Improved time resolved KPI and strain characterization of multiple hosts in shake flasks using advanced online analytics and data science

Shake flasks remain one of the most widely used cultivation systems in biotechnology, especially for process development (cell line and parameter screening). This can be justified by their ease of use as well as their low investment and running costs. A disadvantage, however, is that cultivations in shake flasks are black box processes with reduced possibilities for recording online data, resulting in a lack of control and time-consuming, manual data analysis. Although different measurement methods have been developed for shake flasks, they lack comparability, especially when changing production organisms. In this study, the use of online backscattered light, dissolved oxygen, and pH data for characterization of animal, plant, and microbial cell culture processes in shake flasks are evaluated and compared. The application of these different online measurement techniques allows key performance indicators (KPIs) to be determined based on online data. This paper evaluates a novel data science workflow to automatically determine KPIs using online data from early development stages without human bias. This enables standardized and cost-effective process-oriented cell line characterization of shake flask cultivations to be performed in accordance with the process analytical technology (PAT) initiative. The comparison showed very good agreement between KPIs determined using offline data, manual techniques, and automatic calculations based on multiple signals of varying strengths with respect to the selected measurement signal

Predictive monitoring of shake flask cultures with online estimated growth models

Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of critical events like low dissolved oxygen (DO) levels or when to harvest is hence important to optimize protocols. Established knowledge that unfavorable metabolites of growing microorganisms interfere with the substrate suggests that growth dynamics and, as a consequence, the growth model parameters may vary in the course of an experiment. Predictive monitoring of shake flask cultures will therefore benefit from estimating growth model parameters in an online and adaptive manner. This paper evaluates a newly developed particle filter (PF) which is specifically tailored to the requirements of biotechnological shake flask experiments. By combining stationary accuracy with fast adaptation to change the proposed PF estimates time-varying growth model parameters from iteratively measured BM and DO sensor signals in an optimal manner. Such proposition of inferring time varying parameters of Gompertz and Logistic growth models is to our best knowledge novel and here for the first time assessed for predictive monitoring of Escherichia coli (E. coli) shake flask experiments. Assessments that mimic real-time predictions of BM and DO levels under previously untested growth conditions demonstrate the efficacy of the approach. After allowing for an initialization phase where the PF learns appropriate model parameters, we obtain accurate predictions of future BM and DO levels and important temporal characteristics like when to harvest. Statically parameterized growth models that represent the dynamics of a specific setting will in general provide poor characterizations of the dynamics when we change strain or substrate. The proposed approach is thus an important innovation for scientists working on strain characterization and substrate optimization as providing accurate forecasts will improve reproducibility and efficiency in early-stage bioprocess development

Holistic Design of Experiments Using an Integrated Process Model

Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, they aim to investigate only one process step at a time. Here, we want to develop a new experimental design method that seeks to gain information about final product quality, placing the right type of run at the right unit operation. This is done by minimizing the simulated out-of-specification rate of an integrated process model comprised of a chain of regression models that map process parameters to critical quality attributes for each unit operation. Unit operation models are connected by passing their response to the next unit operation model as a load parameter, as is done in real-world manufacturing processes. The proposed holistic DoE (hDoE) method is benchmarked against standard process characterization approaches in a set of in silico simulation studies where data are generated by different ground truth processes to illustrate the validity over a range of scenarios. Results show that the hDoE approach leads to a >50% decrease in experiments, even for simple cases, and, at the same time, achieves the main goal of process development, validation, and manufacturing to consistently deliver product quality

Architectural and Technological Improvements to Integrated Bioprocess Models towards Real-Time Applications

Integrated or holistic process models may serve as the engine of a digital asset in a multistep-process digital twin. Concatenated individual-unit operation models are effective at propagating errors over an entire process, but are nonetheless limited in certain aspects of recent applications that prevent their deployment as a plausible digital asset, particularly regarding bioprocess development requirements. Sequential critical quality attribute tests along the process chain that form output–input (i.e., pool-to-load) relationships, are impacted by nonaligned design spaces at different scales and by simulation distribution challenges. Limited development experiments also inhibit the exploration of the overall design space, particularly regarding the propagation of extreme noncontrolled parameter values. In this contribution, bioprocess requirements are used as the framework to improve integrated process models by introducing a simplified data model for multiunit operation processes, increasing statistical robustness, adding a new simulation flow for scale-dependent variables, and describing a novel algorithm for extrapolation in a data-driven environment. Lastly, architectural and procedural requirements for a deployed digital twin are described, and a real-time workflow is proposed, thus providing a final framework for a digital asset in bioprocessing along the full product life cycle

