Methods for the control of biological processes

Abstract

The invention relates to a computer-implemented method (400) performed by a controller (C) configured to control a bioprocess included in a bioreactor (BR), the method comprising: obtaining (410) measurement results by performing spectrophotometric measurements of a bioprocess fluid (FL); generating (420) control parameters based on the measurement results and a model; and controlling (430) the bioprocess using the generated parameters.

Description

Technical Field

[0001] The invention relates to a method for controlling a biological process. The invention further relates to a controller and a system. Background Art

[0002] The biotechnology industry frequently uses bioreactors to perform biological processes such as the cultivation of cells. Performing a biological process typically involves controlling the flow of one or more additive fluids and / or one or more additive gases to a biological process fluid. An example of an additive fluid may be glucose. An example of an additive gas may be oxygen.

[0003] In one example, during a typical bioprocessing manufacturing process, there is often a need to monitor process properties / bioprocessing variables in a bioprocessing fluid. For example, process properties / variables that need to be monitored may include glucose content, lactose / lactic acid content, viable cell content, temperature, fluid pressure, fluid pH, fluid conductivity, etc.

[0004] A problem with controlling bioprocesses is that the nature of the monitored process property / bioprocess variable and / or the nature of the bioprocess may prevent the use of sensors in the bioprocess fluid, for example when the bioprocess involves cell culture.

[0005] In a conventional setup, samples of the bioprocess fluid are periodically taken and analyzed outside of the bioreactor to determine process properties / bioprocess variables in the bioprocess fluid. This has the disadvantage of being a complex and work-intensive activity requiring an operator to be on-site to generate the samples.

[0006] Some conventional solutions identify models for predicting process properties / bioprocess variables.

[0007] An example is shown in "Orthogonal projection to latent structures solution properties for chemometrics and systems biology data" (David J. Biagionia, David P. Astling, Peter Graf, and Mark F. Davis, Journal of chemometrics DOI: 10.1002 / cem.1398).

[0008] Additional examples are described in "In Situ Monitoring of CHO Cell Culture Medium Using Near-Infrared Spectroscopy" (Robert A. Mattes, Denise Root, David Chang, Mike Molony, and Mahalia Ong, BioProcess International, January 2007).

[0009] Further examples are shown in “In Situ Infrared Spectroscopy as a PAT Tool of Great Promise for Real-Time Monitoring of Animal Cell Culture Processes” (Li M, Ebel B, Courtès F, Guedon E, and Marc A, Austin Journal of Analytical and Pharmaceutical Chemistry, May 20, 2016).

[0010] Further examples are shown in “Orthogonal projections to latent structures (O-PLS)” (Johan Trygg and Svante Vold, Journal of Chemometrics, 16:118-128, January 18, 2002, https: / / doi.org / 10.1002 / cem.695).

[0011] A further problem is that the flow rate of one or more additive fluids and / or one or more additive gases to the bioprocess fluid needs to be controlled depending on process properties / bioprocess variables in the bioprocess fluid.

[0012] A further problem is that the behaviour of bioprocesses changes over time, requiring a faster response to changes in process properties / bioprocess variables, for example when controlling the flow of one or more additive fluids and / or one or more additive gases to the bioprocess fluid.

[0013] Some conventional solutions, such as those described in the examples above, use evaluation models for spectral data collected from a large reference set of bioprocess conditions, where manually determined process properties / bioprocess variables are correlated with corresponding spectral data from readings obtained from sensors in the bioprocess fluid. This has the disadvantage of requiring the generation of a broad data set that can serve as the basis for the model. Another disadvantage is that the generated model is usually only applicable with acceptable accuracy to exactly that bioprocess scale and bioreactor system in which the data set is generated. Another disadvantage is that the generated model is usually only applicable with acceptable accuracy to a specific time interval or phase of the bioprocess. Therefore, multiple models are required to obtain process properties / bioprocess variables over the entire duration of the bioprocess.

[0014] Therefore, there exists a need for improved methods for controlling biological processes.

[0015] Purpose of the Invention

[0016] An object of embodiments of the present invention is to provide a solution which alleviates or solves the above-mentioned disadvantages and problems. Summary of the invention

[0017] The above mentioned objects are achieved by the subject matter described herein.Further advantageous implementation forms of the invention are further defined herein.

[0018] According to a first aspect of the present invention, the above and other objects are achieved by a computer-implemented method performed by a controller configured to control a biological process included in a bioreactor, the method comprising: obtaining measurement results by performing spectrophotometry of a biological treatment fluid; generating control parameters based on the measurement results and a model; and controlling the biological process using the generated parameters.

[0019] An advantage of embodiments according to the first aspect is that improved control of a bioprocess is obtained. A further advantage is that computational complexity is reduced and the range of operating bioprocess conditions is increased, since a single model is used for the entire duration of the bioprocess.

