As an example of how digital twins can assist with process optimization and drug development, Atos, a digital transformation consultancy, and Siemens, the engineering firm, announced a project in 2020 designed to assist the pharmaceutical industry in improving production. This was through a new innovation based on digital twins. The specific project involves creating a digital replica of the pharmaceutical production process (a “Process Digital Twin”).
The technological components that help drive the development include Internet of Things (IoT), artificial intelligence, and advanced analytics. Each aspect has been put together to help to provide improved efficiency and flexibility across the manufacturing of different pharmaceutical products.
In terms of benefits, these are presented as being the reduction in experimentation time and waste. Furthermore, the use of digital representations has been configured to ensure experiments are of a constant quality and meet industry expectation around “quality by design” (which, in this context, means getting the product right the first time). For a sector reliant upon data, the solution is also capable of using collected data in order to optimize process quality and machine reliability.
The end product is a complete virtual replica of each step within the manufacturing process. The digital replica connects with IoT sensors which are installed within the physical plant. This is an example of ‘smart pharma’, where the orchestrated interaction between online sensors and digital models leads to enhanced control of the production process.
The process sensors collect real-time data, which can be drilled down in order to provide a real-time assessment of operations. Improvements can be driven through a comparison of digital representations of the process and the physical reality of the process. Where the physical reality does not match the digital model expectations, then improvements can be sought. In addition, the digital model can be used to assess when and where the physical process may go awry; this is something that can be achieved through predictive models which can analyze the real-time data.
In terms of product development, the Atos-Siemens platform has been used to create simulation of chemical mixing processes. This enables scientists to run multiple variants of different processes, with the end result of ensuring that the optimal mix of chemicals is used in live production.
The net effect of this is to reduce waste, and hence cost savings, as well as facilitating getting new medicines to the market more quickly.
