Abstract

Digital Twins (DTs) are revolutionizing pharmaceutical and biopharmaceutical industries by creating dynamic virtual models of physical systems, processes, and products. This review explores their evolving applications across the drug development continuum, from early-stage discovery to continuous manufacturing. By enabling real-time data synchronization and predictive modeling, DTs optimize process efficiency, minimize production costs, and ensure consistent product quality. Their integration with artificial intelligence (AI) and machine learning (ML) unlocks advanced capabilities for anomaly detection, preventive maintenance, and adaptive process control. However, challenges persist in achieving seamless data interoperability, ensuring model fidelity across diverse biological systems, and navigating evolving regulatory frameworks. Emerging technologies such as blockchain for secure data sharing, nanotechnology for precision drug delivery, and “dark factory” automation systems present synergistic opportunities to enhance DT scalability. Furthermore, DTs hold transformative potential for personalized medicine by simulating patient-specific responses to therapies. This analysis underscores the dual imperative of addressing technical and regulatory barriers while leveraging cross-disciplinary innovations to fully realize DTs’ capabilities. As the industry transitions toward data-driven paradigms, DTs are poised to become central enablers of agile, patient-centric pharmaceutical manufacturing, ultimately bridging the gap between computational innovation and tangible improvements in therapeutic outcomes.