Biomass prediction / estimation system based on parameter sensing for fixed-bed bioreactors and related methods

Abstract

A system for biomass sensing includes a fixed bed bioreactor including a vessel and a fixed bed disposed within such vessel. At least one sensor is for sensing one or more parameters representative of biomass within the fixed bed. A controller is adapted to correlate the one or more parameters with an amount of biomass in the fixed bed bioreactor, for example, using a correlation model. Related systems and methods are also described.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 303133, filed January 26, 2022, and U.S. Provisional Patent Application No. 63 / 325701, filed March 31, 2022, the disclosures of which are incorporated herein by reference.

[0002] Technical Field The present disclosure generally relates to a system and associated methods for using correlation models to estimate or predict the amount of biomass in a fixed bed bioreactor based on its particular parameters. [Background technology]

[0003] Fixed-bed bioreactors provide an optimal environment for the growth of biological cells (animal, insect, bacteria, etc.) and can achieve cell cultures with high cell densities, or “biomass”. Accurately measuring cell density during cell growth (or cultivation) is a well-known challenge for users of fixed-bed bioreactors. In particular, it is difficult to ascertain the number of cells immobilized on the fixed bed without direct access to the fixed bed. Such access to the fixed bed is often difficult and carries the risk of contaminating the bioreactor contents. However, the U.S. Food and Drug Administration’s Process Analytical Technology (PAT) initiative also requires understanding cell culture processes to timely monitor critical process parameters (CPPs) that affect critical quality attributes (CQAs), making such biomass measurements necessary for compliance.

[0004] Currently, indirect techniques exist for monitoring the density of animal cells in fixed-bed bioreactors, but known examples of such techniques are complex. For example, sampling a removable portion of the fixed bed where the cells reside can provide an idea of ​​the cell density throughout the bioreactor. However, cells may not be easily counted because they remain attached to the portion of the fixed bed obtained during sampling. Cell counts must be determined by lysing the cells, staining them and counting the cell nuclei. This step must be performed repeatedly during a cell culture event or during each successive event, increasing the cost and complexity of the operation.

[0005] Frequent sampling of the fixed bed while maintaining sterility is also a challenge, as sampling typically requires accessing the fixed bed inside the bioreactor, which may be sterile. Indirect measurement with capacitance biomass probes has been proposed, but typically lacks accuracy, precision, or reproducibility. This is primarily due to the fact that only the volume around the probe is measured. Insertion of the probe into the fixed bed is also not done consistently, which further contributes to these limitations.

[0006] It has therefore been recognized that there is a need for a method to determine the amount of biomass, or cell density, present in a fixed bed bioreactor. This technique minimizes or eliminates the potential risk of contamination associated with physical sampling of the fixed bed while providing a more accurate indication of colonization of the fixed bed than previous techniques. This technique not only allows for real-time estimation of biomass production in the fixed bed, but also allows for prediction of future biomass levels based on current process conditions, providing operators with the information they need to make adjustments in real-time to achieve the desired results. This technique can also be applied via an automated system associated with the fixed bed bioreactor, completely eliminating the need for operator intervention to estimate or predict the amount of biomass. Summary of the Invention

[0007] According to a first aspect of the present disclosure, a system for predicting / estimating biomass based on parameter sensing of a fixed-bed bioreactor is provided. The system may include a bioreactor including a vessel (which may be sealed before or during use) and a fixed bed disposed within the vessel. At least one sensor is provided for sensing one or more parameters representative of biomass in the fixed bed. A controller is adapted to correlate the one or more parameters with an amount of biomass in the fixed bed.

[0008] In one embodiment, the one or more parameters representative of biomass include cell culture by-products such as glucose and lactate. The at least one sensor may include a spectroscopic sensor, an enzymatic sensor, a gas sensor, or any combination thereof. The gas sensor and / or the controller may be adapted to determine one or more parameters of air and oxygen gas flow input, oxygen outlet concentration, oxygen transfer rate, oxygen uptake rate, carbon dioxide evolution rate, and respiration rate. The one or more parameters detected by the sensor may be selected from the group including glucose, lactate, Glu, Gln, Asp, Asn, NH3, or a combination thereof.

[0009] The sensor may be associated with or directly connected to the bioreactor, or it may be independent. For example, the system may use an autosampler to provide samples to the sensor. The sensor may be associated with or located within a recirculation loop connected to the bioreactor, such as part of a dedicated line that draws liquid away from the bioreactor surface to minimize air bubbles.

[0010] The controller may be adapted to estimate the amount of biomass in the bioreactor at a future time. The system (or controller) may further include a display for displaying the amount of biomass. The controller may be adapted to receive one or more parameters as input and process the one or more parameters using the correlation model to output the amount of biomass in the fixed bed using the correlation model.

[0011] According to a further aspect of the present disclosure, a biomass assessment system includes a bioreactor including a vessel and a fixed bed disposed within such vessel. At least one sensor for sensing one or more parameters representative of biomass within the bioreactor. The at least one sensor is associated with a recirculation loop associated with the bioreactor.

