Real-time peak integration for pool recovery criteria via area under the chromatographic curve

Abstract

The elution product yield improvement system includes a processor, a probabilistic model, and an elution controller application including instructions configured, when executed by the processor, to receive initialization data, instantiate a communication link to a remote processing / automation system, determine collection criteria, initiate collection, read data values, calculate area ratios via the probabilistic model, and stop collection. A computer-implemented method for improving elution yield and purity includes receiving initialization data, instantiating a communication link to a remote processing / automation system, determining collection criteria, initiate collection, read data values, calculate area ratios, and stop collection. A non-transitory computer-readable medium includes program instructions that, when executed, cause a computer to receive initialization data, instantiate a communication link to a remote processing / automation system, determine collection criteria, initiate collection, read data values, calculate area ratios via the probabilistic model, and stop collection.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 226,149, filed July 27, 2021, entitled "Real Time Peak Integration for Pool Collection Criteria via Chromatographic Area Under the Curve," the entire disclosure of which is expressly incorporated by reference herein.

[0002] The present disclosure is generally directed to the field of real-time peak integration versus pool recovery criteria via area under chromatographic curves, and more specifically, to methods and systems for improving gradient elution by consistently determining collection points to reduce product-related impurities while simultaneously obtaining desired product. [Background technology]

[0003] Traditional elution techniques that rely on chromatographic elution processes and procedures can be inconsistent in both optimizing the desired product recovery and avoiding impurities. A large gap in product quality exists between the recombinant protein expressed in cell culture and the desired target product quality. This gap can be due to the presence of product-related impurities that negatively affect the product quality. The gap between the actual and desired product quality leads to a series of problems. For example, product-related impurities can disrupt the consistency of the purified eluted product (e.g., pharmaceutical drug substance) resulting in a product that does not meet the product quality requirements. Summary of the Invention [Problem to be solved by the invention]

[0004] Product-related impurities may result from the inadvertent or unavoidable recovery of variants or analogs of one or more products of interest (e.g., one or more proteins recovered during the elution process). For example, product-related impurities include high molecular weight variants, low molecular weight variants, protein aggregates, post-translationally modified proteins, etc. Such impurities may be similar to the desired product and therefore may readily elute from the chromatographic medium along with the desired product (e.g., immediately before or after the elution of the desired product).

[0005] When product-related impurities are mixed with the desired product, the recovered product profile is adversely affected. Such negative consequences affect product quality, product potency, product activity, product purity, etc. Such impurities must then be reduced entirely or to negligible levels, otherwise the product may become spoiled and rendered unusable (e.g., as a pharmaceutical product). Thus, the inability of current technology to maximize product recovery and / or minimize impurity contamination generally causes problems including inefficient use of elution media equipment, as well as wasted capital and human resources in repeating or repairing improper product recovery processes.

[0006] Existing strategies fail to address both consistent product recovery and impurity removal. For example, while anion exchange chromatography purification strategies improve impurity removal, they can fail to produce repeatable results due to inconsistent recovery of the desired product during the elution process. Thus, there is a need for improved techniques that consistently determine collection points to generate processes that efficiently obtain the desired product while consistently avoiding impurities. [Means for solving the problem]

[0007] In one aspect, a computing system for improving control of processes and automated systems to improve elution product recovery consistency and yield while avoiding impurities includes one or more processors, an elution collection controller application including computing instructions configured to be executed by the one or more processors, and a peak area estimation model electronically accessible by the elution collection controller application and configured to analyze real-time process data received from the elution collection controller application to estimate one or more area under the curve recovery criteria. The instructions included in the elution recovery controller application, when executed by the one or more processors, are configured to: (i) receive initialization data including a column loading mass and an initial recovery ratio corresponding to one or more elution products; (ii) instantiate a communications link to a process control or automation system; (iii) determine an estimated total peak area and an area recovery criteria corresponding to the one or more elution products; (iv) cause the process control or automation system to initiate collection of the one or more elution products via the communications link; (v) read one or more data values ​​from the process control or automation system via the communications link; (vi) calculate, via a peak area estimation model that analyzes the one or more data values, a ratio of a recovered area under the curve corresponding to the recovered amount of the one or more elution products to the recovery criteria; and (vii) cause the process control or automation system to discontinue collection of the one or more elution products via the communications link based on the ratio of the recovered area under the curve to the recovery criteria.

[0008] In another aspect, a computer-implemented method for improving control of processes and automated systems to improve consistency and yield of elution product recovery while avoiding impurities includes: (i) receiving, via one or more processors, initialization data including a column loading mass and an initial recovery ratio corresponding to one or more elution products; (ii) instantiating, via the one or more processors, a communication link to a process control or automated system; (iii) determining, via the one or more processors, estimated total peak areas and area recovery criteria corresponding to the one or more elution products; (iv) causing the process control or automated system to start collection of the one or more elution products via the communication link; (v) reading one or more data values ​​from the process control or automated system via the communication link; (vi) receiving, from a peak area estimation model analyzing the one or more data values, a ratio of recovered area under the curve corresponding to the recovered amount of the one or more elution products to the recovery criteria; and (vii) causing the process control or automated system to stop collection of the one or more elution products via the communication link based on the ratio of recovered area under the curve to the recovery criteria.

