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Updated: Jan 9, 2026
Acute Kidney Injury Model Induced by Cisplatin in Adult Zebrafish
Published on: May 15, 2021
A next generation physiologically-based kinetic model for quantitative in vitro to in vivo extrapolation of
Kiri G J Romano Olmedo1, Jiaqi Chen1, Ivonne M C M Rietjens1
1Division of Toxicology, Wageningen University and Research, Stippeneng 4, Wageningen 6708 WE, the Netherlands.
Abstract:
Animal-based toxicity tests inadequately predict human nephrotoxicity, hence the call for human-based new approach methodologies. Nephrotoxic chemicals often accumulate in the proximal tubule as transporters on the polarized membrane of these cells can actively take-up chemicals. The aim of the present study was to develop next generation human physiologically-based kinetic (PBK) models to simulate plasma and kidney concentrations and predict the nephrotoxicity of i.v.-administered cisplatin as the model compound. The PBK models were used to predict nephrotoxic doses of this chemotherapeutic by quantitative in vitro to in vivo extrapolation (QIVIVE) of literature available concentration-response relationships of cytotoxicity in renal cell lines. Two forms of the PBK model were developed: one with a simple single kidney compartment and another including a multi-compartmental kidney that includes organic cation transporter 2 (OCT2)-mediated active renal secretion. Observed single dose of 50 mg/m2 lead to nephrotoxicity in 30 % of patients. Using only in vitro and in silico-derived pharmacokinetic parameters, human benchmark dose levels (BMDL)30 values were calculated for all QIVIVE obtained dose-response curves using the maximum concentration in kidney blood or proximal tubule and the area under the concentration-time curve (AUC) in the proximal tubule. Based on the AUC in proximal tubule, the complex form of the model predicts nephrotoxicity in 30 % of patients best, between 23.1 and 65 mg/m2. This study provides a case study for next generation risk assessment using PBK models that incorporate active renal secretion to better predict plasma concentration-time profiles are well as in vivo nephrotoxic dose levels.
Insights
New physiologically-based kinetic (PBK) models improve prediction of cisplatin nephrotoxicity. These human-based models incorporate active renal secretion, offering better risk assessment for drug development and patient safety.
Area of Science:
- Toxicology
- Pharmacokinetics
- Computational Biology
Background:
- * Traditional animal toxicity tests fail to accurately predict human kidney toxicity.
- * Nephrotoxic chemicals concentrate in proximal tubules due to active uptake transporters.
- * There is a need for human-based New Approach Methodologies (NAMs) for toxicity testing.
Purpose of the Study:
- * Develop advanced human physiologically-based kinetic (PBK) models to simulate drug concentrations in plasma and kidneys.
- * Predict nephrotoxicity of intravenous cisplatin using these PBK models.
- * Utilize quantitative in vitro to in vivo extrapolation (QIVIVE) for dose prediction.
Main Methods:
- * Developed two human PBK models: a single-compartment and a multi-compartment kidney model.
- * The multi-compartment model incorporated organic cation transporter 2 (OCT2)-mediated active renal secretion.
- * Used literature data on renal cell line cytotoxicity and pharmacokinetic parameters for QIVIVE.
Main Results:
- * The complex PBK model, incorporating OCT2-mediated secretion, best predicted cisplatin nephrotoxicity at 30% of patients (23.1–65 mg/m²).
- * Predictions were based on the area under the concentration-time curve (AUC) in the proximal tubule.
- * Calculated human benchmark dose levels (BMDL30) using in vitro and in silico data.
Conclusions:
- * Next-generation PBK models incorporating active renal secretion enhance prediction of plasma concentrations and in vivo nephrotoxic doses.
- * This study demonstrates a case study for advanced risk assessment using human-based in silico models.
- * Improved prediction of nephrotoxicity supports safer drug development and personalized medicine approaches.
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