Molecule simulation and mathematical modeling for Con A-sugar affinity-based glucose-responsive particle insulin delivery systems for personalized insulin delivery
Bibliographic record
Abstract
Diabetes remains a pressing global health challenge, with insulin therapy being central to glycemic control in insulin-dependent patients. However, conventional delivery methods, e.g., subcutaneous injection, often fail to mimic the body's dynamic response to fluctuating glucose levels, resulting in suboptimal control and increased risk of hypoglycemia. This limitation motivates the development of intelligent, glucose-responsive drug delivery systems capable of personalized, feedback-regulated insulin release. This thesis presents a multiscale simulation and mathematical modeling framework for describing and predicting the behavior of Concanavalin A (Con A)-dextran hydrogel nanoparticles engineered for glucose- and lipid-sensitive insulin delivery. The central hypothesis is that integrating structural dynamics and medium viscosity into a unified diffusion model significantly improves the prediction of insulin release, particularly under patient-specific physiological conditions. To address this, Coarse-Grained Molecular Dynamics (CGMD) simulations were employed to model hydrogel nanoparticle self-assembly, Dissipative Particle Dynamics (DPD) to capture hydrogel swelling, and a multiscale approach integrating CGMD and the Discrete Element Method (DEM) to investigate insulin diffusion, accounting for both hydrogel structure and hydrodynamic effects. A novel viscosity-sensitive diffusion model was developed, linking the hydrogel structure (swelling ratio and polymer alignment) and solvent viscosity to an effective diffusion coefficient. This model was numerically implemented via the finite difference method and validated against in vitro experimental data, demonstrating excellent agreement (R² > 0.95) across a range of glucose and cholesterol concentrations. The results reveal that insulin diffusion is jointly regulated by hydrogel network morphology and medium viscosity. Furthermore, swelling-induced anisotropy was shown to influence directional release rates. The proposed framework captures the dynamic interplay between material structure and biochemical environment, offering predictive insights into insulin release behavior in personalized treatment scenarios. This thesis contributes a unified, experimentally validated multiscale modeling approach for intelligent insulin delivery design. The findings provide mechanistic understanding and quantitative tools for tailoring glucose-responsive hydrogel systems to individual patient needs—marking a significant step toward truly personalized and closed-loop insulin therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".