Parent-metabolite PBPK model of methotrexate to inform pediatric extrapolation, transporter-mediated drug–drug interactions, and renal impairment
Abstract
Methotrexate (MTX) is widely used in treating chronic inflammatory diseases and in chemotherapy for both adult and pediatric patients. Its pharmacokinetics are complex, as MTX is a substrate of multiple transporters. Particularly during high-dose chemotherapeutic use, it is associated with severe toxicities including reversible kidney impairment and hematological toxicity. Physiologically based pharmacokinetic (PBPK) modeling offers a mechanistic approach to predict drug pharmacokinetics and guide dosing, especially in pediatric applications where clinical data is limited. A whole-body parent-metabolite PBPK model of MTX and 7-hydroxymethotrexate was developed in PK-Sim ® (version 12.1) for intravenous and oral administration in adults and children. Model development used 42 MTX and 16 metabolite plasma concentration-time profiles and showed strong predictive performance, with 53/58 area under the plasma concentration-time curve and 38/39 maximum plasma concentration values within a 2-fold range of observed data. Pediatric extrapolation incorporating transporter ontogeny yielded adequate predictions. Drug-drug interaction simulations for five perpetrator drugs met established acceptance criteria, and renal impairment simulations revealed markedly increased exposure in the high-dose setting. The model will be made publicly available to support dose optimization across clinical scenarios.
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BibTeX
@article{dette2026parentmetabolite,
title = {{Parent-metabolite PBPK model of methotrexate to inform pediatric extrapolation, transporter-mediated drug–drug interactions, and renal impairment}},
author = {Charlotte Maria Ursula Dette and Fatima Zahra Marok and Veronika Alberg and Christiane Dings and Dominik Selzer and Matthias Schwab and Barbara Faganel Kotnik and Vita Dolžan and Daniela Yildiz and Jörg Bittenbring and Marc Remke and Thorsten Lehr},
journal = {Scientific Reports},
year = {2026},
month = nov,
doi = {10.1038/s41598-026-77381-0},
url = {https://www.nature.com/articles/s41598-026-77381-0},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Parent-metabolite PBPK model of methotrexate to inform pediatric extrapolation, transporter-mediated drug–drug interactions, and renal impairment
AU - Charlotte Maria Ursula Dette
AU - Fatima Zahra Marok
AU - Veronika Alberg
AU - Christiane Dings
AU - Dominik Selzer
AU - Matthias Schwab
AU - Barbara Faganel Kotnik
AU - Vita Dolžan
AU - Daniela Yildiz
AU - Jörg Bittenbring
AU - Marc Remke
AU - Thorsten Lehr
PY - 2026
DA - 2026/11/07
JO - Scientific Reports
DO - 10.1038/s41598-026-77381-0
UR - https://www.nature.com/articles/s41598-026-77381-0
AB - Methotrexate (MTX) is widely used in treating chronic inflammatory diseases and in chemotherapy for both adult and pediatric patients. Its pharmacokinetics are complex, as MTX is a substrate of multiple transporters. Particularly during high-dose chemotherapeutic use, it is associated with severe toxicities including reversible kidney impairment and hematological toxicity. Physiologically based pharmacokinetic (PBPK) modeling offers a mechanistic approach to predict drug pharmacokinetics and guide dosing, especially in pediatric applications where clinical data is limited. A whole-body parent-metabolite PBPK model of MTX and 7-hydroxymethotrexate was developed in PK-Sim ® (version 12.1) for intravenous and oral administration in adults and children. Model development used 42 MTX and 16 metabolite plasma concentration-time profiles and showed strong predictive performance, with 53/58 area under the plasma concentration-time curve and 38/39 maximum plasma concentration values within a 2-fold range of observed data. Pediatric extrapolation incorporating transporter ontogeny yielded adequate predictions. Drug-drug interaction simulations for five perpetrator drugs met established acceptance criteria, and renal impairment simulations revealed markedly increased exposure in the high-dose setting. The model will be made publicly available to support dose optimization across clinical scenarios.
SN - 2045-2322
ER -