Computational Modeling on Binding Interactions of Cyclodextrins with the Human Multidrug Resistance P-glycoprotein

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Molecular docking approaches and DFT ab initio simulations were combined for the first time, to study the key interactions of cyclodextrins (CDs: α-CD, β-CD, and γ-CD) family with potential pharmacological relevance and the multidrug resistance P-gp protein toward efficient drug-delivery applications. [1]

The treatment of neurological disorders and cancer therapy where the multiple drug-resistance phenomenon-mediated by the P-gp protein constitutes the fundamental cause of unsuccessful therapies.

 These theoretical results open new horizons for the evaluation of new nanotherapeutic drugs with potential pharmacological relevance for efficient drug-delivery for nanomedicine applications. P-gp residues were conformationally favored. Despite the structural differences, all the cyclodextrins exhibit very close Gibbs free binding energy values (or affinity) by the P-gp binding site (transmembrane domains – TMDs). The obtained theoretical docking-mechanism of the CDs on the P-gp were fundamentally based on hybrid backbone/side-chain hydrophobic interactions, and also hybrid electrostatic/side-chain interactions of the OH-motifs of the CD-ligands with acceptor and donor properties which theoretically could induce allosteric local-perturbations in the TMDs-inter-residues network of P-gp modulating to the CD-ligand extrusion from the blood-brain-barrier (or cancer cells). Finally, these theoretical results open new horizons for the evaluation of new nanotherapeutic drugs with potential pharmacological relevance for efficient drug-delivery applications and precision nanomedicine.

[1] González-Durruthy M, Concu R, Osmari Vendrame LF, Ortiz Martins M, Zanela I, M Ruso J, D S Cordeiro MN. Computational Modeling on Binding Interactions of Cyclodextrins with the Human Multidrug Resistance P-glycoprotein Toward Efficient Drug-Delivery System Applications. Curr Top Med Chem. 2022 Mar 3. doi: 10.2174/1568026622666220303115102. Epub ahead of print. PMID: 35240960.

Featured image: towardsdatascience.com

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