Leveraging the Therapeutic Role of Nanotechnology in Osteoporosis Management: Current Progress and Perspectives

Abstract

Osteoporosis (OP) is a bone degenerative disease, wherein progressive deterioration of microarchitecture of bones make them porous, fragile and brittle leading to bone hollowing. OP is characterized by decrease in bone mineral density (BMD) wherein spine, shoulder, hip, wrist bones are more prone to fracture risks that causes high morbidity and socio-economic burden. This necessitates early diagnosis of OP and prediction of fragility fracture risk. Conventional diagnostic methods, include Dual-Energy X-ray Absorptiometry (DXA), do not give accurate predictions of risk of fractures as minor changes in bone structure are often overlooked. The fact that most fractures occur at non-osteoporotic BMD values, despite the operative diagnosis being based on BMD, is an obstacle in management of OP. With the advent of artificial intelligence, a series of reports suggest deep learning applications in screening and diagnosis of OP. Deep learning methods can emerge as a promising method for detection and diagnosis of osteoporotic fractures via machine learning (ML).

The ever-expanding field of nanotechnology has witnessed rapid development of its potential application for OP treatment. While traditional therapies for OP are effective, their use are severely limited due to reduced bioavailability, non-specific distribution coupled with adverse effects. Nanotherapeutics have emerged as a promising approach for managing OP. The unique physicochemical properties of nanoparticles include tunable surface characteristics, ultra-small size, high surface-to-volume ratio that empower them to overcome the confines of traditional therapies. Nanotechnology applications in OP therapeutics are still in the early phases, with more research needed to fully uncover its potential.