A high-throughput screening of a chemical compound library in ovarian cancer stem cells

This work was performed under the frame of COST Action collaboration (COST Action CM1106). The generous contribution of AIRC (The Italian Association for Cancer Research) IG14536 to G.D. is gratefully acknowledged. A.H. acknowledges support from the János Bolyai fellowship of the Hungarian Academy of Sciences.Background: Epithelial ovarian cancer has a poor prognosis, mostly due to its late diagnosis and to the development of drug resistance after a first platinum-based regimen. The presence of a specific population of “cancer stem cells” could be responsible of the relapse of the tumor, and of the development of resistance to therapy. For this reason, it would be important to specifically target this subpopulation of tumor cells in order to increase the response to therapy. Method: We screened a chemical compound library assembled during the COST CM1106 action to search for compound classes active in targeting ovarian stem cells. We here report the results of the high-throughput screening assay in two ovarian cancer stem cells and the differentiated cells derived from them. Results and conclusion: Interestingly there were compounds active only on stem cells, only on differentiated cells and compounds active on both cell populations. Even if these data need to be validated in ad hoc dose response cytotoxic experiments, the ongoing analysis of the compound structures will open up to mechanistic drug studies to select compounds able to improve the prognosis of ovarian cancer patients.PostprintPeer reviewe

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

Evidence of Mn-oxide biomineralization, Vani Mn deposit, Milos, Greece

We present evidence that precipitation of primary Mn-oxide minerals in the Vani volcanic hosted hybrid epithermal-VMS-type Mn-oxide and barite deposit was in part biogenically mediated. Manganese-oxides pseudomorphically replace small (1-5 mu m) spherical cell-like structures, and branching filamentous constructions (< 60 mu m long) probably representing manganese oxidizing bacteria. In addition, silicified consortia of spherical (5-10 mu m), filamentous, sheathed, septate and spiral (similar to 50-200 mu m) fossilized bacteria, proposed to represent photosynthetic thermophilic cyanobacteria, were found in quartz paragenetically related to the Mn ore. Fluid inclusions indicate formation temperatures around 100 degrees C. XRD and EMP analyses suggest X-ray-amorphous hollandite-group like Mn-oxide phases, and poorly crystalline todorokite and vernadite. These findings suggest a biological link between bacterial and mineralization processes

Reactions of N-hydroxysuccinimide esters of N-alkoxycarbonyl-α-amino acids with active methylene compounds. Synthesis of 3-substituted tetramic acids

The N-hydroxysuccinimide esters of N-alkoxycarbonyl-α-amino acids react with active methylene compounds (cyanoacetic esters, malonic and acyl acetic esters), under basic conditions, to produce 3-substituted N-alkoxycarbonyl tetramic acids; in the case of the N-hydroxysuccinimide esters derived from L-aminoacids, the corresponding optically active tetramic acids are obtained