Method and system for chromatographic data analysis

Abstract

Embodiments of the invention relate to methods and systems for evaluating the integrity of chromatography columns, systems, and processes. The methods and systems may include one or more of extracting blocks and signal combinations for analysis, performing conversion analysis, performing one or more statistical processing controls, and / or implementing intra-process controls based on the statistical processing controls.

Description

This application is a divisional application of invention patents 201780066406.1 and 202210480528.8. CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Application No. 62 / 412,563, filed on October 25, 2016, the entire disclosure of which is incorporated herein by reference. Technical Field

[0002] The methods of the present invention generally relate to chromatography systems and methods, and particularly to embodiments of methods and systems for chromatography data analysis, eg, for in-process monitoring and control of chromatography systems. Background Art

[0003] Packed bed chromatography plays an important role in the production of biopharmaceutical products. Packed bed chromatography is used to purify many active biological agents, such as proteins, for use in pharmaceutical products. Therefore, column operation may have a significant impact on manufacturing critical process parameters (CPPs) and critical quality attributes (CQAs). Moreover, the complexity and size of biological agents may make it relatively more difficult to analyze biological quality and purity compared to, for example, small molecules. Therefore, monitoring the quality, consistency and integrity of chromatographic methods and equipment through in-process control is very important for ensuring that product quality meets any applicable standards (e.g., government regulations).

[0004] Typically, the integrity of the column can be determined by uniform plug flow of the mobile phase through the stationary phase (e.g., resin) of the column. Examples of loss of integrity of the column can include, for example, evidence of channelization, headspace, flowing contaminated areas, etc. Among other things, channelization may result when the mobile phase is able to travel a distance from the inlet of the column toward the outlet of the column without contacting the stationary phase. Among other things, the headspace can refer to the space that allows non-plug flow of the fluid phase when a lateral region is generated in the column. Flowing contaminated areas can include dirt or other residues on the surface of the inlet or outlet frit or on the resin pores.

[0005] There are several technologies for monitoring chromatographic column performance and integrity.Some technologies, such as pulse injection methods for measuring the height equivalent to theoretical plate (HETP), require buffer solutions that require special preparation.Pulse injection techniques usually require operating chromatographic equipment and posts outside normal processing, thereby causing increased processing time and labor.Other technologies include monitoring key parameters (for example, step yield, forecell volume and maximum optical density during loading) as a part for conventional production.However, setting alarm limits for these parameters is difficult and inaccurate, and may cause false alarms or excessively wide restrictions.

[0006] There is a need for methods, systems and processes for measuring and managing column performance and integrity with accuracy and precision and with minimal disruption to processing. Furthermore, because there are inherent differences between chromatographic columns, column cycles and / or production batches for any given product being chromatographed, there is a need for methods, systems and processes for customizing the analysis of column performance and integrity for a particular column or columns, a particular cycle or cycles and / or a particular batch or batches of product. Finally, there is a need for precise in-process controls using such analyses, as well as methods and systems for responding to deviations from these controls, so that problems with column integrity and performance can be identified and corrected early while minimizing waste and expense. Summary of the invention

[0007] An embodiment of the present invention may relate to a processing control method, the method comprising: receiving raw chromatographic data comprising a plurality of signals, wherein each of the plurality of signals is associated with one of a plurality of blocks; obtaining a data subset by selecting a combination of a first block and a first signal from the raw chromatographic data; generating processed chromatographic data by applying a noise reduction technique to the data subset; generating transformed data by performing a transformation analysis on the processed chromatographic data; and performing an action based on the transformed data.

[0008] In some embodiments, the method may further include performing a chromatography column run, wherein raw chromatography data may be received from the chromatography column run. In other embodiments, raw chromatography data may be received from a chromatography process skid. In further embodiments, each of the plurality of blocks may correspond to a step in a chromatography process. In further embodiments, the selected combination may include a first block, a first signal, and a second signal of the plurality of signals.

[0009] In further embodiments, the method may further include selecting a combination of the first block and the first signal based on a configuration file defining a plurality of selection criteria. In some embodiments, the plurality of selection criteria may include: whether the block occurs at regular chromatographic cycle intervals; the extent to which one of the plurality of signals saturates the detector; the extent to which the plurality of signals approach the stationary phase at different levels; the magnitude of changes in the plurality of signals; and / or a plurality of inflection points shown by the plurality of signals during the transition phase.

[0010] In some embodiments, selecting a combination of a first block and a first signal may include selecting a primary block and a signal combination, and the method may further include selecting a secondary block and a signal combination. In further embodiments, the noise reduction technique may include: using a predetermined set point to select a portion of a subset of data to be analyzed; normalizing the portion to prevent amplitude deviation; using at least one smoothing filter on the portion to generate smoothed data; and analyzing the portion for dynamic signal errors. In yet other embodiments, the method may further include: selecting smoothed data that matches a feature of a chromatographic transition, wherein the feature includes one of the following: derivative duration; maximum intensity; duration from start; or expected background sensor noise. In further embodiments, the transition analysis may include generating a curve using the processed chromatographic data; and analyzing the curve to generate performance parameters.

[0011] In some embodiments, the method may further include generating a single value plot, a moving range plot, or a range plot based on the conversion data; and generating performance data by applying statistical process control to the single value plot, the moving range plot, or the range plot, wherein performing an action based on the conversion data may include performing an action based on the performance data. In some embodiments, applying statistical process control may include performing one of multivariate data analysis or principal component analysis. In some embodiments, performing an action based on the performance data may include generating an event notification, generating an assessment of an event, or generating a deviation notification table. Some embodiments of the present invention may include a chromatographic analysis method, which includes performing a process control method while running a chromatographic column.

[0012] Some aspects of the invention may be directed to a process control method, the method comprising: receiving a selection of raw chromatographic data; generating smoothed data by applying a noise reduction technique to the selection of raw chromatographic data; generating processed chromatographic data by selecting the smoothed data that matches a characteristic of a chromatographic transformation; and performing an action based on the processed chromatographic data. The noise reduction technique may include: selecting a portion of the smoothed data to be analyzed using a predetermined set point; normalizing the portion of the data to prevent amplitude deviation; applying at least one smoothing filter on the portion of the data to generate the smoothed data; and analyzing the portion of the data for dynamic signal errors.

[0013] In some embodiments, receiving a selection of raw chromatographic data may include receiving raw chromatographic data including a plurality of signals and a plurality of blocks, wherein each of the plurality of signals may be associated with a block; and selecting a combination of a first block and a first signal from the raw chromatographic data.

[0014] In some embodiments, the method may further include using the processed chromatogram data to generate one of a single value plot, a moving range plot, or a range plot; and applying statistical processing control to the single value plot, the moving range plot, or the range plot by performing multivariate data analysis or principal component analysis to generate performance data. In some embodiments, performing an action based on the processed chromatogram data may include performing an action based on the performance data. In some embodiments, the action may include generating an event notification, generating an assessment of an event, or generating a deviation notification table.

[0015] Some aspects of the invention may include a process control method including receiving processed chromatographic data including a combination of a first block and a first signal; performing a transformation analysis on the processed chromatographic data; generating one of an individual value-moving range-range (I-MR-R) graph based on the transformation analysis; generating performance data by applying a multivariate statistical analysis method to the I-MR-R graph; and performing an action based on the performance data. The action may include generating an event notification, generating an evaluation of the event, or generating one of a deviation notification table.

[0016] In some embodiments, the processed chromatographic data may include a selection of raw chromatographic data to which a noise reduction technique has been applied. In some embodiments, the selection of raw chromatographic data may be received from a chromatographic processing instrument. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate the disclosed embodiments and, together with the description, serve to explain the principles of the disclosed embodiments. In the drawings:

[0018] Figure 1 An exemplary chromatography system is depicted in schematic form, with which various embodiments of the invention may be practiced.

[0019] Figure 2 Exemplary chromatograms are depicted.

[0020] Figure 3 An exemplary normalized plot of a chromatographic step-up conversion is depicted.

[0021] Figure 4 Depicted are graphs of color spectrum step-up conversions for three batches of balanced conductive blocks in accordance with aspects of the present invention.

[0022] Figure 5 An exemplary process for analyzing chromatographic data and performing process control according to aspects of the present invention is depicted.

[0023] Figure 6 Another exemplary process for analyzing chromatographic data and performing process control according to aspects of the present invention is depicted.

[0024] Figure 7 Depicted are exemplary data files according to some aspects of the invention.

[0025] Figure 8 Depicted are exemplary loading plots for multivariate models according to aspects of the present invention.

[0026] Fig. 9 An exemplary data smoothing process according to some aspects of the present invention is depicted.

[0027] Fig.10 Depicted are loading plots for each variable in the principal components from 27 batches according to aspects of the present invention.

[0028] Fig.11 Depicted are exemplary score plots from 27 batches according to aspects of the present invention.

[0029] Fig.12 Depicted are exemplary loading plots for multivariate models according to aspects of the present invention.

[0030] Fig.13 Depicted are exemplary score graphs according to aspects of the invention.

[0031] Fig.14 Depicting individual value graphs of slopes in a given chromatography unit operation according to aspects of the invention.

[0032] Fig.15 A moving range graph depicting the slope in a given chromatography unit operation according to aspects of the present invention.

[0033] Fig.16 Depicting a range graph of slopes in a given chromatography unit operation according to aspects of the present invention.

[0034] Fig.17 Depicted is a single value graph of a non-Gaussian HETP (NG-HETP) according to some aspects of the present invention.

[0035] Fig.18 A diagram depicting the range of motion of a NG-HETP according to some aspects of the present invention.

[0036] Fig.19 A range diagram of a NG-HETP according to some aspects of the present invention is depicted.

[0037] Fig. 20 Another unique graph of NG-HETP according to some aspects of the present invention is depicted.

[0038] Fig.21 Yet another unique diagram of NG-HETP according to aspects of the present invention is depicted.

[0039] Fig. 22 An exemplary system is depicted upon which aspects of the invention may be implemented.

[0040] Fig.23 Exemplary user interfaces according to some aspects of the invention are depicted.

[0041] Fig.24 Exemplary reports according to some aspects of the invention are depicted. DETAILED DESCRIPTION

[0042] The present invention relates to improvements in pharmaceutical product manufacturing and laboratory processing, and improvements in computer functions associated with pharmaceutical product manufacturing and laboratory processing. In particular, aspects of the present invention relate to chromatography methods and systems, and methods and systems for chromatography data analysis, such as for monitoring and control of chromatography processes and systems.

