assisted combinatorial chemistry methods: selectivity is not limited. ..... 1.2
Modern Combinatorial Chemistry Methods: Receptor Assisted Combinatorial.
Pseudo-dynamic combinatorial chemistry
David Soriano del Amo
Department of Chemistry, McGill University, Montreal October 2008
A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Doctor of Philosophy
© David Soriano del Amo
Abstract Pseudo-dynamic combinatorial chemistry (pDCC) combines the synthesis, screening and destruction of combinatorial libraries to kinetically resolve inhibitors based on their affinity for a target. In our proof-of-principle studies, a library of dipeptides was formed in the presence of a target, carbonic anhydrase (CA), and a destruction mechanism, a protease. Since the target and the protease were separated by a dialysis membrane, only the proportion of dipeptides that was not bound to the target was available for destruction and therefore, the rate of hydrolysis of the pseudo-dynamic combinatorial library (pDCL) could be correlated to the librarie’s relative affinity for the target.
The first set of proof-of-principle pDCC experiments were found to be flawed. Rather than reflecting the binding affinity of the library for the target, the final product distribution reflected the protease’s substrate specificity. The main problems with these pDCLs were insufficient and imbalanced rate of destruction of the peptides in the absence of the target, pH drift, and insufficient permeability across 1000 MWCO cellulose ester membranes. A dipeptide amide based pDCL that was efficiently cleaved by thermolysin was designed. The new pDCL could be used in pH 7.5 75 mM HEPES, 16.6 mM CaCl2 buffer, provided that the chambers were limited by 3500 MWCO membranes. A 4th generation pDCC experiment that evolved reflecting the library’s affinity for the target was performed.
In order to study pDCC behavior a simplified pDCC mimic (pDCCm) was designed. In pDCCm, synthesis was replaced by a static library of compounds and destruction by dilution.
The design of a pDCCm based kinetic model led to a pDCCm simulator
(pDCCmSim). PDCCmSim confirmed the main advantage of pDCC over other receptor
i
assisted combinatorial chemistry methods: selectivity is not limited.
pDCCmSim also
predicted that slight differences in the relative permeability of the pDCLs, the number of library members, their relative binding affinity and the experimental stoichiometry can influence the outcome of pDCC experiments dramatically. A set of guidelines that facilitate pDCL optimization is proposed.
Résumé La chimie combinatoire pseudo dynamique (pDCC) combine la synthèse, le criblage et la destruction de libraires combinatoires afin de sélectionner cinétiquement des inhibiteurs sur la base de leur affinité pour une cible. Nos études préliminaires ont été basées sur une librairie de dipeptides formés en présence de l’anhydrase carbonique (CA) comme cible et détruits grâce à une protéase. Étant donné que la cible et la protéase étaient séparées par une membrane de dialyse, seuls les dipeptides non complexés avec la cible étaient susceptibles d’être détruits. Ainsi, la vitesse d’hydrolyse des membres de la librairie combinatoire pseudodynamique (pDCL) peut correspondre aux affinités relatives des molécules pour la cible.
Les premières expériences prototypes de pDCC étaient défectueuses. Plutôt que de refléter l’affinité des membres de la librairie pour la cible, la distribution finale des produits reflétait la préférence de la protéase pour les substrats. Les principaux problèmes de ces pDCL étaient: une vitesse de destruction des peptides insuffisante et inégale en l’absence de la cible, une déviation progressive du pH et une perméabilité insuffisance au travers les membranes d’ester de cellulose 1000 MWCO. Une pDCL basée sur des amides dipeptidiques, aptes à être efficacement clivés par thermolysine a été élaborée, et utilisée avec succès.
ii
Afin d’étudier les pDCC, une expérience modèle simplifiée (pDCCm) a été créée. Dans ce modèle, la synthèse a été remplacée par une librairie statique de molécules et la destruction par une dilution. Le design d’un modèle cinétique basé sur la pDCCm a mené à la création d’un programme de simulation de pDCCm appelé pDCCmSim. L’utilisation de pDCCmSim a confirmé l’avantage principal de la pDCC sur les autres méthodes de chimie combinatoire assistée par un récepteur : la sélectivité n’est pas limitée. pDCCmSim a aussi prédit que de petites différences dans la perméabilité relative d’une pDCL, le nombre de membres de la librairie, leur relative affinité de liaison ainsi que la stœchiométrie peuvent influencer drastiquement les résultats d’une expérience de pDCL. Une série de conditions qui facilitent l’optimisation d’une pDCL est proposée.
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Acknowledgements The list of people who in one way or another have helped me get to this point is very large. I’ll start with the people who inspired me when I first got to Canada: the late Mrs. Kinsley and Mr. Gary Kirchner. Mrs. Kinsley taught general chemistry and Mr. Kirchner taught physics at Centennial Academy, where I attended CEGEP. I really don’t know how they did it because at the time I couldn’t really understand English but their teaching methods somehow managed to replace constant boredom for curiosity. The two of them did such a good job that it was difficult for me to choose between physics and chemistry once done at Centennial, but at the end I joined the chemistry department at McGill. Thank you both.
My first year at McGill was pretty ordinary and was mostly devoted to perfecting my English at social events. In my second year I had one of those spells of really good “bad luck” when I took the second introductory organic chemistry class with Dr. Patrick G. Farrell, a teacher a lot of people feared because he tested understanding rather than knowledge. I saw this as a positive as I am not very good at holding onto facts. After a few lectures I was in awe! His ability to reduce seemingly complex explanations to a basic set of relatively simple rules was amazing to me. He made everything sound so easy that I felt I didn’t need to study to go to his exams. It didn’t go that well as you can imagine…Nevertheless, even though I wasn’t anywhere near being his best student he always found the time to answer all of my questions. His patience and support ultimately made me consider a life in the lab. Thank you Dr. Farrell.
The summer of 1999 I decided to give research a go and looked for a job around the chemistry department. It was not looking very good initially as my academic record was iv
quite unimpressive to say the least but somehow, Dr. Mark P. Andrews decided to give me a chance and hired me as a summer student to work in the development of polymer optical fibers. Working for him was such a pleasure that in 1999 every September’s resolution – “I’m going to work hard this year” – actually stuck and eventually got me a ticket into grad school. Thank you Dr. Andrews.
In 2003, after one year stays in the labs of Dr. D.S Argyroploulos and Dr. R. J. Kazlauskas, I joined Dr. Jim Gleason’s lab, where I have been ever since. Jim is first and foremost a teacher and he is quite good at it. Jim’s high expectations, make his students lives a bit difficult at times but on the flip side, once he’s done with you, you are guaranteed two things: firstly, you’ll have a very good feel for chemistry and secondly, you’ll say “I understaaand” a lot more you’d like to. I think that over the past five years Jim has developed a lot of trust in my ability. I have to admit that at times I didn’t know whether to feel happy for my self or sad for him as I was sure he had completely lost it.
The best thing about Jim’s lab has been the people I’ve had the chance to get to know though. Dr. Alain Ajamian was a senior grad student when I joined in. He was somewhat of a mentor during my first days in the lab and has later become one of the best friends anyone can have. Soon to be Dr. Chris Drouin also deserves especial mention and not because he’s half man half duck. Chris had the misfortune to be placed in the fume hood right next to mine some 4.5 years ago. He’s helped me in so many ways through all this years…I am deeply indebted to him for that. The same goes for Two of Three, also known as Erica Tiong. These two just know how to get a smile out of me, even in my most miserable state. Thanks. Tim Cernak and I have also spent a lot of time together. It is too
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bad he graduated last year. The lab is starting to fill with spiders again and I am not sure who will help with the HCl this time. It’s also been great to share the lab with the likes of Monzhammed, the smoothest moving chemist you’ll ever see, Jon, the best fed grad student in the world, James, the group’s exhibitionist and worse fed grad student you’ll ever see (Yeah James, the ham and cheese croissants…), Marc, the berry enhancer, Spiderman Tan, Mr. Bin, “Do your dishes” Lise, Cowgirl Neenah, “I’ll beat you one day at poker” Daniel and Racing Melanie.
Many other members of the department have also been important through all this years. The contribution of Drs Bohle, Moitessier and Auclair to my degree has been invaluable. Without their generosity I wouldn’t have been able to do half the analysis I have done. I am also deeply in debt with Dr. Ronis. His ability to translate words into mathematics ultimately led to pDCCmSim. I also need to thank Drs. Lennox, Harpp, Cosa and Kazlauskas for their constant support and useful suggestions. Georges Kopp, Fred Kluck, Bill Bastian, JeanPhilippe Guay, Rick Rossi and Weihua Wang are to blame for making ideas happen. Without their skills it’d be impossible for us to do the work that we do. I also have to thank Nadim Saadeh, Dr. Fred Morin and Dr. Alain Lesimple for maintaining the NMR and MS equipment and for running samples for us when we need to. Chantal Marotte and Sandra Aerssen also deserve special note. They are in charge of making sure the department runs smoothly. We’d be lost without them. I also have to thank Rae and Carla for being annoyingly fun to be with and Andrew O’donnell for being Andrew O’donnell. Finally, I have to thank Smelie for being patient with me. She’s put up with the unthinkable over the years and she’ll probably have to put up with lots more. But as they say love is blind and I
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am not about to remove the blind fold. She’s without a doubt the best thing I’ll steal from the department.
I’d like to dedicate this degree to my family. They are the ones who made this possible. I am grateful to my aunt for coming to Canada in the late 60s. I wouldn’t have been able to discover this great place had she not been here. I also need to thank my brother and sister. They’ve taken such good care of their little brother through the years. I didn’t quite realize for many years but God, I am not sure where it’d be if it wasn’t for the two of them. I also have to say I feel bad sometimes that I was the one who got to pursue this path. My brother would have been so much better at it… I also need to thank my parents. I will never really know the sacrifices they’ve had to make to get me here, specially my mom…Moltíssimes gràcies. Finally I need to thank my other family, the “cracks”: Oscar, Mikki, Sergi, Victor, Pau, Miguelón, Chaparro, Pepo, Palomo, Jordi i Ramon. The only thing that I regret from all this years is not having been able to spend more time with everybody listed in this paragraph.
I am also grateful to the Canadian Institute of Health Research (CIHR) for their financial support from 2004-2006. Their generous donation relieved me of TA duties, thus allowing me to focus on research. I also have to thank the Alma Mater travel fund. This research was supported by the National Sciences and Engineering Research Council of Canada (NSERC).
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Table of Contents Abstract ................................................................................................................................ i Résumé................................................................................................................................ ii Acknowledgements............................................................................................................ iv Table of Contents............................................................................................................. viii List of Figures .................................................................................................................. xiii List of Tables .................................................................................................................... xv Abreviations..................................................................................................................... xvi 1
Introduction................................................................................................................. 1 1.1
Combinatorial Chemistry.................................................................................... 3
1.1.1
Library preparation through parallel synthesis ........................................... 3
1.1.2
Library synthesis through split-pool ........................................................... 5
1.2
Modern Combinatorial Chemistry Methods: Receptor Assisted Combinatorial
Chemistry........................................................................................................................ 7 1.2.1
Glossary of Important Terms Employed in RACC..................................... 8
1.2.2
Dynamic Combinatorial Chemistry ............................................................ 9
1.2.2.1
Thermodynamics of DCC Systems................................................... 10
1.2.2.2
Exchange Processes in DCC............................................................. 12
1.2.2.3
Receptor Molding in DCC ................................................................ 14
1.2.2.4
Receptor Casting in DCC.................................................................. 16
1.2.2.5
Limitations of DCC........................................................................... 17
1.2.2.6
Novel Approaches to DCC ............................................................... 19
1.2.3 1.2.3.1 1.2.4
1.3
Target Accelerated Synthesis.................................................................... 22 Limitations of TAS ........................................................................... 25 Pseudo-dynamic combinatorial Chemistry ............................................... 26
1.2.4.1
Theory of pDCC ............................................................................... 27
1.2.4.2
Early developments in pDCC ........................................................... 31
1.2.4.3
Development of a Setup Suitable for pDCC..................................... 34
1.2.4.4
A pDCL of Carbonic Anhydrase Inhibitors...................................... 37
Thesis Synopsis................................................................................................. 40
viii
2
A pDCL of Carbonic Anhydrase Inhibitors: 2nd and 3rd Generation Systems......... 41 2.1
2nd Generation pDCC Design............................................................................ 41
2.2
2nd Generation pDCC Experiments................................................................... 43
2.3
2nd Generation pDCC Experiments: 2nd interpretation ..................................... 46
2.4
3rd Generation Library design: Solutions to 2nd Generation pDCC Experiments
problems........................................................................................................................ 51 2.4.1
Synthesis ................................................................................................... 51
2.4.2
Destruction – Buffer Interference ............................................................. 54
2.4.3
Destruction – Pronase’s Substrate Scope.................................................. 57
2.4.4
Diffusion ................................................................................................... 58 3rd Generation pDCC Experiments: .................................................................. 60
2.5 3
4
pDCL mimic ............................................................................................................. 65 3.1
Design ............................................................................................................... 65
3.2
pDCC Mimic Experiments ............................................................................... 68
3.3
Results and Discussion ..................................................................................... 68
3.3.1
One Cycle Experiments ............................................................................ 69
3.3.2
Four cycle pDCCm Experiments .............................................................. 79
3.3.3
Development of a Suitable pDCCm Kinetic Model ................................. 84
3.3.4
Simulation Experiments with pDCCmSim............................................... 87
3.3.5
pDCC Optimization ................................................................................ 100
3.3.6
Literature Precedent ................................................................................ 105
pDCL : Substrate Engineering and Proof-of-Principle Experiments..................... 108 4.1
Substrate Engineering ..................................................................................... 108
4.1.1
Substrate Engineering I: Destruction Optimization................................ 109
4.1.2
Substrate Engineering II: Binding of Dipeptide Amides to CA ............. 118
4.2
4th Generation pDCCm experiment ................................................................ 121
4.3
Aqueous Synthesis of Dipeptide Amides ....................................................... 123
4.4
Proof-of-Principle pDCC experiments ........................................................... 124
4.5
Future Work .................................................................................................... 135
5
Conclusions and Contributions to Knowledge ....................................................... 139
6
Experimental ........................................................................................................... 141
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7
References............................................................................................................... 180
8
Appendix................................................................................................................. 184 8.1
Chapter 2A – Representative chromatogram of early pDCC experiments…..184
8.2
Chapter 2B – Representative chromatogram of early pDCC experiments.* ... 185
8.3
Chapter 2C – HPLC/MS traces for repetition of 4x2 library experiment, 6x16h
cycles. Data point obtained at 96h. ............................................................................ 186 8.3
Chapter 3 – Representative pDCCmSim data set ........................................... 187
8.3.1
Input Parameters ..................................................................................... 187
8.3.2
Data Outputs – Screening Chamber Concentration ................................ 187
8.3.3
Data Outputs – Selectivity ...................................................................... 188
8.3.4
Data Outputs – Inhibitor Enzyme Complex Concentration in the Screening
Chamber 188 8.3.5
Data Outputs – Free Inhibitor Concentration in the Screening Chamber189
8.3.6
Data Outputs – Inhibitor Concentration in the Dilution Chamber.......... 189
8.3.7
Data Outputs – Inhibitor Concentration in the synthesis chamber ......... 190
8.3.8
Data Outputs – Tota Inhibitor Concentration in the Screening Chamber
with empirical data.................................................................................................. 190 8.4
Selected NMR data ......................................................................................... 191
8.4.1
AcPhgsaOH – 1H ..................................................................................... 191
8.4.2
AcPhgsaOH – 13C .................................................................................... 192
8.4.3
AcTyrBnsa – 1H ....................................................................................... 193
8.4.4
AcTyrBnsa – 13C...................................................................................... 194
8.4.5
BocAlaBnsa – 1H ..................................................................................... 195
8.4.6
BOCAlaBnsa – 13C .................................................................................. 196
