Transforming clinical research and development through the exploitation of patient level data sources

With over 90% of new medical treatments undergoing clinical trials failing to reach the market and the cost of developing each new successful drug now estimated to be around US$3 billion, there is an urgent need for the life science industry to reduce their study failure rates to achieve a better return on investment and bring down the cost of medical interventions. Whilst some failure is down to the novel drugs themselves being unsafe or ineffective, some failure is a result of poor design of the clinical trials used to test the effects of new drug candidates. Poor study design is completely avoidable with effective prospective clinical trial planning.Our aim is to reduce clinical trial failure by improving study design and accelerating the process of bringing new treatments to market for patients. To achieve this, this project will identify patient-level data sources and use them to create a digital library for key therapeutic areas such as cancer, respiratory and rare diseases for use within an intelligent software information ecosystem, extending the capability of our existing clinical study planning tool, KERUS Cloud. KERUS Cloud already optimizes clinical study design by allowing researchers to generate study simulations to understand how different factors can affect a study's outcome, so that the best design is chosen. This software has already been shown to improve a real study's success when designed with this tool by 40%. A library of information that is based on real patient data will improve this process further by allowing data for different disease types to also be fed into the simulations generated by KERUS Cloud, making them the most realistic study simulations yet possible. Following on from this, a later development stage will involve adding a machine learning element to the ecosystem that can compare simulations with real outcomes to identify the key factors that account for the variability seen in real trials. This information will then be used by our KERUS Cloud software in designing the next study, improving its chances of success. However, the project outlined here will identify suitable data sources and generate the algorithms required to prepare and clean the data for use in creation of the digital data libraries to be used by KERUS Cloud to inform study simulations. This approach of harnessing real patient-level data and machine learning to inform trial design will revolutionize the process, powering real trials for success.