[0012] Optionally, the system according to this embodiment may include a controller adapted to correlate one or more parameters with the amount of biomass in the bioreactor. The controller may be adapted to estimate the amount of biomass in the bioreactor at a future time. More specifically, the controller may be adapted to receive one or more parameters as inputs and process (e.g., via a processor) the one or more parameters using the correlation model to output the amount of biomass in the fixed bed. A display may be associated with the controller to display the amount of biomass.

[0013] According to another aspect of the present disclosure, a system for assessing biomass includes a bioreactor including an enclosed vessel and a fixed bed disposed within such vessel. At least one sensor is provided for sensing one or more parameters representative of biomass within the fixed bed bioreactor. A controller is adapted to predict an amount of biomass within the fixed bed bioreactor at a future time based on the one or more parameters.

[0014] In one example, a display associated with the controller displays the amount of biomass predicted by the controller to be present in the bioreactor. To make the prediction, the controller may be adapted to receive one or more parameters as inputs using a correlation model, and process the one or more parameters using the correlation model to output an amount of biomass in the fixed bed.

[0015] According to yet another aspect of the present disclosure, a system includes a bioreactor including a vessel and a fixed bed disposed within the vessel. At least one sensor is provided for sensing one or more parameters of a liquid provided to the at least one sensor by a conduit in fluid communication with the bioreactor. The conduit may include a dedicated line for withdrawing fluid from the bioreactor other than the surface of the bioreactor to minimize the generation of air bubbles.

[0016] Optionally, the system may further include a controller adapted to correlate the one or more parameters with an amount of biomass in the bioreactor. The controller may be adapted to estimate an amount of biomass in the bioreactor at a future time. More specifically, the controller is adapted to receive the one or more parameters as inputs and process the one or more parameters using the correlation model to output an amount of biomass in the fixed bed. A display associated with the controller is provided for displaying the amount of biomass.

[0017] Yet another aspect of the disclosure relates to a system for assessing biomass in a bioreactor, comprising a vessel and a fixed bed disposed within the vessel and associated with a sensor for sensing one or more parameters representative of biomass in the fixed bed. The system includes an autosampler that obtains a sample from the bioreactor and associates the sample with the sensor. The system further includes a controller adapted to correlate the one or more parameters obtained by and / or received from the sensor with an amount of biomass in the fixed bed.

[0018] The controller may be adapted to estimate the amount of biomass in the fixed bed at a future time. In particular, the controller may receive one or more parameters as inputs and process the one or more parameters using a correlation model to output the amount of biomass in the fixed bed. A display is associated with the controller for displaying the amount of biomass.

[0019] In another aspect, a method for assessing biomass includes various steps such as culturing cells in a fixed bed bioreactor and sensing one or more parameters representative of biomass in the fixed bed bioreactor from a cell culture fluid in or emanating from the bioreactor during or after the culturing step (the one or more parameters include, for example, metabolite levels and / or respiration levels of the cell culture). The method can include transmitting the one or more parameters to a controller and estimating the amount of biomass in the fixed bed using the one or more parameters.

[0020] The using step can include using a controller and a correlation model to correlate one or more parameters with an amount of biomass in the fixed bed. In this method, the one or more parameters can be manually input into the controller. The amount of biomass can be a predicted future amount of biomass.

[0021] The sensing step may include feeding the cell culture to a metabolite sensor external to the fixed-bed bioreactor. Alternatively or additionally, the sensing step includes sensing the respiration level of the cell culture by monitoring one or more of air and oxygen gas flow inlets, oxygen outlet concentrations, oxygen transfer rates, oxygen uptake rates, carbon dioxide evolution rates, respiration rates, or any combination thereof.

[0022] Yet another method according to the present disclosure for determining biomass in a fixed bed bioreactor containing cells includes measuring a parameter of the fixed bed bioreactor, the method further includes obtaining an actual cell density of the fixed bed bioreactor, and the method further includes developing a correlation model of the estimated cell density based on the parameter and the actual cell density.

[0023] The correlation model allows for an estimation of cell density without sampling the cells or directly measuring the cells, such as with an invasive probe. The estimation step can include further measuring parameters using a sensor and applying the measured parameters to the correlation model. The actual measurement can include sampling the fixed bed bioreactor or opening it and counting at least a portion of the cells.

[0024] The method may further include using the correlation model to provide an estimated cell density at a current or future time point, which information can be used in determining when to infect or transfect cells based on the estimated cell density.

[0025] A related aspect of the disclosure is a bioreactor system including a controller adapted to apply a correlation model obtained using the methods described herein to measured parameters of a fixed-bed bioreactor to estimate cell density, thereby eliminating the need to sample or measure cell density during cell culture.A further related aspect of the disclosure is a bioreactor system including a controller adapted to apply a correlation model obtained using the methods described herein to predict cell density of a fixed-bed bioreactor, thereby eliminating the need to sample or measure cell density during cell culture.

[0026] A further aspect of the present disclosure relates to a method for developing a final predictive model of cell density in a first fixed bed bioreactor, the method comprising developing a preliminary model correlating cell density with one or more parameters of one or more second fixed bed bioreactors. The method further comprises obtaining actual cell density measurements from the one or more second fixed bed bioreactors to validate the preliminary model to arrive at a final model. The method further comprises applying the final model to the first fixed bed bioreactor to estimate the cell density.

