A method for analyzing source data generated by one or more bioprocessing devices.

Abstract

In one embodiment, the present invention relates to a computer implementation method 800 performed by an analysis module 630 configured to analyze source data generated by one or more bioprocessing devices 100. The method includes the steps of: sending a request 631 including an identifier in the form of a source path; receiving a response 622 including source data; and analyzing the response 622 to generate a target path and target data, wherein the analysis step includes the step of executing a script, and the message sending step includes the target path 632 and / or target data 633.

Description

[Technical Field]

[0001] The present invention relates to a method for analyzing source data generated by one or more bioprocessing devices. [Background technology]

[0002] Bioprocessing systems are widely used, for example, for biomolecule / protein separation. One example of a bioprocessing system is a chromatography system. Chromatography is a well-known procedure for purifying protein samples. Samples can typically be provided in liquid form, for example, derived from a bioreactor.

[0003] Bioprocessing systems / devices are generally used to provide specific system functions. For example, they are used for the production and / or separation of desired substances, such as protein purification in bioprocessing methods like chromatography or filtration, or production through cell culture or oligo synthesis.

[0004] During such separation / processing, a large amount of data is collected during sample elution and other processes. Furthermore, additional data related to sample elution may be collected, such as the characteristics of the surrounding environment (e.g., temperature) during chromatography, cell culture, filtration, or synthesis.

[0005] For example, to improve the quality of the final analytical results, data from multiple runs or from multiple chromatography instruments are collected and aggregated. This increases the complexity of data aggregation, which is at least partly due to various phenomena such as baseline drift, changes in elution peak shape, and shifts in elution time.

[0006] Traditional solutions, such as chromatography analysis systems, typically attempt to load all raw data from multiple chromatography / instrument runs and derive aggregated data. However, this has the drawback that, due to limitations in the analysis system's memory and processing resources, only a selected portion of the multiple chromatography / instrument runs can be analyzed at a time.

[0007] Therefore, improved methods are needed for analyzing source data derived from bioprocessing equipment. [Overview of the project] [Problems that the invention aims to solve]

[0008] The object of the embodiments of the present invention is to provide solutions that mitigate or resolve the above-mentioned drawbacks and problems. [Means for solving the problem]

[0009] The above and further objectives are achieved by the subject matter described herein. Further advantageous embodiments of the invention are further defined herein.

[0010] According to a first aspect of the present invention, the above and other objectives are achieved by a computer implementation method performed by an analysis module configured to analyze source data generated by one or more bioprocessing devices. The method includes sending a request including an identifier in the form of a source path; receiving a response including source data; analyzing the response to generate a target path and target data, the analysis step including executing a script; and sending a message including a target path and target data.

[0011] The advantage of the embodiment according to the first aspect is that it increases the flexibility of analyzing source data generated by one or more bioprocessing devices, while simultaneously reducing the complexity of performing the analysis of source data generated by one or more bioprocessing devices.

[0012] In one embodiment of the first aspect, the source data represents a point of interest or a subset of data derived from the source data.

[0013] In one embodiment of the first aspect, the script includes, selected from, instructions that define an execution flow, operations that define an algorithm that generates target data using source data, and rules that define whether or not an operation should be performed using an execution context.

[0014] According to a second aspect of the present invention, the above and other objectives are achieved by a computer implementation method performed by a source module configured to use source data generated by one or more bioprocessing devices, the method comprising: receiving a first request including an identifier in the form of a source path; sending a second request using a resolver indicated by the source path; receiving a response including a subset of raw data; generating source data using the subset of raw data; and sending a response including the source data.

[0015] According to a third aspect of the present invention, the above and other objectives are achieved by a computer implementation method performed by a target module configured to output target data using a target path, the method comprising the steps of receiving a message including a target path and target data, transmitting a first message for storing the target data using a resolver indicated by the target path, or transmitting a second message for displaying the target data using a resolver indicated by the target path.

[0016] According to a fourth aspect of the present invention, the above-mentioned and other objectives are achieved by a computer comprising a processor and memory, wherein the memory includes instructions executable by the processor, and the computer is configured to perform the method according to any of the first, second, or third aspects.

[0017] According to a fifth aspect of the present invention, the above-mentioned and other objectives are achieved by a computer program including a computer executable instruction, which, when executed on a processing unit included in the computer, causes the computer to perform any of the method steps according to the first, second, or third aspect.

[0018] According to a sixth aspect of the present invention, the above-mentioned and other objectives are achieved by a computer program product including a computer-readable storage medium, the computer-readable storage medium having a computer program according to the fifth aspect.

