Methods and systems for chromatography data analysis

Abstract

To provide methods and systems for assessing integrity of chromatography columns, systems, and processes.SOLUTION: The methods and systems can comprise one or more of extracting a block and signal combination for analysis, performing a transition analysis, performing one or more statistical process controls, and / or implementing in-process controls based on the statistical process controls.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] Aspects of the present disclosure relate generally to chromatography systems and methods, and more particularly to embodiments of methods and systems for chromatography data analysis, for example, for in-process monitoring and control of chromatography systems. [Background technology]

[0002] Packed-bed chromatography processes play a critical role in the manufacturing of biological pharmaceutical products. Many active biological drugs, such as proteins, are purified using packed-bed chromatography for use in drug products. Therefore, chromatography column operation can have a significant impact on manufacturing critical process parameters (CPPs) and critical quality attributes (CQAs). Furthermore, the complexity and size of biologics can make analyzing their biological quality and purity relatively more challenging compared to, for example, small molecules. Therefore, monitoring the quality, consistency, and integrity of chromatography processes and equipment through in-process control is important to ensure that product quality meets any applicable specifications (e.g., government regulations).

[0003] Generally, column integrity 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 column integrity can include, for example, signs such as channeling, headspace, and contaminated areas of flow. Channeling can occur, among other things, when the mobile phase can travel some distance from the column inlet toward the column outlet without contacting the stationary phase. Headspace can refer, among other things, to the formation of lateral areas in the column that allow non-plug flow of the mobile phase. Contaminated areas of flow can include dirt or other residue on the frit surfaces or resin pores at the inlet or outlet.

[0004] Several techniques exist for monitoring the performance and integrity of chromatography columns. Some techniques, such as the pulse injection method for measuring the height equivalent of a theoretical plate (HETP), require specially prepared buffers. Pulse injection techniques generally require manipulation of the chromatography equipment and columns outside of the normal process, increasing process time and labor. Other techniques include monitoring critical parameters (e.g., step yield, prepool volume, and maximum optical density during loading) as part of normal production. However, setting alarm limits for these parameters can be difficult and imprecise, resulting in false alarms or overly broad limits.

[0005] There is a need for methods, systems, and processes for measuring and controlling column performance and integrity that are accurate, precise, and minimize disruption to the process. Furthermore, due to inherent variations between chromatography columns, chromatography column cycles, and / or production lots of a given product undergoing chromatography, there is a need for methods, systems, and processes for customizing column performance and integrity analysis for one or more specific columns, one or more specific cycles, and / or one or more specific product lots. Finally, there is a need for accurate in-process control using such analysis, as well as methods and systems for responding to deviations from such control, so that problems with column integrity and performance can be identified and corrected early with minimal waste and expense. Summary of the Invention

[0006] An embodiment of the present disclosure may relate to a process control method including receiving raw chromatography data including a plurality of signals, each of the plurality of signals 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 chromatography data, generating processed chromatography data by applying a noise reduction technique to the data subset, generating transition data by performing a transition analysis on the processed chromatography data, and performing an action based on the transition data.

[0007] In some embodiments, the method may further include performing a chromatography column run, and the chromatography raw data may be received from the chromatography column run. In other embodiments, the chromatography raw data may be received from a chromatography process skid. In still 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.

[0008] In still further embodiments, the method may also include selecting the first block and the first signal combination according to a profile defining a plurality of selection criteria, which may include whether the blocks occur at regular intervals between chromatography cycles, the extent to which one of the signals saturates a detector, the extent to which the signals approach a stationary phase at a distinct level, the magnitude of change in the signals, and / or the number of inflection points exhibited by the signals during a transition phase.

[0009] In some embodiments, selecting the first block and the first signal combination may include selecting a first-rank block and signal combination, and the method may further include selecting a second-rank block and signal combination. In further embodiments, the noise reduction technique may include selecting a portion of the data subset for analysis using a predetermined set of values, normalizing the portion to prevent magnitude bias, applying at least one smoothing filter to the portion to generate smoothed data, and analyzing the portion for dynamic signal error. In yet further embodiments, the method further includes selecting smoothed data that matches chromatogram transition characteristics, the characteristics including one of derivative duration, maximum intensity, duration from onset, or expected background sensor noise. In yet further embodiments, the transition analysis may include generating a curve using the processed chromatographic data and analyzing the curve to generate performance parameters.

[0010] In some embodiments, the method may further include generating an individual chart, a moving range chart, or a range chart based on the transition data, and generating performance data by applying statistical process control to the individual chart, moving range chart, or range chart, where performing an action based on the transition 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 a notification of an event, generating an evaluation of the event, or generating a deviation notification form. Some embodiments of the present disclosure may include a chromatography method comprising performing the process control method during operation of a chromatography column.

[0011] Some aspects of the present disclosure may relate to a process control method, the method including 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 smoothed data that match characteristics of chromatogram transitions, and performing an action based on the processed chromatographic data. The noise reduction technique may include selecting a portion of the smoothed data for analysis using a predetermined set point, normalizing the portion of data to prevent magnitude anomalies, applying at least one smoothing filter to the portion of data to generate the smoothed data, and analyzing the portion of data for dynamic signal error.

[0012] In some embodiments, receiving the selection of chromatographic raw data may include receiving chromatographic raw data including a plurality of signals and a plurality of blocks, where each of the plurality of signals may be associated with a block, and selecting a first block and a first signal combination from the chromatographic raw data.

[0013] In some embodiments, the method may further include generating one of an individual control chart, a moving range control chart, or a range control chart using the processed chromatographic data, and generating performance data by applying statistical process control to the individual control chart, moving range control chart, or range control chart by performing multivariate data analysis or principal component analysis. In some embodiments, performing an action based on the processed chromatographic data may include performing an action based on the performance data. In some embodiments, the action may include generating a notification of an event, generating an evaluation of the event, or generating a deviation notification form.

[0014] Some aspects of the present disclosure may include a process control method including receiving processed chromatography data including a combination of a first block and a first signal, performing a transition analysis on the processed chromatography data, generating one of an Individual-Moving Range-Range (I-MR-R) control chart based on the transition analysis, generating performance data by applying a multivariate statistical analysis method to the I-MR-R control chart, and performing an action based on the performance data. The action may include one of generating a notification of the event, generating an evaluation of the event, or generating a deviation notification form.

[0015] In some embodiments, the processed chromatography data may include a selection of raw chromatography data to which noise reduction techniques have been applied. In some embodiments, the selection of raw chromatography data may be received from a chromatography process skid. [Brief explanation of the drawings]

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate disclosed embodiments and, together with the description, serve to explain the principles of the disclosed embodiments. [Figure 1] FIG. 1 is a schematic diagram illustrating an exemplary chromatography system in which various embodiments of the present disclosure may be implemented. [Figure 2] FIG. 1 shows an exemplary chromatogram. [Figure 3] FIG. 1 shows an exemplary normalized plot of a chromatographic step-up transition. [Figure 4] FIG. 1 shows a plot of chromatographic increasing transitions of equilibrium conductivity blocks for three lots, according to some embodiments of the present disclosure. [Figure 5] FIG. 1 illustrates an exemplary process for analyzing chromatography data and performing process control in accordance with some aspects of the present disclosure. [Figure 6] FIG. 10 illustrates a further exemplary process for analyzing chromatography data and performing process control in accordance with some aspects of the present disclosure. [Figure 7] FIG. 1 illustrates an exemplary data file in accordance with some aspects of the present disclosure. [Figure 8] FIG. 1 illustrates an exemplary loading plot of a multivariate model according to some aspects of the present disclosure. [Figure 9-1] FIG. 1 illustrates an exemplary data smoothing process in accordance with certain aspects of the present disclosure. [Figure 9-2] FIG. 1 illustrates an exemplary data smoothing process in accordance with certain aspects of the present disclosure. [Figure 10]FIG. 1 shows a loading plot of each variable on the principal components from 27 lots, according to some embodiments of the present disclosure. [Figure 11] FIG. 1 shows exemplary score plots from 27 lots, according to some embodiments of the present disclosure. [Figure 12] FIG. 1 illustrates an exemplary loading plot of a multivariate model, according to some aspects of the present disclosure. [Figure 13] FIG. 10 illustrates an exemplary score plot, in accordance with some aspects of the present disclosure. [Figure 14] 1 is an individual control chart for skewness in a given chromatography unit operation, according to some aspects of the present disclosure. [Figure 15] 1 is a moving range control chart for skewness in a given chromatography unit operation, according to some aspects of the present disclosure. [Figure 16] 1 is a range control chart for skewness in a given chromatography unit operation, according to some aspects of the present disclosure. [Figure 17] 1 is an individual control chart for non-Gaussian HETP (NG-HETP), according to some embodiments of the present disclosure. [Figure 18] 1 is a moving range control chart for NG-HETP, according to some embodiments of the present disclosure. [Figure 19] 1 is a range control chart for NG-HETP, according to some embodiments of the present disclosure. [Figure 20] 10 is another individual control chart for NG-HETP, according to some embodiments of the present disclosure. [Figure 21] 10 is yet another individual control chart for NG-HETP, according to some embodiments of the present disclosure. [Figure 22] FIG. 1 illustrates an exemplary system in which aspects of the present disclosure may be implemented. [Figure 23] 1A-1C illustrate exemplary user interfaces in accordance with certain aspects of the present disclosure. [Figure 24-1] FIG. 10 illustrates an example report in accordance with some aspects of the present disclosure. [Figure 24-2]FIG. 10 illustrates an example report in accordance with some aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0017] The present disclosure relates to improvements in pharmaceutical product manufacturing and laboratory processes, and in computer functionality related to pharmaceutical product manufacturing and laboratory processes. In particular, aspects of the disclosure relate to chromatography methods and systems, and methods and systems for chromatography data analysis, for example, for the purposes of monitoring and controlling chromatography processes and systems.

