ArticlePharmaceutical research2021
Population Pharmacokinetic Analysis and Dosing Optimization Based on Unbound Daptomycin Concentration and Cystatin C in Nonobese Elderly Patients with Hypoalbuminemia and Chronic Kidney Disease.
Article in Pharmaceutical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Advancing Daptomycin Precision Dosing Through Evaluation of Published Population Pharmacokinetic Models and Development of a Dosing Tool.Drug design, development and therapy · 2026Article
- A pharmacovigilance study of daptomycin use in infants under 1 year old based on the FDA adverse event reporting system.Scientific reports · 2025Article
- Review
- Hypoalbuminemia and Pharmacokinetics: When the Misunderstanding of a Fundamental Concept Leads to Repeated Errors over Decades.Antibiotics (Basel, Switzerland) · 2023Review
- Population Pharmacokinetic Model for Unbound Concentrations of Daptomycin in Patients with MRSA Including Patients Undergoing Hemodialysis.European journal of drug metabolism and pharmacokinetics · 2023Article
- Daptomycin Physiology-Based Pharmacokinetic Modeling to Predict Drug Exposure and Pharmacodynamics in Skin and Bone Tissues.Clinical pharmacokinetics · 2022Article
- Review
- Identification of Risk Factors for Daptomycin-Associated Creatine Phosphokinase Elevation and Development of a Risk Prediction Model for Incidence Probability.Open forum infectious diseases · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study evaluated the population pharmacokinetics of daptomycin in nonobese elderly patients with hypoalbuminemia and chronic kidney disease (CKD) using the glomerular filtration rate estimated from cystatin C (eGFRcys) and estimated its optimal dose.
methodsWe performed population pharmacokinetic analysis of the unbound concentrations of daptomycin. The probability of target attainment of 90% for achieving an area under the concentration-time curve of unbound daptomycin at steady state/ minimum inhibitory concentration ratio of ≥66.6 was stochastically simulated.
resultsIn the population pharmacokinetic analysis of 25 patients aged ≥65 years, the two-compartment model using eGFRcys and age as covariates of clearance in central compartment of unbound daptomycin were optimal. The unbound fraction rate (fu) was 0.05-0.14. According to the Monte Carlo simulation, the optimal doses for patients with eGFRcys of 20-60 mL/min and aged 65-95 years were calculated as 200-500 mg q24h.
conclusionThese results suggest that establishing the dose using total concentrations may result in under- or overestimation caused by alterations in fu. The optimal dose for nonobese elderly patients with hypoalbuminemia and CKD depends on eGFRcys and age, and a standard dose may be insufficient for some patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.