3D Organoids vs 2D Cell Lines in Drug Discovery and Disease Modeling
As drug discovery demands more predictive and accurate models, the limitations of traditional 2D cell cultures become increasingly clear. 3D organoid screening offers a more physiologically accurate alternative but brings its own challenges. This article examines the advantages and limitations of 3D organoids versus 2D cell lines in drug discovery and highlights key considerations for high-throughput applications and future developments.
Advantages and limitations of 3D organoids vs 2D cell lines
“Growing cells in 2D culture has long been a mainstay in drug development and disease modeling due to its simplicity, low cost, and highly controlled conditions,” says Carolina Lucchesi, Ph.D., Principal Scientist, BioNexus, ATCC. However, 2D cultures lack critical microenvironmental cues found in vivo, such as oxygen and sugar gradients, 3D cell-to-cell interactions, and extracellular matrix components, limiting their ability to replicate tissue complexity.
In contrast, 3D organoids offer a more physiologically relevant model by better mimicking organ architecture and function. “This improved physiological relevance leads to better predictions of drug efficacy and toxicity, reducing the chances of false positives and negatives,” says Nikki Carter, Commercial Organoid Innovation Director at Molecular Devices. Organoids provide earlier insight into how a candidate drug interacts with human tissues, helping identify both therapeutic effects and potential side effects more accurately. “This level of insight allows researchers to fail faster and focus investment on the more promising candidates,” adds Vi Chu, Head of R&D, Cell Biology Reagents & Tools at MilliporeSigma, the Life Science business of Merck KGaA, Darmstadt, Germany.
Patient-derived organoids also support personalized medicine by capturing individual variability in drug responses, particularly in oncology. “Unlike 2D models—which often consist of a single cell type and accumulate genomic changes over time—organoids better reflect the body's complexity, including diverse cell types, genetic mutations, and the surrounding microenvironment,” explains Chu. “A promising drug candidate screened in a 2D model might not perform as well as one identified using patient-derived organoids.”
Their tissue-like nature makes organoids effective tools in predictive toxicology, enabling the detection of potential side effects in organs like the liver and kidneys. “3D models allow us to test compounds that seemed safe in 2D but may not be in a more realistic environment,” says Lucchesi. Multiple studies have confirmed their high sensitivity and specificity across various tissue types.1–3
Despite their advantages, 3D organoids can be challenging to culture and scale consistently, often requiring specialized expertise, workflows, and supplies. They are more expensive and lower in throughput compared to 2D cell cultures. “Organoids still lack key in vivo features like vasculature and dynamic fluid flow, which may affect their predictive accuracy,” notes Lucchesi.
Patient-derived colorectal cancer organoid stained with Hoechst (blue) for nuclei, phalloidin (green) for actin filament, and MitoTracker (red) for mitochondria. Images acquired using the ImageXpress® HCS.ai High-Content Screening System equipped with confocal spinning disk. Photo courtesy of Molecular Devices.
Choosing the right model
Selecting between 2D and 3D models depends on the specific research question, disease complexity, and project stage. “2D cell culture is particularly valuable early in drug development for screening large libraries of compounds, as it efficiently narrows down promising candidates for further development before transitioning to more complex and physiologically relevant 3D models,” says Lucchesi.
As the pipeline narrows and the need for physiological relevance grows, 3D organoids become increasingly useful. “By providing a more physiologically relevant model, 3D organoids enhance the predictive power of preclinical studies,” explains Carter. Organoids are particularly well suited for mechanistic studies and modeling complex diseases, where in vivo-like responses are critical—cancer, neurological disorders, infectious diseases, metabolic conditions like MASH, and gastrointestinal disorders such as IBD. Additionally, organoids are useful for invasion/migration assays, tumor-stroma interaction, and drug resistance profiling.
Beyond physiological relevance, organoids can help reduce reliance on animal testing and widen genetic diversity in research. “They create opportunities for genetically diverse populations to be reflected in research, allowing scientists to better understand drug interactions in underrepresented patient populations,” notes Chu.
“In reality, the two systems are very likely to be used in tandem due to their complementary strengths or used as a cell source to build more complex models such as microphysiological systems,” Lucchesi concludes.
Transitioning to 3D systems for high-throughput screening
Moving from 2D to 3D culture systems for high-throughput applications introduces technical challenges, such as consistent and scalable organoid production. Unlike 2D cell lines, organoids require more complex and precise growth conditions—including specialized media, scaffolds or extracellular matrices, and longer culture times—making them more sensitive to environmental changes and harder to scale.