[0019] According to a seventh aspect of the present invention, the above and other objectives are achieved by a bioprocessing support system configured to analyze source data, the method comprising: sending a request by an analysis module, including an identifier in the form of a source path; receiving a first request by a source module, including an identifier in the form of a source path; sending a second request by a source module, using a first resolver indicated by a source path; receiving a first response by a source module, including a subset of raw data; generating source data by a source module, using the subset of raw data; sending a second response by a source module, including source data; receiving a second response by an analysis module, including source data; analyzing the second response by an analysis module, including executing a script, to generate a target path and target data; and sending a first message by an analysis module, including a target path and target data.

[0020] In one embodiment of the seventh aspect, the method further includes the target module receiving a first message including a target path and target data, and the target module transmitting a second message to store the target data using a resolver indicated by the target path, or the target module transmitting a third message to display the target data using a resolver indicated by the target path.

[0021] The advantages of the second to seventh aspects are at least the same as those of the first aspect.

[0022] Further uses and advantages of embodiments of the present invention will become apparent from the following detailed description.

Brief Description of the Drawings

[0023] [Figure 1] A chromatography system embodied as a chromatography device according to one or more embodiments of the present disclosure is shown. [Figure 2A] An example of result data generated by one or more chromatography devices according to one or more embodiments of the present disclosure is shown. [Figure 2B] Derivation of points of interest according to one or more embodiments of the present disclosure is shown. [Figure 3] Operations for generating target data according to one or more embodiments of the present disclosure are shown. [Figure 4] Raw result data generated from a plurality of chromatography systems according to one or more embodiments of the present disclosure is shown. [Figure 5] Raw result data generated in runs of different chromatographies according to one or more embodiments of the present disclosure is shown. [Figure 6] The flow of data between functional modules of a computer executing a method according to one or more embodiments of the present disclosure is shown. [Figure 7] This disclosure shows a computer according to one or more embodiments of the present disclosure. [Figure 8] A flowchart of a method according to one or more embodiments of this disclosure is shown. [Figure 9] A flowchart of a method according to one or more embodiments of this disclosure is shown. [Figure 10] A flowchart of a method according to one or more embodiments of this disclosure is shown. [Figure 11] This document demonstrates a highly practical workflow that can be used in various embodiments of the present invention. [Figure 12-1] A more detailed embodiment of template execution for generating desired termination data is shown. [Figure 12-2] A more detailed embodiment of template execution for generating desired termination data is shown. [Modes for carrying out the invention]

[0024] By examining the detailed description of one or more embodiments below, those skilled in the art will be able to gain a more complete understanding of embodiments of the present invention and recognize their additional advantages. It should be noted that similar reference numerals are used to identify similar elements shown in one or more drawings.

[0025] In this specification and the corresponding claims, “or” should be understood as a mathematical OR encompassing “and” and “or,” and not as XOR (exclusive OR). The indefinite article “a” in this disclosure and the claims is not limited to “one,” but can also be understood as “one or more,” i.e., plural.

[0026] In predefined disclosures, the term “Point of Interest” (POI / POIS) may mean, for example, a critical event in a bioprocessing process (e.g., chromatographic processes such as the start / end of a chromatography run, the start / end of elution, or injection points).

[0027] FIG. 1 shows a chromatography system 100 embodied as a chromatography apparatus according to one or more embodiments of the present disclosure.

[0028] The chromatography system 100 is configured to provide desired system functions and typically receives input substances S input_1 , S input_2 , S input_3 and generates one or more desired substances S Desired1 , S Desired2 , S Desired3 .

[0029] In one example, the chromatography system 100 includes a chromatography apparatus configured to separate a desired substance or sample S Input1 ~S InputN from one or more input substances S, for example, different mixtures of samples and other compositions. Desired

[0030] The chromatography system 100 can include those selected from bioprocess units such as reservoirs 151, 152...-N, column 141, splitter 170, at least one UV sensor, pH sensor 131, and conductivity sensor 132. The chromatography system 100 in the form of a chromatography apparatus will be described in more detail below.

[0031] The chromatography apparatus 100 may typically have at least one inlet 155. The inlet may optionally be coupled to one or more reservoirs 151, 152...-N configured to hold fluid. It is understood that the chromatography apparatus 100 may have any number of reservoirs and corresponding inlets. The inlet 155 may be implemented as a tubular element, for example, a tube or hose. The chromatography apparatus 100 may further include a valve unit (not shown). The valve unit may be coupled to reservoirs 151, 152...-N by an inlet 155 coupled to a fluid inlet 101. The valve unit may be configured to be coupled to a (for example, a first) column 141 by a first pair of fluid ports 130, 140. The first column 141 may be located inside the chromatography apparatus 100 or outside the chromatography apparatus 100.

