First total synthesis of versicotide A, B and C

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.

Marine natural products

Covering: 2020This review covers the literature published in 2020 for marine natural products (MNPs), with 757 citations (747 for the period January to December 2020) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms. The emphasis is on new compounds (1407 in 420 papers for 2020), together with the relevant biological activities, source organisms and country of origin. Pertinent reviews, biosynthetic studies, first syntheses, and syntheses that led to the revision of structures or stereochemistries, have been included. A meta analysis of bioactivity data relating to new MNPs reported over the last five years is also presented.

Synthesis and antiplasmodial assessment of nitazoxanide and analogs as new antimalarial candidates

During the last years, the progression to control malaria disease seems to be slowed and WHO (World Health Organization) reported a modeling analysis with the prediction of the increase in malaria morbidity and mortality in sub-Saharan Africa during the COVID-19 pandemic. A rapid way to the discovery of new drugs could be carried out by performing investigations to identify drugs based on repurposing of "old" drugs. The 5-nitrothiazole drug, Nitazoxanide was shown to be active against intestinal protozoa, human helminths, anaerobic bacteria, viruses, etc. In this work, Nitazoxanide and analogs were prepared using two methodologies and evaluated against P. falciparum 3D7. A bithiazole analog, showed attractive inhibitory activity with an EC50 value of 5.9 μM, low propensity to show toxic effect against HepG2 cells at 25 μM, and no cross-resistance with standard antimalarials.

Three Methods for Peptide Cyclization Via Lactamization

Lactamization is the key step in the synthesis of many compounds with macrocyclic structure. As the interest for these types of molecules grows in various fields such as drug discovery and nanomaterials, different methodologies to access them are being developed. Three different strategies to obtain cyclic peptides via lactamization are described in this chapter: solution-phase macrocyclization following solid-phase peptide synthesis (SPPS) of the linear precursor, SPPS and on-resin cyclization on the 2-chlorotrityl chloride (2-CTC) resin, and SPPS and on-resin cyclization by native chemical ligation on the amino-PEGA resin.

Paracetamol and other acetanilide analogs as inter-molecular hydrogen bonding assisted diamagnetic CEST MRI contrast agents

Paracetamol and a few other acetanilide derivatives are reported as a special class of diamagnetic Chemical Exchange Saturation Transfer (diaCEST) MRI contrast agents, that exhibit contrast only when the molecules form inter-molecular hydrogen bonding mediated molecular chains or sheets. Without the protection of the hydrogen bonding their contrast producing labile proton exchanges too quickly with the solvent to produce any appreciable contrast. Through a number of variable temperature experiments we demonstrate that under the conditions when the hydrogen bond network breaks and the high exchange returns back, the contrast drops quickly. The well-known analgesic drug paracetamol shows 12% contrast at a concentration of 15 mM at physiological conditions. With the proven safety track-record for human consumption and appreciable physiological contrast, paracetamol shows promise as a diaCEST agent for in vivo studies.