WO2024062027A1

WO2024062027A1 - Method for controlling a perfusion process in a bioreactor

Info

Publication number: WO2024062027A1
Application number: PCT/EP2023/076059
Authority: WO
Inventors: Koen DIERCKX; Thomas GEUENS; Saba DANESHGAR; Kimberly TUMLOS; Elena TORFS
Original assignee: Simabs Nv
Priority date: 2022-09-21
Filing date: 2023-09-21
Publication date: 2024-03-28
Also published as: NL2033094B1

Abstract

A computer-implemented method (100) for controlling a perfusion process in a bioreactor, the method comprising: - measuring (110) a plurality of process variables relating to the perfusion process; - estimating (120) a cell viability of a cell population in the bioreactor based on the measured process variables, wherein the cell viability is a parameter representative for a proportion of live cells within the cell population; and - controlling (130) one or more process parameters of the perfusion process based on the estimated cell viability.

Description

Method for controlling a perfusion process in a bioreactor

Field of Invention

The field of the invention relates to a method for controlling perfusion process. The invention further relates to a reactor comprising a processor for performing the method and a computer program performing the method.

Background

Pharmaceutical manufacturing is continuously developing. One of such developments is the transition of traditionally known batch or fed-batch culture processing for the production of, for example, antibodies into a continuous biomanufacturing process. While the continuous biomanufacturing, also known as a perfusion process, has a substantial amount of advantages such as increased production efficiency and a simplified scaling of the process, it also comes with its specific challenges.

In particular maintaining a healthy cell population or a constant population density over a prolonged period of time in a perfusion reactor is a challenge as it depends on a multitude of factors which cannot always be directly measured.

Summary

The object of embodiments of the present invention is to provide a computer-implemented method which allows for controlling a perfusion process in a bioreactor such that a healthy cell population can be maintained.

According to a first aspect of the present invention the method comprises measuring a plurality of process variables relating to the perfusion process, estimating a cell viability of a cell population in the bioreactor based on the measured process variables, wherein the cell viability is a parameter representative for a proportion of live cells within the cell population, and controlling one or more process parameters of the perfusion process based on the estimated cell viability. It has been found that cell viability is an essential variable in a continuous biomanufacturing process as it provides an insight into, for example, when cell bleeding should be performed in order to keep the number of viable cells as high as possible for as long as possible during the perfusion process. However, cell viability is difficult to measure. In particular because, during perfusion, a fraction of cells, which can be dead or alive, cannot be measure by performing on-line sampling. On-line sampling is used to determine the cell viability of only the floating cells. Typically, when a cell dies, the cell attaches to a wall of the bioreactor and the on-line cell viability measurement thus erroneously underestimates the actual cell viability, in particular during longer perfusion processes. In addition to this, cell viability is difficult to measure in a continuous manner and with a high frequency. For example, using raman spectroscopy one can only have an indication of the total amount of cells rather than the distinguishment between the amount of viable cells and dead cells. For this reason the method provides the step of estimating the cell viability of the cell population in the bioreactor. The estimating is performed based on measurements of a plurality of process variables relating to the perfusion process. Said plurality of process variables may be measured continuously, at predetermined time-intervals or may be set as initial values prior to starting the perfusion process. The method uses the directly measurable process variables in order to estimate, or even predict, a cell viability of the cells in the bioreactor. The method can then control one or more process parameters of the perfusion process based on the estimated cell viability.

Preferably, the controlling comprises controlling at least one of a feed composition, an incoming flow rate, Qi_n, or an outgoing flow rate, Q_out, based on the estimated cell viability. More preferably, the controlling of the incoming flow rate, Qi_n comprises controlling a feed rate, Qf_eed, and the controlling of the outgoing flow rate, Q_out, comprises controlling a bleed rate, Qbieed, and/or a harvest flow rate, Qharvest- It has been found that an incoming flow rate and an outgoing flow rate, most notably a feed rate and a bleed rate significantly affect the availability of, for example, nutrients and metabolites in the bioreactor that are either required for cell growth or inhibit cell growth at certain concentrations. Therefore, controlling the feed rate and bleed rate of the bioreactor based on the estimated cell viability has proven to be particularly advantageous in maintaining the cell viability as high as possible.

