# Population Physiologically‐Based Pharmacokinetic Modeling to Determine Ontogeny: A Quantitative Clinical Pharmacology Example in Pediatric Rare Disease

## TL;DR

This paper introduces a new approach using population PBPK modeling to better predict drug behavior in children, especially for rare diseases.

## Contribution

The novel contribution is a population PBPK modeling strategy to estimate drug metabolizing enzyme ontogeny using sparse pediatric and adult data.

## Key findings

- Population PBPK modeling improves pediatric PK predictions by estimating DME/transporter ontogenies.
- The approach addresses inconsistencies in ontogeny data and supports model-informed drug development in children.
- The method is demonstrated with risdiplam for spinal muscular atrophy, aiding optimal dose finding in rare pediatric diseases.

## Abstract

Pediatric physiologically‐based pharmacokinetic (PBPK) modelling plays an increasing role in selecting doses in children and addressing clinical pharmacology questions. Ethical concerns often limit clinical pharmacology studies that have no direct therapeutic benefit in children, highlighting the value of PBPK model predictions. However, regulatory acceptance of pediatric PBPK models remains limited because of uncertainties in system‐specific information and inadequate model qualification. Ambiguous ontogeny data of drug metabolizing enzymes (DME) and transporters are recognized as significant obstacles to the accurate pharmacokinetics (PK) prediction in children and the leading cause of insufficient pediatric PBPK model qualification. To address this challenge, a population PBPK modeling approach is proposed. This method is analogous to whole‐body PBPK modeling and allows the estimation of DME/transporter ontogenies using sparse PK data collected from children and adults by nonlinear mixed‐effect modeling. Well‐characterized ontogeny functions of key DME/transporters enhance the extrapolation ability of PBPK models and facilitate model‐informed drug development (MIDD) in children. This article proposes a strategy for pediatric PK extrapolation using population PBPK modeling, illustrated through the case example of risdiplam, approved for the treatment of spinal muscular atrophy. The ontogeny modeling, extrapolations of PK to unstudied pediatric populations, and drug–drug interaction (DDI) risk assessment are also discussed. The population PBPK modeling approach is intended to address the inconsistencies in ontogeny data and augment PBPK modeling for quantitative clinical pharmacology assessments in children. It will accelerate optimal dose finding and provide guidance for adequate use of drugs in pediatric patients, which is especially important for developing treatments for progressive pediatric rare diseases.