EP3732485A1 - Prédiction de l'etat métabolique d'une culture cellulaire - Google Patents

Prédiction de l'etat métabolique d'une culture cellulaire

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Publication number
EP3732485A1
EP3732485A1 EP19700004.5A EP19700004A EP3732485A1 EP 3732485 A1 EP3732485 A1 EP 3732485A1 EP 19700004 A EP19700004 A EP 19700004A EP 3732485 A1 EP3732485 A1 EP 3732485A1
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EP
European Patent Office
Prior art keywords
extracellular
metabolites
cell
metabolite
intracellular
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
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EP19700004.5A
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German (de)
English (en)
Inventor
Wolfgang Paul
Arthur MOHR
Sayuri HORTSCH
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F Hoffmann La Roche AG
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F Hoffmann La Roche AG
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Publication date
Application filed by F Hoffmann La Roche AG filed Critical F Hoffmann La Roche AG
Publication of EP3732485A1 publication Critical patent/EP3732485A1/fr
Pending legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
    • C12M41/46—Means for regulation, monitoring, measurement or control, e.g. flow regulation of cellular or enzymatic activity or functionality, e.g. cell viability
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
    • C12M41/30—Means for regulation, monitoring, measurement or control, e.g. flow regulation of concentration
    • C12M41/38—Means for regulation, monitoring, measurement or control, e.g. flow regulation of concentration of metabolites or enzymes in the cells
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N5/00—Undifferentiated human, animal or plant cells, e.g. cell lines; Tissues; Cultivation or maintenance thereof; Culture media therefor
    • C12N5/0018—Culture media for cell or tissue culture
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G16B5/30—Dynamic-time models

Definitions

  • the invention relates to the prediction of the metabolic state of cells, in particular cells kept in cell culture. State of the art
  • This tendency also and particularly relates to the operation of cell culture reactors used to produce pharmaceutically interesting molecules, especially macromolecules such as proteins.
  • the method further comprises, at each of a plurality of times during the culture of the cell culture, performing the following operations:
  • intracellular flows at the time of the future using the predicted extracellular fluxes of the extracellular metabolites and the stoichiometric equations of the metabolic model.
  • embodiments of the method according to the invention make it possible to control the quality of the prediction or the intermediate results obtained during this prediction during the cultivation of the cell culture.
  • the method described above involves the prediction of extracellular fluxes. These are easily determinable from the change in concentration of extracellular metabolites during fermentation by repeated sampling and analysis of culture medium samples. Thus, during the fermentation a
  • the method can be advantageous for the following reasons:
  • the metabolism of the cells depends essentially on the conditions in the bioreactor. If factors such as pH value, pO 2 value, pressure and temperature are kept constant, it is above all the metabolite concentrations in the reaction medium that are decisive for the behavior of the cells, which is why they were selected as inputs for the MLP.
  • the prediction of future metabolite fluxes has increased compared to Prediction of future metabolite concentrations to the next
  • Invention thus predicts the metabolic state of a cell culture in real time, since the information basis on which the prediction is based exists in the already defined metabolic model, the already trained trained MLP as well as measurement data, whereby the measurement data can easily be collected in real time.
  • the measurement data can be obtained, which is used to carry out the
  • the method for predicting the metabolic state of a cell culture at a future time in real time is continuously performed during the operation of a bioreactor containing the cell culture.
  • the method further comprises generating the MLP by machine learning.
  • Machine learning algorithms are achieved a high accuracy of the prediction on the other hand an adaptation to the metabolic conditions in other cell types very easily and without much manual effort or
  • the training data set is generated so that at each of a plurality of training times during the
  • Extracellular metabolites are trained as output parameter value.
  • the first relevance score is calculated as the partial mutual information score (PMI score) between a metabolite of the first amount and the single metabolite whose extracellular flow is to be predicted.
  • the second relevance score is calculated as the PMI score - between a metabolite of the first quantity and the single metabolite whose extracellular flux is to be predicted, taking into account all the metabolites already contained in the second quantity.
  • Metabolite concentration profile of a particular extracellular metabolite with the metabolite concentration profile of the metabolite whose extracellular flow serves as the output parameter value of the MLP ie: its extracellular flux is to be predicted.
