Image-based cell-profiling assays are crucial to the development of more targeted, effective treatment strategies. Paint-by-cell integrates artificial intelligence (AI) and machine learning (ML) based methodologies. Here, we discuss the potential, challenges, and future outlook of paint-by-cell as a tool for improved drug discovery pipelines.
Understanding paint-by-cell
Paint-by-cell, also known as high-content screening (HCS) or high-throughput screening (HTS) involves high-content image-based screening assays that evaluate therapeutic candidates for bioactivity, toxicity, and mechanisms of action (1).
The process begins with seeding cells in multiwell plates followed by treatment with compound libraries. After treatment, cells are stained, and high-throughput imaging is performed. The real power of paint-by-cell is its data analysis pipeline that integrates advanced AI and ML algorithms for automated analysis of thousands of compounds with the click of a button (2).
Potential and challenges of paint-by-cell in drug discovery
One of the most significant advantages of paint-by-cell is its ability to classify cell phenotypes and identify compound leads even when the precise mechanism of action is unknown. This feature is powerful for exploring new therapeutic avenues and understanding disease mechanisms.
The vast amount of data generated by paint-by-cell experiments used to pose a significant challenge. Data management, analysis, and interpretation has been revolutionized by incorporating advanced AI and ML algorithms. Today, paint-by-cell represents a mainstream technology in the pharmaceutical and biotech industries. Its application spans the entire drug development process, from initial screening to final testing (3). It is seeing increased use in academic research and translational settings, particularly in fields like stem cell biology and regenerative medicine (4).
Conclusion and future perspectives
Paint-by-cell represents a significant leap forward in drug discovery, offering a more efficient, cost-effective, and insightful approach to therapeutic development. Its integration with AI and ML technologies has only makes it an indispensable tool in modern pharmaceutical research. As AI capabilities continue to advance, paint-by-cell workflows are set to become even more advanced, and will further improve drug discovery pipelines.
If you're interested in learning more about the latest trends revolutionizing drug discovery, you won’t want to miss our latest whitepaper. We delve deeper into the impact of paint-by-cell on research and development and explore other emerging trends in the field.
References
- Zanella F, Lorens JB, Link W. High content screening: seeing is believing. Trends in Biotechnology. 2010;28(5):237-245. doi:/10.1016/j.tibtech.2010.02.005
- Scheeder C, Heigwer F, Boutros M. Machine learning and image-based profiling in drug discovery. Current Opinion in Systems Biology. 2018;10:43-52. doi:10.1016/j.coisb.2018.05.004
- Haney SA, LaPan P, Pan J, Zhang J. High-content screening moves to the front of the line. Drug Discovery Today. 2006;11(19-20):889-894. doi:10.1016/j.drudis.2006.08.015
- Xu Y, Shi Y, Ding S. A chemical approach to stem-cell biology and regenerative medicine. Nature. 2008;453(7193):338-344. doi:10.1038/nature07042