Computational-guided approach for identification of PI3K alpha inhibitor in the treatment of hepatocellular carcinoma by virtual screening and water map analysis

Hepatocellular carcinoma (HCC) is one of the most deadly disorders, with a relative survival rate of 36% in the last 5 years. After an extensive literature survey and pathophysiology analysis, PI3Kα was found to be a promising biological target as PIK3CA gene upregulation was observed in HCC, resulting in the loss of apoptosis of cells, which leads to uncontrollable growth and proliferation. Due to superior selectivity and promising therapeutic activity, the PI3K-targeted molecule library was selected, and the ligand preparation was executed. The study mainly focused on e-pharmacophore development, virtual screening and receptor-ligand docking analysis. Then, MMGBSA and ADME prediction analysis was performed with the top 10 molecules; for further analysis of ligand-receptor binding affinity at the catalytic binding site, induced fit docking was performed with the top two molecules. The analysis of quantum chemical stability descriptors, i.e., frontier molecular orbital analysis, was performed followed by molecular dynamics simulation of 100 ns to better understand the ligand-receptor binding. In this study, water map analysis played a significant role in the hit optimization and analysis of the thermodynamic properties of the receptor-ligand complex. The two hit molecules K894-1435 and K894-1045 represented superior docking scores, enhanced stability, and inhibitory action targeting Valine 851 amino acid residue at the catalytic binding site. Hence, the study has significance for the quest for selective PI3Kα inhibitors through the process of hit-to-lead optimization.Communicated by Ramaswamy H. Sarma.

Identification of natural product as selective PI3Kα inhibitor against NSCLC: multi-ligand pharmacophore modeling, molecular docking, ADME, DFT, and MD simulations

Non-small cell lung cancer (NSCLC) is a widespread and often aggressive form of cancer affecting people worldwide. PIK3CA missense mutations play a significant role in the progression of growth factor signaling in cancer, making PI3Kα an important biological target for inhibition against NSCLC. Natural product molecules with PI3Kα inhibitory activity are promising therapeutic agents for the treatment of NSCLC, owing to their selectivity and potentially lower toxicity compared to synthetic compounds. To discover new natural product molecules, we integrated ligand-based virtual screening with structure-based virtual screening. We developed a multi-ligand pharmacophore hypothesis, validated it with 3D Field-based QSAR, and screened a Natural-Product-Based Library (ChemDiv) containing 3601 molecules. After initial screening, 137 hit molecules were generated and further screened using the extra precision (XP) Glide docking protocol. The best ten molecules were selected for free binding energy (ΔG) analysis using MMGBSA and ADME predictions. For further optimization, the top four hits were subjected to induced fit docking (IFD), quantum chemical descriptors analysis by Frontier Molecular Orbital (FMO) studies, and a 100 ns molecular dynamics (MD) simulation. The compounds-S721-1955, CM4579-5085, S721-1963, and S721-1999-exhibited better results than the PI3Kα selective inhibitor alpelisib. In silico prediction analysis of S721-1955 and alpelisib revealed that the former exhibited superior selectivity theoretically, as evidenced by its higher affinity for the target protein. The selective natural product molecule identified in this study holds promise as a potential anti-cancer drug against NSCLC in the near future, but further in vitro and in vivo studies are necessary to confirm its efficacy.

Calixarenes and their Relevance in Anticancer Drug Development

Calixarenes have always captured the attention of several researchers. They have the ability to entrap multiple molecules and form inclusion complexes with drugs due to their unique structure. Due to this property, they are being widely used in the development of several classes of drugs, most notably anticancer drugs. This review attempted to summarize the potential applications of calixarenes and its derivatives in the development of anticancer drugs, with a focus on the delivery of drug classes such as DNA intercalators, taxanes, DNA alkylators, and topoisomerase inhibitors. Calixarene-based macromolecular chemistry could therefore have a high potential for overcoming the toxicity of cancer chemotherapy and achieving targeted drug delivery.

Molecular docking and dynamics approach to in silico drug repurposing for inflammatory bowels disease by targeting TNF alpha

Inflammatory bowel disease is a chronic disorder of the large intestine with the prevalence of approximately 400 cases in 100000, and it is rising day by day. However, several drugs like sulfasalazine (composed of sulfapyridine and 5-aminosalicylic acid or 5-ASA), corticosteroids, and immunosuppressants manage the disease. But there are no absolute treatments for the pain and inflammation of the disease. TNFα is an important target, and drugs like infliximab and adalimumab have pharmacological potency but with pronounced toxicity. So, we choose this major target TNFα for the virtual screening of US-FDA-approved drugs for its repurposing using the in silico method. The protein TNFα (PDB ID: 2AZ5) with small molecule inhibitor and the US-FDA-approved drug molecules (from Zinc database) were first imported and prepared using Protein Preparation Wizard and LigPrep, respectively, followed by molecular docking, ADMET analysis and prime MMGBSA. After that, the drugs were shortlisted according to dock score, ADMET parameters and MM GBSA dG binding score. After that, the shortlisted drug molecules were subjected to an induced-fit docking analysis. Two of the most promising molecules, ZINC000003830957 (Iopromide) and ZINC000003830635 (Deferoxamine), were chosen for molecular dynamics simulation. Finally, the bioisosteric replacement was used to improve the ADMET properties of these molecules. This research provides an idea for drug exploration and computational tools for drug discovery in treating inflammatory bowel disease.Communicated by Ramaswamy H. Sarma.