Contract development and manufacturing organization selection: critical considerations that can make or break your cell and gene therapy development.

BACKGROUND AIMS: With the increase in cell and gene therapy (CGT) clinical trials in recent years has come a subsequent increase in the number of contract development and manufacturing organizations (CDMOs). Successful transition from development and early-phase clinical trials to commercialization of a CGT product often depends on selecting the best-suited CDMO. However, many CGT companies are small biotech companies that lack expertise in the field or do not have experience selecting and transferring a process to a CDMO. METHODS: Given the interest in this topic, a roundtable with CGT developers and CDMO members at the 2023 annual meeting of the International Society of Cell and Gene Therapy Paris discussed these critical aspects of product development, including technical expertise, risk sharing and timing of partnerships. RESULTS AND CONCLUSIONS: Here, we'll analyze the considerations discussed by the panel and elaborate on other factors crucial for CGT development.

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.

Process development for the production of mesenchymal stromal cell-derived extracellular vesicles in conventional 2D systems.

BACKGROUND: In recent years, the importance of extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) has increased significantly. For their widespread use, a standardized EV manufacturing is needed which often includes conventional, static 2D systems. For these system critical process parameters need to be determined. METHODS: We studied the impact of process parameters on MSC proliferation, MSC-derived particle production including EVs, EV- and MSC-specific marker expression, and particle functionality in a HaCaT cell migration assay. RESULTS: We found that cell culture growth surface and media affected MSCs and their secretory behavior. Interestingly, the materials that promoted MSC proliferation did not necessarily result in the most functional MSC-derived particles. In addition, we found that MSCs seeded at 4 × 103 cells cm-2 produced particles with improved functional properties compared to higher seeding densities. MSCs in a highly proliferative state did not produce the most particles, although these particles were significantly more effective in promoting HaCaT cell migration. The same correlation was found when investigating the cultivation temperature. A physiological temperature of 37°C was not optimal for particle yield, although it resulted in the most functional particles. We observed a proliferation-associated particle production and found potential correlations between particle production and glucose consumption, enabling the estimation of final particle yields. CONCLUSIONS: Our findings suggest that parameters, which must be defined prior to each individual cultivation and do not require complex and expensive equipment, can significantly increase MSC-derived particle production including EVs. Integrating these parameters into a standardized EV process development paves the way for robust and efficient EV manufacturing for early clinical phases.

Combining descriptive and predictive modeling to systematically design depth filtration-based harvest processes for biologics.

Advances in upstream production of biologics-particularly intensified fed-batch processes beyond 10% cell solids-have severely strained harvest operations, especially depth filtration. Bioreactors containing high amounts of cell debris (more than 40% particles <10 µm in diameter) are increasingly common and drive the need for more robust depth filtration processes, while accelerated timelines emphasize the need for predictive tools to accelerate development. Both needs are constrained by the current limited mechanistic understanding of the harvest filter-feedstream system. Historically, process development relied on screening scale-down depth filter devices and conditions to define throughput before fouling, indicated by increasing differential pressure and/or particle breakthrough (measured via turbidity). This approach is straightforward, but resource-intensive, and its results are inherently limited by the variability of the feedstream. Semi-empirical models have been developed from first principles to describe various mechanisms of filter fouling, that is, pore constriction, pore blocking, and/or surface deposit. Fitting these models to experimental data can assist in identifying the dominant fouling mechanism. Still, this approach sees limited application to guide process development, as it is descriptive, not predictive. To address this gap, we developed a hybrid modeling approach. Leveraging historical bench scale filtration process data, we built a partial least squares regression model to predict particle breakthrough from filter and feedstream attributes, and leveraged the model to demonstrate prediction of filter performance a priori. The fouling models are used to interpret and provide physical meaning to these computational models. This hybrid approach-combining the mechanistic insights of fouling models and the predictive capability of computational models-was used to establish a robust platform strategy for depth filtration of Chinese hamster ovary cell cultures. As new data continues to teach the computational models, in silico tools will become an essential part of harvest process development by enabling prospective experimental design, reducing total experimental load, and accelerating development under strict timelines.

