Method in biological process system

Abstract

The present invention relates to a method of estimating the performance of a biological process material when used in a biological process system. The bioprocess material includes at least two components each having a data property. The method comprises: i) obtaining data properties for producing at least two components of the bioprocess material; ii) defining a protocol for processing at least two components; iii) processing the at least two components according to the defined process parameters to obtain at least one product; iv) measuring a data property of each product; v) calculating a data property of each product based on the measured data property of each product and / or the data properties from at least two components; and vi) if the product is a bioprocess material, processing the measured data properties and the calculated data properties to estimate the impact of the bioprocess material on the target product in the bioprocess system; or (vii) if the product is not a bioprocess material, the product is considered as an intermediate material, and steps iii)-vi) are repeated with each product as one of at least two components.

Description

Technical Field

[0001] The present invention relates to methods in bioprocess systems (such as chromatography systems and cell culture systems). Background Art

[0002] Bioprocess systems (such as chromatography systems and cell culture systems) base their processes on raw materials that are typically provided by an external provider. Examples of raw materials are chromatography resins and cell culture media. The quality of the raw materials will affect the bioprocess, and an important part of all manufacturing investigations is related to raw material variability.

[0003] In addition, when preparing columns for a chromatography system, materials from different lots (although within specifications) can be mixed, and thus the performance of the resin in the column will be different compared to when using materials from a single supplier lot.

[0004] Variations in performance are undesirable because the primary goal in bioprocess manufacturing is to keep the manufacturing process under control.

[0005] Accordingly, there is a need for processes to introduce that reduce the impact of variations in raw material characteristics. Summary of the Invention

[0006] The object of the present disclosure is to provide methods and devices configured to perform methods and computer programs that seek to alleviate, mitigate or eliminate one or more of the above-mentioned deficiencies and drawbacks in the art, either individually or in any combination.

[0007] This object is achieved by a method for estimating the performance of a bioprocess material when used in a biological system. The bioprocess material includes at least two ingredients each having data properties. The method includes: obtaining the data properties of at least two ingredients used to produce the bioprocess material; defining a procedure for processing the at least two ingredients; processing the at least two ingredients according to the defined process parameters to obtain at least one product; measuring the data properties of each product; calculating the data properties of each product based on the measured data properties of each product and / or the data properties from the at least two ingredients; and if the product is a bioprocess material, processing the measured data properties and the calculated data properties to estimate the impact of the bioprocess material on the target product in the bioprocess system; or if the product is not a bioprocess material, treating the product as an intermediate material and repeating the following steps: processing the at least two ingredients; measuring and calculating the data properties of each product; and determining whether the product is a bioprocess material.

[0008] Advantages include the recognition of immeasurable characteristics of biological process materials, which can improve product quality and / or process performance when used in a biological process system.

[0009] Additional objectives and advantages can be obtained by those skilled in the art from the detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 Illustrates a biological process system suitable for implementing the disclosed method.

[0011] Figure 2 Illustrates a process for generating a feedstock from components.

[0012] Figure 3 Illustrates unit operations used to illustrate a general biological process flow.

[0013] Figure 4 Illustrates based on Figure 3 the unit operations described in

[0014] Figure 5a and Figure 5b Illustrates an example of the connection between feedstock generation and data properties.

[0015] Figure 6 Illustrates the effect of controlling process parameters based on the performance of the feedstock used in a biological process system.

[0016] Figure 7 Illustrates in-process mixing between batches.

[0017] Figure 8 Is a flowchart illustrating a process for estimating the performance in materials expected to be used in a biological process.

[0018] Figure 9 Is a flowchart illustrating a process for adapting process parameters based on changes in process parameters. DETAILED DESCRIPTION

[0019] Definitions of some terms used in this description When producing raw materials (also known as bioprocess materials) for bioprocesses · Component - is a material used in manufacturing a feedstock · Substance - a component can include one or more substances · Intermediate material - includes processed / treated components but is not the final product. Intermediate products do not always exist as the feedstock can be produced directly from the components · Feedstock - includes processed intermediate materials and is the final product used in a biological process.

[0020] When using raw materials in bioprocesses · Raw materials - are the bioprocess materials used in the bioprocess · Process intermediates - the materials produced after each unit operation (as described in more detail below) · Target product - the product in the bioprocess.