[0043] Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those generally understood by those of ordinary skill in the art to which the invention belongs. Materials, methods and examples are merely illustrative and are not intended to be limiting. It will be appreciated by those of ordinary skill in the art that conventional variations of the disclosed materials, methods and examples are possible without excessive experimentation. All disclosures, patent applications, patents, sequences, database entries and other references mentioned herein are incorporated herein by reference in their entirety. In the event of a conflict, this specification, including definitions, shall prevail.

[0044] As used herein, the terms "include," "comprises," or any other variation thereof, are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements may include more than just those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "exemplary" is used in the sense of an "example" rather than an "exemplary." For such terms and for the terms "for example" and "such as" and their grammatical equivalents, unless expressly stated otherwise, they should be understood to be followed by the phrase "without limitation." As used herein, the terms "about" and "includes" are intended to include but are not limited to: It is intended to account for variations due to experimental error. Unless expressly stated otherwise, all measurements reported herein are understood to be modified by the term "about", whether or not the term is expressly used. As used herein, the singular forms "a", "an", and "the" include plural referents unless the context indicates otherwise. In addition, in the claims, values, limits, and / or other ranges represent values, limits, and / or ranges + / - 10%.

[0045] As used herein, the term "antibody" includes antigen binding molecules and antigen binding fragments of complete antibody molecules. As used herein, the term "antigen binding portion thereof", "antigen binding fragment" of an antibody, etc. specifically binds to antigens to form a complex including any naturally occurring, enzymatically obtainable, synthetic or genetically engineered polypeptides or glycoproteins. The antigen binding fragment of an antibody can use any suitable standard technique, such as proteolytic digestion or recombinant genetic engineering techniques involving manipulation and expression of DNA encoding antibody variable domains and optional constant domains, from, for example, complete antibody molecules are obtained. This DNA is known and / or easy to obtain from, for example, commercial sources, DNA libraries (including, for example, phage-antibody libraries), or can be synthetic. DNA can be sequenced and manipulated by chemical methods or by using molecular biology techniques, for example, for arranging one or more variable domains and / or constant domains into suitable forms, or for introducing codons, producing cysteine ​​residues, modifying, adding or deleting amino acids, etc.

[0046] Non-limiting examples of antigen-binding fragments include: (i) Fab fragments; (ii) F(ab')2 fragments; (iii) Fd fragments; (iv) Fv fragments; (v) single-chain Fv (scFv) molecules; (vi) dAb fragments; and (vii) minimal recognition units consisting of amino acid residues that mimic the hypervariable region of an antibody (e.g., isolated complementary determining regions (CDRs), such as CDR3 peptides) or constrained FR3-CDR3-FR4 peptides. Other engineered molecules, such as domain-specific antibodies, single-domain antibodies, domain-deleted antibodies, chimeric antibodies, CDR-grafted antibodies, diabodies, triabodies, tetrabodies, minibodies, nanobodies (e.g., monovalent nanobodies, divalent nanobodies, etc.), small modular immunopharmaceuticals (SMIPs), and shark variable IgNAR domains are also included in the expression "antigen-binding fragment" as used herein.

[0047] As used herein, the term "biology" may refer to macromolecules (e.g., having a size greater than 30 kDa) produced in living systems, such as cells. Biologics may include proteins (e.g., antibodies), nucleic acids, large sugars, etc. Unlike small molecules that may have a well-defined chemical structure, biologics may have highly complex structures that cannot be easily quantified by laboratory methods. Therefore, it may be desirable to achieve purity, consistency, and quality in the manufacture of biologics to ensure biological quality, particularly when used for medical purposes.

[0048] As used herein, the term "chromatography" may refer to any preparative or analytical chromatography. Although much of the present invention is provided in the context of preparative packed bed chromatography for purification of biologics, it is contemplated that the systems and methods disclosed herein are also applicable to a variety of chromatographic processes.

[0049] As used herein, the term "drug product" may refer to a volume of a formulated drug substance dispensed into a primary packaging component for packaging, shipping, delivery, and / or administration to a patient. A drug product may include an active ingredient, including, for example, a biologic.

[0050] As used herein, the term "raw material" may refer to a mixture including one or more biological agents that is suitable for separation or purification via chromatographic processing.

[0051] As used herein, the term "raw chromatographic data" may refer to chromatographic data in its native data state as originally collected. For example, the raw chromatographic data may be of .RES file type (other types of raw file types) or in a database containing values ​​obtained directly from a measurement device.

[0052] As used herein, the term "extracted chromatographic data" may refer to chromatographic data that has been moved from the raw data without any translation. This may be in Excel or .CSV file format, or in a database located in a chromatographic system or computing system.

[0053] As used herein, the term "noise-reduced data" may refer to chromatographic data that has been normalized, smoothed, derived, and / or peak selected, eg, transformed data.

[0054] As described above, it is necessary to monitor and maintain the quality of the chromatographic column and the process, for example, during multiple chromatographic runs, multiple batches, and at the time elapsed between two runs and between two runs. The systems and methods disclosed herein can allow for analysis of chromatographic conversion data (also referred to as "conversion analysis"), and use such analysis to monitor chromatographic performance, identify changes in chromatographic performance, and perform actions related to the chromatographic system based on such analysis and processing. In addition, in some aspects, the systems and methods disclosed herein can be part of manufacturing or purification control in one or more processes, and / or can allow for in-process control using data collected in standard chromatographic processes, thereby minimizing the increase in cost and work required to implement separate process controls.

[0055] Reference will now be made in detail to the exemplary embodiments of the present invention as described below and illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

[0056] Figure 1 An exemplary chromatography system 100 is depicted in schematic form, with which various embodiments of the present invention may be implemented. System 100 includes a mobile phase liquid supply system 102 , a material injection system 104 , a column 106 , a process controller 108 , a computing device 110 , and a detector 112 .

[0057] System 100 can be all or part of a chromatographic system, including chromatographic column 106. In some cases, system 100 can be a chromatograph. System 100 can include any hardware and / or software required for running a chromatographic column. System 100 can be configured to perform various types of chromatography, such as high performance liquid chromatography (HPLC), ion exchange chromatography, size exclusion chromatography, hydrophobic interaction chromatography (HIC), reverse phase chromatography, mixed mode chromatography or affinity chromatography. System 100 can be used for example to separate the biologics in the original mixture, separate and / or purify a single type of biologics, and / or eliminate pollutants from the mixture. In some cases, system 100 can be a part of a pharmaceutical product manufacturing system, such as a system for manufacturing a pharmaceutical product containing a biologic, such as an antibody.

[0058] The mobile phase liquid supply system 102 can be any suitable system for supplying a mobile phase to the inlet of the column 106. The mobile phase liquid supply system 102 can include one or more reservoirs to hold a fluid phase fluid for driving the raw material injected by the material injection system 104 through the column 106. The mobile phase liquid system 102 can include one or more pumps configured to apply pressure to the mobile phase liquid. In some embodiments, the pump of the mobile phase liquid supply system 102 can be configured to mix two or more solvents (e.g., from two or more reservoirs) in a desired proportion before supplying the combined solution to the inlet of the column 106. In some embodiments, the mobile phase liquid supply system 102 can be configured to supply a first mobile phase to the inlet of the column 106, and then, after the desired volume of the first mobile phase has been supplied, the second mobile phase is supplied to the inlet of the column 106. In some embodiments, the mobile phase liquid supply system can be controlled by a process controller 108 or by human interaction.

[0059] The material injection system 104 can be any suitable system for supplying raw materials to be separated and / or purified in the column 106. In some embodiments, for example, the material injection system 104 can include one or more reservoirs to hold the raw materials. These raw materials can include one or more biological agents, contaminants, solvents, or other materials.

[0060] Column 106 can be any column suitable for separating and purifying the injected raw materials from material injection system 104. One of ordinary skill in the art will recognize that column 106 can have any of a variety of sizes (e.g., a diameter ranging from about 30 cm to about 1500 cm) and can be packed with any of a variety of stationary phases. The size, shape, and packing of column 106 can be selected taking into account the raw materials that need to be separated in column 106.

[0061] The process controller 108 and / or computing device 110 can be adapted to control various aspects of the system 100 during a chromatographic run. The process controller 108 can be linked to one or more parts of the system 100, including the mobile phase liquid supply system 102, the material injection system 104, the column 106, the computing device 110, and the detector 112. In some embodiments, the process controller 108 can be a computer programmed to control the various parts of the system 100 according to a desired program. For example, in some embodiments, the process controller can be programmed to turn on and off the pump of the mobile phase liquid supply system 102, and to turn on and off the detector 112. In some embodiments, the process controller 108 can have a display and / or other user interface elements (e.g., buttons, a mouse, a keyboard, a touch screen, etc.), through which commands can be entered by, for example, a human operator. In other embodiments, the process controller 108 can be programmed using, for example, a computing device 110.

[0062] The computing device 110 may be any computer, such as a desktop computer, a server computer, a laptop computer, a tablet computer, or a personal portable device (e.g., a smart phone). In some embodiments, the computing device 110 may have a display and / or other user interface elements (e.g., buttons, a mouse, a keyboard, a touch screen, etc.), through which commands may be entered by, for example, an operator. The computing device 110 may also collect data from the process controller 108 and / or other parts of the system 100, such as the detector 112. The computing device 110 may include one or more programs configured to display or output such data to, for example, a screen, a hard disk, or to a remote location via an Internet connection. The computing device 110 itself may be connected to other aspects of the system 100 via a wired connection, or may be wirelessly connected to other aspects of the system 100 (e.g., the process controller 108). In some embodiments, the computing device 110 may be remotely located relative to the system 100. In some embodiments, the computing device 110 may be configured to display one or more user interfaces or reports. In some embodiments, the process controller 108 and the computing device 110 may be a single device.

[0063] Detector 112 may be any type of detector suitable for detecting one or more characteristics at the outlet of column 106. Figure 1 1, a single detector 112 is depicted, but the system 100 may also include more than one such detector, which is configured to detect various characteristics at the outlet of the column 106. These characteristics may include, for example, conductivity, pH, optical density, and other characteristics of the column outlet. In some embodiments, the detector 112 may be, for example, a conductivity detector, an ultraviolet (UV) detector, a fluorescence detector, a refractometer, a pH detector, a pressure gauge, or any other type of detector.

[0064] For example, a chromatographic cycle run using system 100 can typically include a series of steps. These steps can include, for example, a cleaning step in situ, an equilibrium step, a loading step, a washing step, an elution step, a stripping step, and a regeneration step. The chromatographic cycle can be tracked and / or recorded using data collected from a detector at the chromatographic column outlet (e.g., detector 112 at the column 106 outlet). For example, UV detection and UV chromatography can be used to track chromatographic processing through, for example, washing, elution, collection, and stripping steps. Figure 2 An exemplary UV chromatogram with a typical profile for collecting a single protein is depicted. As a volume of fluid passes through the column (represented by the x-axis), the UV detector detects a fairly steady rise in absorbance with a single peak near the beginning of the elution step. Collection can begin after the small elution peak, during which the absorbance forms a peak shape as most of the analyte passes through the UV detector.

