A statistical approach to improve compound screening in cell culture media

Abstract

The Chinese hamster ovary (CHO) cell line is widely used for the production of recombinant proteins due to its high growing capacity and productivity, as well as other cell lines derived later than CHO. Adapting cell culture media for each specific cell line is a key to exploit these features for cost effective and fast product generation. Media supplementation is generally addressed by means of one‐factor‐at‐a‐time or classical design of experiments approaches but these techniques may not be efficient enough in preliminary screening phases. In this study, a novel strategy consisting in folding over the Plackett–Burman design was used to increase cell growth and trastuzumab production of different CHO cell lines through supplementation with nonanimal recombinant compounds. Synergies between compounds could be detected with a reduced number of experiments by using this methodology in comparison to more conventional fractional factorial designs. In the particular case reported here, the sequential use of this modified Plackett–Burman in combination with a Box‐Behnken design led to a 1.5‐fold increase in cell growth (10 × 106 cells/mL) and a two‐fold in trastuzumab titer (122 mg/L) in suspension batch culture.

Integrating nanoparticle quantification and statistical design of experiments for efficient HIV-1 virus-like particle production in High Five cells

“…Considering the information from previous reports, we decided to apply these novel techniques for optimizing the Gag VLP production process using the High Five/BEVS platform. Different methods are available for process improvement, from the classical one-variable-at-a-time approach to more sophisticated statistical design of experiments (DoE) (Puente-Massaguer et al 2019). So far, VLP production studies have focused either on the former approach (Krammer et al 2010;Pushko et al 2017) or on simple factorial designs (Pillay et al 2009;Pastor et al 2019), limiting the analysis of higher order effects between the variables influencing VLP production.…”

Machine Learning Optimization of Photosynthetic Microbe Cultivation and Recombinant Protein Production

“…Often DOE is carried out in multiple phases. An initial screening phase applies fractional factorial designs to test combinations of variables at 2 or 3 levels 22,23 . After selecting key variables an optimization phase may follow, which tests additional center and axial points to help estimate linear and quadratic response curves and thus better "tune" each variable 24 .…”

Optimizing Soluble Cues for Salivary Gland Tissue Mimetics Using a Design of Experiments (DoE) Approach

“…Guided by the literature [ 36 ], a 24-run folded-over Plackett–Burman design ( Table 2 ) was used to identify factors with a significant effect on Mist1, Aqp5, and Tmem16a mRNA expression to eliminate factors with negligible or negative effects. Folding over the design avoids obscuring the main effects of the factors with two-factor interactions.…”