[0032] The chromatography apparatus 100 may further include an intelligent packing fluid port or packing fluid port 150 configured to be coupled to the packing port of column 141. The chromatography apparatus 100 may further include a waste fluid port 160 configured to be coupled to a waste reservoir or drain (not shown).

[0033] The chromatography apparatus 100 may further include, or be operablely coupled to, a control unit 110 comprising a circuit configuration, for example, a processor and memory. The memory may contain instructions executable by the processor, thereby operating the chromatography apparatus 100 to perform any of the steps or methods described herein.

[0034] The chromatography apparatus 100 may optionally include a splitter 170 to which a pH sensor 131, a conductivity sensor 132, and an outlet valve 120 are selected and coupled. The splitter 170 may be configured to direct the fluid to the outlet valve 120 or any other unit. Optionally, the splitter 170 may be communicatively coupled to a control unit 110 and capable of performing fluid coupling in response to control signals from the control unit 110.

[0035] The pH sensor 131 may be communicatively coupled to the control unit 110 and configured to measure the pH of the fluid supplied by the splitter 170. One or more UV sensors may also be provided to enable monitoring / detection of target protein products. The chromatography apparatus 100 may further include a conductivity sensor 132 communicatively coupled to the control unit 110 and configured to measure the conductivity of the fluid supplied by the splitter 170. The pH sensor 131 and / or conductivity sensor 132 may further be configured to provide the control unit 110 with the measured pH and conductivity as a control signal including the measurement data.

[0036] The chromatography apparatus 100 may further include an outlet valve 120 coupled to the splitter 170. The outlet valve 120 may have one or more outlets or outlet ports 121-123 and may be configured to supply fluid supplied by the splitter 170 to one or more outlets 121-123 in response to a control signal received from the control unit 110, for example.

[0037] Figure 2A shows an example of result data generated by one or more chromatography apparatuses according to one or more embodiments of the present disclosure. In this example, raw data is generated in the form of chromatogram 210 showing elution peaks 211-214 during a chromatogram run. For example, from a PCC cycle, a first peak may be generated during washing and another peak during elution. However, it should be noted that the techniques described herein are not limited to peak-like data and can also be applied to analyze transitions, stability levels, etc.

[0038] Figure 2B illustrates the derivation of points of interest according to one or more embodiments of the present disclosure. From the chromatogram 210 shown in Figure 2A, the data source 220 derives points of interest 221–224 in the form of data surrounding each of the elution peaks 211–214 from the chromatogram 210. Such points of interest 221–224 can be derived from marks in the run log provided at the start and end points of the peaks of interest, such as by finding all peaks and summing their areas, or by finding the maximum peak or peaks separated by retention limits.

[0039] As can be seen from Figure 2B, the derived points of interest 221-224 include all data related to the processing characteristics of the elution peaks, but also include a significant reduction in raw data.

[0040] This has the advantage of allowing for the consideration of additional chromatographic runs when analyzing the resulting data. In other words, for example, when determining elution peaks, considering a larger dataset can improve the quality / statistical certainty of the analysis.

[0041] For example, when comparing multiple chromatographic runs for a particular sample, a larger number of chromatographic runs can be considered. Comparing elution peaks from multiple chromatographic runs can improve the quality of the estimated characteristics. Various comparisons can be made; for example, the same analysis can be performed on many runs, and the values ​​extracted from each analysis can be compared with those of other analyses (for example, the maximum height of peak 221 can be determined, and the trend values ​​derived from each run for these many runs or many cycles within one run can be compared with the extracted values ​​between cycles for analysis); or the comparison can be used to confirm that the peak height is within a certain range, to check for performance degradation using trend values, or to determine whether a peak is missing in any of the runs.

[0042] Figure 3 shows the operation for generating target data values ​​according to one or more embodiments of the present disclosure. Referring to Figures 2A and 2B, the data source 310 can derive source data in the form of points of interest 221-224, for example, in the form of elution peaks 211-214 derived from result data such as a chromatogram 210. The maximum values ​​of each elution peak 211-214 are shown by circles. The analysis module can execute a script that includes operations that define an algorithm for generating target data 320 using the source data.

[0043] In the example shown in Figure 3, the operation receives points of interest 221-224 in the form of elution peaks and generates the mean peak values ​​as target data 320.

[0044] Figure 4 shows raw result data generated from multiple chromatography systems according to one or more embodiments of the present disclosure.

[0045] In one example, the separation of the same sample is performed using three different chromatography systems 411-413.

