Development of iPSC-based clinical trial selection platform for patients with ultrarare diseases
Bibliographic record
Abstract
A "Leap-of-Faith" approach is used to treat patients with previously unknown ultrarare pathogenic mutations, often based on evidence from patients having dissimilar but more prevalent mutations. This uncertainty reflects the need to develop personalized prescreening platforms for these patients to assess drug efficacy before considering clinical trial enrollment. In this study, we report an 18-year-old patient with ultrarare Leigh-like syndrome. This patient had previously participated in two clinical trials with unfavorable responses. We established an induced pluripotent stem cell (iPSC)-based platform for this patient, and assessed the efficacy of a panel of drugs. The iPSC platform validated the safety and efficacy of the screened drugs. The efficacy of three of the screened drugs was also investigated in the patient. After 3 years of treatment, the drugs were effective in shifting the metabolic profile of this patient toward healthy control. Therefore, this personalized iPSC-based platform can act as a prescreening tool to help in decision-making with respect to patient's participation in future clinical trials.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".