Modern therapeutics are shifting from their reliance on small molecules to biologics produced by cultured cells. The development and production of those biologics increasingly rely on computationally controlled design, automation, and analysis of both the process and product of biomanufacturing.
The FDA’s Process Analytical Technology (PAT) guidance document is 20 years old, and that “forward thinking” document is still valid today with its focus on quality by design, embracing a real-time sampling and analysis, said Jonathan Bones, PhD, principal investigator, characterization & comparability group, at the National Institute for Bioprocessing Research & Training [NIBRT] in Dublin, Ireland. Application and deployment of PAT in small molecule manufacturing has made significant progress, yet “things are more complicated” for biopharmaceutical manufacturing, especially upstream processing.
Particularly in the biopharma space, there is a long time lag from the point a sample is taken to the point that the data comes back, and because the processes are dynamic and things may have changed, Bones pointed out. “The information returned refers to a previous point in time.”
Bones’ research group, in close collaboration with 908 Devices, focuses on automating (near) real-time sample collection and myriad analyses within close proximity to (or in line with) the collection point, employing instrumentation that is mostly easy to use “right out of the box.” A second-generation platform features a control autosampler, “which takes the samples and sends them off to different instrumentation” such as in-line Raman spectroscope, an at-line cell culture analyzer, and a microchip capillary electrophoresis interface coupled to a mass detector for rapid product quality assessment. Process information management software (the “master aggregator”) integrates the data collection and analysis with the bioreactors.
The platform has allowed Bones’ team to increase the frequency at which samples are taken as well as expand the number of vessels from which they sample. “Once we have data sets such as this we can look at models using methods like predictive AI. Where’s my process going? If this is the way it is now, what’s my outcome likely to be? Is that good? Is that bad? Do I need to intervene?” Bones asks. “But the issue with that is you can’t do data analytics without good data.”
Analytics play a pivotal role in developing cell lines for bioprocess, as much for bacterial cells involved in industrial fermentation as it does for eukaryotic cells involved in the manufacture of monoclonal antibodies (mAbs) and other biopharmaceuticals. In the case of the first, for example, analytics “provide a comprehensive framework for monitoring critical fermentation parameters, assessing titer and product quality, and ultimately ensuring the selection of the most optimal strains for further advances,” noted Erik Nordwald, PhD, associate director of cell line development at KBI Biopharma.
Analytics play a pivotal role in developing cell lines for bioprocess, as much for bacterial cells involved in industrial fermentation as it does for eukaryotic cells involved in the manufacture of monoclonal antibodies and other biopharmaceuticals. [XH4D/Getty Images]
The first layer of analytics involves real-time monitoring and control of critical parameters including pH, biomass, dissolved oxygen, temperature, and humidity. These measurements help to ensure a state of control and are the first layer of understanding the fermentation of the different strains. The second layer of analysis, Nordwald said, involves measuring the titer, often accomplished using classical gel electrophoresis to separate soluble, insoluble, and periplasmic fractions, followed by HPLC analysis.
After narrowing down strains based on titer and fermentation data, product quality is analyzed by liquid chromatography-mass spectrometry (LC-MS). At this stage, product variants like post-translational modifications, misincorporations, and the amount of initiator Met excised can be read, he continued. “Using PAT, high-throughput titer, and LC-MS, we can screen broadly and finely to ensure the optimal strain is taken forward.”
From the early stages of mAb cell line development (CLD) through fine-tuning its bioproduction at scale, it is necessary to have an accurate assessment of both the quantity and quality of the product being generated. Given sometimes hundreds or even thousands of clones to draw from, choosing which ones to bring forward is often a matter of which affords the greatest titer under the same conditions. And when a chosen clone is grown under different conditions, a quantitative measurement of its mAb production allows for, e.g., an assessment of which growth conditions allow it to best thrive. Similarly, attributes of the mAbs produced, such as how much they aggregate in solution, are often determining factors in CLD and process development as well.
There are several ways in which to measure titer and aggregation. Among the quickest, easiest, and most cost-effective are plate-based add-mix-read Valita assays from Beckman Coulter Life Sciences. The biggest point of comparison between the Valita Titer assay and its main competitors—bio-layer interferometry (BLI), ELISA, and HPLC—is “speed and reduction in time to result—you can have your results for 96 samples in as little as 15 minutes,” said Anna Boland, PhD, senior development scientist at Beckman Coulter. She also points to a reduction in the number of reagents and steps—the assay measures IgG titer directly from cell culture—as well as the cost of implementing the assays.