[0021] The bioprocess can include several different steps, such as a bioreactor for growing cells and producing the target product, filtration for producing the feed material, chromatography for purifying the target product available in the feed material, etc. Some of these steps require different raw materials (such as feed media, chromatography resins, etc.) in order to be able to deliver the required output. Any change in the material properties can have an impact on the process and thus strict specification limits must be maintained within which the different parameters associated with the material can vary. Examples of such parameters are: - Particle size distribution - Concentration - Ratio between different concentrations of substances in the material - Functional properties Although the raw materials have their parameters within the specified intervals, the mixing of different batches / lots without considering the material properties of the mixture can cause an undesirable decline in product quality and process performance. The method of the present invention is particularly suitable for processes / systems in which at least two components or intermediate materials are different batches of the same material and in which these batches can be different from each other with respect to one or more data properties (such as morphological data properties).

[0022] Figure 1 FIG. illustrates a bioprocess system 10 suitable for implementing the disclosed method. System 10 in this example includes a bioprocess 19, which is controlled by a control unit or controller 11. The controller has access to a data storage device (illustrated by database 12 in this example), which can be arranged locally or implemented externally, for example, in a cloud implementation. The bioprocess requires raw materials 13, as illustrated by the arrows, in order to produce a product (intermediate or target product) 16 with feed 15. The feed 15 can be an intermediate from another bioprocess system (not shown). The controller 11 controls the process based on the data accessible from the database 12, which can include historical data from previous runs, recipes, production process descriptions, etc.

[0023] The raw material 13 also contains a detailed description of the data properties associated with the respective raw materials, as described in more detail below. The (measured and calculated) data properties are provided to the model generator 14, where the impact of each raw material on the product 16 is estimated. This information is used by the controller 11 to adapt the process parameters to ensure product quality and / or process performance.

[0024] The data properties associated with the ingredients, intermediates, and products can include morphological data properties, i.e., properties representing the physical structure. For particulate materials, this can be, for example, the particle size distribution (volume-weighted, number-weighted, overall distribution, distribution mode, distribution width, etc.), particle shape (shape factor, sphericity, etc.), the distribution of the substance within the particle (e.g., the distribution of magnetic material within magnetic adsorption beads, the distribution of high-density material within expanded bed absorption beads, etc.). For porous materials (including porous particles), the morphological data properties can, for example, represent the total porosity, pore size distribution, porous network structure, tortuosity, size exclusion chromatography data (accessible pore volume versus probe molecule size), etc.

[0025] The data properties can also include chemical composition data properties. These can be, for example, spectroscopic data, titration data, analytical chromatography data, elemental analysis data, amino acid composition data, etc.

[0026] A third type of data property is functional data properties. These data properties are derived from functional tests of the ingredients, intermediates, or products, and non-limiting examples of such data properties can be the pressure-flow performance of a packed bed column, the static or dynamic binding capacity of a chromatographic resin, the dissolution rate of a powder, the culture performance of a cell culture medium, etc. The functional data properties can be interrelated with the morphological and / or chemical composition data properties. Generally, the correlation with the morphological data properties is more complex (e.g., non-linear), thus emphasizing the need for the method of the present invention, especially when mixing different batches / lots of materials.

[0027] Optional features are the sensors 17, 18, which measure selected process parameters and are used to monitor the process.

[0028] An example of a bioprocess is continuous chromatography, which is designed for the purification of target products (e.g., proteins, biomolecules from cell culture / fermentation, natural extracts) in a continuous downstream process, for example, using periodic countercurrent chromatography. This technology uses three or four chromatographic columns to create continuous purification steps. The columns are switched between loading and non-loading steps (e.g., washing and elution). Continuous chromatography supports process intensification by reducing floor space and improving productivity. Additionally, continuous chromatography is particularly suitable for the purification of unstable molecules because the short process time helps ensure the stability of the target product.

[0029] Another example of a biological process is batch type chromatography with only one chromatographic column, where the column sequentially performs loading and non-loading steps (such as washing and elution).

[0030] Yet another example of a biological process is cell culture, where cells grow in a bioreactor under the influence of a cell culture medium provided as a raw material.

[0031] Figure 2 The figure shows a process for producing a raw material 20 using components 24 - 26 as a bill of materials. Components are typically characterized and delivered using certificates (Certificate of Analysis CofA) that disclose parameters associated with the component batch (such as substances and their concentrations, ratios between substances, particle size distribution, functional properties, etc.).