Platform development for high-throughput optimization of perfusion processes: Part I: Implementation of cell bleeds in microwell plates.

The promise of continuous processing to increase yields and improve product quality of biopharmaceuticals while decreasing the manufacturing footprint is transformative. Developing and optimizing perfusion operations requires screening various parameters, which is expensive and time-consuming when using benchtop bioreactors. Scale-down models (SDMs) are the most feasible option for high-throughput data generation and condition screening. However, new SDMs mimicking perfusion are required, enabling experiments to be run in parallel. In this study, a method using microwell plates (MWP) operating in semi-perfusion mode with an implemented cell bleed step is presented. A CHO cell line was cultivated in a 24-well MWP (Vw = 1.2 mL) and grown at four high cell density (HCD) setpoints. Quasi steady-state condition was obtained by manually performing cell bleeds followed by a total medium exchange after centrifugation. Further, two HCD setpoints were scaled up (VW = 30 mL), comparing a squared six-well deepwell plate (DWP) to shake flasks (SF). This evaluation showed comparable results between systems (DWP vs. SF) and scales (MWP vs. DWP + SF). The results show that the well-plate-based methods are suitable to perform HCD and quasi steady-state cultivations providing a robust solution to industrially relevant challenges such as cell clone and media selection.

Platform development for high-throughput optimization of perfusion processes-Part II: Variation of perfusion rate strategies in microwell plates.

The biopharmaceutical industry is replacing fed-batch with perfusion processes to take advantage of reduced capital and operational costs due to the operation at high cell densities (HCD) and improved productivities. HCDs are achieved by cell retention and continuous medium exchange, which is often based on the cell-specific perfusion rate (CSPR). To obtain a cost-productive process the perfusion rate must be determined for each process individually. However, determining optimal operating conditions remain labor-intensive and time-consuming experiments, as investigations are performed in lab-scale perfusion bioreactors. Small-scale models such as microwell plates (MWPs) provide an option for screening multiple perfusion rates in parallel in a semi-perfusion mimic. This study investigated two perfusion rate strategies applied to the MWP platform operated in semi-perfusion. The CSPR-based perfusion rate strategy aimed to maintain multiple CSPR values throughout the cultivation and was compared to a cultivation with a perfusion rate of 1 RV d-1. The cellular performance was investigated with the dual aim (i) to achieve HCD, when inoculating at conventional and HCDs, and (ii) to maintain HCDs, when applying an additional manual cell bleed. With both perfusion rate strategies viable cell concentrations up to 50 × 106 cells mL-1 were achieved and comparable results for key metabolites and antibody product titers were obtained. Furthermore, the combined application of cell bleed and CSPR-based medium exchange was successfully shown with similar results for growth, metabolites, and productivities, respectively, while reducing the medium consumption by up to 50% for HCD cultivations.

Bayesian cell therapy process optimization.

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

Mechanistic modeling of empty-full separation in recombinant adeno-associated virus production using anion-exchange membrane chromatography.

Recombinant adeno-associated viral vectors (rAAVs) have become an industry-standard technology in the field of gene therapy, but there are still challenges to be addressed in their biomanufacturing. One of the biggest challenges is the removal of capsid species other than that which contains the gene of interest. In this work, we develop a mechanistic model for the removal of empty capsids-those that contain no genetic material-and enrichment of full rAAV using anion-exchange membrane chromatography. The mechanistic model was calibrated using linear gradient experiments, resulting in good agreement with the experimental data. The model was then applied to optimize the purification process through maximization of yield studying the impact of mobile phase salt concentration and pH, isocratic wash and elution length, flow rate, percent full (purity) requirement, loading density (challenge), and the use of single-step or two-step elution modes. A solution from the optimization with purity of 90% and recovery yield of 84% was selected and successfully validated, as the model could predict the recovery yield with remarkable fidelity and was able to find process conditions that led to significant enrichment. This is, to the best of our knowledge, the first case study of the application of de novo mechanistic modeling for the enrichment of full capsids in rAAV manufacturing, and it serves as demonstration of the potential of mechanistic modeling in rAAV process development.