[0009] In yet another aspect, a non-transitory computer readable medium includes program instructions that, when executed, cause a computer to: (i) receive initialization data including a column loading mass and an initial recovery ratio corresponding to one or more elution products; (ii) instantiate a communications link to a process control or automation system; (iii) determine estimated total peak areas and area recovery criteria corresponding to the one or more elution products; (iv) cause the process control or automation system to start collection of the one or more elution products via the communications link; (v) cause the process control or automation system to read one or more data values ​​from the process control or automation system via the communications link; (vi) calculate, via a peak area estimation model that analyzes the one or more data values, a ratio of a recovered area under the curve corresponding to the recovered amount of the one or more elution products to the recovery criteria; and (vii) cause the process control or automation system to stop collection of the one or more elution products via the communications link based on the ratio of the recovered area under the curve to the recovery criteria. [Brief description of the drawings]

[0010] [Figure 1] 1 illustrates a computing environment for improving control of processes and automated systems to improve consistency and yield of elution product recovery while avoiding impurities, according to one embodiment. [Diagram 2] 1 shows an exemplary graph of a pool recovery process that provides improved control of the process and automated systems to improve consistency and yield of elution product recovery while avoiding impurities, according to one embodiment. [Diagram 3] FIG. 1 illustrates a flow diagram of an exemplary computer-implemented method for improving control of processes and automated systems to improve the consistency and yield of elution product recovery while avoiding impurities, according to one embodiment and scenario. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Embodiments described herein relate, inter alia, to techniques for performing real-time peak integration versus pool collection criteria via chromatographic area under the curve (AUC), and more particularly, to methods and systems for controlling a process control or automation system (e.g., one or more remote process control or automation systems) by determining stopping and / or starting points for product collection during an elution run to optimize collection of product having desired product quality attributes while avoiding impurities.

[0012] As discussed above, conventional techniques are inconsistent. For example, conventional elution can recover a product analog along with the desired product. Existing process control and process automation systems do not include the functionality that allows recovery to be parameterized and / or controlled in a fine-grained and precise manner as the present technology provides.

[0013] This lack of detailed and individualized control of processes is a significant problem in the prior art for several reasons. As mentioned above, in the manufacture of certain products (e.g., biosimilar pharmaceuticals), several process performance indicators (PPIs) must be consistently met or the product produced by such manufacturing process cannot be authenticated and therefore cannot be used. This leads to wasted time and waste of valuable material resources.

[0014] Conventional systems may incorporate peak integration features that do not take into account the specifics of each product used or that cause other problems due to lack of tuning and functionality. For example, the on-board peak integration features of conventional systems (such as AKTA pure and AKTAavant systems) can lead to extreme process variability and inconsistent elution over multiple runs. Delays in gradient start and / or gradient overshoot can result in products that do not meet PPI criteria. Unlike the present technology, conventional systems cannot be finely parameterized at run time. Thus, the present technology specifically solves the problems present in the prior art by removing peak shape sensitivity from the industrial process, allowing for a robust and repeatable process that maximizes yield and minimizes impurities.

[0015] The present invention will be further described by the following examples, which are intended to illustrate the invention and are not intended to limit the scope of the invention in any way.

[0016] Exemplary Computing Environment 1 illustrates a computing environment 100 for improving control of processes and automated systems to improve the consistency and yield of elution product recovery while avoiding impurities, according to one embodiment. The computing environment 100 may include a client computing device 102, a remote processing control system 104 communicatively coupled to a chromatography column 106, and an electronic network 108. Any of the client computing device 102, the remote processing control system 104, and the chromatography column 106 may be communicatively coupled to each other via the electronic network 108. This coupling allows for bidirectional network communication between any / all elements of the computing environment 100. For example, the client device 102 may be in bidirectional communication with the remote processing control system 104 (e.g., via one or more open platform communications (OPC) links).

[0017] The client computing device 102 may include one or more computers, each of which may be implemented in, for example, a desktop, virtualized, and / or cloud computing environment. In some embodiments, the client computing device 102 may be implemented as a mobile computing device (e.g., a laptop, a tablet, a mobile phone, a wearable device, etc.). The client computing device 102 may include a processor 110 and a memory 112. Although the processor 110 is referred to in the singular, it may include any suitable number of processors of one or more types (e.g., one or more central processing units (CPUs), graphics processing units (GPUs), cores, etc.). The memory 112 may include one or more memories of one or more types (e.g., persistent memory, solid-state memory, random access memory (RAM), etc.) and may store one or more modules 120. The client computing device 102 may further include an I / O device 140 and a network interface 142, and may be communicatively coupled to an electronic database 150.

[0018] The remote processing and control system 104 may include one or more electromechanical devices, including one or more devices for controlling the chromatography column vessel 106. For example, the remote processing and control system 104 may include one or more valves in a supply line leading to the chromatography column vessel 106. The remote processing and control system 104 may include a motor for operating a piston flange. The remote processing and control system 104 may include a mechanical device (e.g., a hydraulic device) for opening and closing the top and bottom of the chromatography column vessel 106 and / or for configuring one or more physical ports (i.e., inlets or outlets) of the chromatography column 106. The chromatography column 106 is described in more detail below.

[0019] In some embodiments, the remote processing control system 104 may include one or more gauges (e.g., pressure gauges) that monitor the contents of the chromatography column vessel 106. The remote processing control system 104 may include a computing element (e.g., a programmable breadboard) that remotely controls each device in the remote processing control system 104. The remote processing control system 104 may further include a CPU, RAM, and an operating system (not shown) that enable a remote computing device (e.g., computing device 102) to receive / obtain data from the remote processing control system 104. For example, the remote processing control system 104 may include one or more software libraries that enable the computing device 102 to access properties and information contained within the remote processing control system 104.

