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.

Enantioselective Synthesis of a New Non-Natural Gabosine.

The preparation of a new non-natural gabosine is reported, in which the chirality is transferred from the toluene's biotransformed metabolite (1R,2S)-3-methylcyclohexa-3.5-diene-1,2-diol. Further chemical transformations to introduce additional functionality and chirality to the molecule were also accomplished.

Nanocrystals of Novel Valerolactam-Fenbendazole Hybrid with Improved in vitro Dissolution Performance.

Valero-fenbendazole (VAL-FBZ) is a novel hybrid compound with in vitro anthelmintic activity, designed and synthesized to address the global problem of resistance to anthelmintic compounds. This new molecule derives from fenbendazole (FBZ), a well-known commercially available benzimidazole used in veterinary medicine despite its poor water solubility. In this work, we report for the first time a strategy to solve the solubility problems of FBZ and VAL-FBZ by means of self-dispersible nanocrystals (SDNC). Nanocrystals were prepared by media milling followed by a spray-drying step, and a comprehensive and exhaustive structural and physicochemical characterization was carried out, in order to understand the systems and their behavior. The formulation poloxamer 188 (P188):FBZ 1:1 turned out with the best process yield (53%) and re-dispersability properties, particle size average of 258 nm, and polydispersity index of 0.2 after redispersion in water. The dissolution profile showed a markedly increased dissolution rate compared with the simple mixture of the components (80% FBZ dissolved in 15 min from the SDNC vs 14% from the control formulation). FTIR spectroscopy, thermal analysis, and X-Ray Powder Diffraction (XRPD) studies showed no chemical interactions between components and an extensive confocal Raman microscopy analysis of the formulations showed very homogeneous spatial distribution of components in the SDNC samples. This manufacturing process was then successfully transferred for preparing and characterizing VAL-FBZ:P188 (1:1) SDNC with similar results, suggesting the promising interest of a novel anthelmintic with improved biopharmaceutical behavior. In conclusion, new FBZ and VAL-FBZ SDNC with improved dissolution rate were successfully prepared and characterized. Graphical abstract.