Auto-transduction in lentiviral vector bioprocessing: A quantitative assessment and a novel inhibition strategy.

Lentiviral vectors are highly efficient gene delivery vehicles used extensively in the rapidly growing field of cell and gene therapy. Demand for efficient, large-scale, lentiviral vector bioprocessing is growing as more therapies reach late-stage clinical trials and are commercialized. However, despite substantial progress, several process inefficiencies remain. The unintended auto-transduction of viral vector-producing cells by newly synthesized lentiviral vector particles during manufacturing processes constitutes one such inefficiency which remains largely unaddressed. In this study, we determined that over 60% of functional lentiviral vector particles produced during an upstream production process were lost to auto-transduction, highlighting a major process inefficiency likely widespread within the industry. Auto-transduction of cells by particles pseudotyped with the widely used vesicular stomatitis virus G protein was inhibited via the adoption of a reduced extracellular pH during vector production, impairing the ability of the vector to interact with its target receptor. Employing a posttransfection pH shift to pH 6.7-6.8 resulted in a sevenfold reduction in vector genome integration events, arising from lentiviral vector-mediated transduction, within viral vector-producing cell populations and ultimately resulted in improved lentiviral vector production kinetics. The proposed strategy is scalable and cost-effective, providing an industrially relevant approach to improve lentiviral vector production efficiencies.

Experimental characterization and prediction of Escherichia coli host cell proteome retention during preparative chromatography.

Purification of recombinantly produced biopharmaceuticals involves removal of host cell material, such as host cell proteins (HCPs). For lysates of the common expression host Escherichia coli (E. coli) over 1500 unique proteins can be identified. Currently, understanding the behavior of individual HCPs for purification operations, such as preparative chromatography, is limited. Therefore, we aim to elucidate the elution behavior of individual HCPs from E. coli strain BLR(DE3) during chromatography. Understanding this complex mixture and knowing the chromatographic behavior of each individual HCP improves the ability for rational purification process design. Specifically, linear gradient experiments were performed using ion exchange (IEX) and hydrophobic interaction chromatography, coupled with mass spectrometry-based proteomics to map the retention of individual HCPs. We combined knowledge of protein location, function, and interaction available in literature to identify trends in elution behavior. Additionally, quantitative structure-property relationship models were trained relating the protein 3D structure to elution behavior during IEX. For the complete data set a model with a cross-validated R2 of 0.55 was constructed, that could be improved to a R2 of 0.70 by considering only monomeric proteins. Ultimately this study is a significant step toward greater process understanding.

Cryopreserved leukapheresis material can be transferred from controlled rate freezers to ultracold storage at warmer temperatures without affecting downstream CAR-T cell culture performance and in-vitro functionality.

Chimeric antigen receptor (CAR) T-cell therapies are increasingly adopted as a commercially available treatment for hematologic and solid tumor cancers. As CAR-T therapies reach more patients globally, the cryopreservation and banking of patients' leukapheresis materials is becoming imperative to accommodate intra/inter-national shipping logistical delays and provide greater manufacturing flexibility. This study aims to determine the optimal temperature range for transferring cryopreserved leukapheresis materials from two distinct types of controlled rate freezing systems, Liquid Nitrogen (LN2)-based and LN2-free Conduction Cooling-based, to the ultracold LN2 storage freezer (≤-135 °C), and its impact on CAR T-cell production and functionality. Presented findings demonstrate that there is no significant influence on CAR T-cell expansion, differentiation, or downstream in-vitro function when employing a transfer temperature range spanning from -30 °C to -80 °C for the LN2-based controlled rate freezers as well as for conduction cooling controlled rate freezers. Notably, CAR T-cells generated from cryopreserved leukapheresis materials using the conduction cooling controlled rate freezer exhibited suboptimal performance in certain donors at transfer temperatures lower than -60 °C, possibly due to the reduced cooling rate of lower than 1 °C/min and extended dwelling time needed to reach the final temperatures within these systems. This cohort of data suggests that there is a low risk to transfer cryopreserved leukapheresis materials at higher temperatures (between -30 °C and -60 °C) with good functional recovery using either controlled cooling system, and the cryopreserved materials are suitable to use as the starting material for autologous CAR T-cell therapies.