[0020] For example, if the remote process control system 104 is an AKTA pure protein purification system, it can be accessed via a digital protocol (e.g., an open platform communication (OPC) link). In that case, the computing device 102 can override or override the default functionality of the remote process control system 104 to enhance it. For example, the AKTA pure system can perform peak integration itself by default, with the client device specifying start and end collection thresholds as a percentage of the peak maximum. However, as discussed above, lack of fine control over such peak integration can lead to gradient overshoots, irreproducible elution results, etc. The same problem can exist with other remote process control system 104 embodiments (e.g., if the remote process control system 104 is an Emerson DeltaV automated system). The override / override functionality can be performed by one of the modules 120 of the client computing device 102 (e.g., the elution controller module 130).

[0021] The chromatography column vessel 106 may be a chromatography column vessel of any suitable size. For example, in some embodiments, the chromatography column vessel 106 may be a 1.6 m diameter column. In some embodiments, the vessel 106 may be made of polymers, plastics, and / or metals (e.g., various steel alloys). In some embodiments, a portion of the chromatography column vessel 106 is filled with a material (e.g., a slurry, a buffer, etc.). The media in the chromatography column vessel 106 may be any suitable material. The media may be unpressurized or pressurized to various pressure levels (e.g., 1-30 psi or more). A piston in the chromatography column vessel 106 may compress the media to a predetermined height to achieve pressurization.

[0022] One of ordinary skill in the art will appreciate that the specific configuration of the vessel 106, including its size, may be determined for each embodiment with reference to the product being eluted (e.g., the composition of one or more proteins), the media contained within the column, the configuration / characteristics of the remote process control system 104, etc. For example, a bench-scale remote process control system 104 may require a bench-sized vessel 106, while an industrial-scale remote process control system 104 may require a much larger vessel 106.

[0023] Network 108 may include one or more suitable wireless networks, such as a WAN (e.g., the Internet), a LAN, a 3G or 4G network, a WiFi network or other wireless local area network (WLAN), a satellite communication network, and / or a terrestrial microwave network, etc. In some embodiments, network 108 also includes one or more wired networks, such as an Ethernet.

[0024] The one or more modules 120 may include, for example, one or more sets of computer-executable instructions configured to perform a respective function. For example, the one or more modules 120 may include an elution controller module 130, a calibration constructor module 132, an estimation model 134, and a parsing and storage model 136.

[0025] The elution controller module 130 may include computer executable instructions for accessing the remote process control system 104. For example, the elution controller module 130, in one embodiment, may include a class library having a number of methods / functions (e.g., AktaClass) that allow the elution controller module 130 to access the functionality of the AKTA pure system. The library of functions in the elution controller module 130 may include an object oriented class having a collection of methods. The collection of supported methods may include, for example, the following table of methods, included functions, and return codes (if applicable):

[0026] [Table 1]

[0027] In some embodiments, fewer or more methods may be included to support additional / different functionality. In yet further embodiments, the elution controller module 130 instructions may include a constructor class that creates a connection (e.g., an OPC connection) once when the AktaClass is instantiated, and the connection remains connected until the AktaClass instance is garbage collected. The above example of AktaClass is only one example. Those skilled in the art will appreciate that additional classes (e.g., DeltaVClass) may be included that include fewer or more class methods, additional subclasses, and / or entirely different functions / interfaces. Notwithstanding the above, the elution controller module 130 generally includes any collection of computer-executable instructions necessary to control one or more remote process control systems 104, which may be provided by many different models / manufacturers.

[0028] The calibration configurer module 132 may include computer executable instructions to determine one or more coefficients by analyzing one or more past calibration curves for each product. For example, the calibration configurer module 132 may use a suitable technique, such as linear regression, machine learning, or the like, to determine the coefficients corresponding to the products. The calibration configurer module 132 may store the determined coefficients in the database 150 in association with the product identifier. In this manner, another module (e.g., the elution controller module 130) may query the database passing the product identifier to obtain the associated coefficients to use in a calculation (e.g., calculation of the total peak area in terms of the loaded mass parameter at run time). In some embodiments, the calibration configurer module 132 may include computer executable instructions to generate a calibration curve by generating a lab experiment that measures the area under the curve for each loaded mass and performing a linear regression.

[0029] The estimation model 134 may include computer executable instructions that receive one or more data values ​​read from the process control or automation system 104 via the elution controller 130. In some embodiments, the one or more data values ​​may be parsed / formatted values. The estimation model 134 may include instructions to calculate an estimated peak area under the curve using a trapezoidal rule. The estimation model 134 may include one or more ordered data structures (e.g., lists or arrays). The estimation model 134 may iteratively store each of the one or more data values ​​in one of the one or more data structures for a number of data points (i.e., a time series) corresponding to data read from the chromatography column. The estimation model 134 may calculate an estimated peak area under the curve for each time step such that the area under the curve can be monitored over time. For example, the elution controller 130 may iteratively read data from the chromatography column 106 after initializing data collection.

[0030] At each iteration or time step, the estimation model 134 can recalculate an updated area under the curve using the most recent data values. Additionally, at each iteration / time step, the estimation model 134 can store a delta (i.e., change) value, such as a cumulative volume delta calculated by subtracting the most recent cumulative volume value from the next most recent cumulative volume value. This delta can be kept in its indexed data structure as it iterates, enabled by a persistent array of cumulative volume values ​​that the estimation model 134 can reference by index during subsequent iterations. In some embodiments, the estimation model 134 can calculate aggregate or snapshot values ​​based on the respective data structures, such as a 5-minute moving average, a rate of change, etc.