[0020] According to a second aspect of the present invention, the above and other objects are achieved by a controller comprising a processing circuit system and a memory, wherein the memory contains instructions executable by the processor, whereby the controller operates to perform any of the method steps according to the first aspect.

[0021] According to the third aspect of the present invention, the above and other objects are achieved by a bioprocessing system, which includes: a sensor, which is configured to perform spectrophotometric measurement of a bioprocessing fluid and provide measurement results included in a control signal; a first controllable flow unit, which is configured to control the flow of one or more additive gases to a bioreactor in response to a control signal; a second controllable flow unit, which is configured to control the flow of one or more additive fluids to the bioreactor in response to a control signal; and a controller according to the second aspect, which is further configured to receive / send control signals to and from the sensor, the first controllable flow unit and the second controllable flow unit.

[0022] According to the fourth aspect of the present invention, the above and other objects are achieved by a computer program comprising computer executable instructions, wherein when the computer executable instructions are executed on a processing circuit system included in the controller, the controller performs any method steps in the method steps according to the first aspect.

[0023] According to a fifth aspect of the present invention, the above and other objects are achieved by a computer program product comprising a computer readable storage medium having embodied therein a computer program according to the fourth aspect.

[0024] The advantages of the second, third, fourth and fifth aspects of the invention are at least the same as those for the first aspect.

[0025] Further applications and advantages of embodiments of the present invention will become apparent from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A bioprocessing system in accordance with one or more embodiments of the present disclosure is shown.

[0027] Figure 2 The diagram illustrates functional modules of a controller according to one or more embodiments of the present disclosure.

[0028] Figure 3 A controller according to one or more embodiments of the present disclosure is shown.

[0029] Figure 4 A flow chart illustrating a method in accordance with one or more embodiments of the present disclosure.

[0030] A more complete understanding of the embodiments of the present invention and a recognition of their additional advantages will be provided to those skilled in the art by considering the following detailed description of one or more embodiments.It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the drawings. DETAILED DESCRIPTION

[0031] The "or" in this description and the corresponding claims is to be understood as a mathematical OR covering "and" and "or", rather than being understood as XOR (exclusive OR). The indefinite article "a" in this disclosure and the claims is not limited to "one", but can also be understood as "one or more", that is, a plurality.

[0032] In the present disclosure, the term "period" or "bioprocess period" refers to a distinguishable period in a bioprocess. In the example of a cell culture bioprocess, the period can be a stagnant period, a LOG period or a logarithmic period, and a stationary period. Cells in culture typically surge after a standard growth pattern. The first period of growth after the culture is seeded is a stagnant period, which is a period of slow growth when the cells adapt to the culture environment and are ready to grow rapidly. The stagnant period is followed by a LOG period (i.e., a "logarithmic" period), i.e., a period of time in which the cells surge exponentially and consume nutrients in the growth medium. When all the growth medium (e.g., glucose) is exhausted, the bioprocess enters a stationary period (i.e., a plateau period), in which the surge of cells is greatly reduced or completely stopped. Typically, this occurs when nutrients in the bioprocess fluid are exhausted or when the cells occupy all available substrates. The bioprocess can, for example, be a cell culture process, such as the culture of mammalian cells (e.g., Chinese hamster ovary (CHO) cells).

[0033] In the present disclosure, the term "wanted / unwanted bioprocess variables" refers to properties of a bioprocess that are obtainable from a measurement result, for example in the form of a near infrared (NIR) absorption spectrum. A subset of bioprocess variables may be classified as wanted variables, while another set may be classified as unwanted bioprocess variables that interfere with the wanted bioprocess variables. Examples of wanted variables may be any selection of glucose content, lactate content, and viable cell density. Examples of unwanted variables may be gassing or additive gas flow rate / content, bioprocessing time, ambient air temperature, and temperature of the bioprocessing fluid FL.

[0034] Figure 1A bioprocessing system SYS according to one or more embodiments of the present disclosure is shown. The bioprocessing system SYS includes a sensor S configured to perform spectrophotometric measurement of a bioprocessing fluid FL and provide measurement results included in a control signal. The sensor S can be, for example, a probe designed for direct contact with a bioprocess fluid and / or configured to generate an absorption spectrum in the NIR wavelength region. Additionally or alternatively, the probe can be, for example, a probe designed for direct contact with a bioprocess fluid and / or configured to generate a Raman spectrum, such as a spectrophotometric sensor commercially available from Hellma GmbH & Co. (Germany).

[0035] The biological treatment system SYS further comprises a controller C, relative to Figure 3 The controller C is further described. The bioprocessing system SYS further includes a first controllable flow unit V, which is configured to control the flow of one or more additive gases AG1-AG2 to the bioreactor BR in response to a received control signal. The first controllable flow unit V may, for example, include one or more electrically controlled valve units, which are configured to control the flow of one or more additive gases AG1-AG2 to the bioreactor BR by at least partially opening / closing one or more valves in response to a control signal. The gas may be, for example, oxygen O2 and / or carbon dioxide CO2. The bioprocessing system SYS further includes a second controllable flow unit P, which is configured to control the flow of one or more additive fluids AF1-AF2 to the bioreactor BR in response to a control signal. The second controllable flow unit P may, for example, include one or more pumps and / or one or more valve units. The one or more additive fluids AF1-AF2 may, for example, include any one of glucose, lactose / lactic acid, amino acids, carbohydrates, vitamins, minerals, growth factors or hormones.