  • the predictive relevance can be determined by various methods, e.g. Principal component analysis or PMI ("partial mutual information") as will be described below for embodiments of the invention.
  • Input parameters (extracellular metabolites whose concentration is measured and entered into the MLP) are completely independent of the MLP.
  • the selection process is performed in the form of a "wrapper" as described in Chapter 4.7.3 of the Appendix, eg as a functionality provided by the neural network.
  • Metabolites whose extracellular flux is to be predicted in each case are Metabolites whose extracellular flux is to be predicted in each case.
  • the calculated intracellular flows are checked for plausibility and consistency and / or with regard to further quality criteria and if necessary by supplementing, removing or changing
  • the calculation of the intracellular flow of one or more intracellular metabolites at each of the future times involves a calculation of multiple or preferably all intracellular flows of the metabolic model.
  • all intracellular flows of the model are calculated. The more intracellular flows are taken into account, e.g. in a plausibility assessment, the reliability of the prediction increases.
  • the invention in another aspect, relates to a method for monitoring and / or controlling a bioreactor that includes the cell culture of cells of a particular cell type.
  • the method includes a calculation
  • the method may serve to control the bioreactor and include sending a control command to the bioreactor.
  • the control command is sent to automatically initiate steps that change the state of the bioreactor or the media it contains, so that the
  • the method for monitoring and / or controlling a bioreactor includes identifying that reaction within the metabolic model of the cells which acts as a limiting factor for cell growth or production of a desired biomolecule according to the embodiments described herein and examples of the method of predicting the bioreactor metabolic state and multiple intracellular flows of cells. The method further comprises automatically adding selectively to those
  • a strong uptake and metabolism of amino acids does not necessarily mean that the cells also use the amino acids to synthesize the desired target protein.
  • the ingested amino acids are metabolized for completely different purposes.
  • this can be achieved by a metabolic flux analysis based on a specific metabolic model of the cell using the predictions of MLP.
  • Method includes:
  • Bioreactors with the different cell clones are determined whether the intracellular fluxes indicate that the gene encoding the target protein was incorporated into the genome of the cell and the target protein in the cell in
  • Embodiments described procedures was observed, so that the predicted flows were not consistent with the measurable flows but rather had a systematic error. It was observed that this error correlated with the LDFI concentration in the medium, which is an indicator of the presence of lysed cells in the medium. This enzyme is not released by an intact cell into the medium. The detection of LDFI in the reaction medium thus indicates destroyed cells.
  • the LDFI-corrected cell density value is used as the output variable. Accordingly, according to embodiments of the invention, cell densities measured by measured LDFI are used in training the MLP for extracellular flux calculation
  • This method includes:
  • the measurements comprising concentrations of several extracellular metabolites of the metabolic model in the culture medium of the cell culture and a measured cell density of the cells in the cell culture;
  • Control computer for one or more bioreactors act or on
  • intracellular metabolite is meant herein a metabolite of which is known or believed to be within cells of the particular cell type, e.g. because it is taken from the medium of cell culture or is produced by the cells.
  • a flow is therefore also referred to as "reaction rate” or "transport rate”. If several processes run concurrently in the given volume, it is possible that, due to the opposition of some processes, there is no net change in a substance concentration. , eg by substance conversion, uptake or delivery.
  • the volume to which a flow statement relates is the volume of a cell in the case of intracellular as well as extracellular metabolites.
  • Intracellular flows are formulated in the metabolic model for stoichiometric equations which specify a reversible or irreversible response of the above categories (intracellular transport between cell compartments, metabolism).
  • Embodiments of Invention are based on the assumption that the sum of the input and output flows on an intracellular metabolite (the concentration of which is unknown) is identical and therefore its concentration does not change. This is not correct, depending on the mode of operation of the reactor, but at least
  • the use of metabolic models with a large number of stoichiometric equations in MFA can also be used to monitor cell cultures and, in the event of a deviation of one or more metabolite flows from a setpoint range, specific changes in the amount and / or or composition of
  • the model is an MFA model that is suitable for performing a metabolic flux analysis and is based on the so-called steady-state assumption, according to which the amount of substance intracellular
  • Process duration is limited by the consumption of the substrates.
