The validation of pharmacogenetics for the identification of Fabry patients to be treated with migalastat
Bibliographic record
Abstract
PURPOSE: Fabry disease is an X-linked lysosomal storage disorder caused by mutations in the α-galactosidase A gene. Migalastat, a pharmacological chaperone, binds to specific mutant forms of α-galactosidase A to restore lysosomal activity. METHODS: A pharmacogenetic assay was used to identify the α-galactosidase A mutant forms amenable to migalastat. Six hundred Fabry disease-causing mutations were expressed in HEK-293 (HEK) cells; increases in α-galactosidase A activity were measured by a good laboratory practice (GLP)-validated assay (GLP HEK/Migalastat Amenability Assay). The predictive value of the assay was assessed based on pharmacodynamic responses to migalastat in phase II and III clinical studies. RESULTS: Comparison of the GLP HEK assay results in in vivo white blood cell α-galactosidase A responses to migalastat in male patients showed high sensitivity, specificity, and positive and negative predictive values (≥0.875). GLP HEK assay results were also predictive of decreases in kidney globotriaosylceramide in males and plasma globotriaosylsphingosine in males and females. The clinical study subset of amenable mutations (n = 51) was representative of all 268 amenable mutations identified by the GLP HEK assay. CONCLUSION: The GLP HEK assay is a clinically validated method of identifying male and female Fabry patients for treatment with migalastat.Genet Med 19 4, 430-438.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".