Why Controlling Critical Material Attributes (CMAs) Matters in Pharmaceutical Technology Transfer
Why Controlling Critical Material Attributes (CMAs) Matters in Pharmaceutical Technology Transfer
Your process was validated. Your equipment matches. Your batch records are identical. Yet the receiving site still struggles to reproduce the sending site’s results. The root cause is often hiding in a place no one thought to look: the raw materials.
The Unseen Variable
Technology transfer is typically framed as a transfer of process knowledge. Documents move, equipment specifications are compared, operators are trained. But processes do not run on documents. They run on materials.
In pharmaceutical manufacturing, the properties of incoming materials, the particle size distribution of an API, the moisture content of an excipient, the polymorphic form of a drug substance determine how a process behaves. When these properties vary beyond a certain range, even a perfectly transferred process can produce a product that fails its critical quality attributes.
This is why Critical Material Attributes (CMAs) matter. A CMA is a physical, chemical, biological, or microbiological property of an input material that must be controlled to ensure the process consistently produces a product meeting its Critical Quality Attributes (CQAs) [1]. Without controlling CMAs, the control strategy has a hole in it.
What Makes an Attribute “Critical”
Not every material property is a CMA. A property becomes critical when its variability has a demonstrated impact on a CQA. This link is established through risk assessment and experimentation during development, and it must survive the scale change inherent in technology transfer.
The ICH Q8(R2) guideline makes this explicit: a control strategy should include “control of input material attributes (e.g., drug substance, excipients, primary packaging materials) based on an understanding of their impact on processability or product quality” [1]. The word understanding is the operative term. A specification without understanding is not a control strategy; it is a hope.
The identification of CMAs is typically risk-based. Failure Mode and Effects Analysis (FMEA) commonly screens potential material attributes, followed by Design of Experiments (DoE) to quantify their impact on CQAs [2]. This two-step process distinguishes attributes that seem critical from those that data show are critical.
Why Transfer Amplifies the Risk
In development, a process is characterized using the materials at hand. Those materials may come from a single supplier, a single lot, or a narrow range of lots that happened to arrive in the lab. The CMA ranges established during development describe that material history, not the material universe.
Technology transfer changes the material reality. A receiving site may qualify a second supplier. The original supplier may adjust its manufacturing process within the compendial monograph. Storage and shipping conditions may differ. The material that arrives at the receiving site may be within specification and still fall outside the range that the process was designed to handle.
A study on continuous manufacturing of a high-drug-load formulation demonstrated this risk concretely. While product CQAs depended primarily on process parameters, process stability was strongly affected by API CMAs [3]. API batch-to-batch variability could be managed but only by applying suitable granulation conditions, meaning the process parameters had to compensate for material variability. Without understanding that relationship, the receiving site would have no basis for adjustment.
The Consequence of Getting It Wrong
When CMAs are under-specified or poorly understood, the failure modes are predictable:
Process instability. Material variability that exceeds the process’s tolerance can cause feeding issues, content uniformity failures, or dissolution drift. In continuous manufacturing, where materials are continuously redistributed and cannot be re-measured, this risk is amplified [4].
Fragile control strategies. A control strategy defined by tight CPP ranges that were achievable in a benchtop reactor may fail at commercial scale, where thermal mass, mixing dynamics, or shear profiles differ. Without CMA understanding, there is no room to adjust [5].
False confidence in specifications. A material can meet its compendial specification and still fail to perform. USP monographs describe minimum quality, not fitness for a specific process. Treating the monograph as a CMA range is a common but dangerous shortcut.
Investigative burden. When a batch fails and the cause is material variability, the investigation is expensive. The material passed release testing. The process stayed within CPP ranges. The failure appears inexplicable until someone traces it back to an uncharacterized CMA.
A Practical Framework for Transfer Teams
When preparing a technology transfer, the following questions help identify and manage CMA risks:
- Has the CMA been linked to a CQA through data? Not an assumption. Not a supplier certificate of analysis. Not a textbook correlation. Actual experimental evidence or mechanistic understanding showing that variability in this attribute affects this quality attribute.
- What range of material was studied? The CMA range should describe the material, not the lots that happened to be available. If a second supplier is possible, the range must account for supplier-to-supplier variability.
- Can the receiving site measure it? A CMA that cannot be measured at the receiving site cannot be controlled there. Method transfer for CMA testing is as important as method transfer for product testing.
- Does the control strategy account for drift? Some material properties change with storage time, temperature, or humidity. The CMA range at release may not describe the material at point of use.
- Is the CMA range compatible with the receiving site’s supply chain? A range that only one supplier can meet is not a control strategy. It is a dependency.
References
[1] ICH Q8(R2) Pharmaceutical Development: definitions of CQA, CMA, Design Space, and control strategy elements
[2] ICH Q9(R1) Quality Risk Management: risk assessment frameworks for linking material attributes to CQAs
[3] Stauffer, F. et al. “Managing API raw material variability in a continuous manufacturing line; Prediction of process robustness.” International Journal of Pharmaceutics (2019): empirical demonstration of CMA impact on process stability
[4] Pasindu Herath Pathirannahalage, Ayumu Nabetani, Shota Kato, Kanta Sato, Keita Yaginuma, Shuichi Tanabe b, Manabu Kano, Proactive residence time distribution method for determining low-risk setpoints of critical material attributes in pharmaceutical continuous manufacturing
[5] Kossi Molley, Navigating the Pitfalls of MSAT for Successful Technology Transfer, 2026-03-30, Mivado GlobalPerformance
[7] DeepSeek. (2026). DeepThink Model, Large Language Model, https://www.deepseek.com/