  • Reaction volume during the fermentation is not. With a suitable choice of the volume flows, a steady state equilibrium is established in the reactor in which the cell density and the nutrient concentrations remain constant.
  • a "split-batch bioreactor” is a batch or fed-batch type bioreactor operated to charge the reactor one or more times a substantial portion of its medium, e.g. over 10% or over 30% for the purpose of harvesting the cells contained therein.
  • the PMI is a measure of the dependence between a random input variable X and a random output variable Y, taking into account already selected inputs.
  • Various approaches for calculating the PMI of two variables are known, eg Sharma A (2000): “Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 - A Strategy for System predictor Identification ", Journal of Hydrology Vol. 239, Issues 1 - 4, 232-239.
  • a PMI criterion refers to a feature or characteristic that has been defined with respect to the PMI to make a decision.
  • a PMI criterion may be a limit whose overshoot or undershoot affects the course of a process.
  • FIG. 2 shows, by way of example, the process of obtaining information in several
  • FIG. 3 shows a block diagram of a system for predicting the
  • FIG. 7 shows a number of metabolite flows which successfully use the
  • Figure 13 shows input parameter values and output parameter values of an NN
  • FIG. 14 shows a histogram of the obtained RMSE for intracellular flows in FIG.
  • Metabolite flux and two for identical cell clones in two different bioreactors measured extracellular Metabolitflüssen;
  • FIG. 17 shows eleven plots with two curves each, all obtained for a fed batch bioreactor by means of two different calculation methods;
  • FIG. 18 shows eleven plots with two curves each of calculated extracellular flows of a cell clone ZK1;
  • FIG. 19 shows eleven plots with two curves each of calculated extracellular flows of a cell clone ZK2;
  • the reactions can then be in a stoichiometric matrix AE hold on, in which the
  • the system (2.4) is underdetermined, if applicable.
  • equation (2.6) is only for specific ones
  • the second method differs from the first one in that its solution satisfies the steady-state assumption (the measured flows are then called "balanced"). For this, the values of the measurable flows in the solution only approximately coincide with the actually measured values. This can certainly be considered useful if the steady-state assumption is given a higher reliability than the measured values for the flows, which are always faulty. In the solution of (2.8), all those measured flows that can be balanced are adjusted. The unbalanced, however, remain unchanged. A generalization of the just described method for the adaptation of balanceable flows results from a more statistically motivated one
  • v m be again the vector of the measured, error-prone flows and let v m be the vector of the corresponding true values.
  • the measurement error vector S denotes the difference between the true and the measured values:
  • Table 1 Comparison vectors and associated error quantities for three different error sources.
  • the metabolic model created according to embodiments of the invention comprises a network which should comprise the central intracellular material flows and nevertheless should have as little complexity as possible.
  • the model exemplified here is based essentially on the
  • the biomass balance was taken from the above publication by Nolan (2011), as well as the conversion of the living and total cell density into the unit mol / l.
  • the formulation of the stoichiometry for product formation follows from the amino acid composition of the target protein. Also regarding the
  • the network model contained in the metabolic model should encompass the central intracellular material fluxes and still minimize it
  • NAD (P) H and ATP were not included in the formulation of stoichiometry.
  • some metabolic branches have not been considered in detail but have been integrated into biomass formation (e.g.
  • the stoichiometric matrix A was formulated.
  • the columns which correspond to the known (in this case extracellular) material fluxes have been combined to form the sub-matrix A m , the remainder to the sub-matrix A u .
  • a metabolic model 402 of CHO cells was provided as shown in FIG.
  • the metabolic model involves a variety of intracellular 410 and 408 extracellular fluxes, using the metabolic model specifies at least a stoichiometric relationship between an intracellular 406 and an extracellular 404 metabolite.
  • the following steps 106-112 are performed for a plurality of times during the culture of a cell culture in a bioreactor.
  • a profile of actually measured extracellular material flows and for the next time (after expiration of an interval of defined length, for example 24 h) predicted extracellular flows can be created.
  • the deviation of these two profiles from each other indicates the quality of the prediction.
  • Step 106 Receiving Measured Values
  • a sample is taken at several times during the culture of a cell culture in a bioreactor 208 of this cell culture and automatically or manually transferred to one or more analyzers 250, as shown in Figure 2.
