Acceptance criteria
Numerical limits, ranges, or other suitable measures for acceptance of results from analytical procedures that a drug substance, drug product, or materials at other stages of their manufacture should meet (
1
).
Numerical limits, ranges, process signatures, or other suitable measures that are necessary for making a decision to accept or reject the result of a process, in-process variable, a product, or any other convenient subgroups of manufactured units (
2
).
Numerical limits, ranges, or other suitable measures for acceptance of test results (
3
).
Accuracy
The accuracy of an analytical procedure expresses the closeness of agreement between the value that is accepted either as a conventional true value or an accepted reference value and the value found (
4
).
Active pharmaceutical ingredient (API) See
drug substance
.
Analytical procedure
Refers to the way of performing an analysis. It should describe, in detail, the steps necessary to perform each analytical test. This may inclu...
Elements of the biopharmaceutical industry’s new operating paradigm have inevitably created an immediate need to condense, interpret, and relate their implications to existing regulatory and industry practices. This also provides us with an opportunity to look at them in a broader context and in relationship to one another. Such a perspective may open up new directions in discussion on how design and control aspects of biopharmaceutical manufacturing are likely to evolve. These are exciting times for scientists, engineers, statisticians, quality professionals, and regulators alike because contributions from each are needed to establish new practices that will deliver on the intentions and expectations of these new initiatives.
In Chapters 2 and 3, we explore in a harmonized context the fundamental concepts of
quality by design
(QbD) (
1
,
2
),
process analytical technology
(PAT) (
3
),
quality risk management
(QRM) (
4
) and pharmaceutical quality (
5
,–
6
) initiatives, as well as the emerging fie...
The level or intensity of product and process understanding that can or should be achieved beyond the acceptable minimum level promises to be the scope of a continuing debate among biotech industry and its regulators. In practice, the path of increased understanding may follow a series of incremental steps toward the
desired state
(Figure 1) after a product launch. Realistically that is expected to occur when the level of product and process understanding has reached or slightly exceeded the minimum regulatory acceptable level. Development of such understanding beyond information collected from product and process characterization studies (see the appendices online) during development can come from using a
process analytical technology
(PAT) approach for process monitoring or using newer scientific approaches such as
systems biology.
Continual or Continuous Improvement
Some authors prefer the term
continual improvement
over
continuous improvement
.
Although both are similar, they may invoke diffe...
A rich cup of coffee is what comes to mind for many people when you mention the word
robust
. For biotechnologists it is often a comfortable term, generally referring to the overall strength or ruggedness of a
manufacturing process
. However, the origin of the robustness concept for manufacturing is found in the field of robust design, which has for decades been a rigorous discipline with its own metrics, algorithms, and mathematical tools. Lately it has experienced a renewed interest in further development of its formal approaches, mathematical modeling, and applications. Applications of robust design exist in many specialized disciplines from economics to engineering (including bioprocess engineering), with each emphasizing different elements of the concept.
Process Robustness
For bioprocessing professionals, an appropriate definition of
process robustness
is provided in ICH Q8: “ability of a process to tolerate variability of materials and changes of the process and equipment without negative impac...
Today, there is much discussion regarding the promise of improved insight into bioprocess industry processes. Look to the pages of industry publications such as this one, and you’ll see that industry leaders in process measurement and control have begun to discuss openly the potential for simulating and modeling bioprocesses.
“Important opportunities such as the application of mass spectrometers, dissolved carbon dioxide probes, and inferential measurements of metabolic processes have come to fruition today opening the door to more advanced process analysis and control technologies,” says Greg McMillan with Emerson Process Management.
However, speak to such professionals for any length of time, and you’ll learn that unlike processes associated with petroleum manufacturing, for example, which have been modeled for nearly 40 years, biotechnology involves living processes, which are much more challenging to predict. But if the industry is going to improve its process reliability and product quality while red...
Broadley-James Corporation, Emerson Process Management, and the University of Texas at Austin are working together to examine and quantify the potential for faster optimization of batch operating points, process design, and cycle times. We’re also looking for more reproducible and predictable batch endpoints.
The objective of this effort is to show that the impact of PAT can be maximized through the integration of dynamic simulation and multivariate analytics in a laboratory-optimized control system during product development.
Data from bench-top and pilot-plant cell culture runs are being used to create multivariate analytic and high-fidelity, first-principle cell culture models to prototype process changes. The tools that are being used could provide significant improvement in process development and process control by laying a foundation for real-time release capabilities as defined by the PAT guidelines (
1
). Potential benefits include a more automated, seamless, and effective commercialization proce...
+2Currently the biopharmaceutical industry is transitioning to a new business model of production efficiency through implementing operational excellence (Op Ex). Borrowing from such principles as “lean manufacturing” and
“Six Sigma”
(6σ), and incorporating
quality by design
(QbD) (
1
), Op Ex is being applied through the implementation of such advanced enabling concepts and technologies as
quality risk management
(QRM) (
2
),
process analytical technology
(PAT) (
3
), and
systems biology
(SB) (
4
).
Some people see a conflict here: This paradigm shift is occurring amid ever-increasing product development costs, looming biogenerics and biosimiliars, and shortened
product life-cycles
. On the contrary, however, those very burdens contribute to the industry’s increasing need for innovation and efficiency, whether that be in product and process development (including control strategies), manufacturing, or quality assurance. And that is precisely what the FDA intended to address with its guidances on ...
