Drug discovery depends on experimental models that can reliably anticipate how compounds will behave in humans. For decades, the field has relied on two-dimensional (2D) cell cultures and animal models. Both possess well-documented limitations: 2D cultures fail to replicate the complexity of human tissue, while animal models are constrained by interspecies differences. More physiologically relevant in vitro models, particularly three-dimensional (3D) formats such as organoids, offer a path toward more accurate drug predictions and a meaningful reduction in animal use during preclinical development.
What Is the Difference Between 2D and 3D Cell Culture in Drug Screening?
In 2D cell culture, cells grow as a monolayer on a flat plastic or glass surface, where all cells are equally exposed to nutrients, oxygen, and signaling molecules from the culture medium.
3D cell culture, by contrast, produces structures in which cells grow in a three-dimensional arrangement that more closely reflects physiological conditions. The main formats include:
- Spheroids self-assemble into sphere-like formations and develop gradients of oxygen, nutrients, metabolites, and soluble signals, favoring cell-to-cell and cell-to-extracellular matrix (ECM) interactions.
- Organoids originate from stem cells that self-organize in vitro, preserving tissue-specific architecture, morphology, polarity, and gene expression profiles that closely resemble native tissue.
- Scaffold-based cultures use biopolymers arranged to imitate the physiological ECM, supporting cell growth in a structured 3D matrix.
- Organ-on-a-chip systems integrate microfluidic technology with living cell cultures, enabling the controlled flow of fluids and the simulation of mechanical and biochemical stimuli that static 3D models cannot reproduce.
The distinction matters for drug screening because the ability of cells to sense cell-cell and cell-ECM interactions and diverse gradients of biochemical cues, such as oxygen, nutrients, and the tested compounds, can determine their response to drugs.
Advantages and Limitations of 2D and 3D Cell Culture Models
The main strength of 2D cultures is practical: simple, cost-effective, and compatible with high-throughput workflows. Culture formation takes minutes to a few hours, and the medium is commercially available at a low cost. These advantages explain why 2D models are still early drug development.
Their biological limitations are equally clear. When cultured on a flat surface, cells change morphology, losing polarity and cell-to-cell interactions. Gene expression, mRNA splicing, and biochemical behavior are all altered. Primary human hepatocytes clearly depict the main limitation of 2D culture. This cell type, despite being the gold standard for human drug metabolism and toxicity studies, dedifferentiates in 2D conditions, and hepatic function declines within a few days in culture. In contrast, when cultured in 3D spheroids, primary human hepatocytes prolong maintenance of hepatic phenotype and function, including the expression of CYP450 enzymes, drug transporters, albumin, and urea secretion. They remain phenotypically stable and viable for at least 5 weeks, according to Bell et al.
When focusing on cancer cultures, cells cultured in 2D have unlimited and homogeneous access to nutrients and oxygen, unlike cells in vivo, where availability varies due to natural tumor architecture; 2D cultures fail to reproduce this physiological microenvironment that shapes real drug responses.
3D models address many of these shortcomings, but they come with drawbacks that still need to be addressed. Culture formation ranges from a few hours to a few days, reagents are more expensive, and achieving reproducibility and uniformity of spheroids is a practical challenge even for experienced researchers. In organoids specifically, not all cells will fully mimic the structure and function of the actual organ, and the lack of vasculature, critical for nutrient and waste transport, is a consistent limitation across all 3D model types.
Adapted from: Morais AS et al. Organ-on-a-Chip: Ubi sumus? Fundamentals and Design Aspects. Pharmaceutics. 2024 May 2;16(5):615.
Cost, Scalability, and Adoption: Why 2D Models Are Still Widely Used
2D cell culture remains the most established and widely adopted model in drug screening. Its protocols are well understood, its reagents are commercially available, and the vast majority of existing preclinical datasets have been generated using monolayer formats, making it the default starting point for most drug discovery pipelines.
That said, the landscape is shifting. Over the past decade, the number of organoid studies has grown rapidly, and major pharmaceutical companies are increasingly integrating 3D platforms into their drug discovery and toxicological assessment workflows. On the regulatory side, the FDA announced its intention to phase out animal testing in the development of monoclonal antibody therapies and other drugs, and regulatory bodies such as the FDA, NIH, and European agencies have formally recognized organoids as promising alternatives to conventional preclinical models. These shifts are creating both the scientific rationale and the regulatory space for 3D models to evolve and become the gold standard in drug development.
Predictivity in Drug Screening: Why 3D Cell Culture and Organoids Improve Translational Relevance
3D cell culture has shown better cellular responses to drug treatments that more closely resemble in vivo conditions. Organoid culture retains the genetic background of its source tissue and preserves gene expression profiles that more accurately reflect in vivo biology. The translational potential of organoids is increasingly being demonstrated in practice:
- Human cortical organoids produced at scale have enabled standardized screening of approximately 300 FDA-approved compounds across more than 2,400 organoids, supporting high-throughput neurotoxicity testing. (Narazaki et al. 2025)
- Patient-derived lung cancer organoids have provided clinically meaningful drug response profiles within a week, in agreement with tumor mutation profiles and clinical outcomes, a turnaround that could meaningfully shorten the biopsy-to-decision timeline in oncology. (Hu et al. 2021)
- In a landmark example, the bispecific antibody MCLA-158 (petosemtamab) was identified entirely through functional screening of patient-derived colon cancer organoid lines, progressing from discovery to Phase I clinical trials within 30 months, now in Phase III registration trials for head and neck squamous cell carcinoma. (Herpers et al. 2023)
- Cardiac organoid platforms have outperformed conventional 2D monolayers in detecting cardiotoxicity signals from a panel of FDA-recalled compounds, showing dose-dependent ATP depletion and beat disruption at clinically relevant concentrations, suggesting superior sensitivity for identifying cardiac safety liabilities early in development. (Skardal et al. 2020)
However, realizing this potential broadly requires overcoming two persistent barriers: batch-to-batch variability (organoids are sensitive to minor fluctuations in culture conditions, which can lead to inconsistent results across experiments) and limited scalability, as conventional culture methods remain largely manual and low-throughput. When these are not controlled, observed differences in drug response can reflect technical noise rather than genuine biological variation, undermining the reliability of the assay. Addressing these challenges through defined culture materials, spatial control of morphogenesis, and automated workflows is what will determine whether organoids can fulfill their promise as standardized, high-throughput platforms for drug discovery.
At MO:RE we are committed to contributing to this goal. The MO:BOT is an automated 3D cell culture platform that standardizes and scales the entire organoid culture workflow, from cell seeding and medium exchange to real-time quality control and endpoint assays. By removing manual handling from the equation, the MO:BOT reduces the batch-to-batch variability that undermines assay reliability and generates the throughput needed to make 3D models a practical tool in drug discovery and disease modeling.
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Hu Y et al. Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week. Nat Commun. 2021 May 10;12(1):2581. doi: 10.1038/s41467-021-22676-1.
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