[0018] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The materials, methods, and examples are illustrative only and are not intended to be limiting. Those skilled in the art will recognize that routine variations in the disclosed materials, methods, and examples are possible without undue experimentation. All publications, patent applications, patents, sequences, database entries, and other references mentioned in this application are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

[0019] As used herein, the terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements does not include only those elements, but may also include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "exemplary" is used to mean "example," not "ideal." Such terms, as well as the terms "for example" and "such as" and their grammatical equivalents, are understood to be followed by the phrase "and without limitation," unless expressly stated otherwise. As used herein, the terms "about" and "to" are intended to account for variations due to experimental error. All measurements reported in this application are understood to be modified by the term "about," whether or not the term is expressly used, unless otherwise expressly stated. As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Furthermore, in the claims, values, limits, and / or other ranges mean + / - 10% of the value, limit, and / or range.

[0020] In this application, the term "antibody" refers to full antibody molecules. This includes antigen-binding molecules and antigen-binding fragments. The terms "antigen-binding portion" of an antibody, "antigen-binding fragment" of an antibody, and the like, as used herein, include any naturally occurring, enzymatically derived, synthetic, or genetically engineered polypeptide or glycoprotein that specifically binds an antigen to form a complex. Antigen-binding fragments of antibodies may be derived from, for example, an intact antibody molecule using any suitable standard technique, such as proteolytic digestion or recombinant genetic engineering techniques, including the manipulation and expression of DNA encoding the variable and, optionally, constant domains of the antibody. Such DNA is known and / or readily available, for example, from commercial sources, DNA libraries (including, for example, phage antibody libraries), or can be synthesized. The DNA may be sequenced and manipulated chemically or by using molecular biological techniques, for example, to construct one or more variable and / or constant domains in the appropriate form, or to introduce codons, form cysteine ​​residues, or modify, add, or delete amino acids.

[0021] 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 a hypervariable region of an antibody (e.g., an isolated complementarity determining region (CDR), such as a CDR3 peptide) or a constrained FR3-CDR3-FR4 peptide. 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, bivalent nanobodies, etc.), small modular immunopharmaceuticals (SMIPs), shark variable IgNAR domains, and the like, are also encompassed by the term "antigen-binding fragment" as used herein.

[0022] As used herein, the term "biologic" may refer to a macromolecule (e.g., having a size greater than 30 kDa) formed within a biological system, such as a cell. Biologics may include proteins (e.g., antibodies), nucleic acids, large sugars, and the like. Unlike small molecules, which may have well-defined chemical structures, biologics may have highly complex structures that cannot be easily quantified by laboratory methods. Thus, achieving purity, consistency, and quality to ensure biological quality is desirable in the manufacture of biologics, particularly for medical purposes.

[0023] As used herein, the term "chromatography" may refer to any preparative or analytical chromatographic method. While much of this disclosure is provided in the context of preparative packed bed chromatography for the purification of biopharmaceuticals, it is contemplated that the systems and methods disclosed herein may be applied to a wide variety of chromatographic processes.

[0024] As used herein, the term "pharmaceutical product" may refer to a quantity of a dispensed pharmaceutical substance dispensed into a primary packaging element for packaging, shipping, delivery, and / or administration to a patient. A pharmaceutical product may contain an active ingredient, including, for example, a biologic.

[0025] As used herein, the term "feedstock" may refer to a mixture containing one or more biologics suitable for separation or purification by a chromatographic process. As used herein, the term "raw chromatography data" may refer to chromatography data in its native data state, as originally collected. For example, the raw chromatography data may be in a .RES file format or other type of raw file format, or may reside in a database containing values ​​obtained directly from an instrument.

[0026] As used herein, the term "extracted chromatography data" may refer to chromatography data that has been transferred from raw data without any conversion. This may be in Excel or .CSV file format, or may reside in a database located within a chromatography system or computer system.

[0027] As used herein, the term "noise-reduced data" may refer to chromatographic data such as normalized, smoothed, derived, and / or peak-selected transition data.

[0028] As discussed above, chromatography column and process quality needs to be monitored and maintained over time, for example, across multiple chromatography runs, across multiple lots, and both during and between runs. The systems and methods disclosed herein may enable the analysis of chromatography transition data (also known as "transition analysis") and the use of such analysis in monitoring chromatography performance, identifying changes in chromatography performance, and taking action on chromatography systems based on such analysis and processes. Furthermore, the systems and methods disclosed herein may, in some embodiments, be part of one or more in-process manufacturing or purification controls and / or enable in-process control using data collected in standard chromatography processes, thereby minimizing the increased cost and effort required to implement separate process controls.

[0029] Reference will now be made in detail to the exemplary embodiments of the present disclosure, which are described below and illustrated in the accompanying drawings. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or like parts.

[0030] 1 shows a schematic diagram of an exemplary chromatography system 100 in which various embodiments of the present disclosure may be implemented. The 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.

[0031] System 100 may be all or part of a chromatography system, including chromatography column 106. In some cases, system 100 may be a chromatography skid. System 100 may include any hardware and / or software necessary to operate a chromatography column. System 100 may be configured to perform any of a variety of 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 may be used, for example, to separate biologics in a raw material mixture, to isolate and / or purify a single biologic, and / or to remove contaminants from a mixture. In some cases, system 100 may be part of a pharmaceutical product manufacturing system, such as a system for producing a pharmaceutical product containing a biologic, such as an antibody.

[0032] The mobile phase liquid supply system 102 may be any system suitable for supplying a mobile phase to the inlet of the column 106. The mobile phase liquid supply system 102 may include one or more reservoirs for holding the mobile phase liquid used to drive the feedstock injected by the material injection system 104 through the column 106. The mobile phase liquid system 102 may include one or more pumps configured to apply pressure to the mobile phase liquid. In some embodiments, the pumps of the mobile phase liquid supply system 102 may be configured to mix two or more solvents (e.g., from two or more reservoirs) in a desired ratio before delivering the combined solution to the inlet of the column 106. In some embodiments, the mobile phase liquid supply system 102 may be configured to deliver a first mobile phase to the inlet of the column 106 and then deliver a second mobile phase to the inlet of the column 106 after a desired amount of the first mobile phase has been delivered. In some embodiments, the mobile phase liquid supply system may be controlled by a process controller 108 or by human interaction.

[0033] The material injection system 104 may be any system suitable for supplying raw materials requiring separation and / or purification in the column 106. In some embodiments, for example, the material injection system 104 may include one or more reservoirs for holding raw materials. Such raw materials may include one or more biologicals, contaminants, solvents, or other materials.

[0034] Column 106 may be any column suitable for separating and purifying the material injected from material injection system 104. Those skilled in the art will appreciate that column 106 may have any of a wide range of sizes (e.g., diameters ranging from about 30 cm to about 1500 cm) and may be packed with any of a wide variety of stationary phases. The size, shape, and packing of column 106 may be selected taking into consideration the material requiring separation in column 106.

[0035] The process controller 108 and / or computing device 110 may be suitable for controlling aspects of the system 100 during a chromatography run. The process controller 108 may be connected to one or more elements 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 may be a computer programmed to control the elements of the system 100 according to a desired procedure. For example, in some embodiments, the process controller may be programmed to turn on and off the pumps of the mobile phase liquid supply system 102 and to activate and deactivate the detector 112. In some embodiments, the process controller 108 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, for example, by a human operator. In other embodiments, the process controller 108 may be programmed, for example, using the computing device 110.

[0036] The computing device 110 may be any computer, such as a desktop computer, a server computer, a laptop, a tablet, or a personal mobile device (e.g., a smartphone). 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 touchscreen, etc.) through which commands may be entered, for example, by an operator. The computing device 110 may also collect data from other elements of the system 100, such as the process control device 108 and / or the detector 112. The computing device 110 may include one or more programs configured to display or output such data, for example, on a screen, a hard disk, or remotely 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 connected wirelessly to other aspects of the system 100 (e.g., the process control device 108). In some embodiments, the computing device 110 may be located remotely 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 control device 108 and the computing device 110 may be a single device.

[0037] Detector 112 may be any type of detector suitable for detecting one or more properties at the outlet of column 106. While a single detector 112 is shown in FIG. 1 , system 100 may include two or more such detectors configured to detect different properties at the outlet of column 106. Such properties may include, for example, column outlet conductivity, pH, optical density, and other properties. In some embodiments, detector 112 may be, for example, an electrical conductivity detector, an ultraviolet (UV) detector, a fluorescence detector, a refractive index detector, a pH detector, a manometer, or any other type of detector.

[0038] A chromatography cycle, e.g., a run using system 100, may typically include a series of steps. Such steps may include, for example, a clean-in-place step, an equilibration step, a loading step, a wash step, an elution step, a strip step, and a regeneration step. A chromatography cycle may be tracked and / or recorded using data collected from a detector at the outlet of the chromatography column (e.g., detector 112 at the outlet of column 106). For example, UV detection and UV chromatograms may be used to track the chromatography process through, for example, the wash, elution, collection, and strip steps. Figure 2 shows an exemplary UV chromatogram with a typical profile for collection of a single protein. As the volume of liquid passed through the column (illustrated 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 may begin after a small elution peak, during which the absorbance rises sharply as the majority of the analyte passes through the UV detector.

[0039] A chromatography (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., from a wash step to an elution step, or from an elution step to a strip step) when one mobile phase is exchanged for another. Depending on which parameters are detected at the outlet of the column (e.g., by one or more of the detectors 112), the transition may be detected as an increase (a step-up transition) or a decrease (a step-down transition) in one or more parameters, and the transition is followed by a plateau for that parameter. For example, FIG. 3 shows an exemplary normalized plot of a step-up transition in a three-phase chromatography analysis. Before the transition, the detector detects the baseline value of the parameter. During the transition, the parameter "steps up," or increases, and then reaches a plateau after the transition. In some cases, the plateau after the step-up transition is due to detector saturation. Data derived during the transition is quantitative and sensitive to subtle changes in column performance.