[0036] The controller C is communicatively coupled to the sensor S, the first controllable flow unit V, and the second controllable flow unit P. The controller is further configured to receive / send control signals to / from the sensor S, the first controllable flow unit V, and the second controllable flow unit P.

[0037] The biological treatment system SYS may further optionally be coupled to a bioreactor BR, such as Figure 1As shown in . The first controllable flow unit V and the second controllable flow unit P can be coupled to one or more inlets of the bioreactor BR, thereby allowing one or more additive gases AG1-AG2 and / or one or more additive fluids AF1-AF2 to be mixed into the bioprocessing fluid FL. The sensor S can be configured to be included in the bioreactor BR, or configured to be inserted into the bioreactor BR so that the sensor S is at least partially in contact with the bioprocessing fluid FL. The bioreactor BR can, for example, include a single-use flexible bag as a bioreactor container, which is supported in a rigid support container (such as Xcellerex by GE Healthcare LifeSciences). TM XDR bioreactor) or placed on a rocking platform (such as the WAVE TM Bioreactors).

[0038] In one example, the controller C includes a single model that is used to generate and / or predict the value of a bioprocess property / bioprocess variable. The controller C obtains / receives measurement results in a control signal from a sensor S, such as one or more NIR spectra. The measurement results are obtained by the sensor S by performing a spectrophotometric determination of the bioprocess fluid FL included in the bioreactor BR. The controller C then inputs the measurement results into the model to generate / predict control parameters based on the measurement results and a model. The generated control parameters can, for example, define the degree to which the flow of one or more additive gases AG1-AG2 or the flow of one or more additive fluids AF1-AF2 should be adapted in response to the measurement results. The controller C can be further configured to control the bioprocess using the generated parameters. In one example, the generated control parameters include proportional integral differential PID controller parameters, which are configured to control the operation of a PID regulator included in the controller C or arranged separately to the controller C. The PID regulator may be further communicatively coupled to any selection of the controller C, the first controllable flow unit V and the second controllable flow unit P to control the flow of one or more additive gases AG1-AG2 or one or more additive fluids AF1-AF2 to the bioreactor BR.

[0039] In another example, a model is generated using orthogonal partial least squares O-PLS analysis of a reference data set. A starting data set can be obtained by manually determining process properties / bioprocess variables or reference measurements for a specific bioprocess of a specific scale in a specific bioreactor of a specific size. The reference data set can then be used as a starting data set or obtained by expanding the starting data set. For example, the starting data set can be expanded by increasing or decreasing the content or concentration of various components in the bioprocess fluid FL (for example, by spike-in or diluting the bioprocess fluid FL) and obtaining measurements of light reflection or absorption. In this way, spectral measurement data within a range span outside the normal biological range used in upstream culture can be generated. A subset of bioprocess variables can be classified as useful variables, while another set can be classified as useless bioprocess variables that interfere with useful bioprocess variables. Examples of useful variables can be any selection of glucose content, lactic acid content, and viable cell density. Examples of useless variables can be gassing or additive gas flow rate / content, bioprocessing time, ambient air temperature, and the temperature of the bioprocess fluid FL.

[0040] Expanding the starting data set can be further performed by systematically varying the waste bioprocess variables in a systematic manner, for example by applying an empirically designed approach where variables such as concentration, pH or temperature are systematically varied and spectral data are obtained for the different variations.

[0041] The enlargement of the starting dataset can be further performed by repeating the above steps for bioreactors with different volumes. The reference dataset generated by enlarging the starting dataset using any of the above methods can be further analyzed using orthogonal partial least squares (O-PLS) analysis to filter out the effects caused by bioreactor scale / volume.

[0042] In other words, O-PLS can be used to generate a model that receives input variables or measurements and produces predictor variables / prediction parameters. The model is tuned / generated by providing reference measurements as input variables and then adapting the model so that the output substantially matches the reference predictor variables / prediction parameters of the reference data set.

[0043] In other words, O-PLS analysis identifies and separates systematic changes in input variables (e.g., measurements in the form of near infrared (NIR) spectroscopy) that are not correlated or orthogonal from predicted changes in predictor variables to changes seen in reference predictor variables. The result is a simplified model with better predictive power for useful bioprocess variables and interpretation of the model.

[0044] O-PLS can be described as a general preprocessing method for multivariate data. O-PLS analysis removes variations from input / descriptor variables X that are not correlated with predictor variables Y (e.g., glucose levels). In mathematical terms, this is equivalent to removing systematic variations in X that are orthogonal to Y. Removing non-correlated systematic variations in X makes the interpretation of the resulting PLS model easier and has the added benefit of enabling further analysis of the non-correlated variations themselves.

