BioProcessing 2020

This presentation is about a client-focused approach used to find an all synthetic ligand which is developed into an affinity adsorbent – followed by working with the client to develop the affinity chromatography strategy and deliver a robust purification platform with GMP-ready columns.

Learning Objectives:

There has been an increasing number of successful gene therapy clinical trials, leading to regulatory approvals of numerous gene therapy products, in particular ones based on the adeno-associated virus (AAV).  That said, vectors based on natural versions of AAV face a number of delivery challenges that limit their efficacy and will thus preclude the extension of these successes to the majority of human diseases.  These delivery limitations arise since the parent viruses upon which these vectors are based were not evolved by nature for our convenience to use as human therapeutics.  We have been developing and implementing a high throughput approach termed directed evolution – involving the iterative genetic diversification of a viral genome and functional selection for desired properties – to engineer highly optimized variants of AAV for a broad range of cell and tissue targets.

In parallel, the advent of genome editing technologies such as the CRISPR/Cas9 system raise the possibility of using gene delivery not only for gene replacement but for repair or knockout of endogenous genes.  We have thus been combining engineered AAVs with CRISPR/Cas9 for a range of applications, in particular, to treat central nervous system disorders.  The integration of these new technologies – Cas9 cargo with AAV delivery – can enable a broad range of basic and therapeutic applications.

Learning Objectives:

1. To gain an understanding of how preclinical and clinical research in gene therapy is leading to strong advances and clinical approvals

2. To discuss how improvements in delivery technologies, such as engineered AAV delivery vectors, are still needed to fully unlock the potential of gene therapy and genome editing

Digital PCR has become the emerging technique for the sequence-specific detection and quantification of nucleic acids for various applications. During the recent years, numerous articles of new digital PCR methods have been published. Considering the development work into reliable analytical methods suitable for diagnostics requires their validation for the intended use. The results of an in-house evaluation of a droplet digital PCR compared to other technologies will be presented in application to different genomic assays applied in gene therapy.

Learning Objectives:

1) Introduction to Droplet Digital PCR

2) Explain the need for Droplet Digital PCR compared to current methodologies in different gene therapy platforms

3) Present the key evaluations required to establish the assay for research and good manufacturing purpose.

Within this talk the advantages of using intensified Design of experiments (iDoE) in combination with hybrid modeling will be demonstrated. In detailed upstream and downstream showcases the applied hybrid modeling structure significantly reduced the development times while simultaneously enabled the application of soft-sensors and digital twin to screen for ideal process conditions. While the downstream hybrid model could be used for different operation modes and simple model predictive control, the upstream iDoE approach reduced the experimental effort by 66%, significantly reducing development costs.

Learning Objectives:

1. Faster Drug Development by modeling along the whole value chain

2. Hybrid Modeling to strengthen bioprocess understanding

3. intensified Design of Experiments to speed up process characterization

Bioreactor modeling can inform understanding of cellular metabolism—including aspects of growth kinetics, productivity, media consumption, and metabolite secretion—and can be used to optimize bioreactor operations. Models for bioreactors are developed with different level of details and complexity depending on their purpose. For methylotrophic yeast Pichia pastoris, which is widely used as the microbial host for recombinant protein production, complex cellular models such as genomic-scale metabolic models have been developed to describe detailed cellular metabolism. These models, however, have too many parameters to be accurately estimated from experimental data. As such, most of the parameters are highly uncertain, even when additional assumptions and/or approximations are made such as the metabolic fluxes being at steady state. In contrast, macroscopic models can yield a reasonable description of the bioreactor while using simpler mathematical formulations with low computational costs. These types of models can also facilitate real-time applications such as adaptive parameter estimation and model predictive control.

This presentation describes an extensive macroscopic bioreactor model for substrates, biomass, total protein, other medium components, and off-gas components. Species and elemental balances are introduced to describe uptake and evolution rates for medium components and off-gas components. Additionally, a pH model is constructed using an overall charge balance, acid/base equilibria, and activity coefficients to describe production of recombinant protein and precipitation of medium components. The extent of run-to-run variability is modeled by distributions of a subset of the model parameters, which are estimated using the maximum likelihood method. The uncertainty description in this macroscopic bioreactor model identifies the model parameters that have large variability and provides guidance as to which aspects of cellular metabolism should be the focus of additional experimental studies. The model for medium components with pH and precipitation can be used for improving the chemically defined medium by minimizing the amount of components needed while meeting cellular requirements.

Learning Objectives:

1. Discuss current trends in biopharmaceutical manufacturing operations and control

2. Describe a mathematical model that incorporates solution chemistry to more accurately predict bioreactor data