Highlights from Future Lab Informatics 2022

Pharma IQ’s online event Future Laboratory Informatics 2022 focused on how industry professionals can get the best out of their data to advance drug discovery and speed to market.

Featuring speakers from AstraZeneca, Merck, Moderna and Johnson & Johnson, the event explored how to create the lab of the future through the use of technologies including automation, artificial intelligence (AI) and machine learning (ML).

Sylvain Demanze, senior analytical scientist at AstraZeneca, opened with a session discussing automation in the context of small molecule research, emphasizing that the adoption of automation in the lab is a necessity. “Typically, from a drug discovery point of view, the Design-Make-Test-Analyze (DMTA) cycle can take anything from a few weeks, if you are lucky, to several months per integration,” Demanze said.

“Within a drug discovery project you need to go through this cycle 10, 20 or 30 times. What is really important to understand is, if you are able to go through this iteration quicker, you will be able to progress your pipeline of projects quicker," Demanze added.

Making the lab more efficient

Demanze illustrated this by explaining that delays to the process lead to huge losses in revenue. “Take the example of any blockbuster drug on the market, which by definition can make at least US$1 bn in revenue every year. Any single day of delay from the moment that you have patented your invention and the clock starts ticking will cost you at least US$3 m, so this is really big money.”

Handling data more efficiently is one benefit, Demanze noted. At AstraZeneca, data generated by different instruments is being migrated onto a global, cloud-native database, giving easy access to scientists as well as integration with the electronic lab notebook.

In the next session Aurélie Chambon, business development manager at Merck, introduced the LANEXO System, an in-house inventory management tool that helps lab staff keep track of consumables. As well as making workflows more efficient, Chambon explained that automated tools like this one are better in terms of sustainability, as they can reduce the likelihood of reagents expiring or going unused and then needing to be disposed of.

The system uses RFID tags that track expiry dates, reducing the need for paper. “We have conducted surveys showing that you can reduce the time spent on inventory tasks by 97 percent,” Chambon said.

Capturing and analyzing data

Pharma IQ advisory board member and digital process development lead at Moderna, Clark Leininger, spoke about the importance of capturing and accessing relevant data, using a framework called data resolution. He said: “It is looking at what data you are capturing in your systems and then what you are able to do with that level of data. This gives you the framework to think about what you need to do to get the result that you want.”

This includes considering whether you need to capture pass fail data, whether values need to be included and if an event has occurred that has impacted the data.

Watch on-demand: Future Lab Informatics 2022

“If you are capturing those actual values, as well as data on associated processes, you can start doing some real analytics and understanding what other factors could have contributed to negative or positive results, what are the upstream and downstream changes you should be thinking about and who the right people might be to help you answer those questions,” Leininger said.

Adopting good data resolution has typically been difficult in pharma, Leininger added. “We are so focused on getting projects done and moving things through that we are not taking the time to really understand the data that we are collecting,” he said.

“If you are just collecting the bare minimum of data to get your process moving, the next time you want to make an update to that process, change anything, deliver any improvements, or understand why something failed, you are going to struggle because that data is not going to be available,” Leininger continued.

Adopting a digital mindset in the lab

The final session of the day was led by another member of Pharma IQ’s advisory board, Christos Varsakelis, team lead in AI/ML at The Janssen Pharmaceutical Companies of Johnson & Johnson.

Varsakelis explained the role AI and ML will play in the lab of the future, and how to foster a culture that capitalizes on digital tools and Industry 4.0. He also outlined some of the challenges the sector faces when it comes to making the best use of data.

“If we worked at Target and were capturing point of sales information from a cash register, that is all highly structured [data] and you can compare with other highly structured datasets where almost every interaction follows the same pattern. In a lab, you could have up to 100 different instruments generating different data types, with different standards and ways of measuring things, so we have a much more diverse and challenging data set to deal with. On top of that, the volume of data that we deal with is enormous.”

Varsakelis explained that he has witnessed a change in methods of approaching data collection. “Historically there has been this focus on people looking at data as their own, as opposed to allowing it to be integrated. There is a huge mentality shift towards collecting data for the organization to use, which will enable us to do a much better job.”

He also advised against the lure of “shiny new tools”. He said: “It is so easy to purchase or build a new system, but it is very difficult to optimize and integrate it. Often new tools and solutions are not the answer to our problems, it is really getting in at a governance level and understanding what is going on and what people need to do that will drive us forward.”

Quick links

• Why digital transformation is essential to get value from R&D data
• Three ways AI is speeding up drug discovery
• What is the digital lab?

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