Digital Twins in Drug Discovery: A Paradigm Shift Shaping Pharmaceutical Innovation

Abstract

Digital twin technology is a groundbreaking concept of constructing virtual replicas of physical entities and processes. It operates at the juncture of Artificial Intelligence and the Internet of Things, emerging as a significant competence in Model-Based Systems Engineering (MBSE). This transformative technology fosters a profound link between the digital and physical realms by incorporating physical models, sensor updates, multidisciplinary models, and multi-scale and multi-probability simulations in a virtual space. The applications of digital twins extend across diverse domains, including industrial production, product design, healthcare, and drug discovery. Benefits encompass predictive modelling, cost reduction, personalized medicine, and improved quality control. Nevertheless, digital twin technology challenges complexity, validation, quality assurance, and availability. In the pharmaceutical sector, digital twins are poised to revolutionize drug development, accelerate clinical trials, and enhance the safety and efficacy of pharmaceuticals. In networking, they also promise efficient data-driven network management, enabling innovation and rich network applications. However, this evolution has challenges, particularly the stringent regulatory requirements, data privacy concerns, and ethical implications. As digital twin technology matures, it will be pivotal in reshaping the future of healthcare and pharmaceuticals. This dynamic transformation signifies the industry's commitment to innovation, with the promise of delivering enhanced patient outcomes while remaining steadfast in compliance with evolving regulatory standards.