Population Pharmacokinetic Analysis of Daptomycin in Patients With Osteoarticular Infections

May 10, 2017 updated by: Hospices Civils de Lyon

Daptomycin is validated as a treatment of bone and joint infections by the Infectious Disease Society of America. However, most of studies did not investigate daptomycin pharmacokinetics in this indication while it is known that efficacy and toxicity concentration studies show a close therapeutic margin.

Evaluation of P-Glycoprotein (P-gp), a transmembrane transport protein, has demonstrated its influence on the concentration and intracellular activity of daptomycin. Recent work has linked the genetic polymorphism of P-gp to the pharmacokinetics of daptomycin, which may explain inter-individual variability but requires further explorations. Previous studies demonstrated existence of interindividual variabilities as sex, renal function and p-glycoprotein polymorphism couple with an intraindividual variabilities unexplained yet.

A population approach will be used to determinate the pharmacokinetics factors, their intra and interindividual variabilities, the parameters associated to those variabilities (as the p glycoprotein).

The investigator's goal is to evaluate different posology and to try to increase daptomycin efficacy and security in bone and joint infection.

Study Overview

Status

Unknown

Study Type

Observational

Enrollment (Actual)

189

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients having bone or joint infection, with or without implant, having an antibiotherapy with daptomycin

Description

Inclusion Criteria:

Patients

  • having had a bone or joint infection, with or without implant,
  • having an antibiotherapy with daptomycin between December 2012 and December 2016 at the Croix-Rousse hospital
  • are at least 18 years old

Exclusion Criteria:

  • None

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Peak plasma concentration (Cmax)
Time Frame: Month 6
Month 6

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area under the concentration-time curve
Time Frame: up to 6 months
up to 6 months
typical daptomycin clearance and volume of distribution in the population
Time Frame: Month 6
Month 6
Mean daptomycine plasma clearance
Time Frame: Month 6
(unit, liters per hour)
Month 6
Mean daptomycine volume of distribution
Time Frame: Month 6
(unit, liters)
Month 6
Inter-individual coefficient of variation of daptomycin clearance
Time Frame: Month 6
(unit, %)
Month 6
Inter-individual coefficient of variation of daptomycin volume of distribution
Time Frame: Month 6
(unit, %)
Month 6
Intra-individual coefficient of variation of daptomycin clearance
Time Frame: Month 6
(unit, %)
Month 6
Intra-individual coefficient of variation of daptomycin volume of distribution
Time Frame: Month 6
(unit, %)
Month 6
influence of demographic and biological covariates on pharmacokinetics (e.g. : renal function, gender)
Time Frame: Month 6
the influence of demographic and biological covariates on pharmacokinetics will be assessed statistically by using the Akaike Information Criterion (AIC, no unit). AIC = -2xLL + 2P, where LL is the log-likelihood computed by the population algorithm and P is the number of parameters in the model. A covariate will be considered as significant if it is associated with a decrease in the AIC value compared with the base model without covariate.
Month 6
influence of p-glycoprotein pharmacogenetics on daptomycin pharmacokinetics
Time Frame: Month 6
the influence of P-glycoprotein pharmacogenetics on pharmacokinetics will be assessed statistically by using the Akaike Information Criterion (AIC, no unit). AIC = -2xLL + 2P, where LL is the log-likelihood computed by the population algorithm and P is the number of parameters in the model. The P-glycoprotein genotype will be considered as significant if it is associated with a decrease in the AIC value compared with the base model without covariate.
Month 6

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Investigators

  • Principal Investigator: Tristan Ferry, Hospices Civils de Lyon - Hôpital de la Croix Rousse

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 1, 2016

Primary Completion (Anticipated)

June 30, 2017

Study Completion (Anticipated)

June 30, 2017

Study Registration Dates

First Submitted

March 29, 2017

First Submitted That Met QC Criteria

April 28, 2017

First Posted (Actual)

May 1, 2017

Study Record Updates

Last Update Posted (Actual)

May 11, 2017

Last Update Submitted That Met QC Criteria

May 10, 2017

Last Verified

May 1, 2017

More Information

Terms related to this study

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Bone Infection

3
Subscribe