A Human Ovarian Tumor & Liver Organ-on-Chip for Simultaneous and More Predictive Toxo-Efficacy Assays

Arianna Fedi1,2, Chiara Vitale2,3, Marco Fato1

  • 1Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, 16126 Genoa, Italy.

Insights

This study introduces a novel organ-on-chip (OOC) platform to accurately predict anti-cancer drug efficacy and toxicity. This advanced model offers a reliable alternative to traditional preclinical testing methods.

Area of Science:

  • Oncology
  • Biotechnology
  • Pharmacology

Background:

  • Preclinical oncology trials face low success rates due to unreliable in vitro and animal models.
  • There is a critical need for advanced models that better predict human responses to anti-cancer drugs.

Purpose of the Study:

  • To develop and validate a multicompartmental organ-on-chip (OOC) platform for evaluating anti-cancer drug efficacy and hepatotoxicity.
  • To assess the predictive capability of the OOC platform compared to traditional preclinical models.

Main Methods:

  • A multicompartmental OOC platform fluidically connected 3D ovarian cancer tissues with hepatic cellular models.
  • Computational fluid dynamics simulated capillary-like blood flow and cisplatin diffusion.
  • Cytotoxicity assays were performed on the OOC platform, static co-cultures, and dynamic single-organ models.

Main Results:

  • Increasing cisplatin concentrations linearly decreased ovarian cancer and liver cell viability.
  • 3D ovarian cancer models exhibited greater cisplatin resistance than 2D models under static conditions.
  • The OOC platform demonstrated superior predictive accuracy for drug toxicity and efficacy compared to clinical therapy data.

Conclusions:

  • The developed OOC platform accurately mimics systemic drug administration in a physiological context.
  • Organ-on-chip technology provides a reliable 3Rs (Replacement, Reduction, Refinement) alternative for preclinical drug evaluation.
  • This approach enhances the predictability of anti-cancer drug efficacy and toxicity assessments.

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