As the biotech sector emerges from a challenging funding cycle, its effects continue to shape how companies design trials, generate data and make go/no‑go decisions.
Sponsors are seeking earlier and more comprehensive clinical trial data to attract investment and sustain the development of novel therapies.
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In the wake of the funding pullback, biotechs and contract research organisations (CROs) have had to adapt rapidly. Dr Deborah Phippard, CSO at Precision for Medicine, explains how more selective, data‑driven capital is reshaping translational strategies, trial design, and the global landscape of drug innovation.
This interview has been edited for length and clarity.
Abigail Beaney (AB): Based on your experience, can you talk about the difficult funding period biotechs have faced and what impact you’ve seen working at a CRO?
Deborah Phippard (DP): The post‑Covid-19 period was very challenging for many CROs. After the initial surge of Covid-19‑related investment, there was a clear pullback in funding from around 2022 to 2024, and many organisations had to cut back or restructure. I feel that Precision Medicine was relatively protected; our leadership did a very good job of steering the business through that period.
During Covid-19, there was a huge amount of money chasing science. In my opinion, some ideas and drugs that weren’t particularly strong still got funded because the world was in a state of understandable panic and everyone wanted to back potential solutions. When that phase ended, the money didn’t vanish, but it became far more risk‑averse. Investors wanted more robust data before they would commit capital.
Precision has always led with science and data, and our core clients are well‑funded small and mid‑sized biotechs with strong ideas that need expert execution. One of the biggest shifts I’ve seen is that development has become overtly more data‑driven. Translational medicine used to have a more exploratory flavour – large omics programmes, lots of data generation, and interpretation later. That work could be quite research‑oriented, sometimes more for understanding biology or publishing than for making pivotal decisions.
Now sponsors want more tightly focused, hypothesis‑driven translational plans. They want clear answers to specific questions: is the drug engaging its target in humans, and are we seeing early signs of the intended downstream effect? Crucially, they want those answers much earlier in the study. Instead of clustering assays late in dose expansion or later phases, we’re increasingly asked to deliver data cohort by cohort, and sometimes almost patient by patient. That changes how you run a lab. Many translational assays are bespoke to a given drug, so you don’t have the economies of scale that a central lab has. Running “ones and twos” at high frequency increases costs and workload, and scientifically, very small early datasets are limited in what they can truly tell you.
AB: Sponsors are increasingly under pressure to show early data to investors to prove efficacy and validity. What are the risks of reading out data too early? Can it either give investors too much confidence or make an asset look weaker than it really is?
DP: I’m less worried about early data giving investors too much confidence and more concerned about the opposite: that weak or negative early data can be over‑interpreted and kill good drugs too soon. It’s quite difficult to get a strong, clean positive signal very early in a first‑in‑human study, especially in a standard dose‑escalation design. Phase I is still fundamentally about safety. Expectations have crept up over the last decade, and now we also want pharmacodynamic signals or even hints of efficacy, but the early doses are often not fully efficacious.
The real danger comes when the translational assays aren’t well aligned with the drug’s mechanism or when they’re too indirectly related to the biology you care about. As you move one or two steps away from the direct interaction between the drug and its target, many other pathways and factors can influence your readout. That can lead to negative or inconclusive early data that don’t truly reflect the drug’s potential.
If that kind of early data is then used to drive “quick kill” decisions, you can easily terminate a drug that might have worked if the translational strategy had been better designed. There is a strong appetite now for quick kills to avoid wasting money, and I understand the rationale. But if you haven’t collected and preserved the right samples at the right time points, you lose the ability to go back and ask, for example, whether there was a responder subgroup or a biomarker‑defined population that benefited.
In an ideal world, a well‑designed translational programme gives you a second chance: even if the primary endpoint is missed, you can use stored samples to understand why. Once a study is labelled a failure, it’s very hard to get anyone to fund that deeper analysis. That’s why we now see companies returning to targets that were abandoned 10 or more years ago. With AI and better chemistry, they’re designing more specific drugs and finding that the target itself was never the problem; the original drug or development strategy was.
AB: We’ve seen more adaptive trial designs, especially in rare diseases and situations where traditional RCTs are difficult. What are your thoughts on using adaptive designs?
DP: For small and mid‑sized biotechs, I think adaptive designs are often exactly the right approach. These companies may have a single lead asset and limited runway. They need meaningful signals quickly – whether that’s the right indication, dose, biomarker subgroup, or combination partner – to justify further investment and keep the company alive. Adaptive designs can be an efficient way to search that space.
Where the picture changes is later in the life cycle. Once an asset shows promise and is acquired or partnered with big pharma, larger organisations are very good at life cycle management. They can systematically explore new indications, dosing strategies and combinations. The checkpoint inhibitor story illustrates this well: they started with strong signals in cancers like melanoma and are now used across many tumour types, often in very sophisticated regimens. So, I see adaptive designs as particularly valuable in the hands of smaller companies trying to find a viable path quickly, with big pharma then building out the broader development programme.
AB: With reduced funding and fewer biotechs pushing assets forward, there’s concern about a lull in innovative medicines in later stages in the coming years. Is that something you’re worried about, or do you think the money has gone into the right places to sustain drug development?
DP: I’m not especially worried about an overall drop in the number of new drugs moving into clinical trials. What I see instead is a shift in where those drugs come from. Increasingly, many assets are originating in China and then entering clinical trials in the US, because the US remains a very large and attractive market. Europe is also an important source and destination.
If you walk around a major conference like ASCO, you can see the change very clearly: there’s a growing presence of ex‑US companies presenting impressive data. So, I don’t think the innovation engine is slowing. I think the geographic balance of that innovation is shifting, with a greater contribution from China and Europe relative to purely US‑originated programmes.
AB: Coming back to funding challenges: how are you seeing sponsors adapt, and have you noticed an uptick over the past six or seven months as the sector has begun to recover?
DP: I do think we’re seeing a real recovery. One very practical indicator is the number of projects that cross my desk, which has increased. That matches what we’re hearing more broadly about funding coming back into the sector.
In terms of behaviour, sponsors are more targeted and disciplined. There’s much less tolerance for add‑on studies that are primarily of academic interest or aimed at publications. The focus is on what’s essential to demonstrate that the drug is doing what it’s supposed to do and to support regulatory and investor decisions. So, programmes are narrower but more purpose‑built.
I also see a clear push for faster data. Sponsors want smaller, decision‑focused datasets with shorter turnaround times. We’ve had to retool our labs to be able to deliver that, which isn’t trivial when you’re dealing with complex, bespoke translational assays. You don’t get the throughput efficiencies of central lab work, but the market expectation is increasingly “fewer questions, answered faster.”
