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Interactions Between Recreational Drugs and Antiretroviral Agents

2002· article· en· W4240468010 on OpenAlexaff
Tony Antoniou, Alice Tseng

Bibliographic record

VenueAnnals of Pharmacotherapy · 2002
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsUniversity of TorontoToronto General HospitalSt. Michael's Hospital
Fundersnot available
KeywordsMedicineANTIRETROVIRAL AGENTSRecreational DrugRecreationHuman immunodeficiency virus (HIV)Antiretroviral therapyPharmacologyIntensive care medicineVirologyDrugViral load

Abstract

fetched live from OpenAlex

OBJECTIVE:To summarize existing data regarding potential interactions between recreational drugs and drugs commonly used in the management of HIV-positive patients.DATA SOURCES:Information was obtained via a MEDLINE search (1966–August 2002) using the MeSH headings human immunodeficiency virus, drug interactions, cytochrome P450, medication names commonly prescribed for the management of HIV and related opportunistic infections, and names of commonly used recreational drugs. Abstracts of national and international conferences, review articles, textbooks, and references of all articles were also reviewed.STUDY SELECTION AND DATA EXTRACTION:Literature on pharmacokinetic interactions was considered for inclusion. Pertinent information was selected and summarized for discussion. In the absence of specific data, prediction of potential clinically significant interactions was based on pharmacokinetic and pharmacodynamic properties.RESULTS:All protease inhibitors (PIs) and nonnucleoside reverse transcriptase inhibitors are substrates and potent inhibitors or inducers of the cytochrome P450 system. Many classes of recreational drugs, including benzodiazepines, amphetamines, and opioids, are also metabolized by the liver and can potentially interact with antiretrovirals. Controlled interaction studies are often not available, but clinically significant interactions have been observed in a number of case reports. Overdoses secondary to interactions between the “rave” drugs methylenedioxymethamphetamine (MDMA) or γ-hydroxybutyrate (GHB) and PIs have been reported. PIs, particularly ritonavir, may also inhibit metabolism of amphetamines, ketamine, lysergic acid diethylmide (LSD), and phencyclidine (PCP). Case series and pharmacokinetic studies suggest that nevirapine and efavirenz induce methadone metabolism, which may lead to symptoms of opiate withdrawal. A similar interaction may exist between methadone and the PIs ritonavir and nelfinavir, although the data are less consistent. Opiate metabolism can be inhibited or induced by concomitant PIs, and patients should be monitored for signs of toxicity and/or loss of analgesia. PIs should not be coadministered with midazolam and triazolam, since prolonged sedation may occur.CONCLUSIONS:Interactions between agents commonly prescribed for patients with HIV and recreational drugs can occur, and may be associated with serious clinical consequences. Clinicians should encourage open dialog with their patients on this topic, to avoid compromising antiretroviral efficacy and increasing the risk of drug toxicity.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.263
GPT teacher head0.496
Teacher spread0.233 · 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 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

Citations8
Published2002
Admission routes1
Has abstractyes

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