[0027] In one example, developing the preliminary model includes correlating the metabolites with cell density in the plurality of second fixed bed bioreactors. Similarly, in this example or another example, obtaining includes obtaining one or more samples representative of cell density from the one or more second fixed bed bioreactors. The obtaining may alternatively or additionally include opening the one or more second fixed bed bioreactors and counting at least a portion of the cells on the fixed bed therein. [Brief description of the drawings]

[0028] [Figure 1] FIG. 1 shows a schematic of a biomass sensor system for a fixed-bed bioreactor.

[0029] [Diagram 2]FIG. 2 illustrates a schematic of another embodiment of a biomass sensor system for a fixed-bed bioreactor.

[0030] [Diagram 3] FIG. 3 illustrates a schematic of another embodiment of a biomass sensor system for a fixed-bed bioreactor.

[0031] [Figure 4] FIG. 4 shows various locations of metabolite sensors relative to the input and output lines of a fixed-bed bioreactor. [Figure 4A] FIG. 4A shows various locations of metabolite sensors relative to the input and output lines of a fixed-bed bioreactor. [Figure 4B] FIG. 4B shows various locations of metabolite sensors relative to the input and output lines of the fixed-bed bioreactor.

[0032] [Diagram 5] FIG. 5 shows a model correlating specific parameters with biomass or cell density in a fixed-bed bioreactor.

[0033] [Figure 6] FIG. 6 shows a graph for estimating and / or predicting biomass in a fixed-bed bioreactor based on the use of a correlation model and corresponding metabolite data. [Figure 7] FIG. 7 shows a graph for estimating and / or predicting biomass in a fixed-bed bioreactor based on the use of a correlation model and corresponding metabolite data.

[0034] [Figure 8] FIG. 8 is a flow chart showing one possible example of steps for developing and using a model.

[0035] [Figure 9]FIG. 9 depicts a graphical user interface for entering and viewing current or updated information regarding the use of a model to correlate biomass with specific parameters in a fixed bed bioreactor. [Figure 10] FIG. 10 depicts a graphical user interface for entering and displaying current or updated information regarding the use of a model to correlate biomass with specific parameters in a fixed bed bioreactor. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] In one aspect, the present disclosure relates to a system for biomass estimation (present) or prediction (future) of a fixed-bed bioreactor. In particular, the disclosed system can utilize real-time information on the cell culture (e.g., one or more parameters such as respiration information (oxygen consumption and carbon dioxide production) and / or metabolic information (glucose consumption and lactate production)) to obtain accurate biomass estimates. Using this information, a correlation model can be employed to estimate the cell density in the fixed-bed bioreactor. By employing this correlation model, the system can also be used to predict the future state of the fixed-bed in terms of biomass evolution (which can be an increase or decrease in cell density), and allows for fully automated estimation and prediction in a reliable, reproducible, low-risk and cost-effective manner compared to traditional approaches. The estimates (which can be future predictions) can be used to evaluate when to perform additional processing steps, such as, for example, a step of infecting or transfecting the cells.

[0037] Referring to FIG. 1, an exemplary cell culture system 10 includes a bioreactor 12 that includes an internal structure for growth of adherent or suspension cells. As shown in FIGS. 1, 4, 4A, and 4B, the bioreactor 12 includes an outer vessel 12a or vessel that includes a fixed bed 12b as an internal structure for cell growth, and the vessel 12a or bioreactor can be sealed to maintain a suitable environment for cell culture (e.g., a sterile or aseptic environment). The fixed bed 12b can include, for example, a 3D printed matrix, or can be composed of a woven or nonwoven material (e.g., one or more sheets of such material in direct contact with each other or with intervening spacers, beads, hollow fibers, or other suitable cell culture structures to promote growth of adherent cells, etc.). The fixed bed 12b can be of any desired shape, orientation, or form, and can include, for example, a 3D porous monolith, stacked layers (see, e.g., U.S. Pat. No. 11,111,470, the disclosure of which is incorporated herein by reference), vertically arranged parallel layers, layers arranged in a spiral or wound configuration, or a packed bed (see, e.g., U.S. Pat. No. 8,137,959, the disclosure of which is incorporated herein by reference).

[0038] In one example, the system 10 includes in-line or in-situ access to information for determining the biomass concentration of the fixed-bed bioreactor 12. Information obtained from the fluid contents in or emanating from the bioreactor may include information regarding one or more parameters of the bioreactor 12 that can be determined in a non-invasive manner (e.g., using an in-line, in-situ sensor or analyzer, and possibly connected to an automatic sampler 19, as shown in FIG. 3 and further outlined in the description below). Such parameters include, for example, respiratory information, metabolic information, and the like. In particular, respiratory information may include, by way of example, one or more of the following parameters: Real-time monitoring of gas flow inlets (mass flow controllers) for air, oxygen (and potentially carbon dioxide) -Real-time monitoring of oxygen concentration in the culture medium Real-time monitoring of oxygen (and potentially carbon dioxide) outlet concentrations (using systems such as the Bluesens analyzer) Real-time calculation of OTR (Oxygen Transfer Rate) in bioreactors Oxygen / CO2 real-time mass balance calculation based on Oxygen Uptake Rate (OUR), Carbon Dioxide Generation Rate (CDER), and Respiratory Quotient (RQ) may be included.

