The application of digital twins and drug development has been shown with an example from Atos, relating to the path towards a vaccine for the novel coronavirus SARS-CoV-2. It follows, according to Niels Thomsen (Vice President and Global Head of the Insight (IoT and AI) practice), that once a vaccine has been proven, the challenges around mass production can be equally as complex as the path towards developing the actual vaccine in the first place. Such pressures are especially acute when there is an imperative to accelerate the time to get the drug product to market.
Atos contends that the time to deliver a drug to market can be reduced through the collection and analysis of data. Big data analytics together with advances in computing can not only help with the core development of a drug product (as with formulation and disease modelling run as overlapping simulations); such approaches can also assist in the streamlining of the pharmaceutical production process.
An example of such technology helping to foster improvements to manufacturing, and for taking a developmental drug to scale, is digital twins. This requires a digital replica to be created. The process of constructing such a replica starts with having each manufacturing stage equipped with in-line sensors. These sensors enable various sets of data to be collected an interpreted, in actual processing time. When the analyzed data is combined with physical, chemical, and biological data, models can be produced and these models provide the foundation to the digital twin.
The developed digital twin becomes a real-time replica of the physical production processes, enabling pharmaceutical manufacturers to optimize each step and to introduce required changes to improve the process or to create simulations to predict what a particular modification will do to the next stage or to the finished product. The success of predictive improvements become better through machine learning algorithms.
This approach also assists with making more general process improvements through gaining more accurate assessments of changes prior to implementing change control processes. This method is in keeping with the ‘quality by design’ paradigm for pharmaceuticals which dictates that quality built in at the design sage leads to more robust and better quality results.
