Pharmaceutical manufacturing has been moving, slowly, from batch to continuous operation. The case was made on quality and agility grounds by authors from the United States Food and Drug Administration (Lee et al., 2015) and on cost grounds by comparative economic analysis (Schaber et al., 2011). An end-to-end pilot plant that carried a drug from chemical intermediates to finished tablets in one continuous train showed that the idea was technically feasible (Mascia et al., 2013). Regulatory guidance has followed, most recently in the form of the ICH Q13 guideline on continuous manufacturing (ICH, 2022).

A continuous line has no natural pause at which material is held and tested. Assurance of quality has to come from measurement and from models that connect what is measured to what matters. This is why the language of the digital twin has been taken up so readily in the sector. A virtual counterpart of the line that is kept current by plant data promises real-time knowledge of where every portion of material is and what state it is in.

The promise and the practice are not the same. This review asks three questions of the published literature. What counts as a digital twin, and how mature are the examples reported for continuous pharmaceutical processes? How should such a twin be validated when it bears on product quality? And what stands in the way of wider use?

The article is a narrative review of the published literature. It draws on review articles and perspectives in pharmaceutical engineering and process systems engineering, on the general literature on digital twins in manufacturing, on textbooks and papers on the verification and validation of simulation models, and on regulatory guidelines. Sources were chosen for their standing and relevance. No systematic search was performed, and the review does not attempt to count or classify every publication. It reports no new data and no case from any particular company.

The concept is usually traced to Grieves, who described a physical product, its virtual counterpart and the flows of data and information that tie them together (Grieves and Vickers, 2017). The aerospace community gave it a demanding form: a simulation of an individual as-built vehicle that uses the best physical models, sensor updates and service history to mirror the life of that vehicle (Glaessgen and Stargel, 2012). Later reviews found that the term had spread across manufacturing with very uneven meaning (Jones et al., 2020).

The most useful clarification is that of Kritzinger et al. (2018), who classify by the direction of automatic data flow. In a digital model, data pass between the physical and virtual objects only by hand. In a digital shadow, data flow automatically from the physical object to the virtual one, so that the model follows the plant. Only when the flow is automatic in both directions, and a change in the virtual object can alter the physical one, do they speak of a digital twin. Wright and Davidson (2020) make a related point: a twin is a model of a specific object that is updated by measurements of that object and is useful only so long as it is accurate enough for its purpose. By these tests a flowsheet simulation run in the development laboratory is a model, however detailed it may be.

The components from which a twin of a continuous line might be assembled are well developed individually. They fall into process models, measurement, and control.

If the published work is ordered by the degree of coupling between model and plant, four stages can be distinguished, as in Figure 1. The ordering is this review's own arrangement of the categories of Kritzinger et al. (2018), with the twin divided into advisory and closed-loop forms because the two carry very different validation burdens in a regulated plant.

The review of Chen et al. (2020), which covers both small-molecule and biopharmaceutical manufacturing, concluded that the pieces of a twin, the models, the sensors and the data platforms, had each advanced, but that their full integration into a twin with automatic two-way exchange was at an early stage. That assessment matches the picture given by earlier perspectives on the field (Ierapetritou et al., 2016; Rantanen and Khinast, 2015). Most published examples sit at the first two stages: models built and exercised offline, and models that receive plant data for monitoring, material tracking or soft sensing. Demonstrations of model-based control on pilot lines exist and are important, but they are generally presented as control studies and not as twins maintained over a product's commercial life. Table 1 summarises the stages with the typical use of each and what its validation must cover, and adds as a final row the special case of a twin that supports real-time release.

Validation is where the digital twin meets the particular demands of a regulated industry. Three bodies of guidance bear on it, and none was written for twins as such.

The barriers named in the literature are consistent across sources. Material properties come first. Powder flow, cohesion and compaction behaviour are hard to measure, vary between lots, and enter the unit models as parameters that are often fitted and not predicted (Ierapetritou et al., 2016). A twin inherits that weakness and is tested by it whenever a new lot or supplier is introduced. Measurement is the second barrier: in-line spectroscopic probes foul, sample a small volume of a moving powder stream, and depend on multivariate calibrations that must themselves be maintained.

Integration and upkeep form the third group. Plant data sit in control systems, historians, laboratory systems and batch records that were not designed to feed a common model, and the engineering needed to join them is substantial (Chen et al., 2020). Once built, the twin needs people who understand both the process and the model for as long as the product is made, a commitment of skills that Rantanen and Khinast (2015) identified as a general constraint on the manufacturing sciences. The fourth barrier is uncertainty about regulatory expectations for adaptive models, which makes the conservative choice, a fixed model with a narrow role, the rational one for many firms. Finally, the economic argument is harder to make for a twin than for continuous manufacturing itself, because the benefits, fewer deviations and faster investigations, are diffuse and difficult to attribute.

Two structural obstacles complete the list. A twin with a path back to the plant is a new route by which a fault or an intrusion can reach the process, and it has to be protected and qualified with that in mind. The data integrity principles that regulators summarise as attributable, legible, contemporaneous, original and accurate apply to the inputs and outputs of a model as they do to any other record. In addition, equipment suppliers, control system vendors and modelling tools each bring their own formats and interfaces. A twin built for one line is therefore hard to move to another, and each implementation remains close to bespoke, which keeps the cost high and the pool of shared experience small.

Several needs follow. Published accounts that follow a twin through technology transfer, routine production and post-approval change would be more informative than further demonstrations of individual models. A credibility framework written for process twins, combining the ICH impact levels with the context-of-use reasoning of V&V 40, would give developers and assessors a common language. Methods for quantifying and displaying the uncertainty of twin predictions to operators deserve attention, since an advisory twin is only as good as the use people make of it. And open benchmark problems for continuous lines, with shared models and data, would let competing approaches be compared on equal terms, which is not possible at present.

The coverage of the literature is also uneven. Solid oral dosage lines, with their feeders, blenders and tablet presses, appear to have received the most attention, and continuous synthesis and crystallisation of the drug substance rather less under the heading of the digital twin, although both fall within the scope of ICH Q13 (ICH, 2022). Work that carries a single twin architecture across drug substance and drug product, where a disturbance in the first arrives as raw material variation in the second, would test whether the concept scales beyond one unit train. It would also show how residence time models of separate sections should be joined for the purpose of tracing material.

Measured against a strict definition, digital twins of continuous pharmaceutical lines are still uncommon in the published record. The components exist: residence time and flowsheet models, hybrid modelling methods, in-line analysers and model-based control. What is mostly missing is their sustained integration and a settled view of how a model that changes with the plant should be validated. Progress is likely to be stepwise, from shadow to advisory twin to closed loop, with the validation burden rising at each step. Treating maturity and model impact as two sides of one question would help manufacturers decide how far along that path a given product justifies going.

To be declared by the author(s) before publication.

To be declared by the author(s) before publication.