Exploring the logic and conducting a comprehensive evaluation of AdipoRon-based adiponectin replacement therapy against hormone-related cancers-a systematic review

The potential benefits of adiponectin replacement therapy extend to numerous human diseases, with current research showing particular interest in its effectiveness against specific cancer forms, especially hormone-related. However, limitations in the pharmacological use of the intact protein have led to a focus on alternative options. AdipoRon is an extensively studied non-peptidic drug candidate for adiponectin replacement therapy. While researchers have explored the efficacy and therapeutic applications of AdipoRon in various disease conditions, their effects against cancer models advanced more, with no review regarding AdipoRon's efficacy against hormone-related cancers being published. The present systematic review aims to fill this gap. Preclinical evidence was compiled from PubMed, EMBASE, COCHRANE, and Google Scholar following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the manuscript's quality assessment was conducted using the Joanna Briggs Institute (JBI) Checklist Critical Appraisal Tool for Systematic Reviews' Quality. The included nine studies incorporated various cell and animal models of the pancreas, gynaecological system, and osteosarcoma cancers. AdipoRon demonstrated effectiveness against pancreatic cancer by activating p44/42 MAPK, mitochondrial dysfunction, and AMPK-mediated inhibition of ACC1. In gynaecological cancers, it exhibited promising anticancer effects through the activation of AMPK, potential inhibition of mTOR, and modulation of the SET1B/BOD1/AdipoR1 signaling cascade. Against osteosarcoma, AdipoRon worked by perturbing ERK1/2 signaling and reducing p70S6K phosphorylation. AdipoRon shows promise in preclinical studies, but human trials are crucial for clinical safety and effectiveness. Caution is needed due to potential off-target effects, especially in cancer therapy with multi-target approaches. Structural biology and computational methods can help predict these effects.