Evidence map›Paper›PMID 41607354›Full record

ReviewCPT: pharmacometrics & systems pharmacology2026

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

Yumi Cleary, Bhagwat Prasad, Kayode Ogungbenro, Michael Gertz, Aleksandra Galetin

Abstract readReview
In one paragraph

Review in CPT: pharmacometrics & systems pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yumi ClearyCentre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-9703-7269
Bhagwat PrasadDivision of Translational and Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Kayode OgungbenroCentre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK.
Michael GertzRoche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland.
Aleksandra GaletinCentre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-3933-5217

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Models, BiologicalPharmacology, ClinicalRare DiseasesChildDrug DevelopmentHumansPharmacokineticsontogenypediatricspharmacometricsphysiologically‐based pharmacokineticrare disease

Identifiers

PMID41607354
PMCPMC12853142

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.