[0031] The estimation model 134 can estimate the peak area under the curve using the trapezoidal rule. Specifically, the estimation model 134 can calculate the area under the curve fraction at each iteration or time step, for example, by multiplying the most recent delta volume by the average of the two most recent UV absorption values. The area under the curve value may be updated at each time step by adding the fractional area under the curve value to the area under the curve value at the previous time step / iteration. The estimation module 134 can calculate the area under the curve as a percentage of the peak recovery criterion at each iteration / time step by dividing the most recent area under the curve value by the total peak area.

[0032] The serialization and storage module 136 may include computer executable instructions to convert data (e.g., data values ​​read from the process control or automation system 104) into serialized data, such as by converting formatted and parsed data from the process control or automation system 104 into a serialized data format (e.g., data frames, deposition data formats, etc.). In some embodiments, the serialization and storage module 136 may include instructions to persist all data collected during the execution of the method 300. For example, the storage module 136 may store all input parameters, time series data structures, and raw data received from the remote process control system 104 in the database 150 and / or memory 112. Once serialized and stored, the stored data can be queried and retrieved by another module (e.g., the calibration configurer module 132). In some embodiments, the memory 112 may include fewer or more modules.

[0033] The computer-executable instructions for executing module 120 may be written in one or more suitable programming languages ​​(e.g., C, C++, R, Java, Python, JavaScript, LISP, etc.) and may include algorithms that perform the techniques described above, as well as additional instructions to assist an end user (e.g., an engineer, scientist, programmer, quality assurance tester, statistician, etc.) in constructing, parameterizing, evaluating, and / or interpreting (e.g., by visualizing) the data, as shown in FIG. 2 .

[0034] The I / O device 140 can provide input and access capabilities to the computing device 102. For example, the I / O device 140 can include one or more drivers for receiving input from computer peripherals (e.g., a keyboard, mouse, microphone, etc.) and for sending output to computer peripherals and other devices (e.g., one or more computer monitors). In general, the I / O device 140 allows a user of a client computing device to input data via one or more graphical user interfaces for both managing the software modules 120 and providing run-time parameters (e.g., product-specific parameters as described herein). The I / O device 140 can display one or more applications (e.g., an elution control application) to the user on a computer monitor, smartphone / tablet display, etc.

[0035] The network interface 142 may include one or more wired or wireless Ethernet interfaces that enable low-level network connectivity of the client computing device. In particular, the network interface 142 may enable the client computing device 102 to connect to the electronic network 108 using operating system (i.e., kernel) level functionality.

[0036] The electronic database 150 may be a Structured Query Language (SQL) database, a key-value store database (e.g., MongoDB), a flat file database, etc. The database 150 may be stored in the local transient memory 112 of the client computing device 102, and / or on a persistent disk in the memory 112, and / or on a remote computing cloud accessible via the network 108. The electronic database may include one or more databases, each containing a collection of tables that allow the module 120 to create, read, update, and delete information. For example, the elution controller 130 may load pre-configured parameter and coefficient information from the database 150. The serialization and storage module 136 may insert information into the database 150 at run time.

[0037] In operation, one or more users of the client computing devices 102 can access a graphical user interface displayed by the elution controller module 130. The graphical user interface can be packaged as part of an application (e.g., a desktop application, an iPhone or Android downloadable application for mobile devices, etc.). A user can provide initialization data. For example, a user can select one or more products and one or more respective elution columns (e.g., corresponding to the chromatography columns 106). In some embodiments, the environment 100 can include multiple remote process control systems 104. In that case, a user can select from the multiple remote process control systems 104 via the graphical user interface. In some embodiments, a user can specify a remote process control system by typing one or more host names.

[0038] After selecting the remote process control system 104 and one or more products, the user may provide additional input parameters, which are described in more detail below. The elution controller module 130 may maintain in memory 112 a list of the selected remote process control systems 104, products, and their respective sets of parameters. The user may then designate one or more selected remote process control systems 104 to indicate that the elution process should begin. For example, the user may press a "Go" button for the first remote process control system 104 for which the user has provided a number of parameters. In response to the user selecting the button, the elution controller 130 may pass control flow to the calibration configuration module 132, at which point the calibration configuration module 132 selects pre-configured coefficients corresponding to the selected product. The calibration configuration module 132 may return the selected coefficients to the elution controller.

[0039] The elution controller module 130 can instantiate a communication link to the selected remote process control system 104 and store an object (i.e., a handle or reference) to the communication link in memory 112. The elution controller module 130 can determine an estimated total peak area and area recovery criterion corresponding to the selected elution product. The elution controller module 130 can begin reading data values ​​periodically over the communication link. The elution controller module 130 can offload processing of the data values ​​to a peak area estimation model module 134 corresponding to the selected product and the remote process control system 104. The remote process control system 104 can store one or more ordered data structures in memory 112 and / or database 150 and perform calculations on values ​​stored in the ordered data structures and data values ​​received from the elution controller module 130 as described herein.

[0040] The elution controller module 130 can receive the calculation results from the estimation module 134 and pass the calculation results to the serialization and storage module 136. It should be understood that the elution controller module 130 can store separate communication links in the memory 112 and that one or more processors 110 of the client computing device 102 can use multiple communication links in parallel to operate different stages of the above-mentioned process. The processing performed by the modules 134, 136 can block the elution controller 130 and prevent it from reading data from the remote process control system 104. By parallelizing the processing of input data to the estimation model module 134 and the serialization and storage module 136 using a multi-threading or multi-processing approach, the present technique advantageously allows a single client computing device 102 to finely manage the operation of an almost unlimited number of remote process control systems 104, improving over prior art techniques that only provide a single-threaded (and coarse) peak integration function.