Assay compatibility is another challenge, as traditional 2D assays often don’t translate well to 3D systems. “Researchers need to optimize or develop new assays that can accurately measure endpoints in 3D organoids,” says Carter. Lucchesi adds that scaffold materials can introduce background noise like autofluorescence, complicating signal detection.
Chu points to systemic challenges, including limited access to diverse patient-derived organoids and high production costs. “Genetic diversity is essential for effective patient stratification and understanding how different populations respond to drug treatments,” she explains. Chu also highlights the need for global harmonization, data integration, and broader acceptance of organoids within the research community.
Accounting for cell variability is another concern. “The ratio of cell types, their orientation within the culture, and even the size of the 3D culture can vary, introducing a lot of variability,” Lucchesi notes. Researchers address this by leveraging miniaturized, microplate-compatible formats, such as 96- or 384-well spheroid systems, and integrating them with automated liquid handling and imaging systems. Advances in hydrogel scaffolds, microfluidics, and bioprinting also provide better control over the microenvironment and uniformity.
Patient-derived colorectal cancer organoid imaged on the ImageXpress® HCS.ai High-Content Screening System. The Hoechst stain highlights the nuclei, while phalloidin labels F-actin. Photo courtesy of Molecular Devices.
To support the transition to 3D culture systems, Molecular Devices has developed tools like the CellXpress.ai Automated Cell Culture System, which overcomes the challenge of producing large quantities of uniform organoids. “It enables the scalable in-plate production of organoids by automating the culture process and ensuring consistent quality, while reducing transfer steps between culture and end-point assay,” explains Carter. Their 3D Ready Organoids support the scale-up by providing researchers with patient-derived, assay-ready organoids for their studies.
Molecular Devices’ IN Carta Image Analysis Software uses advanced AI to process massive datasets generated from high-throughput screening of 3D organoids and transform complex images into easily interpretable results, supporting 2D, 3D, and even 4D experiments. “Its user-friendly workflows allow researchers to create highly customized image analysis protocols, ensuring they get robust results even for the most complex assays,” Carter notes.
On the assay side, automated high-content imaging paired with machine learning is becoming a preferred approach to extract rich, actionable data. Ultimately, standardization of both culture protocols and data analysis will be essential to make 3D high-throughput screening a scalable and reliable tool for drug discovery.
What’s next for 3D organoids
Experts anticipate advances in organoid complexity, automation, and data integration. “Improved culture systems supporting even more complex cellular make-ups, like co-cultures of epithelial and immune cells, should further boost the physiological relevance of organoids,” says Chu. Carter adds that advances in bioprinting will allow the precise spatial arrangement of multiple cell types, creating highly sophisticated, patient-specific organoid models for tailored therapies.
Automation remains a major focus. “Automated organoid formation improves consistency and scalability, supports high-throughput screening, and advances personalized medicine by generating reproducible patient-specific 3D models,” says Lucchesi. She also notes that advances in iPSC reprogramming will drive precision medicine, allowing for more reliability and reproducibility across experiments.
Chu foresees the rise of integrated multi-omics platforms combining genomic, transcriptomic, proteomic, metabolomic, and epigenetic data with high content imaging and drug-response profiles. Such systems would let researchers compare patient-derived organoids across molecular and phenotypic levels, identify biomarkers, stratify patient responses, and refine predictions of drug sensitivity and resistance.
Emerging organoid-on-a-chip systems may enable real-time monitoring of drug effects across multiple organ models simultaneously. “The long-term vision could be a human-on-a-chip—a combination of organoids being analyzed to understand how one drug affects multiple organs at once,” notes Chu.
Lucchesi predicts AI will streamline drug response prediction and target identification, accelerating 3D model adoption. She also highlights the need for clear decision-making frameworks to integrate organoid data with other models.
Finally, regulatory guidance from agencies like the FDA and EMA will be crucial to ensure organoid-based assays are reliable, reproducible, and meet quality standards. As Carter notes, “Harnessing the full potential of 3D organoids requires a joint effort from academia, industry, and government.”
References
- Bell, C.C., Dankers, A.C.A., Lauschke, V.M., et al. (2018). Comparison of Hepatic 2D Sandwich Cultures and 3D Spheroids for Long-term Toxicity Applications: A Multicenter Study. Toxicol Sci, 162(2):655-666.
- Caleb, J., Yong, T. (2020). Is It Time to Start Transitioning From 2D to 3D Cell Culture? Front Mol Biosci, 7:33.
- Imamura, Y., Mukohara, T., Shimono, Y., et al. (2015). Comparison of 2D- and 3D-culture models as drug-testing platforms in breast cancer. Oncology Reports, 33, 1837-1843.