8.4.7
BOCAla-N-Me-Bn – 1H ......................................................................... 197
8.4.8
BOCAla-N-Me-Bn – 13C ........................................................................ 198
8.4.9
BOCAlaTyrBnsa – 1H.............................................................................. 199
8.4.10
BOCAlaTyrBnsa – 13C............................................................................. 200
8.4.11
BOC-D-AlaBnsa – 1H.............................................................................. 201
8.4.12
BOCTyrBnsa – 1H ................................................................................... 202
8.4.13
BOCTyrBnsa – 13C .................................................................................. 203
x
8.4.14
BOCTyr-N-Me-Bn – 1H ......................................................................... 204
8.4.15
EtocLys(Etoc)OH – 1H ........................................................................... 205
8.4.16
EtocLys(Etoc)OH – 13C .......................................................................... 206
8.4.17
EtocLys(Etoc)PhgsaOH– 1H.................................................................... 207
8.4.18
EtocLys(Etoc)PhgsaOH– 13C................................................................... 208
8.4.19
EtocAlaAlaBnsa – 1H............................................................................... 209
8.4.20
EtocAlaAlaBnsa – 13C.............................................................................. 210
8.4.21
EtocAlaPhesaOH – 1H.............................................................................. 211
8.4.22
EtocAlaPhesaOH – 13C ............................................................................ 212
8.4.23
EtocAlaTyrBnsa – 1H............................................................................... 213
8.4.24
EtocAlaTyrBnsa – 13C.............................................................................. 214
8.4.25
EtocPheLys(Bzsa)OH – 1H ...................................................................... 215
8.4.26
EtocPheLys(Bzsa)OH – 13C ..................................................................... 216
8.4.27
EtocPheLys(Z)OH – 1H .......................................................................... 217
8.4.28
EtocPheLys(Z)OH – 13C ......................................................................... 218
8.4.29
EtocPheLys(Tos)OH – 1H....................................................................... 219
8.4.30
EtocPheLys(Tos)OH – 13C...................................................................... 220
8.4.31
EtocPheTyrBnsa – 1H............................................................................... 221
8.4.32
EtocPheTyrBnsa – 13C.............................................................................. 222
8.4.33
EtocTyrOH – 1H...................................................................................... 223
8.4.34
EtocTyrOH – 13C..................................................................................... 224
8.4.35
EtocTyrPhesaOH – 1H.............................................................................. 225
8.4.36
EtocTyrPhesaOH – 13C ............................................................................ 226
8.4.37
F4ZAlaAlaBn – 1H .................................................................................. 227
8.4.38
F4ZAlaAlaBn – 13C ................................................................................. 228
8.4.39
F4ZAlaAlaBnsa – 1H ................................................................................ 229
8.4.40
F4ZAlaAlaBnsa – 13C ............................................................................... 230
8.4.41
F4ZAlaAla-N-Me-Bn – 1H ...................................................................... 231
8.4.42
F4ZAlaAla-N-Me-Bn – 13C ..................................................................... 232
8.4.43
F4ZAlaOH – 1H ....................................................................................... 233
8.4.44
F4ZAlaOH – 13C ...................................................................................... 234
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8.4.45
F4ZAlaTyrBn – 1H .................................................................................. 235
8.4.46
F4ZAlaTyrBn – 13C ................................................................................. 236
8.4.47
F4ZAlaTyrBnsa – 1H ................................................................................ 237
8.4.48
F4ZAlaTyrBnsa – 13C ............................................................................... 238
8.4.49
HClAlaBn – 1H........................................................................................ 239
8.4.50
HClAlaBn – 13C ...................................................................................... 240
8.4.51
HClAlaBnsa – 1H ..................................................................................... 241
8.4.52
HClAlaBnsa – 13C .................................................................................... 242
8.4.53
HClAla-N-Me-Bn – 1H ........................................................................... 243
8.4.54
HClAla-N-Me-Bn – 13C .......................................................................... 244
8.4.55
HClPhgsaOMe – 1H ................................................................................. 245
8.4.56
HClPhgsaOMe – 13C ................................................................................ 246
8.4.57
HClTyrBnsa – 1H ..................................................................................... 247
8.4.58
HClTyrBnsa – 13C .................................................................................... 248
8.4.59
HClTyr-N-Me-Bn – 1H ........................................................................... 249
8.4.60
HClTyr-N-Me-Bn – 13C .......................................................................... 250
8.4.61
ZAlaAlaBn – 1H ...................................................................................... 251
8.4.62
ZAlaAlaBn – 13C..................................................................................... 252
8.4.63
ZAlaAlaBnsa – 1H.................................................................................... 253
8.4.64
ZAlaAlaBnsa – 13C................................................................................... 254
8.4.65
ZAlaAla-N-MeBn – 1H ........................................................................... 255
8.4.66
ZAlaAla-N-MeBn – 13C .......................................................................... 256
8.4.67
ZAlaBnsa – 1H.......................................................................................... 257
8.4.68
ZAlaBnsa – 13C ........................................................................................ 258
8.4.69
ZAla-D-AlaBnsa – 1H .............................................................................. 259
8.4.70
ZAla-D-AlaBnsa – 13C ............................................................................. 260
8.4.71
ZAlaTyrBn – 1H ...................................................................................... 261
8.4.72
ZAlaTyrBn – 13C..................................................................................... 262
8.4.73
ZAlaTyrBnsa – 1H.................................................................................... 263
8.4.74
ZAlaTyrBnsa – 13C................................................................................... 264
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List of Figures Figure 1.1: Traditional drug discovery (A) vs. parallel synthesis based drug discovery (B). .............................................................................................................................. 4 Figure 1.2: Split-pool library synthesis.............................................................................. 6 Figure 1.3: RACC approaches to drug discovery. ............................................................. 8 Figure 1.4: Diagram used by Goodwin and Lynn to display the 1st DCC example. ......... 9 Figure 1.5: Thermodynamics of exchange reactions. ...................................................... 13 Figure 1.6: Exchange Reactions Used in DCC................................................................ 14 Figure 1.7: Library of pPFm used by Sanders to display receptor casting...................... 15 Figure 1.8: Library of sugar containing disulfides used by Lehn to display receptor molding .................................................................................................................... 16 Figure 1.9: Approach to inhibitors of galactosyltransferase............................................ 21 Figure 1.10: Schematic Representation of TAS .............................................................. 23 Figure 1.11: Huisgen (Click) Reaction. ........................................................................... 24 Figure 1.12: Preparation of an HIV protease inhibitor through TAS. ............................. 25 Figure 1.13: Schematic representation of pDCC. ............................................................ 27 Figure 1.14: Evolution of a 2 member pDCC experiment............................................... 31 Figure 1.15: Two chamber set-up for early, proof-of-principle experiments................. 32 Figure 1.16: Comparison of exponential curves obtained from theory using S= 3.67 and experiment (*)............................................................................................................ 33 Figure 1.17: General coupling used to probe pH and concentration dependence. .......... 34 Figure 1.18: Three chamber pDCL setup. ...................................................................... 36 Figure 1.19: Early pDCC setup....................................................................................... 36 Figure 1.20: 1st Generation pDCL library of CA inhibitors. .......................................... 38 Figure 1.21: Results of a 1st generation pDCL with 4x12 h cycles. ................................ 39 Figure 2.1: 2nd generation pDCL .................................................................................... 42 Figure 2.2: 2nd Generation pDCL experiment. 4x2 library, 6x16 h cycles of 0.8 eq. of each acyl T-Tentagel................................................................................................. 43 Figure 2.3: 2nd Generation pDCL experiment. 4x2 library, 6x8 h cycles of 0.8eq of each acyl T-Tentagel. ........................................................................................................ 44
xiii
Figure 2.4: 2nd Gen. pDCL experiment. 4x2 library, 6x8 h cycles of 0.8eq of each acyl T-Tentagel. 2nd analysis ............................................................................................ 46 Figure 2.5: 2nd Gen. pDCL experiment. 4x2 library, 6x16 h cycles of 0.8eq of each acyl T-Tentagel. 2nd analysis ............................................................................................ 47 Figure 2.6: 2nd Gen. pDCL experiment. Repeat of 4x2 library, 6x16 h cycles of 0.8eq of each acyl T-Tentagel................................................................................................. 49 Figure 2.7: 2nd Gen. pDCL experiment. Repeat of 4x2 library, 6x16 h cycles of 0.8eq of each acyl T-Tentagel. HPLCMS trace at 96h. ........................................................ 50 Figure 2.8: Known strong metal ion chelators structurally analogous to bicine ............ 52 Figure 2.10: 3rd Generation 4x2 pDCL............................................................................ 62 Figure 2.11: Summary of conditions used in 3rd generation pDCL experiments. ........... 62 Figure 3.1: Ideal pDCC experiment................................................................................. 66 Figure 3.2: pDCL mimic set up. ...................................................................................... 67 Figure 3.3: pDCCm experiment with 4 eq. of each library member at the start. No cycles......................................................................................................................... 69 Figure 3.4: Two chamber infinite dilution experiment………………………………….73 Figure 3.5: Selectivity evolution in a one cycle pDCCm experiment. ............................ 77 Figure 3.6: 4 x 48 hour cycles pDCCm experiment.. ...................................................... 79 Figure 3.7: Evolution of selectivity in pDCCm experiment performed with 4 x 48 hour cycles......................................................................................................................... 81 Figure 3.8: Model used to develop of pDCCm kinetic theory........................................ 84 Figure 3.9: Theory vs. Experiment; 2 member pDCCm experiment. ............................. 87 Figure 3.10: Evolution of selectivity in a one cycle pDCCmSim experiment where 4×KiA = KiB; KiA = 1 µM. The Atlantic Ocean was used as dilution chamber (Vol. = 3.55×1023 cm3). 1×1200h cycle experiment. PA = PB = 1.26×10-5 s-1. [A] = [B] = 2×[E] = 3.34×10-4 M.. ............................................................................................... 89 Figure 3.11: Evolution of selectivity in a one cycle pDCCmSim experiment where 4×KiA = KiB; KiA = 1 µM. Dil. Chamber Vol. = 1.8 L. 1×4800h cycle experiment. PA = PB = 1.26×10- s-1. [A] = [B] = 2×[E] = 3.34×10-4 M. ....................................... 90 Figure 3.12: Evolution of selectivity in a one cycle pDCCmSim experiment where 4×KiA = KiB; KiA = 1 µM. The Atlantic Ocean was used as dilution chamber (Vol. = xiv
3.55×1023 cm3). 1×1200h cycle experiment. PA = PB = 1.26×10-5 s-1. [A] = [B] = 2×[E] = 3.34×10-4 M. ................................................................................................ 91 Figure 3.15: Relationship between total inhibitor concentration relative to the target and yield at fixed selectivity. In that case the selectivity was set to 3............................ 96 Figure 3.16: Relationship between total inhibitor concentration relative to the target and time taken to reach a fixed selectivity. In this case the selectivity was set to 3. ..... 97 Figure 3.17: Analogous set of experiments where the [I]0total/[Target]0 was the only variable. This ratio was set to 20 in plot A, 2 in plot B and 0.2 in plot C. .............. 97 Figure 4.1: General structure of a dipeptide inhibitor of CA......................................... 109 Figure 4.2: 4th Generation pDCL. ................................................................................. 120 Figure 4.3: Kinetic evolution of a pDCCm experiment using 2 equivalents of each sulfonamide member of the 4th generation library:................................................. 122 Figure 4.4: 2nd generation pDCL cell………………………………………………….127 Figure 4.5: 4th generation proof-of-principle pDCC experiment………………………130 Figure 4.6: A) Chromatogram obtained by injecting 30 µL of the liquor in the screening chamber 172 h after the start of the experiment. B) Same as A) but spiked with an equimolar mixture of the pDCL components. ........................................................ 132 Figure 4.7: Evolution of selectivity for the 2 × 48 h cycle 4th generation pDCL experiment............................................................................................................... 133 Figure 4.8: Proposed 5th generation pDCL. Solid support is made of 100% PEG. ...... 138
List of Tables Table 1.1: pH dependence of coupling in Figure 1.17..................................................... 35 Table 1.2: Concentration dependence of coupling at pH 9.0 and 10.. ............................. 35 Table 2.1: Conditions used in preliminary two chamber experiments versus pDCLs..... 51 Table 2.2: Measured Ki of compounds used in preliminary 3rd generation pDCL.. ........ 64 Table 3.1: Measured Ki of compounds used in preliminary pDCCm experiments.. ....... 69 Table 3.2: P in 12000 kD MWCO RC membrane vs. MW ratios. .................................. 72 Table 3.3: Effect of cycle time on yield and time at fixed selectivity. ............................ 83 Table 3.4: Parameters used to validate pDCCmSim........................................................ 86
xv
Table 3.5: Effect that different experimental stoichiometries have on the conversion, yield and the time it takes for the resolution process to reach a fixed selectivity ratio in a simple two inhibitor system............................................................. 95 Table 3.6: pDCC behavior in cycle containing experiments I......................................... 99 Table 3.7: pDCC behavior in cycle containing experiments II...................................... 100 Table 4.1: Substrate engineering: dipeptide side chain analysis................................... 110 Table 4.2: Substrate engineering: dipeptide side chain analysis with sulfonamide at K. ....................................................................................................................... 112 Table 4.3: Protease screening: Subtilisin substrate scope............................................. 115 Table 4.4: Protease screening: Elastase substrate scope.. ............................................. 116 Table 4.5: Protease screening: Thermolysin substrate scope........................................ 118 Table 4.6: Ki values of selected sulfonamidated dipeptide amides. .............................. 119 Table 4.7: Inhibitor permeability across a 3500 MWCO RC cellulose membrane versus molecular weight........................................................................................... 121 Table 4.8: Comparison of 1st and 2nd generation pDCL cell properties......................... 127
Abreviations AA
amino acid
Ac
acetyl
AChE
acetylcholine esterase
Ala
alanine
Aq
aqueous
Ara
arabinose
Arg
arginine
Asn
asparagine
Asp
aspartic acid
Bicine
N, N-bis(2-hydroxyethyl)glycine
Bn
benzyl
Bnsa
4-sulfonamido-benzyl
BOC
tert-butoxycarbonyl
xvi
Bz
benzoyl
Bzsa
4-sulfonamido-benzoyl
CA
carbonic anhydrase
Cbz
benzyloxycarbonyl
CE
cellulose ester
CHCl3
Chloroform
Con A
concanavalin A
d
doublet
DCC
dynamic combinatorial chemistry
DCL
dynamic combinatorial library
DCM
dichloromethane
dd
doublet of doublets
ddd
doublet of doublet of doublets
DMAP
4-(dimethylamino)pyridine
DMF
N,N–dimethylformamide
DMSO
dimethylsulfoxide
DNA
deoxyribonucleic acid
DRAM
3.697 mL
EDC•HCl
1-(3-dimethylaminopropyl)-3ethylcarbodiimide hydrochloride
EPPS
4-(2-Hydroxyethyl)-1piperazinepropanesulfonic acid
eq.
equivalents
ESI
electrospray ionization
Et
ethyl
EtOAc
ethyl acetate
Etoc
ethyloxycarbonyl
F4Z
tetrafluorobenzyloxycarbonyl
fM
fentomolar
g
gram(s)
Gal
galactose
xvii
Glc
glucose
GluNHOH
L-glutamic
Gly
glycine
h
hour(s)
HATU
2-(1H-7-Azabenzotriazol-1-yl)--1,1,3,3-
acid γ−monohydroxamate
tetramethyl uronium hexafluorophosphate HBTU
O-Benzotriazole-N,N,N’,N’-tetramethyluronium-hexafluoro-phosphate
HEPES
(4-(2-hydroxyethyl)-1piperazineethanesulfonic acid )
HOBt
1-hydroxybenzotriazole
HPLC
high performance liquid chromatography
Hz
hertz
J
coupling constant, flux
Ka
affinity constant
Kd
dissociation constant
Ki
inhibition constant
Ks
equilibrium constant of synthesis
kon
rate constant for enzyme inhibitor complex formation
koff
rate constant for enzyme inhibitor complex brakeup
kD
kilodalton
L
litre
Leu
leucine
Lys
Lysine
m
milli, multiplet
M
moles per litre, metal
m/z
mass to charge ratio
Man
mannose
xviii
Me
methyl
MIC
minimum inhibitory concentration
mL
millilitre
mmol
millimole
mol
mole
MS
mass spectrometry
MW
molecular weight
MWCO
molecular weight cut off
n
normal
ND
not determined
nM
nanomolar
NMR
nuclear magnetic resonance
P
Permeability
p
quintet
pDCC
pseudo-dynamic combinatorial chemistry
pDCCm
pDCC mimic
pDCCmSim
pDCCm experiment simulator
pDCL
pseudo-dynamic combinatorial library
PEG
polyethylene glycol
pH
-log[H+]
Ph
phenyl
Phe
phenylalanine
Phesa
4-sulfonamidophenylalanine
Phg
Phenylglycine
Phgsa
4-sulfonamido phenylglycine
PhMe
toluene
pKa
-log(Ka)
pNPA
para-nitrophenol acetate
Pr
propyl
Pro
proline
RACC
receptor assisted combinatorial chemistry
xix
RC
regenerated cellulose
RNA
ribonucleic acid
RT
room temperature
s
singlet
SM
starting material
T
target
t
tertiary
t
triplet
TAS
targuet accelerated synthesis
TEA
triethylamine
tert
tertiary
TFA
trifluoroacetic acid
THF
tetrahydrofuran
Tos
tosyl
Troc
trichloroethyloxycarbonyl
T-tentagel
tetrafluorophenol tentagel
Tyr
tyrosine
UV
ultraviolet
v/v
volume by volume comparison
Val
Valine
vDCL
virtual dynamic combinatorial library
w/w
weight by weight comparison
Z
benzyloxycarbonyl
μg
microgram
μM
micromolar
~
approximately
˚C
degree Celsius
xx
1 Introduction The theory of evolution through natural selection was first proposed by Darwin in 1859 in On the origin of the Species.
