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Updated: Mar 19, 2026
Generation of 3D Tumor Spheroids for Drug Evaluation Studies
Published on: February 24, 2023
High-Throughput 3D Tumor Spheroid Screening Method for Cancer Drug Discovery Using Celigo Image Cytometry
Sarah Kessel1, Scott Cribbes1, Olivier Déry1
11 Department of Technology R&D, Nexcelom Bioscience LLC, Lawrence, MA, USA.
Abstract:
Oncologists have investigated the effect of protein or chemical-based compounds on cancer cells to identify potential drug candidates. Traditionally, the growth inhibitory and cytotoxic effects of the drugs are first measured in 2D in vitro models, and then further tested in 3D xenograft in vivo models. Although the drug candidates can demonstrate promising inhibitory or cytotoxicity results in a 2D environment, similar effects may not be observed under a 3D environment. In this work, we developed an image-based high-throughput screening method for 3D tumor spheroids using the Celigo image cytometer. First, optimal seeding density for tumor spheroid formation was determined by investigating the cell seeding density of U87MG, a human glioblastoma cell line. Next, the dose-response effects of 17-AAG with respect to spheroid size and viability were measured to determine the IC50 value. Finally, the developed high-throughput method was used to measure the dose response of four drugs (17-AAG, paclitaxel, TMZ, and doxorubicin) with respect to the spheroid size and viability. Each experiment was performed simultaneously in the 2D model for comparison. This detection method allowed for a more efficient process to identify highly qualified drug candidates, which may reduce the overall time required to bring a drug to clinical trial.
Insights
This study introduces a high-throughput screening method for 3D tumor spheroids to improve cancer drug discovery. The new technique efficiently identifies effective drug candidates for clinical trials.
Area of Science:
- Oncology
- Drug Discovery
- Cell Biology
Background:
- Traditional 2D in vitro models often fail to predict drug efficacy in 3D environments.
- Identifying effective cancer drug candidates requires robust screening methods.
- 3D tumor spheroids better mimic in vivo conditions than 2D cultures.
Purpose of the Study:
- To develop an image-based, high-throughput screening method for 3D tumor spheroids.
- To optimize spheroid formation and drug response measurement.
- To enable efficient identification of qualified cancer drug candidates.
Main Methods:
- Utilized the Celigo image cytometer for high-throughput screening of 3D tumor spheroids.
- Determined optimal seeding density for U87MG glioblastoma cell line spheroid formation.
- Measured dose-response effects of 17-AAG, paclitaxel, TMZ, and doxorubicin on spheroid size and viability.
Main Results:
- Established an optimized method for generating uniform 3D tumor spheroids.
- Quantified the IC50 values for 17-AAG in 3D spheroids.
- Demonstrated the method's ability to compare drug responses in both 2D and 3D models simultaneously.
Conclusions:
- The developed high-throughput screening method enhances the efficiency of cancer drug candidate identification.
- This approach may accelerate the drug development pipeline by providing more predictive preclinical data.
- Image-based analysis of 3D spheroids offers a more accurate assessment of drug efficacy.
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