학술논문

Dosing regimen optimisation for oseltamivir in immunocompromised paediatric patients with influenza: Extrapolation of efficacy.
Document Type
Article
Source
British Journal of Clinical Pharmacology. Mar2022, Vol. 88 Issue 3, p1189-1201. 13p.
Subject
*CHILD patients
*IMMUNOCOMPROMISED patients
*OSELTAMIVIR
*EXTRAPOLATION
*INFLUENZA
*MEDICAL model
Language
ISSN
0306-5251
Abstract
Aims: To optimise the dosing regimen of oseltamivir for immunocompromised (IC) paediatric patients (<18 years) with influenza, we used an extrapolation approach alongside clinical data. Methods: Efficacy was extrapolated from adult IC patients to paediatric IC patients by leveraging existing efficacy, safety, pharmacokinetic (PK)/pharmacodynamic (PD), and disease‐progression models of oseltamivir and oseltamivir carboxylate (OC). Data of IC paediatric patients from two studies (NV25719 and NV20234) were included in the population PK (n = 30), PK/PD analysis (n = 22) and disease modelling approach (n = 36). Simulations were performed to identify the optimal dosing regimen. Results: Clearance of oseltamivir (CL) and OC (CLM) were similar in IC and otherwise‐healthy (OwH) patients <10 years, but decreased by 44.4% (95% CI: 26.8–62.0) and 49.1% (95% CI: 34.5–63.8), respectively, in IC patients aged 10–17 years versus OwH patients. There were no notable exposure–response relationships for any of the virologic PD analyses. Thus, no additional benefit was seen with oseltamivir carboxylate exposures higher than achieved with the conventional dose (75 mg twice daily, age‐ and weight‐adjusted for children <13 years). The disease model illustrated that doses above the conventional oseltamivir dose had limited impact on viral kinetics in IC paediatric patients and a prolonged treatment duration of 10 days was favoured to limit potential viral rebound. Conclusion: An oseltamivir dosage recommendation (conventional dose, twice daily for 10 days) was established in IC paediatric patients with influenza, based on extrapolation of efficacy from IC adults, leveraging population PK, PK/PD, and disease modelling, whilst taking resistance and safety data into account. [ABSTRACT FROM AUTHOR]