Plasmodium falciparum causes the most lethal and widespread form of malaria. Eradication of malaria remains a priority due to the increasing number of cases of drug resistance. The heat shock protein 90 of P. falciparum (PfHsp90) is a validated drug target essential for parasite survival. Most PfHsp90 inhibitors bind at the ATP binding pocket found in its N-terminal domain, abolishing the chaperone's activities, which leads to parasite death. The challenge is that the NTD of PfHsp90 is highly conserved, and its disruption requires selective inhibitors that can act without causing off-target human Hsp90 activities. We endeavored to discover selective inhibitors of PfHsp90 using pharmacophore modeling, virtual screening protocols, induced fit docking (IFD), and cell-based and biochemical assays. The pharmacophore model (DHHRR), composed of one hydrogen bond donor, two hydrophobic groups, and two aromatic rings, was used to mine commercial databases for initial hits, which were rescored to 20 potential hits using IFD. Eight of these compounds displayed moderate to high activity toward P. falciparum NF54 (i.e., IC₅₀s ranging from 6.0 to 0.14 μM) and averaged >10 in terms of selectivity indices toward CHO and HepG2 cells. Additionally, four compounds inhibited PfHsp90 with greater selectivity than a known inhibitor, harmine, and bound to PfHsp90 with weak to moderate affinity. Our findings support the use of a pharmacophore model to discover diverse chemical scaffolds such as FM2, FM6, F10, and F11 exhibiting anti-Plasmodium activities and serving as valuable new PfHsp90 inhibitors. Optimization of these hits may enable their development into potent leads for future antimalarial drugs.

The expression of the efflux pump systems is the most important mechanism of antibiotic resistance in bacteria, as it contributes to reduced concentration and the subsequent inactivity of administered antibiotics. NorA is one of the most studied antibacterial targets used as a model for efflux-mediated resistance. The present study evaluated shikimate pathway-derived phenolic acids against NorA (PDB ID: 1PW4) as a druggable target in antibacterial therapy using in silico modelling and in vitro methods. Of the 22 compounds evaluated, sinapic acid (−9.0 kcal/mol) and p-coumaric acid (−6.3 kcal/mol) had the best and most prominent affinity for NorA relative to ciprofloxacin, a reference standard (−4.9 kcal/mol). A further probe into the structural stability and flexibility of the resulting NorA-phenolic acids complexes through molecular dynamic simulations over a 100 ns period revealed p-coumaric acid as the best inhibitor of NorA relative to the reference standard. In addition, both phenolic acids formed H-bonds with TYR 76, a crucial residue implicated in NorA efflux pump inhibition. Furthermore, the phenolic acids demonstrated favourable drug likeliness and conformed to Lipinski’s rule of five for ADME properties. For the in vitro evaluation, the phenolic acids had MIC values in the range 31.2 to 62.5 μg/mL against S. aureus, and E. coli, and there was an overall reduction in MIC following their combination with ciprofloxacin. Taken together, the findings from both the in silico and in vitro evaluations in this study have demonstrated high affinity of p-coumaric acid towards NorA and could be suggestive of its exploration as a novel NorA efflux pump inhibitor.

A global pandemic caused by the SARS-CoV-2 virus that started in 2020 and has wreaked havoc on humanity still ravages up until now. As a result, the negative impact of travel restrictions and lockdowns has underscored the importance of our preparedness for future pandemics. The main thrust of this work was based on addressing this need by traversing chemical space to design inhibitors that target the SARS-CoV-2 papain-like protease (PL^pro). Pathfinder-based retrosynthesis analysis was used to generate analogs of GRL-0617 using commercially available building blocks by replacing the naphthalene moiety. A total of 10 models were built using active learning QSAR, which achieved good statistical results such as an R² > 0.70, Q² > 0.64, STD Dev < 0.30, and RMSE < 0.31, on average for all models. A total of 35 ideas were further prioritized for FEP+ calculations. The FEP+ results revealed that compound 45 was the most active compound in this series with a ΔG of −7.28 ± 0.96 kcal/mol. Compound 5 exhibited a ΔG of −6.78 ± 1.30 kcal/mol. The inactive compounds in this series were compound 91 and compound 23 with a ΔG of −5.74 ± 1.06 and −3.11 ± 1.45 kcal/mol. The combined strategy employed here is envisaged to be of great utility in multiparameter lead optimization efforts, to traverse chemical space, maintaining and/or improving the potency as well as the property space of synthetically aware design ideas.