When a patient reads the label on their medicine bottle, he or she naturally relies on the medicine to contain the correct drug, be safe, work as intended and list the correct dosage. The pharmaceutical companies that produce these medicines similarly must rely on their internal manufacturing processes and quality control testing to generate the medicine responsible for this patient trust.
For the development of biologic medicines, the process of generating a quality product is less straightforward than that of a small-molecule medicine, like pain relievers such as aspirin. Selecting the right partner, such as the ‘Central GMP Testing Laboratory’ model, can smooth the path to validation and consistent manufacturing quality for your biologic.
As complex and unique entities, biologic medicines require more significant and challenging analytical testing than small molecules. For example, one of the critical attributes of a biologic medicine to characterize is its structure, found in a number of dimensions: (i) the primary structure or how the amino acids are connected together, (ii) the three-dimensional structure or how the biologic exists in space and (iii) the how the biologic molecules interact with each other in solution. Appropriately assessing the structure in multiple dimensions is a critical element to defining overall analytical control strategy for each biologic medicine.
The Process is the Product
The multi-dimensional structure of a biologic isn’t the only element of complexity. Manufacturing a biologic requires living systems (themselves complex) and multi-step purification processes in order to generate the active pharmaceutical ingredient (API) and finally the drug product. Throughout this process, the multitude of steps involved must be sufficiently characterized and validated in order to identify and quantify necessary critical quality attributes (CQAs).
The resulting analytical data measures the output of this process and defines the associated CQAs that constitute the unique signature for each biologic medicine. These data serve as the foundation for demonstrating control of the process and by extension, the product. And it’s not just data for one particular batch—analytical data establishing that acceptance criteria have been met must span all the way from preclinical testing through launch in order to sufficiently characterize the process as well as the product.
Analytical support includes structural characterization using general and specialized protein chemistry methods, comparability testing, as well as GMP release and stability under the oversight of the Quality Assurance Unit. Analytical testing is performed numerous times throughout the lifecycle of development, a period spanning a number of years. As assays improve and the manufacturing processes evolve to support scale-up, these stringent processes must be repeated to ensure the same predictable outcome of producing a high-quality product.
Even with a rigorous and systematic approach to control the quality of one’s process, data variability is inevitable. Too much variability can result in an out-of-specification (OOS) or out-of-trend (OOT) event. These quality events quite often are the result of hand-offs in the process, for example, when testing is transferred from one lab to another.
Use of multiple testing labs can also cause overall control to slip. While each lab individually may have shown acceptable accuracy and precision, the aggregate data from all labs throughout the molecule’s lifecycle are more likely to exhibit increased variability that can result in a failure to meet product acceptance criteria. This lack of control, caused by the analytical bias inherent to testing performed across multiple labs, makes it difficult (or impossible) to link early nonclinical trial material with late-phase manufacturing—a requirement for a successful regulatory filing. A lack of control over one’s process and product as determined by analytical testing can potentially have adverse effects on lot comparability and regulatory registrations.
Establishing Analytical Control
While variability cannot be completely eliminated, it can be controlled to a manageable, acceptable state. Analytical methods should be robust enough to allow one to link critical process parameters to CQAs of the API and drug product. By reducing and controlling variability at the beginning of the product’s lifecycle, it’s possible to establish control over the process (and hence the product) from the beginning of development through commercial launch.
The best opportunity for establishing control over the process and the product is one in which a single testing lab delivers analytical data, thereby eliminating the analytical bias inherent to testing done at multiple labs. Centralized analytical testing offers the greatest potential for a high-quality registration package.