Challenges of Scaling Drug Screening with Complex Cell-Based Models

Danaher

1) Reproducibility of complex human-relevant models

Drugs fail in clinical trials because data generated across pre-clinical R&D and CMC don’t always provide the necessary clinical predictive power or relevance. Monolayer cell cultures and animal models are limited in their ability to predict adverse effects and accurately reflect the physiological conditions of human. Additionally, they often fail to replicate the complex structure, cellular interactions and functional responses of native tissues. In contrast, organoid systems are becoming more widely used as pre-clinical models because they:

Recapitulate key features of cellular architecture and function
Provide relevant, translatable data on drug functionality, kinetics and toxicity
Present a more cost-effective and ethical solution than animal models

However, manual culturing and maintenance of organoids and other complex 3D microphysiological models is cumbersome and fails to meet the demand for large-scale drug screening studies. The process starts with a 2D pre-culture phase, progressing to 3D cell culture and organoid formation, which typically takes weeks. Manual handling increases inter-batch variability, potentially undermining the consistency and reliability of organoid data.

Increasing complexity in cellular biology presents challenges, as highlighted by the scientific community’s emphasis on scaling, reproducibility and accessing large, diverse model populations with batch consistency. One way to achieve the required reproducibility is by providing platform technologies that help industrialize these processes, converting manual approaches into more reliable, routine ones through automation.

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2) Antibody specificity and sensitivity

Precise detection of biomarkers, antigens and identified targets in multiplex experiments largely hinges on the specificity and sensitivity of the antibodies used. In the realm of spatial profiling, particularly when characterizing human-relevant models, assay-validated and highly specific antibodies are essential. Promiscuous antibodies increase off-target binding, which can call into question the reliability of the data obtained. Prioritizing antibody specificity and sensitivity is paramount to advancing your understanding of complex biological systems.

Inaccurate, non-reproducible results can lead to unreliable interpretations of complex biological phenomena
Lost samples lead to the need for repeated experiments and result in the loss of important materials, including primary tumor samples
Increased spending on reagents, equipment and time for troubleshooting and reevaluating experiments

The goal should always be to avoid risks such as poor imaging resolution, non-reproducible results and wasted resources. This starts with selecting the right antibodies when building your panel. A properly planned spatial profiling experiment can deliver high-quality data, including the identification of new biomarkers, by leveraging antibodies that accurately target the intended antigen without cross-reactivity. Your panel must also be validated for your assay. This attention to detail enhances the overall robustness and validity of the findings.

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3) Phototoxicity and Photobleaching during imaging

Evaluation of 3D models through microscopy remains the principal method for assessing physiological responses and obtaining accurate, high-resolution imaging results . However, using conventional imaging techniques presents its challenges:

Prolonged and repetitive exposure to intense light, elevated temperatures or toxic imaging agents can cause phototoxicity and compromise cell viability
High-intensity laser exposure during manual optimization of imaging parameters can lead to photobleaching, which impacts the accuracy of results
Maintaining culture-like conditions to ensure accurate evaluation of organoid growth and morphology over time
Difficult image acquisition and resolution due to light scattering and attenuation of the depth and opacity of the samples

When imaging 3D human-relevant models, we often want to image samples for days or weeks, which isn’t always easy with many platforms. We aim for high resolution, relatively fast imaging and enough temporal resolution to observe cell divisions and movements without causing phototoxicity or photobleaching. Conventional microscopes typically require prolonged light exposure, which can harm the sample.

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4) Data Analysis

Organoid screening is increasingly leveraged in drug discovery due to its ability to model complex tissue architecture and cellular interactions. However, fully characterizing these multicellular, heterogeneous systems demands a broad array of assays—including imaging, luminescence, cytokine profiling, calcium signaling and gene expression analysis. Manual assay analysis and gathering data from multiple tools can prove cumbersome and unreliable.

The diversity of data types introduces significant challenges in assay analysis and data integration
Manual processing and the use of different tools slow down analysis workflows and introduce inconsistencies that compromise data reliability
Improper or fragmented analysis practices can create noise, reduce reproducibility and hinder the translational potential of organoid-based findings

Swift insights and connectivity across departments, organizations and external collaborators are fueling the labs of the future. Automated and AI-driven assay analysis is table stakes for global analytical teams. Organizations that embrace advanced digital tools to contextualize their scientific data will benefit from more efficient research processes.

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The life sciences companies of Danaher provide comprehensive solutions for human-relevant models and spatial profiling, addressing the need for more predictive models in drug discovery. Contact an expert today to learn how we enhance data accuracy, optimize drug response insights and drive the next generation of precision medicine.