rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing

Abstract

Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist’s perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.

High‐yield recombinant adeno‐associated viral vector production by multivariate optimization of bioprocess and transfection conditions

“…Building models on cell density using certain process analytical technologies (PAT) such as capacitance probes may be particularly useful to continuously monitor cell growth and trigger transfection when the target cell density is reached. 43,44 The transfection complexation time (X 8 ) is another critical parameter that may pose challenges to process scale up. Identifying strategies to deliver transfection complex to the bioreactor within a reasonable timeframe is crucial to success at large scale.…”

How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models

“…However, these characteristics of UMM in biomanufacturing, together with the use of P (t = 0) and Q with uncorrelated elements and the presence of a single measured state variable, represent a failure case that occurs when the JEKF cannot estimate the unshared parameters and the state simultaneously. There are many new bioprocesses for which the literature contains no prior knowledge that the biopharmaceutical industry aims to monitor, such as recombinant adeno-associated virus (rAAV) production [ 31 ]. Therefore, enabling the JEKF to side-step the failure case described above may help the industry perform biomanufacturing with the real-time monitoring of bioprocesses with unknown mechanisms.…”

“…They are based on first-principle mechanisms that drive the bioprocess under consideration [ 34 ]. Examples of bioprocesses are (i) the production of therapeutic monoclonal antibodies (mAbs), which is projected to bring in USD 300 billion by 2025 [ 34 ], and (ii) the rAAV production that is a viral vector technology for gene therapy considered the safest and most effective way to repair single-gene abnormalities in non-dividing cells [ 19 , 31 ]. It is essential to point out that despite UMM being the most suitable option to describe the dynamic behavior of bioprocesses and being considered a crucial foundation for soft sensors in DT development, its industrial use is still in its early stages [ 28 , 39 , 40 ].…”