Why digital transformation is essential to get value from R&D data

Before digital tools, scientists in R&D labs relied on paper and spreadsheets to document their research, record experiments and track their inventory.

This is highly inefficient, as not only is there a risk of human error, but it requires a system to store and locate experiment results so that they can be retrieved years later. If and when the person who performed the experiment leaves the company, finding this information can then become very difficult.

The Covid-19 pandemic boosted the adoption of digital technologies, with 49 percent of biopharma companies now using the cloud for day-to-day operations. This still leaves a majority reliant on paper-based systems, due to concerns over security, resistance to change or simply not knowing where to begin with digital transformation.

The shift to a digital lab

The first step along the digital transformation journey is acknowledging the limitations and missed opportunities associated with traditional solutions, including outmoded data structures, physical storage limitations, or outdated equipment.

This requires identifying legacy systems that are slowing down operations. They may have performance issues, do not support mobile devices, are incompatible with modern software and browsers or do not support integration with third-party solutions.

Read the full report: Why R&D must digitalize and become data-centric

The next requirement is aligning across R&D and IT, essentially getting on the same page about modernizing processes with data-centric, cloud-native solutions. Kate Quigley, Product Marketing Lead at Benchling, says there has been a large shift in the relationship between R&D and IT teams in the last few years. “They used to be somewhat at odds with each other,” she notes.

She continues: “I think partly it is the elevated stature of IT these days, with companies realising their value is based on the technology and data they have. There are a lot more hand-in-glove working models now between the scientists and the IT teams, and that helps a lot in progressing towards common objectives and strategies.”

Most organizations undergo a form of digital maturity assessment at the start of their journey to understand where they are in the process. “There are customers that come to us and are taking their first step, which is centralizing data for the first time,” Quigley adds.

“It is a huge undertaking for those organizations that have been working on all these point solutions to gather them and set up an interoperable system, then put data into particular locations so it is findable and usable,” she says.

Implementing lab automation

The last step of digital transformation is automation. The volume of data in R&D is rising exponentially, especially with the growth in new data-heavy techniques in therapeutics and gene-editing. The opportunity for this data to contain inconsistencies and errors is high, especially when manually captured.

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Fausto Artico, Global R&D Tech Head and Director of Innovation and Data Science at GSK, explains, “The more we automate the processes, the better it will be. We can log and track all that is happening in an automated way through software systems and therefore, even a posteriori, be able to verify what happened and correct, fix and improve processes, procedures and data on an ongoing basis.”

GSK is working on a portable IT infrastructure for automated data gathering and data sharing activities for clinical trials. “Such infrastructure will allow us to greatly reduce the enormous costs needed to run clinical trials,” Artico says. “Furthermore, it will allow data scientists and doctors to get much greater visibility, practically in real time, into all activities happening in a clinical trial.”

Unlocking the value of data with artificial intelligence

A digital lab can unlock the potential of vast quantities of data, far more than the human brain is capable of analyzing. For this purpose, artificial intelligence (AI) and machine learning (ML) have transformed the way scientists research new compounds.

Christos Nicolaou, Director in Digital Chemistry at Recursion Pharmaceuticals, explains that “the ability of an AI-driven algorithm to analyze billions of cell images and identify whether there has been a favorable interaction between the ligand and the cell during phenotypic screening, is something that no human can do. AI can do this pretty accurately.”

Read more: The benefits of remote monitoring for sample storage and lab processes

Nicolaou says that it currently takes months for a molecule to be designed, sent off for synthesis – where expert chemists must order reagents and perform the reaction before purifying it, making sure the synthesis effort has worked – and then passing it on for testing. “Imagine having all these done under one roof executed by an automated platform,” he says. “The goal is to go from months to weeks to days.”

Ultimately the creation of a digital lab simplifies the work of scientists to reduce the time it takes to develop new drugs. “It allows us to essentially alleviate the human expert workload by passing on the more mundane tasks to the machines,” Nicolau adds. “In this manner the human will be able to do what it does best, which is create and design.”

Quick links

• How Sanofi is using AI to accelerate time to market for new drugs
  
• Achieving excellence in lab data management
• Three ways AI is speeding up drug discovery

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