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Simulation-Guided Design for Biotechs

Simulation-guided design is quickly becoming a novel feature of modern drug development. Its foundational promise is to harness the power of data to create robust trial strategy. With high-compute power and incredible speed, decision-makers can now create strategy that de-risks clinical trials while offering necessary flexibility when challenges arise, and clarity to align on goals.

While large pharmaceuticals are clearly positioned to make use of such tools, small biotechs also have much to gain from simulation-guided design. New case studies testify to the immense transformative power when simulation-guided design is used wisely by biotech leaders.

Case Study 1: Ways to Optimize for Small Treatment Effect and Small Patient Sample

In the first case study, a small biotech had to find a design that needed fewer than 1,100 patients. Earlier studies led to concerns about a small treatment effect, which added considerable risk to the clinical trial. The biotech had one shot to prove the value of its asset.

Under these parameters, Cytel’s statisticians produced 21 different options for the Chief Medical Officer, each with various risks quantified. All 21 options also presented clear details on time and cost of implementing the design. Within two hours, the CMO was able to find a new design that met various parameters under consideration.

Case Study 2: Ways to Optimize for Innovation & Operations

Sometimes a conventional design might yield strong outcomes but be too expensive to deploy. This happened to a former client whose Phase 3 trial needed only 412 patients and 2.5 years to implement. It was still too expensive without furthering funding.

Sample-size re-estimation designs have historically been used not only to de-risk trials, but also to alter investor risk profiles. A common strategy is a higher return if a trial falls in the promising zone (i.e., higher rewards for higher risks). Since the number and timing of interim looks can affect financial prospects, optimizing such a strategy requires assessing a number of possible interim looks to determine reasonable returns on risk.

So far, a statistician can help optimize interim looks for financial strategy. The additional challenge arises when sponsor teams realize that each interim look timing also affects operations. The number of patients (and therefore sites) necessary for earlier interim looks will be different from later interim looks. Two interim looks will affect trial costs differently from three or four.

Using simulation-guided design platform Solara®, ­the biotech was able to quickly align optimal design with optimized operations.

Case Study 3: Optimizing Smaller Trials

Sometimes we think of long, unwieldy clinical trials that need optimization. Smaller clinical trials can also be optimized in significant ways using simulation guided design. Our client had a one-year trial it was planning to implement, but wondered if it could be shortened. After about fifteen minutes working with a statistician using simulation-guided design, they were able to shave off 15% of the clinical trial duration.

To learn more about Solara, our clinical trial strategy platform for simulation-guided clinical study design and selection, click below:


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