Experts Map a Five-Level Maturity Path for Digital Twins in Freeze-Drying

Digital twins, virtual models of physical processes that are continuously updated with real-time data, are moving from concept to practice in one of pharmaceutical manufacturing’s most conservative unit operations: freeze-drying. A new consensus paper published in AAPS Open brings together experts from academia, equipment suppliers, and biopharmaceutical manufacturers to define how such twins should be built, validated, and progressively deployed for lyophilization, the three-stage process of freezing, primary drying, and secondary drying used to stabilize sensitive drugs.

The paper’s central contribution is a five-level maturity model for digital twin development in lyophilization, mapping the technologies, methodologies, and regulatory concepts needed to advance through each level. The authors are careful to note that the framework does not describe a single, fully realized digital twin deployment at commercial scale. Instead, it synthesizes state-of-the-art modeling approaches, process analytical technology, and regulatory principles to guide implementation efforts during development, technology transfer, and commercial manufacturing.

At the foundation lies the modeling challenge itself. Comprehensive first-principles models of primary and secondary drying have existed for nearly two decades, but early commercial software proved too complex for broad industry adoption. Simplified models built on widely available platforms such as Python and Excel have since gained wider acceptance, with researchers demonstrating that such models can predict product temperature profiles to within roughly one degree Celsius from a practical standpoint. That accuracy, the authors emphasize, depends less on mathematical simplification than on the quality of input parameters, which can vary significantly from cycle to cycle.

Variability is the persistent enemy of freeze-drying models. Fill volume, shelf temperature, cake resistance, and the vial heat transfer coefficient all influence drying time and product temperature, with the latter two parameters identified as the dominant sources of variability and both difficult to control. Controlled nucleation and annealing can reduce variability in cake resistance, but vial heat transfer remains stubborn: the edge effect shifts as lyophilizer wall temperatures change during a cycle, vial bottom concavity varies between lots, and improperly seated stoppers can introduce unpredictable mass transfer resistance severe enough to cause product collapse even when everything else is well controlled.

Scale-up compounds these difficulties. Shelf heat transfer coefficients and edge effects differ at manufacturing scale, greater supercooling can alter crystallization kinetics and increase both cake resistance and vial-to-vial heterogeneity, and commercial freeze dryers can reach sublimation conditions rarely seen in laboratory equipment. Duct geometry matters as well: small pipes between chamber and condenser in lab dryers can create choked flow, while most commercial dryers have larger duct areas and are less likely to choke under the same conditions. The authors argue that equipment should be characterized carefully at every scale, and that process analytical technology tools such as tunable diode laser absorption spectroscopy and residual gas analyzers become essential for managing these transitions.

To standardize the digital side, the paper translates the ISO 23247 reference architecture, an international standard for digital twins in manufacturing structured across four parts covering general principles, reference architecture, digital representation of manufacturing elements, and information exchange, into a lyophilization-specific context. On the sensing side, the framework catalogs wireless temperature probes, TDLAS, pressure-based mass-flow calculation methods, residual gas analysis, and near-infrared spectroscopy, alongside heat flux sensors, as the measurement backbone feeding real-time data into the twin.

Modeling advances described in the paper push toward an equipment- and scale-aware design space. A three-dimensional design space incorporating shelf temperature, chamber pressure, and time captures how sublimation rates evolve during drying; in one cited case, sublimation rates during the initial ramp exceeded those at the end of primary drying by up to 16 percent, illustrating the risk of designing cycles solely around end-of-drying conditions. Equipment capability curves, obtained experimentally or through computational fluid dynamics, clip the design space to what a specific freeze dryer can physically support. Newly published coupled models integrate shelf-to-vial heat transfer, vial-to-chamber mass transfer, and equipment-scale CFD, and have been applied to examine wall temperature effects, chamber pressure changes during power outages, and valve closure impacts. Probabilistic approaches add statistical distributions for vial location, fill volume, and product resistance, converting deterministic predictions into risk-weighted outputs validated through laboratory and pilot-scale testing.

The regulatory dimension receives detailed treatment. Because validated lyophilization processes currently allow parameter variation within proven acceptable ranges only to accommodate natural variation rather than real-time optimization, deploying a digital twin requires rethinking process validation itself, shifting the focus from validating fixed target parameters to validating the performance of the twin. The authors synthesize guidance from the FDA, ICH, and the ASME V&V 40 standard into a risk-based verification and validation strategy in which model credibility requirements scale with model risk, a combination of model influence and decision consequence. FDA guidance on artificial intelligence in manufacturing also flags data-related challenges, including bias, explainability, integrity, privacy, relevance, and reproducibility, that apply directly to twin development.

Commercial platforms illustrate implementation pathways. Siemens’ Executable Digital Twin uses model order reduction, state estimation, and co-simulation for real-time interaction; Ansys Twin Builder and TwinAI support physics-based, data-driven, and hybrid models with calibration and uncertainty quantification tools; and IMA Sentinel applies AI-driven analytics for leak detection and anomaly prediction in freeze dryers, with its machine-learning modules typically requiring around thirty cycles of consistent operation to establish a statistically robust baseline. Machine learning has also improved automated vial inspection detection and false reject rates, while 100 percent near-infrared or laser headspace moisture measurement of every vial, benchmarked against Karl Fischer methods, can feed full-batch moisture mapping back into twin models.

The paper closes with a roadmap of actionable steps spanning sensor selection, model calibration, uncertainty quantification, operator training, and data governance, and highlights emerging technologies, spray freeze-drying and radio-frequency or microwave heating among them, that will demand adaptable hybrid mechanistic-machine-learning twins. Microwave-assisted drying models have only preliminary validation, and dielectric loss data for frozen formulations remain scarce, leaving uncertainty. Industry 4.0, the authors caution, remains an aspirational framework rather than a regulatory mandate, but if the outlined criteria are met, digital twins could shorten cycle times, enhance robustness, and accelerate delivery of high-quality lyophilized products within a GMP-compliant framework.

Subject of Research: A consensus framework for developing digital twins in pharmaceutical lyophilization

Article Title: A consensus framework for digital twin development in lyophilization

Article References: Kazarin, P., Metsi-Guckel, E., Alexeenko, A., Tchessalov, S., Aglave, R., Al Bakri, W., Authelin, J.-R., Dowling, D., Ganguly, A., Gong, E., Horner, M., Fontana, L., Iyer, L., Kessler, W., Kiehl, D., Lapointe-Garant, P.-P., Longwell, G., Moussa, E., Najarian, J., … Bhatt, S. (2026). A consensus framework for digital twin development in lyophilization. AAPS Open, 12(1), Article 52. https://doi.org/10.1186/s41120-026-00150-w

Image Credits: AI Generated

DOI: 10.1186/s41120-026-00150-w

Keywords: digital twin, lyophilization, freeze-drying, pharmaceutical manufacturing, process analytical technology, design space, Industry 4.0, model validation, machine learning, closed-loop control, ISO 23247, GMP