Robots for Drug Discovery

Recent laboratory automation and robotics advances, including developments in artificial intelligence and machine learning, have opened a new frontier in the life science and pharmaceutical space. Frost & Sullivan has identified several major strides in this space.

Robots have been used in biomedical research since the 1980s, mainly for sample processing. Their role in the drug discovery process remained limited, which is why the development, testing and commercialization process generally takes 15 to 20 years. However, recent laboratory automation and robotics advances, including developments in artificial intelligence and machine learning, have opened a new frontier in the life science and pharmaceutical space. Tasks can now be performed at rates and precision beyond human capability.

Collaboration among researchers and well-funded companies is crucial to the development and use of fully automated, ultrahigh-throughput systems for the study of myriad substances’ pharmacological actions to find optimal candidates for new therapeutic products. Don’t think of it as robots taking the place of humans, but rather working alongside humans to ultimately better their lives. Frost & Sullivan has identified several major strides in this space.

AstraZeneca (Cambridge, England) and HighRes BioSolutions (Beverly, Mass.)

Pharmaceutical giant AstraZeneca is a leader in ultrahigh-throughput screening with its open innovation initiative to develop next-generation medicines and technologies. The initiative focuses on solving research and development challenges with access to a clinical compound bank, a preclinical toolbox, screening technologies and a data library. The initiative has allowed AstraZeneca’s academic and industry partners to access more than 250,000 compounds for screening and benefit from other technology and expertise. Proposals from researchers in more than 25 countries have resulted in 150 collaborations: since 2014, more than 100 preclinical and clinical studies have been designed or launched in the areas of cardiovascular and metabolic diseases, oncology, respiratory diseases, inflammation and autoimmunity, and neuroscience.

A cornerstone of the initiative is NiCoLA-B, a drug discovery robot at the U.K. Center for Lead Discovery, the company’s research center on the Cambridge campus. Its name is a variation of the moniker for the entire robot system: CoLAB (collaborative laboratory), which was developed by HighRes Biosolutions. The robot can test more than 300,000 compounds a day in a ballet of procedures with a central, mechanical arm as the lead dancer: the company says it is the fastest of its kind in the world. AstraZeneca explains that the robot uses sound waves to move droplets of potential drugs, billionths of liters at a time, from storage tubes into miniature wells on assay plates. Droplets of cells or biochemical solutions are added to the wells, and the robot monitors interactions for any activity that could indicate a promising drug. It works 24 hours a day, seven days a week so scientists don’t have to, though they can interact with or easily reconfigure it at any time though an app. Intelligent motion and load-sensing features ensure safety even with direct user interaction: the system can slow or stop if it detects resistance or other changes to its environment. AstraZeneca hopes to be able to screen 40 million potential drugs for dozens of diseases each year.

Aberystwyth University (Wales), Cambridge University and the University of Manchester

This collaboration may not have been happening since the beginning of time, but it did start with Adam. Aberystwyth and Cambridge researchers developed a robot scientist in 2009 that automatically carries out scientific processes. The computer system, which they named Adam, was able to formulate and test hypotheses, and proved itself with a simple discovery: by devising good.and running experiments using laboratory robotics, it found that genes in baker's yeast code for specific enzymes to catalyze biochemical reactions.  The same team followed up on its success with robot scientist Eve (of course), which uses artificial intelligence to accelerate the drug discovery process by applying the knowledge gained during initial screenings to predict new compounds that would pass assays. The robot, housed at the University of Manchester, has already discovered compounds that could be used as antimalarials.

Eve can screen more than 10,000 compounds a day against assays and generate faster results that are more efficient and cost-effective than standard assays. In 2015, Eve discovered that a compound used to treat cancer also can be used to inhibit dihydrofolate reductase (DHFR), an enzyme in the malaria parasite. Earlier this year, scientists revealed that Eve determined that triclosan, a common toothpaste ingredient, could be used against drug-resistant malaria—also by inhibiting DHFR. Study results were published in January in the journal Scientific Reports. Scientists hope that Eve can be used for finding new pathways for other drugs and compounds to treat some of the rarest conditions that pharmaceutical companies have found to be unattractive for research because of a low return on investment.

3Scan (San Francisco, Calif.)

3Scan, a digital biotech start-up, is developing a robotic microscopy platform for the advancement of tissue morphology; drug response, discovery, and development; and automated cell counting and diagnosis. It is leveraging its proprietary robotic microscope and computer vision systems to generate a digital, 3-D spatial screening map. Vasculature and blood flow also can be evaluated. The system will allow life scientists to work with more comprehensive data sets from tissue samples. Its knife-edge scanning microscope can process more than 3,000 slices from a tissue sample per hour.

The Road Ahead

Lab automation and robotics to support pharmaceutical research has taken tremendous strides in the past few years. The miniaturization of reaction mixtures and the creation of new molecules will present many more opportunities for stem cell research and the treatment of rare genetic diseases, especially if funding remains strong and governments remain engaged. Other start-ups that may make a difference in this arena include Deep Genomics (Toronto, Ontario), which is developing deep learning-based technology that can estimate a cell’s response to genetic variations by screening hundreds of millions of previously known genetic mutations, thus providing powerful tools for drug discovery; and Enlitic (San Francisco), which is using deep learning to help life scientists and physicians mine actionable information from large stores of medical images, notes, laboratory tests, and equipment reports.

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