Manufacturing: Digital CMC

Will the next generation of blockbuster therapies be won by science alone or by the speed of manufacturing execution?

For the past three decades, pharma has poured its energy into breakthrough science focused on new modalities, novel targets and transformative medicines. The results have been remarkable, with five to six genuinely new drug types reaching market in the last five to ten years – from antibody-drug conjugates and oligonucleotides to cell and gene therapies, RNA vaccines and therapies. The science has never been more exciting, nor the potential for patients greater.

But as development timelines have stretched beyond ten years or more, and the cost of bringing a single asset to market approaching £2bn, the industry’s biggest challenge has started to shift. It is moving from discovery to delivery. The pressure of commercial viability is firmly on accelerating chemistry, manufacturing and controls (CMC), and optimising manufacturing supply chains. The race to market success is being won and lost on the ability to launch and scale with speed.

Once a compound is registered, the clock starts ticking. There is roughly twenty years of patent protection before a competitor can manufacture an equivalent. At peak year sales for a significant asset, getting to market six months earlier can be worth hundreds of millions in additional revenue, not to mention reaching patients faster. Every month lost in tech transfer, manufacturing deviation investigations or batch release delays is unrecoverable. Senior leaders in this industry fear two things above all: making a major compliance error or getting in the way of a product launch. That says everything about how much time matters and why manufacturing excellence is no longer operational, it is strategic.

The transfer from R&D into manufacturing remains, across most of the industry, a fundamentally human-centric and document-heavy process. Batch instructions, process changes and analytical methods are still too often mediated by static PDFs, spreadsheets and email, with critical context lost between systems that do not talk to each other. When a batch fails, information arrives retrospectively. The plant may run for 24 to 40 hours before the problem surfaces. Root cause analysis takes weeks, sometimes months. The plant stops, supply chains back up, submissions wait when connected, interrogable data would resolve the same investigation in minutes. Disconnected systems are not just an inconvenience but a commercial liability.

Part of why this has persisted is structural. Over the past two decades, pharma made a deliberate strategic choice to outsource large portions of its development and manufacturing capability. A rational decision to transfer capability, reduce capital burden and provide supply chain flexibility. But has pharma outsourced so much, for so long, that it gradually ceded both control and capability across complex supply chains? One impact has been on the internal know-how needed to digitalise effectively – to define a coherent data model, to understand how systems should talk to each other. What good looks like, in practice, was quietly eroded. The growth in third-party partners, operating on a different business model with different priorities, did not fill that gap and added complexity to the digital landscape.

Against that backdrop, the single biggest debate in pharmaceutical manufacturing right now is systems architecture. What combination of technologies, connected, will unlock the value in data and enable autonomous decision-making at scale? What are the advantages and disadvantages of standardising on a single data platform versus orchestrating multiple platforms across an organisation? What factors should determine whether a cloud-first, hybrid, or on-premise infrastructure strategy is most appropriate, particularly when balancing latency, security and intellectual property considerations? Is it more effective to build a data intelligence layer that virtualises and harmonises information from existing systems, or to replace legacy platforms entirely with a new digital architecture?

It is a genuinely difficult judgment and many organisations continue to wrestle with it. The technology landscape is moving faster than ever – cloud capabilities that did not exist two years ago are standard today and connectivity between operational systems that was not commercially viable eighteen months ago is now mature. In that environment, companies regularly commit large sums to replace entire operational technology system estates when a data product built on existing infrastructure or the cloud would deliver the same outcome for a fraction of the cost, with the same or better result and far less disruption.

No single organisation can answer this question alone. The complexity is too great, the pace of change too fast and the consequences of getting it wrong too significant. Approaching this through genuine collaboration, with partners who have the expertise and the reference points from working across the industry, is a de-risking strategy.

Because the question every leadership team needs to answer honestly before making a commitment is: how can it be known that the right decision has been made? Once an architectural pattern has been chosen – centralised versus federated data, single-vendor versus composable stack, cloud versus hybrid – the organisation is effectively locked into that approach.

The companies making real progress share one characteristic – a genuine risk appetite at the very top. Chief executive officers (CEOs) and chief operating officers who are prepared to make the financial commitment, back the right people and hold the course across what is a three-to-five-year transformation. That level of investment looks significant but relative to what it returns, pays for itself many times over. Accelerating multiple assets to market earlier and driving a 10 to 30% productivity improvement in the supply chain delivers three key benefits simultaneously: faster time to market; stronger compliance performance; and a measurably reduced cost and carbon footprint.

But the organisations moving fastest are not simply those with the largest budgets. They are those bringing in people who understand the context and culture of a manufacturing environment, the people who have sat in those seats, carried that accountability, and can distinguish between a technically elegant solution and one that will actually work at scale on a factory floor. Generic solutions, deployed without that domain depth, do not deliver. The cultural transformation required – bringing scientists and engineers who have worked the same way for 25 years into a genuinely different way of operating – cannot be managed by people who have never lived it.

Full autonomous manufacturing across a complete supply chain is more likely a 2035 story than 2030. But the direction is clear and the momentum is building. The window for getting the foundational decisions right is not as long as many boardrooms believe. The next blockbuster will not come from a molecule alone. It will come from the organisation that gets its medicines to patients faster, more reliably and at lower cost than its competitors. That is a CMC acceleration and manufacturing problem, and it starts with having a clear data strategy supported by the right systems architecture.

Dave Tudor is CEO and co-founder of DTG, chairman of Plvs Ultra and visiting Professor at the University of Strathclyde, Glasgow, Scotland. Dave spent over twenty years at GSK, rising to vice president head of Global Manufacturing Strategy and vice president Primary Supply Chain. He holds a doctorate in Chemistry from the University of Glasgow, Scotland, and a Masters in Manufacturing Leadership from Cambridge University, UK.