[0040] Examples of measurable parameters that change during a transition may include conductivity, pH, salt concentration, light absorption, fluorescence after excitation with light of an appropriate wavelength, refractive index, electrochemical response, and data generated by mass spectrometry, although one skilled in the art will recognize that any other measurable parameter that can change during a transition may be useful for transition analysis in accordance with the present disclosure.

[0041] To perform transient analysis to determine the quality and / or integrity of a chromatography column and / or process, the chromatography data can be divided into multiple blocks. Each block corresponds to a step in the chromatography process (e.g., a clean-in-place block, an equilibration block, a loading block, a wash block, an elution block, a strip block, a regeneration block, a storage block, etc.). Each block includes multiple signals provided by one or more detectors during that block. Any number or combination of blocks and signals can be used to perform transient analysis, 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). Larger numbers of blocks and / or signals can also be used.

[0042] Figure 4 shows an exemplary plot of detected conductivity as a function of volume during the incremental transition in the equilibrium block for three chromatographic runs. Each run involved the same chromatographic process on the same feedstock on the same column, including the isolation of the same protein, but with different feedstock lots. The first spike (in all three runs) represents the system prime. After the spike occurs, as can be seen, the three runs exhibit variability in the transition phase. The shortest dashed line represents the closest to the ideal transition phase, since the transition is most "vertical" (i.e., occurs over the shortest volume). The long dashed line shows some characteristics indicative of column failure, i.e., the early onset of the transition phase and the tapering end. Overall, this transition occurs over a larger volume. The solid line shows stronger characteristics of column failure, since the transition phase starts very early and takes an excessive amount of time to reach saturation. While these differences are visually apparent, they may not be easily quantifiable or comparable to each other. This disclosure provides systems and methods for using these data to perform analysis and for using such analysis to reliably perform process control.

[0043] Figures 5 and 6 illustrate exemplary processes for analyzing chromatography data and using such analysis to implement process control according to some embodiments of the present disclosure. Figure 5 illustrates the exemplary process at a more general level of detail, and Figure 6 illustrates the exemplary process in greater detail. Although they are described separately below, details and characteristics of the process in Figure 6 may be applicable to the process in Figure 5, and vice versa.

[0044] 5 illustrates an exemplary general process 500 for analyzing chromatography data and performing process control according to some aspects of the present disclosure. According to step 510, raw chromatography data may be processed. According to step 520, data may be obtained from the raw chromatography data. According to step 530, the obtained data may be processed. According to step 540, the processed data may be analyzed (e.g., transition analysis). According to step 550, one or more statistical process controls may be performed. According to step 560, the data may be reported.

[0045] According to step 510, the chromatographic raw data can be processed. The chromatographic raw data can be obtained by performing one or more chromatographic cycles and obtaining signals from one or more detectors (e.g., detector 112 of column 106). These signals can include, for example, UV signals, conductivity signals, pressure signals, pH signals, and / or other signals. The data can be obtained, for example, in the process control device 108 and / or the computing device 110 and can be stored, for example, in a database or a .RES file. The data can include, for example, a series of signal values ​​and the corresponding volumes at which the signal values ​​were measured. The data can also include indicators of the start and end of each block / step in the chromatographic cycle.

[0046] Processing the data may include, for example, extracting the data on a computing device, such as computing device 110, and organizing the data into a data file. Exemplary data files include, for example, spreadsheets, text files, databases, and combinations thereof. The data files containing the extracted chromatographic data may be assigned various metadata to enable consistent storage and processing. The metadata may include, for example, name, designation, date, column run time, column run volume, column mobile phase, feed mixture identification, manufacturing process identifier in which the column was used, and / or other data that may enable consistent automated or manual processing of the data file.

[0047] According to step 520, data may be obtained from the data files for analysis. In some embodiments, an automated software program (e.g., Cron, Jobber, Macro, or other automation or scheduling software) may monitor one or more possible data file storage locations for one or more data files that match one or more profiles. Data files may be assigned profiles based, for example, on metadata associated with the data files. A profile may be, for example, a pre-created set of selection criteria for selecting one or more block and signal combinations suitable for performing transition analysis. A profile may be assigned based, for example, on the type of column being run, the characteristics of the mobile phase, the volume of mobile phase being run, the column run time, or other characteristics of the data files.

[0048] Acquiring data for analysis may include selecting one or more block and signal combinations based on the assigned profile, where the block corresponds to a step in the chromatography process and the signal corresponds to the type of data being collected (e.g., UV data, conductivity, pH, etc.). In some embodiments, a first-ranked block and signal combination may be selected. In further embodiments, a first-ranked block and signal combination and one or more second-ranked block and signal combinations may be selected. Transition analysis may be performed first with the first-ranked block and signal combination, and optionally then with one or more second-ranked block and signal combinations. Profiles, selection criteria, and block and signal combinations are described in further detail with respect to process 600.

[0049] According to step 530, the acquired data may once again be processed to obtain noise-reduced data. Processing the acquired data may include applying one or more smoothing and / or noise reduction techniques to datasets within the acquired data, such as data associated with the first block and signal combination and, optionally, data associated with the second block and signal combination. In some embodiments, processing the data may include standardizing the size of the datasets to allow for a consistent effect of the smoothing window. In some embodiments, processing the data may include normalizing the data to eliminate variation based on the magnitude of the transition. This variation may be due to unique preparations of mobile phase buffers that contain inherent variability in the end values ​​of the baseline or saturation phases.

[0050] Noise reduction techniques may include removal of implicit errors introduced by measurement tools (e.g., detector 112 in system 100) and random errors introduced by batch processes when data is collected (e.g., during earlier steps of method 500). Noise reduction may include de-duplication of records in the dataset, detection and removal of outliers, and / or other techniques for increasing the signal-to-noise ratio within the dataset. Noise reduction may also include data smoothing and signal rejection, which is described in more detail below with respect to process 600.

[0051] 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 the normalized solute signal data array.

[0052] According to step 540, the noise-reduced data may be processed. Such analysis may be a transition analysis. Transition analysis may include performing one or more mathematical methods on the processed data. For example, one or more curves may be generated from the processed data by, for example, taking the first derivative of the curve to generate another curve characterized by peaks. The curve may be analyzed to generate performance parameters such as, for example, several inflection points, maximum rate of change, breakthrough volume, cumulative error, NG-HETP, curve asymmetry, and Gaussian HETP. These performance parameters, alone or in combination with historical data, may assist in determining column integrity.

[0053] For example, an increase in the number of inflection points may indicate that some amount of transition solution is prematurely breaking through, which may be associated with an integrity breach. A decrease in the maximum rate of change over multiple columns may indicate that transition is occurring to a larger amount, which may be a sign of an integrity breach. A decrease in breakthrough volume may similarly characterize an integrity breach. An increase in either NG-HETP or Gaussian HETP may indicate a decrease in column integrity. Other transition characteristics may be generated based on changes in dataset variability, skewness, kurtosis, peak asymmetry, breakthrough or washout volume, and total error. Transition analysis is described in more detail below. Systems and methods for performing transition analysis are also described, for example, in Larson et al., Use of Process Data To Assess Chromatographic Performance in Production-Scale Protein Purification Columns, Biotechnol. Prog., 2003, 19:485-492, which is incorporated herein by reference in its entirety.

[0054] The results of the transition analysis may be stored, for example, along with other data, in a memory element of computing device 110 or in another computing device. For example, the raw data, the initial data set, the smoothed data set, and the transition analysis data may all be stored.

[0055] According to step 550, one or more statistical process controls may be performed using the results of the transition analysis. In some embodiments, the statistical process control involves performing techniques in one of several categories, including: 1) non-traditional control chart analysis (e.g., individual charts, moving range charts, and / or range chart analysis); 2) multivariate analysis (MVA); or 3) a combination of non-traditional control chart analysis and MVA. These processes may include, for example, analyzing the results of the transition analysis as part of a larger data set that includes transition analysis results from previous chromatographic runs, such as runs within the same production cycle, runs of the same product lot, or runs of the same raw material mix. These processes are described more particularly below with respect to process 600.

[0056] The results of one or more statistical process control exercises may be referred to as performance evaluation data, which may refer to any process data that is meaningful in assessing the repeatability and success of a process, including transition analysis results.

[0057] According to step 560, the data may be reported. In some embodiments, one or more reports may be generated. For example, the disclosed methods and systems can generate a tabular report of any results analyzed using a given profile. Reports may be generated for a particular time frame, for a particular run, and / or for a particular lot, based on a desired number of previous chromatography runs. Examples of reports are shown in Figures 24-1 and 24-2 and are described in more detail below.

[0058] FIG. 6 illustrates an exemplary process 600 for analyzing chromatography data and performing process control in accordance with some embodiments of the present disclosure in more detail than FIG. 5 . According to step 610, raw chromatography data may be received. According to step 620, the raw chromatography data may be processed according to a profile. According to step 630, a noise reduction technique may be applied. According to step 640, transition analysis may be performed on the processed chromatography data to generate transition data representative of column integrity. According to step 650, at least one of an Individual (I) control chart, a Moving Range (MR) control chart, or a Range (R) control chart may be generated based on the transition data. According to step 660, one or more multivariate statistical analysis methods may be applied to at least one of the I control chart, the MR control chart, or the R control chart to generate performance data. According to step 660, an action may be performed based on the performance data.