In this work Darwin proposed that natural selection in living
organisms occurs because some organisms possess hereditary traits that are beneficial for their survival in a particular environment.1 He reasoned that these organisms are more likely to survive and reproduce, thus passing on these traits to a new generation. Over long periods of time, this biased reproduction ultimately results in the elimination of the weaker members of a species.
In a broader sense, natural selection explains how certain species are able to adapt to environmental changes. In most instances this adaptive process is usually very slow and remains barely noticeable in a single human life span. Multi-cellular organisms with low mutation rates and longer life durations are probably better suited for evolution in a slow changing environment. However, it is quite apparent that the action of man on Earth is dramatically increasing the speed of environmental change and most species cannot adapt fast enough. The present rate of species extinction is estimated by some experts to be 1000 to 10000 times faster than the expected natural extinction rate.
Smaller bodies such as DNA viruses and bacteria however are better suited for fast environmental change. Even though the rate of mutation of their nuclear genomes per generation is not faster than for most animals and plants, bacteria and DNA viruses adapt to
1
a fast changing environment by maturing fast and proliferating with astonishing ease.2 As a result, the list of bacterial strains resistant to known antibiotics increases every day.
Even more difficult to treat are RNA viruses such as HIV or influenza. These viruses are known to cause acute, severe illness, including severe respiratory disease, hemorrhagic fever and encephalitis, and have a high case fatality rate.3 Fighting these viral infections is a very difficult challenge as these acellular organisms combine fast proliferation with a 103 fold increase in the rate of mutation per generation as compared to humans.2 It is the interplay between fast mutation and fast proliferation that makes these infectious agents so difficult to combat. Nowadays for example, infected patients need to be administered cocktails of several antiretroviral drugs designed to inhibit the activity of several different strains of the virus in order to extend their lives.3-5
Lessons learnt from Darwin’s principles of natural selection and evolution, have also had a profound influence on many other fields of science. For instance, the genetic makeup of single cell organisms such as E. coli is often altered by biochemical engineers to generate RNA and proteins with properties not found in nature. Another application for example resides in the combinatorial optimization of the algorithms that are often used in computer science and mathematics to tackle problems with difficult solutions. In that case, a solution, or a good approximation, is usually found by efficiently scanning the solution space. This same type of combinatorial optimization is also used very frequently in chemistry. This field of science is known as combinatorial chemistry.
2
1.1 Combinatorial Chemistry Combinatorial chemistry is a technique that allows the efficient preparation of libraries of structurally related compounds. The synthesis of these combinatorial libraries can be carried out using solid phase synthesis. In solid supported synthesis the necessary reagents are used in large excess so that reactions can be driven to completion. Once the reaction is complete, the polymeric support containing the product is rid of any remaining reagents and sideproducts through repeated solvent washing and filtration cycles. This simple synthetic procedure can be used to prepare combinatorial libraries as assemblies of pure compounds (parallel synthesis) or as mixtures of solid supported compounds, one bead containing one compound (split-pool synthesis).
1.1.1
Library preparation through parallel synthesis
Prior to the introduction of combinatorial chemistry, compounds were prepared in a linear, step-wise fashion, one reaction vessel at a time using standard solution phase protocols. Any intermediates that were prepared on route to the targeted compounds were purified and fully characterized so that the reactivity of each subsequent step could be controlled. Once the final product was obtained, the activity of the substrate was tested against a particular target in a low throughput fashion. After the data was analyzed, a new molecule was designed and the process was repeated (Figure 1.1 A).
Combinatorial libraries are prepared with higher efficacy through parallel synthesis. In parallel synthesis, libraries of compound analogues are also prepared in a stepwise fashion. However, the same reaction is applied to multiple reaction vessels at a time, each one
3
containing one common reactant or reagents and another different but structurally related reactant or reagent. For example, a library of 96 compounds containing a biaryl moiety can be prepared very quickly in a plate containing 96 wells by cross-coupling 96 different aryl bromides with phenyl boronic acid to give a single product in each well. This way, 96 different but structurally related structures are obtained, each well containing one sole product. If the synthetic procedure applied to each well is more complex and lengthy, solid phase synthesis may be used to simplify purification. After assembly, the library is usually screened without complete purification and without doing extensive characterization of the obtained products (Figure 1.1 B).
Figure 1.1: Traditional drug discovery (A) vs. parallel synthesis based drug discovery (B).6
The number of compounds (Ntotal) that can be obtained through parallel synthesis can be described by the following equation:
Ntotal = NR1 x NR2 x NR3 x …x NRn
(1)
where N represents the number of different constituents used per point of diversity and R1, R2, R3,…,Rn are the points of diversity. Even though this combinatorial approach represents a significant improvement over traditional synthesis, it has drawbacks. The total number of
4
molecules that can be generated per time period is still low, which complicates the preparation of large libraries for example. The amount of target necessary to screen the library is also large, as each compound is assayed independently. In addition, the method usually requires the development of a new assay for each target.
1.1.2
Library synthesis through split-pool
The combinatorial chemistry technique that grants the highest output of compounds is known as split and pool. As opposed to parallel synthesis, where analogues are prepared at the same time, one reaction vessel per analogue, in split-pool each vessel contains a complex mixture of all library members each loaded on a solid support bead. Since each one of the vessels is used to subject the library to a different derivatization, rather than single compounds mixtures are always obtained (Figure 1.2). These mixtures can be separated by picking the solid support beads, one by one.
5
Figure 1.2: Split-pool library synthesis
In a system where the order of addition of the reagents matters (i.e., peptide libraries), the number of compounds that can be obtained though split and pool is:
N Total = A × n m
(2)
where A is the number of starting materials, n is the number of building blocks used in each split pool step (typically 10) and m is the number of steps. In a system where the order of addition does not matter on the other hand (i.e., AB = BA), the number of compounds that can be formed per library is:
6
N (n, m) =
(n + m − 2)! (n − 1)!(m − 1)!
(3)
Comparing equations 1 and 2 shows that the number of compounds that can be generated through split and pool is much larger than through parallel synthesis.
The superior output of the split-pool technique poses some deconvolution problems though.
A split-pool target library always contains very small amounts (few hundred
picomols) of a very large number of compounds (up to 10 split pool cycles worth usually), each loaded on one sole solid support bead. Consequently, the identification of the active compounds in a split-pool library can be rather grueling. Polymer beads are separated and their contents uncovered. This is usually handled through the judicious installation of molecular tags at each synthetic step. Molecular tags are markers that spell the chemical path followed by a library member along the combinatorial reaction coordinate. Even though, as a rule, tags are easier to analyze than their associated compounds, tagging can be complex, cumbersome and error prone at times.7
1.2 Modern Combinatorial Chemistry Methods: Assisted Combinatorial Chemistry
Receptor
In receptor assisted combinatorial chemistry (RACC) a library of compounds is prepared in the presence of a target. Libraries prepared in this way may evolve and adapt to their environment yielding libraries of biased composition because the target can influence the kinetics and/or the thermodynamics of synthesis. There are three RACC techniques that
7
have been developed to date: dynamic combinatorial chemistry (DCC), target (or receptor) accelerated synthesis (TAS) and pseudo-dynamic combinatorial chemistry (pDCC).
Figure 1.3: RACC approaches to drug discovery.8
1.2.1
Glossary of Important Terms Employed in RACC
Template, Target or Receptor: Substance with which library members interact. Molding: The template is a small molecule. The DCL forms a receptor for that small molecule. Casting: The target is a macromolecule. The DCL forms molecules that bind to specific parts of the macromolecule. Amplification (A): Ratio of the concentration of compound I N when the experiment is done in the presence of a target as compared to when it is done in its absence
AI N =
[ I N ]T arg et [I N ]
Selectivity (S) : Ratio of the amplification of two library members relative to the ratio of their affinity constants
8
AI A S IA = IB
1.2.2
AI B AI Ka I B = A Ka I A AI B Ka I a Ka I B
Dynamic Combinatorial Chemistry
The use of reversible reactions in template directed synthesis was introduced in 1992 by Goodwin and Lynn.9 In this work, DNA was used to template the assembly of favored complementary strands from a mixture of nucleotides. The assembly was performed using conditions that favored the reversible imine exchange of a library of amines and aldehydes. As a direct consequence of Le Chatelier’s principle, the addition of a DNA to the rapidly equilibrating mixture of nucleotides favored the formation of substances that produced a more stable duplex combination (see Figure 1.4). Due to the instability of imines in water, the authors added excess NaBH3CN to the buffer mixture. Rather than following imines the author’s quantified the resulting amine reduction products.
Figure 1.4: Diagram used by Goodwin and Lynn to display what is now considered the 1st DCC example.9
9
Progress in the field was slow in the next few years but boomed once the groups of Sanders and Lehn got involved. In 1997 Sanders showed that the equilibrium distribution of a library of compounds that was under dynamic exchange could be altered by the addition of metal ions. Acid catalyzed transesterification was the dynamic process used in this study.10 At around the same time Lehn’s group showed that libraries under dynamic exchange could be used effectively to find compounds that bound to biological targets other than DNA. Carbonic anhydrase was the receptor they targeted in those early studies. Based on the findings of Goodwin and Lynn, Lehn’s group chose to prepare the library using imine exchange in the presence of NaBH3CN.11 1997 is often considered the beginning of dynamic combinatorial chemistry as these two papers were the first to use the term. Since then the field has exploded yielding more than 300 contributions that have appeared in all the major journals from groups from all over the world.12
1.2.2.1 Thermodynamics of DCC Systems The evolution that a dynamic combinatorial library experiences when a receptor is added to a rapidly equilibrating mixture is a thermodynamic effect. The Gibbs free energy change (ΔG) that occurs when a moles of A at concentration [A] react with b moles of B at [B], etc…is given by:
u ⎡ ⎛ ...[Y ] y [ Z ] z ⎞ ⎤ o ⎟⎟ ⎥ ΔG = − RT ⎢ln K − ln⎜⎜ a b ⎢⎣ ⎝ [ A] [ B ] ... ⎠ ⎥⎦
10
(4)
where R is the ideal gas constant, T is temperature, Ko is the equilibrium constant of the system, y and z are the number of moles of products Y and Z that are produced at concentration [Y ] and [Z ] . At equilibrium ΔG = 0 and therefore:
⎛ ...[Y ] y [ Z ] z ⎞ ⎟⎟ ln K = ln⎜⎜ a b ⎝ [ A] [ B] ... ⎠
u
o
(5)
In a DCC system there are two reversible processes that occur simultaneously: dynamic exchange and selective binding (equation 6).
(6)
Binding of a molecule by a receptor is a spontaneous and exothermic process for which ΔG binding can be described by equation 4. Thus, if the receptor binds Q, a product in the
dynamic exchange, the dynamic combinatorial library (DCL) will need to adapt to the presence of the receptor and generate more products so that the equilibrium condition in equation 6 can be satisfied.
ΔG can also be defined by equation 7, where ΔH is the
enthalpy of binding, T is the temperature and ΔS is the entropy of binding. ΔG = ΔH − TΔS
(7)
In order to maximize the loss of free energy, or in other words, in order for the system in equation 6 to reach a new thermodynamic minimum ΔS needs to stay as small as possible; 11
the system needs to remain as disordered as possible. Accordingly, the formation of more of product Q is the most favorable process. In this system, the degree of library reorganization caused by binding will be directly proportional to the binding affinity of Q for the receptor and the amount of receptor present. Thus, in a DCC experiment the receptor is always used in stoichiometric amounts.
1.2.2.2 Exchange Processes in DCC Before it was realized that rapidly exchanging mixtures of compounds could be used as reporters in combinatorial chemistry, dynamic equilibrium processes were seen as a handicap by synthetic chemists. Reactions with accessible kinetic barriers that yielded thermodynamic products of energy similar to their starting materials were generally low yielding. The reason for that was that starting materials and products existed in a dynamic equilibrium. The thermodynamic parameters of the system limited the amount of product that such a reaction could afford.
Over time however, procedures that were able to drive the reactions to completion based on Le Chatelier’s principle were developed. The position of the synthetic equilibrium was usually shifted either by using excess reagents, by derivatizing some of the products or by simply removing them.
12
Figure 1.5: In a dynamic system at equilibrium, the relative concentration of starting materials (SM) and products (P) is determined by ΔG. The time the system needs before reaching equilibrium on the other hand depends on the transition state energies (TS).
The interest in DCC induced a renaissance in the study of reactions that yield mixtures that are under thermodynamic equilibrium that has already lasted a decade.13 For the most part research is focusing on the optimization of conditions rather than the discovery of new transformations. Finding conditions that guarantee the fast interconversion of the library members and the stability of the template can be difficult sometimes, especially when the target of the DCL is a biological receptor. A recent review describes exchange processes that have been used in DCC14 (see Figure 1.6).
13
Figure 1.6: Exchange Reactions Used in DCC. a) Transesterification; b) Transallylesterification c)Transamidation; d) Aldol exchange; e) Transthioesterification; f) Michael reaction; g) Acetal metathesis; h) Thioacetal metathesis; i) Pyrazolotriazone metathesis; j) Imine exchange; k) Hydrazone exchange; l) Oxime exchange; m) Olefin metathesis; n) Alkyne metathesis; o) Disulfide exchange; p) Diels-Alder reaction; q) MetalLigand exchage; r) Hydrogen bond exchange.14
1.2.2.3 Receptor Molding in DCC The example that best displays the potential of DCC in receptor molding to date was published by the group of Prof. Sanders in 2005.15 This is one of the few examples where a DCC experiment actually yielded an otherwise unpredictable result thus validating the use of libraries that can respond to the presence of templates.16 The experiment aimed at finding an artificial receptor for the neurotransmitter acetyl choline (ACh). The DCL library was prepared using acyl hydrazone exchange. Library formation was initiated with the addition of excess trifluoroacetic acid (TFA) (43 equivalents) to a 20 mM solution of a peptide monomer pPFm at room temperature. All of the species detected over the course of the
14
experiment are shown in Figure 1.7. The cyclic dimer, the cyclic trimer and the cyclic tetramer were the only species observed when the library was equilibrated in the absence of ACh. 44 days after initiation of the exchange, the experiment that contained ACh reached equilibrium.
Unexpectedly, the authors found that the library selected for a catenane
(interlinked ring structure). The yield of formation of the catenane was also very high (67%). The dissociation constant of the ACh-Catenane adduct was measured to be 100 nM. As will be discussed in section 1.2.2.5, the large amplification of the catenane and the selectivity of the experiment are rather unusual as DCLs usually have much lower A and S values.
Catenane diastereomers Figure 1.7: Library of pPFm.15
15
1.2.2.4 Receptor Casting in DCC The first to realize the potential of receptor casting through DCC was J. M. Lehn. In one of many examples, in the year 2000 his group reported that a plant lectin called Concanavalin A (Con A) selected from a DCL a disulfide linked carbohydrate dimer that exclusively contained mannose.17 This was an expected result as the natural substrate of Con A is a trisaccharide composed of three mannose units. In order to simplify the analysis of the library, the authors immobilized the enzyme on a support so that unbound substances could
Figure 1.8: Library of sugar containing disulfides.18
be filtered off. Even though all mannose containing dimers could be detected after working up the samples, the symmetric dimer was by far the compound found in highest concentration. Unfortunately, the dissociation constant of the disulfide dimer-Con A adduct was not reported.
