Evidence map›Paper›PMID 29479308›Full record

ArticleFrontiers in cellular neuroscience2018

The Importance of the Dissociation Rate in Ion Channel Blocking.

Hugo Zeberg, Johanna Nilsson, Peter Århem

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.1field-weighted citation impact, top 60% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Hugo ZebergDepartment of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Johanna NilssonDepartment of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Peter ÅrhemDepartment of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Karolinska Institutet · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the relationships between the rates and dynamics of current wave forms under voltage clamp conditions is essential for understanding phenomena such as state-dependence and use-dependence, which are fundamental for the action of drugs used as anti-epileptics, anti-arrhythmics, and anesthetics. In the present study, we mathematically analyze models of blocking mechanisms. In previous experimental studies of potassium channels we have shown that the effect of local anesthetics can be explained by binding to channels in the open state. We therefore here examine models that describe the effect of a blocking drug that binds to a non-inactivating channel in its open state. Such binding induces an inactivation-like current decay at higher potential steps. The amplitude of the induced peak depends on voltage and concentration of blocking drug. In the present study, using analytical methods, we (i) derive a criterion for the existence of a peak in the open probability time evolution for a model with an arbitrary number of closed states, (ii) derive formula for the relative height of the peak amplitude, and (iii) determine the voltage dependence of the relative peak height. Two findings are apparent: (1) the dissociation (unbinding) rate constant is important for the existence of a peak in the current waveform, while the association (binding) rate constant is not, and (2) for a peak to exist it suffices that the dissociation rate must be smaller than the absolute value of all eigenvalues to the kinetic matrix describing the model.

Indexed as

dissociation rate constantion channel blockMarkov chain modelMonte Carlo simulationpeak currentvoltage-clamp

Identifiers

PMID29479308
PMCPMC5811512
OpenAlexW2793921115

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.