[0041] In some embodiments, the serialization and storage module 136 can continuously store data in the database 150 as the elution controller module 130 reads the data from the remote process control system 104. In further embodiments, the serialization and storage module 136 can cache the data in the memory 112 until the elution controller module 130 causes the remote process control system 104 to stop collecting the elution product, at which point the serialization and storage module 136 can perform a batch insert of the cached records into the database 150.

[0042] Exemplary Visualization Embodiments As described above, the computer executable instructions in memory 120 of FIG. 2 allow visualization of the pool recovery process. The pool recovery process may correspond to one or more chromatographic analyses using buffers. A process control system, local or remote (e.g., remote process control system 104 of FIG. 1), may be attached to one or more chromatography column buffer reservoirs (e.g., cylindrical vessels such as column vessel 106). In operation, the remote process control system 104 may control the chromatography columns via tubing valves and pumps. For example, the remote process control system 104 may draw buffer from a buffer source and pump the buffer onto the column to wash, equilibrate, and operate the column (e.g., column washing after sample loading). The remote process control system 104 may pump protein into the column and remove waste from the washing, equilibration, and washing steps. The remote process control system 104 may elute the attached sample from the column. Elution can be isocratic, where the buffer is constant, or gradient, where multiple buffer formulations with different pH / conductivity are mixed during the elution process to form a gradient based on salt / conductivity / pH changes. Regardless of the elution method used, the buffer can wash away proteins attached to the chromatography column and flow out of the column into the eluent. The remote processing and control system 104 can collect the eluent flowing through the column.

[0043] 2 shows an exemplary graph 200 of a pooled recovery process that improves control of the process and automated systems to improve the consistency and yield of elution product recovery while avoiding impurities, according to one embodiment. Upon elution from the column, the absorbance of the fluid exiting the column can be measured (e.g., by a remote processing control system 104). The absorbance can be measured in mAU by ultraviolet light at a particular wavelength (e.g., 280 nm). This absorbance can be used to calculate the protein concentration in the sample compared to a standard curve, past knowledge, published information, etc. The column may be eluted in fractions (i.e., column volume fractions (CV)) corresponding to one column of liquid, with each fraction of liquid being collected independently.

[0044] During the course of elution, the absorbance of each fraction may change as more protein is eluted from the column with increasing gradient. The client computing device 102 may measure this change in absorbance and graph the change as a chromatogram curve showing a trace of absorbance readings for each fraction. For example, graph 200 includes a Y-axis 202-A representing milliabsorbance units (mAU) and an X-axis 202-B representing milliliters. Graph 200 may represent a visual representation of a lab-scale run of an elution process (e.g., as described above) with a particular percentage load mass and gradient. One of skill in the art will appreciate that in some embodiments, graph 200 may represent a change in conductivity or pH of the elution buffer. In some embodiments, graph 200 may represent a fraction (e.g., from 1 to a given point). Graph 200 includes an elution region 204 and a pool start point 206-A and a pool end point 206-B, which represent where elution begins and ends and / or where waste collection begins. For example, pool start point 206-A may begin when a particular percentage of the total peak area based on the total peak area is reached and collection should begin. The pool collection start point may be controlled by elution controller 130 as described herein. Pool end point 206-B may correspond to a percentage of the peak maximum (e.g., 10%) based on a given wavelength.

[0045] The elution region 204 may correspond to the region between the lines designating the fractions containing the protein of interest, or the region containing a percentage of the protein of interest, in which there are some negligible impurities. Such fractions may be pooled (i.e., combined) for further elution. The cut may be a point designating the start of the fractions containing the protein of interest. In the graph 200, the area under the curve may be the area from the start of elution to the cut point where the protein of interest begins to elute.

[0046] The area under the curve may correspond to the area from the start of elution to the cut-off point where the protein of interest begins to elute. This area may correspond to the area where impurities that are attached to the column and have a lower affinity than the protein of interest elute from the column. These impurities may be forms of the protein of interest that are not suitable for recovery. For example, these impurities may be halves of an antibody (e.g. single heavy and single light chains), parts of the protein of interest, aggregates of parts, or the protein of interest with incorrect post-translational characteristics (e.g. incorrect structural sugars, incomplete addition, etc.). In the case of biosimilars, it is essential that the post-translational modifications are identical, since such impurities may affect the activity of the protein and reduce its similarity to the innovator protein. So-called "product-related" impurities share characteristics with the desired product, including binding affinity for the chromatographic matrix, but are not the protein of interest, and therefore must be excluded from the final drug substance.

[0047] In some embodiments, the present technology can cause a visualization, including the graph 200, to be displayed on a user's device (e.g., the client computing device 102) during or after the elution procedure is performed. In this manner, a user can advantageously monitor the elution process as it progresses, providing the user with feedback regarding the fine-grained automated process that is lacking in the prior art.

[0048] Exemplary Computer-Implemented Method 3 illustrates a flow diagram of a computer-implemented method 300 for improving control of processes and automated systems to improve the consistency and yield of elution product recovery while avoiding impurities, according to one embodiment and scenario. As a non-limiting example, one or more steps of method 300 may be performed by computing environment 100 of FIG.