![Small molecules rotate faster than larger molecules and this rotation rate can be determined by Fluorescence Polarization. This technique allows users to rapidly and accurately measure target abundance in solution. [Beckman Coulter Life Sciences]](https://www.genengnews.com/wp-content/uploads/2025/04/CellLineDevelopmentRoberts_BeckmanCoulter_ValitaTiterMechanism-1024x449.jpg "Small molecules rotate faster than larger molecules and this rotation rate can be determined by Fluorescence Polarization. This technique allows users to rapidly and accurately measure target abundance in solution. [Beckman Coulter Life Sciences]")
Small molecules rotate faster than larger molecules and this rotation rate can be determined by fluorescence polarization. This technique allows users to rapidly and accurately measure target abundance in solution. [Beckman Coulter Life Sciences]
The Valita Aggregation Pure assay is similar in many respects, including speed, ease of use, and cost-effectiveness. Both assays are read on a standard fluorescence polarization-capable plate reader. It is considered a “critical quality attribute assay,” she said, “used to determine the relative abundance of aggregated sample in a solution.”
Both assays are automation-friendly and easy to scale, allowing the use of the same tools from early-stage CLD through PD and QC, “so you can kind of keep that consistent measurement.”
Labs dedicated to CLD may want a work cell purpose-built for the end-to-end CLD workflow. CYTENA’s C.STATIONTM is just such a system, integrating “proven technologies for single-cell cloning, imaging, and advanced cell culture with cutting-edge automation software and a clone-centric data management system to ensure full traceability and regulatory readiness for the production of modern therapeutics,” said Pierre-Henri Ferdinand, PhD, the company’s head of product.
The C.STATION is basically a BSL-1 or -2 housing that embeds several pieces of instrumentation, including a shaker and incubators, a liquid handler, a single-cell dispenser with imaging, and a robotic arm to move plates around, integrated with software that orchestrates that instrumentation and analyzes the results. It offers an intuitive interface through which a pre-configured library of methods can be accessed, and “can be run without the need of having an automation engineer on-site,” he noted. The platform also offers powerful customization for more advanced users.
The “beating heart” of the C.STATION is the benchtop UP.SIGHT, a high-efficiency single-cell dispenser combined with “double assurance of clonality,” said Ferdinand. The instrument takes images of the cells both before dispensing “to show that the droplets dispensed contained only one cell,” as well as “detecting a single cell on the bottom of the well after dispensing.”
This assurance of clonality is “part of an environment that will facilitate regulatory approval, by having everything documented so it can be directly put on the table,” he explained, “allowing for full traceability and regulatory readiness for the production of modern therapeutics.”
Bioprocessing has always been a bit late in adopting the latest engineering techniques—applying computational fluid dynamics to bioreactor design, for example, said Will Johnson, head of process modeling, Asimov. His company’s mission is to bring CAD (computer-aided design) to bioprocessing, combining an understanding of biology with engineering to optimize the processes that produce biologics such as mAbs.
![Several computational methods exist to represent and optimize the biophysical and physiological phenomena spanning genetic design to bioprocess performance. Genetic circuit simulation uses dynamic equations to represent transcription and translation of a gene of interest from an exogenous genetic circuit. [Asimov]](https://www.genengnews.com/wp-content/uploads/2025/04/CellLineDevelopmentRoberts_Asimov_Screenshot-1024x573.jpg "Several computational methods exist to represent and optimize the biophysical and physiological phenomena spanning genetic design to bioprocess performance. Genetic circuit simulation uses dynamic equations to represent transcription and translation of a gene of interest from an exogenous genetic circuit. [Asimov]")
Several computational methods exist to represent and optimize the biophysical and physiological phenomena spanning genetic design to bioprocess performance. Genetic circuit simulation uses dynamic equations to represent the transcription and translation of a gene of interest from an exogenous genetic circuit. [Asimov]
There are different approaches to optimization, such as maximizing the density of the cells in the bioreactor so there are more cells producing the product, for example, or maximizing the expression rate by tuning feeding or fluid exchange. “What is novel about what Asimov is doing now is starting to look even further upstream and saying, ‘Well, let’s not just try to optimize the process. Let’s try to optimize the cell line itself’,” Johnson says.
One of Asimov’s core products is a software tool named Kernel for designing genetic parts and genetic circuits. This tool basically allows you to put all the genetic parts together to design, for example, a vector for expressing a mAb or a bi-specific antibody in a host cell.