[0065] A chromatographic (or chromatographic) transition is the response at the outlet of a column (e.g., column 106) to a step change at the inlet of the column (e.g., a change from a wash step to an elution step or from an elution step to a stripping step) when one mobile phase is replaced by another. Depending on what parameter is detected at the outlet of the column (e.g., by one or more of detectors 112), a transition may be detected as an increase (upward transition) or decrease (downward transition) in one or more parameters, followed by a plateau of the parameter after the transition occurs. Figure 3 An exemplary normalized plot of a chromatogram increasing transition that is separated into three phases is depicted. Before the transition, the detector detects a baseline value for the parameter. During the transition, the parameter "rises" or increases, then stabilizes after the transition. In some cases, the plateau after the rising transition is due to detector saturation. The data obtained during the transition is quantitative and sensitive to slight changes in column performance.

[0066] Examples of measurable parameters that may change in a conversion include conductivity, pH, salt concentration, light absorption, fluorescence after excitation with light of a suitable wavelength, refractive index, electrochemical response, and data generated by mass spectrometry. However, one of ordinary skill in the art will appreciate that any other measurable parameter that may change in a conversion may be used in a conversion analysis according to the present invention.

[0067] In order to perform a conversion analysis to determine the quality and / or integrity of the chromatographic column and / or process, the chromatographic data can be divided into a plurality of blocks, each block corresponding to a step in the chromatographic process (e.g., a clean-in-place block, a balance block, a load block, a wash block, an elution block, a strip block, a regeneration block, a storage block, etc.). Each block includes a plurality of signals provided by one or more detectors during the block. In order to perform a conversion analysis, any number or combination of blocks and signals can be used, such as 1 to 8 blocks (e.g., 1 block, 2 blocks, 3 blocks, 4 blocks, or 5 blocks), and about 1 to 8 signals (e.g., 1 signal, 2 signals, 3 signals, 4 signals, 5 signals, 6 signals, or 7 signals). More blocks and / or signals can also be used.

[0068] Figure 4 Depicted are example graphs of the conductivity detected as a function of volume during the elevated conversion in the balance block of three chromatographic runs. Each run includes the same chromatographic treatment of the same raw material in the same column, including the separation of the same protein, but different batches of raw materials are used. The first peak (in all three runs) represents the main part of the system. After the peak appears, as can be seen, the three runs show changes in the conversion phase. The shortest dashed line depicts the dashed line closest to the ideal conversion phase, because the conversion is the most "vertical" (i.e., appears on the shortest volume). The longer dashed line represents some characteristics that indicate column failure, namely the early start and tapered end of the conversion phase. In general, this conversion occurs on a larger amount of volume. The solid line represents a stronger characteristic of column failure, because the conversion phase starts very early and takes too much time to reach saturation. Although these differences are visually obvious, they may not be easy to quantify or given context in the case where they cannot be compared to each other. The present invention provides systems and methods for performing analysis using these data and optionally performing process control using such analysis.

[0069] Figure 5 and 6 Depicted is an exemplary process for analyzing chromatographic data and using such analysis to perform process control according to aspects of the present invention. Figure 5 An exemplary process at a more general level of detail is depicted, while Figure 6 The following describes more details of the exemplary process. Figure 6 Details and particulars of the processing may also apply to Figure 5 and vice versa.

[0070] Figure 5An exemplary general process 500 for analyzing chromatographic data and performing process controls according to some aspects of the present invention is depicted. According to step 510, raw chromatographic data can be processed. According to step 520, data can be acquired from the raw chromatographic data. According to step 530, the acquired data can be processed. According to step 540, the processed data can be analyzed (e.g., a transformation analysis). According to step 550, one or more statistical process controls can be performed. According to step 560, the data can be reported.

[0071] According to step 510, raw chromatographic data can be processed. Raw chromatographic data can be obtained by running one or more chromatographic cycles and obtaining signals from one or more detectors (e.g., detector 112 of column 106). Signals can include, for example, UV signals, conductivity signals, pressure signals, pH signals, and / or other signals. Data can be obtained, for example, at processing controller 108 and / or computing device 110 and can be stored in, for example, a database or .RES file. Data can include, for example, a series of signal values ​​and the corresponding volume where the measured signal is located. Data can also include indicators of the start and end of each block / step in the chromatographic cycle.

[0072] Processing the data may include extracting the data and organizing the data in a data file in a computing device, such as computing device 110. Exemplary data files include, for example, spreadsheets, text files, databases, combinations thereof, and the like. Various metadata may be assigned to the data file containing the extracted chromatographic data to allow for consistent storage and processing. The metadata may include, for example, the name, date, column run time, column run volume, column mobile phase, identification of the original mixture, identification of the manufacturing process using the column, and / or any other data that may allow for consistent automatic or manual processing of the data file.

[0073] According to step 520, data can be obtained from the data file for analysis. In some embodiments, an automated software program (such as Cron, Jobber, macro or other automatic or scheduling software) can monitor one or more possible data file storage locations for one or more data files for matching one or more configuration files. Configuration files can be assigned to data files based on, for example, metadata associated with the data files. Configuration files can be, for example, a pre-established series of selection criteria for selecting one or more block and signal combinations suitable for performing conversion analysis. Configuration files can be assigned based on, for example, the type of column being run, the characteristics of the mobile phase, the volume of the mobile phase being run, the column run time, or other characteristics of the data file.

[0074] Acquiring data for analysis can include selecting one or more block and signal combinations based on an assigned profile, where the blocks correspond to steps in a chromatographic process and the signals correspond to the type of data being collected (e.g., UV data, conductivity, pH, etc.). In some embodiments, a primary block and signal combination can be selected. In other embodiments, a primary block and signal combination and one or more secondary block and signal combinations can be selected. First, a conversion analysis can be performed using the primary block and signal combination, and a second conversion analysis can be performed using one or more secondary block and signal combinations. Profiles, selection criteria, and block and signal combinations are described in more detail with respect to process 600.

[0075] According to step 530, the acquired data can be processed again to obtain noise-reduced data. Processing the acquired data can include applying one or more smoothing and / or noise reduction techniques to data sets in the acquired data, such as data associated with a major block and signal combination, and optionally data associated with a minor block and signal combination. In some embodiments, processing the data can include standardizing the size of the data set to allow for a consistent effect of the smoothing window. In some embodiments, processing the data can include normalizing the data to eliminate variations based on the amplitude of the conversion. This variation may be due to the unique preparation of the mobile phase buffer, which contains inherent variability in the final value of the baseline phase or saturation phase.

[0076] Noise reduction techniques may include removing implicit errors introduced by measurement tools (e.g., detector 112 in system 100), as well as random errors introduced by batch processing when collecting data (e.g., in an earlier step of method 500). Noise reduction may include deduplication of records in a data set, outlier detection and removal, and / or any other technique for increasing the signal-to-noise ratio in a data set. Noise reduction may also include data smoothing and signal rejection, which will be described in further detail below with respect to process 600.

[0077] The processed data may include, for example, measurements of step yields and / or other mobile phase parameters, which may be in the form of one or more smoothed curves corresponding to one or more chromatographic step transitions. The one or more curves may represent a normalized array of solute signal data.

[0078] According to step 540, the noise reduction data can be analyzed. Such analysis can be a transformation analysis. The transformation analysis can include performing one or more mathematical processing on the processed data. For example, one or more curves can be generated from the processed data by, for example, taking the first derivative of the curve to generate another curve characterized by a peak. The curve can be analyzed to generate performance parameters such as, for example, multiple inflection points, maximum rate of change, penetration volume, cumulative error, NG-HETP, curve asymmetry, and Gaussian HETP. These performance parameters (alone or in combination with past data) can help determine the integrity of the column.

[0079] For example, an increase in the number of inflection points may indicate that a small amount of early penetration of the conversion solution is occurring, which may be associated with integrity damage. A reduction in the maximum rate of change used by multiple columns may indicate that a conversion has occurred on a larger column, which may be an indication of integrity damage. A reduction in penetration volume may also characterize integrity damage. An increase in NG-HETP or Gaussian HETP may indicate a reduction in column integrity. Other characteristics of conversion can be generated based on data set variance, slope, kurtosis, peak asymmetry, penetration or washout volume, and total error. Conversion analysis is described in more detail below. For example, by citing the use of processing data by Larson et al., which is incorporated herein in its entirety, to evaluate the chromatographic performance in a production-scale protein purification column, Biotechnol, Prog., 2003, 19, 485-492 also describes a system and method for performing conversion analysis.

[0080] The results of the transformation analysis may be stored with other data, for example, in a memory element of computing device 110, or in another computing device. For example, all raw data, initial data sets, smoothed data sets, and transformation analysis data may be stored.

[0081] According to step 550, the results of the conversion analysis can be used to perform one or more statistical process controls. In some embodiments, the statistical process control can include techniques that perform one of several categories, including 1) non-routine control chart analysis (e.g., single value chart, moving range chart, and / or range chart analysis), 2) variable analysis (MVA), or 3) a combination of non-routine control chart analysis and MVA. These processes can include, for example, analyzing the results of the conversion analysis as part of a larger data set, which includes conversion analysis results from previous chromatographic runs, for example, runs in the same production cycle, runs of the same product batch, or runs of the same raw materials. These processes are described in more detail below with respect to process 600.

[0082] The results of performing one or more statistical process controls may be referred to as performance evaluation data. Performance evaluation data may refer to any process data, including transformation analysis results, that is meaningful in evaluating the reproducibility and success of a process.

[0083] According to step 560, the data can be reported. In some embodiments, one or more reports can be generated. For example, the disclosed methods and systems can generate a tabular report of any results analyzed using a given profile. Reports can be generated based on a desired number of previous chromatographic runs for a specific time range, a specific run, and / or a specific batch. Fig.24 An example report is depicted in and described in more detail below.

[0084] Figure 6 Compare Figure 5 An exemplary process 600 for analyzing chromatographic data and performing process control according to some aspects of the present invention is depicted in more detail. According to step 610, raw chromatographic data can be received. According to step 620, the raw chromatographic data can be processed according to a configuration file. According to step 630, noise reduction techniques can be applied. According to step 640, a transformation analysis can be performed on the processed chromatographic data to generate transformed data representing column integrity. According to step 650, at least one of an individual value (I) graph, a moving range (MR) graph, or a range (R) graph can be generated based on the transformed data. According to step 660, one or more multivariate statistical analysis methods can be applied to at least one I graph, MR graph, or R graph to generate performance data. According to step 660, actions can be performed based on the performance data.

