Physiologically Based Pharmacokinetic (PBPK) Model-Informed Dosing Guidelines for Pediatric Clinical Care: A Pragmatic Approach for a Special Population

Abstract

Physiologically based pharmacokinetic (PBPK) modeling can be an attractive tool to increase the evidence base of pediatric drug dosing recommendations by making optimal use of existing pharmacokinetic (PK) data. A pragmatic approach of combining available compound models with a virtual pediatric physiology model can be a rational solution to predict PK and hence support dosing guidelines for children in real-life clinical care, when it can also be employed by individuals with little experience in PBPK modeling. This comes within reach as user-friendly PBPK modeling platforms exist and, for many drugs and populations, models are ready for use. We have identified a list of drugs that can serve as a starting point for pragmatic PBPK modeling to address current clinical dosing needs. Supplementary Information The online version contains supplementary material available at 10.1007/s40272-022-00535-w.

Apixaban and rivaroxaban's physiologically‐based pharmacokinetic model validation in hospitalized patients: A first step for larger use of a priori modeling approach at bed side

“…The final model for rivaroxaban accurately predicted PK parameters in healthy volunteers in two states: fasting and fed after a single dose of rivaroxaban 10 mg, 15 mg, and 20 mg (Table S2). The input parameters are detailed in Table 1 4,12,17,25,26 …”

“…Indeed, this bottom‐up approach in its purest form could help physicians prescribe drugs such as direct oral anticoagulants (DOACs) in vulnerable populations, including children, the elderly, or polymedicated patients 3 . There is increasing evidence that shows promising results in various areas, such as psychiatry, oncology, or pediatrics 4–7 . However, PBPK has not yet gained the full attention of other fields, such as that of cardiovascular disease, which could benefit from PBPK to predict over‐ and under‐dosing of DOACs, both of which are associated with clinical events.…”

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

“…This influx of data can lead to more accurate and comprehensive models and identify subtle patterns and associations that may not be apparent in smaller or less diverse datasets. As a result, ML-driven PBPK and popPK models can be continuously validated against real-world patient data [ 174 ]. This validation process ensures that the models are not only accurate in controlled clinical settings but also in the messy and complex environment of real-world healthcare, which enhances confidence in model predictions and their utility in clinical practice [ 169 ].…”

Physiologically‐Based Pharmacokinetic Modeling for Drug Dosing in Pediatric Patients: A Tutorial for a Pragmatic Approach in Clinical Care

“…A less time-consuming PBPK modeling approach in which pre-existing compound models are combined with pediatric physiology models can be pragmatic and feasible to predict PK and guide drug dosing in pediatric clinical care. 12,13 Rapid growth of PBPK to support drug development decisions in the last decade resulted in active engagement of major regulators to develop guidelines around the use of this technology. The process is a work in progress.…”

“…Often, these models were developed to simulate PKs in adult populations, and were rarely explored for their potential to guide drug dosing in children. A less time‐consuming PBPK modeling approach in which pre‐existing compound models are combined with pediatric physiology models can be pragmatic and feasible to predict PK and guide drug dosing in pediatric clinical care 12,13 …”