Pharmacophoric analogs of sotorasib-entrapped KRAS G12C in its inactive GDP-bound conformation: covalent docking and molecular dynamics investigations

Driver Mutations in Pancreatic Cancer and Opportunities for Targeted Therapy

Pancreatic cancer is the sixth leading cause of cancer-related mortality globally. As the most common form of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC) represents up to 95% of all pancreatic cancer cases, accounting for more than 300,000 deaths annually. Due to the lack of early diagnoses and the high refractory response to the currently available treatments, PDAC has a very poor prognosis, with a 5-year overall survival rate of less than 10%. Targeted therapy and immunotherapy are highly effective and have been used for the treatment of many types of cancer; however, they offer limited benefits in pancreatic cancer patients due to tumor-intrinsic and extrinsic factors that culminate in drug resistance. The identification of key factors responsible for PDAC growth and resistance to different treatments is highly valuable in developing new effective therapeutic strategies. In this review, we discuss some molecules which promote PDAC initiation and progression, and their potential as targets for PDAC treatment. We also evaluate the challenges associated with patient outcomes in clinical trials and implications for future research.

Identification of promising small-molecule inhibitors targeting STK17B for cancer therapeutics: molecular docking and molecular dynamics investigations

Cancer is a complex disease characterized by the uncontrolled growth of abnormal cells, leading to the formation of tumours. STK17B, a member of the DAPK family, has been implicated in various cancers and is considered a potential therapeutic target. However, no drug in the market has been approved for the treatment of STK17 B-associated cancer disease. This research aimed to identify direct inhibitors of STK17B using computational techniques. Ligand-based virtual screening and molecular docking were performed, resulting in the selection of three lead compounds (CID_135698391, CID_135453100, CID_136599608) with superior binding affinities compared to the reference compound dovitinib. While molecular docking simulation revealed specific interactions between the lead compounds and key amino acid residues at the binding pocket of STK17B, molecular dynamics simulations demonstrated that CID_135453100 and CID_136599608 exhibit stable conformations and comparable flexibility to dovitinib. However, CID_135698391 did not perform well using this metric as it displayed poor stability. Overall, small-molecule compounds CID_135453100 and CID_136599608 showed promising binding interactions and stability, suggesting their potential as direct inhibitors of STK17B. These findings could contribute to the exploration of novel therapeutic options targeting STK17B in cancer treatment.Communicated by Ramaswamy H. Sarma.

Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study

The Kirsten rat sarcoma viral G12C (KRAS^G12C) protein is one of the most common mutations in non-small-cell lung cancer (NSCLC). KRAS^G12C inhibitors are promising for NSCLC treatment, but their weaker activity in resistant tumors is their drawback. This study aims to identify new KRAS^G12C inhibitors from among the FDA-approved covalent drugs by taking advantage of artificial intelligence. The machine learning models were constructed using an extreme gradient boosting (XGBoost) algorithm. The models can predict KRAS^G12C inhibitors well, with an accuracy score of validation = 0.85 and Q²_Ext = 0.76. From 67 FDA-covalent drugs, afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride were predicted to be active inhibitors. Afatinib obtained the highest predictive log-inhibitory concentration at 50% (pIC₅₀) value against KRAS^G12C protein close to the KRAS^G12C inhibitors. Only afatinib, neratinib, and zanubrutinib covalently bond at the active site like the KRAS^G12C inhibitors in the KRAS^G12C protein (PDB ID: 6OIM). Moreover, afatinib, neratinib, and zanubrutinib exhibited a distance deviation between the KRAS^G2C protein-ligand complex similar to the KRAS^G12C inhibitors. Therefore, afatinib, neratinib, and zanubrutinib could be used as drug candidates against the KRAS^G12C protein. This finding unfolds the benefit of artificial intelligence in drug repurposing against KRAS^G12C protein.