3.1 Verification of the sequential/parallel cultivation approach
In the methylotrophic yeast Pichia pastoris, AOX is a key enzyme involved in the dissimilation of methanol . AOX catalyzes the first step in the methanol utilization pathway, the oxidation of methanol to formaldehyde and hydrogen peroxide .
Therefore the cell-specific internal AOX content gP2/X was chosen for investigations relating changes in Pichia pastoris metabolism during long-term cyclic cell-breeding cultivations.
A start up, inoculated from shake flasks, and three cell-breeding cycles, following the strategy described before, are shown in Fig. 4, by means of cell density cXL, methanol concentration cS2M, specific cell internal AOX content gP2/X, and specific methanol uptake rate qS2/X. Each cycle consists of a glycerol (S1) batch, a substrate-limited fed batch on glycerol and a pre-induction phase on methanol (S2).
Figure 4. Cyclically recurring cell-breeding cultivations with Pichia pastoris. Every cycle consists of a glycerol (S1) batch (u: S1 unlimited), a fed-batch phase (l: S1 limited), and a pre-induction phase on methanol (S2). cXL: cell density reconstructed from cell dry weight (○) (determined in duplicate); cS2M: methanol concentration in media phase; gP2/X: cell internal cell-specific AOX content () (determined in triplicate); qS2/X: specific methanol uptake rate (▿).
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As shown in Fig. 4, gP2/X was not observed during glycerol phase in the start up cultivation. The AOX promoter is repressed by unlimited growth on glycerol, which is well known from literature [22–24]. After methanol supply, gP2/X and qS2/X began to increase rapidly. During harvest/refresh operations and after initialization of a further batch process on glycerol (S1), associated with a fully depletion of methanol (S2), the gP2/X decreased rapidly. This has been reported from Jungo et al.  as well.
The recurring enrichment to the same level and the subsequent decrease of gP2/X in the glycerol batch phase of gP2/X show reproducible metabolic turnovers of the Pichia pastoris cells during cyclic substrate change. Combined with final cell densities cXL, equally in each cycle, the functionality of the parallel/sequential cell-breeding approach is proven.
The developed multi-bioreactor plant met the requirement of reproducibility in cell-breeding and ensures stable initial conditions in the screening cultivations, which is achieved by implementing complex automation structures. By using linked DoE tools and extended PAT, investigations regarding process optimization in pharmaceutical protein production become more manageable and reliable.
3.2 Optimization of malaria vaccine expression using the multi-bioreactor plant
The measured product concentrations were used to calculate the related performance index in Eq. (1). Data, shown in Table 1, was used to generate the coefficients a0 to a123 by fitting the model (Eq. (2)) to the measurements with MLR.
The goodness of fit R2 of 0.98 shows that only 2% of the total variation is not explainable by the model. The Prediction Error Sum of Squares (PRESS) value of 0.59 (AUs/h)2 related to the Sum of Squares Total (SST) value of 3.96 (AUs/h)2 indicates a low predicted variation. The goodness of prediction Q2, calculated from PRESS and SST, of 0.85 is close to the determination coefficient R2.
could be calculated to 0.92 with the Sum of Squares Center Points (SSCP), the SST, the number of experiments ntot and the number of independent Center Point experiments nCP.
High significance of the model can be claimed after consideration of ANalysis Of Variance (ANOVA) for a significance level of 95%. The p-value for regression (<0.001) supports the analysis. The model shows no lack of fit with a plof-value of 0.76.
Figure 5 illustrates the results of the optimization via a response surface plot by fitting the model to the experimental data.
Figure 5. Response surface plot of the DoE optimization results as a function of cultivation temperature ϑL and pH-value at a methanol concentration cS2M of 1.0 g/L. Model data was fitted to the data determined experimental by using MLR. A p-value for regression of < 0.001 claims high model significance. The model shows no lack of fit.
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The conclusive result indicates optimal productivity PRD for a methanol concentration cS2Mopt of 1.0 g/L, a pHopt of 5.55 and a cultivation temperature ϑLopt of 25.8°C.
As known from literature, optimum protein production varies in accordance to the used Pichia pastoris strain, especially relating to the geno-, respectively, phenotype of organism (e.g. [25–27]) and to the secreted foreign protein, which are directly influenced by cultivation conditions (e.g. [28–30]).
Gasser et al.  reported that cultivation temperature strongly impacts the regulation of specific genes. Many important cellular processes, including the central carbon metabolism, stress response, and protein folding are affected by changing the growth temperature . However, Cos et al.  observed less protein expression above 32°C with Pichia pastoris, which matches the results obtained in this report. The resulting optimum temperature ϑLopt is specific for the expression of the potential malaria vaccine protein but commonly used in Pichia pastoris cultivations as well.
The ability of Pichia pastoris to grow across a relatively broad pH range  is well known. In contrast, the recombinant protein stability is closely coupled to the pH-value, controlled in cultivations. Different investigations in this field relating to optimal protein production have shown a wide range in adjusted pH-values [34, 35].
Compared to our results, pH-values have been fixed around 5.5 to reduce protease effects, reported in several works [11, 36].
The stability of the target protein was tested in culture supernatant at different temperatures and pH-values (data not shown). The investigations have shown a significant increase of protease effects at temperatures ≥ 27°C. The degradation of the product increases with lowering the pH-value from 6.0 to 4.8.
In summary, the optimal target protein productivity seems to be a sensitive steady state between accelerating cellular processes and decreasing protease effects in the culture supernatant.
3.3 Contemplation of system sensitivity
In the robustness testing procedure the sensitivity around a response variable optimum was investigated by varying the factor levels in a small range.
In this context ϑL was varied by 1°C and the pH-value by 0.15 pH-units around the center point with the factor settings ϑL = 25.8°C and pH = 5.55. A full factorial design in two levels was applied as regression model. The methanol concentration was kept constant in the optimum at 1 g/L. The statistical insignificance of small methanol concentration changes has been proven in pretests (data not shown). The experimental set up, the start values for cXL, respectively IAP1M, as well as the calculated results are shown in Table 1.
The productivity PRD varies around the mean of the center points within a given tolerance limit of ± 2 SDs.
The calculated Q2-value of 0.13 for the chosen model indicates an extremely weak relationship between the factors and the response.
The ANOVA of the regression model demonstrates the insignificance of the robustness test model.
The low predictive power of the selected model and the high p-value verify robustness of the response secretion productivity for small variations in factor settings around the response variable optimum.