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An Oncogenic Hepatocyte-Induced Orthotopic Mouse Model of Hepatocellular Cancer Arising in the Setting of Hepatic Inflammation and Fibrosis
Published on: September 12, 2019
AI-Driven discovery and experimental validation of covalent FGFR4 inhibitors for hepatocellular carcinoma
Mingjie Gao1,2, Weiyi Zhao3, Pengfei Zhang4
1Shandong Provincial Key Medical and Health Laboratory of BT and IT for Thoracic Oncology, Weifang People's Hospital, Shandong Second Medical University, Guangwen Street, Weifang, Shandong, 261041, People's Republic of China.
Background:
FGFR4 signaling is an essential driver in hepatocellular carcinoma. However, traditional screening is often time-consuming, highlighting a need for efficient strategies to identify covalent chemotypes and accelerate FGFR4 drug discovery.
Methods:
We established an integrated AI-driven virtual screening framework to discover FGFR4 covalent inhibitors. The theoretical predictions were evaluated through a biochemical pipeline, encompassing in vitro FGFR4 kinase assays, immunoblotting of intracellular signaling cascades, and bottom-up LC-MS/MS peptide mapping.
Results:
Biological validation of the computational predictions identified five distinct chemical scaffolds (hits 1, 2, 5, 7, and 8) exhibiting antiproliferative activity. The two most active candidates, hit 1 and hit 2, were selected for further mechanistic profiling. These compounds demonstrated dose-dependent FGFR4 kinase inhibition with IC50 values of 1.06 μM and 3.57 μM, respectively. Cellular assays revealed that both compounds attenuate FGFR4 autophosphorylation and its downstream FRS2/ERK1/2 signaling cascade without inducing non-specific protein degradation. Furthermore, bottom-up LC-MS/MS peptide mapping provided direct structural evidence that hit 1 and hit 2 engage the target cysteine residue via a Michael addition mechanism.
Conclusions:
Our AI-guided computational workflow identified multiple covalent FGFR4 inhibitors with measurable biological activity. Hit 1 and hit 2 represent structurally characterized covalent scaffolds. This study provides chemical starting points for targeted HCC therapy and demonstrates the integration of theoretical prediction and experimental validation in covalent drug discovery.
Insights
An AI framework identified novel covalent FGFR4 inhibitors for hepatocellular carcinoma (HCC) treatment. Two lead compounds show potent activity and target specific mechanisms, offering new therapeutic starting points.
Area of Science:
- Oncology
- Drug Discovery
- Computational Chemistry
Background:
- Fibroblast Growth Factor Receptor 4 (FGFR4) signaling drives hepatocellular carcinoma (HCC) progression.
- Conventional drug screening methods for identifying covalent inhibitors are inefficient.
- Accelerating the discovery of FGFR4-targeted therapies is crucial for HCC treatment.
Purpose of the Study:
- To develop and validate an AI-driven virtual screening framework for identifying novel covalent FGFR4 inhibitors.
- To discover and characterize chemical scaffolds targeting FGFR4 signaling in HCC.
- To provide starting points for the development of targeted HCC therapies.
Main Methods:
- Integrated AI-driven virtual screening framework.
- Biochemical validation including in vitro kinase assays and immunoblotting.
- Bottom-up LC-MS/MS peptide mapping for structural elucidation.
Main Results:
- Identified five distinct chemical scaffolds with antiproliferative activity against HCC cells.
- Two lead compounds (Hit 1 and Hit 2) demonstrated potent FGFR4 kinase inhibition (IC50 values of 1.06 μM and 3.57 μM).
- Confirmed target engagement via Michael addition mechanism and inhibition of downstream signaling pathways (FRS2/ERK1/2).
Conclusions:
- An AI-guided workflow successfully identified potent covalent FGFR4 inhibitors.
- Hit 1 and Hit 2 represent structurally characterized covalent scaffolds for HCC therapy.
- Demonstrated the synergy of computational prediction and experimental validation in covalent drug discovery.
