<p>Human 3D in vitro models are increasingly adopted in preclinical drug discovery, as they better recapitulate tissue-like architecture, cell-cell interactions, and organ-specific functions compared with conventional 2D cultures. In parallel, initiatives such as the FDA Modernization Act 2.0 and the proposed 3.0 encourage the use of human-relevant in vitro systems as New Approach Methodologies (NAMs) alongside animal models. However, most 3D cell-based workflows rely on manual protocols that are difficult to standardize, producing variable spheroid morphology and fragmented culture steps that impair assay robustness and cross-study comparability. To address these challenges, we present the MO:BOT, a modular benchtop platform designed to standardize and automate complex 3D cell-based workflows. The MO:BOT integrates critical steps of 3D cell culture, including accurate cell seeding, precise medium exchange, on-deck image-based quality control, scalable compound dosing, and downstream assays. Here, we describe a proof-of-concept automated protocol to generate HepG2 liver spheroids and perform acetaminophen (APAP) drug-response testing. Compared with a manual workflow, the MO:BOT not only reduces hands-on time but also minimizes well-to-well variability in liver spheroid size and improves overall spheroid viability. We demonstrate that standardized, well-tuned pipetting routines can maintain the integrity of delicate 3D structures. An MO:BOT-automated APAP dose-exposure experiment with seven concentrations yields a clear sigmoidal cell-toxicity profile with matching changes in viability, LDH release, and ALT activity, demonstrating that improved spheroid uniformity enhances the sensitivity and robustness of downstream assays. The MO:BOT advances the standardization, scalability, and reproducibility of 3D in vitro models, supporting their broader adoption in preclinical research. </p>

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Automation of 3D liver spheroid generation and acetaminophen dose–response on the MO:BOT enhances assay robustness and precision

  • Dana Hellmold,
  • Daniel S. Ziemianowicz,
  • Frowin Ellermann,
  • Philipp Depperschmidt,
  • Max Appold,
  • Ahmed S. Omar,
  • Jonathan Kurz,
  • Frank Krieg-Schneider,
  • Thorben Pascal Hoppe,
  • David Hackenberger,
  • Lukas Gaats,
  • Julia Vallverdú

摘要

Human 3D in vitro models are increasingly adopted in preclinical drug discovery, as they better recapitulate tissue-like architecture, cell-cell interactions, and organ-specific functions compared with conventional 2D cultures. In parallel, initiatives such as the FDA Modernization Act 2.0 and the proposed 3.0 encourage the use of human-relevant in vitro systems as New Approach Methodologies (NAMs) alongside animal models. However, most 3D cell-based workflows rely on manual protocols that are difficult to standardize, producing variable spheroid morphology and fragmented culture steps that impair assay robustness and cross-study comparability. To address these challenges, we present the MO:BOT, a modular benchtop platform designed to standardize and automate complex 3D cell-based workflows. The MO:BOT integrates critical steps of 3D cell culture, including accurate cell seeding, precise medium exchange, on-deck image-based quality control, scalable compound dosing, and downstream assays. Here, we describe a proof-of-concept automated protocol to generate HepG2 liver spheroids and perform acetaminophen (APAP) drug-response testing. Compared with a manual workflow, the MO:BOT not only reduces hands-on time but also minimizes well-to-well variability in liver spheroid size and improves overall spheroid viability. We demonstrate that standardized, well-tuned pipetting routines can maintain the integrity of delicate 3D structures. An MO:BOT-automated APAP dose-exposure experiment with seven concentrations yields a clear sigmoidal cell-toxicity profile with matching changes in viability, LDH release, and ALT activity, demonstrating that improved spheroid uniformity enhances the sensitivity and robustness of downstream assays. The MO:BOT advances the standardization, scalability, and reproducibility of 3D in vitro models, supporting their broader adoption in preclinical research.