[0046] Each of the chromatography systems 411 to 413 is provided with at least one data source 421 to 423, each data source configured to derive source data in the form of points of interest or data subsets derived from the result data of the respective chromatography systems 411 to 413. The source data obtained from the data sources 421 to 423 can then be provided to the analysis module 430.

[0047] By analyzing the result data from all chromatography systems 411-413, the quality of target data can be improved. For example, the average of elution peaks can be calculated in the analysis. Various target data values ​​320 can be determined. These can then be used to help optimize protein product yield, set boundaries to ensure the process functions as intended, measure lifetime parameters, and identify any declines / efficiency drops in the process. These can also be used for specific process analysis (PAT) requirements and can be recorded over many production runs for use in obtaining and complying with various regulatory requirements.

[0048] Figure 5 shows raw result data 521-523 generated by different chromatographic runs performed at different time points according to one or more embodiments of the present disclosure. In one example, the chromatography system 100 subsequently performs chromatographic runs on the same sample.

[0049] In one example, the separation of the same sample is performed by chromatography system 510 at three different time points T1 to T3.

[0050] The same data sources 531-533 are provided for each chromatography run and are configured to derive source data in the form of points of interest or data subsets derived from the result data of the chromatography system 510. The source data from data sources 531-533 can then be provided to the analysis module 530.

[0051] By analyzing result data from all three different time points T1-T3, the quality of the target data can be improved. For example, the mean of the elution peaks can be calculated in the analysis.

[0052] Figure 6 shows the data flow between functional modules of a computer 700 performing a method according to one or more embodiments of the present disclosure. The computer will be further described in relation to Figure 7. The computer 700 is configured to analyze source data / result data generated by one or more chromatography apparatuses.

[0053] A computer comprises one or more functional modules, or is communicatively coupled to one or more functional modules.

[0054] The raw data storage module 610 is configured to store result data received from one or more chromatography systems 100 and to provide the stored data in response to requests.

[0055] Source module 620 is configured to derive source data and / or data sources from result data. Source data and / or data sources typically represent points of interest or data subsets derived from result data. In one embodiment, source module 620 is typically configured to receive one or more source paths in a request 631 from analysis module 630. A source path is an identifier that identifies one or more raw data storage modules 610 containing the result data of interest. Source module 620 then resolves one or more source paths. A source path is to identify source data in the form of points of interest or subsets of data derived from result data. Next, source module 620 sends request 621 to one or more raw data storage modules 610. Then, one or more raw data storage modules 610 send a response 611 to source module 620 containing source data and / or points of interest and / or data subsets of result data. Next, source module 620 sends a response containing source data and / or data sources to analysis module 630.

[0056] In various embodiments, the source path is i) A resolver that acts as an integral code to resolve the path, ii) A data path for identifying a data subset from a data source, iii) A set of parameters that function as filter parameters for selecting specific values ​​within a data range. This is pointed out.

[0057] for example, “Path”:aa: / / results / run / curve?run_id=${selected_run_id}&curve_name=${curve_identifier}&retention_type=${retention_type}&segment=${segment} This is an example template, where "${variables}" are the values ​​of the template variables in the template, "results" refers to the resolver, " / run / curve" selects the curve data, and the rest after the "?" relates to the parameters that the results resolver uses to find the desired curve data.

[0058] This has the advantage of at least reducing the amount of data transferred. A further advantage is that the physical storage of the resulting data is hidden from the analysis module 630. Any reconfiguration of the physical storage can be performed without notifying the analysis module 630.

[0059] Some basic functions for carrying out embodiments of the present invention may be provided by existing software (e.g., UNICORN®) that can provide a framework for loading an analysis engine. Then, specific embodiments of the present invention may be provided as platform extensions (e.g., extensions to UNICORN®) that include an analysis engine and base algorithms. Application-specific extensions may also be provided.

[0060] The analysis module 630 is further configured to receive a response 622 containing source data and / or data sources. The analysis module 630 is further configured to analyze the response 622 to generate a target path and target data, where the analysis includes executing a script (see, for example, the example described below). In one embodiment, the script includes, selected from, instructions defining the execution flow, operations defining an algorithm that generates target data using the source data, and / or rules defining whether the operations should be executed using the execution context. The analysis module 630 is further configured to send a message 631 containing the target path and / or target data to the target module 640.

[0061] Examples of target data include baseline, theoretical plate equivalent height (HETP), etc.

