From Bench to Bedside: Role of Digital Twins in Nanocarrier Pharmacokinetics and Biodistribution

The integration of digital twin technology into nanomedicine offers transformative opportunities to address longstanding challenges in predicting nanocarrier pharmacokinetics and biodistribution. Conventional experimental models, while informative, are constrained by species–human variability, ethical limitations and incomplete representation of patient-specific heterogeneity. Digital twins — dynamic, data-driven virtual replicas — bridge this gap by combining physiologically based pharmacokinetic models, molecular dynamics simulations, machine learning frameworks and biosensor feedback to generate real-time, adaptive predictions of nanocarrier fate. This review critically evaluates the conceptual underpinnings, modeling strategies and applications of digital twins for nanocarrier optimization. Mechanistic frameworks capture systemic drug distribution, whereas AI-driven approaches leverage complex nano–bio datasets to refine predictive accuracy. Case studies, including lipid nanoparticles for RNA delivery and PEGylated liposomal formulations, demonstrate strong concordance between in silico predictions and experimental/clinical data, validating the translational potential of this approach. Nonetheless, key challenges persist, including data heterogeneity, computational intensity of multiscale simulations, limited validation across species and regulatory acceptance barriers. Looking forward, the convergence of AI, quantum computing and multiomics integration is anticipated to accelerate the evolution of digital twins into patient-centric ecosystems, supporting precision nanomedicine, adaptive dosing and virtual clinical trials.