Evidence map›Paper›PMID 22274748›Full record

ReviewThe AAPS journal2012

Covariate pharmacokinetic model building in oncology and its potential clinical relevance.

Markus Joerger

Open access · bronzeAbstract readReview
In one paragraph

Review in The AAPS journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

43 citing papers in PubMed, 87 citations in OpenAlex.

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  5. Exposure-Toxicity Association of Cyclophosphamide and Its Metabolites in Infants and Young Children with Primary Brain Tumors: Implications for Dosing.Clinical cancer research : an official journal of the American Association for Cancer Research · 2020
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  6. Bayesian Population Pharmacokinetic Modeling of Eltrombopag in Chronic Hepatitis C Patients.European journal of drug metabolism and pharmacokinetics · 2019
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  15. Review
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  17. Go beyond the limits of genetic algorithm in daily covariate selection practice.Journal of pharmacokinetics and pharmacodynamics · 2024
    Article
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  19. Population pharmacokinetics of methotrexate and 7-hydroxymethotrexate and delayed excretion in infants and young children with brain tumors.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2024
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  20. 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

1 author at 1 institution in 1 country.

Markus JoergerDepartment of Oncology and Hematology, Cantonal Hospital, St. Gallen, Switzerland. markus.joerger@gmail.com
University of St. Gallen · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When modeling pharmacokinetic (PK) data, identifying covariates is important in explaining interindividual variability, and thus increasing the predictive value of the model. Nonlinear mixed-effects modeling with stepwise covariate modeling is frequently used to build structural covariate models, and the most commonly used software-NONMEM-provides estimations for the fixed-effect parameters (e.g., drug clearance), interindividual and residual unidentified random effects. The aim of covariate modeling is not only to find covariates that significantly influence the population PK parameters, but also to provide dosing recommendations for a certain drug under different conditions, e.g., organ dysfunction, combination chemotherapy. A true covariate is usually seen as one that carries unique information on a structural model parameter. Covariate models have improved our understanding of the pharmacology of many anticancer drugs, including busulfan or melphalan that are part of high-dose pretransplant treatments, the antifolate methotrexate whose elimination is strongly dependent on GFR and comedication, the taxanes and tyrosine kinase inhibitors, the latter being subject of cytochrome p450 3A4 (CYP3A4) associated metabolism. The purpose of this review article is to provide a tool to help understand population covariate analysis and their potential implications for the clinic. Accordingly, several population covariate models are listed, and their clinical relevance is discussed. The target audience of this article are clinical oncologists with a special interest in clinical and mathematical pharmacology.

Indexed as

Models, BiologicalPharmacokineticsAnimalsAntineoplastic AgentsDose-Response Relationship, DrugHumansNeoplasmsNonlinear DynamicsSoftwareAntineoplastic Agents

Identifiers

PMID22274748
PMCPMC3291194
OpenAlexW2065831867

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.