[0062] The target module 640 is configured to store and / or visualize target data. In one embodiment, the target module 640 is typically configured to receive one or more target paths in a message 631 from the analysis module 630. A target path is an identifier that identifies one or more target data storage modules and / or target data visualization modules 660. The target module 640 then resolves one or more target paths. The target module 640 then optionally sends one or more messages 641, 642 to a target data storage module 650 configured to store target data and / or a target data visualization module 660 configured to visualize target data.

[0063] Sources and targets may include integral codes. Sources can be created to read data from any data source, including external databases and external files. Targets can be local databases, visualizations within a platform (e.g., UNICORN®), or any external target. Examples of these formats may include CSV and Excel file targets. However, the specific data format is not a critical issue, as the source resolver can convert complex data types into an information format that the appropriate analysis engine can understand.

[0064] Figure 7 shows a computer 700 according to one or more embodiments of the present disclosure. The computer 700 may take the form of, for example, a chromatography system, a computer, a server, an onboard computer, a fixed computing device, a laptop computer, a tablet computer, a handheld computer, a wrist-worn computer, a smartwatch, a smartphone, or a smart TV. The computer 700 may include a processing circuit configuration 712 communicatively coupled to a transceiver 704 configured for wired or wireless communication. The computer 700 may further include at least one optional antenna (not shown). The antenna may be coupled to the transceiver 704 and configured to transmit and / or radiate and / or receive wired or wireless signals within a communication network such as Wi-Fi, Bluetooth, 3G, 4G, or 5G. In one example, the processing circuit configuration 712 may be any of a selection of processors and / or central processing units and / or processor modules and / or multiple processors configured to work together. Furthermore, the computer 700 may further include memory 715. The memory 715 may include, for example, hard RAM, a disk drive, a flash drive, or other removable or fixed media drives, or any other suitable memory known in the art, as selected from among these. The memory 715 may include instructions that can be executed by the processing circuit configuration to perform any of the steps or methods described herein. The processing circuit configuration 712 may be communicatively coupled to either the transceiver 704 or the memory 715 as selected from among these. The computer 700 may be configured to send and receive control signals directly to any of the above-described units or external nodes, or to send and receive control signals via wired and / or wireless communication networks.

[0065] The wired / wireless transceiver 704 and / or wired / wireless network adapter may be configured to send and receive data values ​​or parameters as signals between the processing circuit configuration 712 and other external nodes.

[0066] In one embodiment, the transceiver 704 communicates either directly with an external node or via a wireless communication network.

[0067] In one or more embodiments, the computer 700 may further include an input device 717 configured to receive input or instructions from a user and transmit a user input signal indicating the user input or instruction to a processing circuit configuration 712.

[0068] In one or more embodiments, the computer 700 may further include a display device 718 configured to receive display signals from a processing circuit configuration 712 that indicate rendered objects such as text or graphical user input objects, and to display the received signals as objects such as text or graphical user input objects.

[0069] In one embodiment, the display device 718 is integrated with the user input device 717 and is configured to receive a display signal from the processing circuit configuration 712 that indicates a rendered object such as text or a graphical user input object, and to display the received signal as an object such as text or a graphical user input object, and / or to receive input or instructions from the user and transmit a user input signal indicating the user input or instructions to the processing circuit configuration 712.

[0070] In further embodiments, the computer 700 may further include and / or be coupled to one or more additional sensors (not shown) configured to receive and / or acquire and / or measure physical characteristics relating to the computer and / or chromatography system and to transmit one or more sensor signals indicating physical characteristics to a processing circuit configuration 712.

[0071] In one or more embodiments, the processing circuit configuration 712 is further coupled to an input device 717 and / or a display device 718 and / or additional sensors in a communicative manner.

[0072] In the embodiment, the communication network includes Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Networks (GSM), Extended Data GSM Environment (EDGE), Universal Mobile Telecommunications System, Long-Term Evolution, High-Speed ​​Downlink Packet Access (HSDPA), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth®, Zigbee®, Wi-Fi, Voice over Internet Protocol (VoIP), LTE Advanced, IEEE 802.16m, Wireless MAN Advanced, Evolved High-Speed ​​Packet Access (HSPA+), 3GPP Long-Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), and Ultra Mobile Broadband (UMB) (formerly Evolution-Data). Communication is conducted using wired or wireless communication technologies that may include, but are not limited to, at least one of the following: Optimized (EV-DO) Rev.C), High-Speed ​​Low-Latency Access with Seamless Mobile Handoff Orthogonal Frequency Division Multiplexing (Flash-OFDM), High-Capacity Spatial Division Multiple Access (iBurst®), and Mobile Broadband Wireless Access (MBWA) (IEEE 802.20) systems, High-Performance Wireless Metropolitan Area Network (HIPERMAN), Beam Division Multiple Access (BDMA), Global Interoperability for Microwave Access (Wi-MAX), and ultrasonic communication.

