High Precision Biomass Growth Fingerprinting with In-line Total Cell Density Measurement

Discover how our in-line optical density sensors could revolutionize your bacterial fermentation processes.

Quantification of fermentation growth metrics such as biomass concentration are key performance indicators relating to fermentation productivity; however, growth profiling is often collected using off-line optical density measurements which are limited by discrete, time-delayed sampling and measurement quantification.

In this article, we introduce our in-line optical density technology, designed for continuous, real-time measurement of fermentation processes; and demonstrate how real-time predictive analytics collected in-line can improve process control in biomass fermentation processes leading to optimized productivity and ensuring batch quality.

Biomass Measurements in Fermentations

Bacterial fermentations are a critical unit operation for the production of a variety of pharmaceutical, food-additive, and bulk chemical products. One key performance indicator critical for fermentation bioprocessing involves quantifying fermentation growth metrics, with biomass concentration directly relating to fermentation productivity, in-process control criteria such as induction or harvest timing, and as a parameter for batch quality assessments.

Bacterial growth profiles are historically characterized using off-line optical density (OD) measurements (typically at 600 nm wavelength = OD600) with a UV/Vis spectrophotometer or via gravimetric methods such as wet or dry cell weight. ⁴Off-line sampling techniques are widely used for biomass quantification despite suffering from key limitations, namely the discrete nature of the measurement, time delay between sampling and measurement quantification, and large measurement uncertainty.

In-line biomass measurement presents one technology to overcome off-line measurement limitations, enabling continuous in-process measurement of biomass growth trends within fermentation processes without the need for discrete sampling. Common technologies used within the biopharma industry to measure biomass include capacitance-based or optical-based in-line sensors.

Capacitance sensors such as the Hamilton Incyte Arc rely on measuring charge distribution within a fermentation to determine viable biomass concentrations. Capacitance measurements typically require higher biomass concentrations compared to optical-based sensors and off-line measurements, thus limiting usability in lower biomass bacterial fermentations. Conversely, optical sensors rely on absorbance or backscatter measurements to derive a total biomass measurement and generally have comparable concentration thresholds to off-line biomass measurements.

Figure 1. Fermentation growth profile captured by both in-line and off-line OD measurements.

Growth Profiling for Process Consistency

Establishing an optimal growth profile and characterizing batch-to-batch reproducibility is a critical first step to implementing PAT and improving process consistency. Off-line OD sampling provides one tool to develop a growth profile framework but has critical factors limiting profiling robustness (mainly discrete data points and sampling error). Limited data points from different times within different batches results in an incomplete picture of replicate batch profiles. Additionally, the error associated with off-line sampling, dilution, and human variance limits the accuracy of a given batch profile. As a result, any quantitative characterization such as statistical analysis of reproducibility inherently suffers from large standard deviations and limited sampling size.

In-line optical density measurements provide one solution to mitigate the limitations of off-line OD datasets. An in-line sensor eliminates many of the error sources associated with sampling by providing a continuous in-line signal correlatable to off-line biomass metrics, while reducing measurement variation between individual batches and allowing for a more consistent characterization of fermentation growth dynamics than achievable with off-line sampling only.

Case Study: Profiling of Biomass Growth Curves with In-line OD Measurement

In the following case study, a series of Lactobacillus acidophilus fermentations for probiotic production were performed utilizing a Dencytee in-line total cell density sensor as a measurement for biomass growth.¹ Kinetic modeling with general dynamic equations was performed on subsequent OD-derived growth profiles and provided additional data metrics to better assess individual fermentation batch performance characteristics.

In-fermentation data generated from a near-infrared OD measurement was fitted against a shifted logistic function^E1 to characterize the growth curve characteristics. Logistic functions are part of the sigmoidal class of functions and are commonly used to model microbial growth processes.² Furthermore, the empirical coefficients can be directly related to biologically meaningful parameters such as maximum specific growth rate.^E2

Results from the empirical fitting determined that the logistic model was well suited to characterizing the OD profiles from multiple batch E. Coli processes. A high level of linearity was observed between the predicted OD values against measured OD profiles as shown by the corresponding Pearson correlation coefficients, indicating the empirical correlation was appropriate.

time post inoculation, in line sensors — Figure 2. Empirical model fit against experimental data demonstrate high correlation strength throughout the duration of a fermentation.

The same equation applied to two different OD profiles was able to effectively characterize subtle differences between the rate constants and inflection timing while still maintaining a high predictive strength throughout the run. In the context of certain production strategies such as pDNA expression in E. coli, differences in growth rate directly translate to fermentation productivity and product quality.³ Within the case study parameter fitting was applied only in post processing of datasets. However, there exists significant potential for real-time predictive analytics for improved process control in a variety of biomass fermentation processes.

In the instance of well-characterized batch fermentations, a historical fingerprint of an ideal growth curve will already be established, and thus some of the terms in the empirical fitting may be pre-defined. With a known nutrient and sugar concentrations, the expected max biomass concentration (Ax term) can be estimated from previous growth profiles. As well, the cell-free baseline signal can be clearly determined with an in-line OD probe and accounted for with an in-situ product calibration (b0 term). With these terms pre-determined, a non-linear parameter fit can be more easily performed using real-time data, providing the ability to estimate maximum specific growth rate and inflection time throughout the course of a fermentation. For process operations such as induction timing, knowing the process specific growth rate and inflection point may be more advantageous for adjusting to batch-to-batch variation and determining an optimal induction time compared to a set biomass or fixed time approach.

For fed-batch processes, predictive estimates for specific growth rate can translate directly to improved fermentation productivity. For example, a variable feed rate based upon growth rate post-induction has been shown to provide increased productivity and higher batch-to-batch yield consistency for a fed-batch E. coli process compared to a fixed-feed approach.⁴Growth rate can be directly implemented into a feed pump equation; enabling direct growth rate based feed control schemes.

Overall, an in-line optical density sensor provides a path toward increased process reproducibility and implementing advanced analytic controls previously unachievable with only off-line measurements of cell density.

Time after induction constant vs controlled — Figure 3. Model-based feed control demonstrates the potential to double product yield with the same volume of feed while increasing product consistency.

References

Narayana, S.; Christensen, L.; Skov, T.; van den Berg, F., Mid-Infrared Spectroscopy and Multivariate Analysis to Characterize Lactobacillus acidophilus Fermentation Processes. Applied Spectroscopy 2019, 73(9):1087-1098. https://pubmed.ncbi.nlm.nih.gov/31008650.
Longhi, D.; Dalcanton, F.; de Aragão, G.; Carciofi, B.; Laurindo, J., Microbial Growth Models: A General Mathematical Approach to obtain μmax and λ Parameters from Sigmoidal Empirical Primary Models. Brazilian Journal of Chemical Engineering 2017, 34(2):369-375. https://www.scielo.br/j/bjce/a/G9YGDWmWgXq3CRsLNLBnwJn.
O’Kennedy, R.; Ward, J.; Keshavarz-Moore, E.; Effects of Fermentation Strategy on the Characteristics of Plasmid DNA Production. Biotechnol. Appl. Biochem 2003, 37(1):83-90. https://pubmed.ncbi.nlm.nih.gov/12578555.
. Zalaia, D.; Koppb, J.; Kozmab, B.; Küchlera, M.; Herwig, C.; Kager, J.; Microbial Technologies for Biotherapeutics Production: Key Tools for Advanced Biopharmaceutical Process Development and Control. Drug Discovery Today 2020, 38:9-24. https://pubmed.ncbi.nlm.nih.gov/34895644.

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