Monitoring the Recombinant Adeno-Associated Virus Production using Extended Kalman Filter

Iglesias, Cristovão Freitas; Xu, Xingge; Mehta, Varun; Akassou, Mounia; Venereo-Sánchez, Alina; Belacel, Nabil; Kamen, Amine; Bolić, Miodrag

doi:10.3390/pr10112180

Cited by 3 publications

(9 citation statements)

References 49 publications

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“…However, until now, the failure case (biomanufacturing conditions) where JEKF fails as an unshared parameter estimator has not been formally reported. Recently, the JEKF was applied for monitoring rAAV production [ 19 ]. In developing this application, the authors dealt with a situation that resembles the failure case reported here.…”

Section: Related Workmentioning

confidence: 99%

“… P and Q with uncorrelated elements. In case of the limited amount of data, it is very common to assume P and Q with uncorrelated elements in EKF applications [ 19 , 20 , 21 , 47 ]. This assumption means that the error covariance matrices P and Q are diagonal, with the diagonal elements being the noise variances (P

and Q

) and off-diagonal elements equal to zero (P

and Q

).…”

Section: Theoretical Analysismentioning

confidence: 99%

“…The real dataset (RD) contains data regarding rAAV productions, which are described and available in [ 19 ]. rAAV is a viral vector technology for gene therapy that is considered the safest and most effective way to repair single-gene abnormalities in non-dividing cells [ 60 ].…”

Section: Empirical Evaluationmentioning

confidence: 99%

“…The first discussions and applications of the JEKF approach started in the 1960s for the estimation of linear systems (in which there is a bilinear relation between the states and parameters) [ 6 , 7 , 8 , 9 , 10 ]. However, the JEKF is still very popular, with several new applications in different areas [ 5 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], and with unsolved problems [ 22 , 23 ]. Furthermore, the JEKF has been established as the least expensive nonlinear estimator for moderate-size systems in terms of computational cost because the practical implementation of adaptive controllers using microcontrollers (and/or minicomputers and/or microprocessors) requires numerically economical and robust algorithms, such as the JEKF [ 11 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Iglesias,

Bolic

2024

Sensors

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The unstructured mechanistic model (UMM) allows for modeling the macro-scale of a phenomenon without known mechanisms. This is extremely useful in biomanufacturing because using the UMM for the joint estimation of states and parameters with an extended Kalman filter (JEKF) can enable the real-time monitoring of bioprocesses with unknown mechanisms. However, the UMM commonly used in biomanufacturing contains ordinary differential equations (ODEs) with unshared parameters, weak variables, and weak terms. When such a UMM is coupled with an initial state error covariance matrix P(t=0) and a process error covariance matrix Q with uncorrelated elements, along with just one measured state variable, the joint extended Kalman filter (JEKF) fails to estimate the unshared parameters and state simultaneously. This is because the Kalman gain corresponding to the unshared parameter remains constant and equal to zero. In this work, we formally describe this failure case, present the proof of JEKF failure, and propose an approach called SANTO to side-step this failure case. The SANTO approach consists of adding a quantity to the state error covariance between the measured state variable and unshared parameter in the initial P(t = 0) of the matrix Ricatti differential equation to compute the predicted error covariance matrix of the state and prevent the Kalman gain from being zero. Our empirical evaluations using synthetic and real datasets reveal significant improvements: SANTO achieved a reduction in root-mean-square percentage error (RMSPE) of up to approximately 17% compared to the classical JEKF, indicating a substantial enhancement in estimation accuracy.

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Section: Related Workmentioning

confidence: 99%

and Q

) and off-diagonal elements equal to zero (P

and Q

).…”

Section: Theoretical Analysismentioning

confidence: 99%

Section: Empirical Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Iglesias,

Bolic

2024

Sensors

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Multivariate data analysis on multisensor measurement for inline process monitoring of adenovirus production in HEK293 cells

Xu,

Farnós,

Paes

et al. 2024

Biotech & Bioengineering

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In the era of Biopharma 4.0, process digitalization fundamentally requires accurate and timely monitoring of critical process parameters (CPPs) and quality attributes. Bioreactor systems are equipped with a variety of sensors to ensure process robustness and product quality. However, during the biphasic production of viral vectors or replication‐competent viruses for gene and cell therapies and vaccination, current monitoring techniques relying on a single working sensor can be affected by the physiological state change of the cells due to infection/transduction/transfection step required to initiate production. To address this limitation, a multisensor (MS) monitoring system, which includes dual‐wavelength fluorescence spectroscopy, dielectric signals, and a set of CPPs, such as oxygen uptake rate and pH control outputs, was employed to monitor the upstream process of adenovirus production in HEK293 cells in bioreactor. This system successfully identified characteristic responses to infection by comparing variations in these signals, and the correlation between signals and target critical variables was analyzed mechanistically and statistically. The predictive performance of several target CPPs using different multivariate data analysis (MVDA) methods on data from a single sensor/source or fused from multiple sensors were compared. An MS regression model can accurately predict viable cell density with a relative root mean squared error (rRMSE) as low as 8.3% regardless of the changes occurring over the infection phase. This is a significant improvement over the 12% rRMSE achieved with models based on a single source. The MS models also provide the best predictions for glucose, glutamine, lactate, and ammonium. These results demonstrate the potential of using MVDA on MS systems as a real‐time monitoring approach for biphasic bioproduction processes. Yet, models based solely on the multiplicity and timing of infection outperformed both single‐sensor and MS models, emphasizing the need for a deeper mechanistic understanding in virus production prediction.

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rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing

et al. 2023

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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.

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Monitoring the Recombinant Adeno-Associated Virus Production using Extended Kalman Filter

Cited by 3 publications

References 49 publications

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

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

Multivariate data analysis on multisensor measurement for inline process monitoring of adenovirus production in HEK293 cells

rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing

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