ArticleCPT: pharmacometrics & systems pharmacology2023
Interspecies and in vitro-in vivo scaling for quantitative modeling of whole-body drug pharmacokinetics in patients: Application to the anticancer drug oxaliplatin.
Article in CPT: pharmacometrics & systems pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Metabolomics of per- and polyfluoroalkyl substances (PFAS) exposure: Advances in LC-MS technology and applications.Journal of food and drug analysis · 2026Review
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- Modeling the Circadian Control of the Cell Cycle and Its Consequences for Cancer Chronotherapy.Biology · 2023Article
- Interspecies and in vitro-in vivo scaling for quantitative modeling of whole-body drug pharmacokinetics in patients: Application to the anticancer drug oxaliplatin.CPT: pharmacometrics & systems pharmacology · 2023Article
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Authors and funding
6 authors.
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Abstract
Quantitative systems pharmacology holds the promises of integrating results from laboratory animals or in vitro human systems into the design of human pharmacokinetic/pharmacodynamic (PK/PD) models allowing for precision and personalized medicine. However, reliable and general in vitro-to-in vivo extrapolation and interspecies scaling methods are still lacking. Here, we developed a translational strategy for the anticancer drug oxaliplatin. Using ex vivo PK data in the whole blood of the mouse, rat, and human, a model representing the amount of platinum (Pt) in the plasma and in the red blood cells was designed and could faithfully fit each dataset independently. A "purely physiologically-based (PB)" scaling approach solely based on preclinical data failed to reproduce human observations, which were then included in the calibration. Investigating approaches in which one parameter was set as species-specific, whereas the others were computed by PB scaling laws, we concluded that allowing the Pt binding rate to plasma proteins to be species-specific permitted to closely fit all data, and guaranteed parameter identifiability. Such a strategy presenting the drawback of including all clinical datasets, we further identified a minimal subset of human data ensuring accurate model calibration. Next, a "whole body" model of oxaliplatin human PK was inferred from the ex vivo study. Its three remaining parameters were estimated, using one third of the available patient data. Remarkably, the model achieved a good fit to the training dataset and successfully reproduced the unseen observations. Such validation endorsed the legitimacy of our scaling methodology calling for its testing with other drugs.
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