Artificial intelligence-driven digital twins in Pharma 4.0: transforming smart manufacturing, predictive quality assurance, and personalized drug delivery.

PURPOSE: The rapid advancement of Pharma 4.0 has accelerated the integration of artificial intelligence (AI), digital twins, and data-driven technologies into pharmaceutical research, manufacturing, and healthcare. This review aims to critically evaluate the role of AI-driven digital twins across the pharmaceutical lifecycle, with emphasis on smart manufacturing, predictive quality assurance, continuous manufacturing, regulatory validation, personalized drug delivery, and precision medicine. The review addresses how AI-enabled digital twins can overcome limitations of conventional pharmaceutical systems and support predictive, adaptive, and patient-centric approaches. METHODS: A comprehensive literature-based review was conducted by analyzing recent scientific publications, regulatory perspectives, and technological developments related to digital twins, AI, Process Analytical Technology (PAT), Quality by Design (QbD), Pharma 4.0, and emerging Pharma 5.0 concepts. Relevant evidence from pharmaceutical manufacturing, bioprocessing, healthcare applications, and supporting industrial domains was critically evaluated. RESULTS: AI-driven digital twins enable real-time process monitoring, prediction of critical quality attributes, optimization of manufacturing operations, predictive maintenance, and support for real-time release testing. Integration with advanced technologies, including nanotechnology, biosensors, wearable devices, and computational modeling, provides new opportunities for personalized drug delivery and precision medicine. However, challenges associated with data integrity, model validation, interoperability, cybersecurity, regulatory acceptance, and limited pharmaceutical datasets remain significant barriers. CONCLUSION: AI-driven digital twins represent a transformative approach for developing intelligent pharmaceutical ecosystems. Their successful implementation requires robust validation frameworks, regulatory guidance, interdisciplinary collaboration, and continued advancement toward reliable, sustainable, and patient-centered pharmaceutical systems.