Studies of Cytotoxicity Effects, SARS-CoV-2 Main Protease Inhibition, and in Silico Interactions of Synthetic Chalcones

Inhibitory effects against SARSCoV-2 main protease (Mpro) of biflavonoids and benzophenones from the fruit of Platonia insignis

The SARS-CoV-2 mutation and the limitation of the approved drug against COVID-19 are still a challenge in many country healthcare systems and need to be affronted despite the set of vaccines to prevent this viral infection. To contribute to the identification of new antiviral agents, the present study focused on natural products from an edible fruit with potential inhibitory effects against the SARS-CoV-2 main protease (Mpro). First, LC-ESIMS analysis of Platonia insignis fruits was performed and showed the presence of biflavonoids and benzophenones in the seed and pulp, respectively. Then, maceration and chromatographic purification led to the identification of two triglycerides (1 and 2) alongside chamaejasmine (3) and volkensiflavone (4) from the seed and isogarcinol (5) and cycloxanthochymol (6), from the pulp. Compounds 1-6 after evaluating their inhibitory against Mpro, displayed from no to significant activity. Compound 5 was the most potent with an IC50 value of 0.72 μM and was more active than the positive control, Ebselen (IC50 of 3.4 μM). It displayed weak and no cytotoxicity against THP-1 (CC50 of 116.2 μM) and Vero cell lines, respectively. Other active compounds showed no cytotoxicity against THP-1. and Vero cell lines. Molecular docking studies revealed interactions in the catalytic pocket between compound 5 and amino acid residues that composed the catalytic dyads (His 41 and Cyst 145).

The Essential Oil from Conyza bonariensis (L.) Cronquist (Asteraceae) Exerts an In Vitro Antimelanoma Effect by Inducing Apoptosis and Modulating the MAPKs, NF-κB, and PKB/AKT Signaling Pathways

The characterization and cytotoxicity of the essential oil from Conyza bonariensis (L.) aerial parts (CBEO) were previously conducted. The major compound was (Z)-2-lachnophyllum ester (EZ), and CBEO exhibited significant ROS-dependent cytotoxicity in the melanoma cell line SK-MEL-28. Herein, we employed the Molegro Virtual Docker v.6.0.1 software to investigate the interactions between the EZ and Mitogen-Activated Protein Kinases (MAPKs), the Nuclear Factor kappa B (NF-κB), and the Protein Kinase B (PKB/AKT). Additionally, in vitro assays were performed in SK-MEL-28 cells to assess the effect of CBEO on the cell cycle, apoptosis, and these signaling pathways by flow cytometry and the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay using MAPKs inhibitors. CBEO induced a significant increase in the sub-G1 peak, as well as biochemical and morphological changes characteristic of apoptosis. The in-silico results indicated that EZ interacts with Extracellular Signal-Regulated Kinase 1 (ERK1), c-Jun N-terminal Kinase 1 (JNK1), p38α MAPK, NF-κB, and PKB/AKT. Moreover, CBEO modulated the ERK1/2, JNK, p38 MAPK, NF-κB, and PKB/AKT activities in SK-MEL-28 cells. Furthermore, CBEO's cytotoxicity against SK-MEL-28 cells was significantly altered in the presence of MAPKs inhibitors. These findings support the in vitro antimelanoma effect of CBEO through apoptosis induction, and the modulation of ERK, JNK, p38 MAPK, NF-κB, and PKB/AKT activities.

Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro

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.