[0039] Information for monitoring or calculating such respiration information can be obtained using one or more gas sensors 14 associated with a vessel, such as the closed vessel 12a of the bioreactor 12. Such gas sensors 14 can provide information in the form of an output signal (arrow 21) to a controller that includes the model. The controller can include, for example, a microprocessor-based device that includes an input device for receiving data, a microprocessor chip for processing the data, and an output device for transmitting the processed data. The controller may include or be considered as a general-purpose computer, a special-purpose computer, a programmable logic controller, a processor, a microprocessor, or other automated control unit that can computerize calculations for applying the model 18 to estimate and / or predict the amount of biomass. As such, the controller forms part of the system 10, as outlined in the following description, and may be a physical part of the system or may be located remotely from the system.

[0040] To collect metabolite information from the bioreactor 12, for example, one or more of the following parameters may be collected: Real-time monitoring of glucose and lactate concentrations in cell culture media Potential monitoring of other metabolites that reflect biomass, such as Glu, Gln, Asp, Asn, ammonia (NH3), and pyruvate may be necessary.

[0041] Information for monitoring one or more metabolites can be obtained using one or more metabolite sensors 16 associated with the bioreactor 12, such as based on enzymatic (e.g., CCIT device) or spectroscopic (e.g., Irubis device) technologies. Arrangements for achieving sensing can include, for example, an in-line sensor 16(1) connected to a bioreactor medium recirculation loop 17, as shown in Figures 1 and 4, an in situ (i.e., integrated directly into the bioreactor) sensor 16(2), as shown in Figure 2, and / or a sensor 16(3) associated with an autosampler 19 (e.g., "Trace" / ISI / Novabiomedical) for sampling from the bioreactor 12, as shown in Figure 3.

[0042] In one particular example, the metabolite sensing function may involve the integration of a sensor into the system. For example, the metabolite sensor may include an in-line flow cell sensor 16, as shown in FIG. 1, that can measure metabolites flowing from the bioreactor 12 and provide measurements (e.g., in the form of an output signal to a controller, such as a computer 20, or other processor) representative of the cell culture conditions in the associated fixed bed 12b. One way to integrate such a sensor 16 into a system 10 including a fixed bed bioreactor 12 is to place it "in-line" with a conduit for delivering fluid (supernatant) from the bioreactor in real time, for example as a recirculation loop 17 (with appropriate filtering if necessary).

[0043] As shown in Figures 4, 4A, and 4B, which show a recirculation loop 17 in communication with the fixed-bed bioreactor 12 and the recirculation tank T, the sensor 16 may be located in the input line 17a (Figure 4A) or in a dedicated line 17b (Figure 4B) that communicates between the bioreactor 12 and the input line 17a. This is in contrast to the output line 17c (Figure 4), which may contain air bubbles in the liquid obtained from the bioreactor 12, which may affect the detection of the metabolic products in some cases (such as if the sensor is sensitive to the presence of air bubbles). The dedicated line 17b may include, for example, a conduit located below the surface of the liquid in the bioreactor 12, as shown in Figure 4B.

[0044] As mentioned above, one or more different types of metabolite sensors 16 may be used in connection with the system 10. For example, one or more spectroscopic sensors (e.g., Irubis) may be provided in the recirculation loop 17. One or more enzymatic sensors (e.g., CCIT) may also be used, alone or in combination with other sensors. As noted above, in situations where any of the sensors 16 used may be susceptible to air bubbles in providing an accurate measurement, a "bubble-free" setup may be used.

[0045] Using information from either or both of the gas sensor 14 or metabolite sensor 16, a biomass estimate can be automatically calculated in real time by a correlation model 18. The model 18 may also be implemented by a controller, such as a computer 20, as shown, forming part of the system 10. As further shown diagrammatically in FIGS. 1-3, the computer 20 receives as input 23 metabolite information representing parameters from the sensors 14, 16, applies that information to the model 18 via a processor (or which may be entered into the model manually), as indicated by arrow 25, and through its processing power generates an output 27 from the model 18 of an estimated or predicted amount of biomass (e.g., cell density) in the fixed bed bioreactor 12.

[0046] The nature of the model 18 used may vary, and versions of it are known to those skilled in the art. An example of a metabolite-based cell growth rate correlation model 18 is shown in FIG. 4, which is an example of an unstructured mechanistic kinetic model with mono-type equations including macroscopic mass balance and inhibitors. The selected model 18 may also include predicted state estimates, corrected state estimates, corresponding gains used to calculate these estimates, associated prediction and estimation error covariances corresponding to these estimates, and functions for the estimation output, such as a discrete-discrete extended Kalman filter, as in the following example:

[0047] 1. Sampling time Δt s =t s -t s-1 A prediction equation in discrete time (one simulation time t s From the next time t s+1 (a recursive equation that directly computes the solution up to JPEG2025503196000002.jpg64128JPEG2025503196000003.jpg85153

[0048] 2. Sampling time Δt k The discrete-time correction equation (at each new measurement time t k in JPEG2025503196000004.jpg85159

[0049] Using the selected correlation model 18, the computer 20 correlates information relating to one or more parameters, such as oxygen consumption and metabolites, to provide a biomass estimate 22. As shown in Figures 1-3, this estimate 22 may be displayed graphically to a user on an associated display D, which includes an estimated amount of biomass associated with the fixed-bed bioreactor 12 and forms part of the system 10 (see also Figures 9-10). This display D may be part of or associated with the computer 20, which functions as a controller, as shown in Figures 1-3. The display D also provides measured parameter levels 24 (e.g., one or more metabolites), and is shown in more detail in Figure 6. Thus, an indication of biomass may be provided and sterility may be ensured without the need for sampling or other invasive techniques.

