Small molecule NS5B RdRp non-nucleoside inhibitors for the treatment of HCV infection: A medicinal chemistry perspective

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules' simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.

Investigating the effect of polymerase inhibitors on cellular proliferation: Computational studies, cytotoxicity, CDK1 inhibitory potential, and LC-MS/MS cancer cell entrapment assays

Directly acting antivirals (DAAs) are a breakthrough in the treatment of HCV. There are controversial reports on their tendency to induce hepatocellular carcinoma (HCC) in HCV patients. Numerous reports have concluded that the HCC is attributed to patient-related factors while others are inclined to attribute this as a DAA side-effect. This study aims to investigate the effect of polymerase inhibitor DAAs, especially daclatasivir (DLT) on cellular proliferation as compared to ribavirin (RBV). The interaction of DAAs with variable cell-cycle proteins was studied in silico. The binding affinities to multiple cellular targets were investigated and the molecular dynamics were assessed. The in vitro effect of the selected candidate DLT on cancer cell proliferation was determined and the CDK1 inhibitory potential in was evaluated. Finally, the cellular entrapment of the selected candidates was assessed by an in-house developed and validated LC-MS/MS method. The results indicated that polymerase inhibitor antiviral agents, especially DLT, may exert an anti-proliferative potential against variable cancer cell lines. The results showed that the effect may be achieved via potential interaction with the multiple cellular targets, including the CDK1, resulting in halting of the cellular proliferation. DLT exhibited a remarkable cell permeability in the liver cancer cell line which permits adequate interaction with the cellular targets. In conclusion, the results reveal that the polymerase inhibitor (DLT) may have an anti-proliferative potential against liver cancer cells. These results may pose DLT as a therapeutic choice for patients suffering from HCV and are liable to HCC development.

Discovery of Terpenes as Novel HCV NS5B Polymerase Inhibitors via Molecular Docking

Hepatitis C virus (HCV) is a dangerous virus that is responsible for a large number of infections and deaths worldwide. In the treatment of HCV, it is important that the drugs are effective and do not have additional hepatotoxic effects. The aim of this study was to test the in silico activity of 1893 terpenes against the HCV NS5B polymerase (PDB-ID: 3FQK). Two drugs, sofosbuvir and dasabuvir, were used as controls. The GOLD software (CCDC) and InstaDock were used for docking. By using the results obtained from PLP.Fitness (GOLD), pKi, and binding free energy (InstaDock), nine terpenes were finally selected based on their scores. The drug-likeness properties were calculated using Lipinski's rule of five. The ADMET values were studied using SwissADME and pkCSM servers. Ultimately, it was shown that nine terpenes have better docking results than sofosbuvir and dasabuvir. These were gniditrin, mulberrofuran G, cochlearine A, ingenol dibenzoate, mulberrofuran G, isogemichalcone C, pawhuskin B, 3-cinnamyl-4-oxoretinoic acid, DTXSID501019279, and mezerein. Each docked complex was submitted to 150 ns-long molecular dynamics simulations to ascertain the binding stability. The results show that mulberrofuran G, cochlearine A, and both stereoisomers of pawhuskin B form very stable interactions with the active site region where the reaction product should form and are, therefore, good candidates for use as effective competitive inhibitors. The other compounds identified in the docking screen either afford extremely weak (or even hardly any) binding (such as ingenol dibenzoate, gniditrin, and mezerein) or must first undergo preliminary movements in the active site before attaining their stable binding conformations, in a process which may take from 60 to 80 ns (for DTXSID501019279, 3-cinnamyl-4-oxoretinoic acid or isogemichalcone C).

Novel 6-Aminoquinazolinone Derivatives as Potential Cross GT1-4 HCV NS5B Inhibitors

Chronic hepatitis C virus (HCV) infections are a worldwide medical problem responsible for diverse types of liver diseases. The NS5B polymerase enzyme has become a very interesting target for the development of anti-HCV drugs owing to its fundamental role in viral replication. Here we report the synthesis of a novel series of 1-substituted phenyl-4(1H)-quinazolinone and 2-methyl-1-substituted phenyl-4(1H)-quinazolinone derivatives and evaluate their activity against HCV in HCV subgenomic replicon assays. The biological data revealed that compound 11a showed the highest activity against HCV GT1b at a micromolar concentration (EC₅₀ = 0.984 µM) followed by compound 11b (EC₅₀ = 1.38 µM). Both compounds 11a and 11b had high selectivity indices (SI = CC₅₀/EC₅₀), 160.71 and 71.75, respectively, which make them very interesting candidates for further development of more potent and selective anti-HCV agents.

The IQA Energy Partition in a Drug Design Setting: A Hepatitis C Virus RNA-Dependent RNA Polymerase (NS5B) Case Study

The interaction of the thumb site II of the NS5B protein of hepatitis C virus and a pair of drug candidates was studied using a topological energy decomposition method called interacting quantum atoms (IQA). The atomic energies were then processed by the relative energy gradient (REG) method, which extracts chemical insight by computation based on minimal assumptions. REG reveals the most important IQA energy contributions, by atom and energy type (electrostatics, sterics, and exchange–correlation), that are responsible for the behaviour of the whole system, systematically from a short-range ligand–pocket interaction until a distance of approximately 22 Å. The degree of covalency in various key interatomic interactions can be quantified. No exchange–correlation contribution is responsible for the changes in the energy profile of both pocket–ligand systems investigated in the ligand–pocket distances equal to or greater than that of the global minimum. Regarding the hydrogen bonds in the system, a “neighbour effect” was observed thanks to the REG method, which states that a carbon atom would rather not have its covalent neighbour oxygen form a hydrogen bond. The combination of IQA and REG enables the automatic identification of the pharmacophore in the ligands. The coarser Interacting Quantum Fragments (IQF) enables the determination of which amino acids of the pocket contribute most to the binding and the type of energy of said binding. This work is an example of the contribution topological energy decomposition methods can make to fragment-based drug design.