Outsourcing Partners Providing End-to-End Service

As part of its coverage for CPHI Frankfurt 2025, Pharmaceutical Technology® spoke with Christian Dowdeswell, managing director, Arcinova, a Quotient Sciences company, about what makes a contract development and manufacturing organization (CDMO) that offers end-to-end service beneficial to sponsor companies and how digital technologies are changing the industry.

PharmTech: How is early development of drug products enhanced by a CDMO that offers end-to-end services?

Dowdeswell (Arcinova): I think to some extent, this depends a little bit on who you're doing the work for. So, if I was to take a small biopharma company, for example—and they're important because they own the majority of clinical molecules in early clinical development—typically, that sort of company has limited resources to conduct that sort of work themselves. So, they outsource heavily. They perhaps have a limited number of people who are really going to be in a position to manage their service providers. So, that really brings you to the first benefit, but actually having a single partner reduces the complexity you have in managing multiple partners; you have a single point of contact across the continuum.

And, actually, there can be some really more tangible benefits than that. So where you have a single partner developing both your drug substance and drug product, and in fact, in some of the areas around it, what you can find is that they're able to talk to each other directly without you having to get in the way of communication because they're part of the same company, there's no confidentiality issues.

It also means that maybe there's some work that can be overlapped. So, you can start work on a drug product before you've finished all the work on the drug substance, which might not be quite so easy when you have separate parties.

It may be an example to bring that to life. One of the things that we find here is that we can have great communication between the team that's working on the API around the material form of the drug substance, with the drug product team who's doing the work to translate that into a viable drug product. So [there’s] that interplay between how do we optimize the drug substance [and] how does that feed into what we're doing with drug product that can actually bring you to stronger technical outcomes?

I think in our particular case as well, because we're part of Quotient, we're able to go further downstream, into clinical work. The main benefits of that continuity of work through a single provider brings those key benefits, reduction in complexity, especially those smaller companies, reduction in time, which can be quite meaningful, and the reduction in the total cost of ownership.

What role do artificial intelligence (AI) and digital technologies play in accelerating drug discovery and improving manufacturing efficiency?

One of the clearest examples of how digitalization and AI are changing drug discoveries is the use of AI to help design new molecules that target a specific protein site. Now that is quite effective in tailoring a drug to the activity or molecule’s activity you hope it will have, but it has an unexpected consequence in that the number of synthetic steps is increased quite dramatically with that approach. I have some data that shows around the year 2000 there was a paper published by Pfizer and AZ [AstraZeneca] that showed the average number of synthetic steps to get to an API was eight. Round about 2020, you do the same analysis, and you see that number of steps has almost doubled—so, it's like 14 or 15.

And earlier this year, I found some more data, which suggested that the new molecules coming out of novel approaches to drug discovery have often in excess of 30 steps of chemistry to get to the target. So that is a really dramatic step change—that over the past 25 years, you've gone from eight to 30 steps, and that's quite an unexpected consequence. CDMOs are going to have to find a way to deal with that. Now, the time that we have available for CMC [chemistry, manufacturing, and controls] is not going up. In fact, if anything. [it] is coming down. So, you have more complexity, more work to do on the API synthesis. We can leave aside the problems that you have in drug delivery beyond that. But just for the API synthesis, you have to find a way to get all of the work done on CMC and building the synthesis towards providing the small-molecule API in the same time. So, you have to find ways to do that much more efficiently, much more quickly.

One of the things that we've been looking at is around the use of digital twins. We're not alone in doing that, but one of the reasons we've done that work is because it gives us a really great pathway to look at how do we move from the small-scale lab experiment into the initial scale up, which might be to a small kilo lab, 5, 10, 20 liters, and then take that further into the next scale, which might be 100-, 150-, 200-liter scale. And even then, how do we provide data that enable a customer or a future service provider to take that further? So, we've done that work to enable us to model how the reactor is going to behave, how the kinetics of the reaction are going to behave, to enable and facilitate that scale up. And we actually did that. It was part of a sustainability push, originally, that we wanted to be able to do this in a quicker fashion, that we're using less resources to get to the same endpoint. But when you tie that into the complexity part, the amount of work you've got to do, that starts to become essential.

The difficulty in the work that we did was to look at the data architecture. The way that you receive the data from all of the different sensors, because it's not standardized today, requires a lot of data manipulation/adjustment to get it into a format where you can do the modeling. Here's where we're planning on bringing AI in, because AI can help do that data manipulation work in a much smarter, quicker [way], frankly. Quicker is the key here; [AI] enables us to get that modeling done much more quickly, which starts to build this then from a single product or single reaction solution into something that's much more of a platform that you can use across the board.

Some of the other advantages you have is how you can also look at operating in batch or continuous and attend on a particular stage of development. You might want to change that [to] be more appropriate [at] a certain stage of development, to do it continuous and later fit to batch, because that fits a large-scale CD, most manufacturing assets.

I think, as an industry, that's maybe a microcosm of the challenges we face. There's an inconsistency around the state of digitization, and not just company-to-company, but asset-to-asset within companies, because different assets have different ages and different ability to support digitalization.

Innovation, the lack of standardized data architecture, is something that we've seen, really in a microcosm, but [also on] a macro level [and] is something everybody faces. And I think the third one, I'm pretty sure, is a [challenge] across the industry as well, [which] is the availability of expertise. Is there a suitably qualified workforce? Do we have access to the right people that can really support and drive digital initiatives, and, in fact, even who fully understand the true potential of what could be done? I think that's one of the key challenges that, as an industry, we're still coming to grips with.

How is the sector reimagining supply chains to protect against rising tariffs, geopolitical shocks, and global disruptions?

Quite frankly, at the moment, nobody knows. There's a huge amount of change around tariffs and what's happening with some of the other geopolitical situations. And certainly, I don't look to base strategy on social media posts without really understanding how that is going to be implemented. But I think overall, this is part of an ongoing regionalization trend that has really been driving for quite some time, certainly over the past 10 years, this has been happening certainly for innovative pharma. So, companies working in the innovative space, their focus has moved away slightly from just being let's get things done at the lowest cost, which meant moving to Asia, to a realization that their success is much more dependent upon getting their drug advancing through the clinic and hopefully to market in a timely fashion.

Not being first to market or being delayed is a real hit on their revenue. It's a real hit on the business case for developing the drug in the first place. So we're seeing them move away from a cost-first basis to look at how do they best ensure their drug gets through that. The way they do that is by making sure that their CDMO providers are underpinned by strong science, are able to provide good data, are able to execute a plan, have no compromise on quality, are able to really communicate within a very optimal fashion, depending on what that means for an individual organization. Overall, finding that they are able to get more security of supply and more security of delivery.

You can really see that in the past 10 years, the CDMO industry has had quite a revival, and a number of companies that are public demonstrate that really well across Europe and United States. So, I see the trends, the questions around tariffs, BIOSECURE, re-emerged again. The geopolitical factors around the over-dependence on China, I just see that simply accelerating that trend. Because I think our industry has been moving that way anyway. This just maybe makes people much more conscious of it … What it means for us is we have to be consistent or continually re-evaluate what we offer as services to understand [if what we are offering] is still addressing customer problems. What aren't we doing that we should be doing? Are we relevant? Are we going to remain relevant in the future? So really having that continuous dialog with our customers and future customers about what do we need to be doing to really be giving you what you need.