10 rue Georges Jacquet
38000 Grenoble, France
The concept of digital twins appeared in the early 2000s, initially in engineering and aerospace, to optimize the maintenance of complex industrial equipment. Popularized by NASA, this term refers to a virtual replica of a physical object, allowing for simulation, prediction, and optimization of its behavior. Over time, this technology evolved to extend into healthcare, where it now enables highly accurate modeling of patient profiles and diseases. However, to maximize the potential of digital twins, integrating them with real-world data (RWD) and real-world evidence (RWE) is crucial, as these data enrich the models with practical clinical insights. Additionally, RWD and RWE fill gaps in the fine mechanics of biological systems that are still beyond current in vivo monitoring technologies (such as local and systemic concentrations, association and dissociation constants, and circadian variability).
For biotechs developing therapeutic leads, digital twins combined with RWD and RWE offer revolutionary solutions to optimize clinical trials and maximize the potential of molecules under development. These technologies enable not only a reduction in operational costs but also compress the time to market through a predictive and personalized approach.
Digital twins, by creating dynamic replicas of patient profiles, offer great flexibility in trial design. Biotechs can test multiple treatment scenarios and adjust cohorts with unparalleled precision, allowing for optimized patient selection from the outset. This approach minimizes in-study adjustments, reduces patient requirements, and prevents costly interruptions—a critical advantage for biotechs aiming to quickly demonstrate the efficacy of their clinical-phase leads.
Integrating RWD and RWE into digital twin models enriches trials by providing a concrete, representative view of real-world patients. These data and evidence allow biotechs to better predict the efficacy and safety of treatments in real-life settings, reducing the risk of costly surprises at the end of trials. RWD and RWE also enhance the relevance of studies to regulators, facilitating acceptance of results and expediting clinical validation.
In oncology, for example, digital twins enable the simulation of tumor progression and treatment adjustments based on the unique characteristics of the tumor microenvironment (TME) and immune responses. This modeling capability, enriched by RWD and RWE, allows biotechs to design highly targeted treatment strategies, thus reducing failure rates in advanced clinical phases. Response biomarkers can be identified early, enhancing treatment accuracy and increasing the chances of success.
Digital twins also offer access to predictive simulations, allowing biotechs to virtually test treatment responses and identify potential side effects. By exploring different therapeutic scenarios before the study, and leveraging RWD and RWE, biotechs can proactively adjust their trials, avoiding unforeseen complications that could delay or jeopardize development. These analyses strengthen decision-making while keeping costs and timelines under control.
By integrating digital twins and RWD, biotechs have a powerful tool to accelerate clinical development phases. Reduced patient requirements, improved stratification, and predictive simulations help compress trial timelines and optimize allocated resources. As a result, these innovations shorten the time-to-market while preserving R&D budgets, offering a dual benefit in terms of return on investment.
While promising, integrating digital twins and RWD involves overcoming certain challenges, particularly around data quality and understanding the fine mechanics of biological dynamics. However, biotechs adopting this approach today position themselves as pioneers of an innovative precision medicine, optimizing each step of therapeutic development for safer, more personalized treatments. Anticipating responses and controlling costs make this technology indispensable for companies looking to move quickly in a competitive sector.
10 rue Georges Jacquet
38000 Grenoble, France