[0032] The raw material 20 is provided with data for use in a biological process control strategy, which is required when controlling a biological process, even if this data is not measured at the raw material level. At an intermediate level, potential critical material properties are measured, and at the component level, components 24 - 26 are characterized. However, the properties of intermediates 21 - 23 and components 24 - 26 cannot be propagated upwards without considering batch mixing. It should be emphasized that the CofA of the raw material 20 may not provide all the information necessary to fully characterize the material. Measurements and calculations of intermediate materials are required in order to be able to trace the causes of unexpected behavior of the raw material.

[0033] Figure 2 The figure also shows a method for manufacturing a biological process material 20 for a biological process, the biological process material comprising at least two components 24 - 26 each having data properties. The method includes: processing at least two components 24 - 26 using predefined process parameters to produce at least two intermediate materials 21 - 23; obtaining the data properties of each intermediate material; repeatedly processing at least two intermediate materials to obtain the biological process material 20; and identifying the data properties of components and intermediate materials related to the performance of the biological process material.

[0034] Figure 3 The figure shows a unit operation UO - 2 30 used to illustrate a general biological process flow 40. Intermediate I - 1 (the result of a previous step in a biological process system) is fed into UO - 2 together with a raw material R and a buffer / liquid. Process parameter P - 2 determines the operation of UO - 2 to deliver the resulting material PM - 2, which may be an intermediate or a target product of the biological process system. In addition, sensors of the UO monitor and measure process parameters to estimate the product quality of the intermediate and / or target product. Alternatively, the UO estimates process performance to optimize the process.

[0035] Data related to product quality (e.g., purity or host cell protein impurity levels) and / or process performance (e.g., yield) is provided to a controller from a UO or some other data source, and the controller can use this information to adjust process parameters in order to compensate for unexpected deviations in product quality and / or process performance. Quality attributes are measured on "in-process" materials. Each step corresponding to the functionality of the UO has its specific critical quality attribute CQA. CQAs are valuable for trend analysis. Performance attributes are typically yield, volume, etc.

[0036] Figure 4 The figure is based on combining Figure 3 the general biological process flow 40 of several connected unit operations 30 described above. This is a simplified illustration of a rather complex process model. A directed (and in most cases non-cyclic) process flow is shown, which in special cases can include a loop path (as illustrated by the dashed arrow 41 between UO-3 and UO-2). For the purpose of analysis, the output of the previous step (upstream step) is considered as the input to the downstream step, as described in combination with Figure 3 above.

[0037] UO-1 has no inputs and is, for example, a working cell bank (vial). Inoculates is the output from UO-1 and is introduced into UO-2 together with the raw material R-X. UO-2 can be a seed bioreactor and / or a production bioreactor depending on the purpose of the UO, and process parameters are provided as inputs to UO-2. The raw material R-X is also provided to UO-Y where the intermediate I-Y is produced. The intermediate I-Y and the output from UO-2 are provided to UO-3 where I-3 is produced. Data related to product quality and / or process performance is provided at each step, and a controller (not shown) receives data for controlling the process flow. The target product 42 is provided as the output from the last UO-n.

[0038] Figure 5a and Figure 5b An example illustrating the link between raw material generation and data nature is shown. In this example, ingredients from four different batches and two suppliers are provided. Batches G1 - G3 are from supplier 1, while batch G4 is from supplier 2. The relevant data properties are measured, i.e., R1...Rn etc. for batches G1...Gc, and the relevant data properties are calculated, i.e., cR1...cRm for batches G1...Gc. These attributes are inherited to the next level with intermediate materials (represented as batches M1 - M4). The mixing ratios between levels are indicated in Figure 5a the figure.

[0039] Some of the mixing ratios are 1.0 in this example, which means that no mixing of components into intermediate materials is performed. However, the components can undergo different processes, such as washing, sieving, grinding, dilution, etc., which change the properties of the materials compared to the properties of the components. Measure the relevant data properties, i.e., for B1...Bk, etc. of batches M1...Mb, and calculate the relevant data properties, i.e., cB1...cBk for batches M1...Mb.

[0040] The first batch of raw material (denoted as R1) in this example includes materials from two batches of intermediate materials M1 and M2, with a mixing ratio of 60% M1 and 40% M2, and both M1 and M2 have components from Supplier 1. The second batch of raw material (denoted as R2) includes 100% of batch M3 with components from Supplier 1, and the third batch of raw material (denoted as R3) includes 100% of batch M4 with components from Supplier 2.

