Evidence map›Paper›PMID 36323974›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2023

Individualized optimization of colistin loading doses.

Aline Vidal Lacerda Gontijo, André V G Cavalieri

Abstract read
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Article in Journal of pharmacokinetics and pharmacodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. Colistin use and outcomes in Kuwait public hospitals.Frontiers in drug safety and regulation · 2026
    Article
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

2 authors.

Aline Vidal Lacerda GontijoDepartment of Pharmacy, Anhanguera University, Av. Dr. João Batista de Souza Soares, 4009 - Cidade Morumbi, São José dos Campos, SP, 12236-660, Brazil. aline.gontijo@gmail.com.
André V G CavalieriDivisão de Engenharia Aeroespacial, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colistin remains one of the few available options for the treatment of infections caused by resistant bacteria. Pharmacokinetic (PK) studies have been successful in estimating the appropriate colistin methanesulfonate (CMS) dose to achieve a target colistin concentration. Currently, there is a consensus that the dose of CMS should vary according to the patient renal function since CMS is mainly eliminated by renal route. For this same reason, the loading dose should vary according to the patient's renal capacity; however, this is not the current clinical practice. In this study we develop a framework to determine two key parameters for the loading dose regimen: (1) the optimal dose according to the characteristics (renal function and weight) of the patient; (2) the waiting time before the maintenance dose. Based on a previous PK model, our framework allows a fast parameter sweep so as to select optimal loading dose and waiting time minimizing the deviation between the plasma concentration and a target value. The results showed that patients presenting low creatinine clearance (CrCL) should receive a lower CMS loading dose with longer interval to start maintenance treatment to avoid nephrotoxic colistin concentrations. In cases of high CrCL, the dose should be higher and the interval to the next dose shorter to avoid subtherapeutic concentrations. Optimization of the loading dose should considerably improve colistin therapy, as the target concentration is reached more quickly, without reaching toxic values.

Indexed as

Anti-Bacterial AgentsColistinCritical IllnessHumansAnti-Bacterial AgentsColistinColistinColistin methanesulphonateLoading doseOptimization

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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.