Bringing a single new drug to market takes years of research and costs billions – with the COVID-19 pandemic highlighting even more how critical it is to accelerate this process. The high costs and failure rates in drug discovery can be traced down to lack of access to reliable, structured, high quality data. They are critical to power the right decisions at the right time, and to choose the right drug candidates to enter into clinical testing.

Arctoris is an Oxford-based company that just celebrated its 5th anniversary in February this year. Arctoris developed and operates Ulysses, the world’s first fully automated drug discovery platform powered by its proprietary robotic technologies. Arctoris partners with biotech and pharma companies, AI drug discovery companies as well as academic centres across the globe, delivering integrated drug discovery projects from the idea stage right up to IND-enabling studies. Co-Founder and Chief Executive Officer Martin-Immanuel Bittner MD DPhil said: “The drug discovery ecosystem is undergoing rapid change at the moment, with an increasing focus on data quality and data capture, a core concept that in many areas has largely been disregarded in the past.”

However, two reports published by pharmaceutical companies Bayer (Germany) and Amgen (USA) sounded a wake-up call when they each concluded that between 80% and 90% of landmark studies in the field of biomedicine could not be reproduced by independent scientific teams.

When minor differences in the interpretation of protocols can account for huge differences in results, precision, consistency, and reliability become critical. The answer is automation – using robotics to conduct experiments on behalf of the human scientists, so that they can focus on higher value tasks, such as hypothesis generation, data interpretation, and project planning, all while relying on the best possible data for their decision making.

 Arctoris provides just that – 24/7 automated laboratory operations, guided by an experienced team, with real-time data access for its partners and clients via its powerful digital portal, streamlining project planning, tracking and data analysis.

The wide range of biochemical, cell biology and molecular biology assays Arctoris offers enable rapid, informed decision making in basic biology, target validation, hit-to-lead, lead optimisation, toxicology etc. , leading to better decisions taken earlier.

“Ulysses, the technology platform we developed at Arctoris, offers the type of precision, accuracy and reproducibility that simply cannot be obtained manually,” said Martin-Immanuel Bittner. “Furthermore, thanks to fully automated experiment execution coupled with our comprehensive sensor array, we know exactly how each experiment is being conducted and how the data is being generated. We track all these parameters – the so-called experimental meta-data – throughout the whole experiment lifecycle, which enables us to provide full audit trails for each step of the process, and every dataset generated.”

Established in 2016, Arctoris grew out of the CEO’s own research experience while completing his DPhil (PhD) in Oncology as a Rhodes scholar at Oxford.

A medical doctor by background, he was surprised to see how much time researchers spent manually performing experiments, rather than discussing their ideas and focusing on the bigger picture.

In many ways, drug research has not kept pace with other industries in employing technology to increase efficiency and speed, and the poor level of reproducibility is one of the results.

Whilst laboratory robotics have been commonplace in large pharmaceutical corporations for more than 20 years, they have almost exclusively been used solely for high-throughput screening, until recently.

Technological advances and a change in understanding over the past few years though have created a shift in the ecosystem, with more and more research leaders realising the unique benefits of automated data generation, and the impact it has on the discovery process.

The interest in automation and robotics is growing rapidly – to the same degree as Artificial Intelligence is also playing an increasing role in drug discovery. The two trends reinforce each other, as it is well known in the AI community that any algorithm can only be as good as its input data. In other words, to reap the benefits of AI-driven drug discovery, researchers have to have access to highly structured, well annotated, robust and reproducible data. “And this is what we are here for”, said Martin-Immanuel Bittner.

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