Systematic DOE Approach for Dry Granulation Study at Early Clinical Stage of Novel Drugs: An Industrial Case.

In order to introduce a cost-effective strategy method for commercial scale dry granulation at the early clinical stage of drug product development, we developed dry granulation process using formulation without API, fitted and optimized the process parameters adopted Design of Experiment (DOE). Then, the process parameters were confirmed using one formulation containing active pharmaceutical ingredient (API). The results showed that the roller pressure had significant effect on particle ratio (retained up to #60 mesh screen), bulk density and tapped density. The roller gap had significant influence on particle ratio and specific energy. The particle ratio was significantly affected by the mill speed (second level). The tabletability of the powder decreased after dry granulation. The effect of magnesium stearate on the tabletability was significant. In the process validation study, the properties of the prepared granules met the requirements for each response studied in the DOE. The prepared tablets showed higher tensile strength, good content uniformity of filled capsules, and the dissolution profiles of which were consistent with that of clinical products. This drug product process development and research strategies could be used as a preliminary experiment for the dry granulation process in the early clinical stage.

Practical Synthesis of 7-Bromo-4-chloro-1H-indazol-3-amine: An Important Intermediate to Lenacapavir.

7-Bromo-4-chloro-1H-indazol-3-amine is a heterocyclic fragment used in the synthesis of Lenacapavir, a potent capsid inhibitor for the treatment of HIV-1 infections. In this manuscript, we describe a new approach to synthesizing 7-bromo-4-chloro-1H-indazol-3-amine from inexpensive 2,6-dichlorobenzonitrile. This synthetic method utilizes a two-step sequence including regioselective bromination and heterocycle formation with hydrazine to give the desired product in an overall isolated yield of 38-45%. The new protocol has been successfully demonstrated on hundred-gram scales without the need for column chromatography purification. This new synthesis provides a potential economical route to the large-scale production of this heterocyclic fragment of Lenacapavir.

The quest for down scale representativeness: how to exploit CFD to design a shear study for vaccines.

In this work, we exploit computational fluid dynamics (CFD) to evaluate stirred tank reactor (STR) process engineer parameters (PEP) and design a scale-down system (SDS) to be representative of the formulation and filling process steps for an Aluminum adjuvanted vaccine drug product (DP). To study the shear history in the SDS we used the concept of number of passages, combined with an appropriate stirring speed down scale strategy comprising of either (i) tip speed equivalence, widely used as a scale-up criterion for a shear-sensitive product, or (ii) rotating shear, a shear metric introduced by Metz and Otto in 1957 but never used as scaling criterion. The outcome of the CFD simulations shows that the tip equivalence generates a worst-case SDS in terms of shear, whereas the rotating shear scaling approach could be used to design a more representative SDS. We monitored the trend over time for "In Vitro Relative Potency" as DP Critical Quality Attribute for both scaling approaches, which highlighted the crucial role of choosing the appropriate scaling-down approach to be representative of the manufacturing scale during process characterization studies.

Ten years of the manufacturing classification system: a review of literature applications and an extension of the framework to continuous manufacture.

The MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and considers how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture. The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications. The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent.

Machine Learning Classifier-Based Metrics Can Evaluate the Efficiency of Separation Systems.

This paper highlights that metrics from the machine learning field (e.g., entropy and information gain) used to qualify a classifier model can be used to evaluate the effectiveness of separation systems. To evaluate the efficiency of separation systems and their operation units, entropy- and information gain-based metrics were developed. The receiver operating characteristic (ROC) curve is used to determine the optimal cut point in a separation system. The proposed metrics are verified by simulation experiments conducted on the stochastic model of a waste-sorting system.

