The Sims of Pharmaceutical Manufacturing
Okay, it’s 2009. The world as we know it is being utopianly replicated onto the grainy pixels of living room televisions across the world. Yup, you guessed it, a new edition of The Sims has just come out. For those of you who may be unfamiliar, or who may have been doing arguably more productive things with their time, The Sims (aka “Sims”) was a video game that was captivating in its banality. Rather than creating a mystical universe, a crash-and-burn car chase, or a heroic adventure, the world of Sims was a world that purported to be just like the one inhabited by the remote-control wielder. And, while many users undoubtedly created alternative realities, others (or maybe just me?) created a digital copy of their own. Down to my very cute, very fluffy, soon very distorted, dog.
Now, fast forward to the present moment. And to the pharma industry. Oh, and to reality-reality, not digital reality. Plagued by supply chain disruptions and dire drug shortages, the healthcare industry continues to reckon with the complexities and challenges of manufacturing drugs (see our previous article for more context). While a myriad of economic, network, and regulatory factors contribute to drug shortages, pharma companies are increasingly turning to various technologies to optimize their manufacturing operations. One such innovation is digital twin technology, which refers to the digital representation of physical processes or products that, leveraging real-time data and advanced analytics, can simulate current and future states. While this technology has gained significant attention for patient settings, for example to simulate treatment responses, its application in pharma manufacturing is less established.
With the backdrop of Sims, imagine that an entire manufacturing system and supply chain was represented in a digital form, allowing users to observe processes, identify problems, simulate different scenarios, and intervene as needed. And, rather than relying on manual inputs, imagine that this whole system was supercharged by all of the abbreviations: large language models (LLM), machine learning (ML) and artificial intelligence (AI). What would happen?
Well, the more apt question is actually, what is happening? Just this past month, the pharma digital twin company Leucine raised a $7M series A round. Leucine’s platform creates a digital representation of a drugmaker’s production floor. In addition to enabling real-time monitoring and compliance management of products, Leucine’s AI technology provides insights on how to improve system efficiency. Leucine’s key point of differentiation compared to legacy systems is their proprietary LLM technology that translates paper SOPs into digital workflows almost instantly. Instead of arduous and time-consuming manual onboarding, this technology allows Leucine’s system to be implemented within just eight months. Even moreso, this LLM technology facilitates the automated generation of compliant electronic batch manufacturing records (eBMRs) based on digital monitoring, which are key to the FDA’s audit process. While over 30 companies are using Leucine’s technology, drugmaker’s like GSK are using other systems, like Siemens, to create their own digital twins.
Importantly, digital twin technology spans beyond just monitoring existing systems. Rather than building expensive test plants, drugmakers can simulate different manufacturing processes, and even entirely new plants, digitally. In October of last year, for instance, Sanofi announced it would use Dassault’s 3D simulation to help qualify the facilities, equipment, and processes for their new EVolutive vaccine factories in France and Singapore. These digital environments serve as practically limitless testing grounds for important, and expensive, elements of the manufacturing process.
Lastly, returning to our current state of drug shortages, supply chain digital twin technology promises to help avoid such crises. By simulating a range of different scenarios across the entire supply chain, drugmaker’s can assess the resilience (or lack thereof) of their supply chains. While certain circumstances, like the plant closure of a competitor, may not be avoidable or predictable in the moment, these simulations allow drugmakers to understand how their supply chain would respond to hypothetical situations and in turn plan ahead of time. Just as you may have changed your hair color and nervously awaited how your neighbor would react in the world of Sims, digital twin technology allows drugmakers to understand the ripple effects of internal and external factors, and act accordingly. With our investor hats on, we will be on the lookout for companies leveraging digital twin technology to support and enhance pharma manufacturing, especially as drug shortages continue to force the healthcare and broader community to reckon with the consequences of supply chain frailty.