Product temperature data, digital twin context, and AI turn lyophilization experiments into transferable process knowledge
See how LyoINSIGHT worksPharmaceutical lyophilization generates ever-growing volumes of data. Yet one decisive question often remains unanswered:
Which insight from the current experiment will actually improve the next industrial decision?
The next lyophilization run should confirm a hypothesis - not merely generate another collection of data.
Pharmaceutical lyophilization generates ever-growing volumes of data. Yet one decisive question often remains unanswered:
Which insight from the current experiment will actually improve the next industrial decision?
At laboratory scale, additional experiments can be performed comparatively easily.
During scale-up, technology transfer, engineering runs or PPQ, however, the same experimental approach becomes time-consuming, expensive and risk-intensive.
More data alone does not solve this problem.
Data creates business value only when it:
The next lyophilization run should confirm a hypothesis - not merely generate another collection of data.
01 | Product Temperature
Product Temperature Measurement Provides the Foundation
The challenge is to transform robust measurement data systematically into transferable process knowledge.
For two decades, Tempris has supported pharmaceutical companies in measuring the actual product temperature directly inside the vial – from laboratory scale through scale-up and technology transfer to commercial production.
Product temperature data closes an essential gap: equipment parameters describe how the freeze dryer operates. Product temperature shows how the product actually responds under those conditions.
Product temperature data enables companies to:
Experience also shows, however, that the availability of robust measurement data is no longer the only challenge.
The next challenge is to transform that data systematically into transferable process knowledge.
The challenge is to transform robust measurement data systematically into transferable process knowledge.
02 | Data Usability
The Problem Is Not a Lack of Data – The problem is its limited industrial usability
The next economic lever:
Consistently develop documented data into a reusable process model.
Process development often generates more experimental data than can be justified theoretically or transferred to industrial scale.
A single run provides temperature curves, process parameters, endpoint information, and quality values. Initially, these data describe only what occurred under the particular experimental conditions.
They do not automatically explain:
The result is a series of isolated experimental outcomes. With every new scale-up, transfer or equipment comparison, part of the interpretation begins again.
Data is documented – but not consistently developed into a reusable process model.
This is precisely where the next economic lever lies.
The next economic lever:
Consistently develop documented data into a reusable process model.
03 | Process Understanding
From the Temperature Curve to Mathematical Process Understanding
Stop treating individual runs only as completed experiments.
Every run extends an existing model of product and process behavior.
A measured product temperature curve is not a complete explanation of the process. It is the product’s real response to heat transfer, pressure, drying progress, product resistance, and equipment conditions.
When this response is linked to physical heat- and mass-transfer models, further relationships can be evaluated:
The decisive advance is to stop treating individual runs only as completed experiments. Every run extends an existing model of product and process behavior.
This changes the development logic:
| Conventional Experimental Logic | Advanced Tempris Logic |
|---|---|
| Evaluate the data from one run | Develop knowledge across multiple runs |
| Review individual temperature curves | Compare complete time series |
| Assess averages | Differentiate critical vial groups |
| Transfer process parameters | Transfer product behavior |
| Explore broad experimental ranges | Verify well-founded hypotheses selectively |
| Document results | Build reusable process knowledge |
Stop treating individual runs only as completed experiments.
Every run extends an existing model of product and process behavior.
04 | Digital Twin
The Digital Twin Shows the Location – the Time Series Explains the Development
The Digital Twin provides a spatial visualization and a comparative model of actual product behavior.
The Tempris Digital Twin enables real product temperatures to be linked to their positions inside the freeze dryer. Changing color visualizations show how temperature develops at the instrumented vial positions during the process.
This representation answers an important question: Where does product behavior change?
The image alone, however, does not yet explain the recurring relationships behind that change.
The additional insight emerges when complete temperature time series are compared:
The Digital Twin thereby evolves from a spatial visualization into a comparative model of actual product behavior.
The color visualization makes a change visible. Time-series comparison makes its structure analyzable.
The Digital Twin provides a spatial visualization and a comparative model of actual product behavior.
05 | ANN
The Additional Contribution of ANN
Use Tempris LyoCLC® with Artifical Neural Networks (ANN) to turn hidden potential into measurable customer success.
A person can compare individual temperature curves and recognize clear differences. As the number of sensors, positions, process runs, and equipment increases, however, the number of possible relationships grows very quickly.
Artificial Neural Networks (ANN) can systematically compare complete temperature time series. They search for subtle, recurring, or group-specific patterns that are not readily apparent from a single-value view or purely visual evaluation.
ANN can, for example, support the ability to:
ANN does not calculate the Kv value independently, nor does it replace a physical model. Rather, time-series analysis reveals which structures and differences should be considered in a Kv-oriented evaluation.
Three levels of insight therefore complement one another:
Use Tempris LyoCLC® with Artifical Neural Networks (ANN) to turn hidden potential into measurable customer success.
06 | Learning
A New Way of Working in Lyophilization:
Not more measurement, but more learning from every measurement
Tempris creates the foundation for learning purposefully from every run.
Connecting real product temperature, spatial context, physical models and time-series analysis changes the role of every experiment. A run is no longer merely an isolated result. It becomes part of a growing, comparable knowledge base spanning product, process and equipment.
This shifts the way of working:
The economic benefit emerges wherever this knowledge improves the next decision: in experimental planning, scale-up, technology transfer, engineering runs and PPQ, and later in CPV and deviation assessment.
Tempris therefore provides more than data from a run. Tempris creates the foundation for learning more purposefully from every run for the next industrial decision.
Tempris creates the foundation for learning purposefully from every run.
Implementation
From Perspective to Practical Implementation
The strategic consequence is clear: more data alone does not create process knowledge. What matters is how real product data is placed in spatial and temporal context, linked to physical models and translated into a robust process decision.
LyoINSIGHT shows how direct product temperature measurements, spatial process context, physical models and time-series analytics are brought together in a structured approach.
Measure
Contextualize
Compare
Model
Decide
Learn