[0045] Control parameters may be further generated / predicted based on the measurements and useful variables predicted by a model.

[0046] In one example, the model predicts that the glucose level is decreasing and not remaining at a constant level using the current control parameters. An updated set of control parameters can then be generated by determining that the decreasing glucose level predicted by the model indicates that the biological process has entered the LOG phase and that, for example, an increasing proportional term of a proportional-integral-derivative (PID) controller requires an updated set of control parameters to be generated.

[0047] Figure 2 The functional modules of the controller C according to one or more embodiments of the present disclosure are illustrated. It is appreciated that the functionality of the controller C may be distributed across fewer or additional functional modules depending on the application, and the purpose of the concept of functional modules is for illustrative purposes. In other words, the functionality of the controller may be concentrated into a single functional module or distributed across multiple functional modules without departing from the scope of the present disclosure.

[0048] In some embodiments, the controller C includes a measurement acquisition module 210. The measurement acquisition module 210 is primarily configured to obtain a measurement by performing spectrophotometric measurements of the bioprocessing fluid FL. The measurement is typically obtained by receiving a control signal from the sensor S. The control signal typically includes an indication of a measurement resulting from performing spectrophotometric measurements of the bioprocessing fluid FL, for example, an indication of a quantitative measurement of a reflectance or transmission property of the bioprocessing fluid FL as a function of the wavelength of light emitted by the sensor S.

[0049] In one example, the measurement results include a generated spectrum of the bioprocess fluid that indicates specific reflection / absorption values ​​of a spectrum of light (e.g., near infrared NIR). In other words, the reflection / absorption values ​​as a function of the wavelength of the emitted light. The reflection / absorption values ​​at specific wavelengths can be related to the molecular structures present in the bioprocess fluid and, accordingly, indicate the chemical composition of the fluid. The spectrum generated from a full NIR scan may include 4000-10000 cm -1 If the variable of interest includes glucose content, the spectrum resulting from the NIR scan may preferably include 5450-4497 cm -1Between and / or 7501-5630 cm -1 If the useful variable includes lactic acid content, the spectrum generated from the NIR scan preferably may include 8921-7146 cm -1 The wavelength / wavenumber range between .

[0050] In some embodiments, the controller C includes a bioprocess controller module 220. The bioprocess controller module 220 is typically configured to control the flow of one or more additive fluids AF1-AF2 to the bioreactor BR and / or the bioprocess fluid FL of the bioreactor BR and / or to control the flow of one or more additive gases AG1-AG2 to the bioreactor BR and / or the bioprocess fluid FL of the bioreactor BR. The flow is typically controlled in response to the value of a useful variable generated or predicted by a model.

[0051] In one example, the controller C controls the flow of one or more additive gases AG1-AG2 to the bioreactor BR by sending a control signal to a first controllable flow unit V. The first controllable flow unit V typically includes a valve unit, and the control signal activates or controls one or more valves of the valve unit. For example, the flow of oxygen into the bioprocess fluid FL is controlled to a certain volume per time unit.

[0052] In one example, the controller C controls the flow of one or more additive fluids AF1-AF2 to the bioreactor BR by sending a control signal to a second controllable flow unit P. The second controllable flow unit P typically comprises a pump, and the flow of the pump is controlled by the control signal. For example, the flow of glucose into the bioprocess fluid FL is controlled to a certain volume per time unit.

[0053] In one example, the controller C controls the flow of one or more additive gases AG1-AG2 and / or one or more additive fluids AF1-AF2 to the bioreactor BR by sending control parameters in the form of proportional integral derivative controller parameters to one or more PID controllers.

[0054] In some embodiments, the controller C includes a parameter generator module 230. The parameter generator module 230 is generally configured to generate control parameters based on measurements of the sensor S and / or a model. The control parameters generally reflect the responsiveness of one or more PID controllers external to the bioprocess controller module 220 and / or the controller C that control the flow of one or more additive gases AG1-AG2 and / or one or more additive fluids AF1-AF2 to the bioreactor BR to changes in measurement values ​​(e.g., the reduced content of glucose in the bioprocess fluid FL or the density of viable cells in the bioprocess fluid FL).

[0055] In one example, a change in the reflective or transmissive properties of the bioprocess fluid FL indicated by the measurement results may indicate that the bioprocess is in a particular phase of the bioprocess, such as the LOG phase, when the content of glucose in the bioprocess fluid FL may drop rapidly. The parameter generator module 230 may then generate control parameters to provide a higher responsiveness to the content of glucose. For example, a parameter indicating an increased proportional term of a PID controller.

