How Data and Analytics Can Improve Clinical Trial Feasibility

Venkat Sethuraman, Sharma R D and Jessica Rine


Today, many clinical trials don’t meet their deadlines. While many life sciences companies are leveraging data to help support their trial feasibility decisions, few have made it a priority to consistently implement and automate the use of data in those decisions—a potentially costly oversight. In this article, ZS experts present seven building blocks that will help life sciences companies make evidence-based, end-to-end feasibility decisions to improve their clinical trial efficiency.

You’ll learn:

  • How epidemiological data can help you find eligible trial patients
  • Why mapping the KOL landscape is essential to effective site selection
  • How to choose investigators that are participating in complementary rather than competitive trials

About the Experts

Venkat Sethuraman is the global clinical lead within ZS’s R&D excellence practice. He has nearly 20 years of experience in R&D drug development life cycle with deep expertise in biostatistics, clinical trial design strategy, clinical trial optimization, and regulatory approvals. Venkat has a Ph.D. from Temple University and an MBA from Wharton Business School.

Sharma R D is a manager within ZS’s global R&D excellence practice. Sharma has more than 10 years of experience in life sciences, with a special focus in clinical trial design and operations, pre-clinical toxicology, and patient enrollment and engagement. Sharma holds Global MBA from SP Jain School of Global Management, an executive master’s in information management from Stanford University and a bachelor’s in electronics and communication engineering.

Jessica Rine is a consultant within ZS’s R&D excellence practice. She has nearly 15 years of experience in R&D, managing clinical research programs across multiple therapeutic areas. Jessica has a bachelor’s in business administration from Centenary University.