[0073] Furthermore, those skilled in the art will understand that the computer 700 may be equipped with the communication capabilities necessary to implement the present solution, for example, in the form of functions, means, units, elements, etc. Other examples of such means, units, elements, and functions include processors, memory, buffers, control logic, encoders, decoders, rate matchers, derate matchers, mapping units, multipliers, decision units, selection units, switches, interleavers, deinterleavers, modulators, demodulators, inputs, outputs, antennas, amplifiers, receiver units, transmitter units, DSPs, MSDs, TCM encoders, TCM decoders, power supply units, power feeders, communication interfaces, communication protocols, etc., which are appropriately arranged together to implement the present solution.

[0074] In particular, the processing circuitry configurations of the present disclosure may comprise one or more instances of processors, processor modules, and a plurality of processors, central processing units (CPUs), processing units, processing circuits, processors, application-specific integrated circuits (ASICs), microprocessors, field-programmable gate arrays (FPGAs), or other processing logic capable of interpreting and executing instructions, configured to work in conjunction with one another. Accordingly, the expressions “processing circuitry” and / or “processing means” may represent a processing circuitry configuration comprising a plurality of processing circuits, such as any, some, or all of the above. The processing means may further perform data processing functions for data input, output, and processing, including data buffering and device control functions such as processing control and user interface control.

[0075] In one embodiment, a computer is provided, which is configured to perform any or all of the method steps of the method described herein.

[0076] In one embodiment, a chromatography apparatus and / or system is provided, which includes all or selected computer features described in relation to Figure 7. The chromatography apparatus or system is configured to perform any or all method steps of the methods described herein.

[0077] In one embodiment, a computer program is provided which includes a computer executable instruction, and when the computer executable instruction is executed on a processing unit included in the computer, the computer causes the computer to perform one of the method steps of the methods described herein.

[0078] In one embodiment, a computer program product is provided which includes a computer-readable storage medium, the computer-readable storage medium having the computer program embodied therein.

[0079] In one embodiment, a carrier containing the above-mentioned computer program is provided, the carrier being one of an electronic signal, an optical signal, a wireless signal, or a computer-readable storage medium.

[0080] Figure 8 shows a flowchart of Method 800 according to one or more embodiments of the present disclosure. The computer implementation method is performed, for example, by an analysis module 630 configured to analyze source data generated by one or more chromatography apparatuses. The method includes the following steps:

[0081] In step 810, request 631 is sent, which includes an identifier in the form of the source path.

[0082] In step 820, response 622 is received, which includes the source data and / or data source.

[0083] In step 830, the response 622 is analyzed to generate the target path and / or target data. In one embodiment, the analysis includes executing a script and sending a message / request 631 containing the target path and / or target data. For example, the analysis of the response to generate the target path and / or target data may be performed using an analysis engine configured as shown in Figure 11 below.

[0084] In one embodiment, the source data and / or data source represent points of interest or data subsets derived from the resulting data.

[0085] In one embodiment, the script includes selected instructions that define the execution flow, operations that define an algorithm that generates target data using source data, and rules that define whether or not the operations should be performed using the execution context.

[0086] Figure 9 shows a flowchart of Method 900 according to one or more embodiments of the present disclosure. The computer implementation method is performed, for example, by a source module 620 configured to generate source data and / or data sources using result data generated by one or more chromatography apparatuses. The method includes the following steps:

[0087] In step 910, a first request 631 is received, which includes an identifier in the form of one or more source paths.

[0088] In step 920, a second request 621 is sent using the resolver indicated by the source path.

[0089] In step 930, response 611 is received, which includes a subset of the raw data.

[0090] In an optional step 940, source data and / or a data source are generated using a subset of raw data. For example, the resolver may return property packets of selected data from the data source and feed them into this operation step.

[0091] In step 950, send response 622, which includes the source data and / or data source.

[0092] Figure 10 shows a flowchart of Method 1000 according to one or more embodiments of the present disclosure. The computer implementation method is performed by a target module 640 configured to output target data using a target path. The method includes the following steps:

[0093] In step 1010, a message 631 containing the target path and / or target data is received.

[0094] In step 1020, send a first message 641 to store the target data using the resolver indicated by the target path, or send a second message 642 to display the target data using the resolver indicated by the target path.

[0095] Figure 11 shows a high-level, practical workflow 2000 that can be used in various embodiments of the present invention. For example, the workflow 2000 can be used by an analysis module 630 to analyze the response and generate a target path and target data. The target path may be set by a template script that defines the workflow 2000, so that the analysis module 630, which reads / executes those scripts, becomes aware of the data storage location and so on.

