Stepping up the pace in vaccine development and production
Global healthcare company GSK is collaborating with digitalization expert Siemens and the digital transformation leader ATOS to optimize its vaccine development and production process. A key benefit of digitalizing the entire process is expected to be a much shorter development time for vaccines, allowing them to reach people faster and with the optimum quality.
Vaccinations help protect against diseases and are one of the most important and effective preventive health measures available. The principle behind vaccination is to boost the individual’s own immune defenses: vaccines expose the body to a minute quantity of pathogen-derived materials. The immune system responds by developing the appropriate immune cells and stores these protective cells as a kind of “immunological memory”. Not only does the vaccinated patient build up protection against the disease through vaccination, but in some cases, high vaccination rates among the general population make it harder for the individual pathogens to spread (called herd immunity), which may ultimately even result in their elimination.
The long journey of vaccine development
Developing vaccines is usually a costly and time-consuming process. It always starts with biology – live samples or gene sequences of the pathogen. Researchers begin by analyzing the components that are particularly important and thus determining where an immune response could be effective. This can take up to several years but is crucial to the vaccine development process and is followed by a series of stringent testing phases to confirm the safety and efficacy of the candidate vaccine. If the protection provided by the vaccine is proven and its safety profile is acceptable, the manufacturing company can then apply to register it. In parallel, the company develops the method to produce the vaccine in an efficient and robust way. It is a long phase that requires many experiments. It starts in the laboratory at small scale: The process is designed, scientific knowledge is generated to understand the impact of all process parameters on product quality. Then the process is scaled up in a pilot plant to check its robustness before transferring to the manufacturing site. The manufacturing plant prepares for mass production and sets up the appropriate production facilities.
Digitalization as an acceleration factor
Right now, vaccine development typically progresses in many small silos, each digitalized to some extent in its own environment, but with few connections between them. Often one group depends on the completion of the work by another team to be able to access data. This is where there is potential for optimization. Being able to consider the process as a whole and digitalizing the entire value chain would represent a significant improvement: it would enable easier and earlier access to results, faster and more comprehensive feedback, better ability to predict and share outcomes and provide better oversight of the entire process. Thanks to its innovative portfolio of Digital Enterprise solutions – covering product design (i.e. developing the vaccine and making the active ingredient (primary processing)), and manufacturing the pharmaceutical itself (secondary processing) – Siemens have collaborated with GSK and Atos to develop an innovative concept named ‘digital twin’, that combines the virtual and real worlds in a closed loop. Using the digital twin, a virtual representation of the real thing, it’s possible to test stages in the process and gain insights in a virtual environment right at the outset. Connecting the digital twin to a running process, it predicts the performance of the process, anticipates any deviation and steers the control back to the optimal production. That allows vaccines to be developed faster and always be produced with the best information available. In addition, it helps ensure reliable supply. The data obtained from real runs is fed back with machine learning into the models, the “brain” of the digital twin and thus helps optimize both the digital twin and the products and processes from an early stage.
Twins of the overall process
Every day, GSK manufactures around two million vaccine doses, making it one of the world’s largest suppliers. Now GSK is turning to digitalization to help speed things up: collaborating with Siemens, it aims to create and introduce digital twins of the entire vaccine manufacturing process for all new vaccines. In other words, the digital twins of the product, production, and performance will be linked together. Digital twins offer particularly high added value in biological processes, or in cases where particular elements like physical models have to be better understood – as in the vaccines process. Quality management will be improved thanks to the use of soft sensors and process analytical technology (PAT).
Case study: adjuvant manufacture
GSK utilizes adjuvants in the manufacture of its vaccines, which are additives that boost the immune response. This can play an essential role to help protect people with weaker immune systems (e.g. older adults or immune-depressed people) and can help reduce the volume of antigen required for each dose of vaccine, allowing the supply of more doses of the vaccine when demand is high. As first application to test the digital twin, GSK, Siemens and ATOS have developed a proof-of-concept digital twin specifically for the development and manufacturing of adjuvant technologies, with ATOS providing its expertise in IT infrastructure, consulting, integration and data science, Siemens providing its Digital Enterprise expertise, and GSK providing the business case, data and its product and process modelling expertise. For the simulation, the “black box” of adjuvants’ particles had first to be decoded. Using mechanical models and artificial intelligence (AI), the partners developed a hybrid model to simulate and monitor the process. As such, the digital twin links the process parameters to the quality of the adjuvant. The sensors and process analytical technology (PAT) provide information that feed the twin to predict the quality of the product. Any deviation from the optimal quality is anticipated and the twin acts on the process parameters to rectify and meet the target requirements. This digital twin is therefore based on simulation, artificial intelligence, automation, and PAT. This makes use of various software solutions: PAT is provided by SIMATIC SIPAT, which ensures unrestricted data transparency starting with product development and feeds the correlated data back into the process. The TIA Portal (TIA stands for Totally Integrated Automation) integrates hardware, software and services facilitating complete access to the entire digitalized automation system and forming the basis for the engineering process used in implementation. Simulation software was used in process modeling and visualization. The process is also supported by Machine Learning. The time factor, however, posed a particular challenge for adjuvant simulation.
Because the adjuvants’ particle simulation is highly computation-intensive, the computation process can take several hours. That’s a problem for real-time interaction between the digital twin and the real world. The project partners therefore extracted the process illustrated here and simulated it using computational flow dynamics (CFD). This enabled them to generate and save simulation files for all kinds of cases in advance. In combination with data from statistical trial planning (DoE) and machine learning, this gives them the ability to predict the adjuvants’ particles that will be created with each change in critical parameters. As a result, the model is real-time-capable. The digital twins for the next part-processes will be developed as the project progresses.
With digital twins, it is now possible to collect data to understand exactly what is happening in real time during the vaccine production, enabling optimization of operations. It allows not only monitoring of complex processes, but also predict how changes would affect them. It allows researchers to run simulations in a matter of hours instead of building test plants. This new model – based on artificial intelligence, machine learning techniques, predictive and prescriptive models – provides new insights for development and full control over the vaccine production process. It enables a more robust process, delivering improved product quality – and ultimately helps deliver more vaccines faster to those who need them.