Interpreting SFR vario online data gathered in plant cell suspension culture : characterization of plant cell lines for successful production processes

We used the SFR vario for online monitoring in plant cell suspension culture at shake flask scale. By comparing the online gathered data with offline determined values, we were able to confirm, that the SFR vario backscattered light measurement is ideal to predict biomass development. Furthermore, the online pH and oxygen measurements gave insight in different metabolic processes during the whole cultivation

Characterizing CHO cell lines with SFR vario : what online measured data can tell about your cell culture

There are numerous CHO cell lines with different properties. They need to be characterized before starting a production process, to ensure the cells are suitable for the planned application. The SFR vario is a multiparameter monitoring platform for shake flask culture and delivers online data for oxygen, pH and biomass. In this report we give a few examples on how this online monitoring device can be used to characterize precultures of CHO cell lines before starting a bioreactor run

Small scale bioprocess optimization using soft sensors

The development and optimization of cell and microbial cultures is usually done in small scale vessel like shake flasks. However, these vessels lack the sensors and hence information provided by larger bioreactors like biomass concentration, oxygen saturation and pH-values. The proposed presentation will show approaches to overcome these hurdles for mammalian (CHO), animal (e.g. insect cells Sf9) and plant cell cultures as well as microbial fermentations. The first part covers measurement improvements for shake flasks with focus on back-scattered light for biomass growth monitoring and the utilization of sensor spots for a deeper insight into the “black box” shake flasks. In the second part, soft sensors based on engineering and biological characterizations of these vessels are introduced and explained, enabling researchers to predict process values and estimate growth curves. The last part consists of the application of mechanistic and kinetic models for an improved process understanding and an enhanced culture growth predictio

Automated cell line characterization in shake flasks for multiple organisms

Shaken Erlenmeyer flasks are a commonly used cultivation system in biotechnology and are regularly employed in early-stage process development (e.g., inoculum production, media development, and strain characterization). However, they are mostly used as black box systems without the sensoring capabilities a stirred tank bioreactor can provide. Several measurement systems have been developed to overcome this issue, but there is still a lack of comparability or a uniform, automated approach to using online data for process characterization in shake flasks. To overcome this, we compared online backscattered light, dissolved oxygen, and pH data for plant, animal, E. coli, and S. cerevisiae cultivations using the PreSens SFR vario. With these data, key performance indicators (KPIs), such as the specific growth rate and the cell-specific oxygen demand, were evaluated automatically. For algorithm validation, manually calculated KPIs based on offline biomass data, online data and algorithm-based automatically calculated KPIs using online data were compared. The developed algorithm is based on Python and searches for the exponential phase in the corresponding online signal. The exponential fit set by the algorithm and the observed signal were compared and the fit optimized so that the root-mean-square error was as low as possible. With this technique, an accurate estimation of the growth rate and further calculation of the cell-specific oxygen demand can be performed using either the oxygen uptake rate or a biomass estimation based on the backscattered light signal. This enables the comparison and evaluation of different media, strains, and process conditions in a standardized cost-effective and automated manner, reducing human effort and errors

Multivariate Monitoring Workflow for Formulation, Fill and Finish Processes

Process monitoring is a critical task in ensuring the consistent quality of the final drug product in biopharmaceutical formulation, fill, and finish (FFF) processes. Data generated during FFF monitoring includes multiple time series and high-dimensional data, which is typically investigated in a limited way and rarely examined with multivariate data analysis (MVDA) tools to optimally distinguish between normal and abnormal observations. Data alignment, data cleaning and correct feature extraction of time series of various FFF sources are resource-intensive tasks, but nonetheless they are crucial for further data analysis. Furthermore, most commercial statistical software programs offer only nonrobust MVDA, rendering the identification of multivariate outliers error-prone. To solve this issue, we aimed to develop a novel, automated, multivariate process monitoring workflow for FFF processes, which is able to robustly identify root causes in process-relevant FFF features. We demonstrate the successful implementation of algorithms capable of data alignment and cleaning of time-series data from various FFF data sources, followed by the interconnection of the time-series data with process-relevant phase settings, thus enabling the seamless extraction of process-relevant features. This workflow allows the introduction of efficient, high-dimensional monitoring in FFF for a daily work-routine as well as for continued process verification (CPV)