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Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing
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Organoids in drug development: from predictive models to regulatory integration

Xuyang Song1, Xinrong Chen2, Qing Chen3

  • 1Department of Clinical Pharmacology and DMPK, Adlai Nortye Ltd, 685 US Hwy 1, North Brunswick Township, NJ 08902, USA.

Drug Discovery Today
|January 23, 2026
PubMed

Insights

Organoids provide better human tissue models for drug development. Integrating artificial intelligence and harmonized regulations will accelerate their use in preclinical oncology research.

Area of Science:

  • Biotechnology
  • Drug Development
  • Oncology

Background:

  • Organoids offer enhanced physiological relevance compared to traditional 2D and animal models.
  • They recapitulate human tissue architecture, enabling predictive assessments of drug efficacy, toxicity, and pharmacokinetics (DMPK).

Purpose of the Study:

  • To review current applications of organoids in drug development.
  • To discuss scientific and regulatory requirements for bridging the translational gap.
  • To emphasize the need for harmonized regulatory frameworks for organoid technology adoption.

Main Methods:

  • Literature review of organoid applications in preclinical drug development.
  • Analysis of current scientific and regulatory landscapes.
  • Discussion of challenges and opportunities for organoid technology integration.

Main Results:

  • Organoids are valuable tools for predicting drug efficacy, toxicity, and DMPK.
  • Artificial intelligence (AI)-driven modeling enhances organoid utility, especially in oncology.
  • Regulatory agencies are developing standards for organoid validation and qualification.

Conclusions:

  • Harmonized regulatory frameworks are crucial for ensuring scientific rigor.
  • Broader adoption of organoid technologies in drug development requires standardized validation and qualification processes.
  • Organoids, augmented by AI, represent a significant advancement in preclinical research.
Keywords:
DMPKcomputational and AI-driven modelingmicro-physiological systemsorganoidsregulatory strategytoxicity assessmenttranslational research

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