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Record W2094755494 · doi:10.3109/03602532.2013.848214

Bioactivation of drugs in the skin: relationship to cutaneous adverse drug reactions

2013· review· en· W2094755494 on OpenAlexaff
Amy Sharma, Jack Uetrecht

Bibliographic record

VenueDrug Metabolism Reviews · 2013
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDrugImmune systemDrug metabolismPharmacologyMonooxygenaseImmunologyContact dermatitisMedicineChemistryCytochrome P450MetabolismBiochemistryAllergy

Abstract

fetched live from OpenAlex

Drug-induced skin rashes are poorly understood idiosyncratic reactions, and current methods cannot predict their occurrence. Most idiosyncratic drug reactions are thought to be caused by chemically reactive metabolites, and the skin is a frequent site of idiosyncratic reactions; however, the skin has a very limited capacity to metabolize drugs. To balance this, the skin represents a protective barrier with a very active immune response against pathogens and other types of skin injury. Therefore its response to reactive metabolites is quite different from that of the liver. The purpose of this review is to integrate emerging findings into proposed mechanisms of drug and carcinogen metabolism in the skin that are likely responsible for rashes and other immune responses of the skin. Current evidence suggests the skin possesses significant sulfotransferase and flavin monooxygenases activities, but very low cytochromes P450 activity. However, there are skin-specific P450s that are not present in the liver. The manner in which the skin responds to neoantigens through local antigen presentation and innate immune sensing is reviewed with a focus on insights gained from the contact hypersensitivity (CHS) field. The roles of keratinocytes and Langerhans cells, and the emerging function of NOD-like receptors, are highlighted.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.003

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 head0.073
GPT teacher head0.356
Teacher spread0.284 · 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

Citations34
Published2013
Admission routes1
Has abstractyes

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