Company insight
Understanding digital transformation in pharma
With companies around the world adopting digital transformations, Toni Manzano, cofounder and CSO of Azion speaks to World Pharmaceutical Frontiers about how their company’s artificial intelligence and cloud solutions can be used by the pharmaceutical industry to optimise its processes, remove repetitive tasks, and leave human minds to do what they do best – solving complex problems.
We hear about ‘digital
transformation’ all the time – but what does this actually mean for the pharma industry? Toni Manzano: Operational processes are so important in drug manufacturing as patients depend on consistent quality, safety and efficacy of their medication. In a typical drug development cycle, there are often multiple, varied, manual actions that are particular to the product or production phase. In pharma manufacturing, nothing is static, nothing is simple – what we can rely on is complexity and variability. The goal of digital transformation in pharma is to minimise the repetitive tasks that operators perform to avoid any adverse effects on the drugs and patients. Each step in the manufacturing process is specific, and they are all connected. The only way to ensure full connectivity along the manufacturing chain and to enable all parties to have the right information at the right time is with digital transformation. Digital transformation refers to the connectivity that transforms actions to data.
From a practical perspective, what is the first thing organisations should be thinking about if they’re starting a digital transformation effort? TM: A change of mindset must come first. Working on paper is not an option when a process must be optimised. Assigning repetitive tasks to operators when they could be performed digitally (by machines and computers, for example) in a more efficient, secure way, is not the solution. Our challenges are significant: efficient processes, sustainable sites, distributed and
subcontracted manufacturing, logistical complexity, and global distribution agents. They are all components of an exceptionally complex network, and they are difficult to govern without digital help.
Organisations need to invest differently – instead of investing in data centres, on-premise storage or resources dedicated to development, upkeep or software maintenance, they need to be investing in processes and solutions based on how they add value and extract knowledge from development processes. Again, this brings us back to a change in mindset and how people think.
What are the benefits organisations get from moving manufacturing data into the cloud, and how quickly do you think they will start to see the impact of that decision? TM: Data silos are a problem for every organisation. The way to solve this is through digitisation, which doesn’t mean to work completely on electronic systems and off paper but to be fully digital from a management perspective. That means that interdepartmental information (such as logistics, scheduling, production, quality) and systems records (like LIMS, MES, WHMS, and so on) can be shared to extract knowledge out of seemingly disparate data sources.
At Aizon, we see that before we even start to apply AI, companies are seeing the value in connected, contextualised data. The best solution for this challenge is cloud technology. Data integration can’t happen on premise without significant cost and resources. With the cloud, IT departments work in partnership with their cloud providers, delegating IT tasks
World Pharmaceutical Frontiers /
www.worldpharmaceuticals.net
to the software, allowing manufacturers to focus on what they do best: bring value to the drug development cycle.
What do you think is the single biggest benefit to using AI in pharma manufacturing? TM: Artificial intelligence is the best mechanism to robotise and automate human knowledge. AI only works well with quality data and that should be the focus of any biopharma company in the digital space: generate quality data. Humans are good at solving unique and complex problems, not doing repetitive tasks, and this is precisely what AI can do instead. With AI, we are demonstrating value in drug manufacturing by detecting anomalies while observing real time data 24/7 and recommending the right values for CPP between limits in order to always achieve maximum yield. We use AI to quickly classify issues and enable research processes to identify root causes, preventing unexpected downtimes in critical operations. We can supervise complex biotech processes in real time with networks of AI models, and we can suggest actions to avoid quality defects.
Operators can bring value to manufacturing operations by training AI systems and continuously improving the automatic mechanisms of supervision, adding more variables and factors which affect the final product. This is robotisation of human knowledge. The immediate benefit is the knowledgeable automation of complex processes ensures perfect execution of manufacturing tasks. ●
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