Digitalization platform and supervisory control of a continuous integrated bioprocess based on Raman spectroscopy

In the last years, the implementation of Raman spectroscopy, multivariate data analysis (MVDA) and advanced control algorithms gained increasing interest in the biopharmaceutical industry through the PAT initiative. However, there is still a huge gap towards an efficient implementation of modern process analyzers, a centralized data mining combined with online use of MVDA and the integration of process knowledge into a supervisory control frame. To bridge this gap, a digitalization platform for a fully continuous integrated manufacturing bioprocess was developed in collaboration with leading companies for process digitalization solutions, advanced monitoring sensors and cell cultures (Siemens, Kaiser Optical Systems and Merck). The potential of online Raman spectroscopy in upstream and downstream was tested to gain as much as possible process information. Different media, products and cell lines were monitored and diverse spiking strategies and advanced modeling algorithms were investigated to improve the robustness and predictive power of the models. Finally, dedicated runs were performed to develop and tune control algorithms. The developed IT platform facilitates the efficient collection and centralized storing of all process data. In addition, it is able to interact with the control systems of each process unit and close the control loop. Advanced multivariate statistical and mechanistic models as well as process control and optimization tools, can be integrated. In particular, the possibility to decently predict the dynamic evolution of central process variables including glucose, viable cell density and product titer, all amino acids and even quality attributes (aggregates and glycosylation patterns), outlines the important role of online Raman spectroscopy in the supervisory control. The hierarchical control system enables the handling of process perturbations and optimization of diverse objectives such as productivity, efficiency and product quality. The efficient implementation of Raman spectroscopy, facilitated by the IT platform, and the innovative control system provides a very important basis to intensify the main advantages of continuous integrated manufacturing and fully follows the trend of industry 4.0. Please click Additional Files below to see the full abstract

Hybrid Models for the simulation and prediction of chromatographic processes for protein capture

The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsolete, and more generalized tools for process development and optimization are required to keep pace with the emerging trends. Thus, advanced model-based methods that can reduce the can ensure accelerated development of robust processes with minimal experiments are necessary. Though mechanistic models for chromatography are quite popular, their success is limited by the need to have accurate knowledge of adsorption isotherms and mass transfer kinetics. As an alternative, in this work, a hybrid modeling approach is proposed. Thereby, the chromatographic unit behavior is learned by a combination of neural network and mechanistic model while fitting suitable experimental breakthrough curves. Since this approach does not require identifying suitable mechanistic assumptions for all the phenomena, it can be developed with lower effort. Thus, allowing the scientists to concentrate their focus on process development. The performance of the hybrid model is compared with the mechanistic Lumped kinetic Model for in-silico data and experiments conducted on a system of industrial relevance. The flexibility of the hybrid modeling approach results in about three times higher accuracies compared to Lumped Kinetic Model. This is validated for five different isotherm models used to simulate data, with the hybrid model showing about two to three times lower prediction errors in all the cases. Not only in prediction, but we could also show that the hybrid model is more robust in extrapolating across process conditions with about three times lower error than the LKM. Additionally, it could be demonstrated that an appropriately tailored formulation of the hybrid model can be used to generate representations for the underlying principles such as adsorption equilibria and mass transfer kinetics.Fil: Narayanan, Harini. Institute of Chemical and Bioengineering; SuizaFil: Seidler, Tobias. Institute of Chemical and Bioengineering; SuizaFil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina. Institute of Chemical and Bioengineering; SuizaFil: Sokolov, Michael. No especifíca;Fil: Morbidelli, Massimo. Politecnico di Milano; ItaliaFil: Butté, Alessandro. No especifíca

Generating glycan variants for biological activity testing by means of parallel experimental design and multivariate analysis

For more than 20 years, the industry has mainly invested in productivity enhancements. Recently, the focus of cell-culture process development began to shift. The modulation of quality attributes of recombinant therapeutic protein has gained substantial interest as demonstrated by the plethora of recent publications describing the effect of cell culture media on post-translational modifications of recombinant proteins1. Focusing on glycosylation, our team has developed a toolbox of media design beyond the commonly known media components and a rational high-throughput experimental design method. We identified and tested a large variety of novel cell culture compatible chemical components in industrial relevant Chinese hamster ovary cell lines (CHO) expressing recombinant antibodies and antibody fusion molecules. The compounds were evaluated in five different parallel 96-DWP fed-batch experiments, considering their mode of biological action. Viable cell density, viability and product titer were monitored and purified supernatants underwent N-glycan analysis by 2AB-UPLC and site-specific glycan-peptide analysis. Multivariate analysis identified the best performing glycosylation modulators, which were confirmed in spin tubes. Intracellular nucleotide and nucleotide sugar levels were analyzed by capillary electrophoresis, the gene expression by next-generation sequencing technologies, and the impact of the generated glycan variants on the biological activity was assessed. Non-targeted metabolite profiling was carried out to build a multivariate model linking metabolites with the glycan fingerprint. The screening experiments in 96-DWP produced a large glycosylation distribution diversity2,3. Subsequent D-optimal quadratic design in shake tubes confirmed the outcome of the selection process and provided a solid basis for sequential process development at a larger scale. The glycosylation profile with respect to the glycosylation specifications was greatly improved in shake tube experiments: 75% of the conditions were equally close or closer to the specifications than the best 25% in 96-deepwell plates. Further enhancement enabled us to generate extreme glycosylation variants, including high mannose, afucosylated, galactosylated as well as sialic acid species of both a mAb and an antibody fusion molecule with three N-glycosylation sites. The glycan variants induced significant responses in the respective in vitro biological activity assays. Moreover, metabolites correlating with time-dependent glycan profiling data were pinpointed and the glycan distribution of an external data set predicted. Our data highlight the great potential of cell culture medium optimization to modulate product quality and show the feasibility of the generation of a wide range of glycan variants suitable for biological activity testing. [1] Brühlmann D, Jordan M, Hemberger J, Sauer M, Stettler M and Broly H, Tailoring recombinant protein quality by rational media design, Biotechnology Progress 2015, 31:615–629. [2] Brühlmann D, Muhr A, Parker R, Vuillemin T, Bucsella B, Torre S, La Neve F, Lembo A, Haas T, Sauer M, Souquet J, Broly H, Hemberger J, Jordan M, Cell culture media supplemented with raffinose reproducibly enhances high mannose glycan formation, Journal of Biotechnology 2017, 252:32-42. [3] Brühlmann D, Sokolov M, Butté A, Sauer M, Hemberger J, Souquet J, Broly H, Jordan M, Parallel experimental design and multivariate analysis provides efficient screening of cell culture media supplements to improve Biosimilar product quality, Biotechnology and Bioengineering 2017, 114(7):1363-1631

