Introduction
Achieving high volumetric productivities of biologic drugs in
cultivation is a key step in advancing candidate biologic drugs. The
outcome of this effort ultimately impacts manufacturing costs as well as
readiness for transitioning clinical-stage development (Love, Love, &
Barone, 2012). The development of standard, chemically defined media for
established manufacturing hosts, such as CHO, has made such transitions
efficient for monoclonal antibodies by achieving high biomass
accumulation, cell viability, operational consistency, and specific
productivities, streamlining development efforts (McGillicuddy, Floris,
Albrecht, & Bones, 2018; Rodrigues, Costa, Henriques, Azeredo, &
Oliveira, 2012). Nonetheless, optimizing productivity or quality
attributes for a specific product often still requires further
refinement of media (Ritacco, Wu, & Khetan, 2018). Such development may
require evaluating dozens of variants derived from a common standard
formulation to address the specific challenges encountered (Gagnon et
al., 2011; Loebrich et al., 2019). Media development for entirely new
biomanufacturing technologies, such as alternative hosts (Matthews, Kuo,
Love, & Love, 2017a) or new product modalities (Lu et al., 2016), may
also require new formulations or extensive optimizations due to limited
prior knowledge.
Common approaches to develop a medium to optimize a phenotype of
interest are often labor intensive, low throughput, or rely heavily on
extensive analytical capacity (Galbraith, Bhatia, Liu, & Yoon, 2018).
For example, analysis of residual media after cultivation requires
extensive capabilities for analytical characterization and prior
experience with the manufacturing host to identify potentially limiting
or toxic media components (Mohmad-Saberi et al., 2013; Pereira,
Kildegaard, & Andersen, 2018). As a result, optimizations can be slow
and iterative. Furthermore, for an alternative host such asKomagataella phaffii (formerly known as Pichia pastoris ),
there is substantially less, if any, prior knowledge available to
establish profiles for residual components in media after fermentation.
Other analytical techniques like RNA-seq combined with methods for
reporter metabolite analysis can guide media optimization, to generate
testable hypotheses regarding beneficial modifications to media
(Matthews et al., 2017a). Such genome-scale approaches, however, require
prior host-specific knowledge, such as well-annotated genomes, and are
still limited by slow iteration and labor-intensive preparations of new
media to test the hypotheses generated from computational analyses.
Alternative strategies for blending basal components for media allow
linear combinations of existing media to explore many variations rapidly
(Jordan, Voisard, Berthoud, & Tercier, 2013). While this approach
avoids slow iterative analyses, the typical experiment is labor
intensive to perform, often requiring independent preparations of over a
dozen stock media to combine (Rouiller et al., 2013). Similar to
analytical-based approaches for optimization, the selected variations of
media are simultaneously guided and constrained by prior experience and
media designs, which may limit the breadth of components examined
(Kennedy & Krouse, 1999). For less established hosts with fewer
available formulations of media, media blending may also require fullyde novo formulations for initial studies. Further complicating
such designs, different and new components for media can present
challenges in solubility or unanticipated interactions with other
elements in the formulations (Ritacco et al., 2018). New approaches to
blending could, however, enable fast, flexible experimentation and
minimize the time, labor, and analytical development needed initially to
optimize media for new applications and phenotypes.
Here, we present a novel and generalizable approach for the modular
development of media and demonstrate its use to create optimized media
for two different phenotypes—cellular growth and recombinant
expression of a protein (as measured by the secreted heterologous
protein titer) from Pichia pastoris . Our approach comprises two
modular parts for blending and optimization. We determined that a set of
simple concentrated stock solutions constructed in defined modules could
generate many media by blending or dilution. We then automated a simple,
inexpensive liquid handling system (Opentrons OT-2) to enable
high-throughput screening for the effects of diverse media on a
phenotype of interest in milliliter-scale batch cultures. To maximize
the benefit of this automated blending, we also developed an algorithmic
framework for systematic modular media optimization, beginning from a
simple minimal media (here a YNB-based one). This framework provides
insights pertaining to key media components during stages of
optimization, as well as overall mapping of the design space for the
media. In the examples presented here, the resulting defined media
developed with this strategy outperformed commonly used BMGY and BMMY
complex formulations for biomass accumulation and secreted heterologous
protein production.