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British Journal of Clinical Pharmacology· 2026Q1

Comparison of pooled and Bayesian approaches for the analysis of paediatric pharmacokinetic data leveraging prior adult knowledge: A simulation case study for monoclonal antibodies against soluble targets

Misako Takenaka, Scott Marshall, Frances Stringer, Masanori YAMADA et al.

Short summary

When paediatric-adult drug differences are driven by body size, pooled pharmacokinetic (PK) modelling estimates paediatric clearance 1.1%-1.2% more accurately than Bayesian approaches (4.1%-10.4% absolute prediction error). When age-related differences dominate and paediatric sample size is small (≤30), Bayesian modelling yields more accurate clearance estimates for 11-year-olds (3.4%-8.2% vs. 7.4%-15.7% absolute prediction error).

AI-generated from the title and abstract; the full text is not read.

Key points

  • Pooled popPK modelling estimated paediatric clearance more accurately (1.1%-1.2% median absolute PE) than Bayesian approaches (4.1%-10.4%) when PK differences were driven by body size.
  • Bayesian popPK modelling provided more accurate paediatric clearance estimates (3.4%-8.2% median absolute PE) than pooled approaches (7.4%-15.7%) when age-related PK differences were present and paediatric sample size was small (≤30).
  • Individual exposure estimates were comparable between pooled and Bayesian modelling approaches.
  • The optimal popPK approach for paediatric analysis depends on the specific context, including the source of PK differences, paediatric age group, and study design.

AI-generated from the title and abstract; the full text is not read.

Abstract

Aims Population pharmacokinetic (popPK) modelling is widely used to support paediatric dose optimization, yet consensus on appropriate modelling approaches remains limited. This study compared pooled and Bayesian popPK approaches for paediatric pharmacokinetics (PK) analysis using monoclonal antibody (mAb) examples. Methods Paediatric and adult PK datasets were simulated using popPK models inspired by a published mepolizumab model, incorporating two clinically relevant scenarios reflecting different mechanisms of paediatric–adult mAb PK differences. Six paediatric study designs were evaluated. Paediatric PK was analysed using pooled and Bayesian approaches, and model performance was assessed based on absolute prediction error (PE) and precision of population and individual PK parameters. Results When paediatric–adult PK differences were explained primarily by body size, pooled approach estimated paediatric population clearance more accurately (median absolute PE: 1.1%–1.2% with pooled approach vs . 4.1%–10.4% with the Bayesian approach) and was robust across study designs. In contrast, when age‐related PK differences were present and paediatric sample size was small (≤ 30 subjects), the Bayesian approach provided more accurate population clearance estimates for 11‐year‐old population (median absolute PE: 3.4%–8.2% with the Bayesian approach vs .7.4%–15.7% with pooled approach). Individual exposure estimates were comparable between modelling approaches. Conclusions In this case study based on mAbs acting on soluble targets, relative performance of pooled and Bayesian popPK approaches varied with the analysis objective, paediatric age group, ADME characteristics and paediatric study design. These findings provide practical considerations for applying pooled and Bayesian approaches in this context and may help inform the selection of popPK strategies to support model‐informed paediatric dosing decisions.

The authors' abstract, as published at the source. British Journal of Clinical Pharmacology, 2026 · DOI ↗

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Field: Pediatrics, Perinatology and Child Health

Pediatrics, Perinatology and Child HealthMedicine