8July - 2020TECH OUTLOOKIN MY OPINIONBy James Streeter, Global VP Life Sciences Strategy, Oracle Health Sciences [NYSE:ORCL]It's a logical conclusion that expanding data sources in clinical trials can lead to more therapies, but does this additional data speed up or slow down a clinical trial? In the past, clinical trials have used only structured, clinically-sourced data, which was relatively easy to organize and mine, but clinical trials today are more complex and utilize data from a plethora of sources such as mHealth devices for remote monitoring of trial patients, mobile health applications, biomarker data clinically-sourced data and more. If the end goal is getting more therapies to market, and currently only 20-30 percent of drugs make it to market, the question becomes, can expanding our data sources and improving the aggregation and management of both unstructured and structured data help improve the likelihood of regulatory approval and getting safe and effective drugs into the hands of patients? The answer is "yes" and we believe that the combination of advanced technology and the emerging role of the data scientist hold the key. If we can get to a place where larger and more comprehensive datasets are made available to pharmaceutical companies and their clinical researchers, we can move beyond the questions of what data to collect and how to use it, to more meaningful questions such as, what new theory can I prove regarding the population of this trial, should my study continue based on new insights I've received, or what new patterns can I uncover to lead me to a new hypothesis. The answer to these questions lies in technology--technology with advanced metadata management capabilities that can offer the flexibility and scalability needed to handle all real-world data in the format, size, and frequency required as clinical trials evolve. Today, data from various domains is exploding to give us a more comprehensive picture of clinical studies that can improve our decision making. There are tremendous challenges in aggregating, storing and preparing the data for high speed analysis because it is no longer just structured data. It is both structured and unstructured data coming from a growing number of systems, devices and publications such as EMRs, medical devices and documented research. This influx, while valuable, is making it difficult to capture and analyze at different intervals throughout a study. Take for example a biopharmaceutical company focused on cancer drug development. To improve their study and time to market, study teams want the ability to quickly combine and analyze data collected through the clinical trial along with genomics data, to have insight into whether or not the patients admitted into the trial are the right people based on their genes and likelihood of responding to the treatment. Armed with this information early on in the process, study teams can determine if they are on the right track or if they need to course correct with more precise patients. This correlation between the study and genomics enables biopharmaceuticals to better identify the exact type of patient who has a better chance of responding positively to the drug. It is a big step toward precision medicine. At first glance the notion of combining multiple data types for better outcomes may seem time consuming, daunting and not worth the additional effort as it could slow down the time to market, but in reality, the combining of these invaluable data types and making that information available throughout a study actually increases the probability Can Expanding Data Sources in Pharma lead to Faster Drug Development?James Streeter
< Page 7 | Page 9 >