[0050] Using predictive techniques (e.g., an extended Kalman filter as described above), the model 18 can also be used to predict the future amount of biomass in the fixed-bed bioreactor 12, as shown in graph 26 of FIG. 7. Predictions can also be made regarding the levels of parameters (e.g., metabolites), as shown in graph 28 (glucose and lactate as examples). This information can be displayed in graphical or numerical form via the computer 20, at specific points in time or over a range of time. This information allows an operator to understand the future biomass production potential of the fixed-bed bioreactor 12 (whether the amount is positive or negative) without having to directly access the fixed-bed or sample the fixed-bed to directly measure cell density. This not only avoids concerns regarding compromised sterility, but also avoids the limitations previously discussed with respect to past approaches for in situ sensing of biomass, such as cell density probes.

[0051] Sensing of parameters of the bioreactor 12 to determine the current biomass level or to predict future biomass levels in the fixed bed can be performed periodically, if deemed necessary in a particular situation. Whether performed automatically or manually, sampling and / or sensing may be performed frequently, for example, every few seconds, or less frequently, such as once an hour, once a day, or even longer, depending on the circumstances.

[0052] According to further aspects of the present disclosure, the development of a custom correlation model associated with sampling of one or more test runs of the bioreactor may be performed later to enable real-time non-invasive modeling of biomass production. As shown in the flowchart of FIG. 8, this can be achieved by using a biomass / metabolite correlation method 100 that is independent of the type of cell being cultured. The method 100 may require performing multiple calibration runs of the fixed-bed bioreactor, possibly at different scales, to develop a preliminary correlation model, as shown in step 102. This step 102 involves periodically measuring or sensing one or more parameters indicative of biomass production (such as metabolites), combined with an actual measurement of the amount of biomass associated with the fixed bed (either during the bioprocess by sampling or after completion).

[0053] Once full parameter estimation and validation of the preliminary model is complete (step 104), the model can be used as a final model in connection with a fixed-bed bioreactor without the need for sampling (step 106), providing a real-time indication of biomass production without the risk of contamination. More specifically, the process of creating and validating a preliminary correlation model may include running a small-scale version of the fixed-bed bioreactor multiple times. As shown in substep 102a of FIG. 8, this may include measuring one or more parameters, such as glucose and lactate production, while simultaneously evaluating the biomass production of the fixed-bed bioreactor to establish a corresponding cell density value. This can be done in a variety of ways, including dismantling the bioreactor once the bioprocess is complete, or sampling during a bioprocess event. A specific example of a sampling system suitable for a fixed-bed bioreactor is described in U.S. Patent Application Publication No. 2021 / 009933, the disclosure of which is incorporated herein by reference.

[0054] In either case, the acquired and stored measurements (substep 102b) can be used to develop a preliminary model correlating one or more parameters with biomass, as shown in substep 102c. This can also be achieved, for example, by using a relatively small-scale version of a fixed-bed bioreactor designed to be easily opened at the end of the culture to facilitate sampling of the fixed-bed material for cell lysis and estimation of cell density after staining to count cell nuclei. Such an easily accessible bioreactor may be equipped with a lid that is removably fixed to the bioreactor vessel.

[0055] Optionally, multiple medium-scale validation runs may be performed to check the scalability of the developed model and to fine-tune its parameters, which may involve a combination of measuring and sampling the same parameters as in the initial "small" step, as described above.

[0056] Validation of the preliminary model may also be performed as part of the construction step 102. This may include, for example, comparing the model with the data used to construct it, as shown in sub-step 102d.

[0057] The validation step 104 may also include a sub-step 104a of performing a validation run to validate the preliminary model. The data may be stored, as shown in sub-step 104b. A validation sub-step 104c may also be performed by comparing the predictions obtained by the model with the validation data performed in step 104a.

[0058] Once the preliminary model is fully developed using these steps, it can be applied as a final correlation model to a larger scale run of the fixed bed bioreactor 12 for biomass prediction (substep 106a) without the need for sampling. This can include, for example, inputting information about metabolite values ​​(e.g., daily glucose / lactate measurements), which can be done manually or automatically (substep 106b). The final model can output an estimated cell density for such conditions (substep 106c).

[0059] As can be appreciated, the disclosed biomass prediction / estimation techniques can be used to provide real-time estimates of cell density based on measurements of metabolite and / or respiration levels, independent of cell type and dependent on process parameters. As discussed above and further below, including measuring the initial cell density in the inoculum to perform the estimate, future predictions of cell density can also be made to predict productivity of the fixed-bed bioreactor. This information can be used to determine when to perform further process steps, such as infection or transfection, or to adjust other aspects or parameters of the bioprocess operation to achieve a particular outcome with respect to biomass, time, etc. considerations.