  • the analyzer may be a system of one or more analyzers, for example, a Thomassch or an optical counting station for determining the cell density.
  • a high performance liquid chromatograph or other suitable method known in the art may be used.
  • the sampling can be done for example in an interval of 24 hours.
  • the measurement data thus obtained are transmitted to a data processing system 252.
  • the data processing system 252 may be a computer that monitors and / or controls one or more bioreactors as a control unit.
  • At least some of the measured values are also determined by way of corresponding sensors of the bioreactor 208 themselves and transmitted to a data processing system 252.
  • Step 108 Enter the Measured Values in a Trained MLP
  • the data processing system 252 includes an MLP, for example, a neural network (NN) or a cooperative system of multiple neural networks, which has been trained to provide one or more extracellular responses based on input parameter values measured at a particular time (in particular, extracellular metabolite and cell density) Rivers of the metabolic model 254 predicts or estimates.
  • the data processing system 252 may include program logic that automatically transfers the measurement data obtained at a time to an MLP trained on test data sets obtained from cell cultures of the same type of cell that also includes the cells of the cell culture metabolic state at the future time (for example, next day) should be predicted.
  • Step 110 Predict future intake rates and delivery rates through the MLP
  • feeds are made during operation of the bioreactor, they should preferably be in and taken into account
  • Step 112 Perform an MFA
  • this predictive step can also be described as a prediction of a hybrid model relationship.
  • the coupling of the results of the predictions of the MLP with the information of the metabolic model in the course of the substance flow analysis can for example be implemented as follows:
  • Equation 4.5 in the appendix, which will be explained below, only to be understood as a rough approximation to the actual covariance matrix.
  • the covariance matrix is chosen to be a diagonal matrix for the purpose of predicting future intracellular flows since the different metabolite flows are estimated over separate networks and therefore the errors can be considered largely independent of each other.
  • embodiments of the invention for descriptive metabolic flux analysis of the current metabolic state of a cell use a covariance matrix in which the diagonal entries do not
  • Covariance matrix was formulated as a diagonal matrix and has the structure:
  • Equation 2.10 of the appendix refers to the descriptive MFA in which, according to the
  • an error analysis of the model based e.g. on a Gaussian error propagation as described in Section 4.7.8 of the Appendix.
  • these bioreactors include one or more fed-batch reactors and one or more other bioreactors from other types of reactors.
  • Inoculum concentration were chosen identically for each bioreactor, but different modes of operation were used:
  • Glucose concentrations in the medium which were adjusted by different strategies in the feeding.
  • the Fed-batch approaches will often be numbered. According to this numbering, in the first two approaches a short full glucose limitation occurred in the second half of the process before the bolus additions were made. The third and fourth approaches experienced the same limitation, but the subsequent boluses adjusted higher glucose concentrations. In the fifth and sixth approach there was always a positive minimum concentration of glucose.
  • a neural network was created which was to estimate the mean flows of the extracellular metabolites between the current and the next sampling time from the current state in the bioreactor (so-called one-step prognosis). It was for some embodiments for each
  • the extracellular metabolites named in the metabolic model are ranked according to their relevance for the prediction of the respective flow.
  • extracellular metabolites with redundant information content were not considered here.
  • Network 3 The third network training data set included the data from three of the fed-batch fermentations and the test data set the data from the remaining three fed-batch fermentations. f) One-step prediction of extracellular metabolite concentrations
  • the goal is to create a trained MLP that is as accurate as possible
  • the extracellular flow v of a component at time t is the amount of substance taken up by a cell per time.
  • the concentration of the substance in the feed is constant over time or is assumed to be approximately constant.
  • both the concentrations of extracellular metabolites in the previous sampling and the computationally based on the extracellular flows calculated since the last sampling are known and can be transferred together as reference value sets to the MLP to be trained, the As a result, based on the concentrations of the extracellular metabolites measured for the last sampling, the calculated extracellular body learns to predict that the smallest possible deviation from the calculated extracellular fluxes exists.
