Preclinical Testing in Translational Animal Models of Prader-Willi Syndrome: Overview and Gap Analysis
Bibliographic record
Abstract
Prader-Willi syndrome (PWS) is a rare neurodevelopmental disorder causing endocrine, musculoskeletal, and neurological dysfunction. PWS is caused by the inactivation of contiguous genes, complicating the development of targeted therapeutics. Clinical trials are now underway in PWS, with more trials to be implemented in the next few years. PWS-like endophenotypes are recapitulated in gene-targeted mice in which the function of one or more PWS genes is disrupted. These animal models can guide priorities for clinical trials or provide information about efficacy of a compound within the context of the specific disease. We now review the current status of preclinical studies that measure the effect of therapeutics on PWS-like endophenotypes. Seven categories of therapeutics (oxytocin and related compounds, K + -ATP channel agonists, melanocortin 4 receptor agonists, incretin mimetics and/or GLP-1 receptor agonists, cannabinoids, ghrelin agents, and Caralluma fimbriata [cactus] extract) have been tested for their effect on endophenotypes in both PWS animal models and clinical trials. Many other therapeutics have been tested in clinical trials, but not preclinical models of PWS or vice versa. Fostering dialogs among investigators performing preclinical validation of animal models and those implementing clinical studies will accelerate the discovery and translation of therapies into clinical practice in PWS.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".