16
1.2.2.5 Limitations of DCC The two DCC examples shown above are unique because the target selected for compounds that were significantly different from any other library members. These systems can be approximated by equation 6, where only 1 library member binds to the target. In general, however, large DCLs can be expected to produce multiple molecules that compete for the target. Consider the following approximation:
where SMIN is a starting material that forms IN. The equilibrium constant of synthesis is KsIN. Once formed, product IN binds to target T with an affinity constant KaIN. In this system the concentration of inhibitor IA can be described as:
KsI A =
[I A ] [ SM I A ]
(8)
KaI A =
[I A • T ] [ I A ][T ]
(9)
[ I A ]Total = [ I A ] + [ I A • T ]
Combining equations 8-10 and solving for [ I A ]Total yields equation 11.
17
(10)
[ I A ]Total = KsI A [ SM I A ](KaI A [T ] + 1)
(11)
The fraction of [ I A ]Total that the library contains relative to all other members is:
KsI A [ SM I A ](KaI A [T ] + 1) [ I A ]Total = [ I B ]Total + ... + [ I N ]Total KsI B [ SM I B ](KaI B [T ] + 1) + ... + KsI N [ SM I N ](KaI N [T ] + 1)
(12)
As stated above, the target is always used as a stoichiometric reagent in a DCC experiment. In a DCL of tight binders KaI N [T ] + 1 ≈ KaI N [T ] . Thus, equation 12 can be simplified to
KsI A KaI A [ SM I A ] [ I A ]Total = [ I B ]Total + ... + [ I N ]Total KsI B KaI B [ SM I B ] + ... + KsI N KaI N [ SM I N ]
(13)
Assuming ideal DCC conditions, that is that all the SM I N were found in equimolar amounts at the beginning of the DCC experiment and that the synthesis equilibrium of the library members lies far to the side of I N , equation 13 can be simplified into equation 14. Equation 14 shows the main limitation of DCC: the larger the number of tight binders in a DCL, [ I A ]Total KaI A = [ I B ]Total + ... + [ I N ]Total KaI B + ... + KaI N
(14)
the more difficult it is to detect the reorganization of the library when exposed to the target. Nevertheless, computational simulations estimate that libraries of 10-106 members may be used in DCC. For large libraries operating under ideal DCC conditions, the concentration of the best member appears to decrease with the square root of the library size.19
18
Amplification of compounds that do not bind to the target is another problem that DCLs can have. The boundary conditions under which a DCL needs to be operated such that amplification and binding affinity can be correlated have been derived.20 The computational study considered parameters such as library topology, affinity of the DCL for the target and the initial concentrations of the components of the DCC experiment. The model shows that, as a general rule, it is advantageous to use about a 10 fold excess of library members when competitive binding is expected. In that case, the amplification factors of binders correlates well with their affinity for the target.
The solubility of the library components may not permit increasing the concentration of the DCL by a factor of 10 sometimes, especially when dealing with biological targets and aqueous buffers.
Decreasing the target concentration is the most plausible solution.
Although this presents obvious financial benefits, it complicates library deconvolution. This section and section 1.2.2.1 show that library reorganization depends on the binding affinity of a substrate for the target, the amount of target, and the number of binders. Thus, reducing the target concentration diminishes the amplification of guest molecules.
1.2.2.6 Novel Approaches to DCC The deconvolution of DCLs may be simplified by working with what has been termed “virtual” dynamic libraries (vDCL). At equilibrium, the product content of these libraries is maintained just below the limit of detection. Compounds cannot be detected unless the library responds to the presence of the target by increasing the concentration of some of its members.20-22 Working in this detection regime however poses serious concerns. Since real
19
DCLs may not be iso-energetic for all compounds for example, the amplification factor required for the detection of selected species could be different and therefore, in vDCLs containing multiple binders, some binders could be mistaken for non binders. Furthermore, it is important to remember that expecting all the members of a DCL to respond to the excitation energy delivered by an analytical tool with the same intensity is rather unreasonable.
An alternative simplification of DCL analysis that involved separating unbound substances from bound compounds prior to analyzing the DCC mixture was proposed.20 In order to do so however, conditions that permitted freezing the dynamic exchange without altering the integrity of the receptor needed to be found first. This is probably feasible for inorganic templates but less so for biological receptors, as the range of conditions under which they are stable is usually rather restricted. Later on in this thesis, it will be shown that removing unbound substances can be accomplished effectively in the presence of a biological receptor if a pseudo-dynamic combinatorial chemistry (pDCC) set up is used.
Another alternative was recently reported by Beau and coworkers.23 For the first time they showed that DCLs could yield much higher As than was allowed by the thermodynamics of the system. Their DCL targeted galactosyltransferase, an enzyme involved in the synthesis of oligosaccharides. The natural substrate of this particular enzyme is UDP-galactose, an adduct of galactose and uridine diphosphate. Based on this substrate, the authors designed a library that was composed of a galactose acetal, a uridine acetal and set of diamine phosphodiester mimics as linkers (Figure 1.9). The combinatorial library was generated using imine exchange.
20
Figure 1.9: Approach to inhibitors of galactosyltransferase.
In DCC, imine exchange is usually induced under a slightly acidic pH and in the presence of NaBH3CN.24 These conditions however, were not applicable to this target. NaBH3CN (1.19 mM) in a 17 mM Tris–acetate buffer (pH 7.9), 6.8 mM MgCl2 containing 34% glycerol was used instead. The starting concentrations of aldehydes (82 μM each) and diamines (542 μM each), were in large excess as compared to the target (1.6 μM) because imine formation is not favored at the specified pH. Still, these conditions were not sufficient to induce the formation of 2:1 acetal:amine adducts. In fact, only 8 products were detected by HPLC. These stemmed from the 1:1 combination of the 2 acetals and the 4 diamines used in the experiment.
When the exchange was performed in the presence of the receptor the authors found that the target selected for three compounds as 1:1 adducts once more. While two of these compounds resulted from the combination of the nucleoside with the aromatic diamines, the third one stemmed from the sugar and the pyridine containing diamine. Surprisingly, the
21
concentration of these compounds well exceeded the amount of target present, or in other words, the compounds were amplified by more than what the thermodynamic parameters of the DCC system permitted.
The concentration of the 1:1 adduct composed of the
nucleoside and the diamines containing benzene for instance was amplified by a factor of 8. In terms of yield, the concentration of this same compound had surpassed that of galactosyltransferase by a factor of four. It is believed this occurred because the reduced library had lower binding affinity for the target than the imine library and therefore the amine products could not effectively compete for the enzyme’s active site.
This is one of the rare examples where the selectivity of a DCL largely exceeds 1 (see section 1.2.1 for a definition). Although this is a very exciting and promising finding, this behavior is system specific and is difficult to predict at this juncture. DCC procedures that upon library derivatization predictably and consistently yield stable molecules with diminished binding ability will have to be developed before this procedure can be of practical use. These types of exchange processes are currently under investigation.25
1.2.3
Target Accelerated Synthesis
As opposed to DCC, where the target strictly influences the position of the library’s synthetic equilibrium, in TAS the target influences the kinetics of synthesis of selected molecules by catalyzing the formation of an irreversible covalent bond. Catalysis occurs when the target is able to increase the effective molar concentration of the components of the library by bringing two complementary pieces in close proximity (Figure 1.10). This principle is often used in many biological processes but the products yielded are usually not
22
inhibitors of the assisting biomolecules because those biomolecules have evolved to stabilize the TS, not the products. In TAS however, the libraries evolve very differently as the optimization is geared towards diminishing the target’s activity and not towards its improvement. Early examples, applied this concept to prepare a small, thioether based library of carbonic anhydrase inhibitors,26 and either disulfide bond formation or olefin metathesis to prepare a library vancomycin dimers, built around a fragment of the vancomycin receptor.27,28
Figure 1.10: Schematic Representation of TAS
In the context of click chemistry, this concept was first introduced by the Sharpless group. In their seminal studies, small molecule triazole inhibitors of model biological targets were formed using target induced click chemistry. Click chemistry is perfectly suited for this type of experiment as triazoles are often found in drug like molecules. Furthermore, alkyl azides and alkynes react readily with each other when they are placed in close proximity or are activated with copper salts to produce [3+2] cycloadducts but require forcing conditions – neat and high temperature29 – and produce mixtures of regiosomers in the absence of a catalyst (Figure 1.11). This transformation called the Huisgen [3+2] cycloaddition is often referred to as the “click reaction”.
23
Figure 1.11: Huisgen (Click) Reaction.
The high concentration environment necessary to promote the “click” reaction under physiological conditions can be emulated if an alkyne and an alkyl azide bind to two adjacent sites on a receptor for example.30,31 Studies have shown in numerous occasions that such targets can be used to condense mixtures of azides and alkynes to yield high affinity triazole compounds.32 For instance, fM inhibitors of acetyl choline esterase were prepared in this fashion.31,33 The only prerequisite being that the selected triazole precursors contain linkers that permit enough orbital overlap for the [3+2] process to occur. Screening of TAS systems can be very easy when in-situ click chemistry is used as only compounds with binding affinity for the target should form. Facile library analysis renders target-guided insitu click chemistry the practical congener of “virtual libraries” in dynamic combinatorial chemistry.
The potential of TAS was recently shown when Sharpless and co-workers found a high affinity ligand of HIV protease using in situ click chemistry (Figure 1.12).34
The 1.7 nM
inhibitor was derived by uniting a 4.2 mM azide inhibitor with an alkyne. The authors reported that the alkyne was not an inhibitor of HIV protease. This does not mean that the enzyme could not host the alkyne though. Even though the alkyne may have bound with moderate or high affinity at a site adjacent to the active site, this area may not have interfered with the binding of the substrate used in the inhibition assay.
24
Figure 1.12: Preparation of an HIV protease inhibitor through TAS.
1.2.3.1 Limitations of TAS Since its conception in 2002,30 and until the aforementioned example on HIV protease,34 TAS had been performed on a variety of well-studied biological targets with known neighboring binding pockets.31,33,35-37
The library design was always based on known,
selective guests that had moderate (μM) to high affinity (nM) for the clefts of the target. fM inhibitors of acetyl choline esterase were prepared this way.31 TAS was viewed as an elegant technique with a lot of potential but that could not be applied to the plethora of human receptors for which structural knowledge is scarce. Even though the HIV protease example tackled some of these issues, TAS still requires a receptor with two adjacent binding pockets.
An important limitation of TAS is that it can miss appropriate target guests. This was clearly shown in work that targeted carbonic anhydrase II (CA II) published in 2004.36 The results obtained after TAS were compared to the binding affinity of all library members. It was
25
found that the experiment failed to select ~30% of the inhibitors, possibly because the click reaction relies on the proper alignment of the azide and the alkyne. As mentioned before, if these two functionalities do not adopt the right conformation in the binding site of the receptor, the cycloaddition will not occur, even though the resulting adduct might be a strong binder.
1.2.4
Pseudo-dynamic combinatorial Chemistry
The preparation of the library in pDCC is reminiscent of traditional combinatorial chemistry: a set of starting materials are randomly combined through an irreversible reaction that yields, with similar efficiency, all the possible substrate combinations (see Figure 1.13). The library, however, is prepared in the presence of a target and in the presence of a mechanism that destroys unbound substances, thus regenerating parts of the library precursors. We call this a pseudo-dynamic system because these regenerated library precursors can be reused in further synthetic cycles, thus mimicking an equilibrium process. As will be shown in the remainder of this thesis, synthesis recycling improves the selectivity profile of the library. Since a true state of equilibrium cannot be attained in pDCC, the selectivity is measured based on the concentration of the remaining library members at the conclusion of the experiment. As opposed to DCC, in pDCC the selectivity is not limited by the relative binding affinity of the library members and therefore, selectivity factors higher than one can be obtained.
26
Figure 1.13: Schematic representation of pDCC.
1.2.4.1 Theory of pDCC In pDCC, guest molecules are kinetically resolved in order of binding affinity. Poor binders are destroyed faster than good binders. The compound that is hydrolyzed slowest is the best binder in the mixture. As pDCC experiments progress, the concentration of the best binder increases exponentially relative to all other library members. This situation resembles a kinetic resolution of enantiomers.38 The theory derived in the next few paragraphs models the destruction of pDCLs based on this analogy.39
27
Consider a system with two inhibitors, A and B, that compete for binding to target T. P and Q are the products generated after A and B are metabolized at rate k 2 A and k 2 B respectively.
k2 A k2B
(15)
The rate of disappearance of compound A is
d [ A] = − k 2 A [ A] free dt
(16)
The total concentration of A ( [ A]total ) is described by summing the concentration of A that is bound to the target ( [ A]bound ) and the concentration of A that is found free in solution ( [ A] free );
[ A]total = [ A]bound + [ A] free
(17)
[ A] free depends on the dissociation constant ( Kd A ) of the T•A complex. Thus,
⎛ [T ] ⎞ ⎟ [ A]total = [ A]⎜⎜1 + Kd A ⎟⎠ ⎝
28
(18)
Solving for [ A] and substituting in equation 16 yields that A disappears at a rate
k Kd [ A] d [ A] = − 2 A A total dt Kd A + [T ]
(19)
When the target is in excess of the inhibitor Kd A 1),
both ratios cooperate thus accelerating the rate of resolution. For the remainder of this thesis, this type of evolution will be termed cooperative adaptation.
Equation 30 also showed that under conditions of excess target, the influence of the
[ A]1
[ B]1
ratio on the relative flux of the two compounds (
JA ) is maximized. In a system JB
composed of an equimolar mixture of two inhibitors that are in very large excess relative to the target for example, the relative free inhibitor concentrations would approximate 1
A ( [ ]1
[ B]1
≈1) and therefore the relative flux would be equal to the ratio of the permeability
constants (
JA = PA / B ). Under excess target however, [ A]1 and [ B ]1 strictly depend on the JB
dissociation constants of A and B from the target ( K d A and K d B ), while the influence of
PA / B remains constant. In general, a pDCL will evolve displaying its binding affinity for the
74
target only when the relative unbound concentration of its members in the screening chamber surpasses their reciprocal relative permeabilities (i.e., [ A]1
[ B]1
> PB
PA
).
In summary, at any point in time during a pDCC experiment, the relative flux of the library members will reflect their binding affinity for the target if and only if their relative unbound concentrations in the screening chamber surpass their reciprocal relative permeabilities (i.e.,
[ A]1
[ B]1
> PB
PA
).
Assuming that [ A]1
[ B]1
> PB
PA
, the resolution of any pair of
compounds will occur faster than expected from the ratio of their Kis if the pair undergo cooperative adaptation (i.e., PA / B and [ A]1
[ B]1
> 1). On the other hand, the resolution of
any pair of compounds will occur slower than expected if the pair undergo competitive adaptation (i.e., PA / B < 1 and [ A]1
[ B]1
> 1).
In principle, this type of analysis could be used to discuss pDCL evolution with great detail, provided that diffusion was rate limiting, as the free inhibitor concentrations in the screening chamber ( [ I n ]1 ) depended on the number of library members, their relative K i , and the experimental stoichiometry.
The cycle free experiment described in Figure 3.3 was performed with one full equivalent of each one of the library members at the start. Since 12 and 11 adapted cooperatively, the concentration of both compounds evolved synchronous to their K i s throughout the
75
experiment. Not only was 12 a better inhibitor than 11, but also diffused slower in the absence of the target (see Table 3.2).
Early on in the experiment on the other hand, the concentration of the other library members could not be correlated to their binding affinity for the target. While the inhibitors were well in excess, the target’s binding pocket was almost exclusively populated by the two best inhibitors and therefore it was likely that the proportion of the three remaining inhibitors found free of target would be large. Thus, assuming that an equimolar mixture of inhibitors was added at the beginning of the experiment, it was likely that the ratio of their free concentrations approached 1 and therefore, it was possible that [ A]1
[ B]1
≤ PB
PA
for
the remaining three compounds.
However, at around the 50 hour mark, time when the target became the excess reagent, Ki/concentration synchronicity was achieved for all library members and was maintained for the remainder of the experiment. Indeed, 50 hours after the start of the experiment, the concentration of 12 > [11] > [24] > [23] >> [8], thus matching the order of K i s. As stated previously, in equation 30, the weight born by the unbound inhibitor concentrations is maximized when the target is in excess. Since the K i discrepancy between the library members was larger than the difference in their permeability, it was understandable that over time, the relative concentration of the library members relaxed into the expected arrangement (i.e., [ A]1
[ B]1
> PB
PA
for all compounds in the library).
