How precision oncology is helping to address the dichotomy of patient scarcity versus an abundance of disparate data
Precision oncology refers to the diverse strategies in cancer medicine ranging from the use of targeted therapies to using data from next-generation sequencing to select therapy for a person independent of cancer type. Christian Hebenstreit, general manager & senior vice president EMEA, Medidata Solutions discusses how it is helping to address the dichotomy of patient scarcity versus an abundance of disparate data.
As precision oncology trials increase and become more complex, drug developers are facing the paradox of patient scarcity in the presence of an abundance of disparate data. Today, data includes real-world patient information from electronic health records, lab tests, and mobile sensors and apps, as well as new basic biochemical and clinical research. Incorporating such information into clinical response models has the potential to increase their predictive power far beyond what is possible with today’s biomarkers, generating insights and efficiencies that were impossible even a few years ago.
However, successfully incorporating masses of disparate data into clinical development programmes also presents daunting technical challenges. To solve this paradox, developers are adopting clinical development technologies incorporating advanced analytics thereby allowing for the efficient collection, aggregation, and integration of the data.
The field of oncology has been leading the precision medicine revolution, and precision oncology treatments have already provided several phenomenal treatments that have improved outcomes for many patients. Clinical development technologies for example are increasingly being used by drug sponsors to more efficiently recruit patients and seamlessly deal with the increasing number and volume of disparate data streams. These technologies provide a powerful partner for meeting the growing data demands of complex precision oncology trials.
The most technically advanced healthcare providers know that in today’s world, digital innovation and platform hyper connectivity are necessary. Technological innovations in precision oncology trials will also increasingly enable drug development by non-traditional sponsors. Big data approaches for example that integrate disparate data sources will be the best positioned to leverage information that facilitates the development of novel targeted therapies and help with drug repurposing and identification of novel drug combinations. Likewise, artificial intelligence (AI) has the biggest potential to support the analysing of the vast amounts of data patients and healthcare institutions record and will lead clinicians and healthcare workers to be able to spend more time with their patients, improving the human touch. Overall, there will always be more data than we’ll ever be able to consume. That’s where innovations like machine learning can turn the processing, the understanding, the deriving of meaning from that data over to machines.
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