A Digital Twin is a virtual, dynamic replica of a physical entity, process, or system, used for real-time simulation, analysis, and predictive modeling. This concept, borrowed from engineering and aerospace, is rapidly gaining traction in the life sciences. Pharmaceutical companies are creating Digital Twins of manufacturing plants to optimize production flows (Continuous Manufacturing) and predict equipment failures. Crucially, they are also developing in silico (computer-simulated) Digital Twins of patients, organs, or disease pathways.
These patient-specific models integrate genomic data, physiological measurements, and real-world clinical history to simulate how a drug candidate will perform, predict potential side effects, or personalize dosing regimens. By running simulated clinical trials on digital twin populations, researchers can drastically reduce the cost and duration of traditional trials, accelerate the selection of the most promising drug candidates, and reduce failure rates. The growing capability and accuracy of these models make Digital Twins a pivotal technology for improving R&D productivity and enabling the vision of truly personalized medicine.
