Preparation and Preliminary Structure-Activity Relationship Studies of Schwarzinicine A Analogs as Vasorelaxant Agents.

Schwarzinicines A-D, a series of alkaloids recently discovered from Ficus schwarzii, exhibit pronounced vasorelaxant activity in rat isolated aorta. Building on this finding, a concise synthesis of schwarzinicines A and B has been reported, allowing further investigations into their biological properties. Herein, a preliminary exploration of the chemical space surrounding the structure of schwarzinicine A (1) was carried out aiming to identify structural features that are essential for vasorelaxant activity. A total of 57 analogs were synthesized and tested for vasorelaxant activity in rat isolated aorta. Both efficacy (Emax) and potency (EC50) of these analogs were compared. In addition to identifying structural features that are required for activity or associated with potency enhancement effect, four analogs showed significant potency improvements of up to 40.2-fold when compared to 1. Molecular dynamics simulation of a tetrameric 44-bound transient receptor potential canonical-6 (TRPC6) protein indicated that 44 could potentially form important interactions with the residues Glu509, Asp530, Lys748, Arg758, and Tyr521. These results may serve as a foundation for guiding further structural optimization of the schwarzinicine A scaffold, aiming to discover even more potent analogs.

Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies.

Protein kinase p38γ is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38γ with active small molecules represents an attractive alternative for developing anti-cancer drugs. In this work, we present a rigorous and systematic virtual screening framework to identify potential p38γ inhibitors against cancer. We combined the use of machine learning-based quantitative structure activity relationship modelling with conventional computer-aided drug discovery techniques, namely molecular docking and ligand-based methods, to identify potential p38γ inhibitors. The hit compounds were filtered using negative design techniques and then assessed for their binding stability with p38γ through molecular dynamics simulations. To this end, we identified a promising compound that inhibits p38γ activity at nanomolar concentrations and hepatocellular carcinoma cell growth in vitro in the low micromolar range. This hit compound could serve as a potential scaffold for further development of a potent p38γ inhibitor against cancer.

Repurposing FDA-approved drugs as HIV-1 integrase inhibitors: an in silico investigation.

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to-date. One of the most efficacious treatments for naïve or pretreated HIV patients is the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is life-long, the emergence of HIV strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r2 = 0.998, q210CV = 0.721, q2external_test = 0.754) and a boosted K* algorithm (r2 = 0.987, q210CV = 0.721, q2external_test = 0.758) to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies and accelerated Molecular Dynamics simulation. Lastly, their potential as INSTIs were also evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.Communicated by Ramaswamy H. Sarma.