[0085] According to step 610, raw chromatogram data can be received. As with process 500, the raw chromatogram can be obtained from, for example, a chromatographic system, such as system 100. The raw chromatogram data can include multiple signals associated with multiple blocks. Receiving the raw chromatogram data can include retrieving the raw chromatogram data directly from one or more detectors (e.g., detector 112 of system 100) or from a computing device (e.g., computing device 110) and / or can include monitoring a network location for raw chromatogram data files. In some embodiments, the raw chromatogram data can be processed as described above in process 500 with respect to step 510.

[0086] exist Figure 71000 of the chromatographic data extracted is depicted in the figure. Data file 1000 may include, for example, a data file name, which can help identify the data file by an automated system. As shown, the chromatographic data extracted in data file 1000 can be in the form of an electronic spreadsheet (e.g., Microsoft Excel). Data file 1000 may include volume measurements in the first column 1002, which may correspond to regular measurements of the total volume that has passed through the chromatographic system. The second column 1004 may include signal measurements (e.g., UV, conductivity, pH, etc.) corresponding to each of the volume measurements in the column 1002. In this case, the second column 1004 includes conductivity data represented by mS / cm. Other columns may provide additional data. Here, for example, the third column 1006 includes volume measurements corresponding to log entries in the fourth column 1008. This can allow identification of various characteristics of the chromatography run, such as block / step start and end points (CG002_START, CG002_END, CG003_START), flow rates, and points at which various aspects of the chromatography system are activated (e.g., Pump 1 can correspond to the time when a pump associated with, for example, the mobile phase liquid supply system 102 is activated). One of ordinary skill will appreciate that many variations of the data file 1000 are possible. For example, while volume measurements are shown in the data file 1000 as a marker of progress in the chromatography run, other measurements, such as time, can also be used. Additional columns for other signal data can be included, and additional log data can be included (e.g., identifying the mobile phase, identifying the analyte, etc.).

[0087] Return to reference Figure 6 According to step 620, the chromatographic data can be processed according to the configuration file. As briefly described with respect to step 520, a configuration file can be selected for the chromatographic data according to the characteristics of the chromatographic data in the file. For example, a configuration file has been previously created for a given type of chromatographic run, a given chromatographic column, and / or a given analyte. Thus, such a configuration file can be matched to a chromatographic data file for an appropriate run, column, and / or analyte.

[0088] In some aspects, a profile can be created by a user. A profile can be associated with a specific drug or drug product. In one aspect, the drug is a small molecule. In other aspects, the drug is a peptide or polypeptide.

[0089] In some aspects, the drug is a vascular endothelial growth factor (VEGF) derivative. In other aspects, the drug is aflibercept, which is described in U.S. Pat. Nos. 7,070,959, 7,303,746, 7,303,747, 7,306,799, 7,374,757, 7,374,758, 7,531,173, 7,608,261, 7,972,598, 8,029,791, 8,092,803, 8,343,737, and 8,647,842, each of which is incorporated herein by reference in its entirety.

[0090] In other aspects, the medicine is an antigen binding molecule. In some aspects, the antigen binding molecule is an antibody or an antigen binding fragment. In some aspects, the medicine is alikaumab, which is described in U.S. Patent Application Publication Nos. 2014 / 0356371 and 2014 / 035670, each of which is fully incorporated herein by reference. In another aspect, the medicine is sarrelumab, which is described in U.S. Patent Application Publication Nos. 2016 / 0152717, 2014 / 0302053 and 2013 / 0149310, each of which is fully incorporated herein by reference. In another aspect, the medicine is dupilumab, which is described in U.S. Patent Application Publication No. 2014 / 0356372, which is fully incorporated herein by reference. In another aspect, the drug is selected from eloumab, bevacizumab, ranibizumab, tocilizumab, certolizumab pegol, etanercept, adalimumab, abatacept, infliximab, rituximab, anakinra, trastuzumab, pegfilgrastim, interferon beta-1a, insulin glargine [rDNA derived] injection, erythropoietin alfa, darbepoetin, filligasis, and golimumab.

[0091] In some embodiments, the configuration file can be configured to instruct a Sentinel software program (e.g., a macro, Jobber, Cron, or other scheduling software) to periodically scan a network location specified for chromatographic data files. When the file name matches the file name identifier in the configuration file, the configuration file can instruct data acquisition from the file.

[0092] Once a profile has been selected or a profile has been selected for a data file or a profile has been matched to a data file, the data file can be scanned. Figure 7, the fourth column 1008 including log entries may be scanned for indications of block start time, end time, flow rate, etc. For example, with respect to the data file 1000, the volume measurements corresponding to "CG002_START" and "CG002_END" include volume measurements corresponding to the chromatographic operation and signal transition of interest. The first column 1002 and the second column 1004 may then be used to extract the entire data set of signal and volume measurements for that operation.

[0093] The values ​​in the configuration file can also define one or more selection criteria for selecting one or more combinations of blocks and / or signals in the chromatographic data file for performing the conversion analysis. Therefore, the configuration file can be a tool for obtaining a preferred data subset from the chromatographic data file. The selection criteria in the configuration file can be predetermined according to, for example, experimental experience, structured optimization and / or processing archives. Such selection criteria can make it possible to identify blocks and signal combinations that can allow for more precise, accurate or otherwise more useful analysis. Such selection criteria can include, for example, whether the conversion material is easy to obtain. This includes blocks that are converted to or from product solutions. If necessary, this allows additional column evaluations between manufacturing operations. Such selection criteria can also or alternatively include whether the blocks occur at regular intervals. This includes blocks that are not executed after the final collection cycle of the manufacturing batch ends. Such selection criteria can also or alternatively include whether the signal reaches detector saturation before or after the conversion. Such selection criteria can also or alternatively include whether the signal approaches the stationary phase at different and identifiable levels and does not connect drift. Such selection criteria can also or alternatively include whether the signal in a given block has a large difference between the minimum and maximum values. Such selection criteria may also or alternatively include whether the signal has many inflection points during transitions. Fewer inflection points may indicate more reliable data collection.

[0094] In some cases, previous chromatographic runs can help identify appropriate selection criteria for selecting blocks and signal combinations in future chromatographic runs. Figure 8For example, a NG-HETP calculation diagram of two different blocks and signal combinations (elution step-UV signal combination and re-equilibrium step-conductivity signal combination) of six different chromatographic batches (batches AF) is shown. A solid bar indicating three standard deviations for each group is provided as a reference. As can be seen from the figure, the NG-HETP calculation of the elution step-UV signal combination shows a greater change than those for the re-equilibrium step-conductivity signal combination. It can be seen that the scale and standard deviation of the trend are different. When monitoring performance transitions, it may be desirable to have smaller changes between batches that are considered typical. This allows for increased sensitivity when monitoring performance transitions. Therefore, the selection criteria for the chromatographic run of a batch similar to batch AF can include giving priority to the re-equilibrium step and conductivity signal combination over the elution step and UV signal combination. Those skilled in the art will appreciate that analysis of previous chromatographic runs in a similar manner can reveal other possible block and signal combination selection criteria.

[0095] In some embodiments, a configuration file may include instructions for applying one or more selection criteria to a data file having associated chromatographic data. Thus, processing the chromatographic data according to the configuration file may include identifying and extracting a preferred (e.g., primary) block and signal combination for a conversion analysis, and / or one or more additional (e.g., secondary) block and signal combinations for a conversion analysis. In some embodiments, the primary block and signal combination will satisfy the majority of selection criteria in the configuration file for all possible block and signal combinations in the chromatographic data file. In some embodiments, the secondary block and signal combination will satisfy the second most selection criteria in the configuration file for all possible block and signal combinations in the chromatographic data file. While the primary block and signal combination may include the most likely to provide a valuable conversion analysis for evaluating the integrity of the column and the process, the secondary block and signal combination may also provide a secondary measurement and cross-check of the integrity of the column.

[0096] In some embodiments, the configuration file according to step 620 may be a data file in or of itself, which may contain instructions for extracting certain data from or changing a chromatogram data file with associated metadata. In some embodiments, such instructions in the configuration file may be executed by a computer program.

[0097] Return to reference Figure 6, after the chromatographic data has been processed according to the profile, noise reduction techniques may be applied to the processed data according to step 630. As with step 530 of process 500, this step may include applying one or more smoothing and / or noise reduction techniques to the processed data (e.g., data associated with the selected block and signal combination). In some embodiments, this step includes standardizing the size of the data set to allow for a consistent effect of the smoothing window. In some embodiments, this step may include normalizing the data to eliminate variations based on the magnitude of the transitions. Such variations may be due to unique preparations of mobile phase buffers that contain inherent variability in the final values ​​of the baseline phase or saturation phase.

[0098] Noise reduction techniques may include removing implicit errors introduced by measurement tools (e.g., detector 112 in system 100), as well as random errors introduced by batch processing when collecting data (e.g., in earlier steps of method 500). Noise reduction may include deduplication of records in a data set, outlier detection and removal, and / or any other technique for increasing the signal-to-noise ratio in a data set.

[0099] Noise reduction may also or alternatively include applying data smoothing and signal error rejection algorithms. Fig. 9 An exemplary algorithm 900 in this regard is depicted in flowchart form. According to steps 902 and 904 of algorithm 900, the algorithm may begin and retrieve relevant signal data (e.g., data that has been processed according to step 620). According to step 906, the retrieved data may be normalized to remove amplitude bias.

[0100] Subsequently, a multi-stage smoothing algorithm 950 may be applied. This may include applying one or more initial smoothing filters (steps 908, 910) according to desired smoothing filter set points (909, 911). According to step 912, derivation may optionally be performed. One or more additional smoothing filters (steps 914, 916) may then be applied according to additional desired smoothing filter set points (913, 915). The number of smoothing filters applied (steps 908, 910, 914, 916) and the number and nature of the set points 909, 911, 913, 915 may vary according to, for example, data conditions, desired results, signal type, and other factors. Whether derivation is performed on the data may also depend on these factors.

[0101] Processing can then continue to a dynamic signal error rejection algorithm 980. The algorithm can be configured to remove data that is not due to chromatographic conversion from the retrieved data. For example, errors that should be removed to allow meaningful conversion analysis include alarms, machine stops, instrument sensor failures, or data gaps. This can be achieved by identifying expected features of chromatogram conversions, such as derivative duration, maximum intensity, duration from start, or expected background noise. For example, initial point rejection 918 can be performed based on expected conversion positions 919, initial dead zone rejection 920 can be performed based on expected background noise levels 921, derivative height and width rejection can be performed based on expected signal error characteristics, and final dead zone rejection can be performed based on expected background noise levels 925. For example, expected conversion features can be generated based on previously accumulated conversion data. After completing the algorithm 900, the data can be ready for conversion analysis according to step 990.

