Population Pharmacokinetic Analysis of Daptomycin in Patients With Osteoarticular Infections
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
Status
Conditions
Conditions
Study Type
Study Type
Enrollment (Actual)
Enrollment
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
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
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
Peak plasma concentration (Cmax)
Time Frame: Month 6
|
Month 6
|
Secondary Outcome Measures
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
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Tristan Ferry, Hospices Civils de Lyon - Hôpital de la Croix Rousse
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 69HCL17_0182
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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