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Record W1967285730 · doi:10.1016/j.ijpddr.2011.10.001

P-glycoproteins and other multidrug resistance transporters in the pharmacology of anthelmintics: Prospects for reversing transport-dependent anthelmintic resistance

2011· review· en· W1967285730 on OpenAlexafffund
Anne Lespine, Cécile Menez, Catherine Bourguinat, Roger K. Prichard

Bibliographic record

VenueInternational Journal for Parasitology Drugs and Drug Resistance · 2011
Typereview
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsMcGill University
FundersInstitut National de la Recherche AgronomiqueMcGill UniversityFonds Québécois de la Recherche sur la Nature et les TechnologiesAgence Nationale de la RechercheNatural Sciences and Engineering Research Council of CanadaMinistère de l'Agriculture, des Pêcheries et de l'AlimentationWellcome Trust
KeywordsAnthelminticMultiple drug resistancePharmacologyDrug resistanceP-glycoproteinEffluxBiologyIvermectinATP-binding cassette transporterDrugTransporterMicrobiologyGeneBiochemistry

Abstract

fetched live from OpenAlex

Parasitic helminths cause significant disease in animals and humans. In the absence of alternative treatments, anthelmintics remain the principal agents for their control. Resistance extends to the most important class of anthelmintics, the macrocyclic lactone endectocides (MLs), such as ivermectin, and presents serious problems for the livestock industries and threatens to severely limit current parasite control strategies in humans. Understanding drug resistance is important for optimizing and monitoring control, and reducing further selection for resistance. Multidrug resistance (MDR) ABC transporters have been implicated in ML resistance and contribute to resistance to a number of other anthelmintics. MDR transporters, such as P-glycoproteins, are essential for many cellular processes that require the transport of substrates across cell membranes. Being overexpressed in response to chemotherapy in tumour cells and to ML-based treatment in nematodes, they lead to therapy failure by decreasing drug concentration at the target. Several anthelmintics are inhibitors of these efflux pumps and appropriate combinations can result in higher treatment efficacy against parasites and reversal of resistance. However, this needs to be balanced against possible increased toxicity to the host, or the components of the combination selecting on the same genes involved in the resistance. Increased efficacy could result from modifying anthelmintic pharmacokinetics in the host or by blocking parasite transporters involved in resistance. Combination of anthelmintics can be beneficial for delaying selection for resistance. However, it should be based on knowledge of resistance mechanisms and not simply on mode of action classes, and is best started before resistance has been selected to any member of the combination. Increasing knowledge of the MDR transporters involved in anthelmintic resistance in helminths will play an important role in allowing for the identification of markers to monitor the spread of resistance and to evaluate new tools and management practices aimed at delaying its spread.

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)0.001
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.052
GPT teacher head0.400
Teacher spread0.348 · 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

Citations199
Published2011
Admission routes2
Has abstractyes

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