March 2008 Supplement

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...