Pirfenidone (5-methyl-1-phenyl-2(1H)-pyridone, PFD) is a small-molecule compound acting on multiple targets involved in pathological fibrogenesis and is effective to increase the survival of patients with fibrosis, such as idiopathic pulmonary fibrosis. However, PFD is not active enough, requiring a high daily dose. In this study, to keep the multiple target profiles, N₁-substituted phenylhydroquinolinone derivatives, which retain the 1-phenyl-2(1H)-pyridone scaffold were designed and synthesized. The preliminary anti-fibrosis activities for all target compounds were evaluated on a NIH3T3 fibroblast cell line using MTT assay methods. Most compounds showed significant inhibition on NIH3T3 cell proliferation with a IC₅₀ range of 0.09–26 mM, among which 5-hydroxy-1-(4'-bromophenyl)-5,6,7,8-tetrahydroquinolin-2(1H)-one (6j) displayed 13 times higher potency (IC₅₀ = 0.3 mM) than that of AKF-PD (IC₅₀ = 4.2 mM). These results suggest that N₁-substituted phenylhydroquinolinone is a promising scaffold which can be applied for further investigation and for developing novel anti-fibrosis agents.

Bladder cancer is the most common cancer of the urinary system. Bladder urothelial cancer accounts for 90% of bladder cancer. These two cancers have high morbidity and mortality rates worldwide. The identification of biomarkers for bladder cancer and bladder urothelial cancer helps in their diagnosis and treatment. circRNAs are considered oncogenes or tumor suppressors in cancers, and they play important roles in the occurrence and development of cancers. In this manuscript, we developed an Ensemble model, CDA-EnRWLRLS, to predict circRNA-Disease Associations (CDA) combining Random Walk with restart and Laplacian Regularized Least Squares, and further screen potential biomarkers for bladder cancer and bladder urothelial cancer. First, we compute disease similarity by combining the semantic similarity and association profile similarity of diseases and circRNA similarity by combining the functional similarity and association profile similarity of circRNAs. Second, we score each circRNA-disease pair by random walk with restart and Laplacian regularized least squares, respectively. Third, circRNA-disease association scores from these models are integrated to obtain the final CDAs by the soft voting approach. Finally, we use CDA-EnRWLRLS to screen potential circRNA biomarkers for bladder cancer and bladder urothelial cancer. CDA-EnRWLRLS is compared to three classical CDA prediction methods (CD-LNLP, DWNN-RLS, and KATZHCDA) and two individual models (CDA-RWR and CDA-LRLS), and obtains better AUC of 0.8654. We predict that circHIPK3 has the highest association with bladder cancer and may be its potential biomarker. In addition, circSMARCA5 has the highest association with bladder urothelial cancer and may be its possible biomarker.