Acceleration of vaccine development by improvement of process understanding - Analysis of the host cell proteome

While regulatory agencies require stringent product quality and safety to be upheld in biopharmaceutical products, today’s competitive biopharmaceutical market requires short process development times. The demand to accelerate especially the development of vaccines became obvious with the COVID-19 pandemic. By expanding process understanding with the use of process design tools the development time of the purification could be significantly shortened. High throughput experimentation (HTE) provides an automated experimentation platform, which minimizes the amount of used samples and saves experimental time. In this approach, HTE is used to acquire experimental data to regress parameters used as inputs for a chromatographic mechanistic model with the objective to establish an E. coli vaccine purification process development platform for a recombinant subunit vaccine. To provide a generic process development strategy that can be applied to novel antigens, the focus lies on the description of the adsorption behavior of the impurities such as host cell proteins (HCPs) during the capture step. Therefore our approach focuses on the present impurities, in specific the HCPs (Figure 1). When using the same E.coli strain the knowledge regarding the host cell proteins could be transferred to a new product. The first step is the identification of HCPs. Over a thousand HCPs are identified in the E.coli harvest sample investigated by means of mass spectrometry based proteomics. A database containing the properties of these proteins can provide assistance in the decision on chromatography resins suited for the purification process of a new developed antigen. Please click Download on the upper right corner to see the full abstract

Model-based process development for complex vaccine mixtures

The regulations, safety and purity demands are extremely high for vaccine processes and likewise reflected in process development time and cost. Reducing time-to-market is key for pharmaceutical companies, hence saving lives and money, and therefore the need raised for systematic, general and efficient process development strategies (Hanke & Ottens, 2014). Despite the tremendous variation between vaccine purification processes, platform processes for similar types of vaccines could aid to generally accelerate the process development and would be beneficial in terms of knowledge, resources, costs and regulatory aspect. High throughput process development (HTPD) approaches can be used to establish platform processes. HTPD combines high throughput technologies and statistical or mechanistic modeling in an efficient manner. In particular mechanistic models, that aim to describe the real process based upon physical processes occurring, can be of great merit to extend the level of process understanding and thereby support in making decision regarding the process design (Pirrung et al., 2019). Please click Download on the upper right corner to see the full abstract

Ulvan Activates Chicken Heterophils and Monocytes Through Toll-Like Receptor 2 and Toll-Like Receptor 4

Responsiveness to invasive pathogens, clearance via the inflammatory response, and activation of appropriate acquired responses are all coordinated by innate host defenses. Toll-like receptor (TLR) ligands are potent immune-modulators with profound effects on the generation of adaptive immune responses. This property is being exploited in TLR-based vaccines and therapeutic agents in chickens. However, for administering the TLR agonist, all previous studies used in ovo, intra-muscular or intra-venous routes that cannot be performed in usual farming conditions, thus highlighting the need for TLR ligands that display systemic immune effects when given orally (per os). Here we have demonstrated that an ulvan extract of Ulva armoricana is able to activate avian heterophils and monocytes in vitro. Using specific inhibitors, we have evidenced that ulvan may be a new ligand for TLR2 and TLR4; and that they regulate heterophil activation in slightly different manner. Moreover, activation of heterophils as well as of monocytes leads to release pro-inflammatory cytokines, including interleukin1-β, interferon α and interferon γ, through pathways that we partly identified. Finally, when given per os to animals ulvan induces heterophils and monocytes to be activated in vivo thus leading to a transient release of pro-inflammatory cytokines with plasma concentrations returning toward baseline levels at day 3

Characterisation of the E. coli HMS174 and BLR host cell proteome to guide purification process development

Mass-spectrometry-based proteomics is increasingly employed to monitor purification processes or to detect critical host cell proteins in the final drug substance. This approach is inherently unbiased and can be used to identify individual host cell proteins without prior knowledge. In process development for the purification of new biopharmaceuticals, such as protein subunit vaccines, a broader knowledge of the host cell proteome could promote a more rational process design. Proteomics can establish qualitative and quantitative information on the complete host cell proteome before purification (i.e., protein abundances and physicochemical properties). Such information allows for a more rational design of the purification strategy and accelerates purification process development. In this study, we present an extensive proteomic characterisation of two E. coli host cell strains widely employed in academia and industry to produce therapeutic proteins, BLR and HMS174. The established database contains the observed abundance of each identified protein, information relating to their hydrophobicity, the isoelectric point, molecular weight, and toxicity. These physicochemical properties were plotted on proteome property maps to showcase the selection of suitable purification strategies. Furthermore, sequence alignment allowed integration of subunit information and occurrences of post-translational modifications from the well-studied E. coli K12 strain.</p

Activités antibactérienne et immunomodulatrice d'un extrait d'algue verte riche en polysaccharides sulfatés

National audienc

Using artificial neural networks to accelerate flowsheet optimization for downstream process development

An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches enable to screen a broad range of the design-space, in contrast to traditional statistical or heuristic-based approaches. Though, chromatographic mechanistic modeling (MM), one of the advanced model-based approaches, can be speed-limiting for flowsheet optimization, which evaluates every purification possibility (e.g., type and order of purification techniques, and their operating conditions). Therefore, we propose to use artificial neural networks (ANNs) during global optimization to select the most optimal flowsheets. So, the number of flowsheets for final local optimization is reduced and consequently the overall optimization time. Employing ANNs during global optimization proved to reduce the number of flowsheets from 15 to only 3. From these three, one flowsheet was optimized locally and similar final results were found when using the global outcome of either the ANN or MM as starting condition. Moreover, the overall flowsheet optimization time was reduced by 50% when using ANNs during global optimization. This approach accelerates the early purification process design; moreover, it is generic, flexible, and regardless of sample material's type.</p

Predicting protein retention in ion-exchange chromatography using an open source QSPR workflow

Protein-based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open-source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open-source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross-validated R2 of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design.BT/Bioprocess EngineeringBT/Design and Engineering Educatio