Translation of a human induced pluripotent stem cell-derived ovarian support cell product to a Phase 3 enabling clinical grade product for <i>in vitro</i> fertilization treatment
Bibliographic record
Abstract
Abstract Human induced pluripotent stem cells (hiPSCs) show great promise in the development of novel strategies to mitigate reproductive diseases and promote successful reproductive outcomes. Recently, a novel approach for the fast and efficient differentiation of ovarian support cells (OSCs) to generate a versatile platform for basic research and clinical applications was demonstrated. This study details the clinical process development and application of an OSC product, known as Fertilo , to improve the in vitro maturation (IVM) of human oocytes, a method referred to as OSC-IVM. First, transcription factor (TF) mediated OSC differentiation using research-grade raw materials was shown to produce granulosa-like cells that improve the MII maturation rate of human oocytes. To support clinical application, several raw material upgrades were initiated, including substitution of the differentiation matrix with a higher-quality alternative, laminin-521, and the generation of a clinically suitable hiPSC seed bank and master cell bank. Single cell RNA sequencing of OSCs generated using the updated protocol for clinical translation demonstrated the consistency and reproducibility of cellular outcomes. Next, analytical release testing of the clinical product was performed and a murine oocyte maturation assay was developed to establish the potency of OSCs for use in OSC-IVM. Finally, the qualified Fertilo product was applied in a two-phase longitudinal cohort analysis, with the results showing improvement in key outcomes compared to traditional IVM treatment. Our findings demonstrate the first-time clinical development and application of an hiPSC-derived product to improve reproductive outcomes after IVM and advance women’s health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".