Digitalisation in Life Sciences Ireland

What are the biggest challenges that the biopharma industry faces in adopting digital technologies and AI?

I think it comes to two different aspects. The first thing is system integration. Some organisations would have for example, an MES system, but they are not really capturing all the data points. Maybe the data harvesting part is still done manually, or maybe they have an MES system on the manufacturing side, but they don't have an equivalent in the Lab site. 

The second aspect, and the more critical one, is data skills. When it comes to the use of data, together with statistics, it's very much limited to certain individuals. So, widespread adoption is not really there, and because you have one or two individuals driving data adoption in their own function, they don't see what else is going on, they don't see the impact on the wider business or site. For example, you may have someone in QC who creates great dashboards and but then it is only being used within QC, it is not being used by QA. It is not being used to analyse, for example, the job satisfaction of analysts, so there's no tie in with HR or with Finance. It's very much limited to just their own functions. 

We also see for example, a lot of clients are still Excel based. Excel is being used to capture the data and maybe visualise it but there's no central repository where all the data resides. Issues like data governance are not really explored. How would you update a complex spreadsheet throughout the lifecycle of a molecule over 10 or more years? Are you capturing all the knowledge? So, it's still at early stages when it comes to data adoption. But I think it's all about how you integrate with your existing systems vertically and horizontally across, for instance ERP systems with your lab execution systems, your LIMS systems, and there are just so many different systems and you need to ensure they all talk to each other, and you have a single source of truth.

Considering the needs for digital skills in biopharma, how is NIBRT contributing to workforce development in this area?

What we have done in NIBRT is to try to capture the views of our clients, because when we first started, we were still in the dark in terms of trying to figure out “who wants what”. Although we have different clients with different needs, the main thing that came up during client conversations is the skills piece. A client may have an operator who is really good with their HPLC system, or maybe a mass spec, but how do you capture the data? How do you transition the data from let's say, an instrument to something that you can share and update with your colleagues. I think there's a big question mark around skill sets and the availability of the right talent. 

How important are collaborations between academic institutions, technology providers and biopharma companies in advancing digitalisation?

I think that's a good point to bring up. If you think of what's going on in biopharma at the moment, we have two different legs that actually go together to the same place. The first one we have is cell and gene therapy, and the second one is the digitalisation. CGT is the next wave of innovation, and they're mostly going to come from academic groups and small biopharma. They're not going to be driven by the big pharma. There were 83 different modalities when we at NIBRT last counted, and I don't think any single organisation can capture all these different modalities. So, how do we get all the best science from academic groups and smaller biotech companies and transition them into large scale manufacturing? At the same time, obviously, digitalisation is becoming more and more important. How do you capture all the knowledge, all the process information that you have developed in an academic lab so that you can have a nice package that you can use to get big pharma more interested in your molecule. They can look at it and say, “Okay, I know what you have done, where you are and what you need to do and what the requirements are in terms of scaling up”. There's no way of this happening without having a digital system in place. So I think we are at a nice convergence point if you think about it. You have all the different elements coming together and making sure that yes, you have the digital but at the same time, it's not just digital on its own. It has to be digital with the molecule development, with the clinical trials and everything else. It can’t be seen in isolation.

What emerging technologies you think will have the most impact and biopharma in the next few years?

I think the biggest impact will be the introduction of more and more automation around what I call the “basic things”. I know we all want to talk about AI solving this and that, but on a fundamental level If you look at a typical biopharma operation, there are a lot of small problems that you have to always solve, for example your deviation and events handling. A lot of times, those data points have to be pulled down from somewhere and obviously, it's not something that's available in real time. Let’s say you have an operator or scientist searching for the data and making sure the data is of the right quality. Maybe they have to clean it up. Can you automate all those basic things? Because if those basic things that are automatable can be automated, then it will free up time for more of what I call the next step. So the next step would be, before a deviation happens, can we predict it? Instead of just waiting for a machine to break down, can we do the maintenance now? Maybe some of my more senior staff are leaving, so am I capturing their knowledge in a proper place? Am I still relying on paper records? I think it's really important that we capture as much as possible the basic items, so that the more complex investigations can be done in a more efficient way as we are no longer dealing with the “bread and butter” day-to-day issues.