Digital Twins For Clinical Trial Protocols: From Protocol Design To Predictive Study Execution

By Luca Parisi, Director of Data Science; Bianca De Blasi, Lead Data Scientist; Raymond Heatherly, Director of Data Science; Ming-Da Lei, Director of AI Product Management; Aleksandra Petkova, AI Solution Architect; Skye Hodson, VP of Clinical Solutions; Hassan Malik, Chief Data Scientist; and Suzanne Caruso, President

Digital twins are digital representations of real-world entities, such as computer models of drugs/medicines or patients, leveraged for simulations that optimize the design of therapeutics and treatment strategies. In healthcare and life sciences, most discussions have focused on patient or drug twins. Nevertheless, an equally important opportunity exists upstream in study design itself.

A growing generation of AI-enabled digital twin approaches offers a new path. Instead of treating the protocol as a static document, organizations can model it as a dynamic system, whose likely operational consequences can be simulated prior to study execution.

Citeline has been exploring the utility of protocol digital twins that connect eligibility criteria, endpoints, visit schedules, assessments, disease characteristics, study feasibility, country strategy, site requirements, investigator recommendations, and historical trial performance into a predictive and simulation-based decision framework.