[0049] Method 300 may include receiving, via one or more processors, initialization data including column load masses and initial recovery ratios corresponding to one or more eluted products (block 302). For example, method 300 may display, via I / O device 140, a graphical user interface (not shown) including one or more elements (e.g., drop down menus, one or more input fields, etc.) that allow an end user to select one or more products and provide respective parameters corresponding to the one or more products, aspects of the remote processing control system 104, column load masses, initial recovery ratios, etc. The following table provides exemplary input parameters that may be entered by a user in one embodiment:

[0050] [Table 2]

[0051] Method 300 may include validating the initialization data type. For example, the method may validate that the exit parameters are integer values. In some embodiments, these parameters may be received in standard input (e.g., from a comma separated values ​​(CSV) file, as a flag to an automated script (e.g., via a cron job), etc.). It will be appreciated that a user need not necessarily manually provide parameters via I / O device 140, but may receive the initialization data via automated means.

[0052] The method 300 may include instantiating a communication link to a process control or automation system (block 304) via one or more processors. For example, the process control or automation system of block 304 may correspond to the remote process control system 104 of FIG. 1. Thus, the process control or automation system of block 304 may include a chromatography column vessel corresponding to the chromatography column vessel 106 of FIG. 1. The process control or automation system 104 may be any suitable system, such as an AKTA pure microsystem, or an Emerson DeltaV automation system. The present technique is applicable to bench-scale and larger scale process control / automation environments.

[0053] Instantiating the communications links in block 304 may include, for example, creating one or more OPC links at the client computing device 102. In some embodiments, other suitable communications links may be instantiated (e.g., wireless links, wired links, stateless HTTP links, etc.). In general, the elution controller module 130 creates one or more objects in the memory 112 of the client computing device 102, each object tracking the state of each communications link to the remote process control system 104. Each in-memory object provides the interface methods described above that allow the elution controller to access the functionality of the remote process control system 104.

[0054] Method 300 may include determining, via one or more processors, estimated total peak areas and area recovery criteria corresponding to one or more elution products (block 306). In some embodiments, determining the estimated total peak areas may include calibration constructor 132 performing a linear regression of one or more historical calibration curves to estimate one or more coefficients as described above. In this manner, for a given loading mass initialization parameter, elution controller module 130 can simply retrieve product-specific coefficients (e.g., from electronic database 150) at run time to perform the correct total peak area calculation. For example, method 300 may determine the total peak area by calculating the product of the loading mass initialization parameter and a coefficient (e.g., 30.44) corresponding to the product input parameter (i.e., ABP938AEX, continuing the example above).

[0055] Of course, different coefficients may be selected based on past calibration curves depending on the product selected by the user or provided as command line input. In this manner, the present technique is believed to advantageously improve upon prior art techniques that do not include the ability for the user to programmatically select parameters at run time based on known good product coefficient values. This flexibility translates directly to the present technique allowing for correct determination of collection start / end times, as the correct coefficient selection is used in further modeling calculations. Thus, the present technique provides multiple advantages over prior art techniques that only provide a coarse-grained and general "one size fits all" peak integration function. Method 300 can determine the area under the curve recovery criterion by calculating the product of the total peak area and the collection start percentage. As described above, because the calculation of the total peak area includes fine-grained product-specific coefficients, the recovery criterion benefits from improved accuracy based on empirical observations compared to traditional peak integration approaches that rely on more general (or hypothetical) inputs.

[0056] The method 300 may include having the process control or automation system 104 initiate collection of one or more elution products via the communications link (block 308). For example, in one embodiment, the elution controller 130 may call the collect() method of the AktaClass, as described above. In some embodiments, the elution controller 130 of the client computing device 102 may call another function specific to another remote process control system 104 (e.g., a specific method to have the DeltaV system initiate collection). In some embodiments, the method 300 may include other methods that the elution controller 130 may call, such as starting the flow of a mobile phase (e.g., a buffer) through the chromatography column 106.

[0057] The method 300 may include retrieving one or more data values ​​from the process control or automation system via a communications link (block 310). For example, the method 300 may cause the process control or automation system 104 to initiate collection of one or more elution products, and the elution controller 130 may retrieve data values, such as UV absorbance and / or accumulated volume, via a communications link. In some embodiments, the elution controller 130 may include instructions to repeatedly retrieve data from the process control or automation system 104. In some embodiments, the elution controller 130 may include instructions to introduce a delay in retrieving data values ​​from the process control or automation system 104. For example, the elution controller 130 may include instructions to periodically or repeatedly retrieve data with a configurable delay between repetitions (e.g., every 0.5 seconds).

[0058] The method 300 may include receiving (block 312) a ratio of an area under the recovery curve corresponding to the recovery amount of one or more elution products relative to a recovery standard from a peak area estimation model that analyzes the one or more data values. The method 300 may include the elution controller 130 passing the retrieved one or more data values ​​from the process control or automation system 104 to the estimation model module 134. As described above, the elution controller 130 may periodically calculate the area under the curve of an ongoing elution process.

[0059] The method 300 may include causing a process control or automation system to stop collection of one or more elution products via the communication link (block 314) based on the ratio of the area under the curve recovered to the collection criteria. For example, the elution controller 130 may compare the ratio of the area under the curve recovered to the collection criteria, and if the ratio exceeds the collection criteria, the method may call a method of an object corresponding to the communication link to cause the remote process control or automation system 104 to switch an outlet valve to stop collection of the product. As described above, for example, with reference to FIG. 2, the method 300 may include causing a visual display of the ratio of the area under the curve recovered to the collection criteria during and after collection of one or more elution products has been stopped.