[0060] As noted above and as shown diagrammatically in FIG. 1, the biomass estimation / prediction model 18 may operate as an algorithm or program on a computer 20. The computer 20 may be programmed to display a graphical user interface 30, as shown in FIGS. 9-10. This graphical user interface 30 may include an input 32 for entering a biomass target and a display 34 of measured metabolites (which may be entered manually or obtained automatically via the sensors mentioned above). The interface 30 may also include an activation button 36 for updating the model and display, which may also be performed automatically. If applicable, a selector 38 may also be provided for selecting among available biomass estimation or prediction models. The interface 30 may also allow for the entry of additional information if required (e.g., additional parameters of the bioreactor and associated fixed bed, such as the volume to surface area ratio 40).

[0061] The interface 30 may further provide output 42 from the model 18, for example in the form of a graphical representation 44, regarding a prediction of the time to reach the target. The output 42 may, for example, show an estimated or predicted amount of biomass. The interface 30 may provide a selected indication of the corresponding levels of one or more metabolites, such as by a corresponding selection button 46 for switching the displayed information. In addition to the graphical representation 44, a numerical calculation 48 of the estimated time to reach a particular target may also be displayed.

[0062] 10, metabolite values ​​may be updated over time (again, manually or automatically), which may be displayed in multiple rows on display 34, necessitating the use of input 34a to increase the length of the data set. In such a case, output 42 is modified to indicate the biomass level at that particular time, and graphical representation 44 is updated accordingly, helping the user understand the situation in real time. Output 42 may also use the obtained updated information to modify estimated numerical time calculation 48 to achieve the desired biomass target.

[0063] Respiration, glucose, and lactate are mentioned as possible parameters that may be sensed and correlated with cell density, but other parameters may also be used. For example, parameters may relate to the consumption of nutrients such as glutamine, pyruvate, asparagine, and generally all amino acids, intermediates of the Krebs cycle, and sugars (C5 and C6) present in the culture medium. Parameters may also relate to the production of by-products such as ammonia, ethanol, and alanine. Additional process parameters that may be used in the model may include bioreactor pH, temperature, and agitation speed (as this may affect the oxygen transfer rate). Volume of medium (ml / cm 2 Parameters related to the supply strategy such as the flow rate at the medium inlet (perfusion / recirculation) and medium exchange can also be used in conjunction with the model.

[0064] In summarizing various aspects to which this disclosure may pertain, the following items are identified and can be arranged in any combination:

[0065] Item 1. A system for evaluating the biomass of a bioreactor comprising a vessel and a fixed bed disposed within such vessel, comprising: at least one sensor for sensing one or more parameters indicative of biomass in the fixed bed; a controller adapted to correlate one or more parameters with an amount of biomass in the fixed bed; A system comprising:

[0066] Item 2. The system of item 1, wherein the one or more parameters representative of biomass include a cell culture by-product, such as glucose or lactate.

[0067] Item 3. The system of item 1 or 2, wherein at least one sensor includes a spectroscopic sensor.

[0068] Item 4. The system of any of items 1 to 3, wherein at least one sensor includes an enzyme sensor.

[0069] Item 5. The system of any of items 1 to 4, wherein the at least one sensor includes a gas sensor associated with the bioreactor.

[0070] Item 6. The system of item 5, wherein the gas sensor and / or controller is adapted to determine one or more of air and oxygen gas flow inputs, oxygen outlet concentrations, oxygen transfer rates, oxygen uptake rates, carbon dioxide evolution rates, and breathing rates.

[0071] Item 7. The system of any of items 1 to 6, wherein at least one sensor is located within the bioreactor or within a recirculation loop connected to the bioreactor, such as part of a dedicated line that draws liquid from other than the surface of the bioreactor to minimize air bubbles.

[0072] Item 8. The system of any of items 1 to 7, wherein at least one sensor is associated with an autosampler for sampling from the bioreactor.

[0073] Item 9. The system of any of items 1 to 8, wherein the controller is adapted to estimate an amount of biomass in the bioreactor at a future time.

[0074] Item 10. The system of any of items 1 to 9, further comprising a display associated with the controller for displaying the amount of biomass.

[0075] Item 11. The system of any of items 1 to 10, wherein the one or more parameters are selected from the group including glucose, lactate, Glu, Gln, Asp, Asn, NH3, or a combination thereof.

[0076] Item 12. The system of any one of items 1 to 11, wherein the controller is adapted to receive one or more parameters as inputs using a correlation model and process the one or more parameters using the correlation model to output an amount of biomass in the fixed bed.

[0077] Item 13. The system of any of items 1 to 12, wherein the bioreactor comprises a closed vessel.

[0078] Item 14. A system for evaluating biomass in a bioreactor including a vessel and a fixed bed disposed within such vessel and including a recirculation loop, the system comprising at least one sensor for sensing one or more parameters representative of biomass in the bioreactor, the at least one sensor being associated with the recirculation loop.

[0079] Item 15. The system of item 14, further comprising a controller adapted to correlate one or more parameters with an amount of biomass in the bioreactor.

[0080] Item 16. The system of item 15, wherein the controller is adapted to estimate an amount of biomass in the bioreactor at a future time.

