Investigating interactions between derivatives of graphene nanosheets and articaine for prolonged dental anesthetic effects: A multiscale modeling study

Abstract

The controlled release of anesthetic molecules for extended local anesthesia has emerged as a promising strategy in pain management. In this context, we focus on the interactions between graphene nanosheet derivatives (GND) and the anesthetic molecule articaine (ART). Our motivation for studying ART adsorption stems from our previous extensive computational study, which demonstrated that among local anesthetic molecules, ART shows the strongest binding to pristine graphene. To understand the binding of articaine to graphene nanosheets, we conducted an extensive multiscale modeling study using modern levels of theory. We employed a combination of semiempirical (GFN-xTB) and density functional theory calculations (DFT and TD-DFT) to obtain reliable geometrical parameters, binding energies, and quantum-molecular descriptors, crucial for understanding the binding mechanism of articaine. Detailed electron density analysis within the considered systems provided important insights into the noncovalent binding between graphene derivatives and articaine. Additionally, to observe the dynamics and behavior of the system in a biologically relevant environment, we conducted molecular dynamics (MD) simulations based on the GFN-FF method. This study aims to advance the development of effective drug-carrier systems for sustained pain relief. We intend to identify the graphene modifications that could lead to stronger binding of articaine, thereby enabling slower release and prolonged anesthetic effect.