PHARMACEUTICALS
Building that kind of coursecorrection into how we work, rather than treating a setback as a failure of the methodology itself, is part of what lets a team keep testing assumptions on an established product without losing momentum when an early hypothesis fails to pan out.
CDMO scale advantage
Trust between a customer and manufacturer is what gives teams the room to pursue this kind of improvement work on an established, commercially critical product in the first place. That trust is built over years of on-time, in-full delivery, giving sponsors confidence to support improvement work on a product already performing well.
A CDMO ' s scale advantage lies in the breadth of programmes it supports across sponsors, modalities and manufacturing platforms. Unlike an in-house team focused on a single pipeline, a CDMO builds pattern recognition by repeatedly executing technology transfers, process development and commercial manufacturing across a diverse set of products.
A challenge that looks unfamiliar to one sponsor’ s team may be one the CDMO has already solved elsewhere, for a different product or sponsor entirely. That accumulated experience means problems are recognised faster and solved with more confidence, grounded in precedent, not trial and error.
That advantage matters even more as manufacturing pressures rise. Highpotency compounds and fast-tracked oncology programmes come with added complexity. These treatments often address urgent, unmet patient needs, so they are frequently pushed to market on accelerated timelines, sometimes before manufacturing is fully optimised. Accumulated pattern recognition becomes more valuable as a result, since it gives a team a faster path to closing that optimisation gap after launch instead of before it.
Many established manufacturing sites already run manufacturing execution and data-trending systems, and a CDMO working across a large volume of programmes is well positioned to layer AI and machine learning onto that foundation. Applied across dozens of products rather than one, those tools can flag deviations before they become downtime, surface patterns in batch data that would take a human team months to notice and point to which of hundreds of process variables are actually driving a yield or throughput gap. That turns years of accumulated cross-programme experience into something a team can act on faster.
Improving old processes
The assumption that a stable process is a finished one carries a real cost. Every year a mature product sits untouched because it is already meeting spec is another year of unclaimed potential, capacity that could support a new programme, savings that could improve margins and speed that could get drugs to patients faster.
Multiplied across an industry where the vast majority of manufacturing runs through long-established processes, that value is not confined to one product or one company. It is a standing gap in an industry that treats a stable, validated process as the end goal instead of a milestone along the way.
Closing that gap depends on three things: trust built over years of reliable delivery, a mindset that treats early setbacks as learning opportunities rather than failures and the breadth of experience to recognise an advantage a single company ' s own pipeline might never surface.
As high-potency compounds and fast-tracked programs bring more complexity into commercial manufacturing, fewer products will arrive at maturity already running at their ceiling. That makes the willingness to keep looking for ways to optimise manufacturing, and the experience to know where to look, a growing source of competitive advantage. Organisations that treat maturity as a starting point will keep finding value that their competitors, working from the assumption that stable means finished, will keep leaving on the table. ●
J j
Alessandro Turtu
DIRECTOR OF OPERATIONS- CORK, IRELAND
THERMO FISHER SCIENTIFIC alessandro. turtu @ thermofisher. com www. thermofisher. com
32 SPECIALITY CHEMICALS MAGAZINE ESTABLISHED 1981