  • the trained MLP or trained neural network can now be stored and for one-step predictions of extracellular metabolite concentrations at any chosen future time , for example, next
  • the idea of training the neural network is based on the fact that the concentrations of extracellular metabolites are easily measurable and from which, at least in retrospect, extracellular flows can be empirically determined. By using the measured at a certain time
  • Extracellular metabolite concentrations as input parameter values and extracellular flows as calculated over the time interval between this current time point and a future time point based on the extracellular metabolite concentration difference as output parameter values can be used to train a neural network or in other machine-learning algorithms. That he can at least predict the extracellular fluxes for one future time.
  • the determination of the cell density allows a transfer of the total concentration difference in the medium to the individual cells of the cell culture contained in the medium.
  • Fermentation approaches a similar, approximately linear relationship between the LDFI concentration and the cell density difference to exist.
  • a measured total cell density is recorded continulatively in a fermenter and displayed in a plot.
  • the cell density is calculated.
  • the prediction may be calculated, for example, by a trained MLP trained according to embodiments of the invention, the measured cell concentration being another
  • a first temporal profile of the measured cell density of a cell culture of a particular cell type is determined empirically and also a second temporal profile of one based on the metabolic model and the extracellular
  • the discrepancy between measured and predicted cell density is referred to below as the "density discrepancy profile" and can optionally also be plotted in the plot.
  • the corrected cell density is therefore the sum of the measured cell density and that based on the measured LDH concentration by means of the linear function
  • "Snapshot" image 254 of the metabolic state of a cell can be output and / or stored via a graphical user interface.
  • the data processing system 252 is additionally configured to extracellular flow based on a plot of the measured extracellular metabolite concentrations for the future time interval (ie the time interval from the current time to the next time for which a prediction of the metabolic state is to be made) to be calculated and passed to the MLP as the output parameter value during the training.
  • the system 200 may be a data processing system
  • Example valves, pumps, metering units for boluses, stirrers, etc. which are coupled to the system and may receive and execute control commands from the system if necessary.
  • the system 200 includes one or more processors 202 and a first interface 210 for receiving measurement data from the one or more
  • the interface 210 may serve as a direct interface to the
  • Bioreactors or as an interface to analyzers in which samples are analyzed by the bioreactors, or a graphical user interface that allows a user to manually or otherwise input the obtained measurement data.
  • the system 201 includes or is coupled to volatile or nonvolatile storage media 212.
  • the storage medium may be, for example, memory, hard disk, or storage of a cloud service or a network storage or combinations of the aforementioned types of storage.
  • the storage medium includes a metabolic model 214 of the cells held and propagated in the bioreactors, for example a model as depicted in FIG.
  • the storage medium comprises a trained MLP 218, for example a trained neural network, which is designed to predict one or more extracellular flows at a future time, based on the measured concentrations of several extracellular metabolites received at a particular time.
  • a trained MLP 218 for example a trained neural network, which is designed to predict one or more extracellular flows at a future time, based on the measured concentrations of several extracellular metabolites received at a particular time.
  • the storage medium includes program logic 220 configured to pass the received measurements to the MLP 218 to perform a prediction of extracellular flows.
  • the program logic is designed to perform in real time a metabolic flow analysis (MFA) based on the metabolic model 214 and the predicted extracellular fluxes for future intracellular fluxes
  • MFA metabolic flow analysis
  • the program logic 220 may be in any one of
  • Programming language implemented, eg C ++, Java, Matlab or in form several program modules in different or the same programming language that are interoperable with each other.
  • Reference value ranges include. These reference ranges indicate acceptable or desirable intracellular flows of various intracellular metabolites.
  • the program logic 220 can determine whether the cells in one or more of the bioreactors are heading for an undesirable metabolic state, and optionally
  • a warning can be sent to a user via a user interface 224, for example a display device, for example an LCD display.
  • Display device the user can be informed about the predicted extracellular and intracellular flows and also about any predicted deviations of these rivers from desirable reference areas.
  • FIG. 5 shows the calculation of intracellular flows at several
  • the upper plot 502 shows a profile of the concentration of an extracellular metabolite which was determined at six measurement points (one measurement per day).
  • the middle plot 504 shows that a trained MLP predicts one or more extracellular flows from these measurements for a future time, respectively.
