Assessing the viability of long-acting β2-agonists in paediatric asthma patients: a pharmacokinetic/pharmacodynamic perspective. (1st February 2017)

• Record Type: Journal Article
• Authors: Matera, Maria Gabriella; Rinaldi, Barbara; Calzetta, Luigino; Cazzola, Mario
• Is Part Of: Expert opinion on drug metabolism and toxicology. Volume 13:Number 2(2017:Feb.)
• Page Start: 129
• Page End: 136
• Publication Date: 2017-02-01
• Subjects: Pharmacokinetics -- pharmacodynamics -- children -- long-acting β2-agonists; Drugs -- Toxicology -- Periodicals; Drugs -- Metabolism -- Periodicals; 615.7
• DOI: 10.1080/17425255.2017.1234604
• Languages: English
• ISSNs: 1742-5255

Abstract

ABSTRACT: Introduction: Long-acting β2 -agonists (LABAs) combined with inhaled corticosteroids (ICSs) are still commonly prescribed to asthmatic children. Unfortunately, pediatric LABA use is based primarily on data from adults, despite the fact that children are not simply small adults and the magnitude of changes in dose exposure and/or exposure response may not be solely reflected by differences in body weight. Areas covered: The differences in pharmacokinetics (PK) and pharmacodynamics (PD) of LABAs are described and discussed with reference children and adults. Expert opinion: Data on the PK behavior of LABAs is very limited and there is almost no data on once-daily LABAs available in the pediatric population. We do not believe that this is due to a fundamental lack of information because therapeutic response and adverse effects are more useful for the optimization of β2 -agonist treatment than measurement of plasma drug concentrations per se. Nevertheless, population PK-PD studies in children are needed according to the European rules in order to define rational, patient-tailored dosing schemes. Population PK-PD modeling and simulation using non-linear mixed effect modeling should be considered as the preferred tool to develop effective and safe dosing regimens for children because they present an opportunity to analyze sparse and unbalanced datasets, thereby minimizing the burden for each child.