[0081] Item 17. The system of item 14 or item 15, wherein the controller is adapted to receive one or more parameters as inputs using a correlation model and process the one or more parameters using the correlation model to output an amount of biomass in the fixed bed.

[0082] Item 18. The system of any of items 15 to 17, further comprising a display associated with the controller for displaying the amount of biomass.

[0083] Item 19. A system for evaluating the biomass of a bioreactor including a vessel and a fixed bed disposed within such vessel, comprising: at least one sensor for sensing one or more parameters indicative of biomass in the fixed bed bioreactor; A controller adapted to predict an amount of biomass in the fixed bed bioreactor at a future time based on one or more parameters.

[0084] Item 20. The system of item 19, further comprising a display associated with the controller for displaying the amount of biomass.

[0085] Item 21. The system of item 19 or item 20, wherein the controller is adapted to receive one or more parameters as inputs using a correlation model and process the one or more parameters using the correlation model to output an amount of biomass in the fixed bed.

[0086] Item 22. A system comprising: A bioreactor comprising a vessel and a fixed bed disposed within such vessel; and / or 1. A system comprising: at least one sensor for sensing one or more parameters of a liquid provided to the at least one sensor by a conduit in fluid communication with the bioreactor, the conduit including a dedicated line for withdrawing fluid from the bioreactor other than at a surface of the bioreactor to minimize the generation of air bubbles.

[0087] Item 23. The system of item 22, further comprising a controller adapted to correlate one or more parameters with the amount of biomass in the bioreactor.

[0088] Item 24. The system of item 23, wherein the controller is adapted to estimate an amount of biomass in the bioreactor at a future time.

[0089] Item 25. The system of item 23 or item 24, wherein the controller is adapted to receive one or more parameters as inputs using a correlation model and process the one or more parameters using the correlation model to output an amount of biomass in the fixed bed.

[0090] Item 26. The system of any of items 23 to 25, further comprising a display associated with the controller for displaying the amount of biomass.

[0091] Item 27. A system for evaluating biomass in a bioreactor comprising a vessel and a fixed bed disposed in such vessel, and associated with a sensor for sensing one or more parameters representative of biomass in the fixed bed, comprising: an autosampler that takes a sample from the bioreactor and associates the sample with a sensor; and / or A system comprising: a controller adapted to correlate one or more parameters from the sensor with an amount of biomass in the fixed bed.

[0092] Item 28. The system of item 27, wherein the controller is adapted to estimate an amount of biomass in the fixed bed at a future time.

[0093] Item 29. The system of item 27 or item 28, wherein the controller is adapted to receive one or more parameters as inputs using a correlation model and process the one or more parameters using the correlation model to output an amount of biomass in the fixed bed.

[0094] Item 30. The system of any of items 27 to 29, further comprising a display associated with the controller for displaying the amount of biomass.

[0095] Item 31. A method for evaluating biomass, comprising: Cultivating the cells in a fixed bed bioreactor; and / or Detecting one or more parameters representative of the biomass in the fixed-bed bioreactor from the cell culture fluid discharged from the bioreactor during or after the culturing step, the one or more parameters comprising a metabolite level and / or a respiration level of the cell culture; and / or Sending one or more parameters to the controller; and / or using the one or more parameters to estimate the amount of biomass in the fixed bed.

[0096] Item 32. The method of item 31, wherein the using step includes using a processor and a correlation model to correlate one or more parameters with the amount of biomass in the fixed bed.

[0097] Item 33. The method of item 31 or 32, further comprising manually entering one or more parameters into the controller.

[0098] Item 34. The method according to any one of Items 31 to 33, wherein the amount of biomass is a predicted future amount of biomass.

[0099] Item 35. The method of any of items 31 to 34, wherein the sensing step includes supplying the cell culture medium to a metabolite sensor external to the fixed-bed bioreactor.

[0100] Item 36. The method of any of items 31 to 35, wherein the detecting step includes detecting the cell culture respiration level by monitoring one or more of air and oxygen gas flow inlet, oxygen outlet concentration, oxygen transfer rate, oxygen uptake rate, carbon dioxide evolution rate, and respiration rate.

[0101] Item 37. A method for evaluating biomass in a fixed-bed bioreactor containing cells, comprising: Measuring parameters of a fixed bed bioreactor, and / or Obtaining an actual measurement of the cell density of the fixed bed bioreactor; and / or developing a correlation model of the estimated cell density based on the parameters and the actual measured cell density.

[0102] Item 38. The method of Item 37, further comprising estimating cell density using a correlation model without requiring sampling.

[0103] Item 39. The method of Item 37, wherein the estimating step further includes measuring parameters using a sensor and applying the measured parameters to the correlation model.

[0104] Item 40. The method of any of Items 37 to 39, wherein the step of obtaining an actual measurement value includes sampling the fixed-bed bioreactor.

[0105] Item 41. The method of any of Items 37 to 40, further comprising using the correlation model to provide an estimated cell density at the current time point or a future time point.

[0106] Item 42. The method of any of items 31 to 41, further comprising determining when to infect or transfect the cells based on the estimated cell density.

