Accelerating personalized medicine: miniaturized patient-derived organoid drug screening for predicting cancer

Yasmine Abouleila1, Lidwien P Smabers2, Timo Voskuilen1

  • 1HUB Organoids B.V. (HUB), Utrecht, The Netherlands, now is part of the Life Science Business of Merck KGaA, Darmstadt, Germany.

Npj Biomedical Innovations
|April 24, 2026
PubMed

Insights

This study introduces an automated method using the Yamaha Cell Handler™ to miniaturize patient-derived organoid (PDO) drug screening. This innovation reduces cell requirements and speeds up personalized cancer treatment predictions.

Area of Science:

  • Oncology
  • Biotechnology
  • Drug Discovery

Background:

  • Patient-derived organoids (PDOs) are crucial for predicting individual cancer treatment responses.
  • Current PDO screening methods are limited by high cell requirements, long turnaround times, and restricted clinical applicability.

Purpose of the Study:

  • To streamline and miniaturize PDO drug screening by automating the seeding process using the Yamaha Cell Handler™ (YCH).
  • To optimize and validate a high-throughput, low-cell-input assay for predicting patient responses to cancer therapies.

Main Methods:

  • Automated seeding of up to ten PDOs per well using the YCH, optimizing sample requirements.
  • Miniaturized assay development involving evaluation of seeding densities, devices, readouts, and measurement techniques.
  • Validation by comparing miniaturized assay results with standard screens and correlating organoid drug responses with patient outcomes.

Main Results:

  • The YCH was optimized for efficient PDO seeding, significantly reducing sample needs.
  • Miniaturized assay development and validation confirmed its reliability and efficiency.
  • Miniature patient-derived organoid screening closely mirrored results from traditional assays, demonstrating responsiveness to chemotherapy and targeted treatments.

Conclusions:

  • The automated, miniaturized PDO platform enables efficient, high-quality drug screening with reduced cell input.
  • This approach offers significant potential for accelerating personalized cancer treatment predictions in clinical settings.