Fig. 1: Virtual screen identifies dopamine as a powerful anti-fibrotic agent in primary murine fibroblasts.

A Flowchart showing the data processing of the machine learning predictions. In a previous publication, 600 compounds were screened, identifying haloperidol as a potent anti-fibrotic drug (purple). In the virtual screen, 616 compounds are predicted to decrease αSMA expression by 25% and are divided into two groups. Group 1 (blue) contains all predicted compounds which are clinically approved. Group 2 (green) contains unapproved, novel, predicted compounds. These were further analyzed to identify structurally related, clinically approved, drugs. Of these, 545 compounds were structurally related to drugs present in the original “wet” screen, while the remaining 44 drugs were related to dopamine (19/44), topiramate (2/44), zanamivir (8/44), and others (15/44). B Murine heart fibroblasts were treated ex vivo with TGFβ and dopamine, topiramate, and zanamivir. Representative images are shown (Hoechst in blue, αSMA-RFP in red). C Quantification of the number of αSMA positive cells (shown as fold over control). D Quantification of the total number of cells per condition (shown as fold over control). E Murine lung fibroblasts were treated ex vivo with TGFβ and dopamine, topiramate, and zanamivir. Representative images are shown (Hoechst in blue, αSMA-RFP in red). F Quantification of the number of αSMA positive cells (shown as fold over control). G Quantification of the total number of cells per condition (shown as fold over control). All data are shown as mean ± SD. Statistical significance was determined using a one-way ANOVA followed by Dunnett’s multiple comparison test, *P < 0.05, **P < 0.01, n = 3.