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Record W4406732440 · doi:10.1080/17425255.2025.2455388

A comprehensive update on the <i>human leukocyte antigen</i> and idiosyncratic adverse drug reactions

2025· review· en· W4406732440 on OpenAlexaff
Abdelbaset Elzagallaai, Awatif M. Abuzgaia, Michael Rieder

Bibliographic record

VenueExpert Opinion on Drug Metabolism & Toxicology · 2025
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsWestern University
Fundersnot available
KeywordsDrugImmunologyMedicineAntigenAdverse effectHuman leukocyte antigenDrug reactionPharmacology

Abstract

fetched live from OpenAlex

Introduction Idiosyncratic adverse drug reactions (IADRs) or drug hypersensitivity reactions (DHRs) represent a major health problem because they are unpredictable and can be severe with potential life-long or even lethal consequences. Their pathophysiology is not clear but thought to be immune mediated, supported by the significant statistical association of these reactions with specific alleles of the human leukocyte antigen (HLA) gene.Area covered This comprehensive update review summarizes the currently available evidence on the role of HLA gene locus in IADRs and discusses the present understanding of the pathophysiology of IADRs. We searched the available literature in PubMed and Google Scholar with no date restriction for publications on HLA and adverse drug reactions. Findings are summarized and discussed in the context of the currently available evidence.Expert opinion The role of the immune system in IADRs and the role of pharmacogenetic testing in this field is evident. HLA genetic testing is very promising in the management of these reactions. Many obstacles seem to prevent pharmacogenetic testing to meet its full potential including cost and health care providers’ education. Further work in needed to provide more evidence and allow widespread use of pharmacogenetic testing in the clinical practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.057
GPT teacher head0.372
Teacher spread0.315 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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