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Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 µM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12-20 µM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7-45 µM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.
RESUMO
We report synthesis, characterization, biological evaluation, and molecular-docking studies of 18 thieno[2,3-b]pyridines with a phenylacetamide moiety at position 2, which is disubstituted with F, Cl, Br, or I at position 4, and with electron-withdrawing and electron-donating groups (-CN, -NO2, -CF3, and -CH3) at position 2, to study how the electronic properties of the substituents affected the FOXM1-inhibitory activity. Among compounds 1-18, only those bearing a -CN (regardless of the halogen) decreased FOXM1 expression in a triple-negative breast cancer cell line (MDA-MB-231), as shown by Western blotting. However, only compounds 6 and 16 decreased the relative expression of FOXM1 to a level lower than 50%, and hence, we determined their anti-proliferative activity (IC50) in MDA-MB-231 cells using the MTT assay, which was comparable to that observed with FDI-6, in contrast to compound 1, which was inactive according to both Western blot and MTT assays. We employed molecular docking to calculate the binding interactions of compounds 1-18 in the FOXM1 DNA-binding site. The results suggest a key role for residues Val296 and Leu289 in this binding. Furthermore, we used molecular electrostatic potential maps showing the effects of different substituents on the overall electron density.