Preferably, the estimating comprises determining a biomass growth variable,

representative for the cell growth in the cell population of the bioreactor; and determining a biomass death variable,

representative for cell death in the cell population of the bioreactor.

More preferably, the biomass growth variable,

is determined based at least on a variable representative for a number of viable cells, X_v, a volume of the bioreactor, V, a cell growth rate, p, a cell death rate, pd, and an outgoing flow rate, Q_out. In this way an intangible variable is quantified which can then in turn be used to determine, for example, the respective rates of change in time. More preferably, wherein the biomass growth variable is determined using the following equation:

In this way the biomass growth variable is related to a plurality of independent variables such as the ones mentioned here above.

Preferably, the biomass death variable is determined based at least on a variable representative for a number of dead cells, X_d, on a volume of the bioreactor, V, a cell death rate, pd, and an outgoing flow rate, Q_out. The advantages of the biomass growth variable apply, mutatis mutandis, to the biomass death variable. More preferably, the biomass death variable is determined using the following equation

Preferably, the measuring comprises measuring at least one of a variable representative for a nutrient concentration in the bioreactor and a variable representative for a metabolite concentration in the bioreactor. More preferably, the measuring comprises measuring the nutrient concentration of at least one of a glucose and glutamine concentration. More preferably, the controlling of the feed composition comprises controlling the nutrient concentration of at least one of the glucose concentration and glutamine concentration. The advantage hereof is based on the insight that glucose and glutamine, abbreviated as GLC and GLN respectively, are the main nutrients required for growth of the cells in the bioreactor. Even though the elfect of the concentration of GLC and GLN on the perfusion output is smaller than the feed and bleed rate, controlling the GLC and GLN concentration is beneficial for controlling the perfusion process as it allows to optimize the process conditions and processing strategy in the bioreactor.

Preferably, the measuring comprises measuring the metabolite concentration of at least a lactate concentration. More preferably, the measuring further comprises measuring the metabolite concentration of ammonia. It has been found that lactate and ammonia concentrations, abbreviated as LAC and AMM respectively, may inhibit growth of the cell culture. Measuring the metabolite concentration thus provides valuable insights for the method to control perfusion process. It has also been found that, although measuring the metabolite concentration of ammonia can be advantageous in order to estimate cell viability in a more improved manner, the impact of ammonia on the perfusion process is less than lactate. Put differently, the perfusion process is less sensitive towards the generation of ammonia. Therefore, more preferably, the cell growth rate, p, is determined using the following formula,

where CGLC, CGLN , and CLAC are the concentration of glucose, glutamine, and lactate, respectively, KGLC, KGLN , and KLAC are half- saturation coefficients of glucose, glutamine, and lactate , KLAc.inh is an inhibition coefficient for lactate, kd is a maximum cell death rate and K_M,d is an inhibition coefficient of cell growth. More preferably the cell growth rate, p, is determined using the following formula,

where CAMM is the concentration of glucose, glutamine, lactate and ammonia, respectively, KGLC, KGLN , and KLAC are half-saturation coefficients of glucose, glutamine, and lactate, KAMM and KLAc.inh are inhibition coefficients for ammonia and lactate.

Preferably, the cell death rate, p , is determined using the following formula

Preferably, the estimating further comprises determining a change in at least one of the variable representative for a nutrient concentration in the bioreactor and the variable representative for a metabolite concentration in the bioreactor based on an incoming flow rate, Qi_n, an incoming concentration of nutrient or metabolite, Ci, in, an outgoing flow rate, Q_out, an outgoing concentration of nutrient or metabolite, Ci, out, a rate of consumption or production of the respective nutrient or metabolite, q, and a volume of the bioreactor. In this way the method can estimate the cell viability in an improved and more predictive manner. More preferably, the change is determined using the following formula:

Preferably, the estimating further comprises estimating a concentration of the perfusion process product, C_mAb, based on an outgoing flow rate, Q_out, an outgoing rate of production of the perfusion process product, qmAb, and a volume of the bioreactor. More preferably, the estimating of the concentration of the perfusion process product is determined using the following formula:

According to another aspect of the present invention a reactor configured for a perfusion process is provided, the reactor comprising a processor configured for performing the steps of the method of any one of the method embodiments described above.

