A Reliable Automated Sampling System for On-Line and Real-Time Monitoring of CHO Cultures

What should next-generation analytical platforms for biopharmaceutical production look like?

Biotherapeutic products, particularly complex products such as monoclonal antibodies (mAbs), have as many as 20-30 critical quality attributes (CQAs), thereby requiring a collection of orthogonal, high-resolution analytical tools for characterization and making characterization a resource-intensive task. As discussed in this Opinion, the need to reduce the cost of developing biotherapeutic products and the need to adopt Industry 4.0 and eventually Industry 5.0 paradigms are driving a reappraisal of existing analytical platforms. Next-generation platforms will have reduced offline testing, renewed focus on online testing and real-time monitoring, multiattribute monitoring, and extensive use of advanced data analytics and automation. They will be more complex, more sensitive, resource lean, and more responsive compared with existing platforms.

Touch-free optical technologies to streamline the production of T cell therapies

Currently approved adoptive T cell therapy relies on autologous (obtained from the same patient) T cells, which often suffer from poor quality that diminishes treatment efficacy. Due to the heterogeneous nature of T cell quality between and within patients, significant efforts are aimed at optimizing cell manipulation and growth conditions for potent T cell products. We believe that touch-free imaging and sensing technologies are critical to monitor single-cell features during T cell manufacturing to ensure consistent and optimally timed methods for cell manipulation and growth. Here, we discuss emerging label-free optical imaging and sensing methods, along with machine learning techniques that could enable in-line feedback to optimize T cell quality at multiple stages during manufacturing. These methods have the potential to streamline current workflow, accelerate the manufacture of safe high-quality T cell therapies, and improve our understanding of the dynamic, heterogeneous processes of T cell manufacturing.

On the Use of Surface Plasmon Resonance-Based Biosensors for Advanced Bioprocess Monitoring

Biomanufacturers are being incited by regulatory agencies to transition from a quality by testing framework, where they extensively test their product after their production, to more of a quality by design or even quality by control framework. This requires powerful analytical tools and sensors enabling measurements of key process variables and/or product quality attributes during production, preferably in an online manner. As such, the demand for monitoring technologies is rapidly growing. In this context, we believe surface plasmon resonance (SPR)-based biosensors can play a role in enabling the development of improved bioprocess monitoring and control strategies. The SPR technique has been profusely used to probe the binding behavior of a solution species with a sensor surface-immobilized partner in an investigative context, but its ability to detect binding in real-time and without a label has been exploited for monitoring purposes and is promising for the near future. In this review, we examine applications of SPR that are or could be related to bioprocess monitoring in three spheres: biotherapeutics production monitoring, vaccine monitoring, and bacteria and contaminant detection. These applications mainly exploit SPR’s ability to measure solution species concentrations, but performing kinetic analyses is also possible and could prove useful for product quality assessments. We follow with a discussion on the limitations of SPR in a monitoring role and how recent advances in hardware and SPR response modeling could counter them. Mainly, throughput limitations can be addressed by multi-detection spot instruments, and nonspecific binding effects can be alleviated by new antifouling materials. A plethora of methods are available for cell growth and metabolism monitoring, but product monitoring is performed mainly a posteriori. SPR-based biosensors exhibit potential as product monitoring tools from early production to the end of downstream processing, paving the way for more efficient production control. However, more work needs to be done to facilitate or eliminate the need for sample preprocessing and to optimize the experimental protocols.

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.

Taking the pulse of bioprocesses: at-line and in-line monitoring of mammalian cell cultures

Real-time and near real-time monitoring of cell culture processes are critical to the evolving process analytical technology (PAT) paradigm for upstream bioprocessing. The responses measured from these analytical instruments can enable rapid feedback to perturbations that can otherwise lead to batch failures. Historically, real-time monitoring of bioreactor processes has been relegated to parameters such as pH, dissolved oxygen, and temperature. Other analytical results, such as cell growth and metabolites, are provided through manual daily sampling. In order to reduce sample error and increase throughput, real-time and near real-time instruments have been developed. Here we discuss recent advances in these technologies. This article aims to focus on other developing at-line and in-line technologies that enable monitoring of bioreactor processes, including dielectric spectroscopy, NIR, off-gas spectrometry, integrated at-line HPLC, and nanofluidic devices for monitoring cell growth and health, metabolites, titer, and product quality.

Measurement Technologies for Upstream and Downstream Bioprocessing

This special issue is devoted to new developments in measurement technologies for upstream and downstream bioprocessing [...]