Interview with Rod McGlashing, Subject Matter Expert, Data Science at Medrio
Bringing a new drug to market comes with a high price tag: The median cost for a clinical trial is up to $48 million, and the average trial takes longer than 80 months to complete. Failing to meet any criteria in the regulatory process can cause an entire trial to fail. In fact, the U.S. Food and Drug Administration (FDA) approved just 37 novel drugs in 2022–the fewest to pass regulatory scrutiny since 2016.
Whether clinical trials evaluate new drugs, vaccines, gene therapies or medical devices, research teams must get the data right. Adhering to regulatory requirements and ensuring data accuracy is the only way clinical trials can withstand FDA regulatory scrutiny.
“The FDA looks to enforce the rules and regulations to ensure the safety and efficacy of the intellectual property being researched,” explains Rod McGlashing, Data Science Subject Matter Expert at Medrio. “Having that FDA scrutiny, or at least the threat of that scrutiny, being a part of the process helps to push companies to be compliant, and by being compliant, producing better data to establish the best applications of the IP both during the study and upon approval.”
Rigorous regulatory scrutiny isn’t new, but it has intensified. In FY 2021, 65% of the warning letters the FDA issued cited data integrity issues compared to 47% of the warning letters issued in 2019. Data integrity issues can have serious consequences, from denied drug approvals and recalls to facility closures.
Addressing common data integrity problems is essential for withstanding increasing FDA scrutiny. And implementing the appropriate procedures for each clinical trial is just the beginning. Research teams must also overcome other competing pressures, including cost and speed to market. Choosing the right tools and vendors can not only help researchers overcome those obstacles but also ensure their data meets the integrity standards of regulatory bodies.
The Foundational Layer of Data Accuracy
Good data quality starts with a data management plan. Creating a data validation document and clinical monitoring plan incorporating edit checks and source document verification to validate the data helps ensure that the data is correct and doesn’t fall outside stated parameters.
Electronic data capture (EDC) is foundational to the success of a clinical trial. Replacing paper records and storing patient data in EDC software provides real-time access to data, makes it easier to organize, allows more efficient data analysis and improves data quality. Vendors can ensure that EDC is compliant with regulatory requirements.
Research teams could approach electronic data capture (EDC) as a commodity or stand-alone tool and it may be functional. Still, when EDC is one piece of a larger partnership, it creates another layer of data accuracy. “An EDC system allows clinical data collection into a single source where the data can be reviewed, cleaned and analyzed,” McGlashing says.
Selecting systems that include data collection, review, cleaning and monitoring components helps researchers conduct trials based on existing regulations. Additionally, partnering with a vendor who works closely with research teams to follow the parameters laid out in the data validation document, has a greater likelihood of withstanding FDA scrutiny than a commoditized EDC product.
Throughout the clinical trial process, data is reviewed, cleaned and checked for accuracy. In addition to internal checks, data validation documents may also include external checks. The data is typically verified by reports created by SAS programmers or external reports by the EDC system so data managers can review data listings targeted toward the data validation.
Researchers may collect data from multiple sources, including patients, validated instruments, blood pressure monitors, sleep monitors and other health devices included in research studies. Electronic patient-reported outcomes, or ePRO data, can’t usually be cleaned. Still, it must be checked to ensure it falls within the correct parameters and is fit for purpose.
Planning for Risk-Based Approaches
Data management professionals understand the processes involved in following regulations and ensuring data accuracy and increased scrutiny from the FDA likely won’t require any significant changes to these routine processes.
Risk-based approaches and their potential impact on data processing might need special attention. When risk-based approaches are taken, it increases the risk of poor data integrity; care must be taken to ensure data integrity so it can stand up to FDA scrutiny.
The FDA has released draft guidance for sponsors regarding risk-based approaches used to monitor studies. This guidance expands and adds to the previous guidance. Medrio EDC offers monitoring workflows to help address risk-based approaches and ensure the data is collected appropriately through edit checks and further data validation methods.
Along with improved monitoring workflows, taking a risk-based approach means making risk assessments at multiple stages of the trial. From protocol development through statistical analysis, study teams will focus more on safety and efficacy data and other data that are identified as critical.
The factors differentiating Medrio’s EDC are the same factors that make for high-quality data. With the right partner, McGlashing believes clinical trials can withstand any level of FDA scrutiny and offer the bonus of providing accurate, reliable data.
“If you’ve got bad data, the FDA is going to question why, and this could result in IP rejection, warning letters and additional research—and all of these mean additional costs and potential lost revenues downstream,” he says. “If you have clean, quality data, FDA scrutiny doesn’t matter.”
PricewaterhouseCoopers.(2016). Data integrity problems a growing risk to global pharma companies. Retrieved January 4, 2023.
U.S. Food & Drug Administration. (2019). A Risk-Based Approach to Monitoring of Clinical Investigations Questions and Answers. Retrieved January 4, 2023.