Going beyond CAD, Asimov uses CAE (computer-aided engineering) to “predict and optimize the performance of that design in the context in which we intend it to be used,” Johnson continued. “So for instance, we are looking right now at how best to design those genetic parts so that they are secreted most effectively in a CHO cell in a bioreactor.” This may include optimizing codon usage, or engineering promoters to optimize the ratio at which different chains are expressed.
Johnson’s overall goal—his “holy grail”—is not only to engineer and predict and optimize the performance of the individual circuits “component-by-component, but to do that holistically: to do an end-to-end prediction of performance in genetic design in silico, and then further optimize the biology” in the laboratory.
The post Combining Engineering and Biology to Drive More “Cultured” Therapies appeared first on GEN - Genetic Engineering and Biotechnology News.
The FDA’s Process Analytical Technology (PAT) guidance document is 20 years old, and that “forward thinking” document is still valid today with its focus on quality by design, embracing a real-time sampling and analysis, said Jonathan Bones, PhD, principal investigator, characterization & comparability group, at the National Institute for Bioprocessing Research & Training [NIBRT] in Dublin, Ireland. Application and deployment of PAT in small molecule manufacturing has made significant progress, yet “things are more complicated” for biopharmaceutical manufacturing, especially upstream processing.
Particularly in the biopharma space, there is a long time lag from the point a sample is taken to the point that the data comes back, and because the processes are dynamic and things may have changed, Bones pointed out. “The information returned refers to a previous point in time.”
Bones’ research group, in close collaboration with 908 Devices, focuses on automating (near) real-time sample collection and myriad analyses within close proximity to (or in line with) the collection point, employing instrumentation that is mostly easy to use “right out of the box.” A second-generation platform features a control autosampler, “which takes the samples and sends them off to different instrumentation” such as in-line Raman spectroscope, an at-line cell culture analyzer, and a microchip capillary electrophoresis interface coupled to a mass detector for rapid product quality assessment. Process information management software (the “master aggregator”) integrates the data collection and analysis with the bioreactors.
The platform has allowed Bones’ team to increase the frequency at which samples are taken as well as expand the number of vessels from which they sample. “Once we have data sets such as this we can look at models using methods like predictive AI. Where’s my process going? If this is the way it is now, what’s my outcome likely to be? Is that good? Is that bad? Do I need to intervene?” Bones asks. “But the issue with that is you can’t do data analytics without good data.”
Optimal strain
Analytics play a pivotal role in developing cell lines for bioprocess, as much for bacterial cells involved in industrial fermentation as it does for eukaryotic cells involved in the manufacture of monoclonal antibodies (mAbs) and other biopharmaceuticals. In the case of the first, for example, analytics “provide a comprehensive framework for monitoring critical fermentation parameters, assessing titer and product quality, and ultimately ensuring the selection of the most optimal strains for further advances,” noted Erik Nordwald, PhD, associate director of cell line development at KBI Biopharma.
Analytics play a pivotal role in developing cell lines for bioprocess, as much for bacterial cells involved in industrial fermentation as it does for eukaryotic cells involved in the manufacture of monoclonal antibodies and other biopharmaceuticals. [XH4D/Getty Images]
The first layer of analytics involves real-time monitoring and control of critical parameters including pH, biomass, dissolved oxygen, temperature, and humidity. These measurements help to ensure a state of control and are the first layer of understanding the fermentation of the different strains. The second layer of analysis, Nordwald said, involves measuring the titer, often accomplished using classical gel electrophoresis to separate soluble, insoluble, and periplasmic fractions, followed by HPLC analysis.
After narrowing down strains based on titer and fermentation data, product quality is analyzed by liquid chromatography-mass spectrometry (LC-MS). At this stage, product variants like post-translational modifications, misincorporations, and the amount of initiator Met excised can be read, he continued. “Using PAT, high-throughput titer, and LC-MS, we can screen broadly and finely to ensure the optimal strain is taken forward.”
How much? How many?
From the early stages of mAb cell line development (CLD) through fine-tuning its bioproduction at scale, it is necessary to have an accurate assessment of both the quantity and quality of the product being generated. Given sometimes hundreds or even thousands of clones to draw from, choosing which ones to bring forward is often a matter of which affords the greatest titer under the same conditions. And when a chosen clone is grown under different conditions, a quantitative measurement of its mAb production allows for, e.g., an assessment of which growth conditions allow it to best thrive. Similarly, attributes of the mAbs produced, such as how much they aggregate in solution, are often determining factors in CLD and process development as well.