  • a comparison of the positions of the points in the upper and middle plots shows that the points in time of the collection of the measured data and the times at which the extracellular rivers were predicted in each case were about half a day
  • FIG. 6 shows various flows depicted in the metabolic model of FIG. The flows were based on the measured change in metabolite concentrations over a time interval and the measured
  • FIG. 7 illustrates the successful use of the method for generating biological findings.
  • the plot of the glucose flow top left shows that in the profile of the glucose flow at about 0.65 the glucose flow almost stops. Effects of this lack of glucose can be observed with alanine, series and glycine: alanine becomes larger rates in the case of the limitations
  • Glucose flow compared to the glucose concentration in the medium shows, even in product formation. Obviously, there is a very close relationship between these rivers. At the end of the fermentation, the amount of product was highest in those bioreactors without glucose limitation. The availability of glucose seems to be essential for effective product formation, a deficiency should be strictly avoided.
  • FIG. 8 illustrates another successful use of the method for
  • Laktatshift refers to the often observed in the cultivation of cell cultures effect that the lactate flow changes the sign of the positive to the negative.
  • Mulukutla et al. postulate that the lactate shift is the result of regulatory mechanisms that are due to the increasing Lactatinhibition be set in motion.
  • This biological hypothesis was checked by determining the lactate flow in several bioreactors over a number of measurements by means of the method according to the invention and for this the extracellular lactate concentrations were also measured. The corresponding results are shown in FIG. 9 for four bioreactors.
  • FIG. 9 shows plots with lactate fluxes and glutamine concentrations of four bioreactors of different types, which compares the courses of
  • Glutamine concentration and lactate flux allowed in a given cell culture The glutamine concentrations are characterized by solid lines and the lactate flows by dotted lines.
  • the lactate fluxes and concentrations of the individual rivers and metabolites were previously normalized by the
  • the arrows each indicate a reversal of the sign of the lactate flow.
  • Equation (2.10) of the appendix was used to calculate the extracellular, balanced flows that were intracellular via equation (2.5) of the
  • Figure 11 shows snapshots of intracellular and extracellular flows at various time points during the culture of a cell culture of CFIO cells.
  • phase I in the division shown in Fig. 11A
  • glucose is still present in excess, is rapidly transported into the cells and enters the Krebs cycle, and glutamine is also taken up Conversion to glutamate and further along with pyruvate via river v_8 in metabolites of the citrate cycle and in alanine Lactate and ammonia are released in larger quantities into the medium.
  • Lactate concentration in the medium is very low, so even the intake
  • phase IV glucose is added in a bolus manner. Therefore, there is again an increased intake, the flow through the Citrate cycle is particularly strong. All other reactions are extremely reduced.
  • Figure 13 shows some of the extracellular metabolites whose concentrations are used as input to predict extracellular flows.
  • Biomass which is preferably specified in the form of a cell density, and "TZD" the value corrected by the LDH concentration.
  • the table contains the results of the cross-validation, in which the number of input variables, which was determined by hidden variables H and the iteration number.
  • the input metabolites 1504 their concentrations as
  • Input parameter values entering the neural network are highlighted in yellow.
  • the data from several (for example 3) fed-batch fermentations as well as those from a batch batch served as a training data set.
  • the data obtained from other fermenters of the same cell type formed the test data set.
  • cross validations can be performed with other training / test record splits.
  • the selected metabolite may not be the one that, mechanistically, actually has an impact on the material flow, but is only strongly correlated with it. In fact, it could be observed that for a sample white change in the data selection used to calculate the PMI values, some arrangements were different. In some cases, for example, the position of glutamate and glutamine were exchanged, which are closely linked via the metabolism. However, today's selection based on biological intuition, as has been done in some publications, has sometimes yielded much worse predictions.
  • the core density estimator used is based on city block function, for the calculation of
  • Standardization had e.g. all input variables and the output variable the mean 0 and the standard deviation 0.5. This has distorting effects on the relevance of the sizes due to different
  • Test method using a trained MLP performed on the currently obtained measurements.
  • the PMI for the parameter X e.g. a particular extracellular metabolite
  • the parameter Y e.g. another extracellular metabolite
  • g is the density function of the marginal or common distributions.
  • the residuals contain only the information of X and Y that are not yet contained in U. The larger the value for G, the stronger the dependency.