[0102] Although algorithm 900 is an exemplary model of a smoothing and signal error rejection algorithm, one of ordinary skill in the art will recognize that variations to the algorithm are possible. For example, only the smoothing algorithm 950 may be performed, or only the signal error rejection algorithm 980 may be performed. Additionally or alternatively, more or fewer smoothing filters may be applied, and / or more or fewer points may be rejected.

[0103] After applying noise reduction and / or smoothing techniques to the data, the data may include, for example, measurements of step yields and other mobile phase parameters in the form of breakthrough or washout curves corresponding to step transitions.

[0104] Return to reference Figure 6 According to step 640, a transformation analysis can be performed on the processed chromatographic data to generate transformation data representative of column integrity. The transformation analysis can include performing one or more mathematical manipulations on the processed data to infer discrete parameters from the step transformations. For example, one or more curves can be generated from the processed data by, for example, taking the first derivative of the curve to generate another curve characterized by a peak. The curve can be used to generate performance parameters such as, for example, multiple inflection points, maximum rate of change, breakthrough volume, cumulative error, NG-HETP, curve asymmetry, and Gaussian HETP. As described with respect to step 540, these parameters can be used as indicators of column integrity or lack thereof (e.g., when examined against transformation analysis parameters of previous representative chromatographic data).

[0105] For example, an increase in the number of inflection points may indicate that early breakthrough of a small amount of switching solution is occurring, which may be associated with integrity failure. When the derivative curve is plotted against the aggregated volume data, multiple inflection points may be determined from multiple peaks.

[0106] As another example, a decrease in the maximum rate of change used by multiple bars may indicate that a switch has occurred on the larger bars, which may be an indication of integrity violation.The maximum rate of change is equal to the maximum value of the derivative curve.

[0107] As another example, a reduction in breakthrough volume can also characterize an integrity breach. The breakthrough volume can be determined by finding the first volume value at which the signal is less than 95% of its highest value (for a high to low transition) or greater than 5% of its lowest value (for a low to high transition).

[0108] As another example, an increase in NG-HETP or Gaussian HETP can indicate a decrease in column integrity. Other characteristics of the transformation can be generated based on data set variance, slope, kurtosis, peak asymmetry, breakthrough or washout volume, and total error. Systems and methods for performing transformation analysis are also described in, for example, Larson et al., Biotechnol, Prog., 2003, 19, 485-492, which is incorporated herein by reference in its entirety, using process data to evaluate chromatographic performance in production-scale protein purification columns.

[0109] The results of the transformation analysis may be stored with other data, for example, in a memory element of computing device 110, or in another computing device. For example, all raw data, initial data sets, smoothed data sets, and transformation analysis data may be stored.

[0110] Return to reference Figure 6 According to step 650, at least one of an individual value (I) graph, a moving range (MR) graph, or a range (R) graph may be generated based on the conversion data. For simplicity, the present invention collectively refers to them as I-MR-R graphs; however, "I-MR-R graphs" should be understood to refer to only I graphs, only MR graphs, only R graphs, or any combination and number of such graphs. I-MR-R graphs constitute a separate visualization of the conversion analysis output and can help interpret trends in the conversion analysis data of multiple column runs or batches in the form of NG-HETP, slope, kurtosis, or other parameters. One advantage of I-MR-R graphs is that the data can be viewed quickly and can be easily interpreted from a visual perspective. This allows small trends or immediate data shifts to be identified at an early stage.

[0111] For example, an I plot may plot values ​​(e.g., slopes) for each analysis batch. An MR plot may plot values ​​for each analysis batch and values ​​for the difference between the values ​​of the previous analysis batch. An R plot may plot values ​​for the difference between values ​​within a batch (e.g., the slopes of two transformation analyses completed on one batch for a major block and signal combination and a minor block and signal combination). Each plot may include a mean line, an upper control limit (UCL), and a lower control limit (LCL), which may be calculated using available data that has been determined to represent a typical process and placed equidistant from the mean line in each plot.

[0112] When plotted on an I-MR-R chart, some parameters, such as NG-HETP and slope of a conversion analysis, can depict significant dynamics during the life of certain limits. In this case, using an I-MR-R chart with control limits estimated with short-term standards may result in excessive supertrend signals, even after resetting the control chart when the column is refilled. A solution to this problem is to use a Levey Jennings control chart, which is calculated using a long-term standard deviation from a "representative" column batch, which takes into account the special changes attributed to the start-up of a new column filling. Whether the data is considered representative can be determined by abnormal readings of various performance evaluation data sets that do not have a batch. These sets can be used to calculate standard deviations, sometimes paying special attention to the + / -3 standard deviation (SD) line. Several batches can be run on the column to determine whether most or the entire effective period of the column is "typical". In one aspect, full modeling of feasible column dynamics can be performed on the Levey Jennings control chart, which produces a regression model that explains the special cause changes of the column refilling. However, the Levey Jennings control chart requires longer-term data, so its use will be limited by the data aggregation rate.

[0113] Additionally, since the conversion analysis is known to have changes due to the refilling event of the column, the I-MR-R plot can take into account the filling and refilling of the column, for example, the first batch of runs after refilling the column will not have an MR value based on the change compared to the last batch of runs before refilling the column. In some aspects, the control strategy can be configured to only consider certain violations that exclude known changes due to the fact of refilling when monitoring trend shifts.

[0114] The generation of the I-MR-R diagram may be performed, for example, by an analysis module in the computing device 110 or another analysis module elsewhere. The generation of the I-MR-R diagram may also be performed, for example, by a control diagram module in the computing device 110. For example, Figure 14-21 I-MR-R data for between 21 and 100 chromatography batches are shown and discussed further below.

[0115] Return to reference Figure 6 According to step 660, one or more multivariate statistical analysis methods may also be applied to the I-MR-R data. Alternatively, one or more multivariate statistical analysis methods may be applied to the transformed analysis data. In addition to or as an alternative to step 650, this step may be performed and generated as according to step 650. Figure 1 Similarly, consider the previous transformation analysis of chromatographic data. Multivariate statistical analysis takes multiple variables and reduces them to component vectors. This allows large data sets to be viewed as a whole. Advantages include multiple subtle changes in multiple properties that may not be obvious when viewing a singular data set but may become apparent when its component vectors are graphed. Fluctuations in the data may be caused by differences in materials, equipment, ambient atmospheric conditions, etc., and may be minimized by the perception of the operator or human observer. Examples of multivariate statistical analysis methods may include principal component analysis (PCA), partial least squares (PLS), orthogonal partial least squares (OPLS), multivariate regression, canonical correlation, factor analysis, cluster analysis, graphics programs, and the like. Such multivariate statistical analysis can be performed using, for example, specialized computer software.

[0116] The general purpose of using multivariate analysis is to transform large amounts of data into interpretable information. By enabling the search for correlations and patterns in multidimensional variables and extracting statistically significant values ​​from large amounts of raw data, multivariate analysis enables the interpretation of, for example, any significance of variation between transformed analyses of similar batches of chromatographic data.

[0117] For example, PCA is a multivariate statistical method in which a data set containing many variables (e.g., a transformation analysis containing several parameters) is reduced to a few variables called scores (t). For example, a data set containing many variables can be reduced to each observation represented by two t-scores (e.g., each transformation analysis). T-scores contain information about the variation of each variable in the data set and the correlation of each variable with each other variable in the data set. In this regard, the t-score describes the structure of the variation and correlation of each observation in the data set (e.g., each transformation analysis) with each other observation in the data set. The graphical output of PCA is usually a PCA plot. A PCA plot is a plot of one t-score for each observation versus another. Typically, a PCA plot is a distribution that shows how the structure of variation and correlation compares for all observations in the data set. Therefore, this plot can be used to cluster similar observations together.

[0118] As another example, PLS regression analysis is a technique for analyzing the system of independent and response variables.PLS is a prediction technique that can handle many independent variables, even when the variables show multicollinearity.PLS can also associate the group of independent variables with a group of multiple dependent (response) variables.Usually, in PLS, a group of latent variables can be extracted for the group of explicit independent variables, and another group of latent variables can be extracted for the group of explicit response (or dependent) variables.The extraction process can be based on the decomposition of the cross product matrix involving independent variables and response variables.The scores or x values ​​of the latent independent variables are used to predict the scores or y values ​​of the latent response variables.Then, the predicted y values ​​are used to predict the additional explicit response variables.Select x and y scores so that the relationship between continuous x and y variables is as strong as possible.The advantages of PLS ​​include the ability to model multiple independent and dependent variables, the ability to handle multicollinearity between independent variables, robustness in the face of data noise and missing data (depending on the software used), and creating latent variables directly based on the cross product involving the response variable for stronger prediction.

[0119] In some embodiments, multivariate statistical analysis may be performed on the I-MR-R graph to determine further statistical significance of changes shown in the I-MR-R graph.

[0120] In addition to the analysis described above, the trend of the conversion analysis can be created by calculating the non-fixed range that allows buffer changes to remain within the control limits, and the sharp change of column performance can be marked as a potential super trend. The basic method for defining control limits includes moving average, weighted moving average and various degrees of exponential smoothing. A method of calculating trend limits can be adopted, which is called Holt Winters method or triple exponential smoothing method to obtain high efficiency. The Holt Winters method adopts the seasonality defined as discrete column filling events for predicting appropriate restrictions to be directly applied to chromatographic monitoring. Regression modeling (for example, used in Levey Jennings control chart) constitutes an additional method for establishing trend limits. Once enough empirical data have been obtained, the regression modeling of the execution column integrity can be used about the filling of the cumulative column. This can provide accurate and appropriate range of column performance based on the historical column performance included in the model.

[0121] Return to reference Figure 6, according to step 670, an action can be performed based on the performance data. In some embodiments, this may be due to analyzing the identified transformation as an in-process control (IPC). Generally, the action according to step 670 can include generating a report, generating and / or transmitting an alarm to an operator or a display (e.g., a display of computing device 110), or terminating the chromatographic processing. The action according to step 660 can also include, for example, storing all data acquired during the systems and methods disclosed herein in a database for further analysis.

[0122] The results of performing multivariate analysis and / or I-MR-R plot analysis on the transformation data may be referred to as performance evaluation data. Performance evaluation data may refer to any process data including transformation analysis results that may be meaningful in evaluating the reproducibility and success of a process (e.g., a chromatographic process).

