Hierarchical Bayesian Framework Models Immuno-Regenerative Nanoplatform for Restoration of Beta-Cell Mass in Pediatric Type 1 Diabetes Through 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 (analogous to 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 informed by real trials, the model estimates a posterior probability of sustained C-peptide > 0.5 ng/mL at 1 year of 89% (95% CrI: 82--95%) and HbA1c < 6.5% at 5 years of 82% (95% CrI: 75--88%). 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.