[0056] In one example, a change in the reflective or transmissive properties of the bioprocess fluid FL indicated by the measurement results may indicate that the bioprocess is at a particular stage of the bioprocess when the content of glucose in the bioprocess fluid FL is constantly shifted, for example, by the residual. The parameter generator module 230 may then generate control parameters to provide a higher responsiveness to the residual. For example, a control parameter indicating an increased integral term of a PID controller.

[0057] In one example, changes in the reflective and / or transmissive properties of the bioprocess fluid FL indicated by the measurement results may indicate that the bioprocess is at a particular stage of the bioprocess when the content of glucose in the bioprocess fluid FL is constantly shifting, for example, due to a residual error. The parameter generator module 230 may then generate control parameters to provide a higher responsiveness to the error rate of change. For example, a parameter indicating an increased derivative term of a PID controller.

[0058] In some embodiments, the controller C includes an optional model generator module 240. The model generator module 240 may be configured to generate a model by performing an orthogonal partial least squares analysis using a reference data set and / or a starting data set to generate a model with respect to Figure 1 A model is generated by determining correlations between useful variables and, for example, an extended reference set of reference measurements for bioprocess conditions included in a reference data set and / or a starting data set as further described.

[0059] The reference data set and / or the starting data set may be generated by a validated method in order to determine reference values ​​for various process parameters at various time points in the process. The validated method may involve measuring the reflectance and / or transmission properties of the bioprocess fluid FL as a reference measurement result and simultaneously determining the value of a useful variable of the bioprocess as a reference predictor variable, such as determining the glucose content of the bioprocess fluid FL. Parameters of particular interest for the model are considered useful parameters or parameters of interest to determine and / or control, but also parameters that may affect the recorded spectra but are not of interest to determine or control. An example of a general method for performing multivariate data analysis MVDA, unsupervised principal component analysis PCA, and partial least squares regression method PLS for the prediction of multiple cultivation variables during bioprocess monitoring can be found in "Chemometrics and in-line near infrared spectroscopic monitoring of abiopharmaceutical Chinese hamster ovary cell culture: prediction of multiple cultivation variables" (Clavaud M, Roggo Y, Von Daeniken R, Liebler A, Schwabe JO, Talanta, March 26, 2013, 111: 28-38).

[0060] The reference data set can then be transformed into a computer model by analyzing the absorbance as a function of wavelength and the corresponding determined parameter value (e.g., indicating a process time point) and correlating this absorbance with the parameter value determined by the validated method. An example of how a model generated based on reference data can be used to predict variables can be found in "Designing a calibration set in spectral space for efficient development of an NIR method for tablet analysis" (M Anik Alama, James Drennen III, Carl Anderson, Journal of Pharmaceutical and Biomedical Analysis Volume 145, October 25, 2017, pages 230-239).

[0061] In other examples, a model is generated using an orthogonal partial least squares analysis of the reference data set and / or the starting data set.

[0062] In one embodiment, the starting data set is extended, for example, by generating an extended reference measurement by means of the extended measurement and the corresponding biological process variable value included in the starting data set.

[0063] Additionally or alternatively, the initial data set can be expanded, for example, by increasing or decreasing the content or concentration of various components in the bioprocessing fluid FL (e.g., by spiking the bioprocessing fluid FL or diluting the bioprocessing fluid FL). In this way, spectral data can be generated in a range spanning outside the normal biological range used in upstream cultivation.

[0064] Increasing or decreasing levels or concentrations can, for example, be performed via benchtop experiments where the above-mentioned useful parameters (e.g., glucose and / or lactose / lactic acid content or viable cell density) are varied by, for example, spiking / dilution procedures in order to generate spectral data within a range spanning outside the normal biological range used in, for example, upstream cultures.

[0065] Additionally or alternatively, expanding the starting data set can be further performed by systematically varying the useless bioprocessing variables in a systematic manner, for example by applying an empirical design in which variables such as glucose / lactose / lactic acid concentration, pH or temperature are systematically varied and spectral data for the different variations are obtained.

[0066] In one example, this can be performed by running an experiment in an appropriate screening volume bioreactor and applying systematically varied parameters / bioprocess variables in a manner as dictated by a design of experiments DOE setup. Alternatively and concurrently, the above methods can be applied to generate a model. A DOE setup typically involves varying useful and / or useless variables at specific step sizes within a predetermined range.

[0067] Additionally or alternatively, enlarging the starting data set may be further performed by repeating the above steps for bioreactors with different volumes.

[0068] This is preferably performed by running the process of interest in a bioreactor having a volume larger than the volume of the screening reactor used to expand the starting data set (e.g. in the same volume as the target process expected to be used in production conditions, typically between 50 and 2000 liters, but also larger, e.g. 10,000 liters) and / or simultaneously applying the described method for generating a model.

[0069] A reference dataset generated by scaling up the starting dataset using any of the methods described above can be further analyzed using orthogonal partial least squares (O-PLS) analysis to filter out effects due to bioreactor size / volume.

