Probing (Orphan) Nuclear Receptors in Neurodegeneration

Introduction

Neurodegenerative diseases such as Alzheimer’s, Parkinson’s, or Multiple Sclerosis are severe health burdens and major global challenges for societies and healthcare systems. Therapeutic interventions in these diseases are not satisfying, since there is no treatment strategy that can halt or reverse disease progression.

Research Challenges

Several failed attempts at finding a cure for neurodegeneration have significantly reduced the interest for further research - yet, more than ever, new approaches are needed. Critically missing are pharmacologically validated targets for efficient neurodegenerative disease treatment, and it is exactly here that NeuRoPROBE aims to close this gap.

Target Identification

The orphan nuclear receptors (NR) tailless homologue (TLX) and nuclear receptor related-1 protein (Nurr1) have been identified in knockout studies as prime candidates. However, ligands for their pharmacological control as tools for therapeutic validation are lacking. NeuRoPROBE will enable pharmacological modulation of these transcription factors as a new and groundbreaking strategy to counteract neurodegeneration.

Project Objectives

To meet its overall aim, NeuRoPROBE will:

1. Develop chemical probes (CP) and PROTACs for TLX and Nurr1.
2. Employ generative artificial intelligence (AI) for molecular design to accelerate CP and PROTAC development.
3. Establish phenotypic cellular models in 3D settings to mimic neurodegeneration.
4. Use CPs and PROTACs in the phenotypic models for pharmacological control of TLX and Nurr1 to validate their modulation in neurodegeneration.

Expertise and Impact

This challenging and highly multidisciplinary endeavor will profit from my strong interdisciplinary background in the field of NR ligand discovery and pharmacology, as well as in AI-based molecular design. NeuRoPROBE will open new avenues towards regenerative treatments in neurodegeneration, close the gaps in pharmacological control of TLX and Nurr1, and substantially contribute to consolidating AI techniques for structural optimization.