  • kernel density estimators which also use information from the N samples. These estimators provide - in simplified terms - a continuous density function, the in its course similar to the histogram of the samples. It arises from a weighted superposition of N core functions.
  • the estimate is the density of a q-dimensional
  • the common density distribution of two random vectors X and Y can be determined using a product kernel estimator
  • the conditional expected value must generally be used for two random vectors X and U are estimated.
  • the Nadaraya-Watson estimator can be used for this. This is based on the previously applied principles and can be derived as follows:
  • the selection is made on the principle of a wrapper: For each output variable, 70 networks with the 1 to 7 (according to PMI) most relevant inputs and with 1 to 10 hidden neurons were trained in initially 1000 iterations. For this purpose, a training data set was formed from a part of the entire training data set, which consists of the 8 monitored training fermentations. The remaining data served as test data. At each training, the value of the test error was calculated over the number of iterations recorded and its minimum value, and the associated iteration number were determined. Subsequently, the comparison of the 70 networks based on the total minimum test error. This resulted in the determination of the combination of the input variables for the estimation of the respective metabolite flow, as well as the corresponding iteration number and the number of hidden neurons.
  • extracellular metabolites whose concentration is to be used as input parameter values for training or feeding the trained MLP ("input metabolites"), according to purely statistical criteria, individually for each output metabolite, ie individually for each extracellular flow; should be predicted.
  • Sorting step certain "relevance" or predictive power is not yet dependent on the metabolites in the second set.
  • the Predictive power of the "x" most relevant input metabolites determined by a test data set, x is varied, and then the number x is selected with the best predictive power.
  • the input metabolite within the metabolite remaining in the first set is repeatedly identified, the has the greatest predictive power with respect to the flow of a given output metabolite, taking into account the content of the second set. If the concentration profile of the metabolite with the highest predictive relevance within the remaining members of the first set correlates strongly with a metabolite already contained in the second set, this metabolite is usually not converted to the second set, as its predictive power is high However, its concentration profile does not provide any significant contribution over that of a metabolite already in the second set.
  • Concentration profiles of metabolites of the second amount leads, transferred from the first to the second amount.
  • Transfection was used to generate the clones, the clones due to different insertion and / or different copy number of the integrated DNA sequences metabolic differences.
  • Ten Fed batches were used to train the MLP (here: a neural network model), two Fed batches to test the model. These two Fed batches were used for the RMSE calculations.
  • the intracellular fluxes RMSE are calculated from a difference between the intracellular fluxes predicted by a combination of MLP and MFA and intracellular fluxes, which can be calculated from measured extracellular fluxes. RMSE is never negative, a value of 0 (almost never achieved in practice) would indicate a perfect fit of the predicted to the measured data. In general, a lower RMSE is better than a higher one. RMSE is the square root of the average squared error.
  • the same type of medium and nutrient solutions were used for the 12 fed-batch reactors to make the bispecific antibody.
  • the medium and the nutrient solutions differed from the medium and the nutrient solutions for the bioreactors or cell cultures whose metabolic means.
  • Footprint is shown in Figure 5.9 of the appendix.
  • the MLP here a neural network (NN) has been recalibrated to the new record, i. the NN, which has already been trained once on data obtained from the bioreactors shown in Figure 5.9 of the Appendix, was "retrained” or retrained for data from the 12 fed-batch reactors for production of the bispecific antibody.
  • the models were made in Python with the help of
  • Hyperparameters that best fit the data were stored and used as a "re-trained" NN for future predictions of extracellular and intracellular flows of cell cultures in the 12 bioreactors.
  • FIG. 15 shows a histogram of the obtained RMSE for extracellular flows obtained for the 12 fed-batch fermentation runs mentioned in FIG. All RMSE values are normalized to the error obtained for the glucose metabolite. RMSE values for extracellular flows are calculated from a difference between measured external flows and extracellular fluxes predicted by a combination of MLP, especially a neural network, and MFA.
  • Figure 16 shows a comparison of the predicted extracellular metabolite fluxes (black line) with two for two identical cell clones in different
  • FIG. 17 shows several plots with two curves each, all for a fed batch
  • Descriptive MFA were obtained by using measured concentrations of extracellular metabolites as input to metabolic flow analysis (MFA) to calculate various extracellular flows, each corresponding to one of the 11 plots.
