An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis
Raphaëlle Lesage1,2, Mauricio N Ferrao Blanco3, Roberto Narcisi3
1Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium.
Background:
Without the availability of disease-modifying drugs, there is an unmet therapeutic need for osteoarthritic patients. During osteoarthritis, the homeostasis of articular chondrocytes is dysregulated and a phenotypical transition called hypertrophy occurs, leading to cartilage degeneration. Targeting this phenotypic transition has emerged as a potential therapeutic strategy. Chondrocyte phenotype maintenance and switch are controlled by an intricate network of intracellular factors, each influenced by a myriad of feedback mechanisms, making it challenging to intuitively predict treatment outcomes, while in silico modeling can help unravel that complexity. In this study, we aim to develop a virtual articular chondrocyte to guide experiments in order to rationalize the identification of potential drug targets via screening of combination therapies through computational modeling and simulations.
Results:
We developed a signal transduction network model using knowledge-based and data-driven (machine learning) modeling technologies. The in silico high-throughput screening of (pairwise) perturbations operated with that network model highlighted conditions potentially affecting the hypertrophic switch. A selection of promising combinations was further tested in a murine cell line and primary human chondrocytes, which notably highlighted a previously unreported synergistic effect between the protein kinase A and the fibroblast growth factor receptor 1.
Conclusions:
Here, we provide a virtual articular chondrocyte in the form of a signal transduction interactive knowledge base and of an executable computational model. Our in silico-in vitro strategy opens new routes for developing osteoarthritis targeting therapies by refining the early stages of drug target discovery.
Insights
Developing a virtual articular chondrocyte aids osteoarthritis drug discovery. Computational modeling identified a synergistic effect between protein kinase A and fibroblast growth factor receptor 1, offering new therapeutic strategies.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Pharmacology
Background:
- Osteoarthritis (OA) lacks disease-modifying drugs, creating a significant unmet therapeutic need.
- Articular chondrocyte homeostasis is disrupted in OA, leading to hypertrophy and cartilage degeneration.
- Targeting chondrocyte phenotypic transitions is a promising therapeutic strategy, but complex regulatory networks hinder intuitive outcome prediction.
Purpose of the Study:
- To develop a virtual articular chondrocyte model for OA research.
- To guide experimental identification of drug targets through computational modeling and simulations.
- To screen combination therapies for potential osteoarthritis treatments.
Main Methods:
- Developed a signal transduction network model integrating knowledge-based and machine learning approaches.
- Performed in silico high-throughput screening of pairwise perturbations to identify key regulatory conditions.
- Validated computational findings using murine cell lines and primary human chondrocytes.
Main Results:
- The computational model identified conditions influencing the hypertrophic switch in chondrocytes.
- A synergistic effect between protein kinase A and fibroblast growth factor receptor 1 was discovered.
- This combination was validated in both cell line and primary human chondrocyte models.
Conclusions:
- A virtual articular chondrocyte model and knowledge base were successfully created.
- The in silico-in vitro strategy refines early-stage drug target discovery for OA.
- This approach offers novel routes for developing targeted osteoarthritis therapies.
