10.6084/M9.FIGSHARE.C.6291425.V1
Raphaëlle Lesage
Raphaëlle
Lesage
0000-0003-1041-2407
KU Leuven
Mauricio N. Ferrao Blanco
Mauricio N.
Ferrao Blanco
0000-0003-2639-0724
Erasmus MC
Roberto Narcisi
Roberto
Narcisi
0000-0003-0521-8618
Erasmus MC
Tim Welting
Tim
Welting
0000-0001-8081-4466
Gerjo J. V. M. van Osch
Gerjo J. V. M.
van Osch
0000-0003-1852-6409
Delft University of Technology
Erasmus MC
Liesbet Geris
Liesbet
Geris
0000-0002-8180-1445
University of Liège
KU Leuven
An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis
Abstract 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. Graphical Abstract
Artificial Intelligence and Image Processing
figshare
2022
2022-11-10
2022-11-10
Collection
10.1186/s12915-022-01451-8
10.6084/m9.figshare.c.6291425
CC BY 4.0