Digital twins have recently seen increased adoption as process assistants in the pharmaceutical industry: providing online and real-time access to historical and current data, helping simulate future products and processes, and enabling manufacturers to gauge process efficacy.
With drug manufacturing being long and complex, and with attendant challenges such as declining profit margins, increasing costs, market saturation, tighter restrictions, etc., digital twins are paving the way for innovation – enabling dynamic replicas of entire manufacturing process as well as individual manufacturing units, linked to IoT sensors. Such digital solutions are opening up new avenues for innovation, scalability, and market leadership while simultaneously optimizing production processes to ensure superior quality.
Let’s look at a few ways in which digital twins can be leveraged by the pharmaceutical industry:
Within the manufacturing process, digital twins can enable pharmaceutical companies optimize these 4 pillars of drug manufacturing and distribution: production building, production process, product, and patient.
A 3D model can showcase the dynamic properties of the production building process –visualizing the space within warehouses and factories required to store supplies, products, and machinery; the energy consumption expected under normal and unfavorable conditions; and the functionality of the building process prior to set up. This technology can also be used to find efficient solutions to perfecting the production process, for instance, aiding in the design of products such as insulin pens. 
With AI-enabled drug discovery and clinical trials on the rise in the global market – predicted to grow by 25% between 2019 to 2030, with North America generating 58% of global revenue by 2030  – digital twins can help determine methods of simplifying and accelerating trial design and ensure greater safety during testing. It can also help determine whether the same production processes used for certain drugs can be utilized for mass drug personalization, to cater to evolving patient needs and advances in gene-based technology. For example, in cell therapy, patients are treated with modified cells derived from their bodies; data derived from digital twins can be used to create personalized drugs for patients, based on highly focused gene analysis. 
Digital twins can also be used by life science organizations to improve manufacturing processes for and the scalability of drug development. For example, a major pharmaceutical company recently announced the success of an experiment wherein they manufactured vaccines with the help of digital twins.  The researchers involved in the project were able to innovate on, improve, and simulate the entire process of vaccine manufacturing; obtain insights; as well as fix issues before they occurred with the help of digital twins. Such advances have helped tackle the challenges of the mass production of the SARS-CoV-2 virus vaccine , enabling accelerated drug production.
This process of implementing a digital twin begins with fitting each manufacturing stage and unit with IoT sensors, which can collect and interpret various sets of data in real-time. The amalgamation of analyzed physical, chemical, and biological data then facilitates the creation of a model that lays the foundation for the digital twin, with ML algorithms increasing the success of predicted improvements. This method also aids in the development of more general processes by obtaining exact evaluations of changes prior to adopting change control methods. The digital twin shares real-time data that enables pharmaceutical manufacturers to optimize each step and make necessary changes to improve production processes, as well as create simulations to predict what a particular modification will do to the next stage or the finished product, streamlining the process of drug innovation and manufacturing. With drug discovery augmented through such next-gen technology, it is estimated to become the largest stakeholder in the global biopharmaceutical AI market, growing in valuation from $159.8 million in 2018 to approximately $3 billion by 2025. 
Not only do digital twins streamline drug production processes but also enable logistics partners in the sectors to obtain greater visibility into the product’s manufacturing and delivery aspects, facilitating well-informed decisions on material flows and cold chain processes. Forklift deployment at warehouses, material sortation, delivery conditions, container temperatures, etc., are only a few of the many operational processes that can be tracked, forecasted, and optimized with digital twins.
Digital twins can also share real-time information on the status of commodities and assets across the supply chain. This facilitates a faster response time for logistics providers, as well as decreased waste, safer storage and transport, improved inventory management, and optimized warehouse space. In order to derive optimal benefits from digital twins, cloud computing, AI, and advanced visualization tools can also be combined,  giving stakeholders greater end-to-end visibility into processes.
While digital twin technology has been around for almost two decades, it’s implementation in the pharma industry has been a relatively recent development, with scope for exponential growth. Although immediate factors contributing to this growth in the life sciences sector have been the COVID-19 pandemic and increased demand for healthcare services and drugs , this technology can also be incorporated in various steps of the manufacturing process to reap the greater benefits of scalability and innovation. In an age where uncertainty marks the future of healthcare infrastructure and services, advanced technologies such as digital twins can enable greater predictive capabilities and reduced time to market, helping the industry remain at the forefront of change.
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