[0041] Before using the raw materials in the bioreactor, the user mixes all three batches of raw materials (taking 30% of the first batch, 60% of the second batch, and 10% of the third batch) to obtain the correct amount of raw material for the bioreactor. Figure 5b The measured and calculated properties illustrated in help the end user to determine the properties of the mixed raw materials and to find the causes of potential unexpected behaviors in the biological process.

[0042] As mentioned before, this is not a direct averaging because it is a tree with performance parameters inherited during the processing steps. To illustrate this, an example of batch mixing of intermediate materials is provided.

[0043] Example 1 - Mixing batches of intermediate materials Assume there are two batches of bulk powder, BP1 and BP2, both of which include components A and B. However, the concentrations of the corresponding components are different in the bulk powder batches, as shown in Table 1 below: Bulk powder batch Concentration [A] Concentration [B] Ratio [A] / [B] BP1 20 100 0.2 BP2 40 50 0.8 Table 1 Mix the cell culture liquid medium CCM by using 300 kg of BP1 and 600 kg of BP2.

[0044] If only the concentration ratio [A] / [B] is used to calculate the ratio of [A] / [B] in the CCM, then based on the following calculation, the volume-weighted average will be 0.6: However, this is not the true ratio because knowledge of the actual concentrations in the different bulk powder batches will yield the true ratio of [A] / [B] in the CCM: Concentration of component A: Concentration of Component B: Ratio [A] / [B]: Accordingly, the present disclosure provides a genealogy traceability engine that includes mixing intermediate batches. It also provides the calculation of the properties of the batches mixed by the end user, such as in combination Figure 5a and Figure 5b as illustrated.

[0045] Example 2 - Chromatography Resin Another example relates to chromatography resin data, which illustrates the importance of providing inheritance data from components when manufacturing the raw materials (resins) of a chromatography system.

[0046] The resin in this example is Phenyl Sepharose TM 6FF HS, and is typically provided with an analytical certificate of analysis CofA that includes the following aspects: · Ligand density · Lysozyme retention · RNase A retention The resin is produced from one or more batches of a matrix (intermediate material) called Sepharose TM 6Fast Flow Base Matrix. For each matrix, relevant parameters are measured during manufacturing, such as: · Particle size distribution (PSD) · Porosity · Flow rate These relevant parameters are provided as inheritance properties for the resin. In addition, the matrix is made from the component agarose, which has physicochemical properties that are also inherited by the resin via the matrix.

[0047] This type of data property is crucial when investigating the root cause of production problems (such as yield variations), but can also be used to adjust process parameters to compensate for deviations from a predefined process (i.e., the standard process).

[0048] Figure 6The figure illustrates the effect of controlling process parameters based on the performance of the raw materials used in a bioprocess system. The controller monitors a specific process parameter 60 (e.g., the pressure drop across a chromatography column at a constant flow rate), which varies between a maximum value 61 and a minimum value 62. The pressure drop is monitored to detect signs of column degradation. An event 63 occurs, such as replacing the column resin with a new resin batch, and the process parameter is changed to a new level caused by differences in the resin particle size distribution and varies between a maximum value 64 and a minimum value 66. The controller analyzes the data properties of the new resin and adjusts the baseline for pressure monitoring to the new level to allow continuous monitoring.

[0049] Figure 7 The figure illustrates in-process processing (e.g., mixing) between batches, where inheriting data properties can be important in order to be able to use a controller 74 to adapt process parameters in subsequent steps while maintaining product quality and / or process performance. In this example, a capture step 70 in a chromatography system produces batches A 72 and B 73, which are mixed before a polishing step 71. In order to define the intermediate for further processing, the characteristics of the mixture must be determined. Some parameters are measured and calculated after mixing, and some parameters are inherited from batches A and B, similar to the content disclosed in combination Figure 5a and Figure 5b Even if batch A does not fall within a predetermined specification, it may be possible to mix it with other batches so that the mixture falls within the specification. This will reduce scrap and may also improve the yield of the system and thus improve production efficiency.