[0059] According to step 610, chromatographic raw data may be received. Similar to process 500, the chromatographic raw data may be obtained from a chromatographic system, such as system 100. The chromatographic raw data may include multiple signals associated with multiple blocks. Receiving the chromatographic raw data may include directly reading the chromatographic raw data from one or more detectors (e.g., detector 112 of system 100) or from a computing device (e.g., computing device 110) and / or may include monitoring a network location for chromatographic raw data files. The chromatographic raw data may, in some embodiments, be processed as described above with respect to step 510 of process 500.

[0060] FIG. 7 shows an exemplary data file 1000 of extracted chromatography data. Data file 1000 may include, for example, a data file name, which may assist in identifying the data file by an automated system. As shown, the extracted chromatography data in data file 1000 may be in a spreadsheet format (e.g., Microsoft Excel®). Data file 1000 may include volume measurements in a first column 1002, which may correspond to periodic measurements of the total volume passed through the chromatography system. A second column 1004 may include signal measurements (e.g., UV, conductivity, pH, etc.) corresponding to each of the volume measurements in column 1002. In this case, second column 1004 includes conductivity data in mS / cm. Other columns may provide additional data. Here, for example, third column 1006 may include volume measurements corresponding to logbook entries in fourth column 1008. This may allow for identification of characteristics of the chromatography run, such as block / step start and end points (CG002_START, CG002_END, CG003_START), flow rates, and when aspects of the chromatography system were initiated (e.g., Pump1 may correspond to the time a pump associated with mobile phase liquid supply system 102 is activated, for example). Those skilled in the art will recognize that many variations on data file 1000 are possible. For example, while data file 1000 shows volume measurements as markers of progress in the chromatography run, other measurements, such as time, may be used. Additional columns for other signal data may be included, and additional logbook data (e.g., mobile phase identification, analyte identification, etc.) may be included.

[0061] Referring back to FIG. 6 , according to step 620, the chromatography data can be processed according to a profile. As briefly described with respect to step 520, a profile can be selected for a chromatography data file according to characteristics of the chromatography data in that file. For example, a profile may be pre-created for a given type of chromatography run, a given chromatography column, and / or a given analyte. Such a profile can thus be matched to the chromatography data file for the appropriate run, column, and / or analyte.

[0062] In some embodiments, the profile may be created by a user. The profile may be associated with a particular drug or drug product. In one embodiment, the drug is a small molecule. In another embodiment, the drug is a peptide or polypeptide.

[0063] In some embodiments, the agent is vascular endothelial growth factor (VEGF) derivative.In other embodiments, the agent is aflibercept, described in one or more of U.S. Patent 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 the above documents is incorporated herein by reference in its entirety.

[0064] In other embodiments, the agent is an antigen-binding molecule. In some embodiments, the antigen-binding molecule is an antibody or antigen-binding fragment. In some embodiments, the agent is alirocumab, as described in U.S. Patent Application Publication Nos. 2014 / 0356371 and 2014 / 035670, each of which is incorporated by reference in its entirety. In another embodiment, the agent is sarilumab, as described in U.S. Patent Application Publication Nos. 2016 / 0152717, 2014 / 0302053, and 2013 / 0149310, each of which is incorporated by reference in its entirety. In another embodiment, the agent is dupilumab, as described in U.S. Patent Application Publication No. 2014 / 0356372, each of which is incorporated by reference in its entirety. In another embodiment, the agent is selected from evolocumab, bevacizumab, ranibizumab, tocilizumab, certolizumab, etanercept, adalimumab, abatacept, infliximab, rituximab, anakinra, trastuzumab, pegfilgrastim, interferon beta-1a, insulin glargine [rDNA-derived] injection, epoetin alfa, darbepoetin, filigrastim, and golimumab.

[0065] In some embodiments, a profile may be configured to instruct a sentinel software program (e.g., macro, jobber, cron, or other scheduling software) to periodically scan designated network locations for chromatography data files. A profile may direct data collection from a file if the file name matches a file name identifier in the profile.

[0066] Once a profile has been selected, selected for, or matched to a data file, the data file can be scanned. For example, with respect to the exemplary data file 1000 of FIG. 7, the fourth column 1008 containing the logbook entries can be scanned for indicators such as block start time, end time, flow rate, etc. For example, with respect to data file 1000, the volume measurements corresponding to "CG002_START" and "CG002_END" bracket the volume measurements corresponding to the chromatographic run and signal transition of interest. The first column 1002 and the second column 1004 can then be used to extract the complete data set of signals and volume measurements for that run.

[0067] The values ​​in the profile may also define one or more selection criteria for selecting one or more combinations of blocks and / or signals in the chromatography data file for performing transition analysis. Thus, a profile can be a tool for obtaining a desired subset of data from a chromatography data file. The selection criteria in the profile may be predetermined, for example, from empirical experience, structured optimization, and / or process documentation. Such selection criteria may enable the identification of block and signal combinations that may enable more precise, accurate, or otherwise useful analysis. Such selection criteria may include, for example, whether transition materials are readily available. This includes blocks that transition to or from the product solution. This allows for additional column evaluation between manufacturing runs, if desired. Such selection criteria may also, or alternatively, include whether multiple blocks occur at regular cycle intervals. This includes blocks that are not performed after the conclusion of the final collection cycle of a manufacturing lot. Such selection criteria may also, or alternatively, include whether a signal reaches detector saturation before or after a transition. Such selection criteria may also or alternatively include whether the signal approaches a plateau at a clear and discernible level and does not continually drift. Such selection criteria may also or alternatively include whether the signal in a given block has a large difference between its minimum and maximum values. Such selection criteria may also or alternatively include whether the signal has many inflection points during the transition. A smaller number of inflection points may indicate more reliable data collection.

[0068] In some cases, previous chromatography runs can help identify appropriate selection criteria for selecting block and signal combinations in future chromatography runs. Figure 8, for example, shows plots of NG-HETP calculations for two different block and signal combinations (elution step-UV signal combination and re-equilibration step-conductivity signal combination) for six different chromatography lots (Lots A-F). For reference, solid bars representing three standard deviations are provided for each set. As can be seen from this plot, the NG-HETP calculations for the elution step-UV signal combination show much greater variation than those for the re-equilibration step-conductivity signal combination. It can be seen that both the trend scale and standard deviation are different. When monitoring shifts in performance, it may be desirable to have less lot-to-lot variation considered typical. This allows for increased sensitivity when monitoring changes in performance. Therefore, selection criteria for chromatographic runs of lots similar to Lots A-F may include a preference for the re-equilibration step and conductivity signal combination over the elution step and UV signal combination. Those skilled in the art will recognize that similar chromatographic pre-run analysis may reveal selection criteria for other potential blocks and signal combinations.

[0069] In some embodiments, a profile 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 profile may include identifying and extracting a preferred (e.g., primary) block and signal combination for transition analysis and / or one or more additional (e.g., secondary) block and signal combinations for transition analysis. In some embodiments, the primary block and signal combination will most likely satisfy the selection criteria in the profile among all possible block and signal combinations in the chromatographic data file. In some embodiments, the secondary block and signal combination will most likely satisfy the selection criteria in the profile among all block and signal combinations in the chromatographic data file. The primary block and signal combination may contain data most likely to provide useful transition analysis for assessing column and process integrity, while the secondary block and signal combination may provide a secondary measure and cross-check of column integrity.

[0070] In some embodiments, the profile according to step 620 may itself be a data file that may contain instructions for extracting particular data from or modifying chromatography data files with associated metadata, in some embodiments, such instructions in the profile may be executable by a computer program.

[0071] Referring to FIG. 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. Similar to 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 may include standardizing the size of the data set to allow for a consistent impact of the smoothing window. In some embodiments, this step may include normalizing the data to eliminate variation based on the magnitude of the transition. This variation may be due to unique preparations of mobile phase buffers, including inherent variability in the end values ​​of the baseline or saturation phases.

[0072] Noise reduction techniques may include removal of implicit errors introduced by measurement tools (e.g., detector 112 in system 100) and random errors introduced by batch processes when data is collected (e.g., in earlier steps of method 500). Noise reduction may include de-duplication of records in the dataset, outlier detection and removal, and / or other techniques to increase the signal-to-noise ratio within the dataset.

[0073] Noise reduction may also or alternatively include the application of data smoothing and signal error-rejection algorithms. Figures 9-1 and 9-2 illustrate an exemplary algorithm 900 in this regard in flowchart form. According to steps 902 and 904 of algorithm 900, the algorithm begins and relevant signal data (e.g., data processed according to step 620) is read. According to step 906, the read data may be normalized to remove magnitude anomalies.

[0074] Next, 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 settings (909, 911). Derivation may optionally be performed according to step 912. One or more additional smoothing filters may then be applied (steps 914, 916) according to additional desired smoothing filter settings (913, 915). The number of smoothing filters applied (steps 908, 910, 914, 916), as well as the number and characteristics of settings 909, 911, 913, 915, may vary depending, for example, on the data conditions, expected results, signal type, and other factors. Whether or not differentiation is performed on the data may also depend on these factors.

[0075] The process may then continue with a dynamic signal error rejection algorithm 980. This algorithm may be configured to remove data from the retrieved data that is not due to chromatographic transitions. For example, errors that should be removed to enable meaningful transition analysis include alarms, machine arrests, skid sensor malfunctions, or missing data. This may be achieved by identifying expected characteristics of chromatographic transitions, such as induction time, maximum intensity, duration from onset, and expected background noise. For example, initial point rejection 918 may be made based on expected transition location 919, initial deadband rejection 920 may be made based on expected background noise level 921, derivative height and width rejection may be made based on expected signal error characteristics, and final deadband rejection may be made based on expected background noise level 925. Expected transition characteristics may be generated, for example, based on previously accumulated transition data. Once the algorithm 900 is complete, according to step 990, the data may be ready for transition analysis.