76
The relationship between the relative unbound concentration of library members and their relative permeability also simplified the discussion of selectivity.
A plot depicting the
evolution of selectivity in the cycle free experiment can be found in Figure 3.5. This selectivity plot clearly showed that when P and Ki cooperated, the selectivity increased quickly (navy blue line, see [ A]1
[ B]1
and PB
A P and Ki competed (pink line, see [ ]1
PA
[ B]1
data for 23 in Table 3.2) but it did not when
and
PB
data for 24 in Table 3.2). The
PA
selectivity of 11 increased slowly due to the proximity of both Ki values, even if P and Ki cooperated (light blue line).
5
[EtocLeuPhesaOH]/[EtocXPhesaOH]
4.5 4 23
3.5
24
3
11
2.5 2 1.5 1 0
24
48
72
96
120
144
168
Tim e (h)
Figure 3.5: Selectivity evolution in a one cycle pDCCm experiment.
Figure 3.5 also showed that the rate of increase in selectivity slowed down after the target became the excess reagent, possibly due to a decrease in the overall rate of inhibitor disappearance. The resolution of 11 relative to 12 appeared to be most affected by this change in stoichiometry as the selectivity increase for 11 appeared to plateau after 100h. At
77
around the same time, the selectivity for 24 also decelerated, although to a lesser extent. These results were rather surprising, as they appeared to contradict the theory of pDCC described in section 1.2.4.1, which predicted an exponential increase in selectivity with respect to conversion. On the other hand, the evolution of selectivity for 23 appeared to support this theory, as even though the selectivity increase for 23 appeared to slow down for the 72 - 96h time span, the selectivity appeared to increase sharply after the 120th hour. This seemingly contradictory behavior is actually common in experiments where the inhibitors are first in excess but finish as the limiting reagent. While the inhibitors are in excess, there is competition for the target’s active site and thus accelerating the resolution process. When the inhibitors become the limiting reagent on the other hand, the rate of resolution slows down first but increases sharply later on, thus emulating exponential behavior. The effect that the relative experimental stoichiometry can have on the evolution of pDCLs will be further described in section 3.3.4.
The extent to which the selectivity curves decelerated around the time when the target became the excess reagent was related to the substances relative flux (the higher
less pronounced the deceleration was) and therefore depended on both
K iA
K iB
and
JA the JB PB
PA
.
Thus, since the Ki discrepancy between the two compounds was large, and their relative adaptation was collaborative, the deceleration period was shortest for the 23-24 pair (from 60 h to 100 h). Faster resolution permitted the observation of the expected fast increase in selectivity common of mature exponential functions, starting at 120 h.
For all other
compounds however, even if their relative flux was larger than one, this observation was not made, likely because the experiment was stopped too early. The Ki difference between 11
78
and 12 was small, and the 23-24 pair evolved under competitive adaptation. Nonetheless, the extent of deceleration also reflected the compound’s relative binding affinity for the target as it was more pronounced for 11, the 2nd best inhibitor, than it was for 24.
3.3.2
Four cycle pDCCm Experiments
As previously mentioned, the only difference between the one cycle experiment described above and the pDCCm experiments that will be described in this section was the mode of addition of the library; rather than adding four equivalents of each inhibitor at the beginning of the experiment, one equivalent of each library member was added at four, regular time intervals that defined the beginning of each of the four cycles.
Figure 3.6 shows the concentration changes the library endured over the course of an experiment performed with four 48 h cycles.
80 70 60 23 24 12 11 8
%CA
50 40 30 20 10 0 0
24
48
72
96
120
144
168
192
216
240
264
Time (h)
Figure 3.6: 4 x 48 hour cycles pDCCm experiment. 5 mL of a 1.67×10-4 M solution of CA was placed in the screening chamber and the “synthesis” chamber was topped with 5mL of a 1.67×10-4 M of each library member stock. The apparatus was then dipped in a jar containing 1.8 L of buffer and the mixture was shaken at 150 rpm at room temperature. The contents of the “synthesis” chamber were replaced every 48h with a fresh 5 mL batch of the library stock. The dilution buffer was replaced every 48h. The samples used to generate the plot were once more taken from the screening chamber.
79
As could be expected for experiments of this type, the plot showed clear signs of periodicity. Just as it happened in cycle free experiments, the concentration of the library in the screening chamber increased for the first quarter of each cycle and then declined until a fresh batch of dipeptides was added. At the beginning of each cycle the concentration of the library members in the screening chamber could not be correlated with their K i possibly because the relative unbound concentration of the 3rd and 4th best inhibitors – 23 and 24 respectively – was too small to overcome relative permeability ( [ A]1
[ B]1
≤ PB
PA
); 24
diffused 15% slower than 23 in the absence of the target. As it happened in the one cycle experiment, the relative ratio of 11 and 12 was larger than the relative ratio of 11 and 24, yet the competitive adaptation of 24 relative to 12 appeared to be less pronounced in this instance.
Soon after the beginning of each cycle the library reflected the expected composition. While 50 hours were necessary for the concentrations to match the order of K i s in the one cycle experiment, only 24 hours were needed in the first cycle of this experiment. Proving that the inhibitors were indeed building up in the system, the time to reach K i /concentration synchronicity shortened with each subsequent cycle (only 12 hours were necessary in the fourth). Furthermore, the use of cycles appeared to simplify the identification of the nonbinders in the mix (note the concentration of 23 relative to 8 in Figure 3.3 and Figure 3.6). While 96 h were necessary for the concentration of 8 to drop to 2% relative to CA in the one cycle experiment, this compound was only detectable at the end of the second cycle in this four cycle experiment.
80
The selectivity plot also displayed the expected periodic behavior; the selectivity ratio increased within a cycle but dropped at its onset. As expected, the amplitude of the selectivity curves was more pronounced when the K i difference between the compounds being compared was large (Figure 3.7).
[Best inhibitor]/[Inhibitor X]
4.5 4 3.5 23
3
24 2.5
11
2 1.5 1 0
24
48
72
96
120
144
168
192
216
240
264
Time (h)
Figure 3.7: Evolution of selectivity in pDCCm experiment performed with 4 x 48 hour cycles. See caption in Figure 3.6 for experimental conditions.
Indeed, the rate of increase in selectivity was sharpest when the concentration of the best inhibitor was compared to the concentration of 23, followed by 24, but was less appreciable when the concentration of the best inhibitor was compared to the concentration of the 2nd best inhibitor, 11.
The biggest point of discrepancy between the results obtained in the one cycle and this four cycle experiment was that, while the selectivity ratios appeared to plateau towards the end in the former, there was little evidence for that in the latter.
81
As mentioned during the
discussion the selectivity profile of the one cycle experiment, section 3.3.4 will show the effect that changes in relative experimental stoichiometry can have in the outcome of pDCLs. The differences displayed by the two selectivity profiles shown in Figure 3.5 and Figure 3.7 can be thus ascribed to the fact the inhibitors were in excess most of the time in cycle containing experiments, while the target was the excess reagent for a total of about 90 hours in the one cycle experiment.
Although interesting, this finding did not illuminate any possible advantages that multi-cycle experiments might have over their single cycle analogues. Since the theory presented in section 1.2.4.1 predicted that the selectivity in pDCLs is not limited, we felt that any experimental optimization should focus on reducing the time needed to reach a particular selectivity goal, while maximizing the yield.
The only scalars that change in a pDCC
experiment are time, yield relative to the target, and selectivity.
To that effect, a common selectivity point was found in all the pDCCm experiments that were performed with 11, 12, 23, 24 and 8 (1 cycle, 4×24 h cycles, 4×36 h cycles and 4×48 h cycles). Table 3.3 summarizes the results.
82
Selectivity (12 : 11 : 24 : 23 ) = 1 : 1.5 : 2.2 : 2.8 Cycle Time (h)
Total Yield Rel. to CA
Time (h)
(eq.) 0 (1 cycle)
0.64
90
24
0.80
155
36
1.15
168
48
1.21
192
Table 3.3: Effect of cycle time on yield and time at fixed selectivity. Total Yield = yield of 11+12+23+24.
As shown in Table 3.3, as the cycle time increased so did the yield and the time necessary for the library to reach the specified selectivity ratio.
The yield increase, synonymous to
increasing the detection limit of the experiment, was understandable since in longer cycle times the addition of a fresh batch of inhibitors was performed later on in the resolution process, when the reaction mixture contained a higher proportion of the best inhibitor. Higher yield however occurred at the expense of time. Thus, while fewer cycles should be used in pDCLs designed to probe binding to unstable, or easily accessible receptors (faster access to results), multi-cycle experiments should be performed with stable, difficult to obtain targets.
Even though the selectivity data presented herein did not confirm the predicted exponential increase in selectivity, it reflected that the discussion of selectivity in compartmentalized pDCC systems needed to be handled with caution. The selectivity results shown in Figure 3.5 and Figure 3.7 seemed ambiguous at first, but became quite explicit once Fick’s law of diffusion was taken into account. For any pair of compounds, the selectivity increased slower than expected when the faster diffusing compound was the better inhibitor
83
(competitive adaptation), but the opposite occurred when the faster diffusing compound was the poorer inhibitor (cooperative adaptation). In order to explore if these two types of adaptation satisfied the predicted exponential behavior, a kinetic pDCCm model based on Fick’s law of diffusion was developed.
3.3.3
Development of a Suitable pDCCm Kinetic Model
Since in pDCCm transmembrane diffusion was rate limiting, the kinetic pDCCm model was devised by adapting Fick’s law of diffusion (equation 27) to a three chamber system. This type of analysis was used to a two chamber system in section 3.3.1.
Consider the setup shown in Figure 3.8,
Figure 3.8: Model used to develop of pDCCm kinetic theory.
and let C- be the concentration of substance C found in the synthesis chamber, C± the concentration found in the screening chamber and C+ the concentration found in the destruction/dilution chamber. Other than C, the screening chamber contains target T, which binds C with an equilibrium constant K eq , and K eq = K i−1 .
84
In Figure 3.8, the flux of molecules passing through the screening chamber at any given time is given by, dC ± = − P (C ± − C − ) + P (C ± − C + ) − k off [T .C ± ] + k on [C ± ] × [T ] dt
(32)
where − P (C ± − C − ) describes the flux of molecules across the synthesis/screening boundary, + P (C± − C+ ) describes the flux of molecules across the screening/destruction chamber, and the remaining two terms account for the affinity of compound C for the target.
Finding solutions to this rate equation is difficult and beyond the scope of this thesis, especially when applied to a multi-component system, but could be accomplished with the aid of a computer. It is for that reason that pDCCmSim was coded. pDCCmSim is a computer program that allows the simulation of pDCCm experiments using easily measured experimental parameters such as the permeability of the individual library components, the area of the membrane (A), the volume of each of the chambers (V), the number of inhibitors, their initial concentration, their K i , and the target concentration.
In order to validate pDCCmSim however, a simple, two cycle, two inhibitor (11 and 12) pDCCm experiment was first performed and the results obtained compared to the computed data. Table 3.4 summarizes the parameters that were used for this simulation,
85
Cell
Inhibitor
EtocLeuPhesaOH
EtocPhePhesaOH
(12)
(11)
A (cm2)
8.36
P (×10-5s-1)
1.26 ± 0.05
1.34± 0.05
Vsynthesis (cm3)
5
Ki (×10-6 M)
1.0 ± 0.3
1.5 ± 0.4
Vscreening (cm3)
5
[I]o (×10-4 M)
3.34 ± 0.05
3.34± 0.05
Vdestruction (cm3)
1800
[CA]o(×10-4 M)
1.67 ± 0.1
1.67± 0.1
Table 3.4: Parameters used to validate pDCCmSim; 3500 MWCO RC membranes were used.
which were determined from a version of the cell described in Figure 2.9 (equipped with two 3500 MWCO RC membranes) modified to fit the setup described in Figure 3.2.
The kinetic evolution curve outputted by pDCCmSim closely resembled the data obtained for the analogous experiment in the laboratory.
Figure 3.9 shows how the inhibitor
concentrations present in aliquots originating from the screening chamber (dots) contoured the results predicted by pDCCmSim almost perfectly. The little bit of variance that can be observed when comparing theory and experiment was normal. The scalars shown in Table 3.4 were not exact values, but rather close estimates that contained a certain degree of uncertainty associated. Error margins were reported whenever possible. Furthermore, the theory that led to pDCCmSim was developed using equation 32, which considered concentration gradients rather than chemical potential gradients.
86
EtocLeuPhesaOH EtocPhePhesaOH
Figure 3.9: Theory vs. Experiment; 2 member pDCCm experiment. Total concentration in screening chamber of EtocLeuPhesaOH and EtocPhePhesaOH. Dots represent experimental data while lines represent simulation results. [i]center = free inhibitor concentration in the screening chamber; [Ai]center = enzyme inhibitor complex concentration in the screening chamber. The complete set of data given by pDCCmSim can be found in Appendix 3A.
3.3.4
Simulation Experiments with pDCCmSim
Delighted with the results obtained in the pDCCmSim validation experiment, we tested our understanding of pDCLs by running a vast array of simulations, changing one variable at a time. Given the large amount of data compiled, the next few paragraphs discuss trends rather than individual results. A representative example, corresponding to Figure 3.9 can be found in the Appendix.
The first set of simulations involved a simple system of two compounds, A and B, that diffused across the cell membranes described in Table 3.4 with P = 1.26 x 10-5 s-1. This P value was chosen so that the results obtained in these simulations could be compared to the available experimental data. The behavior of this pDCCm library was studied by altering the inhibition constant of compound B, the worse inhibitor, while maintaining the Ki of
87
compound A at 1.0 µM. Any consequences related to the experimental stoichiometry, and the number and frequency of cycles were also studied. A long experimental time was used for simulations where K i A ≤
K iB
5
.
Under these conditions, the simulated cell
necessitated modification as the volume of the dilution chamber was insufficiently large when compared to the other chambers. In fact, the [A]free and the [B]free reached equilibrium in all chambers of the cell in that instance. This problem was circumvented by setting the volume of the cell to 3.55×1023 cm3, the volume of the Atlantic Ocean.
The first set of pDCCmSim experiments was conducted to confirm that indeed, just as predicted by the pDCC model presented in section 1.2.4.1, at high conversion the selectivity in pDCC (i.e., [A]/[B]) could become much larger than the ratio of the Kis of the two compounds. For instance, when a pDCCmSim experiment was run with an equimolar mixture of two compounds (3.34×10-4 M each, K i A = 1 µM and
K iB
K iA
= 4 ) that had equal
permeability constants (PA = PB = 1.26×10-5s-1) and were in excess relative to the target (1.67×10-4 M), after 1000 h the selectivity was 29 (see Figure 3.10)
88
A)
Ki = 1 µM Ki = 4 µM
8.5% of inhibitors still remain after 1000h; 34% relative to target. Selectivity = 29
B)
[ A] [ B]
Figure 3.10: A) Kinetic evolution of a 2 inhibitor pDCCmSim experiment where 4×KiA = KiB; KiA = 1 µM. The Atlantic Ocean was used as dilution chamber (Vol. = 3.55×1023 cm3). 1×1200h cycle experiment. PA = PB = 1.26×10-5 s-1. [A] = [B] = 2×[E] = 3.34×10-4 M. [i]center = free inhibitor concentration in the screening chamber; [Ai]center = enzyme inhibitor complex concentration in the screening chamber. B) Evolution of selectivity for the experiment depicted in A).
When the volume of the dilution chamber was set to 1.8 L on the other hand, the concentration of the inhibitors equilibrated all through the cell over time and the selectivity relaxed into a plateau (see Figure 3.11). The selectivity was highest at 500 minutes because the best inhibitor was the compound with the highest concentration gradient across the
89
“synthesis” screening boundary early on (i.e., the target will sequester more of the better inhibitor).
[ A] [ B]
Figure 3.11: Evolution of selectivity in a cycle free pDCCmSim experiment where 4×KiA = KiB; KiA = 1 µM. Dil. Chamber Vol. = 1.8 L. 1×4800h cycle experiment. PA = PB = 1.26×10- s-1. [A] = [B] = 2×[E] = 3.34×10-4 M.
The results in Figure 3.10 agreed with the pDCC model presented in section 1.2.4.1; over time, the rate of increase in selectivity appeared to be exponential. This was even more apparent when the simulation time was extended to 4800 h (Figure 3.12), thus suggesting that only time was preventing our libraries from displaying the predicted exponential increase in selectivity. Our pDCCm experiments never exceeded 200 h ( ~8 days).