[0123] In one aspect, step 670 may include generating one or more reports. For example, the disclosed methods and systems may generate a report in a tabular format of any results analyzed using a given configuration file. Reports may be generated based on a desired number of previous batches for a specific time frame and / or a specific batch. The data set may be fully extracted into a variety of formats and may be imported into an external application if further analysis is desired.

[0124] Fig.24 Describes an exemplary report 2400 according to some aspects of the present invention. The exemplary report 2400 includes a report pivot table that includes the results of four chromatographic cycles from a manufacturing batch. Each of the four cycles is listed by its batch and cycle number and includes a list of the date and time at which it runs. The conversion analysis results are reported in columns, including NG-HETP, Gaussian HETP, slope, asymmetry, kurtosis, non-Gaussian N, and Gaussian N. A snapshot of the data source is also provided, which indicates the name of the chromatographic system from which the data comes, the log recorded therein, and the block used to obtain the data. Below the data for each of the cycles, trend data for each of the analysis results is reported. It should be understood that the report is an exemplary report, and many variations are possible. For example, the desired number of chromatographic cycles can be listed and / or included in one or more trend data graphs.

[0125] In some aspects, the systems and methods disclosed herein can be used for continuous monitoring of the integrity of the column and the processing. In this regard, the systems and methods disclosed herein can be about specific columns and / or processing analysis data. In one aspect, one or more alarms can be generated based on data analysis. In another aspect, the chromatographic process can be terminated based on data analysis. For example, one or more notifications (e.g., event notifications, event assessments, or deviation notification tables) can be provided or displayed to the operator to take corrective action. For example, when the analysis is completed, one or more screen overlays can be displayed on, for example, the screen of the computing device 110, and / or a message can be sent to the operator, suggesting whether to continue or stop the chromatographic process.

[0126] In one aspect, the results from the disclosed methods and systems may tend to inform information on current trends in assessing column packing quality before the column is used in manufacturing. In another aspect, the results from the disclosed methods and systems can be used to assess column performance in real time (or off-line), and the integrity of the column can be confirmed before the next product use cycle (e.g., if acceptable ranges and control limits in trend graphs are established).

[0127] In another aspect, the results can be used with statistical information to predict treatment outcomes based on process modeling using multivariate statistical analysis, prior to costly and time-consuming investigations and testing.

[0128] For example, one evaluation criterion for a statistical analysis plot may specifically include that when using PCA to generate a score for a data set, batches that exceed a threshold number of standard deviations from the mean may be identified as issues with column integrity and may result in the generation of an alert or instruction regarding a batch change.

[0129] In particular, one evaluation criterion for an I-MR-R chart may include that any point outside of an upper control limit or a lower control limit for one or more chart types may be the basis for an alarm. Thus, an action performed in accordance with step 670 may be to issue an alarm from, for example, computing device 110, if the batch exhibits a point outside of the control limits. Such an alarm may include, for example, an event notification, an event evaluation, and / or a deviation notification table to be provided to an operator or a database.

[0130] In some aspects, the systems and methods disclosed herein can be implemented as part of an in-process control system that can operate within the framework of an organization's quality system to ensure consistency and compliance with safety requirements. As part of such a program, data from the systems and methods disclosed herein can be used to determine key processing parameters (CPPs) and key quality attributes (CQAs) to be monitored in an in-process control program. Additionally, as part of such a program, signal conversions and integrity transitions of the column can be detected in real time or near real time (e.g., simultaneously with or in parallel with the operation of the column), allowing preventive and corrective actions to be taken in response to performance data.

[0131] Fig.23 An exemplary user interface 2300 according to some aspects of the present invention is depicted. The user interface 2300 depicts a transformation analysis profile creation / editing screen, which a user can use to generate or edit a new transformation analysis profile. The parameters selected during the creation of the profile can be used to adjust the transformation analysis based on the unique characteristics of the chromatographic process and optimize the robustness of the output of each column and program. The parameters listed in the exemplary user interface 2300 include, for example, a profile name, comments, historical data and / or test location, file mode, final value, key indicator, hard reset, window size for moving average, value of the first filter (e.g., SG filter), value of the second filter, percentage of the first Vmax to register the signal as zero, percentage of the maximum width for retaining peaks, height of the chromatographic column, start date, end date, and database name.

[0132] The method and system disclosed herein can be used to monitor the integrity of a column relatively continuously. For example, the method and system disclosed herein can monitor the integrity of a column without interrupting conventional chromatographic processing to perform diagnostics on a chromatographic system. In addition, the method of the system disclosed herein can analyze data about a specific column and a specific process. As discussed, one or more alarms can be generated based on data analysis over time. In another aspect, the chromatographic processing can be terminated based on data analysis. For example, one or more notifications can be displayed to the operator so that corrective action is taken in the event that the integrity of the column is found to be compromised. For example, one or more screen overlays can be displayed, and a message window can be displayed to the operator when the analysis is complete, thereby suggesting to continue or stop the chromatographic processing, or suggesting other actions.

[0133] In some aspects, the results from the disclosed methods and systems may tend to inform information on current trends in evaluating column packing quality before the column is used in manufacturing. In other aspects, the results from the disclosed methods and systems can be used to evaluate column performance in real time (or offline), and the integrity of the column can be confirmed before the next product use cycle (e.g., if acceptable ranges and control limits in trend graphs are established). In some aspects, the results can be used with statistical information to predict process outcomes based on process modeling using MVA, prior to expensive and time-consuming investigations and testing. Example

[0134] Example 1

[0135] The primary block and signal combinations were selected from the affinity capture chromatography data for Protein A as follows. The affinity capture data included eight blocks and two signals (UV and conductivity) in each block for a total of 16 potential block and signal combination selections. The data was assigned a profile containing a series of block and signal selection criteria that were applied in the following order to select the primary block and signal combinations: • By considering the selection criterion that the blocks must occur at fixed intervals in the manufacturing batch cycle, two blocks and their corresponding signals can be eliminated, leaving 12 potential combination choices. • By considering the selection criterion that the signal must reach UV absorbance saturation, the UV signals for three blocks can be eliminated as candidates, leaving nine potential combination choices. - By considering the selection criteria that the signal is close to the stationary phase at different and identifiable levels, the UV signals for three blocks can be eliminated as candidates, leaving six potential combination choices (all with conductivity are selected as signals). • By considering the selection criterion that the signal should have a large difference between the minimum and maximum values ​​at a given block, the conductivities for four blocks can be eliminated, leaving two potential combination choices. • By considering the signal showing the least number of inflection points as the preferred selection criteria, the conductivity for one block can be removed, leaving only one remaining block and signal combination selection.

[0136] The last remaining block and conductivity signal selection is the primary block and signal combination for which the conversion analysis can be performed. The last block and signal combination to be eliminated becomes the secondary block and signal combination.

[0137] Example 2

[0138] The I-MR-R trend slope and NG-HETP data for 100 chromatography batches in a given chromatography "Procedure B" are plotted as shown below. Figures 14 to 16I, MR and R graphs showing the slope are shown respectively. Figures 17 to 16 I, MR and R graphs showing NG-HETP are shown, respectively. UCL and LCL represent 3 standard deviations, as determined by previously accepted data. Interruptions in the mean, UCL and LCL lines represent refilling of the columns. Uninterrupted shifts of these lines represent points at which limits are recalculated.

[0139] Fig.14 The slopes for all 100 batches produced in Procedure B are shown. It can be seen that the first and second column packings exhibit different behaviors during their use. As shown, packing 1 experienced limited displacement after the first four batches and maintained the slope value between 0.055 and 0.855. Packing 2 was super-trending and eventually reached a steady state at batch 67. This may be due to the longer time for displacement and sedimentation of the new column packing than in packing 1.

[0140] Fig.15 MR plots showing the slopes of all batches produced in procedure B. An outlier value for fill 2 may be observed, which indicates a large shift between batches based on the corresponding values.

[0141] Fig.16 The R degree of slope for all batches generated in Procedure B is shown. Several outliers are noted in Fill 1. This increases the limit for Fill 2. There are three fills on the graph, and the batches are plotted in order so that Fill 1 is the leftmost continuous line and Fill 3 is the rightmost continuous line. Note that during the second half of Fill 1, the trend points are outside of the trend. This may indicate that the bar has experienced changes over the period of the batch.

[0142] Fig.17 I plots of NG-HETP are shown for all batches produced in Procedure B. Filling 1 experienced a decrease in NG-HETP, indicating improved column behavior. Filling 2 experienced a continued increase in NG-HETP, which may be associated with decreased column efficacy.

[0143] Fig.18 A moving range plot of NG-HETP is shown for all batches produced in Program B. Outliers can be noted in Packs 1 and 2. This identifies several points that show large shifts between single values.

[0144] Fig.19 An R-chart of NG-HETP is shown for all batches produced in Procedure B. Fill 2 showed consistently elevated range values ​​which were investigated and determined to have a root cause with a changed flow direction within the third cycle of the batch. This caused the third cycle to show different values ​​than the other cycles.

[0145] Example 3

[0146] Individual value (I) plots are made for the transition analysis of two chromatographic batches for a given "Procedure A".

[0147] Fig. 20 Shown are I diagrams for 46 batches of NG-HETP produced in Procedure A. The data show that the column performed within the limits established for consistency of processing.

[0148] Fig.21 I-diagrams are shown for 21 additional batches of NG-HETP produced in Level A. The data show that two batches (56 and 58) exceeded the upper control limit.

[0149] Example 4

[0150] Using 27 chromatographic batches, including Figure 4 Multivariate analysis was performed on the transformed analysis data from the three batches shown in . Figure 4 The loading values ​​were calculated using seven parameters for the three batches shown in . The seven parameters included NG-HETP for each of the I-map, MR-map, and R-map for each batch, the slope for each of the I-map, MR-map, and R-map for each batch, and the kurtosis for the I-map. Fig.10 Loading plots for each of the seven parameters are shown. The size of each bar corresponds to the effect of the parameter on the principal component. The error bars represent the relative error in the loading values.

[0151] Fig.11 An exemplary score plot for 27 batches is shown. For seven parameters from 27 batches, including Figure 4 Score plots calculated for the batches shown in (principal component 1) and the loading values ​​calculated for principal component 2. Batches with similar parameter values ​​are clustered. The ellipses around most of the plotted points exclude outliers at 95% confidence.

[0152] Example 5

[0153] Multivariate analysis was performed on the I-MR-R data for the conversion analysis of 46 chromatographic batches as described below. I-MR-R data was collected for each of the 46 batches. Batches that were considered atypical or unsuitable based on the I-MR-R data were removed from the analysis, and data for the remaining batches were collected in Table 1 below. Batches containing values ​​for multiple conversions were averaged and reported as separate measurements. Range values ​​were calculated by subtracting the minimum value from the maximum value of the conversion within the batch.