[0096] In a workflow 2000 that may be provided by the analysis engine, the user selects several results from a results browser. This is provided in this embodiment by UNICORN® software available from Cytiva®, which is used for flexible control of chromatography, filtration, oligo synthesis, and bioreactors. The results include source data generated by one or more such bioprocessing devices 100 selected for subsequent analysis. Generally, such source data shows how the measured bioprocessing data parameters change over time. For example, such data may show bioprocessing data (y-axis) correlated with time data (x-axis). Thus, this may be presented as a set of peaks showing how the bioprocessing data changes over time (as shown, for example, in Figures 2A, 2B, and 3).

[0097] These results are then passed to a context module that generates a set of data to be retained during the execution of the template by the software.

[0098] After selecting results, the user can also select a template using the advanced analysis tab of the UNICORN™ software, which is then applied to the list of results (for example, as shown in Figure 12). Executing a template involves calling dynamically registered code functions to determine the appropriate target path and target sink data corresponding to the target data. This occurs by first setting the analysis parameters, which are also passed to the context module for later use. The fetch source data module then retrieves the data, obtaining the appropriate source data or metadata and transferring it to the run operation module, as well as copying it to the context module. The run operation module then generates output from the operation data corresponding to the target sink data, using the configuration parameters defined by the analysis parameters and source / metadata, which is passed to both the context module and the output target module.

[0099] The template may be configured to prompt the user for further input as needed, and the code executed by the template can be dynamically swapped within UNICORN™. The output target module can also format the output of the run operation into an appropriate format for further use and send that data as needed. For example, the data may be formatted in formats such as Excel, ELN notebook, or text file.

[0100] Figure 12 shows in more detail an exemplary embodiment 3000 of template execution. In this example, the template is used to perform a relatively simple trend analysis (for example, in UNICORN® software). However, in reality, for example, with large datasets obtained from continuous chromatography (PCC) runs, thousands of instructions / modules may be required to perform the appropriate analytical functions.

[0101] When a template is executed, a list of result names is provided as a variable. Instructions are then extracted from the template and submitted to the analysis engine. These are then all executed in a sequence defined by flow control instructions. Normal program flows of subsequences, loops, and selective branches are supported.

[0102] In this example, the command queue for the trend template includes i) obtaining a UV curve, ii) obtaining a conductivity curve, and iii) obtaining a pH curve. The setting parameters (SET PARAMETERS) are i) retention type, ii) phase name, iii) perform UV analysis, iv) UV curve name, v) perform conductivity (Cond) analysis, vi) Cond curve name, vii) perform pH analysis, viiii) pH curve name, ix) output folder, and x) output file.

[0103] Next, data is retrieved for each instruction in the instruction queue. Point of interest (POIS) detection is performed in each detection phase, followed by phase segment detection. Phases are a known concept in UNICORN™ and are displayed in the run log and may also be displayed in its evaluation module. A novel feature of embodiments of the present invention is that this information can be used to find subsegments of data, which can be read from, for example, a database, thereby limiting the amount of processing required and enabling higher-resolution data.

[0104] For UV analysis, a UV peak integration phase is performed. In this case, source data is extracted from the source path via the results source resolver. The UNICORN™ Peakintegrate function is then called with the settings submitted from the respective templates and source data. The peak data is then stored in the context module for later use via the context path resolver.

[0105] Conductivity analysis is also performed to find the maximum amplitude phase. For pH analysis, the pH find of the maximum amplitude phase is further determined. The data determined from each command execution is also formatted in Excel as needed, allowing for the generation of reports for subsequent analysis.

[0106] Accordingly, various embodiments of the present invention can provide a method, computer, or program such that source data, target data, and / or raw data are generated by or provided to a bioprocessing apparatus / system including one or more of a chromatography apparatus, a cell culture apparatus, a filtration apparatus, and / or an oligo synthesis apparatus.

[0107] Such data may show how bioprocessing-related quantities change over time (e.g., they may provide peaks of different heights / sizes). These analyses can determine peak values / integrated areas / mean values, etc., which may be related to physical, time-varying bioprocessing parameters present at various phases of the bioprocessing, such as volume / concentration / other. Therefore, various embodiments of the present invention mitigate or solve the aforementioned shortcomings and problems associated with conventional systems and apparatus by providing a more flexible method for selecting data and the operations performed on that data.

