Immuno-Regenerative Nanoplatform for Restoration of \( \beta \)-Cell Mass in Pediatric Type 1 Diabetes: A Hierarchical Bayesian Framework for Dual-Target Therapy with Time-Dependent Uncertainty Quantification

Type 1 diabetes (T1D) in children is an autoimmune disorder characterized by progressive loss of insulin-producing β-cells. Current therapies, including insulin replacement and immunotherapies, mitigate symptoms but do not address the underlying autoimmunity or enable β-cell regeneration. We propose a theoretical nanoplatform integrating environment-responsive biomaterials, mRNA-mediated cellular reprogramming, and tolerogenic immunomodulation. The design includes a thermosensitive chitosan-PNIPAAm hydrogel co-loaded with (i) PLGA nanoparticles for sustained release of proinsulin peptide P277 and IL-10, and (ii) ionizable lipid nanoparticles (LNPs) encoding PDX1, MAFA, and NEUROG3. LNPs are functionalized with the GE11 peptide to enhance targeting to EGFR-expressing pancreatic acinar cells. A multiscale mathematical model couples hydrogel degradation, nanoparticle biodistribution, dendritic cell-mediated Treg induction, latent viral trigger dynamics, and dual-target failure analysis. Calibrated using hierarchical Bayesian inference against synthetic clinical data, the model estimates a posterior probability of sustained C-peptide > 0.5 ng/mL at 1 year of 92% (95% CrI: 85–97%) and HbA1c < 6.5% at 5 years of 85% (95% CrI: 78–91%). Time-dependent Sobol’ sensitivity analysis identifies shifts in uncertainty from biodistribution (early phase) to immune control (late phase). The framework is falsifiable and open-source, providing a rigorous blueprint for preclinical evaluation of a potential functional cure for pediatric T1D without chronic immunosuppression.

Keywords: Type 1 diabetes, cellular reprogramming, immune tolerance, Bayesian hierarchical modeling, nanomedicine, uncertainty quantification, mRNA-LNP, active targeting.