[0070] Figure 3A controller C according to one or more embodiments of the present disclosure is shown. The controller C may take the form of, for example, an electronic control unit, a server, an onboard control unit, a fixed computing device, a laptop control unit, a tablet control unit, a handheld control unit, a wrist-worn control unit, a smart watch, a smart phone, or a smart TV. The controller C may include a processing circuit system 312, which is communicatively coupled to a communication interface (e.g., a transceiver 304) configured for wired or wireless communication. The controller C may further include at least one optional antenna (not shown in the figure). The antenna may be coupled to the transceiver 304 and configured to transmit and / or transmit and / or receive wired or wireless signals in a communication network (e.g., WiFi, Bluetooth, 3G, 4G, and 5G, etc.). In one example, the processing circuit system 312 may be any of the selections of a processor and / or a central processing unit and / or a processor module and / or a plurality of processors configured to cooperate with each other. In addition, the controller C may further include a memory 315, which is communicatively coupled to the processing circuit system 312. Memory 315 may include, for example, a hard RAM, a disk drive, a floppy disk drive, a flash drive, or other removable or fixed media drive, or any other suitable memory selection known in the art. Memory 315 may contain instructions executable by processing circuitry to perform any of the steps or methods described herein. Processing circuitry 312 may be communicatively coupled to transceiver 304 and any selection of memory 315. Controller C may be configured to send / receive control signals directly to / from any of the above-mentioned units or to an external node, or to send / receive control signals via a wired and / or wireless communication network.

[0071] Wired / wireless transceiver 304 and / or wired / wireless communication interface may be configured to send / receive data values ​​or parameters as signals to / from processing circuitry 312 and / or other external nodes.

[0072] In an embodiment, the transceiver 304 communicates with an external node / unit directly or via a wireless communication network.In one example, the control parameters are sent to an external PID controller.

[0073] In one or more embodiments, the controller C may further include an input device 317 configured to receive input or instructions from a user and send a user input signal indicative of the user input or instructions to the processing circuit system 312 .

[0074] In one or more embodiments, controller C may further include a display 318 configured to receive a display signal indicating a rendered object (e.g., text or a graphical user input object) from processing circuit system 312 and display the received signal as an object (e.g., text or a graphical user input object).

[0075] In one embodiment, the display 318 is integrated with the user input device 317, and is configured to receive a display signal indicating a rendered object (e.g., a text or graphical user input object) from the processing circuit system 312 and display the received signal as an object (e.g., a text or graphical user input object), and / or is configured to receive input or indications from a user and send a user input signal indicating the user input or indication to the processing circuit system 312.

[0076] In other embodiments, the controller C may further include and / or be coupled to one or more additional sensors (not shown in the figure), which are configured to receive and / or obtain and / or measure physical properties related to the biological treatment system SYS, and send one or more sensor signals indicating the physical properties to the processing circuit system 312. An example of such an additional sensor may be an ambient air pressure sensor, which is configured to measure the ambient air pressure in which the biological treatment system SYS is located.

[0077] In one or more embodiments, processing circuitry 312 is further communicatively coupled to input device 317 and / or display 318 and / or additional sensors.

[0078] In an embodiment, the communication network communicates using wired or wireless communication technology, which may include a local area network (LAN), a metropolitan area network (MAN), a global system for mobile communications (GSM), an enhanced data GSM environment (EDGE), a universal mobile telecommunications system, long term evolution, a high speed downlink packet access (HSDPA), a wideband code division multiple access (W-CDMA), a code division multiple access (CDMA), a time division multiple access (TDMA), Bluetooth®, Zigbee®, Wi-Fi, voice over Internet protocol (VoIP), LTE Advanced, IEEE 802.16m, WirelessMAN-Advanced, Evolved High Speed ​​Packet Access (HSPA+), 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), Ultra Mobile Broadband (UMB) (formerly Evolution-Data Optimized (EV-DO) Rev. C), Fast Low Latency Access with Seamless Handover Orthogonal Frequency Division Multiplexing (Flash-OFDM), High Capacity Spatial Division Multiple Access (iBurst®), and Mobile Broadband Wireless Access (MBWA) (IEEE 802.16e). The present invention may include at least one of IEEE 802.20) system, High Performance Radio Metropolitan Area Network (HIPERMAN), Wavelength Division Multiple Access (BDMA), Worldwide Interoperability for Microwave Access (Wi-MAX), and ultrasonic communication, but is not limited thereto.

[0079] Furthermore, it is recognized by the skilled person that the controller C may include necessary communication capabilities in the form of, for example, functions, components, units, elements, etc., for performing the present solution. Examples of other such components, units, elements and functions are: processors, memories, buffers, control logic, encoders, decoders, mapping units, multipliers, decision units, selection units, switches, modulators, demodulators, inputs, outputs, antennas, amplifiers, receiver units, transmitter units, DSPs, power supply units, power feeders, communication interfaces, communication protocols, etc., which are appropriately arranged together for performing the present solution.