[0076] Although algorithm 900 is one exemplary model of a smoothing and signal error rejection algorithm, one skilled in the art will recognize that variations to this algorithm are possible. For example, only smoothing algorithm 950 may be implemented, or only signal error rejection algorithm 980 may be implemented. Additionally or alternatively, more or fewer smoothing filters may be applied and / or more or fewer points may be rejected.

[0077] After applying noise reduction and / or smoothing techniques to the data, the data may include measurements of step yields and other mobile phase parameters in the form of breakthrough or washout curves corresponding to step transitions, for example.

[0078] Referring back to FIG. 6 , according to step 640, transition analysis can be performed on the processed chromatographic data to generate transition data indicative of column integrity. Transition analysis can include performing one or more mathematical methods on the processed data to infer dispersion parameters from step transitions. For example, one or more curves can be generated from the processed data by, for example, taking the first derivative of a curve to generate another curve characterized by peaks. This curve can be used to generate performance parameters such as, for example, several 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 of column integrity (e.g., when matched with transition analysis parameters of previous representative chromatographic data).

[0079] For example, an increase in several inflection points may indicate that some amount of transition solution is prematurely breaking through, which may be related to an integrity violation. When plotting the derivative curve versus the total volume data, several inflection points can be determined from several peaks.

[0080] As another example, a decrease in the maximum rate of change over the use of multiple columns may indicate that the transition is occurring to a larger volume, which may be a sign of a breach in integrity. The maximum rate of change is equal to the maximum value of the derivative curve.

[0081] As another example, a decrease in breakthrough capacity may similarly characterize a breach of integrity. Breakthrough capacity may be determined by finding the first capacity value at which the signal is less than 95% of its highest value (for a high-to-low transition) or more than 5% of its lowest value (for a low-to-high transition).

[0082] As another example, an increase in either NG-HETP or Gaussian HETP may indicate a decrease in column integrity. Other characteristics of the transition may be generated based on dataset variability, skewness, kurtosis, peak asymmetry, breakthrough or washout volume, and total error modifications. Systems and methods for performing transition analysis are also described, for example, in Larson et al., "Use of Process Data To Assess Chromatographic Performance in Production-Scale Protein Purification Columns," Biotechnol. Prog., 2003, 19:485-492, which is incorporated herein by reference in its entirety.

[0083] The results of the transition analysis may be stored, for example, along with other data, in a memory element of computing device 110 or in another computing device. For example, the raw data, the initial data set, the smoothed data set, and the transition analysis data may all be stored.

[0084] Referring back to FIG. 6 , according to step 650, at least one of an Individual (I) control chart, a Moving Range (MR) control chart, or a Range (R) control chart can be generated based on the transition data. For brevity, this disclosure will collectively refer to them as I-MR-R control charts, but it should be understood that "I-MR-R control chart" can refer to only I control charts, only MR control charts, only R control charts, or any combination and number of such control charts. I-MR-R control charts provide a separate visualization of transition analysis output and can aid in the interpretation of trends in transition analysis data across multiple column runs or lots in the form of NG-HETP, skewness, kurtosis, or other parameters. An advantage of I-MR-R control charts is that the data can be quickly viewed and easily interpreted from a visual standpoint, making subtle trends or immediate data shifts discernible at an early stage.

[0085] An I control chart, for example, may plot values ​​(e.g., skewness) for each lot analyzed. An MR control chart may plot the difference between the values ​​of each analyzed lot and the values ​​of a previously analyzed lot. An R control chart may plot the difference between values ​​within a lot (e.g., the skewness of two transition analyses performed on the top block and signal combination and the second top block and signal combination in one lot). Each control chart may include a mean line, an upper control limit (UCL), and a lower control limit (LCL), which can be calculated using available data determined to represent a typical process and are positioned equidistant from the mean line on each control chart.

[0086] Some parameters, such as NG-HETP and skewness in transition analysis, may exhibit significant dynamics relative to specific limits of life when plotted on an I-MR-R control chart. In such cases, using an I-MR-R control chart with control limits estimated using short-term criteria can result in excessive out-of-trend signals, even after resetting the chart due to column repacking. One solution to this problem is the use of a Levey-Jennings control chart, which uses a calculation of the long-term standard deviation from a "representative" column lot to account for the extra variability resulting from starting a new column pack. Whether data are considered representative can be determined by the absence of abnormal readings for various performance evaluation data sets for the lot. These sets may be used to calculate the standard deviation, sometimes with particular attention to the + / - 3 standard deviation (SD) line. Several lots of a given column may be run to determine whether the useful life of most or all of the columns was "typical." In one embodiment, a complete modeling of feasible column dynamics can be performed for Levey-Jennings control charts, which result in regression models that account for special-cause variability in column repacking. However, Levey-Jennings control charts require longer data periods, and thus their use may be limited by data aggregation rates.

[0087] Additionally, because transition analysis is known to have variation due to column repacking events, the I-MR-R control chart can take into account packing and column repacking; for example, the first lot run after the column is repacked will not have an MR value based on the change from the last lot run before the column was repacked. In some embodiments, the control strategy can be configured to consider only specific violations when monitoring for trending excursions, excluding known variation due to repacking events.

[0088] The generation of the I-MR-R control chart may be performed, for example, by an analysis module within computing device 110 or by a separate analysis module elsewhere. The generation of I-MR-R control charts may also be performed by, for example, a control chart module in computing device 110. For example, Figures 14-21 show I-MR-R data for chromatography lots 21-100, and are discussed further below.

[0089] Referring back to FIG. 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 transition analysis data. This step may be performed in addition to or as an alternative to step 650, and, like the generation of control charts according to step 650, takes into account the transition analysis of previous chromatographic data. Multivariate analysis takes a large number of variables and simplifies them into component vectors. This allows for a holistic view of large data sets. Advantages include the possibility that subtle variations across multiple runs that may not be apparent when considering a single data set may become apparent when those component vectors are plotted. This data variability may arise from differences in materials, equipment, ambient atmospheric conditions, etc., and may be small from the perspective of an operator or human observer. Examples of multivariate statistical analysis methods include Principal Component Analysis (PCA), Partial Least Squares (PLS), Orthogonal Partial Least Squares (OPLS), multivariate regression, canonical correlation, factor analysis, cluster analysis, graphical procedures, etc. Such multivariate statistical analyses may be performed, for example, using specialized computer software.

[0090] The general purpose of using multivariate analysis is to convert large amounts of data into interpretable information. By enabling the search for correlations and patterns among multidimensional variables and the extraction of statistically significant values ​​from large amounts of raw data, multivariate analysis allows the interpretation of any significance for variation between successive analyses of chromatographic data from similar lots, for example.

[0091] For example, PCA is a multivariate statistical method in which a dataset containing a large number of variables (e.g., a transition analysis involving several parameters) is reduced to a small number of variables called scores (t). For example, a dataset containing a large number of variables can be reduced to a dataset in which each observation (e.g., each transition analysis) is represented by two t-scores. The t-scores contain information about the variability of each variable in the dataset and its correlation to all other variables in the dataset. Thus, the t-scores describe the variability and correlation structure of each observation in the dataset (e.g., each transition analysis) relative to each other observation in the dataset. The graphical output of PCA is typically a PCA plot. A PCA plot is a plot of one t-score against another t-score for each observation. Generally, a PCA plot is a variance that shows how the variability and correlation structure compare for all of the observations in the dataset. The plot can thus serve to cluster similar observations together.

[0092] As another example, PLS regression analysis is a technique for analyzing systems of independent and response variables. PLS is a predictive technique that can handle many independent variables, even when the variables exhibit multicollinearity. PLS can also relate a set of independent variables to multiple sets of dependent (response) variables. Often, PLS extracts one set of latent variables for a set of manifest independent variables and another set of latent variables for a set of manifest response (or dependent) variables. This extraction process can be based on decomposition of a cross-product matrix containing both the independent and response variables. The scores of the latent independent variables, i.e., x-values, are used to predict the scores of the latent response variables, i.e., y-values. The predicted y-values ​​are then used to predict additional manifest response variables. The x-scores and y-scores are selected to maximize the relationship between consecutive pairs of x- and y-variables. Advantages of PLS ​​include its ability to model multiple independent and dependent variables, its ability to handle multicollinearity among independent variables, its robustness in dealing with data noise and (depending on the software used) missing data, and its ability to directly generate independent latent variables based on a cross product involving the response variable, resulting in stronger predictions.

[0093] In some embodiments, multivariate statistical analysis can be performed on the I-MR-R control chart to determine further statistical significance of the variations shown on the I-MR-R control chart.

[0094] In addition to the analyses described above, trends in transition analysis can be generated by calculating non-stationary ranges, allowing slow fluctuations to remain within the control limits, while extreme shifts in column performance can be flagged as potential deviations from the trend. Basic methods for defining control limits include moving averages, weighted moving averages, and various degrees of exponential smoothing. One such method for calculating trending limits, known as the Holt-Winters method or triple exponential smoothing, can be used with high validity. The Holt-Winters method uses seasonality to predict appropriate limits, defined as discrete column packing events, for direct application to chromatographic monitoring. Regression modeling (e.g., as used in Levey-Jennings control charts) constitutes an additional method for establishing trend limits. Once sufficient empirical data is available, regression modeling of column integrity can be performed on cumulative column pack usage. This can provide accurate ranges of column performance based on past column performance included in the model.

[0095] 6 , according to step 670, an action may be taken based on the performance data. In some embodiments, this may be to have the transition analysis identified as in-process control (IPC). Generally, the action according to step 670 may include generating a report, generating and / or sending an alert to an operator or display device, such as the display device of computing device 110, or terminating the chromatography process. The action according to step 660 may also include, for example, storing all of the data acquired during the systems and methods disclosed herein in a database for further analysis.

[0096] The results of performing multivariate analysis and / or I-MR-R control chart analysis on transition data can be referred to as performance evaluation data. Performance evaluation data can refer to any process data, including the results of transition analysis, that can be meaningful in assessing the reproducibility and success of a process (e.g., a chromatography process).