According to another aspect of the present invention, there is provided a computer program product comprising a computer-executable program of instructions for performing, when executed on a computer, the steps of the method of any one of the method embodiments described above.

It will be understood by the skilled person that the features and advantages disclosed hereinabove with respect to embodiments of the method may also apply, mutatis mutandis, to embodiments of the computer program product and the reactor.

According to yet another aspect of the present invention, there is provided a digital storage medium encoding a computer-executable program of instructions to perform, when executed on a computer, the steps of the method of any one of the method embodiments described above. According to yet another aspect of the present invention, there is provided a device programmed to perform a method comprising the steps of any one of the methods of the method embodiments described above.

According to yet another aspect of the present invention, there is provided a method for downloading to a digital storage medium a computer-executable program of instructions to perform, when executed on a computer, the steps of the method of any one of the method embodiments described above.

Brief description of the figures

The accompanying drawings are used to illustrate presently preferred non-limiting exemplary embodiments of devices of the present invention. The above and other advantages of the features and objects of the present invention will become more apparent and the present invention will be better understood from the following detailed description when read in conjunction with the accompanying drawings, in which:

Figure 1 schematically illustrates a perfusion bioreactor and a method for controlling the perfusion process according to an exemplary embodiment.

Description of figures

The description and drawings merely illustrate the principles of the present invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the present invention and are included within its scope. Furthermore, all examples recited herein are principally intended expressly to be only for pedagogical purposes to aid the reader in understanding the principles of the present invention and the concepts contributed by the inventor(s) to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass equivalents thereof.

The functions of the various elements shown in the figures, including any functional blocks labelled as “processors”, may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and/or custom, may also be included. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the present invention. Similarly, it will be appreciated that any flowcharts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer.

Figure 1 schematically illustrates a bioreactor 1000 and a controller 1300 for controlling the bioreactor. A bioreactor 1000 is any manufactured device or system that supports a biologically active environment. As an example, a bioreactor 1000 is a vessel in which a biochemical process is carried out which involves organisms or biochemically active substances derived from such organisms. A bioreactor may also refer to a device or system designed to grow cells or tissues in the context of cell culture. Bioreactors exist in all kinds of shapes and sizes but are commonly cylindrical, ranging in size from litres to cubic metres, and are often made of stainless steel or equipped with single use bags The bioreactor 1000 is in particular configured for a perfusion process. In the context of the application a perfusion process is a continuous biomanufacturing process for the production of, but not limited to, monoclonal antibodies, , from a cell culture.

The bioreactor 1000 comprises a feed inlet, a harvesting outlet and a bleed outlet. Via the feed inlet, a feed composition can be input into the bioreactor. This is also referred to as an incoming flow rate, labelled as Qi_n. The feed composition preferably comprises at least one of glucose and glutamine. Via the harvesting outlet, a perfusion process product, such as monoclonal antibodies can be harvested. This is referred to as a harvest flow rate, Qharvest- Via the bleed outlet, waste product such as dead cells or metabolite can be bled from the bioreactor 1000. This is referred to as a bleed rate, labelled as Qbieed. The harvest flow rate and bleed rate form an outgoing flow rate, labelled as Q_out. It will be clear that the step of harvesting and bleeding does not necessarily need to occur simultaneously. The outgoing flow rate Q_out can thus be formed either by the harvest flow rate or the bleed rate, or a combination of the harvest and bleed rate.

In order to generate a flow rate, one or more pumps may be provided. Figure 1 shows that each of the respective flow lines is provided with a respective pump 1101, 1102, 1103.

