Synthesis and antiviral evaluation of a novel series of homoserine-based inhibitors of the hepatitis C virus NS3/4A serine protease

Design, Synthesis, Evaluation and Molecular Dynamics Simulation of Dengue Virus NS5-RdRp Inhibitors

Dengue virus (DENV) is a major mosquito-borne human pathogen in tropical countries; however, there are currently no targeted antiviral treatments for DENV infection. Compounds 27 and 29 have been reported to be allosteric inhibitors of DENV RdRp with potent inhibitory effects. In this study, the structures of compounds 27 and 29 were optimized using computer-aided drug design (CADD) approaches. Nine novel compounds were synthesized based on rational considerations, including molecular docking scores, free energy of binding to receptor proteins, predicted Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) parameters, structural diversity, and feasibility of synthesis. Subsequently, the anti-DENV activity was assessed. In the cytopathic effect (CPE) assay conducted on BHK-21 cells using the DENV2 NGC strain, both SW-b and SW-d demonstrated comparable or superior activity against DENV2, with IC50 values of 3.58 ± 0.29 μM and 23.94 ± 1.00 μM, respectively, compared to that of compound 27 (IC50 = 19.67 ± 1.12 μM). Importantly, both SW-b and SW-d exhibited low cytotoxicity, with CC50 values of 24.65 μmol and 133.70 μmol, respectively, resulting in selectivity indices of 6.89 and 5.58, respectively. Furthermore, when compared to the positive control compound 3'-dATP (IC50 = 30.09 ± 8.26 μM), SW-b and SW-d displayed superior inhibitory activity in an enzyme inhibitory assay, with IC50 values of 11.54 ± 1.30 μM and 13.54 ± 0.32 μM, respectively. Molecular dynamics (MD) simulations elucidated the mode of action of SW-b and SW-d, highlighting their ability to enhance π-π packing interactions between benzene rings and residue W795 in the S1 fragment, compared to compounds 27 and 29. Although the transacylsulphonamide fragment reduced the interaction between T794 and NH, it augmented the interaction between R729 and T794. In summary, our study underscores the potential of SW-b and SW-d as allosteric inhibitors targeting the DENV NS5 RdRp domain. However, further in vivo studies are warranted to assess their pharmacology and toxicity profiles.

The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors

Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC₅₀) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target function 1 (TF1) without applying the index of ideality of correlation (IIC) and target function 2 (TF2) that uses IIC. The constructed models based on TF2 were statistically more significant and robust than the models based on TF1. The determination coefficients (r²) of training and test sets were 0.86 and 0.88 for the best split based on TF2. The promoters of the increase/decrease of activity were also extracted and interpreted in detail. The model interpretation results explain the role of different structural attributes in predicting the pIC₅₀ values of hepatitis C virus NS3/4A protease inhibitors. Based on the mechanistic model interpretation results, eight new compounds were designed and their pIC₅₀ values were predicted based on the average prediction of ten models.

QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM)

In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC₅₀ values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony. The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method. The correlation coefficients (r²) of training sets and test sets were 0.75 and 0.72 for the best MLR model, 0.87 and 0.85 for the best SVM model, respectively. In addition, a series of sub-dataset models were also developed. The performances of all the best sub-dataset models were better than those of the whole dataset models. We believe that the combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline.

Unusual Amino Acids in Medicinal Chemistry

Unusual amino acids are fundamental building blocks of modern medicinal chemistry. The combination of readily functionalized amine and carboxyl groups attached to a chiral central core along with one or two potentially diverse side chains provides a unique three-dimensional structure with a high degree of functionality. This makes them invaluable as starting materials for syntheses of complex molecules, highly diverse elements for SAR campaigns, integral components of peptidomimetic drugs, and potential drugs on their own. This Perspective highlights the diversity of unnatural amino acid structures found in hit-to-lead and lead optimization campaigns and clinical stage and approved drugs, reflecting their increasingly important role in medicinal chemistry.