[0097] In one embodiment, step 670 can include generating one or more reports. For example, the disclosed method and system can generate a tabular report of any results analyzed using a given profile. Reports can be generated based on a desired number of previous lots, for a specific time frame, and / or for a specific lot. The data set can be fully extracted into multiple formats and input into external applications if further analysis is desired.

[0098] 24-1 and 24-2 illustrate an exemplary report 2400 according to some embodiments of the present disclosure. The exemplary report 2400 includes a report pivot table containing the results of four chromatography cycles from one manufacturing lot. Each of the four cycles is listed by its lot and cycle number and includes a column for the date and time it was performed. The results of the transition analysis are reported in columns including NG-HETP, Gaussian HETP, skewness, asymmetry, kurtosis, non-Gaussian N, and Gaussian N. A snapshot of the data source is also provided, indicating the name of the chromatography system from which the data originated, the logbook in which it was recorded, and the block from which the data was acquired. Below the data for each cycle, trending data for each analysis result is reported. It should be understood that this report is an exemplary report and that many variations are possible. For example, any desired number of chromatography cycles may be listed and / or included in one or more plots of the trend data.

[0099] In some embodiments, the systems and methods disclosed herein may be used for continuous monitoring of column and process integrity. Accordingly, the systems and methods disclosed herein may analyze data for a particular column and / or process. In one embodiment, one or more alerts may be generated based on the data analysis. In another embodiment, the chromatography process may be terminated based on the data analysis. For example, one or more notifications (e.g., event notification, event evaluation, or deviation notification form) may be provided or displayed to an operator to take corrective action. For example, one or more screen overlays may be displayed, e.g., on the screen of the computing device 110, and / or a message may be sent to the operator upon completion of the analysis to advise whether to continue or stop the chromatography process.

[0100] In embodiments, results from the disclosed methods and systems can be trended to provide current trend information when assessing column packing quality prior to column use in production. In another embodiment, results from the disclosed methods and systems can be used to assess column performance in real time (or offline) to confirm column integrity prior to the next product use cycle (e.g., when tolerances and control limits in a trend chart are established).

[0101] In a further aspect, the results can be used along with statistical information to predict process outcomes based on process modeling using multivariate statistical analysis, prior to expensive and time-consuming inspection and testing.

[0102] One criterion for evaluating statistical analysis plots in particular may include, for example, that when generating a score plot for a data set using PCA, lots that are more than a threshold number of standard deviations from the mean may be identified as having column integrity issues, which may result in the generation of a warning or instruction regarding lot variation.

[0103] One evaluation criterion for I-MR-R control charts, among others, may include that any point outside the upper or lower control limits of one or more control chart types may be grounds for an alert. Thus, an action performed pursuant to step 670 may be to issue an alert, for example, from computing device 110, if a lot exhibits a point outside the control limits. Such an alert may include, for example, a notification of the event provided to an operator or a database, an evaluation of the event, and / or a deviation notification form.

[0104] In some embodiments, the systems and methods disclosed herein may be implemented as part of an in-process control system that may operate within an organization's quality system framework to ensure consistency and compliance with safety requirements. As part of such a program, data from the systems and methods disclosed herein may be used to determine critical process parameters (CPPs) and critical quality attributes (CQAs) to be monitored within the in-process control program. Additionally, as part of such a program, signal transitions and column integrity shifts may be detected in real time or near real time (e.g., during or concurrent with column operation), allowing preventative and corrective actions to be taken in response to performance data.

[0105] FIG. 23 illustrates an exemplary user interface 2300 according to some embodiments of the present disclosure. The user interface 2300 shows a transition analysis profile creation / edit screen where a user can create or edit a new transition analysis profile. Parameters selected during profile creation can be used to tailor the transition analysis based on the unique characteristics of the chromatography process and to optimize the robustness of the output for each column and program. Parameters listed in the exemplary user interface 2300 include, for example, profile name, comments, historical data and / or test location, file pattern, last value, key indicators, hard reset, window size for moving average, first filter (e.g., SG filter) value, second filter value, percentage of Vmax below which the signal should first register as zero, maximum width percentage to hold peaks, chromatography column height, start date, end date, and database name.

[0106] The methods and systems disclosed herein may be used for relatively continuous monitoring of column integrity. For example, the methods and systems disclosed herein may monitor column integrity without requiring interruption of the normal chromatography process to perform diagnostics on the chromatography system. Furthermore, the methods and systems disclosed herein can analyze data relating to a particular column and a particular process. As discussed, one or more warnings may be generated based on the analysis of data over time. In another embodiment, the chromatography process may be terminated based on the data analysis. For example, if column integrity is found to be compromised, one or more notifications may be displayed to an operator to take corrective action. For example, one or more screen overlays may be displayed, and upon completion of the analysis, a message window may be displayed to the operator advising whether to continue or stop the chromatography process, or to take other action.

[0107] In some embodiments, results from the disclosed methods and systems can be trended to provide information about current trends in assessing column packing quality prior to column use in production. In other embodiments, results from the disclosed methods and systems can be used to assess column performance in real time (or offline) to confirm column integrity prior to the next product use cycle (e.g., if tolerances and control limits in trend charts are established). In some embodiments, results can be used along with statistical information to predict process outcomes based on process modeling using MVA prior to expensive and time-consuming inspection and testing. [Example]

[0108] Example 1 The primary block and signal combinations are selected from affinity capture chromatography data as follows: The affinity capture data contains eight blocks and two signals (UV and conductivity) per block, for a total of 16 possible block and signal combination options. A profile containing a set of block and signal selection criteria is assigned to the data, and the selection criteria are applied in the following order to select the primary block and signal combinations:

[0109] By considering the selection criterion that the blocks must occur at regular intervals between production batch cycles, two blocks and their respective signals can be eliminated, leaving a choice of 12 possible combinations.

[0110] By considering the selection criterion that the signal must reach UV absorbance meter saturation, the UV signals in three blocks can be eliminated as candidates, leaving a choice of nine possible combinations.

[0111] By considering the selection criterion that the signal should be at a clear and distinguishable level approaching the stationary phase, the UV signals of the three blocks can be eliminated as candidates, leaving six possible combination options (all with conductivity as the signal option).

[0112] By considering the selection criterion that the signal should have a large difference between the minimum and maximum values ​​in a given block, the conductivities of four blocks can be eliminated, leaving a choice of two possible combinations.

[0113] By considering the selection criterion that signals that exhibit the minimum number of inflection points are preferred, the conductivity of one block can be eliminated, leaving only one remaining choice of block and signal combination.

[0114] The final remaining block and conductivity signal choices are the first block and signal combinations on which transition analysis can be performed. The final block and signal combination to be eliminated is the second block and signal combination.

[0115] Example 2 I-MR-R trending skewness and NG-HETP data were plotted for 100 chromatography lots for a given chromatography program, "Program B," as follows: Figures 14-16 show the I, MR, and R control charts for skewness, respectively. Figures 17-19 show the I, MR, and R control charts for NG-HETP, respectively. The UCL and LCL represent 3 standard deviations determined by previously accepted data. Breaks in the mean, UCL, and LCL lines indicate column repacking. Solid shifts in these lines indicate points at which limits were recalculated.

[0116] Figure 14 shows the skewness for all 100 lots generated in Program B. It can be seen that the first and second column packs behaved differently during their use. As shown, Pack 1 experienced a marginal shift after the first four lots, maintaining a skewness value between 0.055 and 0.855. Pack 2 deviated from the trend, however, eventually reaching a steady state at lot number 67. This may be because the shifting and stabilization of the new column pack took longer than Pack 1.

[0117] Figure 15 shows the MR control chart of skewness for all lots generated in Program B. An outlier can be observed for Pack 2, indicating a large shift between lots based on individual values.

[0118] Figure 16 shows the R chart for skewness for all lots generated in Program B. Pack 1 shows some outliers, which increased the limits for Pack 2. There are three packs on the chart, and the lots are plotted consecutively, with Pack 1 being the leftmost solid line and Pack 3 being the rightmost solid line. Note the off-trend points during the latter half of Pack 1, which may indicate that the column was experiencing variation within the cycle of that lot.

[0119] Figure 17 shows the NG-HETP I control chart for all lots produced in Program B. Pack 1 experienced a decrease in NG-HETP, representing improving column performance. Pack 2 experienced a continued increase in NG-HETP, which may correlate with a decrease in column effectiveness.

[0120] Figure 18 shows the moving range control chart for NG-HETP for all lots produced in Program B. Outliers can be seen in both Pack 1 and Pack 2. This identified several points that showed dramatic shifts in individual values.

[0121] Figure 19 shows the R chart for NG-HETP for all lots generated in Program B. Pack 2 showed consistently rising range values ​​that were investigated and determined to have a root cause that changed flow direction within the third cycle of the lot, causing the third cycle to show values ​​that differed from the other cycles.

[0122] Example 3 Individual (I) control charts were plotted for transition analysis of two groups of chromatographic lots for a given "Program A."

[0123] Figure 20 shows the NG-HETP I control chart for 46 lots produced in Program A. The data show that the columns perform within established limits for process consistency.

[0124] Figure 21 shows the NG-HETP I control chart for 21 additional lots generated in Program A. The data show that two lots (Lots 56 and 58) exceeded the upper control limit.

[0125] Example 4 Multivariate analysis was performed using transition analysis data from 27 chromatography lots, including the three lots shown in Figure 4. Loading values ​​were calculated for seven parameters from the 27 lots, including the three lots shown in Figure 4. The seven parameters included NG-HETP for each of the I, MR, and R control charts for the lots, skewness for each of the I, MR, and R control charts for the lots, and kurtosis for the I control chart. Figure 10 shows the loading charts for each of the seven parameters. The size of each bar corresponds to the effect of the parameter on the principal component. Error bars indicate the relative error in the loading values.

