Evidence map›Paper›PMID 27299325›Full record

Trial reportClinical pharmacokinetics2016

A Semi-Physiological Population Model to Quantify the Effect of Hematocrit on Everolimus Pharmacokinetics and Pharmacodynamics in Cancer Patients.

Nielka P van Erp, Carla M van Herpen, Djoeke de Wit, Annelieke Willemsen, David M Burger, Alwin D R Huitema, Ellen Kapiteijn, Rob Ter Heine

Open access · hybridAbstract readClinical Trial, Phase II
In one paragraph

Trial report in Clinical pharmacokinetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

11 citing papers in PubMed, 25 citations in OpenAlex.

  1. Trial
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Observational
  11. Age, Weight, andAntimicrobial agents and chemotherapy · 2017
    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

8 authors at 5 institutions in 1 country.

Nielka P van ErpDepartment of Pharmacy, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
Carla M van HerpenDepartment of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
Djoeke de WitDepartment of Clinical Pharmacy and Toxicology, Leiden University Medical Centre, Leiden, The Netherlands.
Annelieke WillemsenDepartment of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
David M BurgerDepartment of Pharmacy, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
Alwin D R HuitemaDepartment of Pharmacy and Pharmacology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Ellen KapiteijnDepartment of Medical Oncology, Leiden University Medical Centre, Leiden, The Netherlands.
Rob Ter HeineDepartment of Pharmacy, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands. r.terheine@radboudumc.nl.
Radboud University Nijmegen · NLRadboud University Medical Center · NLLeiden University · NLLeiden University Medical Center · NLThe Netherlands Cancer Institute · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

INTRODUCTION AND

objectiveEverolimus (a drug from the class of mammalian target of rapamycin [mTOR] inhibitors) is associated with frequent toxicity-related dose reductions. Everolimus accumulates in erythrocytes, but the extent to which hematocrit affects everolimus plasma pharmacokinetics and pharmacodynamics is unknown. We aimed to investigate the everolimus pharmacokinetics/pharmacodynamics and the influence of hematocrit in cancer patients.

methodsA semi-physiological pharmacokinetic model for everolimus was developed from pharmacokinetic data from 73 patients by non-linear mixed-effects modeling. Using a simulation study with a known pharmacodynamic model describing S6K1 (a downstream mTOR effector) inhibition, we investigated the impact of hematocrit.

resultsThe apparent volume of distribution of the central and peripheral compartment were estimated to be 207 L with a relative standard error (RSE) of 5.0 % and 485 L (RSE 4.2 %), respectively, with an inter-compartmental clearance of 72.1 L/h (RSE 3.2 %). The apparent intrinsic clearance was 198 L/h (RSE 4.3 %). A decrease in hematocrit from 45 % to 20 % resulted in a predicted reduction in whole-blood exposure of ~50 %, but everolimus plasma pharmacokinetics and pharmacodynamics were not affected. The predicted S6K1 inhibition was at a plateau level in the approved dose of 10 mg once daily.

conclusionsA population pharmacokinetic model was developed for everolimus in cancer patients. Hematocrit influenced whole-blood pharmacokinetics, but not plasma pharmacokinetics or pharmacodynamics. Everolimus whole-blood concentrations should always be corrected for hematocrit. Since predicted mTOR inhibition was at a plateau level in the approved dose, dose reductions may have only a limited impact on mTOR inhibition.

Indexed as

HematocritAdultAgedAged, 80 and overAntineoplastic AgentsDose-Response Relationship, DrugEverolimusFemaleHumansMaleMiddle AgedModels, BiologicalNeoplasmsRibosomal Protein S6 Kinases, 70-kDaAntineoplastic AgentsEverolimusribosomal protein S6 kinase, 70kD, polypeptide 1Ribosomal Protein S6 Kinases, 70-kDa

Identifiers

PMID27299325
PMCPMC5069307
OpenAlexW2432041768

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.