[0050] Exemplary Example Batch A has a host cell protein concentration of 2000 ng / mL and a target product concentration of 20 mg / mL, while batch B has a host cell protein concentration of 3000 ng / mL and a target product concentration of 10 mg / mL. Host cell protein levels are typically expressed relative to the target product concentration, here, for batch A it is 2000 / 20 = 100 ng / mg, and for batch B it is 3000 / 10 = 300 ng / mg. The subsequent unit operations are verified to purify material with a maximum host cell protein of 240 ng / mg, so batch B cannot be used as is. By mixing the two batches, it may be possible to qualify the material for further processing. If 25% of batch A and 75% of batch B are taken, the volume weighted average of the relative host cell protein concentration will be 250 ng / mL, which is calculated by 0.25*100 + 0.75*300. This value is above the acceptance limit for the subsequent step. However, this is not the correct value for a mixture that must be calculated by separately estimating the host cell protein concentration and the target product concentration according to the following formula before determining the relative host cell protein concentration: Host cell protein concentration = 0.25*2000 + 0.75*3000 = 2750 ng / mL Target product concentration = 0.25*20 + 0.75*10 = 12.5 mg / mL Relative host cell protein concentration = 2750 / 12.5 = 220 ng / mg, which qualifies the mixture for further processing.

[0051] Combined Figure 7 The above-described process can be described as a method for processing at least two intermediates (e.g., batch A 72 and batch B 73) resulting from previous process steps in a bioprocess system. The bioprocess system includes a controller 74 configured to control the process parameters of the bioprocess system, and the method includes the following plurality of steps: - Obtain the data properties of each of at least two intermediates from a previous process step, - Perform in-process processing (e.g., mixing) of at least two intermediates 72 and 73 to produce a resulting intermediate 76, - Obtain the data properties of the resulting intermediate 76 by measuring and calculating some parameters after processing and using the inherited data properties from intermediates 72 and 73, and estimate the performance of the resulting intermediate 76 when used in a subsequent process step 71, - Identify changes in the process parameters of the bioprocess system that indicate the quality of the target product and / or the process performance, which can be performed in the controller 74, and - Adapting the process parameters of the following process step 71 based on the data properties of each intermediate 72 and 73 from the previous process step 70 and the data properties of the generated intermediate 76 to compensate for changes in the process parameters.

[0052] According to some embodiments, at least two intermediates 72 and 73 are selected to be different batches produced in a previous process step 70 .

[0053] According to some embodiments, the bioprocess system is selected as a chromatography system and the preceding process step 70 is a capture step and the following step 71 is a polishing step.

[0054] Figure 8 is a flow chart illustrating a process of estimating a property in a material when used in a bioprocess. The bioprocess material includes at least two components each having a data property. According to some embodiments, the bioprocess material is a chromatography resin. According to another embodiment, the bioprocess material is a cell culture medium.

[0055] The process starts in step 80 and data properties of at least two components used to produce the bioprocess material are obtained in step 81. This may come from a CofA (Certificate of Analysis) provided by the manufacturer of the component, or the properties may be measured and calculated prior to use.

[0056] According to some embodiments, step 81 includes obtaining a particle size distribution for at least one component (e.g., each component). According to some embodiments, each component includes at least one substance, and the process further includes selecting the data properties of each component to include a batch number, a supplier of the component, and data characterizing the at least one substance. According to some embodiments, for a component having at least two substances, the process further includes selecting the data properties of each component to further include a ratio between the at least two substances.

[0057] Other data properties may include the molecular species representing the drug (eg, isoform).

[0058] In step 82, a protocol for producing bioprocess materials is defined. The protocol includes various steps and, in some cases, the production of intermediate materials that are used as ingredients in subsequent process steps, as described below in step 87.

[0059] According to some embodiments, the procedure includes any combination of the following: filtering; reacting; cooling; exciting; mixing; diluting; sieving; washing; grinding; and heating.

[0060] When defining the procedure, the process proceeds to step 83 where at least two components are processed according to the defined procedure to obtain at least one product. The product can be an intermediate material or a bioprocess material (also known as the user's feedstock). In step 84, the data properties of each product are measured, and in step 85, the data properties of each product are calculated based on the measured data properties of each product and / or the data properties from at least two components.

[0061] In some embodiments, step 84 includes obtaining at least data properties related to the particle size distribution and / or porosity and / or flow rate of each intermediate material.

[0062] In step 86, a determination is made regarding the status of the product produced. If the product is a bioprocess material, the process proceeds to step 88, and if the product is not a bioprocess material, the process proceeds to step 87.