90
[ A] [ B]
Figure 3.12: Evolution of selectivity in a cycle free pDCCmSim experiment where 4×KiA = KiB; KiA = 1 µM. The Atlantic Ocean was used as dilution chamber (Vol. = 3.55×1023 cm3). 1×1200h cycle experiment. PA = PB = 1.26×10-5 s-1. [A] = [B] = 2×[E] = 3.34×10-4 M.
In order to confirm the convergence of the pDCC model presented in section 1.2.4.1 and the kinetic model used by pDCCmSim, a simulation under conditions of excess target was performed.i A two component pDCCmSim experiment performed at 1.67×10-4 M target and 1.67×10-5 M of each of the two inhibitors predicted a selectivity ratio of 3 at 78% conversion (hydrolysis) provided that the Ki ratio of the two inhibitors was 2 ( 2 K i A = 2 µM = K iB ). This was in perfect agreement with the original pDCC model (decribed in section 1.2.4.1). The initial model however, could not report the time necessary for the pDCL to reach a selectivity of 3. Using P = 1.26×10-5 s-1 for both compounds, and the cell parameters described in Table 3.4, pDCCmSim predicted a 2300 h (96 day!) resolution time. The only difference between the initial, kinetic resolution based model and the pDCCm based kinetic model was therefore that in the first, assuming excess target, the selectivity increase could
i
During the derivation of the original pDCC time independent model presented in section 1.2.4.1 the target was assumed to be in large excess relative to the library members.
91
only be related to % conversion, while in the kinetic model the selectivity could be related to both time and % conversion under a much wider range of stoichiometry conditions.
PDCCmSim was used to display graphically the effect that the relative permeability of the pDCL can have on the resolution of inhibitors. pDCCmSim for example showed that the outcome of a pDCL could reflect the ease of transmembrane diffusion rather than binding affinity strength sometimes. Figure 3.13 (see A and B) shows that in a simple 2 component pDCCm library, the concentration of the inhibitors in the screening chamber could be correlated to their K i if and only if the inverse of the relative permeabilities was smaller than the ratio of the K i s (i.e.,
Ki A
Ki B
> PB
PA
).
Thus, a clear exponential function was obtained when the two compounds underwent cooperative adaptation in a relatively short amount of time (Figure 3.13 C, selectivity ~ 4600 at 1200), while the rate of increase of the selectivity curve was clearly slower when the inhibitors were assigned the same permeability constant (Figure 3.13 D, selectivity = 4.42 at 1200h), and even more so when they underwent competitive adaptation (Figure 3.13 E, selectivity = 2.5 at 1200h). Later on in this chapter it will be shown that these patterns also hold true for experiments involving more than two compounds for as long as the receptor is in excess, and for experiments containing cycles.
Other than, D and E, the scenarios described above are rather extreme and should only be considered a mere curiosity; the interactions between a high affinity binder and its host are highly specific whereas membrane-solute interactions are not. Thus, the probability that
92
A) Ki ratio = P ratio
B) Ki ratio > P ratio
D) Ki ratio > P ratio
[ A] [ B]
[ A] [ B]
E) Ki ratio < P ratio
Figure 3.13: Influence of the permeability coefficient on the selectivity profile in pDCC. Comparison of five pDCCmSim experiments where 2 K i A = K iB ;
[ A] [ B]
K i A = 1 µM; PA = 1.26×10-5 s-1. A)
PA = 2PB; B) PA = 3PB ; C) 3PA =PB; D) PA = PB; E) 1.58PA = PB; All other parameters were the same as the ones used to produce Figure 3.10
the K i ratio of two substances will exceed the P ratio of two guest molecules of comparable molecular weight is very high. competition between
Ki A
Ki B
and
As already explained earlier in this chapter however,
PB
PA
can become problematic when the pDCC
experiment is carried with excess inhibitors if the library contains a compound that is a significantly better binder than the rest. In such instance, the order of disappearance of
93
compounds other than the best binder may not respect the order of K i s, as the best binder effectively reduces the amount of target available for all the other compounds to bind. When the resolution of a library of four inhibitors (10 nM, 0.5 µM, 4.0 µM, and 1 mM) was simulated (see Figure 3.14), the concentration of the compounds in the screening chamber represented their relative binding affinity for the target when, under excess inhibitors, the Ps were equivalent for all compounds (A), and when the target was in excess, even if the permeability of the third best inhibitor was set 2.25 times smaller than the permeability of all other compounds(B).
When the inhibitors were in excess however, the order of
concentrations did not satisfy the order of K i s (C), even if
K i3 rd
K i2 nd
= 8 and
P2 nd
P3rd
=
2.25.
These simulations (Figure 3.14) also confirmed that running experiments under conditions of excess target simplified the identification of the inhibitors in the library. Thus, whereas the concentration of the 4 µM inhibitor was similar to the concentration of the non-inhibitor throughout most of the experiment (A), there was a clear difference in the relative concentration of the two substrates when the target was the excess reagent (B).
The next set of simulations assessed the influence of the relative experimental stoichiometry on the yield and the resolution time. Since both pDCL models predict that the selectivity in pDCLs is not limited, a set of simulations based on a two member pDCL ( K i A = 1 µM = ½ K iB , and PA = PB = 1.26×10-5 s-1) was performed, and the time, yield, and % conversion (hydrolysis) recorded at a fixed selectivity of 3 (Table 3.5).
94
A
10 nM 0.5 µM 4.0 µM 1.0 mM
B
C
Figure 3.14: pDCCmSim experiments performed with a 10 nM inhibitor, a 0.5 µM inhibitor, a 4.0 µM inhibitor and a 1 mM binder. A) Experiment carried with all Ps set to 1.34×10-5 s-1 and excess inhibitors; B) same as A but P for third best inhibitor = 1.34×10-5s-1/2.25 and excess target; C) same as B) but with excess inhibitors; Ki/concentration synchronicity was lost. [i]center = free inhibitor concentration in the screening chamber; [Ai]center = enzyme inhibitor complex concentration in the screening chamber.
[ A] [ B ] = 3
[I]o total /[Target]o
Conversion %
[I]total /[Target] at S=3
Time (h) S = 3
20
97
0.60
535
4
89
0.46
610
3
86
0.42
665
2
82
0.36
850
1
78
0.22
1375
0.2
78
0.04
2275
0.1
78
0.02
2425
Table 3.5: Effect that different experimental stoichiometries have on the conversion, yield and the time it takes for the resolution process to reach a fixed selectivity ratio in a simple two inhibitor system where KiA = 1 µM and KiB = 2 µM. Both compounds had P = 1.26×10-5 s-1. The target concentration was maintained
95
constant at 1.67×10-4 M. Conversion % equals the extent of hydrolysis of the dipeptides relative to their starting concentration.
PDCCmSim showed that increasing the inhibitor concentration relative to the target shortened resolution times and improved yields, albeit at the expense of % conversion. For this two inhibitor system, three to four total binder equivalents appeared to be optimum as the use of more concentrated inhibitor solutions did not result in considerable gains in yield (see Figure 3.15) nor time (see Figure 3.16), whereas reducing the amount of inhibitor considerably affected both parameters. With four total inhibitor equivalents for example it was possible to obtain the same yield than with one total equivalent but in approximately 56% less time.i However, increasing the total number of inhibitor equivalents from four to twenty only shortened the reaction time by an additional 12% and yielded 25% more product. Thus, the data in Table 3.5 suggested that, as opposed to other RACC methods, using a large excess of library members may be desirable; the resolution time shortens and the reaction yield increases. As explained before though, using a large excess of inhibitors may not be advisable for libraries that contain an inhibitor that is a significantly better binder 0.7
Yield (target equivalents)
0.6 0.5 0.4 0.3 0.2 0.1 0 0
3
6
9
12
15
18
[I]total/[Target]
Figure 3.15: Relationship between total inhibitor concentration relative to the target and yield at fixed selectivity. In that case the selectivity was set to 3. i
At [A]/[B] = 3, the total inhibitor yield was 2.3×10-5 M both when the simulation was run with 3.34×10-4 M total inhibitor and 8.35×10-5 M target, and when the simulation was run with 3.34×10-4 M total inhibitor and 1.67×10-4 M target, thus resulting in a 2 fold increase in yield. Also, the resolution time was 25% shorter when fewer target was used (630h vs 850h).
96
2500
time (h)
2000
1500
1000
500
0 0
3
6
9
12
15
18
[I]total/[Target]
Figure 3.16: Relationship between total inhibitor concentration relative to the target and time taken to reach a fixed selectivity. In this case the selectivity was set to 3.
than the rest, unless the sole purpose of the experiment is to find the best binder in the mixture (see Figure 3.14).
Previously, it was shown that using excess inhibitors promotes the efficient competition for the target’s active site and therefore speeds up the resolution process. Figure 3.17 for example shows the fast increase in selectivity that exists at early time points in experiments that are performed with the target as the limiting reagent (A and B), which is not apparent when the target is in excess (C). Thus, the selectivity at 1200h was largest (~4.8) when the experiment A
C
B
[ A] [ B]
Figure 3.17: Analogous set of experiments where the [I]0total/[Target]0 was the only variable. This ratio was set to 20 in plot A, 2 in plot B and 0.2 in plot C. The remaining parameters were analogous to the ones used for the simulations summarized in Table 3.5.
97
was run with 20 inhibitor equivalents (A). The selectivity at 1200h was ~3.8 when 2 equivalents were used (Y), and only ~1.8 when the target was used in excess (C, 0.2 inhibitor equivalents).
PDCCmSim demonstrated that while the resolution time and the yield improve when the pDCLs are operated under conditions of excess inhibitors, Ki/concentration synchronicity may be jeopardized. As evidenced in Figure 3.14, a library could evolve not reflecting its binding affinity for the target in such instance (C), and even if it did, the library could evolve such that an inhibitor could be easily mistaken for a non-inhibitor (A), or the other way around. For experiments only targeting the identification of the best binder contained in a pDCL, operating under excess inhibitors would be valid for as long as this compound was a significantly better guest compound than the remaining library members.
Using a very high concentration of inhibitors at the start of the pDCC experiment however may not always be possible. Sometimes for example, the solubility of the compounds may not permit the preparation of high concentration solutions. In other occasions, it may be the stability of target that is compromised in such a medium. Using cycles could be an attractive solution to this problem, which, fortunately, is also most beneficial when the target is in excess. As shown in Figure 3.15 and Figure 3.16, a relatively small increase in inhibitor concentration when the target was in excess would significantly improve the yield and shorten the resolution time. The same observations were made when, rather than adding excess inhibitors at the beginning of the experiment, the target’s binding cleft was slowly saturated with inhibitors via cycles (see Table 3.6).
98
Entry
([I]o total /[Target])
([I]total
Cycle
Selectivity
Yield @
#
/Cycle
/[Target])
Time (h)
@ 144 h
144 h
1
0.5
0.5
144
1.35
39%
2
0.5
1
72
1.49
72%
3
0.5
1.5
48
1.78
90%
4
0.5
3
24
2.40
106%
5
0.5
6
12
3.0
120%
6
0.2
0.2
144
1.22
16.4%
7
0.2
0.4
72
1.23
33.7%
8
0.2
0.6
48
1.25
48.5%
9
0.2
1.2
24
1.40
76.0%
10
0.2
2.4
12
1.71
91%
11
0.1
0.1
144
1.23
8.5%
12
0.1
0.2
72
1.19
25%
13
0.1
0.6
24
1.21
42.5%
14
0.1
1.2
12
1.30
58.1%
Table 3.6: pDCC behavior in cycle containing experiments. The total time was 144h in all experiments. Once more the parameters used were analogous to the ones used for the simulations summarized in Table 3.5.
Thus, Table 3.6 shows that when the target was in excess, the yield improved significantly with every cycle (see entries 1, 2, 6, 7, 8, 11, 12, 13), but the selectivity only slightly. This was to be expected because, as a consequence that the pDCL did not need to compete for the enzyme’s active site, the rate of resolution was relatively slow.
As the target became
saturated with inhibitors on the other hand, their rate of exchange at the binding cleft increased and, therefore, so did their rate of dilution. Consequently, the addition of small batches of equimolar mixtures of inhibitors produced gains in selectivity while the rate of material accumulation decreased (see entries 3, 4, 9, 10, 11, 12).
99
When the target was saturated with excess inhibitors in the first cycle however, the addition of large quantities of inhibitors at regular time intervals did not offer any dramatic advantages (see Table 3.7, entries 1, 2 and 3), even when the same number of total inhibitor equivalents was added (see Table 3.7, entries 4 and 5).
Entry #
([I]o total
([I]total
/[Target]) /Cycle /[Target])
Cycle
Selectivity
Yield @
Time (h)
@ 144 h
144 h
1
10
20
72
2.22
157%
2
5
15
48
2.20
157%
3
2
12
24
2.30
150%
4
10
60
24
1.41
355%
5
5
60
12
1.52
402%
Table 3.7: pDCC behavior in cycle containing experiments. The total time was 144h in all experiments. Once more the parameters used were analogous to the ones used for the simulations summarized in Table 3.5.
Unfortunately, at this juncture pDCCmSim only allows the total experimental time to be divided in cycles of equal time. In order to find a common selectivity point that would allow to study how the stoichiometry added per cycle ([I]o total /[Target]) /cycle) affected the time and yield, just as it was done in section 3.3.2, the pDCCmSim code should be modified to incorporate an extended final cycle.
3.3.5
pDCC Optimization
The use of cycles in pDCC introduces experimental versatility because the ([I]o
total
/[Target]o)/Cycle ratio can be altered at any given time during the experiment.
As
mentioned before, binders could be mistaken for non-binders when using the target as
100
limiting reagent. Using the target as excess reagent on the other hand is detrimental to the time efficiency and the yield of the kinetic resolution process. An interesting compromise is found with a sequence of cycles where the first is performed under excess target, while any others are executed with the intent to flood the receptor with library members (see Figure 3.18). In that instance, the first cycle serves to identify the binding members of the pDCL while all others relate their relative binding affinity.
Thus, when the experiment is performed with two substoichiometric cycles (Figure 3.18 A), the concentration of the non-inhibitor (1 mM, black line) remained very low throughout the experiment and significantly different from the concentration of the inhibitors. For instance, at 96 h, the concentration of the non- inhibitor was 12.5 times smaller than that of the 4.0 µM inhibitor, and even more for the remaining two compounds. In fact, at only 2.0 × 10-6 M at 96 h, the concentration of the non-inhibitor was low enough that unless the compound had a very high quantum yield, it would be invisible to most standard HPLC detectors (UV); the remaining three inhibitors on the other hand, would likely be detectable.
In contrast, ranking the binding affinity of the inhibitors included in Figure 3.18 A based on their concentration might prove challenging. Differentiating between the 4.0 µM from the other two would be possible. Whereas the concentration of the former at 96 h was 2.5 × 105
M, the concentration for the two other compounds was 3.18 × 10-5 M for the 2nd best
binder and 3.26 × 10-5 M for the best binder. Assigning the remaining two compounds would be quite difficult. Even though the binding constant for the best binder was 50 times better than that of the second binder, their concentrations at 96 h only differed by 2.5%.
101
A
10 nM 0.5 µM 4.0 µM 1.0 mM
C
B
Figure 3.18: Identical pDCCmSim experiments run with A) excess target in both cycles (1.67×10-4 target 1.67 ×10-5 each inhibitor per cycle); B) excess library members both cycles (1.67×10-4 target 3.34 ×10-4 each inhibitor per cycle); C) mixed (1.67×10-4 target 3.34 ×10-5 each library member 1st cycle and 1.67×10-4 target 3.34 ×10-4 each inhibitor 2nd cycle). 4 inhibitors. P library = 1.26×10-5 s-1. . [i]center = free inhibitor concentration in the screening chamber; [Ai]center = enzyme inhibitor complex concentration in the screening chamber.
When the exact same experiment was performed under conditions of excess inhibitors on the other hand the opposite occurs (Figure 3.18 B). Differentiating the best inhibitor from all other compounds would be very simple in that instance. Since this compound is a 50 and a 400 fold better binder than the second and third best binders respectively, the active site would almost exclusively be saturated with it. Thus, while all other compounds were invariably washed away, the best binder would be retained. The main drawback with performing the experiment in this form would be the loss of the other two inhibitors.
