Identifying Challenges in Implementing Scientific Advancements in Industry
- Fig. 1: Description of process validation in three stages, according to the FDA guidance from January 2011 : Stage 1 Process Design: The commercial manufacturing process is defined during this stage based on knowledge gained through development and scale-up activities; Stage 2 Process Qualification: During this stage, the process design is evaluated to determine if the process is capable of reproducible commercial manufacturing; Stage 3: Continued Process Verification: On-going assurance is gained during routine production that the process remains in a state of control.
- Fig. 2: Real-time estimation of biomass concentration in a fed-batch culture of Kluyveromyces marxianus in defined glucose medium. The white dots represent off-line evaluated dry cell weight measurements. The black line represents continuous, in-line estimates of the cell concentration evaluated from calorimetric measurements.
- Prof. Ian Marison‘s research group at the Laboratory of Integrated Bioprocessing (LiB) at Dublin City University and National Institute of Bioprocessing Research and Training (Nibrt)
Year 2001 was the dawn of a new era for the pharmaceutical industry. Over the past decades, since the Thalidomide scandal, regulatory actions ensured that quality of drugs, active pharmaceutical ingredients and biological products released to market was no longer an issue. However, difficulties still remain with the development and continuous operation of consistent and reliable processes leading to high value products, fit for their intended use. End-product quality control, quarantine and time-dependent end-point characterization govern quality assurance strategies of the sector.
Initial Stages of Process Analytical Technology
While semiconductor and microtechnology companies proudly display 6Sigma values, the biopharmaceutical field have tried to keep up to the 2Sigma values . Low Sigma values associated with biotechnology and pharmaceutical processes can be related to variability. Presently, variability, inherent to the complex nature of biologicals and their production, cannot be eradicated or drastically mitigated. Increased product and process understanding could help to identify inherent process variability and to manage it appropriately, ensuring that quality is built into products "by design". It is in this area that the Food and Drug Administration (FDA) grasped the importance of creating a fertile ground for change and innovation. A series of meetings and conferences resulted in the drafting of "Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach" launched in 2002, which led to the Process Analytical Technology (PAT) initiative .
Advanced Process Validation to Deal with Gap Between Science and Industry
What is the state of the ambitious framework ten years later? Did we reach the point where each process is fully understood, controlled and automated in order to produce consistently high quality products without extensive regulatory over-sight and time-consuming end-point quality control? Many global pharma companies have successfully implemented PAT systems in their R&D departments and production facilities, but interest in the concept is even higher in academic groups that are expanding PAT expertise.
However, there is still a consensus among all stakeholders that a gap exists between scientific advances and industrial reality. The FDA seems to be aware of the challenges the industry is facing by implementing PAT but also knows that encouraging process understanding and promoting continuous improvement for advanced variability management is paramount, especially for the efficient production of biologicals. The "Guidance for Industry - Process Validation: General Principles and Practices" (PV guidance) , published this year might be one way of bridging the gap and bringing process understanding, variation management, and scientifically driven process development and improvement into existing, "non-PAT-conducted" processes. The guidance defines process validation as "the collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product."  Considering all three stages (fig. 1) of a product's life-cycle and insisting on continued process verification and continuous process improvement should be a successful way to achieve implementation of scientific advances in industrial processes.
Reliable, but Simple Process Monitoring - Principal Pivot to Close the Gap
Process analysers are an important pillar of the PAT initiative and are also gaining importance in the PV guidance. Indeed "new analytical technology and modifications to existing technology are continually being developed and can be used to characterize the process or the product" , paving the way for innovation in the field of analytical sciences, including chemical, microbiological and probabilistic risk assessment techniques . Calorimetry is an example of a potentially useful yet completely under-utilised analytical tool. Applied for the real-time estimation of biomass concentration in microbial cultures (fig. 2), it can be employed for characterisation during process development to facilitate technology transfer from bench to industry and for process monitoring at industrial scale .
Smart Data, Information and Knowledge Management as a Key to a Successful Life-cycle Approach
The life-cycle approach, suggested by the PV guidance, enhances the importance of multivariate data analysis to unveil impacts of process parameters on quality attributes  and to elaborate "well-justified rationales"  for changes in the process. Most importantly, data, information and the gained knowledge need to be retrievable. Typically, an industrial process leading to the production of biologicals consists of a series of batch processes, involving various process parameters to be monitored and controlled. For example, a single run for the production of a therapeutic glycoprotein, generates several thousand data points, emanating from different process analysers, stored in various forms. Both the PAT and the PV guidance urge companies to apply knowledge management tools in order to learn from historical data, to gather deeper process understanding and to ensure a holistic approach to produce high-quality products. However, implementation of overall institutional knowledge bases is scarce. There is an urgent need for tools that allow combining expert knowledge, historical data and multivariate analysis techniques to provide meaningful data as a basis for reliable production processes [7, 8, 9]. The appropriate, user-friendly tools to retrieve multivariate data, to analyse it and to use it as a foundation on which to build a process knowledge base are lacking. The academic field did respond to the PAT initiative by focusing research on the development of faster and better performing process analysers, the application of chemometrics and the design of highly complex control strategies. But what about the forth pillar of the PAT initiative? The issue of "continuous improvement and knowledge management tools" has not been tackled extensively so far. Creating well performing expert- or knowledge- based control strategies and dealing with the complexity of the system requires sound and advanced information technology skills . Essentially these techniques and tools provide greater process understanding. Industry needs to embrace the technology in order to extract the knowledge and only then can they be used as acceptable tools for process monitoring and control from a regulatory standpoint. Interdisciplinarity, integrating information technology, chemometrics and engineering with biotechnology, is the key to a successful, holistic approach to close the gap between science and industry.
References are available from the author.