Bioprocesses have long been ubiquitous in the production of modern pharmaceuticals and drugs. Contemporary bioprocesses are
being increasingly used in the production of many other products, ranging from biodegradable plastics, packing materials,
and other throwaways, to non-fossil fuels such as ethanol and biodiesel, and commonly needed human spare parts such as artificial
skin and cartilage. Fluorescence-based sensing technologies, which can greatly decrease overall development time, labor, and
costs, become an increasingly useful tool, particularly because their use permits a degree of miniaturization, scalability,
and multiplexing that was previously unavailable.
Partly because of the growing number of applications, in the last few decades "faster, better, cheaper" has driven advances
in bioprocess techniques in much the same way that it has driven advances in electronics, medical care, and other competitive
industries. Even minor technological improvements in the early screening stages of drug discovery can improve speed to market
and increase profits. Improvements in the pilot and production stages of development lead to reduced production costs that,
for a long-lived drug (the ideal goal of any such development endeavor), can lead to increased profits over the longterm.
(New Brunswick Scientific)
The initial stages of a bioprocess development cycle, whether for a drug or other product, typically require an enormous number
of experiments (thousands or even tens of thousands) to select an optimal cell line and media formulation. Even then, the
cell line and formulation selected is merely the best of those tested. The more combinations tested, the better the chance
that the combination selected will be close to optimal.
These experiments must be done quickly to minimize development time. This requirement mandates that the experiments be done,
to the extent possible, in parallel. Until recently, this initial screening was done almost exclusively in well plates or
flasks, vessels that were reasonably inexpensive and thus economically feasible for a high degree of parallelism. However,
these systems run mostly blind, with little or no instrumentation to track and control relevant process parameters.
Typically, the only data available to the research scientist are the results of offline measurements, which are labor intensive
to take and produce results of questionable accuracy because process parameters are subject to measurable change during the
sampling process. Consequently, little data are obtained regarding optimal process parameters; process optimization is generally
left to be done heuristically in bioreactors. The latter systems are adequately instrumented and capable of measuring and
controlling salient process parameters. However, because of the discrete and expensive nature of experiments run in such systems,
a fairly limited number of experiments may be performed in an attempt to determine what constitutes optimal culture conditions.
Even small errors in what are determined to be (in contrast to what actually are) optimal conditions can result in enormous
increases in production costs over time. In addition, if the range of allowable variances in such conditions is not adequately
explored and documented, the possibility exists that valuable and viable batches of product may be discarded because of small
excursions from so-called ideal culturing conditions, even though the excursions may actually be of little consequence.
Process parameters are needed even during the screening stages of development. This is illustrated in Figure 1, which shows
an experiment performed with E. coli in a 50-mL flask. As shown, the dissolved oxygen (DO) level reaches its minimum (and hence the cell line its viable maximum)
just three hours into the run. Commonly, such an experiment would be started in a flask, which would then be left to continue
overnight without interruption until morning. Offline sampling at that point in time would be misleading because it would
show only the end of run conditions and provide no information as to what actually transpired during the run.
Of course, small laboratory-scale bioreactors could be used for the initial screening of cell lines and media as well as for
process optimization. However, purchasing large numbers of individual bioreactors for the screening stage would be prohibitively
expensive and setting up, running, breaking down, and cleaning a large number of such bioreactors would require an enormous
amount of labor. If bioreactors were to be used, the degree of parallelism (and hence the number of experiments performed
in a given period of time) would be quite limited or else the time required for the initial stages of the development cycle
would increase dramatically.