Artificial Intelligence (AI) in Pharmaceutical Manufacturing

Today's episode focuses on the role of AI in Pharmaceutical Manufacturing, specifically how it is powering personalized medicine.

We will explore AI's impact today and what the future looks like by 2030, with technologies like digital twins driving efficiency and precision in drug development.

Episode Transcript

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Welcome to Supply Chain 360. Your all access pass to the latest innovations and trends

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shaping the supply chain industry.
Today we're focusing on the pharmaceutical and biopharma space, where AI is making major
waves. We'll explore how this technology is enabling the rise of personalized medicine
and how it will evolve by 2030, revolutionizing both the manufacturing process and global

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supply chains.
Let's get into how AI is reshaping the supply chain within the pharma sector and where things
are headed in the next decade.
Here comes a closer look at AI in pharma manufacturing today. AI is already playing a critical role
in pharmaceutical supply chains today, enhancing efficiency, optimizing production processes,

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and reducing time to market. Here's how it's happening right now.
How do AI models work in drug discovery?
AI models are accelerating drug discovery by analyzing huge datasets to predict molecular
behavior and identify promising drug candidates. Companies like Insilico Medicine are using

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AI to dramatically shorten the timeline of drug discovery.
How is it utilized in optimizing manufacturing?
In manufacturing, AI helps with quality control, predictive maintenance, and production optimization,
especially in the manufacturing of biologics. Companies like GSK use AI to streamline vaccine

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production and ensure batch consistency.
How does AI work in personalized medicine production?
AI is critical in scaling the manufacturing of personalized treatments like gene therapies,
which require precise and customized production processes. AI also helps predict demand, ensuring

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the right resources are available at the right time.
One of the most exciting developments in healthcare is the rise of personalized medicine, where
treatments are tailored to individual patients based on their genetic profiles. AI is making
this possible by identifying biomarkers that predict how a patient will respond to a treatment,

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a practice currently being used by companies like Roche in oncology.
In manufacturing, AI is essential in handling the complexity of CAR-T therapies and other
gene therapies, where each batch must be customized for a single patient. AI helps ensure these
processes are both scalable and precise, meeting the strict quality standards required for

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personalized medicine.
So what about digital twins and how will they evolve?
Digital twins are virtual replicas of physical systems, allowing companies to optimize production
and predict outcomes in real time. Today, digital twins are used for manufacturing

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simulations. Companies like GSK model entire production processes to ensure optimal conditions
for biologics. They are also used in clinical trials. Digital twins simulate patient populations,
helping pharma companies predict how drugs will perform, cutting down on the need for
animal models.

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So will digital twins develop into full supply chain optimization? The answer is a clear
yes. Companies will use digital twins to model their entire supply chains, from raw material
sourcing to drug delivery, ensuring optimal logistics and reducing waste.
So what about real-time prescriptive analytics? Future digital twins will not only predict

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outcomes but will also recommend the best actions to optimize manufacturing and supply
chain processes in real time.
Let's have a closer look at hyper-personalized medicines.
Digital twins will help create even more personalized treatments, simulating how individual patients

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respond to drugs in real time, leading to hyper-personalized therapies.
What can we see as opportunities and challenges? As exciting as AI and digital twins technologies
are, implementing them on a large scale comes with its challenges, particularly in data
security, regulatory compliance and integration with existing systems. But the opportunities

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far outweigh the challenges. With AI and digital twins, pharmaceutical companies can streamline
production, accelerate drug discovery and bring highly personalized treatments to patients
faster than ever before.
So let's summarize and conclude for today. AI and digital twin technologies are already

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transforming pharmaceutical manufacturing and by 2030 they will be fully integrated
across the supply chain. These innovations are enabling smarter, faster and more personalized
treatments that will change healthcare forever.
Before we wrap up, I wanted to let you know that I will be attending the CPHI Convention

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on Pharmaceutical Ingredients in Milan, Italy, celebrating 35 years of uniting the pharma
industry. If you are attending and would like to connect, I'd love to meet with you. Feel
free to direct message me on LinkedIn to set up a time to chat about the latest innovations
in pharma and supply chains.
Thanks for tuning in, stay ahead of the curve and see you next time!