[0107] Item 43. A bioreactor system including a controller adapted to apply the correlation model obtained using the method according to any one of items 37 to 42 to the measured parameters of the fixed-bed bioreactor to estimate a cell density, thereby eliminating the need to sample or measure the cell density during cell culture.

[0108] Item 44. A bioreactor system including a controller adapted to apply the correlation model obtained using the method according to items 37 to 42 to predict cell density in a fixed-bed bioreactor, thereby eliminating the need to sample or measure cell density during cell culture.

[0109] Item 45. A method for developing a final predictive model of cell density in a first fixed bed bioreactor, comprising: Developing a preliminary model correlating cell density with one or more parameters of the one or more second fixed bed bioreactors; and / or Validating the preliminary model by obtaining actual cell density measurements from one or more second fixed bed bioreactors to arrive at a final model; and / or applying the final model to the first fixed bed bioreactor to estimate cell density.

[0110] Item 46. The method of Item 45, wherein the step of developing a preliminary model includes correlating cell density and metabolic products in a plurality of second fixed bed bioreactors.

[0111] Item 47. The method of item 45 or item 46, wherein the obtaining step includes obtaining one or more samples representative of cell density from the one or more second fixed bed bioreactors.

[0112] Item 48. The method of any of items 45 to 48, wherein the obtaining step includes opening one or more second fixed-bed bioreactors and counting at least a portion of the cells on the fixed bed therein.

[0113] For purposes of this disclosure, the following terms have the following meanings. As used herein, "a," "an," and "the" refer to both singular and plural referents unless the context clearly indicates otherwise. By way of example, a "compartment" refers to one or more compartments.

[0114] As used herein, "about," "substantially," "generally," or "approximately" referring to a measurable value, such as a parameter, amount, time duration, and the like, is meant to encompass variations of the specified value and of no more than + / -20%, preferably no more than + / -10%, more preferably no more than + / -5%, even more preferably no more than + / -1%, and even more preferably no more than + / -0.1%, to the extent that such variations are appropriate for the practice of the disclosed invention, provided that the value to which the "about" modifier refers is itself specifically disclosed.

[0115] As used herein, "comprise," "comprising," "comprises," and "comprised" are synonymous with "include," "including," or "contain," "containing," or "contains," and are inclusive or open-ended terms that identify the presence of what follows; for example, "a component comprising" does not exclude or preclude the presence of additional, unlisted components, features, elements, materials, or steps that are known or disclosed in the art.

[0116] While specific embodiments have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. The following claims define the scope of protection under applicable law, and it is intended that methods and structures within the scope of these claims, and their equivalents, be covered thereby.

Claims

1. 1. A system for assessing biomass in a bioreactor comprising a vessel and a fixed bed disposed within such vessel, comprising: at least one sensor for sensing one or more parameters indicative of biomass within the fixed bed; a controller adapted to correlate the one or more parameters with the amount of biomass in the fixed bed; Equipped with The system wherein the one or more parameters indicative of biomass include cell culture by-products.

2. 10. The system of claim 1, wherein the one or more parameters representative of biomass include glucose or lactate as the cell culture by-product.

3. The system of claim 1 or 2, wherein the at least one sensor comprises a spectroscopic sensor, an enzymatic sensor, or a gas sensor associated with the bioreactor.

4. The system of claim 3, wherein the at least one sensor includes a gas sensor associated with the bioreactor, and the gas sensor and / or controller is adapted to determine one or more of air and oxygen gas flow input, oxygen outlet concentration, oxygen transfer rate, oxygen uptake rate, carbon dioxide generation rate, and respiration rate.

5. The system described in claim 1, wherein the at least one sensor is positioned within the bioreactor or within a recirculation loop connected to the bioreactor, such as part of a dedicated line that draws liquid from other than the surface of the bioreactor to minimize bubbles.

6. The system described in claim 1, wherein the at least one sensor is associated with an automatic sampler for sampling from the bioreactor.

7. The system described in claim 1, wherein the controller is adapted to estimate the amount of biomass in the bioreactor at a future time.

8. The system described in claim 1, further comprising a display associated with the controller for displaying the amount of biomass.

9. The system of claim 1, wherein the one or more parameters are selected from the group including glucose, lactate, Glu, Gln, Asp, Asn, NH3, or a combination thereof.

10. The system of claim 1, wherein the controller is adapted to receive the one or more parameters as input using a correlation model, process the one or more parameters using the correlation model, and output the amount of biomass in the fixed bed.

11. A system for evaluating biomass, comprising: a bioreactor comprising a vessel and a fixed bed disposed within such vessel, the bioreactor being associated with a recirculation loop; at least one sensor for sensing one or more parameters indicative of biomass in the bioreactor, said at least one sensor being associated with the recirculation loop; A system including:

12. The system described in claim 11, further comprising a controller adapted to correlate the one or more parameters with the amount of biomass in the bioreactor.

13. The system described in claim 12, wherein the controller is adapted to estimate the amount of biomass in the bioreactor at a future time.

14. The system described in claim 12, wherein the controller is adapted to receive the one or more parameters as input using a correlation model, process the one or more parameters using the correlation model, and output the amount of biomass in the fixed bed.

15. The system described in claim 12, further comprising a display associated with the controller for displaying the amount of biomass.

Images