For all of the innovation seen in drug discovery, translating promising molecules into clinical candidates remains one of the most complex stages of pharmaceutical development. Despite advances in formulation science, modeling, and manufacturing, the path from early development to clinical testing often remains slower and more fragmented than many developers would like.
John McDermott has spent more than two decades navigating that transition. As Vice President of Scientific Consulting at Quotient Sciences, he leads a team responsible for designing development programs that help sponsors move molecules more efficiently from early formulation work into clinical testing.
In this interview, McDermott discusses the practical bottlenecks that continue to slow development programs, how integrated approaches used by Quotient Sciences aim to shorten timelines, and what sponsors should consider when navigating early-stage development decisions.
What do you see as the biggest bottlenecks in modern drug development today?
Over my career in pharma, I’ve had the opportunity to deliver drugs to patients in almost every way you can imagine – and a few ways that ultimately didn’t work.
The industry is developing a wealth of technology in formulation science, but we’re finding that the real challenge isn’t the availability of technology but how it is applied to get medicines through development, quickly and efficiently.
Additionally, there's a lot of discourse around the term “integration”. The reality is, moving a drug from formulation into clinical testing – and ultimately into patients – is a traditionally slow and poorly integrated process. At nearly every transition between key milestones, there is significant “white space” where progress stalls.
Quotient Sciences’ platform, Translational Pharmaceutics, addresses this problem. It allows us to move faster from formulation to dosing, rather than waiting months to manufacture a capsule and test it in patients. We are looking to continue to build on that principle: accelerating the path from bench to manufacturing unit and into early clinical studies, then quickly into patient trials to generate the efficacy data that ultimately determines whether a drug has real potential.
How can developers generate meaningful efficacy signals earlier in the development process?
Phase II is still regarded as the “development graveyard,” even 20 years after that hypothesis was first postulated. If we want to change those outcomes, we need to rethink earlier stages of development.
Traditionally, the process has been sequential: here’s your drug, here’s your first-in-human dataset – now pause and move on to generating patient data. What we’re trying to do instead is compress that timeline so developers can reach a point where they already have both the first-in-human and early patient datasets. Positive signals at that stage can help unlock the funding and investment needed to advance the molecule further. Ultimately, that acceleration benefits patients by bringing promising therapies forward more quickly.
One of the approaches we have taken is to apply Translational Pharmaceutics to accelerate entry into first-in-human studies. We’ve recently also partnered with Biorasi, a US-based Contract Research Organization (CRO), to broaden access to patient populations, enabling efficacy signals to be explored earlier within first-in-human trials conducted under standard protocols. Under this model, our patient recruitment strategy expands beyond direct advertising for common conditions, to reach more specialized and rare disease populations through targeted patient referrals for dosing and study participation.
What’s the conversation if it’s a negative signal?
Naturally, nobody likes negative data, but the reality is that some molecules simply don’t work. In those cases, the decision is straightforward: stop investing and move on.
Sometimes, a molecule fails for the original indication but reveals signals that suggest potential elsewhere. If the study is designed to capture those insights, developers can redirect the program into a different therapeutic area. That kind of repurposing allows companies to extract value from an asset that might otherwise be abandoned, which is particularly important in today’s capital-constrained environment.
What should companies watch for when evaluating CDMO timelines and proposals?
An obvious answer is when the conditions seem simply too good to be true. Naturally, sponsors desire aggressive timelines for complex molecules, but there are some development steps that cannot be compressed.
In one case, a sponsor asked us to submit a proposal that included generating a three-month stability dataset before selecting a formulation for clinical development. A competing CDMO presented a timeline of three months to complete the entire program, whereas our proposal estimated five months. The difference was that we had accounted for the full scope of work – including the stability studies – but from the sponsor’s perspective, the comparison initially came down to the headline: three months with them, five months with us.
With this in mind, we often advise sponsors to ensure they have comprehensively dissected and understood the company’s proposal when offered a timeline that seems overly optimistic.
You also need to ask additional questions – such as those around lead time. I’ve had numerous customers come to us to rescue programs where they’ve been working with CDMOs who presented a really aggressive timeline, but that CDMO is booked up for a year. And if you don’t ask that question, you’re just costing yourself time, and then you need to be able to pull it back.
Looking ahead, which technologies do you think will have the biggest impact on how drugs are developed?
One of the themes that comes up constantly at the moment is AI, and I see it as a genuinely exciting development for our industry. What has become clear is that AI is most powerful when it is applied to real scientific problems in a practical way. Over the last few years, I’ve seen first-hand how AI can help identify promising targets for orphan diseases and support better decision-making much earlier in development.
I’m also seeing companies use AI to identify new molecules more intelligently. Rather than relying solely on broad high-throughput screening, AI can help focus attention on stronger candidates earlier, making discovery more targeted, efficient and informed from the outset.
An area that is especially important for us is formulation development. We are actively developing AI approaches that can help guide experimentation to address complex formulation development challenges, with machine learning systems that support faster, better-informed development decisions. We see strong potential for these tools to complement scientific expertise and accelerate progress in areas that have traditionally depended on time-intensive trial and error.
More broadly, we see AI becoming an integral part of in silico drug development. Our modelling and simulation team already works extensively with PBPK and physiologically based biopharmaceutics modelling approaches, and we are integrating AI with those services to generate even richer insights into how molecules and formulations are likely to behave long before extensive clinical testing begins.
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