102
The ideal procedure would combine the advantages of both stoichiometric regimes. Thus, when an experiment was performed under excess target for the first cycle and under excess inhibitors for the second (Figure 3.18 C) all of the information was retained. For the first 48 h cycle, the kinetic evolution proceeded very similarly for the three inhibitors once more but did not for the non-inhibitor. During the second cycle, while the concentration of all substances other than the best inhibitor evolved similarly, its concentration built up significantly. In that case, the first cycle served to differentiate between the non-inhibitor and the inhibitors, and the second cycle discerned the best inhibitor.
Figure 3.19 however, shows that provided the number of inhibitors is known, the stoichiometry of a pDCL experiment should be tailored such that the transition from the excess target to the excess inhibitors regime was slower. In that case, the pDCCmSim experiment was performed with additions of 0.2 equivalents of each library member per 48 h cycle (0.8 equivalents total) for a total of 6 cycles. All other parameters were analogous to the ones used to produce Figure 3.18.
Performing the experiments in this fashion permitted to unequivocally differentiate and rank all of the members in this 4 member library. Thus, after the first cycle (0.8 eq. of library members added, 0.6 inhibitor eq.), the concentration of the non-inhibitor was well below the concentration of all other library members. During the second cycle the total amount of binders added surpassed the enzyme concentration, and therefore, the concentration of the two best binders started to differentiate from the 3rd best. Given the relative binding strength of the library members, there was only enough target available to bind slightly over 0.2 equivalents of the 3rd best inhibitor; 0.4 equivalents of each the best and second best
103
binders were occupying the other 0.8 target equivalents. During the 3rd cycle, enough of the two best binders was added (0.6 equivalents of each, 1.2 equivalents total) to push the 3rd best binder out of the screening chamber completely. In subsequent cycles, this compound virtually behaved like a non-binder. Moreover, also during the third cycle, the best binder occupied for the first time more than 50% of the enzyme (~60%) and therefore, the ratio of its concentration relative to the second best binder increased. The release of the 2nd best binder continued steadily from then on. At the end of the 6th cycle, while the concentration of the best binder was approximately 6.5 × 10-4 M, the concentration for the 0.5 µM inhibitor was 1.0 × 10-5 M, and was below 0.5 × 10-5 M for the remaining two compounds.
10 nM 0.5 µM 4.0 µM 1.0 mM
Figure 3.19: pDCCmSim experiments with the same compounds used to produce Figure 3.18. Simulation conditions: 0.8 eq total library members each cycle (1.67×10-4 target 3.34 ×10-5 each). . [i]center = free inhibitor concentration in the screening chamber; [Ai]center = enzyme inhibitor complex concentration in the screening chamber.
At first glance, Figure 3.19 and Figure 3.18 C appear to provide the same qualitative information about the binding properties of the library, although 3.18 C appears to do so at a much faster rate. However, all the compounds in this small ideal library were assumed to have the same permeability.
Had this not been the case both figures may not have
104
converged, as the Ki of the strongest binder was much smaller than the Kis of all other compounds, and target saturation by the best inhibitor occurred faster in the experiment described by C.
In that instance, the concentration in the screening chamber of the
remaining library members would represent P rather than Ki. The same could eventually happen in the experiment in Figure 3.19, albeit at a much slower pace. As mentioned in the discussion of P in section 3.3.4, in general, the likelihood that the members of a pDCL are not resolved in the right order increases with the Ki distribution of the binders.
Consequently, when dealing with diverse, untested libraries the pDCL or pDCLm experiment should be performed as follows: first a cycle with excess target should be performed and analyzed. Based on the diffusion properties of the compounds at the start of the experiment (binders always diffuse into the screening chamber much faster than non binders because the formation of enzyme inhibitor complexes maintains the concentration gradient across the “synthesis”- screening boundary high, see first 24 h in Figure 3.18 A for example) the stoichiometry of the library in subsequent cycles should be programmed so that kinetic profiles similar to Figure 3.19 are produced. As a general rule, this can be accomplished if the total amount of inhibitors added in subsequent cycles is 0.6 – 0.8 target equivalents.
3.3.6
Literature Precedent
As surprising as it may be, to the best of our knowledge, pDCCm like experiments have not been previously reported in the context of drug screening. The literature however, contains close relatives such as equilibrium dialysis71 and a similar, non-equilibrium dialysis technique
105
that is used to study the partition of drugs in blood.72,73 These two procedures are however always performed on pure compounds.
pDCCm also resembles a number of techniques that are commonly used every day in laboratories around the world. The most popular examples probably are chromatography and the loading of antigens for enzyme linked immunosorbent assays (ELISAs), especially when preparing sandwich ELISAs.74-77 pDCCm can also be considered the reverse of common protein purification procedures such as elution through a gel modified with a known guest molecule or the retention of tagged proteins with an appropriately packed column,78-80 which is the main procedure used to isolate peptides when produced through recombinant expression.81,82
Notably, pDCCm experiments also resemble signaling processes in biology.
Signaling
molecules are constantly produced in cells, then released past the membrane defining the cell wall or not, depending on the location of the recipient receptor; when the signaling molecule finds its target it binds to it, thus initiating a cascade of biochemical processes. Often times the signaling molecule is then destroyed. This type of process is at the core of the cell cycle for example. Cyclins are signaling proteins that are produced by cells to trigger both DNA replication (S-phase) and mitosis (M-phase). In the context of DNA replication, S-cyclins are synthesized, and then bind to the cyclin dependent kinase (Cdk), thus forming a complex. The newly formed protein conjugate exposes the active site of the Cdk, which allows for phosphorylation. The phosphorylated Cdk-S-cyclin conjugate is a very important intermediate in cell biology, as it is able to phosphorylate the plethora of proteins that send
106
the cell cycle into the S phase, thus triggering DNA replication. After the S-phase is completed, cyclins are first ubiquitinated and then degraded by the proteasome.83
107
4
pDCL : Substrate Engineering and Proof-ofPrinciple Experiments
The concept of pDCLs is based upon the simultaneous in-situ formation, selection and destruction of inhibitors. In an ideal pDCL, synthesis and destruction occur at the same rate for all compounds in the absence of the target but destruction reflects ΔGbinding in its presence. As shown in chapters 2 and 3, this condition can only be satisfied if the kinetics of synthesis, the kinetics of binding, and the kinetics of destruction are carefully balanced. Using a three chamber setup simplifies this task, as the passive transport of solutes across dialysis membranes is very slow and should be rate limiting. This chapter describes the effort that was necessary in order to design a library that could be used in our proof-ofprinciple pDCC experiments. All of this work was performed by the author.
4.1 Substrate Engineering Chapters 1 and 2 presented two different ways to approach the design of a pDCL of carbonic anhydrase (CA) inhibitors. The 1st generation library, which is described in section 1.2.4.4, showed how the buildup of inhibitor metabolites could prevent the retention of the selected library members in the screening chamber. In the 2nd and 3rd generation libraries, which are described in chapter 2, the library bias was protease specific rather than CA specific. Designing a 4th generation library of peptide CA inhibitors appeared to be the simplest way to take advantage of the often cited catalytic promiscuity of pronase towards a very wide range of peptide bonds.
108
Pronase is a mixture of several metallo and serine proteases that display excellent exo and endopeptidase activity.70 Overall, the rate of hydrolysis of peptides is fastest at the carboxyl side of glutamate and aspartate, but given time, pronase will hydrolyze a protein into 70% free amino acids in 24 h under relatively dilute conditions.84 It is for that reason that pronase is the preparation of choice in gene purification.85 To the best of our knowledge however, more in-depth studies describing the enzyme mixture’s selectivity are scarce69 and rather unfocussed.
4.1.1
Substrate Engineering I: Destruction Optimization
Up until this point, our experiments suggested that the enzyme preparation did not accept large or partially charged groups at the side chain of carboxy end residue of the peptides; the presence of Gly or cyclic residues at the protected amino end was also problematic. In order to fully understand pronase’s substrate scope the peptides were simplified to the structure shown in Figure 4.1. X, Y, Z and K were systematically substituted. The carbamate functionality was conserved in order to prevent the racemization of the peptides during their synthesis.
Figure 4.1: General structure of a dipeptide inhibitor of CA.
A summary of the dipeptide substrates prepared with the intent to study substitution at Y and Z along with the results obtained in pronase hydrolysis experiments are summarized in Table 4.1.
109
X-YaaZaa-K Compound
Hydrolysis
Site of
#
X
Yaa
Zaa
K
Rate
Cleavage
1
Etoc
Phesa
Phe
OH
++++
Y-Z
2
Etoc
Phesa
Leu
OH
++++
Y-Z
3
Etoc
Phesa
Gly
OH
++++
Y-Z
5
Etoc
Phesa
Pro
OH
++++
Y-Z
6
Etoc
Phe
Gly
OH
+++++
Y-Z
7
Etoc
Phe
Leu
OH
+++++
Y-Z
8
Etoc
Phe
Phe
OH
+++++
Y-Z
9
Etoc
Phe
Pro
OH
+++++
Y-Z
11
Etoc
Phe
Phesa
OH
++
Y-Z
12
Etoc
Leu
Phesa
OH
++
Y-Z
13
Etoc
Gly
Phesa
OH
+
Y-Z
14
Etoc
Pro
Phesa
OH
+
Y-Z
15
Etoc
Leu
Phe
OH
+++++
Y-Z
16
Etoc
Gly
Phe
OH
+++
Y-Z
17
Etoc
Pro
Phe
OH
++
Y-Z
23
Etoc
Tyr
Phesa
OH
++
Y-Z
24
Etoc
Lys(Etoc)
Phesa
OH
+
Y-Z
27
Etoc
Ala
Phesa
OH
++
Y-Z
28
Etoc
Lys(Etoc)
Phgsa
OH
+
Y-Z
29
Etoc
Phe
Lys(Bzsa)
OH
+
Y-Z
30
Etoc
Phe
Lys(Tos)
OH
+
Y-Z
31
Etoc
Phe
Lys(Z)
OH
+
Y-Z
Table 4.1: Substrate engineering: dipeptide side chain analysis. The experiments were performed with 6.68 × 10-4 M substrate in 5.0 ml of an 8 mg/ml pronase solution buffered at pH 8.20 with 75 mM EPPS 10 mM CaCl2. +++++, the compounds completely hydrolyzed in less than thirty minutes; ++++, the compound hydrolyzed in less than three hours; +++, the compound hydrolyzed in less than six hours; ++ The compound hydrolyzed in less than 18 h; + More than 30% of the compound remained did not hydrolyze after 24 h. Yaa = aminoacid containing side chain Y and Zaa = aminoacid containing side chain Z. See Figure 4.1 for a description of X, Y, Z and K.
110
Even though pronase accepts a wide range of substituents at Y, Table 4.1 shows its inability to cleave peptides that contain groups larger that the side chain of tyrosine at Z. With regards to Y, the hydrolysis of the peptides was only compromised when Gly and Pro were found at the site (compounds 13, 14, 16, 17). Substitution at Z proved more problematic. Benzyl (compounds 1, 8, and 15) and 4-hydroxybenzyl69,84 side chains, as found in Phe and Tyr respectively, were well tolerated by the enzyme preparation. However, the hydrolysis of peptides containing a sulfonamidated Phe residue at Z was remarkably slow (compounds 1114, 23, 24, 27). Shortening the length of the side chain by one carbon unit did not improve the results as evidenced by compound 28. Incorporation of the aromatic sulfonamide moiety in a large albeit less rigid Z side chain, as was the case in compounds 29 and 30, did not offer any significant improvement. Compound 31 suggested that the likely cause behind the poor hydrolytic profile of Z sulfonamidated peptides was their overall size and not the functional group itself. Relocating the aromatic sulfonamide moiety to K was a possible solution.
The simplest way to produce dipeptide inhibitors of CA that contained an aromatic sulfonamide moiety at K was to couple 4-sulfonamido-benzylamine to BOC protected amino acids. Once deprotected, these amino acid amides could be extended into the sought dipeptides using standard peptide coupling methods. Table 4.2 summarizes this new set of compounds – binders and non binders of CA – that were prepared to study pronase’s substrate specificity.
111
X-YaaZaa-K Compound
Hydrolysis
Site of
Rate
Cleavage
NH-Bnsa
+++++
Y-Z, Z-K
Tyr
NH-Bnsa
+++++
Y-Z, Z-K
Ala
Tyr
NH-Bnsa
+++++
Y-Z, Z-K
F 4Z
Ala
Tyr
NH-Bnsa
+++++
Y-Z, Z-K
36
Z
Ala
Tyr
NH-Bn
+++++
Y-Z, Z-K
37
F 4Z
Ala
Tyr
NH-Bn
+++++
Y-Z, Z-K
38
Z
Ala
Ala
NH-Bnsa
+++++
Y-Z, Z-K
39
F 4Z
Ala
Ala
NH-Bnsa
+++++
Y-Z, Z-K
40
Z
Ala
Ala
NH-Bn
+++++
Y-Z, Z-K
41
F 4Z
Ala
Ala
NH-Bn
+++++
Y-Z, Z-K
42
Tos
Phe
Phe
OH
-
-
43
Ac
Tyr
-
NH-Bnsa
-
-
44
-
-
Tyr
NH-Bnsa
+++++
Z-K
45
-
-
Ala
NH-Bnsa
+++++
Z-K
46
-
-
Ala
NH-Bn
+++++
Z-K
47
-
-
Tyr
NH-Bn
+++++
Z-K
48
Z
Ala
Ala
NMe-Bn
-
-
49
F 4Z
Ala
Ala
NMe-Bn
-
-
50
Z
Ala
D-Ala
NH-Bnsa
-
-
X
Yaa
Zaa
K
32
Etoc
Ala
Tyr
33
Boc
Ala
34
Z
35
#
Table 4.2: Substrate engineering: dipeptide side chain analysis with sulfonamide at K. The experiments were performed with 6.68 × 10-4 M substrate in 5.0 ml of a 1 mg/ml pronase solution buffered at pH 8.20 with 75 mM EPPS 10 mM CaCl2. +++++, the compounds completely hydrolyzed in less than thirty minutes; ++++, the compound hydrolyzed in less than three hours; +++, the compound hydrolyzed in less than six hours; ++ The compound hydrolyzed in less than 18 h; + More than 30% of the compound remained did not hydrolyze after 24 h; -, the compound survived the hydrolytic medium. Yaa = aminoacid containing side chain Y and Zaa = aminoacid containing side chain Z. See Figure 4.1 for description of X, Y, Z and K.
Locating the aromatic sulfonamide at K also proved problematic. When the peptides were cleaved, the hydrolysis did not stop at Y-Z, but continued to degrade the resulting amino acid amides, thus releasing the corresponding amino acids and benzyl amines (compounds
112
32-41). That hydrolysis at Y-Z was necessary to promote hydrolysis at Z-K was deduced from the fact that significant amounts of Z-K fragments could be detected throughout all successful hydrolysis experiments. Another clue that supported this order of amide bond hydrolysis was the fact that a compound that did not hydrolyze at Y-Z did not hydrolyze at Z-K (43) either, whereas amino acid amides were easily cleaved by the enzyme preparation (44-47). This suggested that the hydrolysis of compounds 32-41 was carried by two different proteolytic activities: an endopeptidase component that acted upon Y-Z and an aminopeptidase component that broke down Z-K.
In an attempt to modulate pronase’s activity a small set of compounds designed to prevent the aminopeptidase activity was prepared. Compounds 48 and 49 incorporated an N-methyl amide at Z-K. These methylated analogues of 40 and 41 however were not substrates of pronase and therefore the hydrolysis was inhibited at both Y-Z and Z-K. Installing D-Ala at Z did not yield the sought hydrolytic profile either. While compound 38 was easily cleaved by pronase, in diastereomer 50 both Y-Z and Z-K survived the hydrolytic medium.
Other means of suppressing pronase’s activity at Z-K were also considered. As explained in Chapter 2 for example, ion chelators can alter pronase’s hydrolytic activity. Since Ca2+ ions are involved in the formation of the tertiary structure of pronase aminopeptidase86 the hydrolysis experiments described in Table 4.2 were attempted in pH 8.2 bicine buffer rather than EPPS. However, Ca2+ ions are not cofactors of pronase aminopetidase87 and therefore, the enzyme preparation could still cleave at Z-K albeit at a slower rate. At this point it became apparent that utilizing a non-specific protease with multiple proteolytic activities
113
such as pronase in pDCC would be problematic. A protease of well defined activity and broad substrate scope was necessary.
