How to Make the Most of Your Clinical Trial Data—All of it

Banks can detect fraud the instant it happens, saving them millions annually. Retail operations seem to recommend products as soon as we think of them. And manufacturers can perform maintenance on machines before they break down, preventing lost production time.

Meanwhile, pharmaceutical and biotech companies spend hundreds of millions of dollars to develop one drug, yet only a small fraction gain regulatory approval. To improve efficiency and produce a higher ratio of successful clinical trials, pharma and biopharma must follow the lead of banking and retail and use something they have in abundance—data.

The average clinical trial generates up to three million data points, and counting.1 CROs and sponsors have access to that data throughout the course of a study, yet they wait until submission time to put that data to use. Why wait?

By taking a data science approach to clinical trials—from patient recruitment to post-regulatory approval—drug developers can up their odds of hitting timelines, staying on budget, and improving success rates.

Why take a data science approach?

Reviewing clinical and operational data throughout a study warms CROs and sponsors of problems at a time when they can more easily correct them. Addressing issues early keeps a study moving forward. Left unchecked, minor issues can snowball into big, study-derailing messes. An early-and-often approach yields the following benefits:

  • Improved site monitoring. Is one site rating patients consistently higher or lower than others on a crucial scale? Is one site reporting significantly more (or less) adverse events than others? Is there a concerning pattern developing? Early monitoring can identify anomalies and highlight issues that on-site visits cannot.
  • Comprehensive safety and efficacy information. With data analytics, sponsors can get an early indication of a drug's safety and efficacy. Even in a blinded study, sponsors can look at the pooled variance and averages across patients or sites and determine if a drug is performing as expected.
  • Fraud detection. Take a look at the data. Do you notice sets of patients that are too similar or dissimilar? That's a sign of fabricated participant data. An early data review can also weed out "professional patients" who enroll in one study at multiple sites to receive multiple payments. Odd as it sounds, it happens.
  • Efficient regulatory submission. Data integrity is critical for gaining regulatory approval. Ongoing data monitoring shows the FDA and/or EMA you've done your homework to protect data integrity. 

How to use data after study completion

Data gathered during a clinical trial has value long after a study completes (or fails to complete). Data reveals where a study succeeded and where it went off track. It can also inform future research in the following ways:

  • Correct efficacy and safety issues. Identify and correct any efficacy and safety issues well before regulatory submission. Sponsors can also integrate safety and efficacy data from multiple studies into a global database to draw conclusions that wouldn't be possible otherwise. With a big enough pool, you can discern patterns in subpopulations or in people with certain genetic mutations.
  • Improve patient recruitment. Powerful analytics tools can mine millions of electronic medical records (EMRs), claims data and other resources to hone in on specific patient groups faster than other methods. Sponsors can then find patients that meet their inclusion and exclusion criteria more easily, which speeds up the timeline and ups the odds of success.
  • Gain insight for future studies. Learn from the past to improve the future. The data acquired by drug developers through the years can inform future research. Use technology to mine through peer-reviewed medical journals, reports of side effects, and past-study data to research a new drug that targets a rare disease or to expand an indication for an existing drug. Leverage real world data for new possibilities.

The data is there. Why not use it? To find out how to put your data to work—from beginning to end—register for Biorasi and Medrio's upcoming webinar.

Event Details

Title: Making the Most of Trial Data: An Approach to Leveraging Ongoing Study Data for Actionable Insights

Date: Thursday, June 20, 2019

Time: 2:00 PM EST

Read the full White Paper on Data Insights: Making the Most of Trial Data

1. Institute of Medicine (US) Roundtable on Research and Development of Drugs, Biologics, and Medical Devices; Davis JR, Nolan VP, Woodcock J, et al., editors. Assuring Data Quality and Validity in Clinical Trials for Regulatory Decision Making: Workshop Report. Washington (DC): National Academies Press (US); 1999. Final Comments. Available from: NBK224576
The editorial staff had no role in this post's creation.