GlaxoSmithKline Tackling Data Challenges to Streamline Drug Development

May 20, 2019
Adrian Cottrell, PhD

Applied Clinical Trials

At some point in every organization’s life, they come to realize that their data environment isn’t set up for success. Adrian Cottrell, PhD, VP of clinical, medical, and regulatory tech for GlaxoSmithKline writes about when his company came to that very conclusion in 2017.

Working closely with Oracle Health Sciences, Accenture Life Sciences and other pharmaceutical companies to tackle the data management challenges in drug development and regulatory reporting

At some point in every organization’s life, they come to realize that their data environment isn’t set up for success. In the case of pharma, breakthrough therapies don’t make it to market without accurate, quality clinical trial data. As data becomes more diverse, coming from many new sources, and volumes continue to grow exponentially, clinical R&D and regulatory reporting are becoming more complicated and time consuming; yet, the mission for pharma companies is to bring more therapies to market as fast as possible.

For GlaxoSmithKline, that realization came in 2017 when we were tasked to find a technical solution to solve clinical R&D and regulatory reporting data challenges.

Data analytics has become an integral and important part of reducing clinical trial cycle time to bring drugs from bench-to-bedside faster, but to do this requires aggregated, clean datasets from various silo-ed systems to be made available to statisticians and regulatory teams in a quick and efficient manner.

For GSK, this wasn’t a nice to have, it was an imperative.

The imperative stemmed from new regulations mandating that clinical studies initiated on or after December 2016 must have standardized data formats – specifically, CDISC data in STDM format for safety and efficacy reporting.

The challenges we faced in our clinical R&D and regulatory departments really came down to speed, quality and process. For us to be successful, we needed to reduce the time it took to aggregate and clean the clinical trial data required for analysis by our statisticians. The only way to do this was to automate these processes to enable real-time access to data.

The traditional process involved many discrete activities to collect, aggregate, clean and format data ready for analysis. This was time consuming and carried inherent risk. So, my colleague, Leonie Christianson, and I collaborated with Oracle Health Sciences, Accenture Life Sciences and others from the pharmaceutical industry, including Merck, Pfizer and Eli Lilly, to define common clinical data management challenges and solve them.

It was important for us to have new levels of transparency with others in the industry if we were to find universal ways of tackling the data management challenge shared across the industry.

“Simplifying the processes associated with acquisition and management of clinical patient data could shave cycle times and speed insights gained from collected data. More importantly, it creates agility in a shifting landscape with newly emerging data sources,” said Mike Stapleton, Managing Director, Global Life Sciences R&D at Accenture.

GSK’s work with Accenture Life Sciences Cloud Coalition to implement Oracle Health Sciences Data Management Workbench (DMW) addressed the challenges of speed, quality and process. Accenture and Oracle Health Sciences worked with GSK to automate the creation of audit trailed datasets from clinical sources that could be used in real-time by clinical R&D for decision making and for regulatory reporting. Today, GSK can aggregate data from 13 internal and external sources including lab data and data from the electronic data capture (EDC) system in eCRF format.

During a clinical trial, GSK can have between six and ten interim analysis points triggered by certain events such as safety or protocol changes, and the data needs to be trustworthy and available in real-time when these analysis points happen. In the past, it would take weeks to get the data converted to CDISC format for this analysis but using DMW, GSK now has the capability to deliver STDM data within minutes of receiving data from any of the data sources.

This new approach to clinical data management unifies previously silo-ed, diverse datasets, eliminates risks associated with duplicate data, inconsistent data and manual data entry, while also rapidly creating STDM data ready for reporting. The new capability delivered through DMW enables GSK to address the challenges of speed, quality and process.

Ultimately, we’re all here to bring new therapies to market to help patients to do more, feel better and to live longer. Tackling the clinical data management challenge helps us do this.

Adrian Cottrell, PhD, is the vice president of clinical, medical and regulatory tech, for GlaxoSmithKline.