The shortcomings of accurate rate estimations in cultivation processes and a solution for precise and robust process modeling

Self Cite

Process characterization is necessary in the biopharmaceutical industry, leading to concepts such as design of experiments (DoE) in combination with process modeling. However, these methods still have shortcomings, including large numbers of required experiments. The concept of intensified design of experiments (iDoE) is proposed, that is, intra‐experimental shifts of critical process parameters (CPP) that combine with hybrid modeling to more rapidly screen a particular design space. To demonstrate these advantages, a comprehensive experimental design of Escherichia coli (E. coli) fed‐batch cultivations (20 L) producing recombinant human superoxide dismutase is presented. The accuracy of hybrid models trained on iDoE and on a fractional‐factorial design is evaluated, without intra‐experimental shifts, to simultaneously predict the biomass concentration and product titer of the full‐factorial design. The hybrid model trained on data from the iDoE describes the biomass and product at each time point for the full‐factorial design with high and adequate accuracy. The fractional‐factorial hybrid model demonstrates inferior accuracy and precision compared to the intensified approach. Moreover, the intensified hybrid model only required one‐third of the data for model training compared to the full‐factorial description, resulting in a reduced experimental effort of >66%. Thus, this combinatorial approach has the potential to accelerate bioprocess characterization.

Section: Introductionmentioning

confidence: 99%

Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization

Bayer

Striedner

Duerkop

2020

Self Cite

“…Bayer et al. [ 33 ] point out that the choice of the rate calculation method largely impacts the accuracy of the calculated rates. They concluded that fitting a cubic smoothing spline function is better than a step‐wise integration.…”

Section: Discussionmentioning

confidence: 99%

Error propagation in constraint‐based modeling of Chinese hamster ovary cells

Széliová

Iurashev

Ruckerbauer

et al. 2021

Chinese hamster ovary (CHO) cells are the most popular mammalian cell factories for the production of glycosylated biopharmaceuticals. To further increase titer and productivity and ensure product quality, rational system-level engineering strategies based on constraint-based metabolic modeling, such as flux balance analysis (FBA), have gained strong interest. However, the quality of FBA predictions depends on the accuracy of the experimental input data, especially on the exchange rates of extracellular metabolites. Yet, it is not standard practice to devote sufficient attention to the accurate determination of these rates. In this work, we investigated to what degree the sampling frequency during a batch culture and the measurement errors of metabolite concentrations influence the accuracy of the calculated exchange rates and further, how this error then propagates into FBA predictions of growth rates. We determined that accurate measurements of essential amino acids with low uptake rates are crucial for the accuracy of FBA predictions, followed by a sufficient number of analyzed time points. We observed that the measured difference in growth rates of two cell lines can only be reliably predicted when both high measurement accuracy and sampling frequency are ensured.

“…The estimation of the rates can be accomplished in two different ways, a differential or integral way. [ 41–44 ] Since the interest was in analyzing changes in the specific rates over time, the differential way was adopted here, following the best practice, [ 45,46,28 ] that is, Starting from the integrate version of the material balance (Equation (5)), the rate related terms were isolated on the right‐hand side since they cannot be measured:

c_{normalex} (t_{i}) \cdot V (t_{i}) - c_{normalex} (t_{0}) \cdot V (t_{0}) - \int_{t_{0}}^{t_{i}} u \cdot d t = \int_{t_{0}}^{t_{i}} q \cdot x \cdot V \cdot d t

Fit arbitrary time dependent functions (e.g., cubic smoothing splines, gaussian process models, polynomials or others), f ( t , w ), to approximate the measured quantities,

y_{m} false(t_{i} false) = c_{ex, m} false(t_{i} false) \cdot V_{normalm} false(t_{i} false) - c_{ex, m} false(t_{0} false) \cdot V_{normalm} false(t_{0} false) - \int_{t_{0}}^{t_{i}} u_{normalm} \cdot d t

, such that the residual ɛ was small, though the function also does not overfit the data.

y_{normalm} = f (t, w) + ε

Build the derivative of f ( t , w ) analytically with respect to time, that is,

\frac{d f false(t, w false)}{d t}

Evaluate the derivative at the time instance t i at which the concentrations have been measured (assuming that the concentrations have the lowest measurement frequency) and divide by the approximated biomass ( x m ( t ) · V m ( t ) = g ( t , ω...…”

Section: Methodsmentioning

confidence: 99%

Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

Richelle¹,

Lee²,

Portela³

et al. 2020

In recent years, multivariate data analysis (MVDA) and modeling approaches have found increasing applications for upstream bioprocess studies (e.g., monitoring, development, optimization, scale-up, etc.). Many of these studies look at variations in the concentrations of metabolites and cell-based measurements. However, these measures are subject to system inherent variations (e.g., changes in metabolic activity) but also intentional operational changes. It is proposed to perform MVDA and modeling on data representative of the underlying biological system operation, that is, the specific rates, which are per se independent of the scale, operational strategy (e.g., batch, fed-batch), and biomass content. Two industrial case studies are highlighted to showcase the approach: one is HEK medium performance comparison study and the other is CHO scale-up/-down study. It is shown that analyzing processes in this way reveals insights into behavior of the underlying biological system, which cannot to the same degree be deducted from the analysis of concentrations.