Biopharmaceuticals represent a significant and growing sector of the pharmaceutical industry. The global biopharmaceutical industry is currently worth over €107 billion, according to research conducted by BioPlan Associates (1). The industry should exceed €123 billion in 2015, says the International Market Analysis Research and Consulting Group (2).
Biopharmaceuticals are used in the prevention and treatment of disease and there are over 300 approved biopharmaceuticals on the market, with many in clinical development yet to come.
As with all pharmaceutical compounds, the cost and time required to bring a biopharmaceutical product to market has huge economic implications. The average cost of researching and developing a new medicine is claimed by the pharmaceutical industry to be approximately €1.4 billion (3). Process development for biopharmaceutical products is generally more labour intensive, time consuming and expensive than for comparable traditional pharmaceutical processes because of the large number of individual processes and potential variables involved.
In recent years, there has been a growing necessity to increase the speed, efficacy and information content associated to the development and scale-up of biopharmaceutical processes, particularly given the regulatory authorities current shift in philosophy towards quality-by-design (QbD). This QbD requirement for increased process understanding intensifies the work involved in the earlier phases of the drug-product lifecycle.
The benefit, however, is that the increased process knowledge can speed the technical transfer from development into manufacturing, deliver a more optimised, robust process with higher titres and greater reproducibility and aid in troubleshooting and root-cause analysis of deviations during production. Taking these factors into consideration, evolving a process development strategy that reduces costs and timelines while simultaneously providing greater process knowledge would be highly advantageous.
CURRENT STATE OF PLAY
Initial bioprocess development consists of the parallel strands of cell-line optimisation, clone selectio, and screening for media, feed components and strategies, as well as other process conditions.
[caption id="attachment_15960" align="alignright" width="1024"] Figure 1: Bioprocess development streams (click to enlarge)[/caption]
Shake flasks, the most common vessels used in early cell work, have served the biopharmaceutical industry well over the decades, but their limitations for optimising cell culture conditions are well known. Shake flasks allow control of temperature, ambient gas mix and agitation rate, but standard upstream bioprocess monitoring and controlling of critical process parameters such as pH, dissolved oxygen (DO) and feed schedules are beyond the capabilities of these vessels. However, these critical process parameters influence cell metabolism, viability and productivity, and ultimately product quality and stability.
Operation within such broad design-space variability intensifies the difficult task facing bioprocess developers in identifying the superiority of one clone over another or the influences of media, feed and supplementation strategies – factors that are instrumental in improving volumetric productivity. Selecting suboptimal clones during early development when using shake flasks is not uncommon and diminished cell productivity and product quality then persist through development and beyond.
Bioprocess development platforms used during early-stage process development should mimic the physical and mechanical characteristics of production-scale reactors to the greatest degree possible, to ensure consistency throughout development phases. Bench-top bioreactors have the potential to address process consistency and harmonise unit operations between development and production.
[caption id="attachment_15962" align="alignright" width="1024"] Figure 2: Operating the DASGIP bench-top bioreactor workstation[/caption]
However, traditionally bioreactors only routinely monitor pH, temperature and dissolved oxygen (DO) online. Unfortunately, these routinely measured variables do not provide significant insight into the mechanisms of the process. The lack of routine in-line measurements of critical process parameters (CPPs) reduces the potential for increased process understanding and ultimately the potential for direct control of the critical quality attributes (CQAs) of the product by manipulation of the appropriate CPPs.
The level of process understanding that can or should be achieved beyond the acceptable minimum level promises to be the scope of a continuing debate among the biopharmaceutical industry and its regulators. In practice, the path of increased understanding may follow a series of incremental steps toward the desired, optimal bioprocess design space. Development of such understanding beyond information collected from product and process characterisation studies during development can come from using a process analytical technology (PAT) approach for process monitoring.
PAT-ENABLED PROCESS DEVELOPMENT PLATFORM
In 2004, the United States Food and Drug Administration (FDA) defined process analytical technology (PAT) as a mechanism to design, analyse, and control pharmaceutical manufacturing processes through the measurement of CPPs which affect CQAs (4). The philosophy behind the FDA PAT initiative is that the CQAs of a product are directly determined by the CPPs.
[caption id="attachment_16087" align="alignright" width="800"] Figure 3: QbD philosophy for process development[/caption]
Therefore, the delivery of the desired CQAs can be ensured if the CPPs are identified, the nature of their relationships to the CQAs understood and appropriate control strategies then applied to guarantee a high quality, reproducible output from the process. Overall, this QbD philosophy states that designing a robust process depends on the interplay of two distinct factors: the level of process understanding achieved and the level of process control implemented (Figure 3, right). Processes that are both reproducible and robust can be achieved only with high levels of process understanding and process control.
Optimal cell growth is achieved only through a narrow range of environmental conditions. It is evident from Figure 3 that it is possible to operate a reproducible bioreactor process within narrow operating ranges without a high level of process understanding. However, bioreactor control provides special challenges due to significant process variability, the complexity and nonlinearity of biological systems, the need to operate in a sterile environment, and the relatively few real-time direct measurements available that help define the state of the culture.
Bioprocesses employ most of the same types of control as are used in other chemical industries, which consist mainly of traditional single input single output feedback PI (proportional + integral) controllers. These simple controllers are used to control the bioprocess variables (pH, DO, temperature) which are measured routinely online at regular sample intervals.
However, these control algorithms do not take into account the dynamics of the process and thus act solely to reduce the error between a defined set-point and the process variable of interest. Incorporating process knowledge into a control algorithm can enhance robustness and direct the process along the optimal batch trajectory. Processes that are both reproducible and robust can be achieved only with high levels of process understanding and process control. So it is not surprising that the PAT guidance emphasises both factors.
Measurement: Acquisition of Process Data
There is a growing interest in the concepts of product and process optimisation based on PAT to ensure public safety and product efficacy. The positive impacts of PAT that have been seen in small-molecule drug development and manufacturing are a significant part of the reason that the concepts are now being applied to the biopharmaceutical sector.
PAT is enabling a more fundamental understanding of how bioprocesses work and what influences their efficiency. There are many complexities associated with the cell-line chosen, the media, the quality of the nutrients, the processing conditions and the harvesting of the product. PAT can be used comprehensively in bioprocessing to help manufacturers exercise greater control over operations and simplify some of the complexity.
In-line process variables that could not be monitored in the past can now be measured, analysed and used for advanced control schemes. Process analytical technologies act as the ‘eyes’ inside the bioreactor. In bioprocesses, the need for real-time process information is particularly high, due to the complexity and unpredictability of the process.
The media used to support growth and protein production in animal culture are extremely sophisticated mixtures, often containing in excess of eighty different species, almost all of which are at very low concentrations when compared with small molecule production. Many of the species are from similar families, such as the twenty or so amino acids used by mammalian cells. The low concentrations combined with the structural similarities of multiple species means that finding an instrument with suitable sensitivity and specificity is non-trivial.
One promising means of biomass, substrate (glucose, glutamine, glutamate), by-product (lactate, ammonia) and end-product monitoring uses spectroscopic sensors based on near infrared (NIR), mid infrared (MIR) or Raman spectroscopy.. These optical methods have many desirable attributes for bioprocess monitoring: