Structure-Activity Relationship and Quantitative Structure-Activity Relationship of GluN2C/D Subunit Selective Antagonists of the N-Methyl-D-Aspartate Receptor Open Access

Abstract

The N-methyl-D-aspartate (NMDA) receptors belong to the ionotropic glutamate receptor (iGluR) family and contribute to synaptic plasticity and excitatory neuronal transmission in the central nervous system. The functional NMDA receptors are heterotetramers, and they are assembled from two GluN1 subunits with two GluN2 (A-D) subunits. GluN2D subunits regulate synaptic transmission in basal ganglia, which controls movement disorders such as Parkinson’s disease. Our goal is to develop potent GluN2C/D subunit-selective antagonists of the NMDA receptor with a dihydroquinoline-pyrazoline (DQP) scaffold, which can be used as potential therapies for neuropathological diseases.

This dissertation consists of two projects. In the first project, 997 series compounds with DQP scaffold are synthesized and their structure-activity relationship (SAR) are explored in depth. Results indicate that compound (S)-997-74 is the most active candidate with an IC50 value of 46 nM at the GluN2D subunit. Although compound 997-110 is less potent than 997-74, it is more selective (625-fold) at GluN2D subunit over GluN2B subunit. Then, 2D- and 3D-quantitative SAR (QSAR) models are implemented to analyze the favorable and unfavorable moieties of 997-series that contribute to the inhibition activity at the GluN2D subunit and to make predictions of future compounds. The 2D-QSAR models show that the halogen substitution on the phenyl ring and the difluoro substitution on the acyl chain play crucial roles on this series, while the phenyl ring on the quinolone moiety is unfavorable to improve the activity at GluN2D subunit. The 3D QSAR model based on the NAMFIS-1a conformer can be used in combination with the 2D QSAR models to guide prediction of future medicinal efforts of the 997-series. The second project develops another novel series of GluN2C/D subunits selective antagonists of the NMDA receptor with piperazine scaffold. Compound 1121-35 with thiophene substitution on the A-ring provides the best results with IC50 values of 13 mM and 11 mM at GluN2C and GluN2D subunits, respectively, and shows no inhibition at GluN2A and GluN2B subunits. The SAR of this series is relatively flat, allowing only limited advancing progress over the initial screening hit.

Table of Contents

Chapter 1. Introduction 1

1.1    NMDA topology 1

1.2   NMDA receptor pharmacology 6

1.2.1   NMDA receptor agonists 6

1.2.2    NMDA competitive antagonists 6

1.2.3    NMDA uncompetitive antagonists 7

1.2.4    NMDA noncompetitive modulators 8

1.3    Therapeutic rationale for GluN2D-selective NMDA antagonists 11

1.4    DQP-1105 properties 13

Chapter 2. SAR study of 997-series 18

2.1    Background of 997 project: 18

2.2    SAR results of 997-series with DQP-1105 scaffold 19

2.2.1    Acyl chain modification 19

2.2.2    A-phenyl ring modification 28

2.2.3    B-phenyl ring modification 32

2.2.4    Quinolone ring modification 35

2.3    SAR results of 997-series with 997-95 scaffold 38

2.3.1    Development of 997-95 scaffold 38

2.3.2    A-phenyl ring modification 40

2.3.3    B-phenyl ring modification 42

2.4    Chemistry Experimental 44

2.4.1    Synthetic procedures 44

2.4.2    Evaluation of Enantiomers 100

2.4.3    X-ray 101

2.4.4    Two-electrode voltage-clamp recording 102

2.4.5    Data Analysis 103

Chapter 3. QSAR study of 997-series 104

3.1    Introduction 104

3.2    2D-QSAR study of 997-series 105

3.2.1    Introduction of 2D-QSAR 105

3.2.2    AutoQSAR study of 997-series 107

3.2.2.a    Introduction of autoQSAR 107

3.2.2.b    Data preparation and autoQSAR model creation 108

3.2.2.c    AutoQSAR model analysis 108

3.2.2.d    Analyzing predictions and identifying outliers. 110

3.2.3    KPLS model 115

3.2.3.a    Introduction of KPLS 115

3.2.3.b    KPLS model creation 115

3.2.3.c    Results and discussion: 116

3.3    Field-based 3D-QSAR study of 997-series 121

3.3.1    Introduction of 3D-QSAR 121

3.3.2    Field-based QSAR 122

3.3.2.a    Background 122

3.3.2.b    Field-based QSAR experimental section 125

3.3.2.c    Field-based QSAR results 130

3.3.2.d    Field-based QSAR discussion 141

Chapter 4. 1121 series 143

4.1    Introduction 143

4.2    A-ring modification 143

4.3    Linker modification 148

4.4    Piperazine ring modification 153

4.5    B-ring modification 155

Chapter 5. Conclusion and Outlook 185

Appendix A: Crystal data and structure refinement for (R)-(+)-997-74 204

Appendix B: The report for the best AutoQSAR model kpls_dendritic_1 205

Appendix C: Concentration-response data for 997-series at ionotropic glutamate receptors. 210

Appendix D: Concentration-response data for 997-enantiomers at ionotropic glutamate receptors. 212

Appendix E: Concentration-response data for 1121-series at ionotropic glutamate receptors. 213

References 215

 

About this Dissertation

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School	Laney Graduate School
Department	Chemistry
Degree	Ph.D.
Submission	Dissertation
Language	English
Research Field	Chemistry, Organic
Keyword	SAR Parkinson’s disease NMDA DQP QSAR CNS
Committee Chair / Thesis Advisor	Liotta, Dennis C., Emory University
Committee Members	Traynelis, Stephen F., Emory University Weinert, Emily, Emory University McDonald, Frank E., Emory University

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