Year: 2026
Type: article
Abstract: The combination of digital twin technology and nanomedicine has emerged as a promising strategy for advancing predictive modelling and precision therapeutics. Nanomedicine offers significant opportunities for targeted drug delivery and controlled drug release; however, its broader clinical translation remains constrained by complex nano–bio interactions, interpatient variability, heterogeneous biodistribution, and uncertainties in predicting nanoparticle pharmacokinetics. Digital twins, conceptualized as continuously updated, patient-specific virtual representations that integrate real-world biological and clinical data, have the potential to address some of these challenges by supporting individualized computational simulations and treatment optimization. This review critically examines the integration of digital twin frameworks with nanomedicine, focusing on computational modelling of nanoparticle transport, protein corona dynamics, immune interactions, and biodistribution. It further evaluates predictive modelling approaches, including physiologically based pharmacokinetic (PBPK) models, artificial intelligence (AI), machine learning (ML), and hybrid mechanistic–AI frameworks, highlighting their respective strengths, limitations, and current validation status.Applications in cancer, cardiovascular, and neurological disorders, together with smart drug delivery platforms and biosensor-assisted monitoring systems, are also discussed. The review additionally examines current challenges associated with model validation, data standardization, interoperability, regulatory acceptance, and clinical implementation. Although clinically validated digital twin systems for nanomedicine remain limited, continued advances in computational modelling, multimodal data integration, experimental validation, and standardized regulatory frameworks may facilitate the future development of reliable digital twin-enabled precision nanomedicine
Source: Next Nanotechnology
Authors: Sailee Chowdhury, Sithi Sarkar, Anirban Das
Language: English
Subfield: Biomaterials
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,280
OpenAlex ID: W7202392742