Controller design for effective glycosylation control in mAbs

Monoclonal antibodies (mAbs), a class of commercially viable biotherapeutics, undergo post-translational modifications when expressed in mammalian cell lines, resulting in structural and pharmacological changes in the protein. One such post translational modification is N-linked glycosylation, where the non-template driven enzymatic attachment of different sugar moieties (glycans) to the mAb can alter the product quality of the mAb, compromising the efficacy and safety of the drug product. While significant research effort has been devoted to developing techniques for characterizing and monitoring the glycosylation pattern in mAbs, a robust technique for controlling the glycan distribution and ensuring consistent glycosylation does not currently exist. In this work, we present a framework for designing and implementing controllers for effective control of glycosylation in mAbs. The two-step procedure requires first performing output controllability analysis [1, 2] to identify specific inputs that can be manipulated to control particular glycan species (outputs) along with a quantitative relationship between inputs and outputs. Next, this structural information is used to design appropriate proportional integral (PI) controllers. The effectiveness of the controller design technique to track a specified glycan distribution trajectory has been evaluated via simulation for two cases of practical importance: (a) where glycosyltransferase enzyme concentrations are used as manipulated variables (inputs) to control glycan distribution (output) and the input output relationship is represented by a dynamic glycosylation model [3]; and (b) where amino acids are used as manipulated variables (inputs) but the quantitative relationship between the inputs and the outputs is established experimentally. In each case, we design appropriate controllers and then test the controller performance under nominal conditions (i.e., when the process model accurately represents the process in question) and under more realistic model-plant mismatch conditions. The results indicate how effective glycosylation control is possible with appropriate controller design using our proposed technique

ADVANCED CONTROL OF GLYCOSYLATION AND TITER IN FED-BATCH MONOCLONAL ANTIBODY PRODUCTION

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Multiscale modeling of monoclonal antibody (mAb) production and glycosylation in a CHO cell culture process

The production of recombinant therapeutic monoclonal antibodies (mAbs) using cultured mammalian cells accounts for approximately $80 billion in global sales annually. These antibodies are often produced using Chinese hamster ovary (CHO) cell lines that execute the necessary post-translational modifications (e.g., glycosylation) for the drug to be therapeutically efficacious. Glycosylation is an intracellular, enzymatic process by which glycans (i.e., sugar molecules) are attached to a specific location on the antibody. The structure of the glycans attached to the mAb affects the therapeutic function of the molecule, making glycan distribution a critical quality attribute. Consequently, the ability to predict how variations in process parameters and/or media components affect both product formation and glycosylation is important from both a process development and process control viewpoint. A multiscale, mathematical model describing CHO cell growth and antibody production was developed in MATLAB to provide a quantitative understanding of how to manipulate a cell culture process to improve antibody titer and control glycosylation effectively. At the macro (bioreactor) scale, the model uses Monod growth kinetics to describe cell growth, nutrient/metabolite concentrations, and mAb production; at the micro scale, the glycosylation process in the Golgi apparatus is modeled using a glycosylation reaction network governed by Michaelis-Menten enzyme kinetics. Although both macro and micro scale processes are dynamic, disparate time scales makes it possible to solve the (fast) glycosylation model as a static function of the (slowly changing) macro scale state variables. In this multidisciplinary study, we will present a design of experiments approach for (1) identifying significant factors affecting glycosylation—including concentrations of asparagine, glutamine, and copper in the media, and (2) using these factors as macro scale “inputs” to the micro scale model. Model predictions are validated against an independent data set from a representative industrial mammalian cell culture process. Ultimately, the models we discuss will be valuable for biopharmaceutical process development and model-based control system design

Modeling, estimation, and control of glycosylation in monoclonal antibodies produced in CHO cells

