Quantitative clinical pharmacology practice for optimal use of antibiotics during the neonatal period

Abstract

Challenges for optimal use of antibiotics in neonates can largely be overcome with quantitative clinical pharmacology practice. Clinicians should be aware that there is a next step to support the clinical decision-making based on clinical characteristics and therapeutic drug monitoring, through Bayesian-based modeling and simulation methods. Pharmacometric modeling and simulation approaches permit us to characterize population average, inter-subject and intra-subject variability of pharmacokinetic parameters such as clearance and volume of distribution, and to identify and quantify key factors that influence the pharmacokinetic behavior of antibiotics during the neonatal period.

Vancomycin therapeutic drug monitoring and population pharmacokinetic models in special patient subpopulations

“…Modeling and simulation approaches have clear advantages in dosing optimization of antimicrobial agents in neonates . Pharmacometric modeling and simulation approaches allow to characterize population average, pharmacokinetic parameters, intra and intersubject variability, and to identify and quantify key factors that influence antibiotics pharmacokinetic behavior during the neonatal period . Simulations showed that the maintenance dose should be adjusted more precisely to each neonate based on weight and serum creatinine values .…”

Optimizing Antibiotic Drug Therapy in Pediatrics: Current State and Future Needs

“…Pharmacometric modeling, specifically population‐based PK (PopPK), allows us to integrate different factors (or covariates) to ultimately characterize a population average, with intersubject and intrasubject variabilities of PK parameters, including Vd and CL, that determine drug dosing . The incorporation of covariates, particularly weight and age, as explained above, is integral to optimizing the use of drugs in pediatric patients because this approach can address the wide age spectrum across the diverse pediatric age groups . The use and acceptance of PopPK (which integrates nonlinear mixed‐effects modeling) continues to improve our understanding of PKPD in children .…”

“…19 The incorporation of covariates, particularly weight and age, as explained above, is integral to optimizing the use of drugs in pediatric patients because this approach can address the wide age spectrum across the diverse pediatric age groups. 20 The use and acceptance of PopPK (which integrates nonlinear mixed-effects modeling) continues to improve our understanding of PKPD in children. 21 Furthermore, PopPK can be used to individualize drug dosing directly in the patient care setting to improve the precise achievement of a therapeutic goal that minimizes drug toxicity while maximizing clinical benefit.…”

“…Safe and effective antibiotic use in all pediatric age groups, including neonates, requires thorough understanding and application of the quantitative clinical pharmacology that encompasses PKPD. 20 Pharmacologists, pharmacists, and physicians must collectively be equipped to support and integrate PopPK modeling, Bayesian estimation, and simulation methods in the clinical decision-making process that is integral to therapeutic drug monitoring and management to optimize exposure-response and clinical outcome relationships across the pediatric age spectrum. 18 To demonstrate the application of clinical pharmacology to the optimization of pharmacotherapy and its positive impact on the care of pediatric patients with infections, specific antibacterial agents (including beta-lactams, aminoglycosides, and vancomycin) are presented in the subsequent sections.…”

Antimicrobial dosing in neonates

“…PK modeling using compartmental analysis can predict drug concentration at any time, offers a more flexible timetable for PK samples collection, allows less blood sampling while quantifies the effect of several factors (covariates) on PK parameters explaining intra-and intersubject PK variability [16]. Pharmacometric modeling and simulation, tailored to term/preterm neonates, are valuable tools for understanding PK behavior of antibiotics, providing the basis for dose optimization and treatment individualization in this vulnerable population [16]. Population PK-PD modeling and simulation using nonlinear mixed effect modeling is a wellestablished approach of great value and similar studies encouraged by regulatory agencies are constantly increasing [17].…”