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In our recent deep dive into the clinical trial processes of top-10 pharmaceutical companies, we discovered something eye-opening: traditional site selection methods and feasibility processes are not only outdated but are also significantly impacting trial success rates.
Before the advent of advanced AI tools like Spectrum™, pharma companies primarily relied on manual processes and a biased US-centric approach for their trial sites. This method, while seemingly logical due to regulatory and operational conveniences, posed severe risks of over-concentration and inefficiency that ultimately leads to delays and other trial challenges.
We might wonder, why do we stick to this model?
But with Spectrum, this game has changed.
A top-10 pharmaceutical Sponsor faced a daunting task: identify the most suitable sites for a critical phase III Obstructive Hypertrophic Cardiomyopathy(oHCM) trial.
With a target of 500 participants spread across 250 sites, a 730-day enrollment window, and a 365-day site activation period, the stakes were high. Traditional methods left them heavily reliant on US sites, which posed significant risks, including over-concentration and extended timelines.
Spectrum completed a comprehensive, data-driven feasibility analysis and leveraged cutting-edge AI to create two distinct scenarios:
Our machine learning model didn’t just skim the surface; it dove deep into both proprietary and public historical data, predicting trial timelines with remarkable precision. This endeavor was not just about crunching numbers but about harnessing the power of Real-World Data (RWD) and machine learning to transform clinical trial operations. Here’s what we found:
The results were both clear, and compelling:
By integrating Spectrum’s AI-driven insights, this study sponsor didn’t just follow the usual path; they blazed a new trail in clinical trial feasibility.