[0154] Using the data from Table 1, principal components are calculated by creating a loading plot showing the coefficients of each input parameter. Each row of data is transformed into a single value. An assessment of the accuracy and relevance of the model to the physical system is represented by the R2 and Q2 values ​​of the PCA model, where R2 is a statistical measure of how close a test set of data is to the fitted regression line, and Q2 is a statistical measure of how close a test set of data will be to the regression line. R2 and Q2 together represent how well the model will describe the system being analyzed, with 1 being perfect modeling and 0 representing a complete lack of relevance.

[0155] Fig.12 The loading graph of the model is shown. The R2 value of the model is 0.798 and the Q2 value is 0.591, indicating that the model is acceptable for use and all input values ​​have an impact on the principal components of the model because they are not near the center line. Fig.12 The magnitude of the y-coordinate of each point corresponds to the effect of the parameter on the principal component (e.g., the effect on the mean NG-HETP, the range of NG-HETP, the range of slope, and the mean slope). The y-coordinate of each point corresponds to the number of inputs at each point.

[0156] The principal component values ​​are trended and graphed linearly with respect to the corresponding batches. Fig.13 The score plot for the dataset is depicted. The score plot shows the PC1 value (the value contributing to the direction of the highest variance) for each batch used. Fig.13 It can be seen that one batch (Batch 6) is outside the three standard deviation limit and several points are close to exceeding two standard deviations, indicating that the system is experiencing variation across those batches.

[0157] As will be appreciated by those skilled in the art, the methods and systems disclosed herein may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. In addition, the methods and systems according to the present invention may take the form of a computer program product on a computer-readable storage medium having computer-readable instructions (e.g., computer software) embodied in the storage medium. Suitable computer-readable storage media may include a hard disk, a CD-ROM, an optical storage device, or a magnetic storage device. More particularly, the methods and systems may take the form of a network-implemented computer software.

[0158] Embodiments of the present invention are described with reference to block diagrams and flow charts illustrating methods, systems, devices, and computer program products. It will be understood that one or more blocks of the block diagrams and flow charts may be implemented by computer program instructions, respectively. These computer program instructions may be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing device for producing a machine, so that the instructions executed on the computer or other programmable data processing device create a means for implementing the functions specified in the flow chart block or blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory that is capable of directing a computer or other programmable data processing device to operate in a specific manner so that the instructions stored in the computer-readable medium produce a product including computer-readable instructions that implement the functions specified in the flow block or multiple blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps to be performed on the computer or other programmable device produce a computer-implemented process so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flow block or multiple blocks.

[0160] Therefore, the blocks of the block diagrams and flow charts support combinations of means for performing the specified functions, combinations of steps for performing the specified functions and program instructions for performing the specified functions. It will also be understood that each block of the block diagrams and flow charts and combinations of blocks in the block diagrams and flow charts can be implemented by a hardware-based computer system or a combination of hardware (e.g., dedicated chromatography hardware) and computer instructions that perform the specified functions or steps.

[0161] Fig. 22 Depicted is an operating environment 2200 in which some systems and methods according to the present invention may be implemented. As an example, Figure 1 The processing controller 108 and the computer device 110 (or components thereof) may be a computer 2201, such as Fig. 22 As shown in . The computer 2201 can include one or more components, such as one or more processors 2203, a system memory 2212, and a bus 2213 that couples various components of the computer 2201 including the one or more processors 2203 to the system memory 2212. In the case of multiple processors 2203, the system can use parallel computing.

[0162] The bus 2213 can include one or more of several possible types of bus structures, such as a memory bus, a memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. The bus 2213 and all buses specified in this specification can also be implemented via a wired or wireless network connection.

[0163] Computer 2201 generally includes various computer-readable media. Exemplary readable media can be any available media accessible to computer 2201, and include, for example but not limited to, volatile and non-volatile media, removable and non-removable media. System memory 2212 can include computer-readable media in the following forms: volatile memory, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM). System memory 2212 can generally include data, such as chromatographic data 2207 and / or program modules, such as operating system 2205 and chromatographic software 2206, which can be accessed and / or operated by one or more processors 2203. According to specific embodiments, many features and advantages of the present invention are obvious, and therefore it is intended that all such features and advantages of the present invention that fall within the true spirit and scope of the present invention are covered by the attached claims. In addition, since those skilled in the art will easily think of many modifications and variations, it is not desirable to limit the present invention to the exact configuration and operation shown and described, and accordingly, all suitable modifications and equivalents that fall within the scope of the present invention can be adopted.

[0164] In another aspect, the computer 2201 can also include other removable / non-removable, volatile / non-volatile computer storage media. The mass storage device 2204 can provide non-volatile storage of computer code, computer-readable instructions, data structures, program modules, and other data for the computer 2201. For example, the mass storage device 2204 can be a hard disk, a removable disk, a removable optical disk, a cassette or other magnetic storage device, a flash memory card, a CD-ROM, a digital versatile disk (DVD) or other optical storage device, a random access memory (RAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0165] Those skilled in the art will appreciate that the concepts upon which the present invention is based can readily be used as a basis for designing other structures, methods and systems for carrying out the several purposes of the present invention.Accordingly, the claims should not be considered limited to the foregoing description.

Claims

1. A processing control method, comprising: receiving raw chromatographic data including a plurality of signals and a plurality of blocks; processing the raw chromatographic data by selecting a combination of a first block and a first signal from the raw chromatographic data; Performing a transformation analysis on the processed raw chromatographic data, wherein performing the transformation analysis comprises: generating performance parameters, the performance parameters comprising a maximum rate of change or a number of inflection points; and generating conversion data based on performance parameters; Generate an individual value plot, moving range plot, or range plot based on the transformed data; and Performance data is generated by applying statistical process controls to the individual value graph, moving range graph, or range graph.

2. The method of claim 1 , further comprising performing an action based on the performance data, wherein the action comprises at least one of the following: Generate event notifications; generate an assessment of said event; or Generates a deviation notification form.

3. The method of claim 1, further comprising performing a chromatography column run and generating raw chromatography data from the chromatography column run; in, The raw chromatographic data is used to generate the processed chromatographic data.

4. The method according to claim 3, wherein: The raw chromatographic data includes a plurality of signals, and each of the plurality of signals is associated with one of a plurality of blocks, and Each of the plurality of blocks corresponds to a step in the operation of the chromatography column.

5. The method according to claim 3, further comprising: Smoothed data is generated by applying a noise reduction technique to the selection of raw chromatographic data, wherein the noise reduction technique increases a signal-to-noise ratio of the selection of raw chromatographic data.

6. The method according to claim 5, further comprising: The processed chromatographic data is generated by selecting smoothed data matching the chromatographic transition features.

7. The method of claim 5, wherein performing the noise reduction technique comprises: selecting a portion of the raw chromatographic data for analysis using a predetermined set point; Applying at least one smoothing filter to the selected portion of the data to generate smoothed data; Normalize the selected data to prevent amplitude deviation; as well as Analyze the dynamic signal errors of the selected portion of the data.

8. The method of claim 1, wherein the conversion analysis further comprises: generating a curve using the processed chromatographic data; as well as The curves are analyzed to generate performance parameters.

9. A process control method comprising: receiving selected raw chromatographic data; generating smoothed data by applying a noise reduction technique to a selection of the raw chromatographic data, wherein the noise reduction technique comprises: selecting a portion of the raw chromatographic data for analysis using a predetermined set point; and Applying at least one smoothing filter to the selected portion of the data to generate smoothed data; The processed chromatogram data is generated by selecting smoothed data matching a chromatogram transformation characteristic, wherein the chromatogram transformation characteristic comprises: Derivative duration; Maximum strength; Duration from start; or Expected background sensor noise; and One of an individual value plot, a moving range plot, or a range plot is generated using the processed chromatogram data.

10. The method according to claim 9, wherein: The noise reduction technology also includes: The selected part of the data is normalized to prevent amplitude bias; Analyze the selected portion of the data for dynamic signal errors; or Both of the above.

11. The method according to claim 9, further comprising: Apply statistical process controls to the individual value chart, moving range chart, or range chart to generate performance data by: perform multivariate data analysis; or Perform principal component analysis.

12. The method according to claim 11, further comprising: Performing an action based on the performance data, wherein the action includes at least one of the following: Generate event notifications; generating an assessment of said event; or Generates a deviation notification form.

13. The method of claim 9, wherein the selection of raw chromatographic data is received from a chromatographic processing instrument.

14. The method according to claim 9, further comprising: Performing a transformation analysis on the processed raw chromatographic data to generate transformation data, wherein performing the transformation analysis comprises: generating performance parameters, the performance parameters comprising a maximum rate of change or a plurality of inflection points; and generating the conversion data based on the performance parameter; An action is performed based on the transformed data, wherein performing the action includes: generating an event notification; generating an evaluation of the event; or generating a deviation notification table.

15. The method according to claim 9, further comprising: Performing conversion analysis to generate conversion data, wherein performing conversion analysis includes: generating a curve using the processed chromatographic data; analyzing the curve to generate performance parameters; and generating the conversion data based on the performance parameter; and An action is performed based on the transformed data, wherein performing the action includes: generating an event notification; generating an evaluation of the event; or generating a deviation notification table.

16. A process control method comprising: receiving a selection of raw chromatographic data comprising a plurality of signals, wherein each signal of the plurality of signals is associated with one of a plurality of bins; Processed chromatographic data is generated by applying a noise reduction technique to a subset of the raw chromatographic data, wherein applying the noise reduction technique comprises: selecting a portion of the subset of the raw chromatographic data for analysis using a predetermined set point; and applying at least one smoothing filter on the portion to generate smoothed data; generating performance parameters, the performance parameters comprising a maximum rate of change or a plurality of inflection points; and generating the conversion data based on the performance parameter; An action is performed based on the transformed data, wherein performing the action includes: generating an event notification; generating an evaluation of the event; or generating a deviation notification table.

17. The method according to claim 16, further comprising: A chromatography column run is performed, wherein the raw chromatography data is received from the chromatography column run.

18. The method of claim 16, wherein each block in the plurality of blocks corresponds to a step in a chromatographic process.

19. The method of claim 16, further comprising: The combination of the first block and the first signal is selected according to a configuration file defining a plurality of selection criteria, wherein the plurality of selection criteria include: whether the blocks occur at regular chromatographic cycle intervals; the extent to which one of the plurality of signals saturates the detector; the degree to which the plurality of signals are close to a stationary phase at different levels; the magnitude of changes in the plurality of signals; or a plurality of inflection points shown by the plurality of signals during the switching phase, and wherein the subset of data is obtained from a combination of the first block and the first signal.

