A Data-Driven Approach for Leveraging Inline and Offline Data to Determine the Causes of Monoclonal Antibody Productivity Reduction in the Commercial-Scale Cell Culture Process
Abstract
:1. Introduction
- A data-driven approach is proposed to leverage both the inline and offline data to identify the causes of mAb productivity reduction.
- The features of inline data are extracted using the FDA method and then fused with offline data to obtain an extended feature matrix to represent the cell culture process. The causes of mAb productivity are analyzed via the ccPCA method based on the extended feature matrix.
- A commercial-scale case study is provided to verify the effectiveness of the proposed approach.
2. Materials and Methods
2.1. Preliminaries
2.1.1. kNN
2.1.2. FDA
2.1.3. ccPCA
2.2. Proposed Approach
2.2.1. Data Preprocessing
- Missing data caused by network failures: Network failures disconnect the equipment and the real-time database; thus, the values during network downtime are not recorded. The amount of missing data depends on how long it takes to solve network failures.
- Data spikes caused by sensor failures: Data spikes, although not often seen, manifest as a sudden drop or rise in normal data that fall out of the reasonable ranges (e.g., the pH drops to 0 or the temperature rises to 100 °C during the culture process), and can be easily identified by simple rules.
- Variables with little information: There are many sensors equipped in both bioreactors and their accessories, but the variables not directly linked to the bioreactors may not reflect the dynamic characteristics of the cell culture process. Furthermore, some process variables are stationary across the whole batch, and their variances are zero or very low. Such variables are considered to have little contribution to the data analysis of the cell culture process.
2.2.2. Phase Division
2.2.3. Feature Extraction and Fusion
2.2.4. Cluster Comparing
3. Results and Discussion
3.1. The Cell Culture Process
3.1.1. Process Description
3.1.2. Process Data
3.2. Data Preprocessing Result
3.3. Phase Division Result
3.4. Feature Extraction and Fusion Result
3.4.1. Reference Phase Selection
3.4.2. FDA Fitting Result
3.5. Cluster Comparing Result
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. | Inline Process Variables | Unit | No. | Offline Process Variables | Unit |
---|---|---|---|---|---|
1 | pH 1 | ― | 10 | VCD | 106·mL−1 |
2 | pH 2 | ― | 11 | cell viability | % |
3 | DO 1 | % | 12 | average diameter of cells | μm |
4 | DO 2 | % | 13 | cell agglomeration ratio | % |
5 | temperature | °C | 14 | pH | ― |
6 | air flow sparger | L·min−1 | 15 | glucose concentration | g·L−1 |
7 | air flow overlay | L·min−1 | 16 | lactate concentration | g·L−1 |
8 | O2 flow sparger | L·min−1 | |||
9 | volume | L |
Variable No. | Phase 1 | Phase 2 | Phase 3 | Phase 4 | Phase 5 | |||||
---|---|---|---|---|---|---|---|---|---|---|
Type * | Number | Type | Number | Type | Number | Type | Number | Type | Number | |
1 | B | 24 | B | 9 | B | 40 | B | 5 | B | 8 |
2 | B | 24 | B | 11 | B | 40 | B | 5 | B | 8 |
3 | P | 1 | P | 1 | P | 1 | B | 28 | B | 28 |
4 | P | 1 | P | 1 | P | 1 | B | 28 | B | 28 |
5 | P | 1 | P | 1 | P | 1 | P | 1 | P | 1 |
6 | P | 1 | P | 1 | P | 1 | P | 1 | P | 1 |
7 | P | 1 | P | 1 | P | 1 | P | 1 | P | 1 |
8 | P | 3 | P | 3 | P | 3 | B | 22 | B | 20 |
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Zhang, S.; Chen, H.; Wan, Y.; Wang, H.; Qu, H. A Data-Driven Approach for Leveraging Inline and Offline Data to Determine the Causes of Monoclonal Antibody Productivity Reduction in the Commercial-Scale Cell Culture Process. Pharmaceutics 2024, 16, 1082. https://doi.org/10.3390/pharmaceutics16081082
Zhang S, Chen H, Wan Y, Wang H, Qu H. A Data-Driven Approach for Leveraging Inline and Offline Data to Determine the Causes of Monoclonal Antibody Productivity Reduction in the Commercial-Scale Cell Culture Process. Pharmaceutics. 2024; 16(8):1082. https://doi.org/10.3390/pharmaceutics16081082
Chicago/Turabian StyleZhang, Sheng, Hang Chen, Yuxiang Wan, Haibin Wang, and Haibin Qu. 2024. "A Data-Driven Approach for Leveraging Inline and Offline Data to Determine the Causes of Monoclonal Antibody Productivity Reduction in the Commercial-Scale Cell Culture Process" Pharmaceutics 16, no. 8: 1082. https://doi.org/10.3390/pharmaceutics16081082