Jörg Thömmes

Recent advances in large-scale production of monoclonal antibodies and related proteins.

The rapid development of high-yielding and robust manufacturing processes for monoclonal antibodies is an area of significant focus in the biopharmaceutical landscape. Advances in mammalian cell culture have taken titers to beyond the 5 g/l mark. Platform approaches to downstream process development have become widely established. Continuous evolution of these platforms is occurring as experience with a wider range of products is accrued. The increased cell culture productivity has shifted the attention of bioprocess development to operations downstream of the production bioreactor. This has rejuvenated interest in the use of non-chromatographic separation processes. Here, we review the current state-of-the-art industrial production processes, focusing on downstream technologies, for antibodies and antibody-related products and discuss future avenues for evolution.

Alternatives to Chromatographic Separations

Up to now, the productivity of mammalian cell culture has been perceived as limiting the productivity of the industrial manufacture of therapeutic monoclonal antibodies. Dramatic improvements in cell culture performance have changed this picture, and the throughput of antibody purification processes is gaining increasing attention. Although chromatographic separations currently are the centerpiece of antibody purification, mostly due to their high resolving power, it becomes more and more apparent that there may be limitations at the very large scale. This review will discuss a number of alternatives to chromatographic antibody purification, with a particular emphasis on the ability to increase throughput and overcome traditional drawbacks of column chromatography. Specifically, precipitation, membrane chromatography, high‐resolution ultrafiltration, crystallization, and high‐pressure refolding will be evaluated as potential large scale unit operations for industrial antibody production.

Elastic net with Monte Carlo sampling for data-based modeling in biopharmaceutical manufacturing facilities

Abstract Biopharmaceutical manufacturing involves multiple process steps that can be challenging to model. Oftentimes, operating conditions are studied in bench-scale experiments and then fixed to specific values during full-scale operations. This procedure limits the opportunity to tune process variables to correct for the effects of disturbances. Generating process models has the potential to increase the flexibility and controllability of the biomanufacturing processes. This article proposes a statistical modeling methodology to predict the outputs of biopharmaceutical operations. This methodology addresses two important challenging characteristics typical of data collected in the biopharmaceutical industry: limited data availability and data heterogeneity. Motivated by the final aim of control, regularization methods, specifically the elastic net, are combined with sampling techniques similar to the bootstrap to develop mathematical models that use only a small number of input variables. This methodology is evaluated on an antibody manufacturing dataset.

A Systematic Approach to Process Data Analytics in Pharmaceutical Manufacturing: The Data Analytics Triangle and Its Application to the Manufacturing of a Monoclonal Antibody

Abstract This chapter provides a tutorial on the effective application of process data analytics techniques to (bio)pharmaceutical processes. A methodology is proposed in which the process dataset is first interrogated to determine its characteristics in terms of extents of correlation, nonlinearity, and dynamics. The second step is select effective data analytics techniques based on the combination of the identified characteristics. Techniques can be read from a “data analytics triangle,” which has the characteristics at its vertices. This chapter also discusses the value of sparse models and the importance of thorough cross-validation when applying data analytics techniques to (bio)pharmaceutical data sets. Key points and data analytic techniques including lasso and elastic net are described and demonstrated within the context of their application to laboratory- and production-scale data for the manufacture of a monoclonal antibody. A case study illustrates best practices for learning from process data, namely, the importance of maintaining plant-wide connections in the data, implementing procedures to counteract overfitting of the models, and the potential for sparse models in (bio)pharmaceutical operations.

Industry Review of Cell Separation and Product Harvesting Methods

Abstract Once a therapeutic protein has been produced by a chosen production system, the product must be separated and recovered from the host system. Cell harvest and recovery serve this function by removing or isolating host cells so that the product can be recovered from the host system, and the product stream can be clarified prior to the purification process. These process steps are the link between the synthesis and the purification of the therapeutic products. They are critical to yield the product in its native form from the production system and require careful optimization.