[0126] Figure 11 shows an exemplary score plot for the 27 lots. The score plot was calculated for seven parameters from the 27 lots, including the loading values ​​(Principal Component 1) calculated for those lots shown in Figure 4 as well as Principal Component 2. Lots with similar parameter values ​​were clustered. Ellipses around the majority of plot points exclude outliers at a 95% confidence level.

[0127] Example 5 Multivariate analysis was performed on the I-MR-R data for transition analysis of 46 chromatography lots. I-MR-R data was collected for each of the 46 lots. Lots deemed abnormal or inappropriate based on the I-MR-R data were removed from the analysis, and data for the remaining lots is collected in Table 1 below. Lots containing multiple transition values ​​were averaged and reported as individual measurements. Range values ​​were calculated as the maximum minus the minimum transition value within the lot.

[0128] [Table 1]

[0129] Using the data from Table 1, principal components were calculated by creating a loading plot showing the coefficients for each input parameter. Each row of data was converted to a single value. Assessment of model accuracy and relevance to the physical system was performed using the R of the PCA model. 2 Value and Q 2 where R 2 is a statistical measure of how close the test set of data is to the fitted regression line, and Q 2 is a statistical measure of how close a test set of data is to the regression line. 2 and Q 2 Both indicate how well the model describes the system under analysis, with 1 being perfect modeling and 0 representing a complete lack of correlation.

[0130] Figure 12 shows the loading plot of the model.2 The value is 0.798, and Q 2 The value was 0.591, indicating that the model was acceptable for use and that all input values ​​had an influence on the model principal components because they were not located near the center line. In Figure 12, the magnitude of the y-coordinate of each point corresponds to the parameter's influence on the principal components (e.g., the influence of the mean NG-HETP, the range of NG-HETP, the skewness range, and the mean skewness). The y-coordinate of each point corresponds to the number of inputs per point.

[0131] The principal component values ​​were trended for the corresponding lots and graphed linearly. Figure 13 shows a score plot of the data set. The score plot shows the PC1 values ​​(values ​​that contributed in the direction of the highest variation) for each lot used. In Figure 13, it can be seen that one lot (Lot 6) fell outside the 3 standard deviation limit, with several points on the verge of exceeding 2 standard deviations, indicating that the system was experiencing variation in those lots.

[0132] As will be appreciated by those skilled in the art, the methods and systems disclosed herein may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the systems and methods according to the present disclosure 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 hard disks, CD-ROMs, optical storage devices, or magnetic storage devices. More specifically, the methods and systems may take the form of web-implemented computer software.

[0133] Embodiments of the present disclosure are described with reference to block diagrams and flowchart illustrations of methods, systems, devices, and computer program products. It will be understood that one or more blocks of the block diagrams and flowchart illustrations, respectively, can be implemented by computer program instructions. These computer program instructions may be loaded into a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to create a machine such that the instructions, executing on the computer or other programmable data processing apparatus, create means for implementing the functions defined in one or more of the flowchart blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture including computer-readable instructions for implementing the functions defined in one or more of the flowchart blocks. The computer program instructions may also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions executing on the computer or other programmable apparatus provide steps for implementing the functions defined in one or more of the flowchart blocks.

[0135] Thus, the blocks in the block diagrams and flowchart diagrams support combinations of means for performing a specified function, combinations of steps for performing a specified function, and program instructions for performing a specified function. It will also be understood that each block of the block diagrams and flowchart diagrams, and combinations of blocks in the block diagrams and flowchart diagrams, can be realized by a hardware-based computer system that performs the specified function or step, or a combination of hardware (e.g., special-purpose chromatography hardware) and computer instructions.

[0136] Figure 22 illustrates an operating environment 2200 in which some systems and methods according to the present disclosure may be implemented. As an example, the process control device 108 and computing device 110 (or components thereof) of Figure 1 may be a computer 2201 shown in Figure 22. The computer 2201 may include one or more components, such as one or more processors 2203, a system memory 2212, and a bus 2213 that connects 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 may employ parallel computing.

[0137] The bus 2213 may 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 bus or local bus using any of a variety of bus architectures. The bus 2213, and all buses defined in this description, may also be implemented over wired or wireless network connections.

[0138] The computer 2201 typically includes a variety of computer-readable media. Exemplary readable media can be any available media that can be accessed by the computer 2201, including, by way of example and not limitation, both volatile and nonvolatile media, and removable and non-removable media. The system memory 2212 can comprise computer-readable media in the form of volatile memory, such as random access memory (RAM), and / or nonvolatile memory, such as read-only memory (ROM). The system memory 2212 typically includes data, such as chromatography data 2207, and / or program modules, such as an operating system 2205 and chromatography software 2206, that are accessible to and / or executed by the one or more processors 2203. The many features and advantages of the present disclosure will be apparent from the detailed specification, and it is, therefore, intended by the appended claims to cover all such features and advantages of the present disclosure that are within the true spirit and scope of the present disclosure. Further, since numerous modifications and variations will readily occur to those skilled in the art, it is not desired to limit the disclosure to the exact construction and operation illustrated and described; therefore, all suitable modifications and equivalents may be used and are within the scope of the present disclosure.

[0139] In another embodiment, the computer 2201 may also include other removable / non-removable, volatile / non-volatile computer storage media. The mass storage device 2204 may 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 may be a hard disk, a removable magnetic disk, a removable optical disk, a magnetic cassette or other magnetic storage device, a flash memory card, a CD-ROM, a digital versatile disk (DVD) or other optical storage device, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0140] Those skilled in the art will appreciate that the conception upon which this disclosure is based may readily be utilized as a basis for designing other structures, methods and systems for carrying out the several purposes of the present disclosure, and accordingly the scope of the appended claims should not be deemed to be limited by the foregoing description.

[0141] [Appendix 1] receiving raw chromatographic data comprising a plurality of signals, each of the plurality of signals being 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 chromatography data; generating processed chromatography data by applying a noise reduction technique to the data subset, the application of the noise reduction technique comprising: selecting a portion of the data subset for analysis using predetermined settings; normalizing said portion to prevent magnitude deviations; generating smoothed data using at least one smoothing filter on the portion; analyzing the portion for dynamic signal errors; generating transition data by performing a transition analysis on the processed chromatography data, the performing the transition analysis comprising: generating a curve using the processed chromatographic data; analyzing the curve to generate performance parameters; and performing an action based on the transition data, wherein the performing an action includes generating a notification of the event, generating an evaluation of the event, or generating a deviation notification form.

[0142] [Appendix 2] 2. The method of claim 1, wherein the raw chromatography data is received from a chromatography system.

[0143] [Appendix 3] 2. The method of claim 1, wherein the raw chromatography data is obtained from a detector of a chromatography system when one or more chromatography cycles are performed by the chromatography system.

[0144] [Appendix 4] 2. The method of claim 1, wherein each of the plurality of blocks corresponds to a step in a chromatography process.

[0145] [Appendix 5] 2. The method of claim 1, wherein the selected combination includes the first block, the first signal, and a second signal of the plurality of signals.

[0146] [Appendix 6] 10. The method of claim 1, further comprising selecting the combination of the first block and the first signal according to a profile defining a plurality of selection criteria.

[0147] [Appendix 7] The plurality of selection criteria include: whether multiple blocks occur at regular intervals between chromatography cycles; the extent to which one of the plurality of signals saturates a detector; The degree to which the plurality of signals approach a plateau at distinct levels; the magnitude of the change in the plurality of signals; or 7. The method of claim 6, further comprising counting the number of inflection points exhibited by the plurality of signals during a transition period.

[0148] [Appendix 8] 2. The method of claim 1, wherein selecting the first block and the first signal combination includes selecting a first-rank block and signal combination, and the method further includes selecting a second-rank block and signal combination.

[0149] [Appendix 9] The method further includes selecting smoothed data that matches a characteristic of a chromatogram transition, the characteristic comprising: Induction time, maximum strength, Duration from start, or 2. The method of claim 1, including one of expected background sensor noises.

[0150] [Appendix 10] The method comprises: generating an individual control chart, a moving range control chart, or a range control chart based on the transition data; generating performance data by applying statistical process control to the individual control chart, moving range control chart, or range control chart; 2. The method of claim 1, wherein performing an action based on the transition data includes performing an action based on the performance data.

[0151] [Appendix 11] 11. The method of claim 10, wherein applying statistical process control to the individual control chart, moving range control chart, or range control chart includes performing one of multivariate data analysis or principal component analysis.

[0152] [Appendix 12] A chromatography method comprising carrying out the method of claim 1 while running a chromatography system.

[0153] [Appendix 13] receiving a selection of the chromatographic raw data; generating smoothed data by applying a noise reduction technique to said selection of raw chromatographic data, said noise reduction technique comprising: selecting a portion of the smoothed data for analysis using a predetermined set of values; normalizing the data of said portion to prevent magnitude deviations; applying at least one smoothing filter to the portion of data to generate smoothed data; analyzing the portion of data for dynamic signal error; generating processed chromatographic data by selecting smoothed data that matches chromatographic transition characteristics, the chromatographic transition characteristics being: Induction time, maximum strength, Duration from start, or Steps, including expected background noise, performing an action based on the processed chromatography data, said performing an action comprising: generating a notification of the event; generating an assessment of the event; or generating a deviation notification form.

[0154] [Appendix 14] The step of receiving the selected raw chromatographic data comprises: receiving raw chromatography data comprising a plurality of signals and a plurality of blocks, wherein each of the plurality of signals is associated with a block; and selecting a first block and a first signal combination from the raw chromatography data.