  • the solid line curves ( ⁇ "NN-MFA ⁇ ") were obtained by using extracellular metabolite concentrations measured at a given time t0 as input to an MLP (eg, NN) to predict extracellular flows at a future time t1 future predicted extracellular fluxes were used as input for metabolic flux analysis (MFA).
  • MLP eg, NN
  • a comparison of the two curves of each of the 11 plots thus shows that the values predicted using MLP + MFA for a future date are very high
  • FIG. 18 shows several plots with two curves each, all for a fed batch
  • Bioreactor with a first cell clone ZK1 were obtained for the preparation of a bispecific antibody.
  • the dotted line curves (“NN + Descriptive Extracellular MFA") were obtained by using extracellular metabolite fluxes measured at a given time tO as input to an MLP (eg, NN) to detect extracellular flows on one predict future time t1, and these future predicted extracellular fluxes were used as input to metabolic flux analysis (MFA).
  • MLP metabolic flux analysis
  • Extracellular flows of extracellular metabolites were used as input to metabolic flow analysis (MFA) to calculate various extracellular flows, each corresponding to one of the plots shown in FIG.
  • MFA metabolic flow analysis
  • the extracellular fluxes were normalized for each plot and for each day in terms of the concentration of glucose.
  • current extracellular flows which were calculated on the basis of the metabolite concentrations measured currently and at a past point in time, were used as MLP input.
  • a metabolite concentration in the broader sense was used as input. It was observed that alternatively, when the measured metabolite concentrations were used as input, the predictive results of the MLP were ultimately substantially identical.
  • the measured values differed a little from the predicted fluxes.
  • the scaling is due to the glucose normalization corresponds to a very high "dissolution” and the deviations were rather small considering the total amount of the metabolite.
  • a certain tendency of the overfitting data was observed, which is usually remediable by increasing the data set.
  • FIG. 19 shows several plots with two curves each, all for a different fed batch bioreactor BR2 with a second cell clone ZK2 for locating the
  • the clones may have been generated, for example, by a method that does not fully control the position of integration of a new DNA sequence segment into the genome of the cells and / or the number the integrated sequence sections offers.
  • the starting lines used are genetically identical, in the course of integration of new genes or DNA sequences (e.g., in the case of random integration in the course of transfection), the
  • FIGS. 18 and 19 each show, for a particular cell clone ZK1, ZK2, that the combination of a neural network NN and a metabolic one
  • FIG. 20 shows several plots with two curves each, all of which have been obtained for the bioreactor BR1 with the cell clone ZK1 and which each represent calculated intracellular flows.
  • FIG. 20 thus corresponds to FIG. 18 with the difference that the calculated intracellular instead of the calculated extracellular flows are shown.
  • FIG. 21 shows several plots with two curves each, all of which have been obtained for the bioreactor BR2 with the cell clone ZK2 and which each represent calculated intracellular flows.
  • FIG. 21 thus corresponds to FIG. 19 with the difference that the calculated intracellular instead of the calculated extracellular flows are shown.
  • the totality of the plots in FIGS. 19 and 21 thus constitutes one

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Abstract

L'invention concerne un procédé permettant de prédire l'état métabolique d'une culture de cellules d'un type cellulaire particulier. Il comprend la fourniture (102) d'un modèle métabolique (402) d'une cellule du type de cellule particulier et la réalisation des étapes suivantes à chaque instant d'une pluralité d'instants au cours de la culture cellulaire ; la réception (106) de concentrations mesurées de plusieurs métabolites extracellulaires et d'une densité cellulaire mesurée dans le milieu de culture ; l'entrée (108) des valeurs de mesure reçues comme valeurs de paramètre d'entrée dans une logique de programme d'apprentissage automatique MLP (218) entraînée ; la prédiction (110) de flux extracellulaires (408) des métabolites extracellulaires à un instant ultérieur par le MLP ; la réalisation (112) d'une analyse de flux métaboliques pour calculer les flux intracellulaires à l'instant ultérieur sur la base des flux extracellulaires prédits et des équations stœchiométriques du modèle métabolique.
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