Dr. Heiner Niessen - Head of Application Technology Quality & Compliance, Merck
Peter Brandstetter - Quality and Regulatory expert, Accenture
James Man - Quality Subject Expert and R&D Advisory Managing Director, Syneos Health
James Kelleher – CEO, Generis
What influences the way life sciences companies manage Quality today – and what does the future hold for the discipline as smart automation presents new ways for monitoring and pre-emptively intervening in pharma manufacturing? Late last year, experts from Merck, Syneos Health and Accenture took part in a live video debate on the subject, chaired by James Kelleher, CEO of Generis.
The following summarizes the highlights of the discussion.
Could Quality Management be a Thing of the Past?
The panel began by giving their frank views of where the role of Quality is heading in life sciences. James Man of Syneos Health proposed that the activity could now be more embedded within every-day operations and improved incrementally, a vision Peter Brandstetter of Accenture supported.
“I think companies will reach a point where they don’t need Quality Management or people working in Quality,” he suggested. “When everything is digital and automated, Quality Management loses its relevance.”
At the coalface, the situation looks more complex however. At Merck, Heiner Niessen, said he believed Quality demands were actually increasing, as more parameters are measured and the responsibility for Quality extends right along the supply chain. In the future a product’s carbon dioxide footprint might become a Quality parameter, for instance, he noted.
Yet this, in turn, increases the urgency to address the manual burden of Quality monitoring and management. “There’s an opportunity here,” Brandstetter said. “The more we learn about the manufacturing process from all of the data being collected - and about the context - the better we can predict it, rather than retrospectively checking whether everything was created as it should have been. For me, that’s the goal.”
It’s here that smart use of technology offers considerable potential – for example in managing information from different sources. As Merck’s Niessen put it, “With improving digitalization, it’s much easier to capture and track this information, right out into the real world.”
Human Considerations
As ever, thoughts of smarter technology use and automation can instill fear in teams - who may worry about being sidelined, or about whether they can trust technology sufficiently to let it take over from human vigilance.
James Man at Syneos Health pointed out that it is still people who make the decisions and promote change, but that if those decisions can be informed by rich data the outcomes are likely to be better.
In fact, a better approach would be to involve Quality experts more, he suggested. “Right now, there isn’t typically a role of Chief Quality Officer – but perhaps there should be,” he proposed. “If companies want to be more pre-emptive, and for Compliance to add value to the business, there will need to be structural changes - beginning with representation.”
The Role of Machine Intelligence
At Merck, machine intelligence is starting to play a role in Quality management, albeit in quite limited areas currently - such as image analysis, for instance, where use of AI is quite advanced. Although AI has yet to be applied to ‘real decision-making’, Niessen suggested that the Quality considerations would be similar to those already in place now.
“Either you train the human being, or you train the algorithm, so if it doesn’t work the measures to take will be the same,” he noted. The critical requirement, though, would be that the algorithm’s own decision-making process would be transparent.
Syneos’s Man suggested that AI/machine learning might offer potential in the context of individualized/personalized medicine and Quality Management – to keep track of oversight and do this more cheaply/less manually: along the lines of a digital twin perhaps.
“If AI can be applied as a learning tool, I do see potential for collectively improving capability by identifying near misses and so on. In manufacturing there is more of a culture of celebrating this kind of thing, but in R&D we’re a long way behind,” he said.
Niessen noted that when very low quantities of a product are involved, adapted for perhaps just one individual or a small group of people, the need for greater efficiency with quality management grows given that there will be as many quality control measures as there are personalized products.
“Actually, using the technology here would complete the circle quite neatly,” he said. “It was automation and machine learning that led us to the point of having personalized medicine. Using the same technologies to help with the testing workload would make sense.”
Containing the Rising Costs of Quality Management
So, should companies just accept that Quality costs will rise as data and parameters increase, or become better at reducing effort and containing cost, wondered James Kelleher at Generis?
With huge pressure on the industry to reduce the cost of drugs, companies do need to contain costs wherever possible, the panel agreed – especially with the trend toward personalized medicine. Also, from a health insurance perspective, as outcome-based reimbursement becomes more established, the cost of Quality does become a factor, Accenture’s Brandstetter added.
So what of the scope for smarter Quality in delivering tangible business benefits – for example, driving insights for future products, preventing recalls, ensuring the supply chain delivers as expected, and so on?
“Lots of companies recognize that Quality is an under-utilized competitive lever,” James Man acknowledged, conceding however that no one is really leading the way here yet. “People are cautious about investing currently,” he said. “It’s more a case of business as usual – updating the QMS, putting in that new CAPA management system, etc. There’s certainly more that can be done.”
Predictions: The Road to 2025
Generis’s Kelleher ended the debate by asking what concrete steps companies might take between now and 2025, to move closer to where they need to be.
Merck’s Niessen pointed to the corporate imperative to connect individual quality systems across the value chain to enable seamless data transfer. “So you would have your CAPA system, your RIM system, your supplier RIM system all acting more or less as one system,” he suggested. Merck sees a big advantage here, he said, and has initiatives in place to drive standard data exchange formats for exchanging Quality information at a system level - removing the need to send PDF files around and retype/scan information into each system.
Accenture’s Brandstetter concurred that there needs to be more structured data that’s exchangeable across company borders. Blockchain could help here, he suggested - enabling a trusted chain of data. “But the right foundations are needed and this is no small step. We need to break things down into smaller initiatives,” he warned.
As to whether the role of static ‘documents’ would diminish, the panel were undecided.
James Man though not. “The way we access and interact with documents is here to stay,” he said. “But we should connect Quality systems, and a Chief Quality Officer function will be important. Embedding Quality people in the key R&D teams will happen. Achieve that, and you might be piloting more real-time data exchange with the regulators by 2025.”
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