How Can Automated Screening Processes Improve Drug Discovery?

By Sarah MooreReviewed by Megan Craig, M.Sc.

There are thousands of diseases that afflict humanity. While medical science has made huge advances in the last century, which has opened the door to new treatments for this vast repertoire of diseases, there is still an urgent need to develop new treatments to improve survival rates, offer therapeutics to those who do not respond to or are not suitable for current therapies, and, ultimately, to find much-needed cures.

It takes an average of 12 years from start to approval to develop a new drug. In addition, it is estimated that just one in 1,000 drugs that enter preclinical testing progress to clinical trials, and only 20% of those go on to approval. As well as being a lengthy process, drug development is also incredibly expensive. Recent research has estimated that it costs an average of $1.5 million to take a drug all the way through from discovery to approval.

The drug discovery process is fundamental to developing successful new therapeutics. It is the first stage of drug development and the process through which potential therapeutic compounds are identified. The primary method of drug discovery is compound screening, where a biological target relevant to the disease that the drug is being developed for is assed to assay plates, where they are then exposed to a library of compounds. “Hits” are compounds that are identified as those that produce desirable results when introduced to the target. These can move forward in the drug development process while the rest can be discarded.

Measurements taken to define a “hit” can be done manually (e.g. by microscopy), or automatically. Automation can greatly enhance the compound screening process, mostly by speeding it up and reducing the resources needed to be dedicated to it.

Speeding Up the Drug Screening Process

By adopting automation in the screening process, drug discovery can be sped up and streamlined. Laboratories around the globe are already adopting automated systems. Examples include the various rule-based and model-based approaches and prototypical robotic systems such as Adam and Eve, which are leveraged for automated target and hit finding.

In 2009, scientists at the Universities of Manchester, Cambridge and Aberystwyth developed Adam, the world’s first machine capable of discovering scientific knowledge autonomously. In 2015, the same team developed Eve, an artificially intelligent ‘robot scientist’ created to speed up the drug discovery process and improve efficiency.

Eve systematically tests each of a large set of compounds against assays, removing the need for technicians to work manually. This speeds up the process, removes the possibility of human error, and makes the process cheaper. Over recent decades, automation has become increasingly integrated into scientific processes, Eve is just one of many automation systems that are becoming commonplace in the modern laboratory.

The system can screen over 10,000 compounds daily and has already been used to identify compounds with the potential to treat cancer and various tropical diseases.

Integrating Automation with Organ-on-a-chip Technology

Organ-on-a-chip technology has been developed to mimic the natural physiology of cells along with the mechanical forces exerted on them in their natural environment in the body. These chips, therefore, provide an excellent platform for modeling how compounds may behave within human cells in the early-stage of drug development, where it is not yet safe for human participants to be tested on. The use of organ-on-a-chip technology lends itself to drug discovery as it enables the use of human cells in co-culture, which accurately recreate disease states with levels of accuracy that are unachievable with traditional 2D tissue culture.

This technology is combined with automation tools to establish novel platforms of drug discovery that have reduced error rates, shortened cycle times, improved feedback loops and compound optimization, reduced material consumption, and an enhanced design capable of targeting multiple biochemical and biological structures.

To incorporate automation into drug screening processes that use organ-on-a-chip, scientists have automated the stage of gel filling, media filling and cell seeding by incorporating automated liquid handling. This allows numerous wells to be prepared automatically, resulting in plates that can test multiple experiments simultaneously. In addition, there is the opportunity to further leverage automation by integrating artificial intelligence systems that automatically register “hits”.

Automation, alongside organ-on-a-chip and AI is helping to accelerate the current trend in medical science towards personalized medicine. With these tools, scientists can investigate individual responses to different compounds. In recent years, it has been increasingly understood that genetic differences can make a person more or less susceptible to drug treatments.

Some patient cohorts do not respond as well as others to certain drugs. This has left portions of the patient population underserved and in need of novel therapeutics. Personalized medicine offers hope for developing novel medicines for specific patient cohorts, allowing for the tailoring of medicine to the individual’s genetic profile.

References and Further Reading

Jodat, Y.A. et al. (2019) Human-derived organ-on-a-chip for personalized drug development. Current Pharmaceutical Design, 24(45), pp. 5471–5486. https://pubmed.ncbi.nlm.nih.gov/30854951/

Ma, C. et al. (2021) Organ-on-a-chip: A new paradigm for drug development. Trends in Pharmacological Sciences, 42(2), pp. 119–133. https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(20)30264-9

Robot Scientist ‘Eve’ could boost search for new drugs (2015) [Online]. Manchester University. Available at: https://www.manchester.ac.uk/discover/news/robot-scientist-eve-could-boost-search-for-new-drugs/ 

Schneider, G. (2017) Automating drug discovery. Nature Reviews Drug Discovery, 17(2), pp. 97–113. https://www.nature.com/articles/nrd.2017.232

Sparkes, A. et al. (2010) Towards robot scientists for autonomous scientific discovery. Automated Experimentation, 2(1), p. 1. https://aejournal.biomedcentral.com/articles/10.1186/1759-4499-2-1

Wang, Y. et al. (2023) Emerging trends in organ-on-a-chip systems for drug screening. Acta Pharmaceutica Sinica B[Preprint]. https://www.sciencedirect.com/science/article/pii/S2211383523000333?via%3Dihub