[0060] As discussed above, a set of methods in the elution controller 130 allows for managing various aspects of the remote process control or automation system 104 and for checking the status of the process control or automation system 104. Such management / check functions include taking control of the process control or automation system 104 via a communications link, releasing control of the process control or automation system 104 via a communications link, switching an outlet valve of the process control or automation system 104 via a communications link, verifying an outlet valve position of the process control or automation system 104 via a communications link, switching an inlet valve of the process control or automation system 104 via a communications link, verifying an inlet valve position of the process control or automation system 104 via a communications link, and tearing down the communications link to the process control or automation system 104 via a communications link. As discussed above, the communications link may be an OPC communications link.

[0061] It should be understood that the examples provided herein are simplified for illustrative purposes and that some embodiments may include more complex layouts / configurations of chromatography column vessels, process control and / or automation systems, computing devices, etc. For example, the present techniques may be used in some embodiments to monitor the status of an array of chromatography column vessels in a bench setting or in a large-scale commercial production plant.

[0062] Additional considerations All references cited herein, including patents, patent applications, literature publications, and the like, are hereby incorporated by reference in their entirety.

[0063] It should also be understood that when describing a range of values, the present disclosure contemplates each individual value occurring within the range. For example, a "cell aggregate size of about 20 nm to about 200 nm" can be, but is not limited to, 40 nm, 60 nm, 100 nm, etc., and any value between such values. In any range described herein, the endpoints of the range are included in the range. However, the present specification also contemplates the same range excluding the nadir and / or maximal point.

[0064] It should also be understood that unless a term is expressly defined in this patent using "As used herein, the term '_' is herein defined to mean ..." or similar language, no attempt is made to limit the meaning of that term beyond its plain or ordinary meaning, either expressly or implicitly, and such terms should not be construed as limited in scope based on any declaration made in any paragraph of this patent (other than the claim language). To the extent any term recited in the claims at the end of this disclosure is referred to in this disclosure as consistent with a single meaning, this is done solely for clarity so as not to confuse the reader, and no attempt is made to limit such claim term to that single meaning, either implicitly or otherwise. Finally, it is not intended that the scope of any claim element be construed under application of 35 U.S.C. § 112(f) unless the claim element is defined by reference to the terms "means" and function without reference to any structure. The systems and methods described herein are directed to improving the functionality of computers and enhance the functionality of conventional computers.

[0065] Throughout this specification, the term "set", unless expressly defined otherwise, means a set having one or more members, but excluding the empty set.

[0066] Throughout this specification, multiple instances may implement elements, operations, or structures that are described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more individual operations may be performed simultaneously, and there is no requirement that the operations be performed in the order illustrated. Structures and functions shown as separate elements in multiple configurations may be implemented as combined structures or elements. Similarly, structures and functions shown as a single element may be implemented as separate elements. These and other variations, modifications, additions, and improvements are within the scope of the subject matter of this specification.

[0067] Also, certain embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (code embodied in a non-transitory tangible machine-readable medium) or hardware. In hardware, the routines, etc. are tangible devices capable of performing certain operations, and may be configured or arranged in a particular way. In an exemplary embodiment, one or more computer systems (e.g., stand-alone, client, or server computer systems) or one or more modules of a computer system (e.g., a processor or processors) may be configured as modules that operate with software (e.g., an application or application portions) to perform certain operations as described herein.

[0068] In various embodiments, a module may be implemented mechanically or electronically. Thus, the term "module" should be understood to encompass a tangible entity that is physically constructed and permanently configured (e.g., hardwired) or temporarily configured (e.g., programmed) to operate in a particular manner or perform particular operations as described herein. In considering an embodiment in which the modules are temporarily configured (e.g., programmed), each module need not be configured or instantiated at the same time. For example, if the modules include a general-purpose processor configured with software, the general-purpose processor may be configured as each of the different modules at different times. Thus, the software may configure the processor, for example, to configure a particular module at one time and a different module at a different time.

[0069] Multiple modules can provide information to other modules or receive information from other modules. Thus, the above-mentioned modules may be considered to be communicatively coupled. When multiple such modules are present simultaneously, communication can be achieved through signal transmission (e.g., via appropriate circuits and buses) connecting the modules. In embodiments in which multiple modules are configured or instantiated at different times, communication between such modules can be achieved, for example, through the storage and retrieval of information in memory structures accessible to the multiple modules. For example, a module can perform an operation and store the output of the operation in a communicatively coupled memory device. A further module can then access the memory device at a later time to retrieve and process the stored output. Modules can also initiate communication with input / output devices to perform operations on resources (e.g., retrieving information).

[0070] Various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily or permanently configured (e.g., by software) to perform the associated operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. Modules referred to herein may include processor-implemented modules in some example embodiments.

[0071] Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. Performance of certain operations may be distributed among one or more processors and may be spread across any number of machines as well as being present within a single machine. In some exemplary embodiments, a processor or processors may be located in a single location (e.g., in a home environment, an office environment, or as a server farm), while in other embodiments the processors may be distributed across multiple locations.

[0072] Performance of certain operations may be distributed among one or more processors and may reside within a single machine as well as be spread across a number of machines. In some exemplary embodiments, one or more processors or processor-implemented modules may be located in a single geographic location (e.g., in a home environment, an office environment, or a server farm). In other exemplary embodiments, one or more processors or processor-implemented modules may be distributed across a number of geographic locations.

