Quality by Design for Biotechnology Products—Part 3 - Guidance from the Quality by Design Working Group of the PhRMA Biologics and Biotechnology Leadership Committee on how to apply ICH Q8,
Quality by Design for Biotechnology Products—Part 3
Guidance from the Quality by Design Working Group of the PhRMA Biologics and Biotechnology Leadership Committee on how to apply ICH Q8, Q8R1, Q9, and Q10 to biopharmaceuticals.
The data package for a market application (BLA or MAA) is enhanced by including data from full scale manufacturing runs, ideally
from the intended commercial manufacturing site. These data confirm that the normal operating ranges or set points for process
parameters lead to successful manufacturing at commercial scale. (GMPs) require formal process validation, and data from validation
batches traditionally have been expected as part of the marketing application. A successful process validation exercise signals
the beginning of the commercial life of the product and serves as the baseline for future process improvement efforts. Although
important, process validation batches provide limited data for assessing the long-term variability and process capability
of the manufacturing process. Rather, these data serve as one element in the larger dataset of process design and control
experiments. The importance of including full scale data and data from process validation batches in the marketing application
depends on platform experience and the state of qualification of small-scale models used to develop the process design space.2
Refining the Design Space
A refinement of the initial design space for cell culture, drug substance purification, and finished product manufacture may
be driven by several of the following factors:
increased process knowledge
increased product knowledge through clinical and nonclinical trials
stability studies
process changes
outcome of comparability exercises
further development of platform technologies
new analytical technologies.
The refinement of the design space is based on the same principles used to develop the initial design space. Starting from
the quality target product profile (QTPP), a risk assessment should be performed to identify the process parameters that should
be re-evaluated with respect to their potential impact on critical quality attributes using small-scale models and applying
tools such as DOE.
Continuous Verification, Process Changes, and Comparability
Continued process verification has emerged as a key principle of process validation.3 During commercial manufacture, additional data will be collected from postapproval batches. These data will be reviewed
periodically to confirm that the control strategy is appropriate. In some cases, this review may indicate that there is a
need to modify specifications or in-process controls. Process changes may or may not require adjusting process parameter ranges
to achieve a comparable quality in the drug substance or drug product. Testing for pre- and post-change comparability (see
ICH Q5E),4 therefore, can result in refining the ranges for critical process parameters (CPPs) and extending or reducing the number
of critical process parameters.
The prior existence of a design space can make it easier to characterize the influence of a change on the critical quality
attributes of drug substance or drug product or to confirm the robustness of chosen process parameter ranges (e.g., in the
case of scale-up, site transfer, or equipment changes).
Comparability Protocols and Expanded Change Protocols
The traditional comparability protocol (CP) has been available for more than 10 years in the US. The concept of a CP or similar
reporting structure has not been adopted by the other regulatory agencies, however. CPs are pre-approved by the FDA with predetermined
acceptance criteria that will be used to confirm product comparability following a discrete process change.5 The more recently adopted expanded change protocol (ECP) takes a more holistic approach and offers the use of a protocol
providing the approach and acceptance criteria that can be applied to multiple manufacturing process changes or a process
change across multiple related product types or manufacturing process platforms.1 The CP/ECP includes a quality risk assessment plan that provides assurance that changes to the process are formally assessed
for impact to product critical quality attributes. This type of protocol or plan encourages incorporation of technical innovation,
and process optimization, while maintaining product safety and efficacy.6
Table 1. Example of a change control matrix of postapproval reporting requirements for process changes
Table 1 provides an example of a proposed change control matrix that, if approved by the FDA (e.g., as part of the initial
marketing application or a later supplement), could clarify the postapproval reporting requirements in the US for several
process changes based on the impact to normal operating ranges, proven acceptable ranges, or design space limits. Postapproval
manufacturing changes, covered under the manufacturer's approved CP or ECP (or approved CMC postapproval management plan;7 see below), should require regulatory review and approval before implementation only in very limited cases, such as when
the change expands the existing design space of critical process parameters and alters the approved control strategy (see
Table 1). This scenario would permit most changes to or within the design space that result in comparable post-change product,
to be presented during inspections or during regular annual updates to health authorities.
This approach could lead to a future state in which prior approval supplements (assuming a satisfactory site inspection) would
be reserved only for changes where the post-change product or the control strategy are NOT comparable to the current product.
Anurag S. Rathore, PhD, is a consultant, Biotech CMC Issues, and a member of the faculty in the department of chemical engineering at the Indian Institute of Technology. Rathore is also a member of BioPharm International's Editorial Advisory Board.
Articles by Anurag S. Rathore, PhD
