Search

Digital twins promise to reshape pharmaceutical development and manufacturing by creating virtual replicas that mirror physical systems over time. Their value lies in linking process knowledge, real-time data, predictive modelling, and decision support into a single operational framework. In pharmaceutical contexts, this promise is especially relevant because product quality is tightly coupled to process history, material attributes, and patient-facing performance. The core problem is that digital twin development in pharma remains fragmented across manufacturing, drug delivery, regulatory modelling, and digital transformation literatures. Manufacturing studies often focus on process control, PAT, and continuous production, whereas drug delivery studies emphasise physiological prediction, formulation performance, and patient-specific behaviour. These areas share mechanistic foundations, but they are rarely treated as parts of a unified pharmaceutical digital twin ecosystem. This conceptual review analyses digital twin logic across pharmaceutical manufacturing and drug delivery systems. It focuses on how mechanistic models, hybrid modelling, and real-time data infrastructures can be combined to support quality, performance prediction, and regulatory decision-making. The central argument is that digital twins must become not only predictive but also explainable and governable. The synthesis defines the architecture of pharmaceutical digital twins, catalogues their manufacturing and drug delivery applications, and identifies unresolved challenges in model coupling, parameter identifiability, uncertainty handling, and regulatory credibility. It also maps technological, organisational, economic, and regulatory barriers that prevent promising models from becoming routine industrial tools. Five tables summarise the conceptual architecture, application domains, model architectures, integration problems, and implementation barriers. Realising the full potential of pharmaceutical digital twins will require mechanistic rigour, explainable analytics, high-quality data connectivity, and early alignment with regulatory expectations. The future of the field will depend less on isolated demonstrations and more on reusable validation strategies, transparent model governance, and cross-sector collaboration. Digital twins should therefore be understood as evolving regulatory-scientific infrastructures rather than as standalone computational artefacts.