Fig. 2: Structural optimization of the lead compound C40.
A Subdivision of lead compound C40 into core and linker region, R1 is the area altered in the first optimization round, R2 represents the area altered in the second optimization round after R1 was chosen. B Selected structures of functional groups used for R1 substitution. The dark red box indicates the moiety chosen in the first round of optimization based on the activity screen in. C Here, all analogs of the first optimization round of C40 were assessed for progranulin induction through the luciferase-based reporter assay after 48 h of treatment. Treatment concentration of 3 µM is shown normalized to the average value of DMSO controls (n = 4 biological replicates are summarized as mean ± SD). D In optimization round two, based on A03, R2 was substituted with shown functional groups. E Activity of new analogs were assessed via the luciferase-based reporter assay after 24 h of treatment and 3 µM treatment concentration normalized to the average value of DMSO controls is shown (n = 3 except for A24, A8, A29, A33, which are n = 2, biological replicates are summarized as mean ± SD). F Predicted LogS and LogP values of each analog of both optimization rounds as an indicator of solubility. Solubility scale of LogS and Lipophilicity scale of LogP are indicated, increasing blue color depicts increasing solubility. Predictions were made with SwissADME94 and the consensus LogP was used. Initial compounds are shown in black, new lead analogs are shown in red, first round of molecules are colored in pink, second round of molecules are colored in blue. G Principal component analysis of predicted molecular features of all analogs including measured luciferase activity. Closest analogs to A03 are circled. PCA was conducted with ClustVis97 and used parameters were predicted by SwissADME94. H Hierarchical clustering analysis of predicted and measured molecular features of all analogs conducted via ClustVis97. Rows are centered and variance scaling is applied. Correlation distance and average linkage is used for clustering both rows and columns.