Comparison of vector elements and process conditions in transient and stable suspension HEK293 platforms using SARS-CoV-2 receptor binding domain as a model protein.

BACKGROUND: Mammalian cell lines are frequently used as protein expression hosts because of their ability to correctly fold and assemble complex proteins, produce them at high titers, and confer post-translational modifications (PTMs) critical to proper function. Increasing demand for proteins with human-like PTMs, particularly viral proteins and vectors, have made human embryonic kidney 293 (HEK293) cells an increasingly popular host. The need to engineer more productive HEK293 platforms and the ongoing nature of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic presented an opportunity to study strategies to improve viral protein expression in transient and stable HEK293 platforms. RESULTS: Initial process development was done at 24 deep well plate (DWP) -scale to screen transient processes and stable clonal cell lines for recombinant SARS-CoV-2 receptor binding domain (rRBD) titer. Nine DNA vectors that drove rRBD production under different promoters and optionally contained Epstein-Barr virus (EBV) elements to promote episomal expression were screened for transient rRBD production at 37 °C or 32 °C. Use of the cytomegalovirus (CMV) promoter to drive expression at 32 °C led to the highest transient protein titers, but inclusion of episomal expression elements did not augment titer. In parallel, four clonal cell lines with titers higher than that of the selected stable pool were identified in a batch screen. Flask-scale transient transfection and stable fed-batch processes were then established that produced rRBD up to 100 mg/L and 140 mg/L, respectively. While a bio-layer interferometry (BLI) assay was crucial for efficiently screening DWP batch titers, an enzyme-linked immunosorbent assay (ELISA) was used to compare titers from the flask-scale batches due to varying matrix effects from different cell culture media compositions. CONCLUSION: Comparing yields from the flask-scale batches revealed that stable fed-batch cultures produced up to 2.1x more rRBD than transient processes. The stable cell lines developed in this work are the first reported clonal, HEK293-derived rRBD producers and have titers up to 140 mg/L. As stable production platforms are more economically favorable for long-term protein production at large scales, investigation of strategies to increase the efficiency of high-titer stable cell line generation in Expi293F or other HEK293 hosts is warranted.

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.

High-throughput process development from gene cloning to protein production.

In the post-genomic era, the demand for faster and more efficient protein production has increased, both in public laboratories and industry. In addition, with the expansion of protein sequences in databases, the range of possible enzymes of interest for a given application is also increasing. Faced with peer competition, budgetary, and time constraints, companies and laboratories must find ways to develop a robust manufacturing process for recombinant protein production. In this review, we explore high-throughput technologies for recombinant protein expression and present a holistic high-throughput process development strategy that spans from genes to proteins. We discuss the challenges that come with this task, the limitations of previous studies, and future research directions.

Progress in fed-batch culture for recombinant protein production in CHO cells.

Nearly 80% of the approved human therapeutic antibodies are produced by Chinese Hamster Ovary (CHO) cells. To achieve better cell growth and high-yield recombinant protein, fed-batch culture is typically used for recombinant protein production in CHO cells. According to the demand of nutrients consumption, feed medium containing multiple components in cell culture can affect the characteristics of cell growth and improve the yield and quality of recombinant protein. Fed-batch optimization should have a connection with comprehensive factors such as culture environmental parameters, feed composition, and feeding strategy. At present, process intensification (PI) is explored to maintain production flexible and meet forthcoming demands of biotherapeutics process. Here, CHO cell culture, feed composition in fed-batch culture, fed-batch culture environmental parameters, feeding strategies, metabolic byproducts in fed-batch culture, chemostat cultivation, and the intensified fed-batch are reviewed. KEY POINTS: • Fed-batch culture in CHO cells is reviewed. • Fed-batch has become a common technology for recombinant protein production. • Fed batch culture promotes recombinant protein production in CHO cells.