Safety Assessment of Stem Cell Islet Differentiation and Considerations for Clinical Translation
Bibliographic record
Abstract
Type 1 Diabetes (T1D), characterized by the autoimmune destruction of pancreatic β-cells, causes severe fluctuations in blood glucose. T1D patients depend on daily insulin injections to live. While recent advances, such as CD3-targeting immunotherapies (e.g., teplizumab), have shown promise in delaying disease onset, T1D management remains burdensome despite improvements in insulin formulations, insulin pumps, and continuous glucose monitors (CGMs). Since 2000, the Edmonton Protocol for islet transplantation has offered a cell-based therapy for T1D. However, due to limited donor availability, challenges with engraftment, and the need for lifelong immunosuppression, this therapy is limited to adults with brittle glycemic control and severe hypoglycemic risk. This has prompted exploration of stem cell-derived islets (SC-islets) as an alternative cell source. The first section of this thesis summarizes the landscape of transplant therapies for diabetes including pancreas, islet, and islet-alternatives. Multiple groups have developed differentiation protocols to produce SC-islets from human embryonic stem cells (hESCs) or induced pluripotent stem cells (iPSCs) and several clinical trials are already underway. However, safety concerns around these cell products persist. Some cells fail to commit to the β-cell lineage during differentiation, leading to off-target populations that may form cysts or tumors after transplant. Therefore, an in vivo model for rigorous assessment of differentiation protocols, SC-islet products, and microenvironmental factors is critical. The second section of this thesis compares SC-derived pancreatic progenitor cell behavior across multiple transplant sites in mouse and rat models. ESC-derived progenitors were transplanted at four sites in immunodeficient mice (kidney capsule, portal vein, epididymal fat pad, and subcutaneously). iPSC-derived islets were transplanted into the same sites, along with the omentum which has previously been evaluated for clinical islet transplantation. Glucose tolerance testing and human C-peptide levels assessed functional efficacy of the transplanted cells. Transplant site differences influenced cell function, maturation, and cyst formation. Notably, the kidney capsule (KC) was the most prone to cyst development, irrespective of host species or cell line, suggesting its potential as an in vivo model for safety monitoring and quality control assessment. Cells also engrafted and functioned better at the KC – the gold standard for site for human islet transplantation in rodent models compared to the PV which is less accommodating to human islets and more prone to ischemia. This study highlighted the need for further optimization of our differentiation protocols. Recent studies have emphasized the importance of successful definitive endoderm (Stage 1; S1) differentiation to ensure later-stage SC-islet endocrine identity and homogeneity. Many protocols use bovine serum albumin (BSA) for differentiation however, it is not GMP (Good Manufacturing Process)-compliant and prohibited in clinical trials. In the third section, this thesis evaluates serum influence on S1 differentiation of H1 ESCs in planar conditions. Results show that BSA can be replaced with lyophilized HSA (human serum albumin) without impacting cell survival, definitive endoderm achievement, or later-stage endocrine differentiation. To advance SC-islet therapies clinically, it is important to understand the limitations of current diabetes care and surgical treatments. One population of interest are total pancreatectomy (TP) patients. TP is performed selectively for pancreatic malignancies, pancreatitis, and autoimmune conditions, but has historically been regarded fearfully due to concerns of severe hypoglycemia and loss of counter-regulatory hormones. However, with improved insulins and CGMs, outcomes may now be safer. This study evaluated 147 TP patients from the last 15 years, comparing those who underwent TP alone or TP with auto-islet transplant (TPAIT). Post-operative outcomes, morbidity, mortality, and glycemic control using CGMs were compared. TP patients exhibited comparable glycemic control and variability to insulin-dependent TPAIT patients – and better glycemic control than patients with brittle T1D. The final section discusses remaining challenges in translating SC-islets to clinical applications. This thesis contributes to establishing safety and quality control standards for SC-islet differentiation and off-target cell detection. Additionally, it informs differentiation protocol optimization, moving these therapies towards clinical trials and discusses important clinical endpoints for future trials. If successful, SC-islets could overcome the challenges faced by the Edmonton Protocol and offer a new cell-based therapy for millions of patients living with diabetes worldwide.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".