Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.

Cardiac fibrosis is a dysregulation of the normal wound healing response, resulting in excessive scarring and cardiac dysfunction. As cardiac fibroblasts primarily regulate this process, we explored how candidate anti-fibrotic drugs alter the fibroblast phenotype. We identify a set of 137 phenotypic features that change in response to drug treatments. Using a new computational modeling approach termed logic-based mechanistic machine learning, we predict how pirfenidone and Src inhibition affect the regulation of the phenotypic features F-actin assembly and F-actin stress fiber formation. We also show that inhibition of PI3K reduces F-actin fiber formation and procollagen I production in human cardiac fibroblasts, supporting a role for PI3K as a mechanism by which Src inhibition may suppress fibrosis.