[0063] In step 88, the measured data properties and the calculated data properties are processed to estimate the impact of the bioprocess material on the target product in the bioprocess system. According to some embodiments, step 88 further includes retrieving information on the manufacturing process of the target product in the bioprocess system and mapping the estimated performance of the bioprocess material onto the manufacturing process to estimate the impact of the bioprocess material on the target product.

[0064] In step 87, the product is considered an intermediate material, and steps 83 - 86 are repeated in the case where each product is one of at least two components.

[0065] Thus, the above method describes how to manufacture the feedstock (i.e., the bioprocess material) from at least two components. In some embodiments, the feedstock is produced via intermediate materials. The results from the measurement of data properties and the calculation of data properties are stored in a data storage device and are accessible to a control unit for use in controlling a bioprocess (such as a chromatography or cell culture process).

[0066] Figure 9 is a flowchart illustrating the process of adapting process parameters based on changes in process parameters in a bioprocess system (such as a chromatography system or a cell culture system). A controller configured to control the process parameters of a chromatography system includes a controller configured to control the process parameters of a bioprocess system.

[0067] The process starts at step 90, and in step 91, a model is generated based on the estimated performance of the bioprocess material obtained according to the process described in conjunction with Figure 8 which is accessible to the controller.

[0068] The process includes an optional step 92, in which purification of the target product is performed in a predefined process, and the system is further configured to measure parameter values before and / or after an operation in the bioprocess system. The optional step further includes identifying a deviation between the measured parameter values and the parameter values obtained in the predefined process, and adapting process parameters in step 98 to compensate for the identified deviation.

[0069] The process proceeds to step 93, in which changes in process parameters of the bioprocess system that indicate the quality and / or process performance of the target product are identified.

[0070] According to some embodiments, the bioprocess system further includes at least one sensor configured to measure parameter values, and step 93 further includes an additional step 95 of obtaining sensor readings to identify changes in process parameters. According to some embodiments, step 93 further includes an additional step 96 of monitoring bioprocess performance.

[0071] The process proceeds to step 94, in which process parameters of the bioprocess system are adapted based on a model to compensate for changes in process parameters.

[0072] According to some embodiments, the model is externally generated and / or generated in the controller.

[0073] For a chromatography system, the flowchart in Figure 9 may be described in the case where the bioprocess system is a chromatography system having at least one column, where the column material is suitable for purification of the target product from the feed, and the chromatography system further includes a controller configured to control the process parameters of the chromatography system, and where the method includes: generating, in step 91, a model accessible to the controller based on the estimated performance of the column material obtained according to the process described in Figure 8 ; identifying, in step 93, changes in process parameters of the chromatography system that indicate the quality and / or process performance of the target product; and adapting, in step 94, the process parameters of the chromatography system based on the model to compensate for changes in process parameters.

[0074] According to some embodiments, the column material is provided in batches (or lots), each batch / lot having a separate estimated performance. The method further includes adapting the model in step 97 based on the differences in estimated performance between batches / lots.

[0075] According to some embodiments, the chromatography system further includes at least one sensor configured to measure parameter values, and step 93 further includes obtaining sensor readings in step 95 to identify changes in process parameters. According to some embodiments, step 93 further includes monitoring the performance of at least one column in step 96.

[0076] According to some embodiments, the process includes an optional step 92, where purification of the target product is performed in a predefined process, and the system is further configured to measure parameter values before and / or after at least one column, and the process further includes identifying a deviation between the measured parameter values and the parameter values obtained in the predefined process, and adapting (step 98) process parameters to compensate for the identified deviation.

[0077] According to some embodiments, the column material is any of the following: chromatographic resin; membrane; nanofiber; monolith.

[0078] For a cell culture system, it can be described in the case where the bioprocess system is a cell culture system including a controller Figure 9 in the flowchart, the controller is configured to feed a cell culture medium and control process parameters of the cell culture system. The method includes: generating, in step 91, a model accessible to the controller based on an estimated performance of the cell culture medium obtained according to the process described in combination Figure 8 with; identifying, in step 93, a change in a process parameter of the cell culture system indicating the quality and / or process performance of the cell culture; and adapting, in step 94, the process parameters of the cell culture system based on the model to compensate for the change in the process parameter.