Carboxypeptidase Y was the first protease tested.
Carboxypeptidase Y is a serine
carboxypeptidase produced in yeast that has very broad amino acid specificity. In fact, the enzyme accepts proline and amidated amino acid residues, which are difficult to hydrolyze with other carboxypetidases. Carboxypeptidase Y is also active in urea and sodium dodecyl sulfate solutions and can therefore be used in the sequencing of polypeptides.88 In our hands however, the activity of this protease was found to be insufficient. When using a 1 mg/mL buffered solution of Carboxypeptidase Y the hydrolysis of peptides 8 and 11-14 proceeded slowly, and its high cost prohibited the used of high concentrations.
Subtilisin was another of the enzymes tested. Subtilisin is a serine endopeptidase known for its broad substrate specificity, high activity, and low cost. Even though the enzyme prefers to cleave at the carboxyl side of large uncharged residues, it can effectively hydrolyze other types of peptide bonds as well as certain types of amides.89 Table 4.3 summarizes the results obtained in our hydrolysis experiments.
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X-YaaZaa-K Compound
Hydrolysis
Site of
Rate
Cleavage
OH
++
Y-Z
Phesa
OH
++
Y-Z
Gly
Phesa
OH
+
Y-Z
Etoc
Pro
Phesa
OH
+
Y-Z
33
Boc
Ala
Tyr
NH-Bnsa
+++++
Y-Z, Z-K
34
Z
Ala
Tyr
NH-Bnsa
+++++
Y-Z, Z-K
35
F 4Z
Ala
Tyr
NH-Bnsa
+++++
Y-Z, Z-K
38
Z
Ala
Ala
NH-Bnsa
+++++
Y-Z, Z-K
39
F 4Z
Ala
Ala
NH-Bnsa
+++++
Y-Z, Z-K
48
Z
Ala
Ala
NMe-Bn
-
-
49
F 4Z
Ala
Ala
NMe-Bn
-
-
50
Z
Ala
D-Ala
NH-Bnsa
-
-
X
Yaa
Zaa
K
11
Etoc
Phe
Phesa
12
Etoc
Leu
13
Etoc
14
#
Table 4.3: Protease screening: Subtilisin substrate scope. Experiments were carried out with 1 mg/ml subtilisin in pH 8.2 50 mM EPPS 10 mM CaCl2 buffer and 1.67×10-4 m substrate. +++++, the compounds completely hydrolyzed in less than thirty minutes; ++++, the compound hydrolyzed in less than three hours; +++, the compound hydrolyzed in less than six hours; ++ The compound hydrolyzed in less than 18 h; + More than 30% of the compound remained did not hydrolyze after 24 h; -, the compound survived the hydrolytic medium. Yaa = aminoacid containing side chain Y and Zaa = aminoacid containing side chain Z. See Figure 4.1 for a description of X, Y, Z and K.
As shown in the table above, substilisin was not a good replacement for pronase as both of these hydrolases displayed the same reactivity and substrate scope. Indeed, subtilisin was not an efficient promoter of second generation sulfonamide dipeptide hydrolysis (compounds 11-14) but was able to hydrolyze the tested peptide amides at both Y-Z and Z-K (compounds 33-35, 38 and 39) for as long as they were not methylated (48 and 49) or were prepared with D-Ala (50).
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Given the high percentage of Ala containing peptides that were prepared, the activity of the serine endoprotease elastase was also assayed. Elastase is an enzyme whose primary role is to break down elastin. Together with collagen, elastin determines the mechanical properties of connective tissue. Elastase is known to cleave at the carboxyl side of small, hydrophobic amino acid residues such as Gly, Ala and Val.
The results obtained from hydrolysis
experiments are summarized in Table 4.4.
X-YaaZaa-K Hydrolysis
Site of
Rate
Cleavage
NH-Bnsa
+++
Y-Z, Z-K
Tyr
NH-Bnsa
+++
Y-Z, Z-K
Ala
Tyr
NH-Bnsa
+++
Y-Z, Z-K
Z
Ala
Ala
NH-Bnsa
+++
Y-Z, Z-K
39
F 4Z
Ala
Ala
NH-Bnsa
+++
Y-Z, Z-K
48
Z
Ala
Ala
NMe-Bn
-
-
49
F 4Z
Ala
Ala
NMe-Bn
-
-
50
Z
Ala
D-Ala
NH-Bnsa
-
-
Compound
X
Yaa
Zaa
K
33
Boc
Ala
Tyr
34
Z
Ala
35
F 4Z
38
#
Table 4.4: Protease screening: Elastase substrate scope. Experiments were carried out with 1 mg/ml elastase in pH 8.2 50 mM EPPS 10 mM CaCl2 buffer and 1.67×10-4 m substrate. +++++, the compounds completely hydrolyzed in less than thirty minutes; ++++, the compound hydrolyzed in less than three hours; +++, the compound hydrolyzed in less than six hours; ++ The compound hydrolyzed in less than 18 h; + More than 30% of the compound remained did not hydrolyze after 24 h; -, the compound survived the hydrolytic medium. Yaa = aminoacid containing side chain Y and Zaa = aminoacid containing side chain Z. See Figure 4.1 for a description of X, Y, Z and K.
In general, the catalytic efficiency, and the cleavage site selectivity of elastase towards the tested compounds was insufficient. Dipeptide amides 33-35, 38 and 39 were not cleaved at a high enough rate at 1 mg/mL elastase concentration, whereas dipeptides amides 48-50 were
116
not substrates of the enzyme. The insufficient rate of hydrolysis of 33-35, 38 and 39 was not surprising as the peptides were short; elastase is known to use several distant sites along a peptide backbone to strain the intermediate Michaelis complex.90-93 This same type of interaction might also explain why 33-35, 38 and 39 cleaved at both Y-Z and Z-K. The peptide-elastase Michaelis complex would be significantly longer-lived if the intermediate was formed by cleaving Z-K first.
Thermolysin was another protease subjected to testing.
This thermostable, neutral
endoproteinase produced by Bacillus thermoproteolyticus specifically catalyzes the hydrolysis at the amino side of peptide bonds containing hydrophobic amino acids.94,95 The enzyme requires one zinc ion for activity as well as four calcium ions for structural stability96 but, to the best of our knowledge, the enzyme is not known to be inhibited by sulfonamides. In general, thermolysin is assumed to behave like subtilisin,94 but in our hands the enzyme only displayed only the necessary endopeptidase activity (Table 4.5, compounds 33-41), while no aminopeptidase activity could be detected (Table 4.5, compounds 44-47). In addition, thermolysin could achieve the hydrolysis of the peptide bonds present in the tested peptide amides very efficiently provided that the terminal amide was not methylated (Table 4.5, compounds 48 and 49). With those results the optimization of destruction was completed; thermolysin would be the destruction mechanism used in future peptide amide pDCLs targeting CA.
117
X-YaaZaa-K Compound
Hydrolysis
Site of
Rate
Cleavage
NH-Bnsa
+++++
Y-Z
Tyr
NH-Bnsa
+++++
Y-Z
Ala
Tyr
NH-Bnsa
+++++
Y-Z
Z
Ala
Tyr
NH-Bn
+++++
Y-Z
37
F 4Z
Ala
Tyr
NH-Bn
+++++
Y-Z
38
Z
Ala
Ala
NH-Bnsa
+++++
Y-Z
39
F 4Z
Ala
Ala
NH-Bnsa
+++++
Y-Z
40
Z
Ala
Ala
NH-Bn
+++++
Y-Z
41
F 4Z
Ala
Ala
NH-Bn
+++++
Y-Z
44
-
-
Tyr
NH-Bnsa
-
-
45
-
-
Ala
NH-Bnsa
-
-
46
-
-
Ala
NH-Bn
-
-
47
-
-
Tyr
NH-Bn
-
-
48
Z
Ala
Ala
NMe-Bn
-
-
49
F 4Z
Ala
Ala
NMe-Bn
-
-
X
Yaa
Zaa
K
33
BOC
Ala
Tyr
34
Z
Ala
35
F 4Z
36
#
Table 4.5: Protease screening: Thermolysin substrate scope. Experiments were carried out with 1 mg/ml thermolysin in pH 8.2 50 mM EPPS 10 mM CaCl2 buffer and 1.67×10-4 m substrate. Analogous results were obtained when pH 7.5 50 mM HEPES 10 mM CaCl2 was used as buffer. +++++, the compounds completely hydrolyzed in less than thirty minutes; ++++, the compound hydrolyzed in less than three hours; +++, the compound hydrolyzed in less than six hours; ++ The compound hydrolyzed in less than 18 h; + More than 30% of the compound remained did not hydrolyze after 24 h; -, the compound survived the hydrolytic medium. Yaa = aminoacid containing side chain Y and Zaa = aminoacid containing side chain Z. See Figure 4.1 for a description of X, Y, Z and K.
4.1.2
Substrate Engineering II: Binding of Dipeptide Amides to CA
The affinity of a variety of dipeptide amides for the active site of CA was tested (Table 4.6). We were initially discouraged by the results as most of the tested compounds had Kis that were either insignificantly different or were considered too close to be used in a proof-ofprinciple pDCL (compounds 32-34, 52). The overall rate of destruction of the members of a
118
pDCL is coupled to their rate of synthesis, their permeability, their binding affinity for the host, and their rate of hydrolysis in the absence of the target. If Ki/ library evolution synchronicity is to be attained, the difference in binding affinity of the pDCL members must be sufficiently large to counteract the effect of any other competing parameters.
X-YaaZaa-K Compound
X
Yaa
Zaa
K
Ki (µM)
32
Etoc
Ala
Tyr
NH-Bnsa
3.2 ±0.4
33
BOC
Ala
Tyr
NH-Bnsa
2.3±0.4
52
Troc
Ala
Tyr
NH-Bnsa
2.6±0.4
34
Z
Ala
Tyr
NH-Bnsa
2.1±0.3
35
F 4Z
Ala
Tyr
NH-Bnsa
0.7±0.2
38
Z
Ala
Ala
NH-Bnsa
4.5±0.4
39
F 4Z
Ala
Ala
NH-Bnsa
2.8±0.3
41
F 4Z
Ala
Ala
NH-Bn
>100
#
Table 4.6: Ki values of selected sulfonamidated dipeptide amides. Yaa = aminoacid containing side chain Y and Zaa = aminoacid containing side chain Z. See Figure 4.1 for a description of X, Y, Z and K.
Fortunately, substituting the benzyloxycarbonyl group (Z) in ZAlaTyrBnsa(34), for its 2,3,5,6tetrafluorinated analogue, F4ZAlaTyrBnsa (35), afforded a much improved inhibitor of CA, presumably through the reinforcement of aromatic interactions.97-99 The 2,3,5,6-tetrafluoro compound was used instead of its pentafluoro analogue because of the duration and pH requirements of pDCC experiments. Pentafluoroalkyl benzenes are known to react at the 4 position with hydroxide through a nucleophilic aromatic substitution mechanism.100 Substituting the tyrosine at the carboxyl end of the peptide for alanine furnished two other compounds with significantly different binding affinity for the target (38 and 39). A control
119
experiment showed that F4ZAlaAlaBn (41), a non-sulfonamidated analogue of F4ZAlaAlaBnsa, did not bind to CA, thus confirming the irrelevance of the quadrupolar (aromatic) interaction in sulfonamide-free library members.
Figure 4.2 shows the final library selected for the 4th generation proof-of-principle pDCC experiments. The inhibitors in this series, compounds 34, 35, 38, and 39, were selected
40
38, Ki = 4.5 µM
41
39, Ki = 2.8 µM
36 34, Ki = 2.1 µM
37 35, Ki = 0.7 µM Figure 4.2: 4th Generation pDCL.
based on their binding affinity for CA, as well as their narrow molecular weight distribution. As explained in Chapter 3, libraries with a narrow molecular weight distribution are likely to have a narrow permeability profile, and therefore, are more likely to evolve reflecting target binding.
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The range of Ps obtained for of all the 4th generation inhibitors was satisfactory (see Table 4.7).
The permeability difference between the fastest and slowest diffusing peptides –
F4ZAlaTyrBnsa (35) and ZAlaAlaBnsa (38) – was only 18%, whereas the permeability of the two other inhibitors was comparable to that of 38. The fact that PBest
PX
< 1 was also
positive as this meant that the relative permeability of the compounds would assist library evolution. On the other hand, the fact that both Ki 34
Ki 39
and P38
P39
were smaller than
one was considered a challenge as it compromised Ki/concentration synchronicity (see Chapter 3). Our concerns were allayed with a pDCCm experiment performed with the 4th generation binders.
4.2 4th Generation pDCCm experiment The 4th generation pDCCm experiment was performed at pH 7.5, in 50 mM HEPES supplemented with 10 mM CaCl2, and with the target as the limiting reagent (1.67×10-4 M). 3.34×10-4 M of each of the 4th generation binders (see Figure 4.2) was added into the “synthesis” chamber at the start of each one of the two 48 h cycles. Table 4.7 summarizes the properties of all the compounds included in this experiment.
PBest
MW x
Compound Name
Ki (µM)
MW
P (10-5 s-1)
F4ZAlaTyrBnsa (35)
0.7 ± 0.2
626.58
2.83 ± 0.2
1
1
ZAlaTyrBnsa (34)
2.1 ± 0.3
554.61
3.28 ± 0.1
0.86
0.89
F4ZAlaAlaBnsa (39)
2.8 ± 0.3
534.48
3.15 ± 0.1
0.90
0.85
ZAlaAlaBnsa (38)
4.5 ± 0.4
462.52
3.47 ± 0.1
0.82
0.74
PX
MWBest
Table 4.7: Inhibitor permeability across a 3500 MWCO RC cellulose membrane versus molecular weight.
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The pDCCm experiment showed the desired evolution profile (see Figure 4.3). Thus, the concentration of the poorest inhibitor in the series (38, Ki = 4.5 µM) dropped from the screening chamber faster than the concentration of 39 (Ki = 2.8 µM), which in turn dropped faster than the concentration of 34 (Ki = 2.1 µM). The best inhibitor in the series, 35 (Ki = 0.7 µM) was the compound found in highest concentration throughout the entire experiment. 4 inhibitor 2x48 h cycle 80 70 60
% CA
50 40 30 20 10 0 0
10
20
30
40
50
60
70
80
90
100
Time (h)
Figure 4.3: Kinetic evolution of a pDCCm experiment using 2 equivalents of each sulfonamide member of the 4th generation library: F4ZAlaTyrBnsa ( ), ZAlaTyrBnsa ( ), F4ZAlaAlaBnsa ( ) and ZAlaAlaBnsa ( ). The chambers were limited by 3500 kD MWCO membranes.
As encouraging as those pDCCm results were, the information needed to be handled with care, pDCCm experiments are a great pDCC analysis tool but have limitations. In this pDCCm, the 4th generation library was added to the system as an equimolar static mixture of dipeptides to emulate infinitely fast and equal synthesis rates for all compounds. Certainly, this was a highly hypothetical situation that would not apply to a peptide based pDCC
122
experiment performed at pH 7.5 under very dilute conditions.
These experimental
conditions were necessary because of thermolysin’s activity requirements101-103 and because of the high cost of most biological targets respectively.
4.3 Aqueous Synthesis of Dipeptide Amides In 2002, Corbett et al.43 reported that T-Tentagel active esters could be used to perform peptide couplings in water under dilute conditions provided that the pH was sufficiently elevated to guarantee a high enough proportion of free base amino acid nucleophiles (see Table 1.1 and Table 1.2). Indeed, the authors showed that a 10 mM nucleophile solution (Gly, α-NH3+ pKa = 9.7, 1.2 equivalents relative to the active esters) could be used to produce a 50% yield of EtocPheGlyOH over 16 h at pH 10.0. 10% of the active esters hydrolyzed over that span. These results were very encouraging as the proportion of free base glycine present at pH 10.0 was around 6 mM. Since the pKa of the 4th generation amino acid amide nucleophiles was about two units lower – α-NH3+ pKa for HClTyrBnsa and HClAlaBnsa was determined to be 7.20 and 7.80 respectively – we felt there was a high probability that the peptides would couple, even at 1 mM nucleophile concentration. At pH 7.5, the rate of active ester hydrolysis should be much slower as the hydroxide concentration was about 500 fold smaller than at pH 10.0.
The couplings proceeded well with 1.6 mM nucleophile concentration (1.0 equivalent) at pH 7.5. Much to our delight, the extent of active ester hydrolysis was still low (
