The Clinical Trial Of The Future

It is administered to a carefully curated patient cohort, selected using demographic and medical data to recruit a population-representative sample, weighted towards those most susceptible to the disease. They were recruited via social media adverts carefully tailored to them, using natural language processing to understand what messages work well with that group.

In this trial, retention is maximized through a decentralized format, with wearable sensors allowing patients to remain in their home environment, giving researchers rich, real-life and continuous data on everything from sleep to gait. Costs are trimmed thanks to a predictive model that accurately forecasts required drug quantities by crunching data from past trials to understand retention curves. Data collected is detailed and granular, measuring real physical and biological changes in patients as they progress through the trial, as well as traditional subjective measures. These produce data giving clear indications of efficacy, variability between populations, and advance warning of rare adverse events. Analysis of interim data allows the sample size to be refined, doses to be altered for greater effectiveness, and alterations made to the allocation ratio to focus on patients most likely to benefit. The trial concludes early based on clear efficacy data, with clear signals on different optimal dosing regimens for different populations. This allows the medicine to reach the market sooner and lowers costs and improves profits for the company.

Thanks to higher quality data and more predictive endpoints, the trial’s success translates directly into real-world effectiveness, saving lives.

How can we deliver the clinical trial of the future?