Figure 1 furthermore illustrates that the perfusion process in the bioreactor 1000 is controlled using a controller 1300. The controller 1300 is configured to perform a method to maintain a cell viability in the bioreactor 1000 as high as possible for as long as possible. Cell viability is an essential variable in a continuous biomanufacturing process as it provides an insight into, for example, when cell bleeding, Qbieed, should be performed in order to keep the number of viable cells as high as possible for as long as possible during the perfusion process. However, cell viability is difficult to measure directly . In particular because, during perfusion, a fraction cells, which can be dead or alive, cannot be measure by performing on-line sampling. On-line sampling is used to determine the cell viability of only the floating cells. Typically, when a cell dies, the cell attaches to a wall of the bioreactor and the on-line cell viability measurement thus erroneously underestimates the actual cell viability, in particular during longer perfusion processes. In addition to this, cell viability is difficult to measure in a continuous manner and with a high frequency. For example, using raman spectroscopy one can only have an indication of the total amount of cells rather than the distinguishment between the amount of viable cells and dead cells. For this reason the method comprises measuring 110 a plurality of process variables relating to the perfusion process, estimating 120 a cell viability of a cell population in the bioreactor based on the measured process variables, wherein the cell viability is a parameter representative for a proportion of live cells within the cell population, and controlling 130 one or more process parameters of the perfusion process based on the estimated cell viability.

The measuring 110 is performed using one or more sensors 1201, 1202, 1203, 1204. Process variables can include, but are not limited to a concentration of glucose, glutamine, lactate and ammonia, respectively labelled CGLC, CGLN , CLAC and CAMM, a volume of the bioreactor, V, incoming flow rate, Qi_n, an outgoing flow rate, Q_out, a feed rate, Qf_eed, a bleed rate, Qbieed, and a harvest flow rate,

temperature. Although a distinct sensor can be used to measure, for example, temperature, a sensor can be provided which measures a combination of process variables. The sensors can be arranged in a plurality of locations. Also, a plurality of sensors can be arranged at the same location, for example to measure diffierence process variables at the same location. For example, a sensor 1204 can be arranged in the bioreactor to measure at least one of a variable representative for a nutrient concentration in the bioreactor and a variable representative for a metabolite concentration in the bioreactor. The sensor 1204 can be used to measure the nutrient concentration of at least one of a glucose and glutamine concentration in the bioreactor. Additionally, another sensor can be arranged in the bioreactor to measure a temperature of the cell culture for example. Where the sensor 1204 measures the concentration of glucose and glutamine in the bioreactor, an additional sensor 1201 can be arranged at the inlet of the bioreactor. Sensor 1201 can also measure a glucose and glutamine concentration, particularly a glucose and glutamine concentration of the incoming flow

The sensor 1201 can also be configured to measure a feed rate, Qf_eed or a further sensor configured to measure the feed rate can be provided. Moreover, other sensors 1202 can be provided in the bleed line or harvest line, for example to determine an actual bleed rate

or harvest flow rate It is noted that a flow rate can also be indirectly determined by reading an output signal to the pumps 1101, 1102, 1103. The measuring 110 may also comprise measuring the metabolite concentration of at least one of a lactate and ammonia concentration, for example using sensor 1204. It has been found that lactate and ammonia concentration may inhibit growth of the cell culture. Measuring the metabolite concentration in the bioreactor 1000 thus provides valuable insights for the method 100 to control the perfusion process.

The plurality of process variables may be measured continuously, at predetermined timeintervals or may be set as initial values prior to starting the perfusion process. The measurements 110 can be performed offline an/or inline. For example, a process variable such as initial number of cells can be measured offline. Inline measurements such as flow rate measurements or Raman spectroscopy measurements in the bioreactor can be beneficial in monitoring and controlling the perfusion process.

The estimating 120 is performed based on measurements 110 of the plurality of process variables relating to the perfusion process. In other words, the plurality of measured process variables such as a concentration of glucose, glutamine, lactate and ammonia, a volume of the bioreactor, V, incoming flow rate, Qi_n, an outgoing flow rate, Q_out, a feed rate, Qf_eed, a bleed rate, Qbieed, and a harvest flow rate, Qharvest, temperature are used as an input for the method to estimate a cell viability of the cells in the bioreactor, as will be elaborated here below. The estimating 120 preferably comprises determining a biomass growth variable,

The biomass growth variable is representative for the cell growth in the cell population of the bioreactor. Additionally the estimating 120 comprises determining a biomass death variable,

which is representative for cell death in the cell population of the bioreactor. In this way an intangible variable is quantified which can then in turn be used to determine, for example, the respective rates of change in time. More preferably, the biomass growth variable,

is determined based at least on a variable representative for a number of viable cells, X_v, a volume of the bioreactor, V, a cell growth rate, p, a cell death rate, pd, and an outgoing flow rate, Q_out. The biomass growth variable is preferably determined using the following equation:

In this way the biomass growth variable is related to a plurality of independent variables such as a volume of the bioreactor, V, a cell growth rate, p, a cell death rate, pd, and an outgoing flow rate, Q_out. This allows to optimize the perfusion process in order to keep the cell viability as high as possible for as long as possible. Moreover, this also allows to predict process outcomes when one or more of the process variables are changed. Similarly to the biomass growth variable, the biomass death variable is determined based at least on a variable representative for a number of dead cells,

on a volume of the bioreactor, V, a cell death rate, pd, and an outgoing flow rate, Q_out. More preferably, the biomass death variable is determined using the following equation

This allows to optimize the perfusion process ever further. Moreover, this also allows to further improve the predicting of process outcomes when one or more of the process variables are changed.

Also the cell growth rate, p, can be determined based on the measured 110 at least one of the variable representative for the nutrient concentration in the bioreactor and the variable representative for a metabolite concentration in the bioreactor. With respect to the metabolite concentration it is preferred that the concentration of LAC is measured. It has also been found that the impact of ammonia on the perfusion process is less than lactate. Therefore, the cell growth rate, p, can be determined using the following formula,

In order to further improve the estimation of the cell viability the cell growth rate, p, is determined using the following formula [3’]

Similarly to the cell growth rate, the cell death rate, pd, is determined using the measured 110 at least one of the variable representative for the nutrient concentration in the bioreactor and the variable representative for a metabolite concentration in the bioreactor. Using the following formula

In formulas [3], [3’] and [4] CGLC, CGLN , CLAC and CAMM are the concentration of glucose, glutamine, lactate and ammonia, respectively, KGLC, KGLN , and KLAC are half-saturation coefficients of glucose, glutamine, and lactate „ KAMM and KLAC, inh are inhibition coefficients for ammonia and lactate, kd is a maximum cell death rate and K_M,d is an inhibition coefficient of cell growth. The estimating 120 can further comprise determining a change in at least one of the variable representative for a nutrient concentration, CGLC, CGLN, in the bioreactor and the variable representative for a metabolite concentration, CLAC and CAMM, in the bioreactor based on an incoming flow rate, Q_ln, an incoming concentration of nutrient or metabolite, Ci, in, an outgoing flow rate, Q_out, an outgoing concentration of nutrient or metabolite, Ci, out, a rate of consumption or production of the respective nutrient or metabolite, q, and a volume of the bioreactor. In this way the method can estimate the cell viability in an improved and more predictive manner. More preferably, the change is determined using the following formula:

More specifically, determining a change of the variable representative for a nutrient concentration, CGLC, CGLN, in the bioreactor and the variable representative for a metabolite concentration, CLAC and CAMM can be performed as follows:

Formula [5-1] is used to determine the change of the concentration of glucose. Formula [5- 2] is used to determine the change of the concentration of glutamine. [5-1] and [5-2] take into account an incoming and outgoing flow rate, for example a feed rate and bleed rate, as well as a rate of consumption of the respective nutrient. Formula [5-3] is used to determine the change of the concentration of lactate. Formula [5-4] is used to determine the change of the concentration of ammonia. Note that metabolites are produced in the bioreactor and are typically not introduced in the bioreactor via the feed. [5-3] and [5-4] thus only take an outgoing flow rate into account, for example a bleed rate, as well as a rate of production of the respective metabolite.

Moreover, the estimating 120 may further comprise estimating a concentration of the perfusion process product, C_mAb, based on an outgoing flow rate, Q_out, an outgoing rate of production of the perfusion process product, qmAb, and a volume of the bioreactor. More preferably, the estimating of the concentration of the perfusion process product is determined using the following formula:

The cell viability can also be predicted using a trained neural network. Such a neural network can be trained using CGLC, CGLN , CLAC and CAMM, Xtas inputs for training the neural network. The respective training data for each of CGLC, CGLN , CLAC and CAMM, Xt can be obtained by performing the above mentioned method. It is however preferred that such a trained neural network is used in addition to the above described method of determining the cell growth and death. An output of the neural network can be used in combination with formula’s [l]-[6] to compensate for parts of the variables which are difficult to correlate or describe with the mechanistic formula’s [1] -[6]. The respective training data can be obtained using in-line measurements, for example using raman spectroscopy.

