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Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists
Jie Xia1, Zhenyi Wang2,3, Yi Huan4
1State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China.
Abstract:
Farnesoid X receptor (FXR) agonists can reverse dysregulated bile acid metabolism, and thus, they are potential therapeutics to prevent and treat nonalcoholic fatty liver disease. The low success rate of FXR agonists' R&D and the side effects of clinical candidates such as obeticholic acid make it urgent to discover new chemotypes. Unfortunately, structure-based virtual screening (SBVS) that can speed up drug discovery has rarely been reported with success for FXR, which was likely hindered by the failure in addressing protein flexibility. To address this issue, we devised human FXR (hFXR)-specific ensemble learning models based on pose filters from 24 agonist-bound hFXR crystal structures and coupled them to traditional SBVS approaches of the FRED docking plus Chemgauss4 scoring function. It turned out that the hFXR-specific pose filter ensemble (PFE) was able to improve ligand enrichment significantly, which rendered 3RUT-based SBVS with its PFE the ideal approach for FXR agonist discovery. By screening of the Specs chemical library and in vitro FXR transactivation bioassay, we identified a new class of FXR agonists with compound XJ034 as the representative, which would have been missed if the PFE was not coupled. Following that, we performed in-depth biological studies which demonstrated that XJ034 resulted in a downtrend of intracellular triglyceride in vitro, significantly decreased the serum/liver TG in high fat diet-induced C57BL/6J obese mice, and more importantly, showed metabolic stabilities in both plasma and liver microsomes. To provide insight into further structure-based lead optimization, we solved the crystal structure of hFXR complexed with compound XJ034, uncovering a unique hydrogen bond between compound XJ034 and residue Y375. The current work highlights the power of our pose filter-based ensemble learning approach in terms of scaffold hopping and provides a promising lead compound for further development.
Insights
Researchers developed a new computational method to discover novel Farnesoid X receptor (FXR) agonists for nonalcoholic fatty liver disease. This approach identified compound XJ034, a promising therapeutic lead with demonstrated efficacy and metabolic stability.
Area of Science:
- Medicinal Chemistry
- Computational Drug Discovery
- Hepatology
Background:
- Farnesoid X receptor (FXR) agonists show therapeutic potential for nonalcoholic fatty liver disease (NAFLD) by regulating bile acid metabolism.
- Existing FXR agonists face challenges due to low research and development success rates and side effects, necessitating novel chemotypes.
- Structure-based virtual screening (SBVS) for FXR has been limited by challenges in addressing protein flexibility.
Purpose of the Study:
- To develop an improved SBVS approach for identifying novel FXR agonists, overcoming limitations in addressing protein flexibility.
- To discover and characterize new classes of FXR agonists with potential therapeutic applications for NAFLD.
Main Methods:
- Developed human FXR (hFXR)-specific ensemble learning models using pose filters from multiple crystal structures.
- Integrated these pose filter ensembles (PFE) with traditional SBVS (FRED docking, Chemgauss4 scoring).
- Screened the Specs chemical library, performed in vitro FXR transactivation bioassays, and conducted in vivo studies in diet-induced obese mice.
Main Results:
- The hFXR-specific PFE significantly improved ligand enrichment in SBVS, enabling the discovery of previously missed compounds.
- Identified a new class of FXR agonists, exemplified by compound XJ034, which demonstrated efficacy in reducing intracellular triglyceride levels.
- XJ034 significantly decreased serum and liver triglycerides in obese mice and exhibited favorable metabolic stability in plasma and liver microsomes.
- Determined the crystal structure of hFXR complexed with XJ034, revealing a unique interaction with residue Y375.
Conclusions:
- The pose filter-based ensemble learning approach is powerful for scaffold hopping and discovering novel FXR agonists.
- Compound XJ034 represents a promising lead compound for further development as a potential NAFLD therapeutic.
- This study provides a validated computational strategy for accelerating the discovery of FXR-targeted therapeutics.
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