Controllability analysis to identify manipulated variables for a glycosylation control strategy

N-linked glycans affect important end-use characteristics such as the bioactivity and efficacy of many therapeutic proteins, (including monoclonal antibodies), in vivo. However, achieving a precise glycan distribution during manufacturing can be challenging because glycosylation is a non-template driven cellular process, with the potential for significant uncontrolled variability in glycan distributions. As important as the glycan distribution is to the end-use performance of biopharmaceuticals, to date, no strategy exists for controlling glycosylation on-line. In this work, we present a controllability analysis for glycosylation as a first step toward establishing an online glycosylation control strategy. We first assessed the theoretically achievable extent to which the very complex process of glycosylation is controllable. Once theoretic controllability was established, we performed experiments to identify appropriate manipulated variables that can be used to direct the glycan distribution of an IgG1 to a desired state. We found that bioreactor process variables such as glucose and glutamine media concentration, temperature, pH, agitation rate, and dissolved oxygen (DO) had significant but small effects on the relative percentage of various glycans. This indicated that the IgG1 glycan distribution was generally robust to even large perturbations of typical bioreactor variables. Conversely, we found that media supplementation with manganese, galactose, and ammonia had significant and large effects on certain glycans. From this work, we determined that manganese can be used as a manipulated variable to increase the relative abundance of M5 and decrease FA2 glycans simultaneously, and galactose can be used as a manipulated variable to increase the relative abundance of FA2G1 and decrease FA2 and A2 simultaneously. As a final test, we applied machine learning algorithms to validate and enrich these findings from a data-centric point of view. The machine learning algorithms provided an avenue to discover unknown relationships and patterns that refined our findings and provided a framework to explore more variables

Controllability analysis of protein glycosylation in CHO cells.

To function as intended in vivo, a majority of biopharmaceuticals require specific glycan distributions. However, achieving a precise glycan distribution during manufacturing can be challenging because glycosylation is a non-template driven cellular process, with the potential for significant uncontrolled variability in glycan distributions. As important as the glycan distribution is to the end-use performance of biopharmaceuticals, to date, no strategy exists for controlling glycosylation on-line. However, before expending the significant amount of effort and expense required to develop and implement on-line control strategies to address the problem of glycosylation heterogeneity, it is imperative to assess first the extent to which the very complex process of glycosylation is controllable, thereby establishing what is theoretically achievable prior to any experimental attempts. In this work, we present a novel methodology for assessing the output controllability of glycosylation, a prototypical example of an extremely high-dimensional and very non-linear system. We first discuss a method for obtaining the process gain matrix for glycosylation that involves performing model simulations and data analysis systematically and judiciously according to a statistical design of experiments (DOE) scheme and then employing Analysis of Variance (ANOVA) to determine the elements of process gain matrix from the resulting simulation data. We then discuss how to use the resulting high-dimensional gain matrix to assess controllability. The utility of this method is demonstrated with a practical example where we assess the controllability of various classes of glycans and of specific glycoforms that are typically found in recombinant biologics produced with Chinese Hamster Ovary (CHO) cells. In addition to providing useful insight into the extent to which on-line glycosylation control is achievable in actual manufacturing processes, the results also have important implications for genetically engineering cell lines design for enhanced glycosylation controllability

Heat maps representing the significant elements of the process gain matrices for the glycan classes in operating (a) Range 1, (b) Range 2, and (c) Range 3.

<p>Visual inspection suggests that significant process gains are associated with 10 of the 12 glycan classes when the process is operated in Range 1 or 2 indicating that the relative percentage of glycan classes can be changed in these operating ranges. There are no significant process gains for any glycan class in operating Range 3, suggesting that the relative percentage of glycan classes cannot be affected or controlled at all when the process is operated in Range 3.</p

Heat maps representing the significant elements of the process gain matrices for specific glycoforms typically found in biologics in operating (a) Range 1, (b) Range 2, and (c) Range 3.