Finally the method comprises controlling 130 one or more process parameters of the perfusion process based on the estimated 120 cell viability. The controlling 130 can be performed by controlling at least one of a feed composition, an incoming flow rate, Q_ln, or an outgoing flow rate, Q_out, based on the estimated cell viability. More preferably, the controlling of the incoming flow rate, Qi_n comprises controlling a feed rate, Qf_eed, and the controlling of the outgoing flow rate, Qout, comprises controlling a bleed rate, Qbieed, and/or a harvest flow rate, Qharvest- It has been found that an incoming flow rate Qi_n and an outgoing flow rate, most notably a feed rate and a bleed rate significantly affect the availability of, for example, nutrients and metabolites in the bioreactor that are either required for cell growth or inhibit cell growth at certain concentrations. Therefore, controlling 130 the feed rate Qf_eed and bleed rate Qbi_eed of the bioreactor 1000 based on the estimated cell viability has proven to be particularly advantageous in maintaining the cell viability as high as possible. This can be executed by outputting the estimated 120 cell viability and computing a corresponding actuating signal which can in turn be used to as an input signal to, for example, the pumps 1101, 1102, 1103. This can be a simple on-off control or a P/PI/PID control scheme. Alternatively or in combination the control scheme may comprise a predictive aspect. The control scheme may for example estimate one or more process variables based on an estimated effect of controlling one or more parameters. For example, when a nutrient is added, cell growth can be estimated as well as time in the future at which point a cell bleed may be required in order to maintain optimal cell viability. Also a harvest time may be predicted based controlling, for example, a feed rate. The controlling of the feed composition may comprise controlling the nutrient concentration of at least one of the glucose and glutamine concentration. The advantage hereof is based on the insight that glucose and glutamine are the main nutrients required for growth of the cells in the bioreactor. Even though the effect of the concentration of GLC and GLN on the perfusion output is smaller than the feed and bleed rate, controlling the GLC and GLN concentration is beneficial for controlling the perfusion process as it allows to optimize the process conditions and processing strategy in the bioreactor. A person of skill in the art would readily recognize that steps of various above-described methods can be performed by programmed computers. Herein, some embodiments are also intended to cover program storage devices, e.g., digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions, wherein said instructions perform some or all the steps of said above-described methods. The program storage devices may be, e.g., digital memories, magnetic storage media such as magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. The program storage devices may be resident program storage devices or may be removable program storage devices, such as smart cards. The embodiments are also intended to cover computers programmed to perform said steps of the above-described methods.

It should be noted that the above-mentioned embodiments illustrate rather than limit the present invention and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” does not exclude the presence of elements or steps not listed in a claim. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. The present invention can be implemented by means of hardware comprising several distinct elements and by means of a suitably programmed computer. In claims enumerating several means, several of these means can be embodied by one and the same item of hardware. The usage of the words “first”, “second”, “third”, etc. does not indicate any ordering or priority. These words are to be interpreted as names used for convenience.

In the present invention, expressions such as “comprise”, “include”, “have”, “may comprise”, “may include”, or “may have” indicate existence of corresponding features but do not exclude existence of additional features.

Whilst the principles of the present invention have been set out above in connection with specific embodiments, it is to be understood that this description is merely made by way of example and not as a limitation of the scope of protection which is determined by the appended claims.

Summary of formulae

Claims

1. A computer-implemented method (100) for controlling a perfusion process in a bioreactor, the method comprising:

- measuring (110) a plurality of process variables relating to the perfusion process;

- estimating (120) a cell viability of a cell population in the bioreactor based on the measured process variables, wherein the cell viability is a parameter representative for a proportion of live cells within the cell population; and

- controlling (130) one or more process parameters of the perfusion process based on the estimated cell viability.

2. The method according to the previous claim, wherein the controlling (130) comprises controlling at least one of a feed composition, an incoming flow rate, Q_ln, or an outgoing flow rate, Q_out, based on the estimated cell viability.

