 H 2.49741 2.49444 5.99518 H 4.60368 3.09622 2.286 H 5.15061 5.38605 2.95378 H 3.0674 4.8194 6.67686 O -6.11554 -3.83986 -6.2566 O 4.39863 6.50278 5.34217 C 5.15043 7.3922 4.52831 H 6.15094 6.99418 4.31532 H 5.24333 8.31494 5.10338 H 4.63525 7.60386 3.58237 C -7.31174 -4.29796 -5.64244 H -7.22109 -5.34117 -5.31299 H -8.08597 -4.22648 -6.40813 H -7.58876 -3.67058 -4.78546

184

APPENDIX D. SUPPLEMENTARY DATA FOR CHAPTER IV

1H and 13C NMR Spectra of 1

AA115 7.49 7.49 7.47 7.35 7.34 7.34 7.33 7.32 7.30 7.30 7.30 7.29 7.29 7.28 7.27 7.27 7.27 5.32 4.11 4.09 4.07 3.51 3.47 3.46 3.45 3.44 2.67 2.65 2.40 15000

10000

5000

0 53.87 3.00 6.36 3.09 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 f1 (ppm)

AA115-NMR 172.39 77.33 145.29 128.67 128.06 126.66 77.07 76.82 71.36 66.71 54.40 1E+05

50000

0

-50000

-1E+05

190 180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 f1 (ppm)

185

1H and 13C NMR Spectra of 2

AA116 15000 9.36 5.12 5.11 5.09 5.09 4.59 4.33 4.32 4.30 4.30 2.50 2.50 2.50

10000

5000

0

-5000 9.00 2.99 3.07 2.98 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

AA116

165.40 63.20 53.01 40.25 40.08 39.91 39.75 39.58 1E+05

50000

0

-50000

-1E+05

9095100105110115120125130135140145150155160165 8085 354045505560657075 f1 (ppm)

186

1H and 13C NMR Spectra of 3

AA124 8.41 8.19 8.17 7.80 7.78 7.51 7.49 7.48 7.13 7.12 6.97 6.96 6.35 5.26 5.25 4.23 4.22 4.12 4.11 4.10 2.95 2.78 2.21 1.98 1.97 1.97 1.47 3000

2000

1000

0 2.88 3.00 3.01 6.23 2.91 3.00 2.48 2.90 2.97 2.89 19.45

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 f1 (ppm)

AA124 10000 124.69 122.91 117.35 113.63 0.86 0.69 0.53 0.36 182.30 168.99 151.51 131.21 129.28 126.86 126.00 125.80 97.18 64.94 56.42 44.37 26.61 0.20 0.03 -0.14

5000

0

-5000

-10000

190200210 180 90100110120130140150160170 80 -10010203040506070 f1 (ppm)

187

1H and 13C NMR Spectra of 4

AA121 2500 3.48 2.89 2.24 1.45 0.10 4.12 4.14 4.43 4.43 4.45 4.46 5.33 6.90 7.11 7.12 8.57 8.55 8.35 8.34 8.24 8.23 7.50 7.49 7.48 7.47 7.29 7.29 7.29 7.28 7.28

2000

1500

1000

500

0 18.34 2.77 2.97 3.00 2.71 3.16 6.03 3.14 2.82 2.94

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 f1 (ppm)

AA121 30000 133.97 166.94 131.24 130.06 129.75 129.38 128.86 123.24 115.49 77.29 77.04 76.78 66.01 55.46 45.47 26.92

20000

10000

0

-10000

-20000

180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 10 f1 (ppm)

188

UV-Vis absorption titrations of S1

3.0 20mM S1 + Acetate m 2.5 20 M S1 + Benzoate 2.5

2.0 2.0

1.5 1.5

1.0 1.0 Absorbance Absorbance

0.5 0.5

0.0 0.0

200 250 300 350 400 450 200 250 300 350 400 450 Wavelength, nm Wavelength, nm

3.0 3.0 m 20 M S1 + Chloride 20µM S1 + Citrate

2.5 2.5

2.0 2.0

1.5 1.5

1.0 1.0 Absorbance Absorbance

0.5 0.5

0.0 0.0

200 250 300 350 400 450 200 250 300 350 400 450 Wavelength, nm Wavelength, nµ

3 20 µM S1+ glutarate 3 20 µM S1 + Maleate

2 2

Absorbance Absorbance 1 1

0 0

200 250 300 350 400 450 200 250 300 350 400 450 Wavelength (nm) Wavelength (nm) 189