There are several ways in which to measure titer and aggregation. Among the quickest, easiest, and most cost-effective are plate-based add-mix-read Valita assays from Beckman Coulter Life Sciences. The biggest point of comparison between the Valita Titer assay and its main competitors—bio-layer interferometry (BLI), ELISA, and HPLC—is “speed and reduction in time to result—you can have your results for 96 samples in as little as 15 minutes,” said Anna Boland, PhD, senior development scientist at Beckman Coulter. She also points to a reduction in the number of reagents and steps—the assay measures IgG titer directly from cell culture—as well as the cost of implementing the assays.
Small molecules rotate faster than larger molecules and this rotation rate can be determined by fluorescence polarization. This technique allows users to rapidly and accurately measure target abundance in solution. [Beckman Coulter Life Sciences]
The Valita Aggregation Pure assay is similar in many respects, including speed, ease of use, and cost-effectiveness. Both assays are read on a standard fluorescence polarization-capable plate reader. It is considered a “critical quality attribute assay,” she said, “used to determine the relative abundance of aggregated sample in a solution.”
Both assays are automation-friendly and easy to scale, allowing the use of the same tools from early-stage CLD through PD and QC, “so you can kind of keep that consistent measurement.”
End-to-end CLD
Labs dedicated to CLD may want a work cell purpose-built for the end-to-end CLD workflow. CYTENA’s C.STATIONTM is just such a system, integrating “proven technologies for single-cell cloning, imaging, and advanced cell culture with cutting-edge automation software and a clone-centric data management system to ensure full traceability and regulatory readiness for the production of modern therapeutics,” said Pierre-Henri Ferdinand, PhD, the company’s head of product.
The C.STATION is basically a BSL-1 or -2 housing that embeds several pieces of instrumentation, including a shaker and incubators, a liquid handler, a single-cell dispenser with imaging, and a robotic arm to move plates around, integrated with software that orchestrates that instrumentation and analyzes the results. It offers an intuitive interface through which a pre-configured library of methods can be accessed, and “can be run without the need of having an automation engineer on-site,” he noted. The platform also offers powerful customization for more advanced users.
The “beating heart” of the C.STATION is the benchtop UP.SIGHT, a high-efficiency single-cell dispenser combined with “double assurance of clonality,” said Ferdinand. The instrument takes images of the cells both before dispensing “to show that the droplets dispensed contained only one cell,” as well as “detecting a single cell on the bottom of the well after dispensing.”
This assurance of clonality is “part of an environment that will facilitate regulatory approval, by having everything documented so it can be directly put on the table,” he explained, “allowing for full traceability and regulatory readiness for the production of modern therapeutics.”
Engineering cell circuits
Bioprocessing has always been a bit late in adopting the latest engineering techniques—applying computational fluid dynamics to bioreactor design, for example, said Will Johnson, head of process modeling, Asimov. His company’s mission is to bring CAD (computer-aided design) to bioprocessing, combining an understanding of biology with engineering to optimize the processes that produce biologics such as mAbs.
Several computational methods exist to represent and optimize the biophysical and physiological phenomena spanning genetic design to bioprocess performance. Genetic circuit simulation uses dynamic equations to represent the transcription and translation of a gene of interest from an exogenous genetic circuit. [Asimov]
There are different approaches to optimization, such as maximizing the density of the cells in the bioreactor so there are more cells producing the product, for example, or maximizing the expression rate by tuning feeding or fluid exchange. “What is novel about what Asimov is doing now is starting to look even further upstream and saying, ‘Well, let’s not just try to optimize the process. Let’s try to optimize the cell line itself’,” Johnson says.
One of Asimov’s core products is a software tool named Kernel for designing genetic parts and genetic circuits. This tool basically allows you to put all the genetic parts together to design, for example, a vector for expressing a mAb or a bi-specific antibody in a host cell.
Going beyond CAD, Asimov uses CAE (computer-aided engineering) to “predict and optimize the performance of that design in the context in which we intend it to be used,” Johnson continued. “So for instance, we are looking right now at how best to design those genetic parts so that they are secreted most effectively in a CHO cell in a bioreactor.” This may include optimizing codon usage, or engineering promoters to optimize the ratio at which different chains are expressed.
Johnson’s overall goal—his “holy grail”—is not only to engineer and predict and optimize the performance of the individual circuits “component-by-component, but to do that holistically: to do an end-to-end prediction of performance in genetic design in silico, and then further optimize the biology” in the laboratory.
The post Combining Engineering and Biology to Drive More “Cultured” Therapies appeared first on GEN - Genetic Engineering and Biotechnology News.