20. The method of claim 16, further comprising: generating a single value graph, a moving range graph, or a range graph based on the conversion data; as well as by applying statistical process controls to the individual value graph, moving range graph or range graph to generate performance data, Wherein performing an action based on the conversion data comprises performing an action based on the performance data.

21. A process control method, comprising: Generating smoothed data by applying a noise reduction technique to the raw chromatographic data, wherein the noise reduction technique comprises: applying a smoothing filter to a portion of the raw chromatographic data to generate the smoothed data; generating processed chromatographic data by selecting smoothed data matching features of the chromatographic transformation; performing conversion analysis to generate conversion data; and An action is performed based on the transformed data, wherein performing the action includes: generating an event notification; generating an evaluation of the event; or generating a deviation notification table.

22. The method according to claim 21, wherein: Characteristics of the chromatographic transformation include: Derivative duration; Maximum strength; Duration from start; or Expected background sensor noise.

23. The method of claim 21, wherein the noise reduction technique further comprises selecting a portion of the raw chromatographic data using a predetermined set point.

24. The method according to claim 21, wherein: The noise reduction technology also includes: Analyze the dynamic signal errors of the raw chromatographic data in this section; Normalize the selected portion of data to prevent amplitude bias; or Both of the above.

25. The method of claim 21, wherein performing a conversion analysis comprises: generating a performance parameter based on the processed chromatographic data, the performance parameter comprising a maximum rate of change, a plurality of inflection points, a breakthrough volume, a cumulative error, a curve asymmetry, or a combination thereof; as well as The conversion data is generated based on the performance parameter, or based on the performance parameter combined with historical data.

26. The method of claim 21, wherein performing a conversion analysis comprises: generating a curve using the processed chromatographic data; analyzing the curve to generate performance parameters; as well as The conversion data is generated based on the performance parameter.

27. A process control method comprising: processing raw chromatographic data including a plurality of signals and a plurality of blocks to generate processed raw chromatographic data; Performing a transformation analysis on the processed raw chromatographic data to generate transformation data, wherein performing the transformation analysis comprises: generating a curve using the processed chromatographic data; analyzing the curve to generate performance parameters; and Generating the conversion data based on the performance parameter An action is performed based on the transformed data, wherein performing the action includes: generating an event notification; generating an evaluation of the event; or generating a deviation notification table.

28. The method of claim 27, further comprising: Generate single value plots, moving range plots, or range plots; as well as Performance data is generated by applying statistical process controls to the individual value graph, moving range graph, or range graph.

29. The method of claim 28, performing an action based on the conversion data comprises performing an action based on the conversion data and the performance data.

30. The method of claim 27, wherein each of the plurality of signals is associated with one of a plurality of blocks, and Each block in the plurality of blocks corresponds to a step in a chromatography column run.

31. The method of claim 27, wherein processing the raw chromatographic data comprises selecting a combination of a first block and a first signal from the raw chromatographic data.

32. The method of claim 31 , wherein the combination of the first block and the first signal is selected according to a configuration file defining a plurality of selection criteria, wherein the plurality of selection criteria comprises one or more of the following: whether the blocks occur at regular chromatographic cycle intervals; the extent to which one of the plurality of signals saturates the detector; the degree to which the plurality of signals are close to a stationary phase at different levels; an amplitude of changes in the plurality of signals; or A plurality of inflection points are shown by the plurality of signals during the switching phase.

33. The method according to claim 27, wherein the performance parameter comprises two or more of the following: Maximum rate of change; Multiple inflection points; penetration volume; Cumulative error; Curve asymmetry; and Equivalent height of theoretical plate.

34. The method of claim 27, further comprising performing a chromatography column run and generating raw chromatography data from the chromatography column run.

35. The method of claim 34, wherein: The conversion data is indicative of the integrity of the chromatographic column used in the chromatographic column run.

36. The method of claim 27, further comprising: generating smoothed data by applying a noise reduction technique to a selected portion of the raw chromatographic data, wherein the noise reduction technique increases a signal-to-noise ratio of the selected portion of the raw chromatographic data; as well as Processing the raw chromatographic data includes processing the smoothed data.

37. A process control method comprising: Smoothed data is generated by applying a noise reduction technique to the raw chromatographic data, wherein the noise reduction technique comprises: selecting a portion of the raw chromatographic data using a predetermined set point; and A smoothing filter is applied to the portion of the raw chromatographic data to generate smoothed data. Generate performance parameters based on the smoothed data, wherein the performance parameters include: Maximum rate of change; Multiple inflection points; penetration volume; Cumulative error; Curve asymmetry; Equivalent height of theoretical plate; or A combination of the above; generating conversion data based on the performance parameters; and An action is performed based on the transformed data, wherein performing the action includes: generating an event notification; generating an evaluation of the event; or generating a deviation notification table.

38. The method of claim 37, wherein the noise reduction technique further comprises normalizing the selected portion of data to prevent amplitude deviation and analyzing the selected portion of data for dynamic signal errors.

39. The method of claim 37, further comprising: performing a chromatography column run, wherein the raw chromatography data is generated from the chromatography column run; as well as Wherein, the conversion data represents the integrity of the chromatographic column used in the chromatographic column run.

40. The method of claim 37, wherein generating performance parameters comprises generating a curve using the smoothed data; and The curves are analyzed to generate the performance parameters.

41. A process control method comprising: receiving raw data from a detector, wherein the raw data comprises a plurality of signals and a plurality of blocks, and wherein the raw data corresponds to conductivity, pH, salt concentration, light absorption, fluorescence, refractive index, electrochemical response, or mass spectrometry data; applying a noise reduction technique to the plurality of signals and the plurality of blocks to generate processed data; Performing a conversion analysis on the processed data to generate conversion data, wherein performing the conversion analysis comprises: generating curves using the processed data; analyzing the curves to generate performance parameters; and generating conversion data based on the performance parameters; and An action is performed based on the transformed data, where the performed action includes generating a notification of the event, generating an evaluation of the event, or generating a deviation notification table.

42. The method according to claim 41, wherein: Applying a noise reduction technique to the plurality of signals and the plurality of blocks includes selecting a combination of a first block and a first signal from the original data.

43. The method of claim 42, wherein the combination of the first block and the first signal is selected according to a configuration file defining a plurality of selection criteria, wherein the plurality of selection criteria comprises one or more of: whether the blocks appear at regular intervals; the extent to which one of the plurality of signals saturates the detector; the degree to which the plurality of signals are close to a stationary phase at different levels; an amplitude of changes in the plurality of signals; or A plurality of inflection points are shown by the plurality of signals during the switching phase.

44. The method of claim 43, wherein the profile is associated with a pharmaceutical product.

45. The method of claim 44, wherein the conversion data is indicative of the integrity of a chromatographic column used to purify the drug.

46. ​​The method of claim 41, wherein the performance parameter comprises two or more of the following: Maximum rate of change; Multiple inflection points; penetration volume; Cumulative error; Curve asymmetry; and Equivalent height of theoretical plate.

47. The method of claim 41, wherein: The noise reduction technology includes one or more of the following: Analyze dynamic signal errors in a portion of the raw data; The selected portion of data is normalized to prevent amplitude deviation.

48. A process control method comprising: receiving raw data from a detector, wherein the raw data corresponds to conductivity, pH, salt concentration, light absorption, fluorescence, refractive index, electrochemical response, or mass spectrometry data; generating processed data by selecting smoothed data matching the transformed features; Performing a conversion analysis on the processed data to generate conversion data, wherein performing the conversion analysis comprises: generating a performance parameter based on the processed data, the performance parameter comprising a maximum rate of change; multiple inflection points; penetration volume; cumulative error; curve asymmetry; or a combination thereof, and Generate conversion data based on performance parameters or generate conversion data based on performance parameters combined with historical data; An action is performed based on the transformed data, where the performed action includes generating a notification of the event, generating an evaluation of the event, or generating a deviation notification table.

49. The method of claim 48, wherein: Characteristics of the transition include: Derivative duration; Maximum strength; Duration from start; or Expected background sensor noise.

50. The method of claim 48, further comprising: Produce an individual value plot, a moving range plot, or a range plot.

51. The method of claim 50, further comprising generating performance data by applying multivariate data analysis to the single value graph, moving range graph, or range graph.

52. The method according to claim 51, wherein multivariate data analysis includes principal component analysis, partial least squares, orthogonal partial least squares, multivariate regression, canonical correlation, factor analysis, cluster analysis, graphical programs, etc.

53. The method of claim 50 further comprises generating performance data by performing a principal component analysis on the single value graph, moving range graph, or range graph.

54. The method of claim 50, further comprising: Generate performance data by performing principal component analysis or multivariate data analysis on individual value plots, moving range plots, or range plots; and Terminate chromatography based on performance data.

55. The method of claim 48, wherein the raw data comprises a plurality of blocks and a plurality of signals, and the method further comprises selecting a combination of the first block and the first signal from the raw data according to a profile defining a plurality of selection criteria, wherein the profile is associated with a drug.

56. A process control method comprising: receiving raw data from a detector, wherein the raw data comprises a plurality of signals and a plurality of blocks, and wherein the raw data corresponds to conductivity, pH, salt concentration, light absorption, fluorescence, refractive index, electrochemical response, or mass spectrometry data; Generating smoothed data by applying a noise reduction technique to the raw data, wherein the noise reduction technique comprises: selecting a portion of the raw data using a predetermined set point; and Applying a smoothing filter to the raw data of the portion to generate smoothed data; Perform transformation analysis on the smoothed data to generate transformation data; An action is performed based on the transformed data, where the performed action includes generating a notification of the event, generating an evaluation of the event, or generating a deviation notification table.

57. The method of claim 56, wherein: The noise reduction technique also includes selecting the portion of the raw data using a predetermined set point.

58. The method of claim 56, wherein the noise reduction technique further comprises: Analyze dynamic signal errors in the raw data portion; The selected part of the data is normalized to prevent amplitude bias; or Both of the above.

59. The method of claim 56, further comprising: Generate single value plots, moving range plots, or range plots; as well as Generate performance data by performing principal components analysis or multivariate data analysis on individual value plots, moving range plots, or range plots.

60. The method of claim 56, wherein performing a conversion analysis comprises: generating a performance parameter based on the smoothed data, the performance parameter comprising a maximum rate of change, a number of inflection points, a penetration volume, a cumulative error, a curve asymmetry, or a combination thereof, and Conversion data is generated based on the performance parameters in combination with historical data.

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