“CMC was not usually a core part of the planning — it was more of an afterthought. Now that funding has tightened, that is changing.”
Siddhartha Jain, President of Shoolin Management Company, has spent two decades in biologics CMC across Novartis, Regeneron, Sanofi, and other major companies, contributing to the development and manufacturing of over 10 clinical and commercial molecules.
In this episode of the PharmaSource podcast, Sid shares why raw material variability remains one of the industry’s most underestimated risks, what separates a technically proficient CDMO from one that is merely experienced, and why digital CMC transformation fails when it ignores people and process.
1. Funding Pressure Is Finally Making CMC a Strategic Priority
For years, CMC was treated as a downstream concern — something companies would address once the science was proven and capital was available. Sid argues that tight funding conditions have changed this dynamic.
“When the liquidity was high, the companies were more flexible and open to building their internal facilities early on,” he explains. “The level of planning was not as detailed because the companies knew that when they started to run out of funds, they could go back and raise more.”
That behavior has shifted. Companies are now leaner, more selective about which programs they advance, and far more deliberate about cost modeling. “CMC costs are the highest costs in getting to the IND,” Sid notes. “So the companies are planning early on how much it would take, how long it would take to get to the clinic.”
His advice for early-stage companies is to resist the instinct to build physical facilities and instead leverage CROs and CDMOs to preserve capital. “Start with outsourced vendors, get to the critical mass, and then you can slowly start to build piece by piece your physical internal capability.”
2. Experienced CDMOs Vs Technically Proficient Ones
CDMO selection is one of the most consequential decisions an early-stage biotech makes, and Sid argues that experience alone is not a sufficient criterion. The distinction that matters is scientific culture.
He categorizes CDMOs into three broad groups: newer organizations building their track record, those with extensive execution history across multiple molecules, and a smaller subset that goes further — demonstrating genuine investigational capability when processes deviate from expectation.
“The company that is technically proficient will be able to understand what is the reason for this atypical performance, how the process can be changed to mitigate that risk, and they will be able to help us make the process only stronger as we go along,” Sid explains.
“The only way you can find out which is a technically proficient CDMO is either through personal experience or from somebody who has actually seen that CDMO was able to take a step back when there was an atypical performance.” There is, as Sid puts it plainly, no magic bullet.
3. Raw Material Variability Remains One of CMC’s Most Underestimated Risks
While the industry has achieved strong control over process design and manufacturing equipment, raw materials represent a persistent vulnerability. “We don’t have direct control over the raw materials, we don’t control the supply chain, and we don’t necessarily know every impurity that is present,” Sid says.
Variability in raw materials can affect cell growth, chromatography step performance, membrane performance, and ultimately process performance, product yield, and quality. Sid outlines six categories of raw materials in biologics manufacturing: single-use systems, cell culture media, chromatography resins, membranes, salts and chemicals used in buffer preparation, and excipients.
A key reason variability tends to surface at manufacturing scale rather than during development is the exposure to multiple raw material lots over time. “When we go to the manufacturing scale, manufacturing continues for years and years. Not only are we catching variation across multiple lots, but we are also catching variation over time.”
Scale itself amplifies the risk. “Simply because of the stresses and non-linearities involved at a larger scale, a small variation in the raw material can exacerbate those effects — on shear, on cells in the bioreactor, on column packing efficiency, on membrane performance.”
4. Managing Raw Material Risk Requires a Structured, Long-Term Program
Sid is clear that raw material variability cannot be solved with a one-time initiative. “It’s more like a program,” he says. In organizations he has supported, dedicated material science teams manage this systematically across several interconnected activities.
The first is building supplier relationships deep enough to receive early warning of process shifts — even when those shifts don’t yet breach specification. “The value of an attribute may still be within specification, but the vendor may be seeing a shift in their own process. The sooner we can learn about it, the better.”
The second is developing a working understanding of material quality attributes — the specific properties of each raw material that influence process performance or product quality. Critically, this is not the same as the raw materials certificate of analysis. “Not everything on the certificate of analysis may be a material quality attribute. And not everything that is a material quality attribute may be on the certificate of analysis.”
Identifying those attributes is a combination of scientific knowledge, published literature, and accumulated experience. “It’s an ongoing, learning process. We see something happen, we immediately realize it is impacting the process, and that becomes something we monitor.” The quality attributes that matter are also context-specific — the tolerance for an impurity in a buffer component differs from that in an excipient that enters the patient.
5. Digital CMC Transformation Fails Without People and Process
Sid defines digital CMC not as a destination but as a three-stage maturity journey: knowledge management and data capture; predictive modelling of process performance; and ultimately AI-driven process automation. “As we mature, we move to the next stage. Different companies are at different stages of maturity — but all of them are on the journey in their own respect.”
The most common failure mode, in his experience, is treating digital transformation as a technology procurement exercise. “Many in industry tend to have the viewpoint: let’s make an investment, buy a digital product, get it installed, and now we are digital.” That approach consistently underdelivers because it neglects the other two elements of successful transformation: people and processes.
Change management is as important as the technology itself. “We have to make sure that the end users are giving feedback that is included in the design of the solution, and we are explaining what the benefit to them is on a day-to-day basis. There will always be folks who will be skeptical of the digital solution — how can we work with them? How can the digital solution be simple enough that everyone can use it seamlessly?”
For mid-stage companies with limited resources, Sid’s recommendation is straightforward: invest in the foundation first. “Regardless of the size of the company, they should focus on knowledge management — the electronic digital knowledge management — because that is the fundamental layer. Once that has been achieved, the subsequent layers will more easily fall into place.”
6. AI and Continuous Manufacturing Represent the Biggest Long-Term Opportunities
Looking ahead, Sid sees the most transformative potential in two areas: AI-driven process insights and closed end-to-end continuous manufacturing.
On AI, his perspective is deliberately measured. “The intent and the opportunity are not to look at it from the perspective of replacing humans, but looking at it from the perspective of providing new information and new insights that help us improve processes working alongside humans.”
On continuous manufacturing, he identifies a longer-term possibility that the industry is only beginning to explore: if closed end-to-end processing matures sufficiently, it could eventually reduce or remove the need for clean room requirements and environmental monitoring in manufacturing facilities. “We are not there yet, but that’s the blue sky — and if we are able to even just reduce the amount of clean room requirements, that can have a significant impact on the cost of building and operating a facility.”
