Abstract
Purpose :
To assess the pharmacological profile of axitinib, a tyrosine kinase inhibitor (TKI), through a bioinformatic/molecular modeling approach and two in vitro models of diabetic retinopathy (DR).
Methods :
Molecular docking, MM-GBSA and molecular dynamics simulation have been used to study axitinib interaction with Keap1 and its binding on melanocortin receptor 1 (MCR1). Human retinal endothelial cells (HRECs) were challenged with high glucose (HG) concentration (40 mM, fluctuating and non-fluctuating) and treated with axitinib (0.1-25 µM). HRECs tolerability to axitinib has been evaluated by means of LDH and MTT assays. Phosphorylation of VEGFR1, VEGFR2 and ERK, as well as Nrf2 modulation, were investigated though western blot analysis. Additionally, 2′,7′-dichlorofluoresceindiacetate (DCFDA) assay was carried out to measure reactive oxygen species (ROS) production.
Results :
Axitinib exerted a significant (p<0.05) protective effect on HRECs exposed to HG challenge, through reduction of LDH release and increase of cell viability, in comparison to positive control cells (HRECs exposed only to HG). Moreover, we showed the anti-angiogenic and anti-inflammatory activity of axitinib (inhibition of VEGFR1, VEGFR2 and ERK activation) in our two different in vitro models of DR. Therefore, axitinib reduced ROS production, displaying an antioxidant effect. Additionally, axitinib was able to modulate Nrf2 in HRECs exposed to HG, fluctuating and non-fluctuating, through predicted Keap1 interaction and activation of MCR1.
Conclusions :
In conclusion, we demonstrated that axitinib inhibits VEGFR1/VEGFR2/ERK pathways in HRECs exposed to HG stimulus. Our molecular modeling studies showed that axitinib ancillary antioxidant and anti-inflammatory effects, would be linked to its activity as Nrf2 inducer and MCR1 agonist. This pharmacological profile suggests that axitinib could be a valid therapeutic candidate to treat DR, for which basic research is always searching for innovative therapies to counteract its progression.
This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.