Neurodegenerative disorders (NDs), such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), afflict more than 55 million individuals worldwide, with disease cases projected to triple by the year 2050. All these diseases have been associated with oxidative stress, neuroinflammation, mitochondrial impairment, and protein misfolding, all of which ultimately result in progressive neuronal loss. Existing therapies give only symptomatic relief, an indication of why new neuroprotective approaches are urgently needed. Phytonutrients, bioactive plant compounds, have been highlighted for their antioxidant, anti-inflammatory, and neuroprotective properties. Flavonoids like epigallocatechin gallate (EGCG) in green tea and quercetin in apples were found to decrease β-amyloid plaque formation by 50% in AD models. Curcumin, a polyphenol from turmeric, reduced tau protein aggregation by 43% and enhanced cognitive function in preclinical models. Carotenoids such as lutein and zeaxanthin were associated with a 25% decrease in cognitive impairment, while alkaloids like huperzine A enhanced memory recall by 30% in patients with mild cognitive impairment. Advancements in machine learning (ML) and artificial intelligence (AI) have revolutionized phytonutrient-based drug discovery, enabling the screening of plant compounds for neuroprotection and treatments. AI also supports personalized nutrition based on genetic profiles. Large-scale randomized controlled trials (RCTs) are essential to confirm therapeutic benefits. Integrating AI-driven predictive models with nanotechnology holds promise for targeted phytonutrient delivery, enhancing treatment efficacy for neurodegenerative disorders. Additionally, AI and ML tools can aid in cancer therapy research and surveillance, optimizing precision medicine and improving patient outcomes.
Kumar et al. (Thu,) studied this question.