Objectives: Baicalin (BCL) has potential therapeutic benefits, but its clinical outcomes are restricted mainly because of low water solubility. This study sought to improve the water solubility of BCL by the formation of inclusion complex with β-cyclodextrin (β-CD). Methods: The inclusion complex was studied by solubility test, differential scanning calorimeter (DSC), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), 1H Nuclear magnetic resonance (1HNMR) and scanning electron microscopy (SEM). Molecular docking was conducted to verify the experimental findings. The dissolution rate was determined by dialysis membrane method. In vivo absorption studies in rats were conducted and high-performance liquid chromatography (HPLC) was used to analyse the plasma level of BCL after oral administration. Key findings: The DSC, FTIR, XRD, 1HNMR and SEM findings suggested the formation of inclusion complex between BCL and β-CD in 1 : 1 stoichiometry. Molecular docking demonstrated the insertion of benzene ring of BCL into β-CD cavity by hydrophobic interactions and possible H-bond formation. Moreover, β-CD markedly improved the solubility of BCL and displayed AL-type phase diagrams. The improvement in dissolution rate of the inclusion complex was reflected in the earlier Tmax, higher Cmax and larger AUC0–t than that of BCL after oral administration. Conclusions: β-cyclodextrin complex can be used as an effective formulation strategy for development of BCL-loaded delivery system with better therapeutic outcomes.

To improve the reliability of virtual screening for transforming growth factor-beta type 1 receptor (TβR1) inhibitors, 2 docking methods and 11 scoring functions in Discovery Studio software were evaluated and validated in this study. LibDock and CDOCKER protocols were performed on a test set of 24 TβR1 protein–ligand complexes. Based on the root-mean-square deviation (RMSD) values (in Å) between the docking poses and co-crystal conformations, the CDOCKER protocol can be efficiently applied to obtain more accurate dockings in medium-size virtual screening experiments of TβR1, with a successful docking rate of 95%. A dataset including 281 known active and 8677 inactive ligands was used to determine the best scoring function. The receiver operating characteristic (ROC) curves were used to compare the performance of scoring functions in attributing best scores to active than inactive ligands. The results show that Ludi 1, PMF, Ludi 2, Ludi 3, PMF04, PLP1, PLP2, LigScore2, Jain and LigScore1 are better scoring functions than the random distribution model, with AUC of 0.864, 0.856, 0.842, 0.812, 0.776, 0.774, 0.769, 0.762, 0.697 and 0.660, respectively. Based on the pairwise comparison of ROC curves, Ludi 1 and PMF were chosen as the best scoring functions for virtual screening of TβR1 inhibitors. Further enrichment factors (EF) analysis also supports PMF and Ludi 1 as the top two scoring functions.

The transforming growth factor-beta (TGF-β) plays an important role in pathological fibrosis and cancer transformation. Therefore, the inhibition of the TGF-β signaling pathway has therapeutic potential in the treatment of cancer. In this study, the binding modes between 47 molecules with a pyrrolotriazine-like backbone structure and transforming growth factor-beta type 1 receptor (TβR1) were simulated by molecular docking using Discovery Studio software, and their structure–activity relationships were analyzed. On the basis of the analysis of the binding modes of ligands in the active site and the structure–activity relationships, 29,254 new compounds were designed for virtual screening. According to the aforementioned analyses and Lipinski’s rule of five, five new compounds (CQMU1901–1905) with potential activity were screened through molecular docking. Among them, CQMU1905 is an attractive molecule composed of 5-fluorouracil (5-FU), 6-mercaptopurine (6-MP), and 5-azacytosine. Interestingly, 5-FU, 6-MP, and 5-azacytidine are often used as anti-metabolic agents in cancer treatment. Compared with existing compounds, CQMU1901–1905 can interact with target proteins more effectively and have good potential for modification, making them worthy of further study.