[0079] According to some embodiments, the cell culture medium is provided in batches (or lots), each batch / lot having a separate estimated performance, and the method further includes adapting, in step 97, the model based on a difference in the estimated performances between batches / lots.

[0080] According to some embodiments, the model is externally generated and / or generated in the controller.

[0081] The above method can be implemented according to a computer program for controlling process parameters in a bioprocess system (such as a chromatographic system or a cell culture system), the computer program including instructions that, when executed on at least one processor, cause the at least one processor to execute the method described in combination Figure 9 with. A computer-readable storage medium can carry a computer program for controlling process parameters in a bioprocess system.

Claims

1. A method in a chromatography system, the chromatography system having at least one column, the at least one column having column material adapted to purify a target product from a feed, the chromatography system further including a controller configured to control process parameters of the chromatography system, wherein the method includes: a) generating (91) a model accessible by the controller based on an estimated performance of the column material, b) identifying (93) changes in process parameters of the chromatography system indicative of the quality and / or process performance of the target product, and c) adapting (94) the process parameters of the chromatography system based on the model to compensate for the changes in process parameters, wherein the column material includes at least two components, each component having at least one data property, and the estimated performance is generated using the following steps: i) obtaining (81) the data properties of the at least two components used to produce a bioprocess material, ii) defining (82) a protocol for producing the bioprocess material, iii) processing (83) the at least two components according to the defined protocol to obtain at least one product, iv) measuring (84) the data properties of each product, v) calculating (85) the data properties of each product based on the measured data properties of each product and / or the data properties from the at least two components, the data properties being different from the measured data properties of each product, and vi) If the product is the bioprocess material, the estimated performance of the bioprocess material is obtained by processing (88) the measured and calculated data properties of the product; retrieving information on the manufacturing process of the target product in the bioprocess system; and mapping the estimated performance of the bioprocess material onto the manufacturing process to estimate the impact of the bioprocess material on the target product in the bioprocess system, or vii) if the product is not the bioprocess material, treating the product as an intermediate material and repeating (87) steps iii)-vi), wherein each product that is one of the at least two components has both the measured data properties in step iv) and the calculated data properties in step v) of the data properties of one of the at least two components.

2. The method according to claim 1, wherein, The column material is provided in batches, each batch having an individual estimated performance, and the method further includes adapting (97) the model based on differences in the estimated performance between batches.

3. The method according to claim 1 or 2, wherein The chromatography system further includes at least one sensor configured to measure parameter values, and step b) further includes obtaining (95) sensor readings to identify changes in process parameters.

4. The method according to claim 3, wherein Step b) further includes monitoring (96) the performance of the at least one column.

5. The method according to claim 3 or 4, wherein Purification of the target product is carried out in a predetermined process, and the system is further configured to measure parameter values before and / or after the at least one column, and the method further includes identifying a deviation between the measured parameter values and the parameter values obtained in the predetermined process, and adapting (98) the process parameters to compensate for the identified deviation.

6. The method according to any one of claims 1-5, wherein, The model is externally generated and / or generated in the controller.

7. The method according to any one of claims 1 to 6, further including selecting the column material as any of the following: chromatography resin; membrane; nanofiber; monolithic material.

8. A method for processing at least two intermediates (72, 73) from a previous process step in a bioprocess system, the bioprocess system including a controller (74) configured to control process parameters of the bioprocess system, wherein the method includes: - Obtaining data properties of each of the at least two intermediates from the previous process step, - Performing in-process processing on the at least two intermediates to produce a resulting intermediate (76), - Obtaining data properties of the resulting intermediate and estimating the performance of the resulting intermediate when used in a subsequent process step, - Identifying changes in process parameters of the bioprocess system indicative of the quality and / or process performance of the target product, and - Adapting the process parameters of the subsequent process step based on the data properties of each intermediate from the previous process step and the data properties of the resulting intermediate to compensate for the changes in the process parameters.

9. The method according to claim 8, wherein, The in-process processing includes mixing the at least two intermediates.

10. The method according to claim 8 or 9, wherein The at least two intermediates are selected as different batches produced in the previous process step.

11. The method according to any one of claims 8-10, wherein, The bioprocess system is selected as a chromatography system, and the previous process step is a capture step (70), and the subsequent step is a finishing step (71).

