In silico screening of potential Tumor necrosis factor alpha (TNF-α) inhibitors through molecular modeling, molecular docking, and pharmacokinetics evaluations

Abstract

The multifunctional cytokine TNF-α serves as a key biological mediator for several important immune processes, such as inflammation, infection, and antitumor responses. It is crucial for both acute and chronic neuroinflammation, as well as several neurodegenerative diseases. For the treatment of inflammatory diseases, the synthetic antibodies etanercept, adalimumab, and the generic medication Diclophenac directly bind to TNF-α, preventing it from interacting with the tumor necrosis factor receptor (TNFR). These approved drugs have detrimental side effects. There is therefore a lot of interest in the scientific world to identify new small-molecule-based TNF-α inhibition therapies. In this study, a set of molecular modeling techniques have been applied including the QSAR model, docking, and pharmacokinetics prediction to identify and optimize novel TNF-α inhibitors. Based on the modeling techniques applied, the QSAR shows (R2= 0.9534, Q2 = 0.8707, Rpred2= 0.8599, cRr2= 0.8994, SEE = 0.1067). The results showed that the function of these discovered compounds was not connected to lipophilicity, whereas less lengthy NN bonds and long substituents might lead to quite bioactive molecules. The discovered hits indicate promising inhibition against TNF-α and lacked harmful effects. Most of the discovered molecules had higher TNF-binding affinity than the reference substance. Furthermore, comparing the reference drug grading (ds) of 0.38, molecule 74 with PubChem ID 2998055 exhibits superior properties with a drug grading (ds) of 0.76. As a whole, the discovered molecules have favorable pharmacokinetics, pharmacodynamics, and drug interaction properties that suggest promising TNF- inhibition and lacked toxicity, suggesting that they are potential drug candidates.