[0080] In particular, the processing circuit system of the present disclosure may include a processor, a processor module, and multiple processors configured to cooperate with each other, a central processing unit (CPU), a processing unit, a processing circuit, a processor, an application-specific integrated circuit (ASIC), a microprocessor, a field programmable gate array (FPGA), or one or more instances of other processing logic that can interpret and execute instructions. Therefore, the expression "processing circuit system" and / or "processing component" may refer to a processing circuit system including multiple processing circuits, such as any, some or all of the above-mentioned processing circuit systems. The processing component may further perform data processing functions for input, output and processing of data, including data buffering and device control functions, such as call processing control, user interface control, etc.

[0081] Figure 4 A flow chart of a method 400 according to one or more embodiments of the present disclosure is shown. The computer-implemented method 400 is provided and executed by a controller C configured to control a biological process included in a bioreactor BR. The method comprises:

[0082] Step 410: Obtain a measurement result by performing a spectrophotometric measurement of the bioprocess fluid FL. The measurement result is typically obtained by receiving a control signal from the sensor S. The control signal indicates a measurement result, such as a reflectance or transmission property of the bioprocess fluid FL as a function of wavelength, such as relative to Figure 2 Further description.

[0083] In one example, the bioprocess is a cell culture process and the measurement results indicate transmission properties of the bioprocess fluid FL as a function of wavelength, such as the intensity of near infrared light NIR as a function of wavelength. The wavelength of NIR light may typically be comprised in the range of 780 nm to 2500 nm.

[0084] Step 420: Generate control parameters based on the measurements and a (single) model.

[0085] The control parameter generally reflects the responsiveness of one or more PID controllers external to the bioprocess controller module 220 and / or controller C that control the flow of one or more additive gases AG1-AG2 and / or one or more additive fluids AF1-AF2 to the bioreactor BR to changes in measured result values ​​(e.g., the reduced content of glucose in the bioprocess fluid FL or the density of viable cells in the bioprocess fluid FL). Figure 2 Additional details and examples of generating control parameters are further described.

[0086] Additionally or alternatively, in one embodiment, the step of generating control parameters further comprises estimating / predicting a set of useful bioprocess variables by providing the resulting measurements to the model as output from the model. Additionally or alternatively, the generated control parameters may be further based on the estimated / predicted set of useful bioprocess variables. In one example, the estimated / predicted set of useful bioprocess variables indicates that the bioprocess has entered a different phase, and the control parameters are selected from a predefined set of parameters associated with that particular phase (e.g., the LOG phase of the cell culture).

[0087] Additionally or alternatively, the control parameter is further based on the processing time of the bioprocess. The step of generating the control parameter further comprises mapping the set of useful bioprocess variables and / or the processing time to a period in the plurality of periods of the bioprocess. Additionally or alternatively, the generated control parameter is further dependent on the mapped period.

[0088] Additionally or alternatively, the generated control parameters include proportional-integral-derivative controller parameters, for example, any selection of proportional, integral and derivative terms of a PID controller.

[0089] Step 430: Control the bioprocess using the generated parameters. Controlling the bioprocess may generally include controlling the flow of one or more additive gases AG1-AG2 and / or one or more additive fluids AF1-AF2 to the bioreactor BR. Figure 2 Further describe controlling biological processes.

[0090] Can be relative to Figure 1 An example of the execution of the above method is found. As described, it is possible to describe the functionality of the controller C with respect to Figure 2 Find additional details and examples of the method.

[0091] Additionally or alternatively, in one embodiment, the useful bioprocess variables are indicative of any selection of the glucose content, lactose / lactic acid content and viable cell content of the bioprocess fluid FL.

[0092] Additionally or alternatively, in one embodiment, the biological process comprises performing a cell culture.

[0093] Additionally or alternatively, in one embodiment, method 400 further comprises the step of generating 405 a model. Additionally or alternatively, in one embodiment, generating the model further comprises performing an orthogonal partial least squares (O-PLS) analysis using a reference data set to determine correlations between reference useful variables / predictor variables and an extended reference measurement result of a reference set for the bioprocessing conditions.

[0094] Additionally or alternatively, the step of generating 405 a model further includes any selection of the following steps: identifying bioprocessing variables that contribute to a set of reference measurement results; classifying the bioprocessing variables as useful variables or useless variables; generating extended reference measurement results for an extended set of bioprocessing conditions; determining correlations between useful variables and the extended reference measurement results for the reference set of bioprocessing conditions; and generating a model based on the determined correlations.

[0095] Additionally or alternatively, in one embodiment, the step 430 of controlling the bioprocess further comprises controlling the flow rate of one or more additive fluids AF1-AF2. Controlling the flow rate may include controlling the flow rate of a pump that provides the additive fluid to the bioreactor BR, such as relative to Figure 2 Further description.