12. A method for estimating the performance of a bioprocess material when used in a bioprocess system, the bioprocess material including at least two components, each component having data properties, wherein the method includes: i) Obtaining (81) the data properties of the at least two components used to produce the bioprocess material, ii) Defining (82) a protocol for producing the bioprocess material, iii) Processing (83) the at least two components according to the defined protocol to obtain at least one product, iv) Measuring (84) the data properties of each product, v) Calculating (85) the data properties of each product based on the measured data properties of each product and / or the data properties from the at least two components, the data properties being different from the measured data properties of each product, and vi) If the product is the bioprocess material, the estimated performance of the bioprocess material is obtained by processing (88) the measured and calculated data properties of the product; Retrieving information on the manufacturing process of the target product in the bioprocess system; And mapping the estimated performance of the bioprocess material onto the manufacturing process to estimate the impact of the bioprocess material on the target product in the bioprocess system, or vii) If the product is not the bioprocess material, treating the product as an intermediate material and repeating (87) steps iii)-vi), where each product as one of the at least two components has both the measured data properties in step iv) and the calculated data properties in step v) of the data properties of one of the at least two components.

13. The method according to claim 12, wherein, The data properties of at least one component and / or each product include morphological data properties.

14. The method according to claim 12 or 13, wherein, Step i) further includes obtaining a particle size distribution for at least one component.

15. The method according to any one of claims 12 - 14, wherein, Step iv) also includes obtaining at least data properties related to the particle size distribution and / or porosity and / or flow rate of each intermediate material.

16. The method according to any one of claims 12 - 15, wherein The protocol in step ii) also includes any combination of the following groupings: filtration; reaction; cooling; excitation; mixing; dilution; sieving; washing, grinding; and heating.

17. The method according to any one of claims 12 - 15, wherein At least two of the components are different batches of the same material.

18. The method according to claim 17, wherein The batches are different from each other with respect to one or more data properties.

19. The method according to claim 17 or 18, wherein, The batches are different from each other with respect to one or more morphological data properties.

20. The method according to any one of claims 12 - 19, wherein, Each component includes at least one substance, and the method also includes selecting the data properties of each component to include: - Lot number - The supplier of the component, and - Data characterizing the at least one substance.

21. The method according to claim 17, wherein, The method also includes, for a component having at least two substances, selecting the data properties of each component to further include the ratio between the at least two substances.

22. The method according to any one of claims 12-21, wherein, The bioprocess material is selected as a chromatography resin.

23. The method according to any one of claims 12-21, wherein, The bioprocess material is selected as a cell culture medium.

24. A method for manufacturing a bioprocess material (20) for a bioprocess, the bioprocess material including at least two components (24-26), each component having data properties, wherein the method includes: - Processing at least two components (24-26) using a predetermined protocol to produce at least one product (21-23), - Obtaining the data properties of each of the at least one product, - Repeating the processing of the at least one product and the at least two components to obtain the bioprocess material (20), and - Identifying the data properties of the components and the at least one product relevant to the performance of the bioprocess material.

25. The method according to claim 24, wherein, The data properties of the at least one component and / or each product include morphological data properties.

26. The method according to claim 24 or 25, wherein At least two of the components are different batches of the same material.

27. The method according to any one of claims 24 - 26, wherein, The batches are different from each other with respect to one or more data properties.

28. The method according to claim 27, wherein, The batches are different from each other with respect to one or more morphological data properties.

29. A method for generating cell culture for a bioprocess in a cell culture system, the cell culture system further including a controller configured to feed a cell culture medium and control process parameters of the cell culture system, wherein the method includes: a) Generating (91) a model accessible to the controller based on the estimated performance of the cell culture medium obtained according to any one of claims 1-10 or 12-18, b) Identifying (93) changes in the process parameters of the cell culture system indicative of the quality and / or process performance of the cell culture, and c) Adapting (94) the process parameters of the cell culture system based on the model to compensate for changes in the process parameters.

30. The method according to claim 29, wherein, The cell culture medium is provided in batches, each batch having an individual estimated performance, and the method also includes adapting (97) the model based on differences in the estimated performance between batches.

31. The method according to claim 29 or 30, wherein, The model is externally generated and / or generated in the controller.

32. A computer program for controlling a process parameter in a biological process system, comprising instructions which, when executed on at least one processor, cause the at least one processor to perform the method according to any one of claims 1 - 31.

33. A computer-readable storage medium carrying the computer program according to claim 32 for controlling a process parameter in a biological process system.

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