Artificial Intelligence-Guided Nanoformulations for Personalized Management of Obesity and Diabetes
Mwende Muthoni D.
Faculty of Medicine Kampala International University Uganda
ABSTRACT
Obesity and type 2 diabetes are heterogeneous, chronic disorders driven by complex genetic, behavioral and environmental factors, leading to widely variable responses to lifestyle and pharmacologic therapies. Conventional treatment paradigms rely on population averages rather than individual biology, contributing to suboptimal control, weight regain and treatment failure. Nanotechnology offers powerful tools to enhance bioavailability, tissue targeting and safety of metabolic therapeutics, while artificial intelligence (AI) provides data-driven methods for pattern discovery, prediction and optimization across high-dimensional clinical, biochemical and behavioral datasets. Integrating AI with nanoformulation design and deployment enables a new paradigm: AI-guided nanotherapies tailored to the molecular, phenotypic and lifestyle profile of individual patients with obesity and diabetes. This review explores how AI can support rational design of nanoformulations (materials selection, composition, size, surface chemistry), predict pharmacokinetics and tissue distribution, and match patients to specific nano-enabled interventions. It discusses emerging examples of machine learning in nanomedicine and metabolic disease management, the role of digital biomarkers and multi-omics in building personalized models, and the architecture of closed-loop systems that couple AI analytics with smart nanocarriers and sensors. Key ethical, regulatory and equity considerations are addressed, and future directions for AI–nano convergence in “precision diabesity” are outlined.
Keywords: Artificial intelligence; nanoformulations; obesity; diabetes; precision medicine
CITE AS: Mwende Muthoni D. (2026). Artificial Intelligence-Guided Nanoformulations for Personalized Management of Obesity and Diabetes. IDOSR JOURNAL OF BIOLOGY, CHEMISTRY AND PHARMACY 11(1):8-14 https://doi.org/10.59298/IDOSR/JBCP/26/102.814