[0155] [Appendix 15] The method comprises: generating one of an individual control chart, a moving range control chart, or a range control chart using the processed chromatography data; and applying statistical process control to the individual control chart, moving range control chart, or range control chart by performing multivariate data analysis or performing principal component analysis to generate performance data, wherein performing an action based on the processed chromatography data includes performing an action based on the performance data.

[0156] [Appendix 16] receiving raw chromatographic data comprising a plurality of signals, each of the plurality of signals being 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 chromatography data; generating processed chromatography data by applying a noise reduction technique to the data subset, the application of the noise reduction technique comprising: selecting a portion of the data subset for analysis using predetermined settings; normalizing said portion to prevent magnitude deviations; generating smoothed data using at least one smoothing filter on the portion; analyzing the portion for dynamic signal errors; generating transition data representative of column integrity by performing a transition analysis, the performing the transition analysis comprising: generating performance parameters including a maximum rate of change; generating the transition data based on the performance parameters; and performing an action based on the transition data, wherein the performing an action includes generating a notification of the event, generating an evaluation of the event, or generating a deviation notification form.

[0157] [Appendix 17] The method comprises: generating an individual control chart, a moving range control chart, or a range control chart based on the transition data; generating performance data by applying statistical process control to the individual control chart, moving range control chart, or range control chart; 17. The method of claim 16, wherein performing an action based on the transition data includes performing an action based on the performance data.

[0158] [Appendix 18] selecting the combination of the first block and the first signal according to a profile defining a plurality of selection criteria, the plurality of selection criteria comprising: whether multiple blocks occur at regular intervals between chromatography cycles; the extent to which one of the plurality of signals saturates a detector; The degree to which the plurality of signals approach a plateau at distinct levels; the magnitude of the change in the plurality of signals; or 17. The method of claim 16, further comprising counting the number of inflection points exhibited by the plurality of signals during a transition period.

[0159] [Appendix 19] The method comprises: generating an individual control chart, a moving range control chart, or a range control chart based on the transition data; generating performance data by applying statistical process control to the individual control chart, moving range control chart, or range control chart; 17. The method of claim 16, wherein applying statistical process control to the individual control chart, moving range control chart, or range control chart includes performing one of multivariate data analysis or principal component analysis.

[0160] [Appendix 20] 17. The method of claim 16, wherein the raw chromatography data is obtained from a detector of the chromatography system when one or more chromatography cycles are performed by the chromatography system, from a computing device of the chromatography system, or both.

[0161] [Appendix 21] 17. A chromatography method comprising carrying out the method of claim 16 while running a chromatography system.

Claims

1. receiving raw data from a detector, the raw data comprising a plurality of signals and a plurality of blocks, the raw data corresponding to conductivity, pH, salt concentration, optical absorption, fluorescence, refractive index, electrochemical response, or mass spectrometry data analysis; generating processed data by applying noise reduction techniques to the plurality of signals and the plurality of blocks and selecting data that match transition characteristics, the transition characteristics including induction time, maximum intensity, duration from onset, or expected background noise; performing a transition analysis on the processed data to generate transition data, the performing the transition analysis comprising: generating a curve using the processed data; analyzing the curve to generate performance parameters; and generating the transition data based on the performance parameters; and performing an action based on the transition data, wherein the performing an action includes generating a notification of the event, generating an evaluation of the event, or generating a deviation notification form.

2. The method of claim 1 , wherein applying a noise reduction technique to the plurality of signals and the plurality of blocks comprises selecting a first block and a first signal combination from the raw data.

3. Selecting the combination of the first block and the first signal includes selecting the combination according to a profile defining a plurality of selection criteria, the plurality of selection criteria comprising: whether multiple blocks occur at regular intervals; the extent to which one of the plurality of signals saturates a detector; The degree to which the plurality of signals approach a plateau at distinct levels; the magnitude of the change in the plurality of signals; or the number of inflection points exhibited by the plurality of signals during the transition period; The method of claim 2 , comprising one or more of:

4. The method of claim 3 , wherein the profile is associated with a pharmaceutical product.

5. 5. The method of claim 4, wherein the transition data is indicative of the integrity of a chromatography column used in the purification of the pharmaceutical product.

6. 10. The method of claim 1, wherein the performance parameters include two or more of maximum rate of change, number of inflection points, breakthrough volume, cumulative error, curve asymmetry, and height equivalent to a theoretical plate.

7. The noise reduction technique includes: Analyzing a portion of the raw data for dynamic signal error; or Normalizing a portion of the raw data to prevent magnitude deviations The method of claim 1 , comprising one or more of:

8. receiving raw data from a detector, said raw data corresponding to conductivity, pH, salt concentration, optical absorption, fluorescence, refractive index, electrochemical response, or mass spectrometry data analysis; generating processed data by selecting smoothed data that match transition characteristics, the transition characteristics including induction time, maximum intensity, duration from onset, or expected background noise; performing a transition analysis on the processed data to generate transition data, the performing the transition analysis comprising: generating performance parameters based on the processed data, including maximum rate of change, number of inflection points, breakthrough volume, cumulative error, curve asymmetry, or a combination thereof; generating transition data based on the performance parameters, or generating transition data based on the performance parameters in combination with historical data; and performing an action based on the transition data, wherein the performing an action includes generating a notification of the event, generating an evaluation of the event, or generating a deviation notification form.

9. The method of claim 8 , wherein the method further comprises generating an individual control chart, a moving range control chart, or a range control chart.

10. 10. The method of claim 9, further comprising generating performance data by performing multivariate data analysis on the individual control chart, moving range control chart, or range control chart.

11. 11. The method of claim 10, wherein the multivariate data analysis comprises principal component analysis, partial least squares, orthogonal partial least squares, multivariate regression, canonical correlation, factor analysis, cluster analysis, graphical methods, or a combination thereof.

12. The method of claim 9 , further comprising generating performance data by performing a principal component analysis on the individual control chart, moving range control chart, or range control chart.

13. The method comprises: generating performance data by performing principal component analysis or multivariate data analysis on the individual control chart, moving range control chart, or range control chart; terminating the chromatography process based on the performance data; 10. The method of claim 9, further comprising:

14. 9. The method of claim 8, wherein the raw data includes a plurality of blocks and a plurality of signals, the method further comprising selecting a combination of a first block and a first signal from the raw data according to a profile defining a plurality of selection criteria, the profile being associated with a pharmaceutical product.

15. receiving raw data from a detector, the raw data comprising a plurality of signals and a plurality of blocks, the raw data corresponding to conductivity, pH, salt concentration, optical absorption, fluorescence, refractive index, electrochemical response, or mass spectrometry data analysis; generating smoothed data by applying a noise reduction technique to the raw data, the noise reduction technique comprising: selecting a portion of the raw data using a predetermined setting; applying a smoothing filter to the portion of the raw data to generate smoothed data; selecting smoothed data that matches characteristics of chromatographic transitions, the characteristics of the chromatographic transitions including induction time, maximum intensity, duration from onset, or expected background noise; performing a transition analysis on the selected smoothed data to generate transition data; and performing an action based on the transition data, wherein the performing an action includes generating a notification of the event, generating an evaluation of the event, or generating a deviation notification form.

16. The method of claim 15 , wherein the noise reduction technique further comprises selecting the portion of the raw data using a predetermined setting.

17. The noise reduction technique includes: analyzing said portion of raw data for dynamic signal error; or Normalizing a portion of the raw data to prevent magnitude deviations, or The method of claim 15 further comprising both.

18. The method comprises: generating an individuals, moving range, or range control chart; 16. The method of claim 15, further comprising generating performance data by performing principal component analysis or multivariate data analysis on the individual control chart, moving range control chart, or range control chart.

19. Transition analysis is performed by generating performance parameters based on the smoothed data, including maximum rate of change, number of inflection points, breakthrough volume, cumulative error, curve asymmetry, or a combination thereof; and generating transition data based on the performance parameters in combination with historical data.

20. receiving a selection of the chromatographic raw data; generating smoothed data by applying a noise reduction technique to said selection of raw chromatographic data, said noise reduction technique comprising: selecting a portion of the raw chromatographic data for analysis using predetermined settings; applying at least one smoothing filter to the selected portion of data to generate smoothed data; generating processed chromatographic data by selecting smoothed data that matches chromatographic transition characteristics, the chromatographic transition characteristics being: Induction time, maximum strength, Duration from start, or Steps, including expected background noise, and using the processed chromatography data to generate one of an individual control chart, a moving range control chart, or a range control chart.

21. The noise reduction technique includes: normalizing said portion of the raw chromatographic data to prevent magnitude deviations; or analyzing said portion of the raw chromatographic data for dynamic signal error; or 21. The method of claim 20, further comprising both.

22. 21. The method of claim 20, further comprising generating performance data by applying statistical process control to the individual, moving range, or range control chart by performing multivariate data analysis or by performing principal component analysis.

23. 23. The method of claim 22, wherein the method further comprises performing an action based on the performance data, the action comprising at least one of generating a notification of the event, generating an evaluation of the event, or generating a deviation notification form.

24. 21. The method of claim 20, wherein the selection of chromatographic raw data is received from a chromatographic process skid.

25. The method comprises: performing a transition analysis on the processed chromatography data to generate transition data, the performing the transition analysis comprising: generating a performance parameter including a number of inflection points or a maximum rate of change; generating transition data based on the performance parameters; 21. The method of claim 20, further comprising: performing an action based on the transition data, wherein the performing an action comprises generating a notification of the event, generating an evaluation of the event, or generating a deviation notification form.

26. The method comprises: performing a transition analysis to generate transition data, the performing the transition analysis comprising: generating a curve using the processed chromatographic data; analyzing the curve to generate performance parameters; generating transition data based on the performance parameters; 21. The method of claim 20, further comprising: performing an action based on the transition data, wherein the performing an action comprises generating a notification of the event, generating an evaluation of the event, or generating a deviation notification form.

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