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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.

Digital twin technology is increasingly used across pharmaceutical science, pharmaceutical manufacturing, precision medicine, and regulatory science. However, the same term is now applied to systems with very different simulated objects, including drug products, manufacturing processes, patients, and regulatory workflows. This semantic expansion has created a need for clearer conceptual organisation. The central problem addressed in this article is that pharmaceutical digital twin research has developed faster than its classification language. A tablet dissolution model, a continuous manufacturing simulator, a virtual patient population, and a compliance-monitoring model may all be described as digital twins, even though they support different decisions and require different validation logics. Without classification, comparison across studies becomes imprecise. This article proposes a four-category taxonomy of pharmaceutical digital twin systems. The categories are Product Twins, Process Twins, Patient Twins, and Regulatory Twins. The classification is grounded in the primary object of simulation and the principal decision function supported by the twin. The proposed taxonomy shows that digital twins should not be treated as a single technological class. Product Twins primarily simulate drug-product behaviour, Process Twins simulate manufacturing operations, Patient Twins simulate therapeutic response in individuals or populations, and Regulatory Twins simulate or support regulatory evaluation and oversight. Distinguishing these categories is a necessary step toward mature pharmaceutical digital twin science.