Novel Anisotropic Porous Materials through Self-Assembly of Super-Paramagnetic Particles

ISSN:0009-429

Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes

In this work, we aim to introduce the concept of the degree of hybridization for cell culture process modeling. We propose that a family of hybrid models can be created with varying fractions of process knowledge explicitly encoded in the model, defined as the degree of hybridization, with the two extremes being fully data-driven (0%) and fully mechanistic (100%) models. Subsequently, the aim is to compare the different models based on different metrics: model accuracy, the experimental effort for model development, extrapolation capability, the capability of generating new process understanding, and ease of utilization in practice, and to demonstrate that this could provide an additional degree of freedom for model selection. We could quantitatively demonstrate that for the cell culture process, either extreme has limitations. The major drawback of the data-driven model is the poor performance at low data availability as well as poor extrapolation capability, inability to provide process understanding, and subsequently inefficient practical application. On the other hand, the mechanistic model has poor accuracy due to the addition of excessive knowledge that then biases the models. Moving from data-driven to mechanistic models, the performance of the models improves progressively, as long as the knowledge added is not too biased. We show that the choice of the hybrid models to be used is based on the goal of model development. For instance, hybrid models including mass balances on each species show better performance in transferring models across different modes of operation. On the other hand, models with a higher degree of hybridization allow for more process interpretation possibilities. For modeling accuracy, amount of training data, extrapolation, and practical applications, the Hybrid Rate (HR) model is found to have the optimal degree of hybridization. This is likely due to the compromise between adding process knowledge and increasing the model parameters achieved by the HR model. The HR model features the incorporation of mass balance and channels the data-driven modeling to cell-specific rates, and thus these two pieces of information appear to be the most crucial ones. Finally, we believe that the concept will be instrumental in progressively developing and testing hypotheses about complex processes such as cell cultures.Fil: Narayanan, Harini. No especifíca;Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Sokolov, Michael. No especifíca;Fil: Butté, Alessandro. No especifíca;Fil: Morbidelli, Massimo. Politecnico di Milano; Itali

Functional-Hybrid Modeling through automated adaptive symbolic regression for interpretable mathematical expressions

Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledge or black-box data-driven models based on patterns observed in data. However, in the past two decade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these have been largely based on conventional machine learning algorithm (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain-experts. In this work we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of these hybrid models for four benchmark systems and a microbial fermentation reactor, all of which are systems of (bio)chemical relevance. We also demonstrate that compared to a similar implementation with the conventional ANN, the performance of Functional-Hybrid model is at least two times better in interpolation and extrapolation. Additionally, the proposed framework can learn the dynamics in 50% lower number of experiments. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model

Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Capture Chromatographic Step

In process engineering, two paradigms of modeling approaches exist: the mechanistic and the data-driven approaches with the former being completely based on knowledge while the latter completely based on data. In our previous work, we highlighted the advantages of using hybrid models that explores the synergy between mechanistic and data-driven models. Here we introduce the concept of developing a series of hybrid models constituted by a progressively increasing extent of process knowledge. Thus, aligning the models on the "degrees of hybridization"axis with data-driven model being 0% hybridized and mechanistic model being 100% hybridized. In this work, the proposed concept is demonstrated for the application of a chromatographic capture step where the models are evaluated based on (i) prediction accuracy, (ii) extrapolation ability, (iii) providing process understanding, and (iv) practical application. We show the limitations of both model variant extremes. On one hand, the performance of the mechanistic model is compromised due to an excessive imposition of knowledge, thus affecting its predictive capabilities and efficiency in practical utility. On the other hand, the data-driven model inherently is not suitable for application such as multicolumn chromatography or to gain process understanding. In contrast, a series of hybrid models could be developed with better and versatile performance in term of prediction, extrapolation, process understanding, and practical utility. We show that for general process applications the different hybrid model variants and their ensembles have comparable performance. We illustrate the criteria for selection of a particular hybrid model variant based on different considerations such as complexity of training or model development, acquired understanding, and data requirement.Fil: Narayanan, Harini. No especifíca;Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Sokolov, Michael. No especifíca;Fil: Arosio, Paolo. No especifíca;Fil: Butté, Alessandro. No especifíca;Fil: Morbidelli, Massimo. Politecnico di Milano; Itali