Coupling Genotyping and Computational Modeling in Prediction of Anti-epileptic Drugs that cause Stevens Johnson Syndrome and Toxic Epidermal Necrolysis for Carrier of HLA-B*15:02
Bibliographic record
Abstract
PURPOSE: The importance of HLA-B*15:02 genotyping to avoid carbamazepine induced SJS/TEN and molecular modeling to predict the role of HLA-B*15:0 and AEDs induced SJS/TEN are investigated. METHODS: DNA was extracted from eighty-six patients. The patients were genotyped by AS-PCR. Computational modeling of the HLA-B*15:02 followed by docking studies were performed to screen 26 AEDs that may induce ADR among HLA-B*15:02 carriers. RESULTS: Odd ratio for CBZ induced SJS/TEN and HLA-B*15:02 was 609.0 (95% CI: 23-15873; p=0.0002). Molecular modeling studies showed that acetazolamide, ethosuxiamide, lamotrigine, oxcarbazepine, phenobarbital, phenytoin, primidone and sodium-valproate may induce ADR in HLA-B*15:02 carriers alike CBZ. Conclusion. We confirmed HLA-B*15:02 as a predictor of SJS/TEN and recommend pre-screening. Computational prediction of DIHR is useful in personalized medicine.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.000 |
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".