[0096] Additionally or alternatively, in one embodiment, the step 430 of controlling the bioprocess further comprises controlling the flow rate of one or more additive gases AG1-AG2. Controlling the flow rate may include controlling the flow rate of a pump that provides the additive fluid to the bioreactor BR, such as relative to Figure 2 Further description.

[0097] In one embodiment, a controller C is provided that includes processing circuitry 312 and memory 315. The memory 315 contains instructions executable by the processor 312, whereby the controller operates to perform any of the method steps in accordance with the method 400.

[0098] In one embodiment, a bioprocessing system SYS is provided, which includes: a sensor S, which is configured to perform spectrophotometric measurement of a bioprocessing fluid FL and provide measurement results included in a control signal; a first controllable flow unit V, which is configured to control the flow of one or more additive gases AG1-AG2 to a bioreactor BR in response to a control signal; and a second controllable flow unit P, which is configured to control the flow of one or more additive fluids AF1-AF2 to the bioreactor BR in response to a control signal.

[0099] Additionally or alternatively, in one embodiment, the controller C is further configured to receive / send control signals to / from the sensor S, the first controllable flow unit V and the second controllable flow unit P.

[0100] Additionally or alternatively, in one embodiment, a computer program is provided and includes computer executable instructions for causing controller C to perform any of the method steps described herein when the computer executable instructions are executed on a processing circuit system 312 included in controller C.

[0101] Additionally or alternatively, in one embodiment, a computer program product is provided and includes a computer-readable storage medium having the above-described computer program embodied therein.

[0102] Finally it should be understood that the present invention is not limited to the embodiments described above, but also relates to and combines all embodiments within the scope of the attached independent claims.

Claims

1. A computer-implemented method (400) performed by a controller (C) configured to control a bioprocess included in a bioreactor (BR), the method comprising: obtaining (410) a measurement result by performing a spectrophotometric measurement of a bioprocess fluid (FL), generating (420) control parameters based on the measurements and a model, and controlling (430) the biological process using the generated parameters, wherein the bioprocess comprises cell culture, and the bioprocess comprises a plurality of phases, The method further comprises: generating (405) the model, Identify the bioprocess variables that contribute to the set of reference measurements, classifying the bioprocessing variables as useful variables or useless variables, Generate extended reference measurements for an extended set of bioprocessing conditions, determining a correlation between the useful variable and said extended reference measurements of a reference set for bioprocess conditions, The one model is generated based on the determined correlations.

2. The method of claim 1, wherein: The plurality of periods are at least two of a stagnation period, a LOG period, and a static period.

3. The method of claim 1, wherein: The control parameters are generated based on the measurements and a single model.

4. A method as claimed in any one of the preceding claims, wherein: The step of generating the control parameters further comprises: estimating a set of useful bioprocess variables as output from the model by providing the obtained measurements to the model, and Wherein the generated control parameters are further based on the estimated set of useful bioprocess variables.

5. The method of claim 4, wherein: The control parameter is further based on a processing time of the bioprocess, and the step of generating the control parameter further comprises: mapping the processing time to a period in a plurality of periods of the biological process, and The generated control parameters further depend on the mapped period.

6. The method according to any one of claims 1 to 3, wherein: The generated control parameters include proportional-integral-derivative controller parameters.

7. The method of claim 4, wherein: The useful bioprocess variable is indicative of a selection of any of the glucose content, the lactose content, and the viable cell content of the bioprocess fluid (FL).

8. The method of claim 4, wherein: Generating the model further includes performing an orthogonal partial least squares analysis using a reference data set to determine correlations between the useful bioprocess variables and extended reference measurements for a reference set of bioprocess conditions.

9. The method according to any one of claims 1 to 3, wherein: Controlling the bioprocess further comprises controlling the flow rate of one or more additive fluids (AF1-AF2).

10. The method according to any one of claims 1 to 3, wherein: Controlling the bioprocess further comprises controlling the flow rate of one or more additive gases (AG1-AG2).

11. A controller (C), comprising: Processing circuit system (312); as well as A memory (315) containing instructions executable by the processor (312), whereby the controller operates to perform the method steps of any one of claims 1-10.

12. A biological treatment system (SYS), comprising: a sensor (S) configured to perform a spectrophotometric measurement of the bioprocess fluid (FL) and to provide a measurement result included in a control signal, a first controllable flow unit (V) configured to control the flow of one or more additive gases (AG1-AG2) to the bioreactor (BR) in response to a control signal, a second controllable flow unit (P) configured to control the flow of one or more additive fluids (AF1-AF2) to the bioreactor (BR) in response to a control signal, The controller (C) as claimed in claim 11, wherein the controller (C) is further configured to receive / send control signals to / from the sensor (S), the first controllable flow unit and the second controllable flow unit (P).

13. A computer program comprising computer executable instructions for causing a controller (C) to perform the method steps of any one of claims 1 to 10 when the computer executable instructions are executed on a processing circuit system (312) included in the controller (C).

14. A computer program product comprising a computer readable storage medium having embodied therein the computer program of claim 13.

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