3. The method according to the previous claim, wherein the controlling (130) of the incoming flow rate, Qi_n comprises controlling a feed rate, Qf_eed; and the controlling (130) of the outgoing flow rate, Q_out, comprises controlling a bleed rate, Qbieed, and/or a harvest flow rate, Qharvest.

4. The method according to any one of the previous claims, wherein the estimating (120) comprises determining a biomass growth variable,

representative for cell death in the cell population of the bioreactor.

5. The method according to the previous claim, wherein the biomass growth variable,

is determined based at least on a variable representative for a number of viable cells, X_v, a volume of the bioreactor, V, a cell growth rate, p, a cell death rate, pd, and an outgoing flow rate, Q_out.

6. The method according to the previous claim, wherein the biomass growth variable is determined using the following equation

7. The method according to any one of the previous claims 4-6, wherein the biomass death variable is determined based at least on a variable representative for a number of dead cells, X_d, on a volume of the bioreactor, V, a cell death rate, p , and an outgoing flow rate, Q_out.

8. The method according to the previous claim, wherein the biomass death variable is determined using the following equation

9. The method according to any one of the previous claims, wherein the measuring comprises measuring at least one of a variable representative for a nutrient concentration in the bioreactor and a variable representative for a metabolite concentration in the bioreactor.

10. The method according to claim 9, wherein the measuring comprises measuring the nutrient concentration of at least one of a glucose and glutamine concentration.

11. The method according to claims 2 and 10, wherein the controlling of the feed composition comprises controlling the nutrient concentration of at least one of the glucose concentration and glutamine concentration.

12. The method according to any one of the claims 9-11, wherein the measuring comprises measuring the metabolite concentration of at least lactate.

13. The method according to the previous claim, wherein the measuring further comprises measuring the metabolite concentration of ammonia.

14. The method according to any one of claims 5-8 and any one of claims 10-13, wherein the cell growth rate, p, is determined based on the measured at least one of the variable representative for the nutrient concentration in the bioreactor and the variable representative for a metabolite concentration in the bioreactor.

15. The method according to the previous claim, wherein the cell growth rate, p, is determined using the following formula,

where CGLC, CGLN , and CLAC are the concentration of glucose, glutamine, and lactate, respectively, KGLC, KGLN , and KLAC are half- saturation coefficients of glucose, glutamine, and lactate , KLAc.inh is an inhibition coefficient for lactate, kd is a maximum cell death rate and K_M,d is an inhibition coefficient of cell growth.

16. The method according to the previous claim and claim 13, wherein the cell growth rate, p, is determined using the following formula,

where CAMM is the concentration of glucose, glutamine, lactate and ammonia, respectively, KGLC, KGLN , and KLAC are half-saturation coefficients of glucose, glutamine, and lactate , KAMM and KLAc.inh are inhibition coefficients for ammonia and lactate.

17. The method according to the previous claim, wherein cell death rate, pd, is determined using the following formula

18. The method according to any one of claims 9-17, wherein the estimating further comprises determining a change in at least one of the variable representative for a nutrient concentration in the bioreactor and the variable representative for a metabolite concentration in the bioreactor based on an incoming flow rate, Qi_n, an incoming concentration of nutrient or metabolite, Ci, in, an outgoing flow rate, Q_out, an outgoing concentration of nutrient or metabolite, Ci, out, a rate of consumption or production of the respective nutrient or metabolite, q, and a volume of the bioreactor.

19. The method according to the previous claim, wherein the change is determined using the following formula:

20. The method according to any one of the previous claims, wherein the estimating further comprises estimating a concentration of the perfusion process product, C_mAb, based on an outgoing flow rate, Q_out, an outgoing rate of production of the perfusion process product, q_mAb, and a volume of the bioreactor.

21. The method according to the previous claim, wherein the estimating of the concentration of the perfusion process product is determined using the following formula:

22. A reactor configured for a perfusion process, the reactor comprising a processor configured for performing the steps of the method of any one of the previous claims.

23. A computer program product comprising a computer-executable program of instructions for performing, when executed on a computer, the steps of the method of any one of claims 1-21.