Process evolution in cell and gene therapy from discovery to commercialization

Interfacing data science with cell therapy manufacturing: where we are and where we need to be

Although several cell-based therapies have received FDA approval, and others are showing promising results, scalable, and quality-driven reproducible manufacturing of therapeutic cells at a lower cost remains challenging. Challenges include starting material and patient variability, limited understanding of manufacturing process parameter effects on quality, complex supply chain logistics, and lack of predictive, well-understood product quality attributes. These issues can manifest as increased production costs, longer production times, greater batch-to-batch variability, and lower overall yield of viable, high-quality cells. The lack of data-driven insights and decision-making in cell manufacturing and delivery is an underlying commonality behind all these problems. Data collection and analytics from discovery, preclinical and clinical research, process development, and product manufacturing have not been sufficiently utilized to develop a "systems" understanding and identify actionable controls. Experience from other industries shows that data science and analytics can drive technological innovations and manufacturing optimization, leading to improved consistency, reduced risk, and lower cost. The cell therapy manufacturing industry will benefit from implementing data science tools, such as data-driven modeling, data management and mining, AI, and machine learning. The integration of data-driven predictive capabilities into cell therapy manufacturing, such as predicting product quality and clinical outcomes based on manufacturing data, or ensuring robustness and reliability using data-driven supply-chain modeling could enable more precise and efficient production processes and lead to better patient access and outcomes. In this review, we introduce some of the relevant computational and data science tools and how they are being or can be implemented in the cell therapy manufacturing workflow. We also identify areas where innovative approaches are required to address challenges and opportunities specific to the cell therapy industry. We conclude that interfacing data science throughout a cell therapy product lifecycle, developing data-driven manufacturing workflow, designing better data collection tools and algorithms, using data analytics and AI-based methods to better understand critical quality attributes and critical-process parameters, and training the appropriate workforce will be critical for overcoming current industry and regulatory barriers and accelerating clinical translation.

Robust bioprocess design and evaluation of commercial media for the serial expansion of human induced pluripotent stem cell aggregate cultures in vertical-wheel bioreactors

BACKGROUND

While pluripotent stem cell (PSC) therapies move toward clinical and commercial applications at a rapid rate, manufacturing reproducibility and robustness are notable bottlenecks in regulatory approval. Therapeutic applications of PSCs require large cell quantities to be generated under highly robust, well-defined, and economically viable conditions. Small-scale and short-term process optimization, however, is often performed in a linear fashion that does not account for time needed to verify the bioprocess protocols and analysis methods used. Design of a reproducible and robust bioprocess should be dynamic and include a continuous effort to understand how the process will respond over time and to different stresses before transitioning into large-scale production where stresses will be amplified.

METHODS

This study utilizes a baseline protocol, developed for the short-term culture of PSC aggregates in Vertical-Wheel® bioreactors, to evaluate key process attributes through long-term (serial passage) suspension culture. This was done to access overall process robustness when performed with various commercially available media and cell lines. Process output variables including growth kinetics, aggregate morphology, harvest efficiency, genomic stability, and functional pluripotency were assessed through short and long-term culture.

RESULTS

The robust nature of the expansion protocol was demonstrated over a six-day culture period where spherical aggregate formation and expansion were observed with high-fold expansions for all five commercial media tested. Profound differences in cell growth and quality were revealed only through long-term serial expansion and in-vessel dissociation operations. Some commercial media formulations tested demonstrated maintenance of cell growth rates, aggregate morphology, and high harvest recovery efficiencies through three bioreactor serial passages using multiple PSC lines. Exceptional bioprocess robustness was even demonstrated with sustained growth and quality maintenance over 10 serial bioreactor passages. However, some commercial media tested proved less equipped for serial passage cultures in bioreactors as cultures led to cell lysis during dissociation, reduction in growth rates, and a loss of aggregate morphology.

CONCLUSIONS

This study demonstrates the importance of systematic selection and testing of bioprocess input variables, with multiple bioprocess output variables through serial passages to create a truly reproducible and robust protocol for clinical and commercial PSC production using scalable bioreactor systems.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

Bioprocess Economic Modeling: Decision Support Tools for the Development of Stem Cell Therapy Products

Over recent years, the field of cell and gene therapy has witnessed rapid growth due to the demonstrated benefits of using living cells as therapeutic agents in a broad range of clinical studies and trials. Bioprocess economic models (BEMs) are fundamental tools for guiding decision-making in bioprocess design, being capable of supporting process optimization and helping to reduce production costs. These tools are particularly important when it comes to guiding manufacturing decisions and increasing the likelihood of market acceptance of cell-based therapies, which are often cost-prohibitive because of high resource and quality control costs. Not only this, but the inherent biological variability of their underlying bioprocesses makes them particularly susceptible to unforeseen costs arising from failed or delayed production batches. The present work reviews important concepts concerning the development of bioprocesses for stem cell therapy products and highlights the valuable role which BEMs can play in this endeavor. Additionally, some theoretical concepts relevant to the building and structuring of BEMs are explored. Finally, a comprehensive review of the existent BEMs so far reported in the scientific literature for stem cell-related bioprocesses is provided to showcase their potential usefulness.

A Quality by Design Approach in Pharmaceutical Development of Non-Viral Vectors with a Focus on miRNA

Cancer is the leading cause of death worldwide. Tumors consist of heterogeneous cell populations that have different biological properties. While conventional cancer therapy such as chemotherapy, radiotherapy, and surgery does not target cancer cells specifically, gene therapy is attracting increasing attention as an alternative capable of overcoming these limitations. With the advent of gene therapy, there is increasing interest in developing non-viral vectors for genetic material delivery in cancer therapy. Nanosystems, both organic and inorganic, are the most common non-viral vectors used in gene therapy. The most used organic vectors are polymeric and lipid-based delivery systems. These nanostructures are designed to bind and protect the genetic material, leading to high efficiency, prolonged gene expression, and low toxicity. Quality by Design (QbD) is a step-by-step approach that investigates all the factors that may affect the quality of the final product, leading to efficient pharmaceutical development. This paper aims to provide a new perspective regarding the use of the QbD approach for improving the quality of non-viral vectors for genetic material delivery and their application in cancer therapy.