Structural Modification of Ibuprofen as new NSAIDs via DFT, Molecular Docking and Pharmacokinetics Studies

Öz

Inflammations generate uneasiness. This study adopts quantum mechanical and molecular docking approach to model and explore twenty derivatives of ibuprofen as potential non-steroidal anti-inflammatory drug candidates taking ibuprofen as the standard. Optimization and calculation of the drug-like quantum chemical parameters of the compounds were conducted at DFT/B3LYP/6-31G* level of theory. Binding affinity, interaction and inhibition of the potential drug-candidates with human COX-2 receptor were investigated using molecular docking studies. Pharmacokinetic properties were studied. The drug candidates interact effectively and spontaneously with the COX-2 receptor via hydrogen bonding and π-π stacking with great binding affinity. The energy gap, global hardness and softness, and chemical potential of the derivatives suggest that they are kinetically unstable, more chemically reactive than the parent drug and are effective electron donors. From the pharmacokinetic studies, all the derivatives are not substrates to permeability glycoprotein (suggesting reduced therapeutic failure), not efficiently permeable to skin, can be absorbed by human intestine and can cross the blood brain barrier. Some derivatives are potential CYP1A2, CYP2D6 and CYP3A4 inhibitors. All the ibuprofen derivatives exhibit comparable drug-likeness with standard

Anahtar Kelimeler

• Non-steroidal anti-inflammation drugs;
• COX-2 inhibitors;
• Ibuprofen
• Density functional theory;
• Molecular docking

Kaynakça

1. Hazhazi, H., Melkemi, N., Bouachrine, M. (2019). DFT-based reactivity and combined QSAR, molecular docking of 1,2,4,5-Tetrazine derivatives as inhibitors of Pim-1 kinase. Heliyon. 5, 0–9. 10.1016/j.heliyon.2019.e02451.
2. Das, D., Kumar, A., Guruprasad, R., Mahapatra, D.K. (2016). Molecular Docking and Density Function Theory (DFT) Studies of some 4-( 2-Chloroacetamido) Benzoic Acid Derivatives as Local Anesthetics. Molecular Modeling. 2017, 1–5.