A pharmacokinetic-pharmacodynamic model based on multi-organ-on-a-chip for drug-drug interaction studies

Kenta Shinha1, Wataru Nihei, Tatsuto Ono1

  • 1Department of Mechanical Engineering, School of Engineering, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa 259-1292, Japan.

Biomicrofluidics
|January 7, 2022
PubMed

Insights

Organ-on-a-chip (OoC) models can predict drug-drug interactions (DDI). A multi-organ-on-a-chip system combined with a pharmacokinetic-pharmacodynamic model accurately estimated DDI for anticancer drugs.

Area of Science:

  • Pharmacology
  • Drug Discovery
  • Biotechnology

Background:

  • Drug-drug interactions (DDI) pose risks in drug discovery due to poor pharmacokinetic understanding.
  • Organ-on-a-chip (OoC) technology is an in vitro model for drug efficacy and toxicity but not yet applied to DDI studies.

Purpose of the Study:

  • To evaluate the applicability of organ-on-a-chip technologies for drug-drug interaction (DDI) studies.
  • To develop and validate a multi-organ-on-a-chip (MOoC) model integrated with a pharmacokinetic-pharmacodynamic (PK-PD) model for DDI assessment.

Main Methods:

  • A MOoC model was constructed with liver (metabolism) and cancer (drug target) components.
  • An anticancer prodrug, CPT-11, was used to assess metabolite efficacy.
  • The inhibitory effects of simvastatin and ritonavir on CPT-11 metabolism were evaluated to assess DDI.

Main Results:

  • The MOoC model successfully evaluated the efficacy of CPT-11 metabolites.
  • DDI estimation using the MOoC and PK-PD model showed similar results to concomitant administration experiments.
  • The combined PK-PD model and MOoC approach proved useful for predicting DDI.

Conclusions:

  • Organ-on-a-chip technologies, particularly the developed MOoC system, can be effectively applied to drug-drug interaction (DDI) studies.
  • The integration of PK-PD modeling with MOoC facilitates a better understanding of pharmacokinetic mechanisms in DDI.
  • This approach offers a promising in vitro strategy for predicting DDI during drug development.

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