Current and future physiologically based pharmacokinetic (PBPK) modeling approaches to optimize pharmacotherapy in preterm neonates

Introduction There is a need for structured approaches to inform on pharmacotherapy in preterm neonates. With their proven track record up to regulatory acceptance, physiologically based pharmacokinetic (PBPK) modeling and simulation provide a structured approach, and hold the promise to support drug development in preterm neonates. Areas covered Compared to general and pediatric use of PBPK modeling, its use to inform pharmacotherapy in preterms is limited. Using a systematic search (PBPK + preterm), we retained 25 records (20 research papers, 2 letters, 3 abstracts). We subsequently collated the published information on PBPK software packages (PK-Sim (R), Simcyp (R)), and their applications and optimization efforts in preterm neonates. It is encouraging that applications cover a broad range of scenarios (pharmacokinetic-dynamic analyses, drug-drug interactions, developmental pharmacogenetics, lactation related exposure) and compounds (small molecules, proteins). Furthermore, specific compartments (cerebrospinal fluid, tissue) or (patho)physiologic processes (cardiac output, biliary excretion, first pass metabolism) are considered. Expert opinion Knowledge gaps exist, giving rise to various levels of uncertainty in PBPK applications in preterm neonates. To improve this, we need cross talk between clinicians and modelers to generate and integrate knowledge (PK datasets, system knowledge, maturational physiology and pathophysiology) to further refine PBPK models.

Automated summary

The article introduces a structured approach, physiologically based pharmacokinetic (PBPK) modeling and simulation, to inform pharmacotherapy in preterm neonates. Through a systematic search, the authors collected information on the applications and optimization efforts of PBPK software packages in preterm neonates. It is suggested that collaboration between clinicians and modelers can help improve PBPK models despite existing knowledge gaps and uncertainties in their application.