m 20µM S1 + Oxalate 2.5 20 M S1 + Malonate 2.5

2.0 2.0

1.5 1.5

1.0 1.0 Absorbance Absorbance 0.5 0.5

0.0 0.0

200 250 300 350 400 450 200 250 300 350 400 450 Wavelength, nm Wavelength, nµ

3.5 20mM S1 + Tricarballylate 3.0

2.5

2.0

1.5

Absorbance 1.0

0.5

0.0

200 250 300 350 400 450 Wavelength, nm

190

UV-Vis absorption titrations of S2

2.5 20µM S2 + Acetate 2.5 20µM S2 + Benzoate

2.0 2.0

1.5 1.5

1.0 1.0 Absorbance Absorbance 0.5 0.5

0.0 0.0

200 250 300 350 400 450 200 250 300 350 400 450 Wavelength, nµ Wavelength, nµ

2.5 20µM S2 + Citrate 2.5 20mM S2 + Chloride 2.0

2.0 1.5

1.5

1.0

1.0 Absorbance

Absorbance 0.5

0.5

0.0 0.0 200 250 300 350 400 450 200 250 300 350 400 450 Wavelength, nµ Wavelength, nm

3.0 3.0 20mM S2 + glutarate 20mM S2 + Maleate

2.5 2.5

2.0 2.0

1.5 1.5

1.0 1.0 Absorbance Absorbance

0.5 0.5

0.0 0.0

200 250 300 350 400 450 200 250 300 350 400 450 Wavelength, nm Wavelength, nm 191

2.5 µ 20µM S2 + Oxalate 20 M S2 + Malonate 2.5

2.0 2.0

1.5 1.5

1.0 1.0 Absorbance Absorbance 0.5 0.5

0.0 0.0

200 250 300 350 400 450 200 250 300 350 400 450 Wavelength, nµ Wavelength, nµ

3.0 20mM S2 + Tricarballylate 2.5

2.0

1.5

1.0 Absorbance

0.5

0.0

200 250 300 350 400 450 Wavelength, nm

192

Fluorescence titrations of S1

12 Acetate 5 Benzoate 10 4 8 0

0 3 I /

6 I /

4 2

2 1

0 0 360 400 440 480 520 560 600 360 400 440 480 520 560 600 Wavelength (nm) Wavelength (nm)

4 Citrate Glutarate 2 3 0 0 I / I /

2 1

1

0 0 360 400 440 480 520 560 600 360 400 440 480 520 560 600 Wavelength (nm) Wavelength (nm)

Malonate Maleate 3 1.5

0 2 0 I /

1.0 I /

1 0.5

0 0.0 360 400 440 480 520 560 600 360 400 440 480 520 560 600 Wavelength (nm) 193

2 Tricarballylate Oxalate 4

3 K = 7.44 x 104 M-1 0 a 0 I /

I / 1

2

1

0 0 360 400 440 480 520 560 600 360 400 440 480 520 560 600 Wavelength (nm) Wavelength (nm)

Chloride 1.0 0 I / 0.5

0.0 360 400 440 480 520 560 600 Wavelength (nm)

194

Fluorescence titrations of S2

1.2 1.2 Acetate Benzoate 1.0 1.0

0.8 0.8 0 0 0.6 0.6 I / I /

0.4 0.4

0.2 0.2

0.0 0.0 400 440 480 520 560 600 640 680 400 440 480 520 560 600 640 680 Wavelength (nm) Wavelength (nm)

1.0 Citrate 1.0 Glutarate-S2

0.8 0.8 0 0 0.6 0.6 I / I /

0.4 0.4

0.2 0.2

0.0 0.0 400 440 480 520 560 600 640 400 450 500 550 600 650 Wavelength (nm) Wavelength (nm)

1.0 Maleate-S2 1.0 Malonate-S2

0.8 0.8

0 0.6

0 0.6 I / I /

0.4 0.4

0.2 0.2

0.0 0.0

400 450 500 550 600 650 400 450 500 550 600 650 Wavelength (nm) Wavelength (nm)

195

1.0 Oxalate-S2 1.0 Tricarballylate-S2

0.8 0.8

0 0.6 0 0.6 I / I /

0.4 0.4

0.2 0.2

0.0 0.0

400 450 500 550 600 650 400 450 500 550 600 650 Wavelength (nm) Wavelength (nm)

1.0 Chloride-S2

0.8

0 0.6 I /

0.4

0.2

0.0

400 450 500 550 600 650 Wavelength (nm)

196

Fluorescence titrations isotherms of S2

1.0 1.0

0.8 0.8

0.6

0 0.6 -I f

/I 0 0.4 0.4 I-I (I-Io)/(If-Io)

0.2 0.2 4 -1 K = 2.08 x 10 M 4 -1 0.0 a 0.0 Ka = 4.29 x 10 M 0.00000 0.00004 0.00008 0.00012 0.00016 0 50 100 150 200 [Glutarate] (M) [Tricarballylate] (µM)

1.0

0.8 ) 0

-I 0.6 i /(I

) 0 0.4 I-I ( 0.2

3 -1 0.0 Ka = 8.28 x 10 M 0.00000 0.00005 0.00010 0.00015 [Citrate] (µM)