[0108] Finally, it should be understood that the present invention is not limited to the embodiments described above, but is also related to and incorporated into all embodiments within the scope of the attached independent claims. [Explanation of Symbols]

[0109] 100 chromatography systems, chromatography apparatus 101 Fluid inlet 110 Control Unit 120 Outlet valve 121-123 Exit or Exit Port 130, 140 First pair of fluid ports 131 pH sensor 132 Conductivity Sensor 141 First column 150 filling fluid ports 151, 152...-N Reservoir 155 Entrance 160 waste fluid ports 170 Splitter 211-214 Elution peaks 210 Chromatograms 220 data sources 221-224 Points of Interest 310 Data Sources 320 Target data (values) 411-413 Chromatography Systems 421-423 Data Sources 430 Analysis Modules 510 Chromatography System 521-523 Raw result data 530 Analysis Modules 531-533 Data Sources 610 Raw Data Storage Module 611 Response 620 source modules 621 request 630 Analysis Modules 631 Message, Request 640 Target Modules 641 First Message 642 Second Message 650 Target Data Storage Module 660 Target Data Visualization Module 700 Computers 704 Transceiver 712 Processing Circuit Configuration 715 memory 717 Input device 718 Display device 800 ways 900 ways 2000 Workflow 3000 Embodiments S desired Input material S input desired substance Time points T1-T3

Claims

1. A computer implementation method (800) performed by an analysis module (630) configured to analyze source data generated by one or more bioprocessing devices (100), The steps include sending a request (631) which includes an identifier of the form of the source path (810), The steps include receiving a response (622) containing source data (820), A step (830) of analyzing the response (622) in order to generate a target path and target data, the step of analyzing includes a step of executing a script, A step of sending a message including the target path and the target data. Method (800), including.

2. The method according to claim 1 (800), wherein the source data indicates a point of interest or a subset of data derived from the source data.

3. The method according to any one of claims 1 to 2 (800), wherein the script includes selected from any instructions that define an execution flow, an operation that defines an algorithm that generates the target data using the source data, and a rule that defines whether or not the operation should be performed using the execution context.

4. A computer implementation method (900) performed by a source module (620) configured to generate source data generated by one or more bioprocessing devices (100), Step (910) receiving a first request (631) which includes an identifier of the form of the source path, Step (920) of sending a second request (621) using the resolver indicated by the source path, The steps include receiving a response (611) containing a subset of raw data (930), The steps include generating source data using the aforementioned raw data subset (940), The steps include sending a response (622) containing the source data (950), Method (900), including.

5. A computer implementation method (1000) performed by a target module (640) configured to output target bioprocessing data using a target path, The steps include receiving a message (632) containing the target path and target data (1010), Step (1020): Send a first message (641) to store the target data using the resolver indicated by the target path. Or, Step (1020) of sending a second message (642) to display the target data using the resolver indicated by the target path, and A method (1000) including the following.

6. Computer (700), Processor and A memory, the memory including instructions that can be executed by the processor Equipped with, The computer (700) is configured to perform the method described in any one of claims 1 to 5.

7. A computer program comprising a computer executable instruction that, when executed on a processing unit included in the computer, causes the computer to perform any of the steps of the method according to any one of claims 1 to 5.

8. A computer program product comprising a computer-readable storage medium, wherein the computer-readable storage medium comprises the computer program described in claim 7.

9. A method performed by a bioprocessing support system configured to analyze source data, The analysis module (630) sends a request (631) containing an identifier in the form of a source path, The source module (620) receives the first request (631) which includes an identifier in the form of the source path, The source module (620) transmits a second request (621) using the first resolver indicated by the source path, The source module (620) receives a first response (611) which includes a subset of raw data, The source module (620) generates source data using the raw data subset, The source module (620) transmits a second response (622) including the source data, The analysis module (630) receives the second response (622) which includes the source data, The analysis module (630) analyzes the second response (622) to generate a target path and target data, the analysis step including the step of executing a script, The analysis module (630) transmits a first message including the target path (632) and the target data (633). Methods that include...

10. The steps include receiving the first message (632) including the target path and the target data by the target module (640), The target module (640) sends a second message (641) to store the target data using the resolver indicated by the target path, or The target module (640) sends a third message (641) to display the target data using the resolver indicated by the target path. The method according to claim 9, further comprising:

11. The method, computer, or program according to any one of claims 1 to 8, wherein the source data, the target data, and / or the raw data are generated by or provided to a bioprocessing apparatus / system (100) including one or more of a chromatography apparatus, a cell culture apparatus, a filtration apparatus, and / or an oligo synthesis apparatus.

12. The method, computer, or program according to claim 11, wherein the source data, the target data, and / or the raw data include bioprocessing data showing how bioprocessing-related magnitude values ​​change over time.

Images