Ogunnaike, Babatunde A.Robinson, Anne S.Monoclonal antibodies (mAbs) are a class of commercially valuable biopharmaceuticals that are used for treating diseases such as psoriasis, rheumatoid arthritis, and multiple types of cancer. A vast majority of these biotherapeutics are expressed in mammalian cell lines such as Chinese Hamster Ovary (CHO) cells to enable post-translational modifications that generate human-like protein structures. One such post-translational modification that results in structural and pharmacological changes in the protein is N-linked glycosylation, involving the addition and subsequent modification of an oligosaccharide to the protein backbone. The non-template driven, enzymatic modification of the attached oligosaccharide yields a heterogeneous distribution of glycan isoforms, altering the immunogenicity, stability and half-life of the mAb, and hence the final drug product quality. Maintaining the desired product quality of mAbs in the presence of process variations during manufacturing has been difficult for a variety of reasons, including: (i) a lack of quantitative understanding of the effect of input factors on product quality attributes; (ii) the absence of on-line or real-time measurements of quality attributes as these are monitored infrequently or using time-consuming assays; (iii) the lack of effective control strategies that incorporate these infrequent measurements (as and when they become available) to regulate product quality. To ensure product safety and therapeutic efficacy, regulatory agencies are encouraging manufacturers to monitor and control the drug product quality, specifically maintaining the glycan distribution within an acceptable range. The overall goal of this dissertation, therefore, is to develop a rational framework to model the effect of different input factors on the glycosylation profile, estimate the glycan distribution using a dynamic mathematical model supplemented with infrequent measurements, and control the final glycosylation profile in monoclonal antibodies produced in CHO cells. ☐ As the glycosylation profile in mAbs is influenced by several process variables spanning multiple scales—from operating conditions at the bioreactor (macro) scale, to factors at cellular (meso) scale and organelle (micro) scale—we developed an integrated multi-scale model of glycosylation and validated the model predictions using experimental results obtained with an in-house cell line. The model serves as a useful link between nutrient concentrations and cell growth at the macro-scale and the glycosylation profile at the micro-scale. ☐ In parallel, we used a holistic approach that combined factorial design of experiments and a novel computational technique to identify the various combinations of glycan species that are affected by dynamic media supplementation and to quantify mathematically how they are affected. Our experiments demonstrated the importance of taking into consideration the time of addition of trace media supplements, not just their concentrations, and the corresponding mathematical analysis provided insight into what supplements to add, when, and how much, in order to induce specific changes in the glycosylation profile. ☐ We developed a two-step framework to control the glycosylation profile by first generating quantitative input-output relationships using the previously described holistic approach and then designing proportional (P) and proportional integral (PI) controllers based on this quantitative input-output relationship. The set-point tracking performance of these P and PI controllers was evaluated via simulations under nominal conditions (i.e. when the model is assumed to be representative of the actual ‘plant’ or process) and model-plant mismatch conditions. Our results demonstrated that the developed framework can be implemented to design glycosylation controllers to achieve a desired target glycosylation profile under different conditions. ☐ The P and PI controllers that we have developed are suited for batch-to-batch control as they depend on the final glycosylation profile. To achieve real-time control of glycosylation we require real-time information of the glycan distribution obtained from glycan assays; however, current glycan assays are infrequent and characterized by long analysis times. We address this limitation in glycosylation analysis using two approaches: (i) by formulating a rational framework based on observability analysis to guide the development of novel assays that can simplify glycan analysis or reduce analysis time; and (ii) by designing a state estimator to predict the glycan distribution profile in the absence of measurements using the previously developed multi-scale model and updating those predictions as and when measurements become available. ☐ The framework developed in this dissertation will form the basis of an online control scheme to control the final glycosylation profile in the product, thereby achieving consistent product quality.University of Delaware, Department of Chemical and Biomolecular EngineeringPh.D

Controlling the Glycosylation Profile in mAbs Using Time-Dependent Media Supplementation

In order to meet desired drug product quality targets, the glycosylation profile of biotherapeutics such as monoclonal antibodies (mAbs) must be maintained consistently during manufacturing. Achieving consistent glycan distribution profiles requires identifying factors that influence glycosylation, and manipulating them appropriately via well-designed control strategies. Now, the cell culture media supplement, MnCl2, is known to alter the glycosylation profile in mAbs generally, but its effect, particularly when introduced at different stages during cell growth, has yet to be investigated and quantified. In this study, we evaluate the effect of time-dependent addition of MnCl2 on the glycan profile quantitatively, using factorial design experiments. Our results show that MnCl2 addition during the lag and exponential phases affects the glycan profile significantly more than stationary phase supplementation does. Also, using a novel computational technique, we identify various combinations of glycan species that are affected by this dynamic media supplementation scheme, and quantify the effects mathematically. Our experiments demonstrate the importance of taking into consideration the time of addition of these trace supplements, not just their concentrations, and our computational analysis provides insight into what supplements to add, when, and how much, in order to induce desired changes

Preprocessing of Raman Lidar Signal Over a High Altitude Station in India: Practical Considerations

Lidar can provide the range-resolved information about the vertical profiles of optical properties of aerosol content and clouds present in the atmosphere. However, before obtaining the information from received lidar signal we ought to use a few pre-processing techniques such as range-correction, temporal and spatial averaging, dead time correction of the photon counts (PC) signal, correction due to overlap effect, Simulation of molecular Rayleigh signal and gluing analog & PC signals. In this work, we have discussed some of the initial pre-processing techniques ought to be performed before lidar inversion

Lidar Overlap Function Determination Using the Raman Lidar Signals

The determination of vertical distribution of optical properties of clouds and aerosols using the lidar system is affected by the incomplete overlap between the field of view of transmitter i.e. laser beam & the receiver in the near‐field range. Thus, the study of vertical profiles of aerosol optical properties in the lower atmosphere is erroneous without the correction of lidar overlap function. Here we have analysed the effect of overlap using a simple technique proposed by Ansmann and Wandinger to determine overlap function. We have determined the overlap factor for 5 different days of June 2016 and then calculated the mean overlap profile and determined the relative deviation of each day with respect to mean overlap factor. Results reveal that the complete overlap was achieved beyond 300 meters

Lidar Overlap Function Determination Using the Raman Lidar Signals

The determination of vertical distribution of optical properties of clouds and aerosols using the lidar system is affected by the incomplete overlap between the field of view of transmitter i.e. laser beam & the receiver in the near‐field range. Thus, the study of vertical profiles of aerosol optical properties in the lower atmosphere is erroneous without the correction of lidar overlap function. Here we have analysed the effect of overlap using a simple technique proposed by Ansmann and Wandinger to determine overlap function. We have determined the overlap factor for 5 different days of June 2016 and then calculated the mean overlap profile and determined the relative deviation of each day with respect to mean overlap factor. Results reveal that the complete overlap was achieved beyond 300 meters

Preprocessing of Raman Lidar Signal Over a High Altitude Station in India: Practical Considerations

Lidar can provide the range-resolved information about the vertical profiles of optical properties of aerosol content and clouds present in the atmosphere. However, before obtaining the information from received lidar signal we ought to use a few pre-processing techniques such as range-correction, temporal and spatial averaging, dead time correction of the photon counts (PC) signal, correction due to overlap effect, Simulation of molecular Rayleigh signal and gluing analog & PC signals. In this work, we have discussed some of the initial pre-processing techniques ought to be performed before lidar inversion

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