Antibiotics in Adult Cystic Fibrosis Patients: A Review of Population Pharmacokinetic Analyses.

BACKGROUND

Lower respiratory tract infections are common in adult patients with cystic fibrosis (CF) and are frequently caused by Pseudomonas aeruginosa, resulting in chronic lung inflammation and fibrosis. The progression of multidrug-resistant strains of P. aeruginosa and alterations in the pharmacokinetics of many antibiotics in CF make optimal antimicrobial therapy a challenge, as reflected by high between- and inter-individual variability (IIV).

OBJECTIVES

This review provides a synthesis of population pharmacokinetic models for various antibiotics prescribed in adult CF patients, and aims at identifying the most reported structural models, covariates and sources of variability influencing the dose-concentration relationship.

METHODS

A literature search was conducted using the PubMed database, from inception to August 2020, and articles were retained if they met the inclusion/exclusion criteria.

RESULTS

A total of 19 articles were included in this review. One-, two- and three-compartment models were reported to best describe the pharmacokinetics of various antibiotics. The most common covariates were lean body mass and creatinine clearance. After covariate inclusion, the IIV (range) in total body clearance was 27.2% (10.40-59.7%) and 25.9% (18.0-33.9%) for β-lactams and aminoglycosides, respectively. IIV in total body clearance was estimated at 36.3% for linezolid and 22.4% for telavancin. The IIV (range) in volume of distribution was 29.4% (8.8-45.9%) and 15.2 (11.6-18.0%) for β-lactams and aminoglycosides, respectively, and 26.9% for telavancin. The median (range) of residual variability for all studies, using a combined (proportional and additive) model, was 12.7% (0.384-30.80%) and 0.126 mg/L (0.007-1.88 mg/L), respectively.

CONCLUSION

This is the first review that highlights key aspects of different population pharmacokinetic models of antibiotics prescribed in adult CF patients, effectively proposing relevant information for clinicians and researchers to optimize antibiotic therapy in CF.