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Discovery and Pharmacological Investigation of Selective Small-Molecule IL-36R and TLR9 Antagonists for Autoimmune Diseases and Cancer

초록/요약

Interleukin-36 (IL-36) and Toll-like receptor 9 (TLR9) are key mediators of innate immune activation whose dysregulation drives chronic inflammation, autoimmunity, and cancer progression. Despite their pathogenic significance, selective and potent small-molecule antagonists for these pathways remain limited. This dissertation integrates two complementary research projects focused on the rational design, computational modeling, and experimental validation of novel selective inhibitors targeting IL-36R and TLR9. In the first project, we identified IRA10 and its optimized derivative, IRA10L, as the first competitive small-molecule antagonists of the IL-36 receptor (IL-36R). Molecular docking, molecular dynamics simulations, and MMPBSA calculations confirmed strong and stable binding of IRA1L to IL-36R, consistent with its improved potency over the parent compound. IRA10L markedly suppressed IL-36α/β/γ-induced CXCL1, TNF-α, IL-6, and IL-8 expression, and inhibited p65, ERK, and p38 phosphorylation. Functionally, IRA10L reduced IL-36-driven cancer cell metabolic activity, migration, spheroid formation, and colony growth. Biophysical assays including surface plasmon resonance, Schild analysis, and competitive ELISA validated direct receptor-level competition. In the second project, an integrated machine learning–computational pipeline combining QSAR modeling, molecular docking, pharmacophore mapping, and molecular dynamics simulations identified two novel TLR9 inhibitors, TRin7 and TRin8. Both compounds selectively suppressed CpG ODN2395-induced cytokine production in murine RAW264.7 macrophages and human Daudi cells without affecting other TLR pathwas. Mechanistic studies demonstrated blockade of TLR9–CpG DNA interactions and inhibition of downstream NF-κB and MAPK signaling, leading to reduced COX2 and NOS2 expression. In vitro potency aligned with binding free-energy predictions, with TRin7 showing slightly superior activity. Together, these studies establish IRA10L as the first validated small-molecule IL-36R antagonist and TRin7/TRin8 as selective TLR9 inhibitors,highlighting the therapeutic potential of targeting innate immune signaling with rationally designed small molecules. This combined work demonstrates the strength of integrating computational modeling, biophysical assays, and cellular validation for next-generation anti-inflammatory drug development.

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목차

Project 1 1
Summary 3
1. Introduction 5
1.1. IL-36 Signaling Mechanisms 5
1.2. IL-36 in Inflammatory and Autoimmune Diseases 5
1.2.1. Skin and Psoriasis 5
1.2.2. Inflammatory Bowel Disease 6
1.2.3. Rheumatoid Arthritis and Osteoarthritis 6
1.3. IL-36 in Cancer 6
1.3.1. Colorectal Cancer 7
1.3.2. Non–Small Cell Lung Cancer (NSCLC) 7
1.3.3. Breast Cancer 7
1.4. Therapeutic Implications and Rationale 7
2. Materials and Methods 10
2.1. Experimental Studies 10
2.1.1. Cell Lines and Reagents 10
2.1.2. Cell Viability Assay 10
2.1.3. Cytokine Quantification by ELISA 10
2.1.4. Western Blot Analysis 11
2.1.5. Metabolic Activity Assay 11
2.1.6. Migration Assay 11
2.1.7. 3D Spheroid Assay 12
2.1.8. Colony Formation Assay 12
2.1.9. Competitive ELISA 12
2.1.10. Surface Plasmon Resonance (SPR) 13
2.1.11. Schild Analysis 13
2.2. Computational Methods 13
2.2.1. Library Preparation and Receptor Structure 13
2.2.2. Molecular fingerprint-based similarity search 13
2.2.3. Structure-based Virtual Screening 14
2.2.4. Design of the structural analogs based on initial lead compound 14
2.2.5. Molecular Dynamics Simulations (MD) 15
2.2.6. Binding Free Energy Calculations 15
2.2.7. Statistical Analysis 16
3. Results 18
3.1. Identification of IRA10 as a lead against IL-36 receptor 18
3.2. Evaluation and selection of IRA10L as an inhibitor of IL-36 receptor 22
3.3. IRA10L suppresses multiple IL-36 induced cytokines and signaling pathways 24
3.4. IRA10L directly binds to IL-36 receptor 26
3.5. Molecular dynamics simulations and binding free energy analysis 28
3.6. IRA10L inhibits IL-36 α/β/γ-induced metabolic activity and migration in cancer cells 31
3.7. IRA10L suppresses IL-36α/β/γ-facilitated spheroid growth in cancer cells 33
3.8. IRA10L suppresses IL-36-induced colony formation in cancer cells 35
4. Discussion 38
Project 2 41
Summary 43
5. Introduction 45
5.1. Biology and Localization of TLR9 45
5.2. Mechanism of TLR9 Activation 45
5.3. TLR9 in Inflammatory and Autoimmune Diseases 45
5.4. Therapeutic Targeting of TLR9 46
5.5. Rationale of the Study 46
6. Materials and Methods 48
6.1. Computation studies 48
6.1.1. Machine learning-based Quantitative Structure-Activity Relationship (QSAR) Modeling 48
6.1.2. Molecular docking 50
6.1.3. Pharmacophore modeling 51
6.1.4. Molecular dynamics (MD) simulations 52
6.1.5. Binding free energy calculation 52
6.2. Experimental studies 53
6.2.1. Cell lines and reagents 53
6.2.2. Cell viability assay 53
6.2.3. Enzyme-linked immunosorbent assay (ELISA) 54
6.2.4. Proteins quantification and western blot analysis 54
6.2.5. Schild Assay 55
6.2.6. Competitive ELISA 55
6.2.7. Statistical analysis 56
7. Results 58
7.1. Machine learning-driven QSAR computational analysis 58
7.1.1. Exploratory data analysis 58
7.1.2. Dataset composition and class balance 58
7.1.3. Molecular descriptor analysis 59
7.1.4. Model performance comparison and analysis 60
7.1.5. Best overall performers 60
7.1.6. Y-scrambling analysis of the logistic regression QSAR model 62
7.2. Structure-Based Computational Studies 63
7.2.1. Molecular docking analysis 63
7.2.2. Pharmacophore analysis 65
7.2.3. Molecular dynamics (MD) simulation results 67
7.2.4. MM/PBSA binding free-energy analysis 69
7.3. Experimental results 70
7.3.1. Screening and functional characterization of TRin compounds as TLR9 antagonists 70
7.3.2. Mechanistic evaluation of TRin7 and TRin8 as selective TLR9 antagonists 73
7.3.3. Inhibitory effects of TRin7 and TRin8 on ODN2395-induced cytokine production in mouse and human cell lines 75
8. Discussion 79
9. Research findings and Future Perspectives 83
9.1. Research findings 83
9.2. Integrated Insights 84
9.3. Future Perspectives 84
10. References 87
11. Supplementary Materials 94
11.1. Project 1—Supplementary Materials 94
11.2. Project 2—Supplementary Materials 103

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