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Multispectroscopic and Molecular Docking Insight into Elucidating the Interaction of Irisin with Rivastigmine Tartrate: A Combinational Therapy Approach to Fight Alzheimer's Disease.

ABSTRACT: This study was aimed to study the interaction between purified irisin and rivastigmine tartrate (RT), a cholinesterase inhibitor used in Alzheimer's therapy. Irisin mainly promotes brown fat-like features in white adipose tissues; however, it has some important role in the nervous system also, i.e., capable of opposing synapse and memory failure in Alzheimer's disease (AD). The recombinant protein was purified by Ni-NTA chromatography and characterized using spectroscopic and in silico techniques. Further, the mechanism of interaction between irisin and RT was investigated using various biophysical techniques. Fluorescence quenching studies suggested that there exists a moderate binding between irisin and RT with a binding constant (K) of 10⁴ M^-1 and the irisin-RT complex is guided by a combination of both static and dynamic modes of quenching. Thermodynamic parameters suggested the reaction to be driven by hydrogen bonding, making it specific. FTIR and CD spectroscopy suggested no secondary structural alterations in irisin in the presence of RT. Molecular docking investigation provided an insight into the important residues that play a key role in irisin-RT interactions. This study delineates an important finding in AD therapy and can provide a platform further to explore the potential of irisin in AD treatment.

SUBMITTER: Waseem R

PROVIDER: S-EPMC7992156 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Alzheimer's disease (AD) is a neurodegenerative disease that affects a wide range of populations and is the primary cause of death in various countries. The treatment of AD is still restricted to oral conventional medicines that act only superficially. Fabrication of intranasal solid lipid nanoparticulate system for the uptake of therapeutic agents will act as a convincing approach with limited off-site toxicity and increased pharmacological activity. The objective of this study was to formulate, optimize, and evaluate the efficiency of rivastigmine tartrate (RT)-loaded intranasal solid lipid nanoparticles (SLNs) employing the solvent-evaporation diffusion method. To optimize the formulation parameters, the central composite design (CCD) was used. Lipid concentration (X1) and surfactant concentration (X2) were considered to be independent variables, while particle size (Y1), percentage entrapment efficiency (Y2), and percentage drug release (Y3) were considered as responses. The solid lipid was glyceryl monostearate, while the surfactant was polysorbate 80. The optimized formulation has a particle size of 110.2 nm, % entrapment efficiency of 82.56%, and % drug release of 94.86%. The incompatibility of drug excipients was established by differential scanning calorimetry (DSC) and Fourier-transform infrared spectroscopy (FTIR). Nasal histopathology tests on sheep mucosa revealed that the developed SLNs were safe to utilize for intranasal delivery with no toxicity. Ex vivo permeation investigations revealed that the flux and diffusion coefficients for RT solid lipid nanoparticles and RT solution were 3.378 g/cm2 /h and 0.310-3 cm2 /h, respectively. Stability studies demonstrated that the developed SLNs were stable when stored under various storage conditions. The viability and vitality of adopting a lipid particle delivery system for improved bioavailability via the intranasal route were also established in the in vivo pharmacokinetic investigations. According to the histopathological and pharmacokinetic investigations, the developed formulations were safe, non-lethal, efficient, and robust. These results suggest the potentiality provided by rivastigmine tartrate-loaded solid lipid nanoparticles for nasal delivery.

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Multispectroscopic and Molecular Docking Insight into Elucidating the Interaction of Irisin with Rivastigmine Tartrate: A Combinational Therapy Approach to Fight Alzheimer's Disease.

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