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Record W2058743904 · doi:10.1016/j.trsl.2012.01.004

Genetic and immune predictors for hypersensitivity syndrome to antiepileptic drugs

2012· article· en· W2058743904 on OpenAlexaff
Manuela G. Neuman, Lawrence B. Cohen, Radu M. Nanau, Paul A. Hwang

Bibliographic record

VenueTranslational research · 2012
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsSunnybrook Health Science CentreNorth York General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineToxic epidermal necrolysisCarbamazepinePhenytoinPopulationInternal medicineToxicityGastroenterologyImmunologyEpilepsyDermatology

Abstract

fetched live from OpenAlex

Hypersensitivity syndrome reactions (HSR) to antiepileptic drugs (AED) are associated with severe clinical cutaneous adverse reactions (SCAR). We aimed (1) to assess HSRs to AEDs using the in vitro lymphocyte toxicity assay (LTA) in patients who manifested HSRs clinically; (2) to correlate LTA results with the clinical syndrome; (3) to correlate LTA results with the human leukocyte antigen (HLA) allele B∗1502 (HLA-B∗1502) positivity in a Han Chinese-Canadian population; and (4) to determine the cytokine network in this population. Patients that developed fever and cutaneous eruptions in the presence or absence of organ involvement within 8 weeks of exposure to carbamazepine (CBZ), phenytoin (PHY), or lamotrigine (LTG) were enrolled. Control patients received AEDs without presenting HSR. We investigated 10 CBZ-HSR patients (4 with Stevens-Johnson syndrome [SJS]), 24 CBZ-controls, 10 PHY-HSR patients (4 with drug-induced liver injury [DILI]), 24 PHY-controls,6 LTG-HSR patients (1 with SJS and 1 with DILI), and 24 LTG-controls. There were 30 Han Chinese individuals (14 HSR patients and 16 controls) in our cohort. LTA toxicity greater than 12.5%±2.5% was considered positive. Differences among groups were determined by analysis of variance. In addition, we measured cytokine secretion in the patient sera between 1 month and 3 years after the event. All Han Chinese individuals and 30% of Caucasians were genotyped for HLA-B∗1502. A perfect correlation (r=0.92) was observed between positive LTA and clinical diagnosis of DILI and SJS/toxic epidermal necrolysis (TEN). HLA-B∗1502 positivity in Han Chinese is a predictor of CBZ-HSR and PHY-HSR. HLA-B∗1502-negative Han Chinese receiving only CBZ or a combination of CBZ and PHY tolerated the drug(s) clinically, presenting negative CBZ-LTA and PHY-LTA. However, 3 patients presenting negative CBZ-LTA and PHY-LTA, as well as negative HLA-B∗1502, showed positive LTG-LTA (38%, 28%, and 25%, respectively), implying that they should not be prescribed LTG. Three patients had LTA positive to both PHY and CBZ, and 3 others had LTA positive to both PHY and LTG. Clinically, all 6 patients presented HSR to both drugs that they tested positive to (cross-reactivity). Patients were grouped based on the clinical presentation of their symptoms as only rash and fever or as a triad of rash, fever and DILI or SJS/TEN that characterizes "true" HSR. Levels of proinflammatory cytokines were significantly higher in patient sera compared with control sera. More specifically, the highest levels of tumor necrosis factor-α have been measured in patients presenting "true" HSR, as were the apoptotic markers Fas, caspase 8 activity, and M30. The LTA is sensitive for DILI and SJS/TEN regardless of drug or patient ethnicity. HSR prediction will prevent AED-induced morbidity. In Han Chinese, HLA-B∗1502 positivity is a predictor for CBZ-HSR and PHY-HSR. Its negativity does not predict a negative LTG-HSR. There is cross-reactivity between AEDs. Additionally, T-cell cytokines and chemokines control the pathogenesis of SJS/TEN and DILI, contributing to apoptotic processes in the liver and in the skin.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.088
GPT teacher head0.393
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations31
Published2012
Admission routes1
Has abstractno

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