Author: Miguel Pereira

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I was recently told that adaptive trial designs are not something that biotech companies should use. It’s something for ‘big pharma’ only.

In the same conversation, I was told they wanted to be as flexible as possible.

Biotech companies frequently face a lot of pressure to show promising results of their compounds. Their funding and future revenue depend on it. So, I understand that the last thing they want is to think a lot about trial design. They can just use a ‘vanilla’ design that is simple and quick because they need to move fast.

I have designed and run multiple adaptive trials with biotech and small pharma companies and I don’t do it because this is are cool and trendy. We have made their trials more flexible, with less patients, we have killed unpromising drugs early and we have saved effective drugs from being dropped.

Here’s why smaller companies (and smaller budgets) benefit from adaptive trials:

Flexibility

Adaptive designs offer the ability to modify certain elements of the design in response to emerging data. They also have flexibility in sample size and patient number allocation. What’s possible:

• Skip dose levels for faster dose escalation in dose-finding trials
• Test intermediate (and originally unplanned) dose levels
• Drop and add arms
• Use information from monotherapy to inform combination regimes
• Perform a seamless transition between phases → instead of doing 2 or 3 trials, you can do only 1 or 2 trials until drug approval.

Take-away: In a rapidly evolving pharma landscape, flexibility allows us to move fast and adapt → This is precisely what the classical designs don’t offer.

Efficiency

Efficiency comes hand-in-hand with flexibility. If you have a better ability to adapt, you are also more efficient. Adaptive trials provide efficiency because they:

• Reduce time and cost by identifying futility early or using smaller sample sizes
• Enable faster go/no-go decisions
• Use resources more wisely → especially critical for startups with limited capital

Take-away: the clinical development cycle becomes shorter and reduces time-to-market.

Old methods might not work on new drugs

The traditional "one-size-fits-all" clinical trial model was built around conventional small-molecule drugs. Many newer therapies— immunotherapy, gene therapy, cell therapy, precision medicines—have different characteristics with different dose-response and dose-toxicity relationships that don’t fit the classical model. Examples:

• In immunotherapy, you might want find a dose regimen to take us to a target dose instead of a single dose
• You might want to add a biomarker-finding study to our trial and stratify patients accordingly → this stratification can happen even after you start enrolling the patients
• Drugs might have delayed treatment effects that need to be accounted for in the trial design → using a classical design means the trial will take a lot more time

Better-decisions

By leveraging real-time data during the trial, adaptive designs enable smarter, data-driven decisions. Biotech companies can:

• Identify early signs of efficacy or safety issues → For example, in a bayesian unblinded design, you can check efficacy at any point and
• Reallocate resources to the most promising treatment arms
• Adjust enrolment criteria or sample size based on emerging data

Conclusion

It might sound cumbersome to think about adaptive trials in a biotech company. After all, ‘you have so many other problems to solve, that you should just go with the simple stuff’. But… if your platform is truly innovative, why would you think the old designs are the most suitable for your drugs?

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#biotech #pharma #clinicaltrials #biostatistics