<p>Visual inspection suggests that significant process gains are associated with 8 of the 18 glycoforms when the process is operated in Range 1 and 11 of the 18 glycoforms when operated in Range 2, indicating that the relative percentage of glycoforms can be changed in these operating ranges. As with the glycan classes, there are no significant process gains for any glycoforms in operating Range 3, suggesting that the relative percentage of glycoforms cannot be affected or controlled at all when the process is operated in Range 3.</p

Graphical representation of the coefficients associated with each enzyme and sugar nucleotide donor in input modes, µ<i>_i</i>, associated with the controllable output modes for glycoforms, <i>η_i,</i> shown in Figure 6.

<p>Each column shows the glycosylation enzymes and sugar nucleotides of each input modes in each operating range (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087973#pone-0087973-t001" target="_blank">Table 1</a>). No input modes are shown for operating Range 3 since no controllable modes were found in this range. Coefficients were obtained using eq. 10 following singular value decomposition of the process gain matrix corresponding to the glycoform distribution as described in section “Assessing Controllability from the Process Gain Matrix”. How much the enzyme or sugar nucleotide contributes to an input mode is reflected in the coefficient associated with that variable in the linear combination. A dominant contributor to a mode (where one exists) is identified by the variable with the largest coefficient in the weighted sum. The enzyme(s) and/or sugar nucleotide(s) that are dominant contributors of the input mode affect the glycoforms of the associated output mode the most.</p

Operating ranges of input factors used in controllability analysis (i.e., µM concentrations used for each glycosylation enzyme and sugar nucleotide donor investigated as factors in DoE).

<p>Operating ranges of input factors used in controllability analysis (i.e., µM concentrations used for each glycosylation enzyme and sugar nucleotide donor investigated as factors in DoE).</p

Graphical representation of the coefficients associated with each glycan class in the controllable output modes, <i>η_i.</i>

<p>Modes that were not controllable (i.e. associated with singular values, <b><i>σ_i</i></b><<b><i>σ_*</i></b> = 1) are not shown. Each column shows the glycan classes (output modes) that are controllable in each operating range (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087973#pone-0087973-t001" target="_blank">Table 1</a>). No output modes are shown for operating Range 3 since no controllable modes were found in this range. Coefficients were obtained using eq. 9 following singular value decomposition of the glycan class process gain matrix as described in section “Assessing Controllability from the Process Gain Matrix”. How much the glycan class contributes to an output mode is reflected in the coefficient associated with that variable in the linear combination. A dominant contributor to a mode (where one exists) is identified by the variable with the largest coefficient in the weighted sum. Any glycan classes associated with a non-zero coefficient can be affected by perturbations in the associated input mode; however the dominant glycan class will be affected the most.</p

Singular values, <i>σ_i</i>, obtained from singular value decomposition of the glycan class process gain matrices for three operating ranges.

<p>Output modes associated with singular values, <i>σ_i</i>><i>σ_*</i> = 1, are considered controllable.</p

Graphical representation of the coefficients associated with each enzyme and sugar nucleotide donor in input modes, <i>µ_i</i>, associated with the controllable output modes for glycan classes, <i>η_i,</i> shown in Figure 2.

<p>Each column shows the coefficients associated with the enzymes and sugar nucleotides of each input mode in each operating range (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087973#pone-0087973-t001" target="_blank">Table 1</a>). No input modes are shown for operating Range 3 since no controllable modes were found in this range. Coefficients were obtained using eq. 10 following singular value decomposition the process gain matrix for the glycan classes as described in section “Assessing Controllability from the Process Gain Matrix”. How much the enzyme or sugar nucleotide contributes to an input mode is reflected in the coefficient associated with that variable in the linear combination. A dominant contributor to a mode (where one exists) is identified by the variable with the largest coefficient in the weighted sum. The enzyme(s) and/or sugar nucleotide(s) that are dominant contributors of the input mode affect the glycan classes of the associated output mode the most.</p

Singular values, <i>σ_i</i>, obtained from singular value decomposition of the glycoform process gain matrix for three operating ranges.

<p>Output modes associated with singular values, <i>σ_i</i>><i>σ_*</i> = 1, are considered controllable.</p