Structural Optimization of Oxaprozin for Selective Inverse Nurr1 Agonism.

Nuclear receptor related 1 (Nurr1, NR4A2) is a ligand-sensing transcription factor with neuroprotective and anti-inflammatory roles widely distributed in the CNS. Pharmacological Nurr1 modulation is considered a promising experimental strategy in Parkinson's and Alzheimer's disease but target validation is incomplete. While significant progress has been made in Nurr1 agonist development, inverse agonists blocking the receptor's constitutive activity are lacking. Here we report comprehensive structure-activity relationship elucidation of oxaprozin which acts as moderately potent and nonselective inverse Nurr1 agonist and RXR agonist. We identified structural determinants selectively driving RXR agonism or inverse Nurr1 agonism of the scaffold enabling the development of selective inverse Nurr1 agonists with enhanced potency and strong efficacy.

Structural Fusion of Natural and Synthetic Ligand Features Boosts RXR Agonist Potency.

The retinoid X receptors (RXRs) are ligand-activated transcription factors involved in, for example, differentiation and apoptosis regulation. Currently used reference RXR agonists suffer from insufficient specificity and poor physicochemical properties, and improved tools are needed to capture the unexplored therapeutic potential of RXR. Endogenous vitamin A-derived RXR ligands and the natural product RXR agonist valerenic acid comprise acrylic acid residues with varying substitution patterns to engage the critical ionic contact with the binding site arginine. To mimic and exploit this natural ligand motif, we probed its structural fusion with synthetic RXR modulator scaffolds, which had profound effects on agonist activity and remarkably boosted potency of an oxaprozin-derived RXR agonist chemotype. Bioisosteric replacement of the acrylic acid to overcome its pan-assay interference compounds (PAINS) character enabled the development of a highly optimized RXR agonist chemical probe.

De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime.

Generative neural networks trained on SMILES can design innovative bioactive molecules de novo. These so-called chemical language models (CLMs) have typically been trained on tens of template molecules for fine-tuning. However, it is challenging to apply CLM to orphan targets with few known ligands. We have fine-tuned a CLM with a single potent Nurr1 agonist as template in a fragment-augmented fashion and obtained novel Nurr1 agonists using sampling frequency for design prioritization. Nanomolar potency and binding affinity of the top-ranking design and its structural novelty compared to available Nurr1 ligands highlight its value as an early chemical tool and as a lead for Nurr1 agonist development, as well as the applicability of CLM in very low-data scenarios.