[0073] Unless specifically stated otherwise, references herein using terms such as "processing," "computing," "calculating," "determining," "presenting," "displaying," and the like, may refer to machine (e.g., computer) operations or processes that manipulate or transform data represented as physical (e.g., electronic, magnetic, or optical) quantities in one or more memory (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine elements that receive, store, transmit, or display information. Some embodiments may be described using the terms "coupled" and "connected," along with their derivatives. For example, some embodiments may use the term "coupled" to indicate that two or more elements are in direct physical or electrical contact. However, the term "coupled" may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other. These embodiments are not limited in this context.

[0074] Any reference to "one embodiment" or "an embodiment" as used herein means that a particular element, feature, structure, or characteristic described in connection with that embodiment may be included in at least one embodiment. The appearances of the phrase "in one embodiment" in various places in this specification do not necessarily all refer to the same embodiment. Additionally, the use of "a" or "an" is employed to describe elements and components of an embodiment herein. This is done merely for convenience and to give a general sense of description. This specification and the claims that follow should be read to include one or at least one, and the singular also includes the plural unless it is clear that it is meant otherwise.

[0075] As used herein, the terms "include," "includes," "including," "including," "having," "having" or any other variations thereof are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that includes a list of elements is not necessarily limited to only those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Furthermore, unless expressly stated to the contrary, "or" refers to an inclusive or rather than an exclusive or. For example, a condition A or B is satisfied by any one of A being true (or present) and B being false (or absent), A being false (or absent) and B being true (or present), and both A and B being true (or present).

[0076] The detailed description in this specification should be construed as merely exemplary and does not describe all possible embodiments, since describing all possible embodiments is impractical, if not impossible. Many alternative embodiments can be implemented using current technology or technology developed after the filing date of this application. Those skilled in the art will appreciate further alternative structural and functional designs for implementing the disclosed systems and methods through the principles disclosed herein, upon review of this disclosure. Thus, although specific embodiments and applications have been illustrated and described, it should be understood that the disclosed embodiments are not limited to the precise structures and elements disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made to the arrangement, operation and details of the methods and apparatus disclosed herein, without departing from the spirit and scope defined in the appended claims.

[0077] The particular features, structures, or characteristics of any specific embodiment may be combined in any suitable manner, using selected features but not corresponding other features, and in any suitable combination with one or more other embodiments. Many modifications may also be made to adapt a particular application, situation, or material to the essential scope and spirit of the invention. It is to be understood that other variations and modifications of the embodiments of the invention described and illustrated herein are possible in light of the teachings herein and are considered to be part of the spirit and scope of the invention.

[0078] Although the preferred embodiment of the present invention has been described, it is to be understood that the present invention is not limited thereto and that modifications may be made without departing from the present invention. The scope of the present invention is defined by the appended claims, and all devices that come within the meaning of the claims are intended to be encompassed literally or equivalently. Accordingly, the above detailed description is intended to be regarded as illustrative rather than limiting, and it is to be understood that it is the following claims and all equivalents that define the spirit and scope of the present invention.

Claims

A computing system configured to control an automated process for eluate product recovery having improved consistency and yield, comprising: one or more processors; an elution recovery controller application including computing instructions configured to be executed by the one or more processors; a peak area estimation model that is electronically accessible by the elution recovery controller application and configured to analyze real-time process data received from the elution recovery controller application to estimate one or more curve under area recovery criteria; and wherein the computing instructions included in the elution recovery controller application, when executed by the one or more processors, cause the system to: receive initialization data including column load mass and initial recovery ratio corresponding to one or more eluate products; instantiate a communication link to a process control or automation system; determine an estimated total peak area and area recovery criteria corresponding to the one or more eluate products; initiate recovery of the one or more eluate products via the communication link to the process control or automation system; read one or more data values from the process control or automation system via the communication link; calculate a ratio of the recovered curve under area corresponding to the recovery amount of the one or more eluate products to the recovery criteria via the peak area estimation model that analyzes the one or more data values; and based on the ratio of the recovered curve under area to the recovery criteria, configure the system to terminate recovery of the one or more eluate products via the communication link to the process control or automation system; a computing system. Claim 2 wherein the instructions included in the elution recovery controller application, when executed by the one or more processors, cause the system to: acquire control of the process control or automation system via the communication link; release control of the process control or automation system via the communication link; parse the one or more data values received from the process control or automation system via the communication link and convert them into respective formatted data values; switch an outlet valve of the process control or automation system via the communication link; check the position of the outlet valve of the process control or automation system via the communication link; Switch the inlet valve of the process control or automation system via the communication link, Check the position of the inlet valve of the process control or automation system via the communication link, or Further configured to discard the communication link via the communication link, The computing system according to claim 1.

3. The computing system according to claim 1 or 2, wherein the communication link is an Open Platform Communications (OPC) communication link.

4. If the instructions included in the elution recovery controller application are executed by the one or more processors, Further configured to determine the estimated total peak area and the area recovery criterion corresponding to the one or more elution products using linear regression of a calibration curve specific to the one or more elution products, The computing system according to claim 2.

5. The computing system according to claim 2, wherein the one or more data values read from the process control or automation system include one or both of (i) ultraviolet absorbance and (ii) cumulative volume.

6. The computing system according to claim 5, wherein the process control or automation system is at least one of (i) an AKTA pure microsystem or (ii) an Emerson DeltaV automation system.

7. If the instructions included in the elution recovery controller application are executed by the one or more processors, Further configured to display the visualization of the recovery of the one or more elution products on a graphical user interface of a user device, the visualization including a display of a recovery start percentage and a display of a recovery end percentage, The computing system according to any one of claims 1 to 2 or 4 to 6.

8. A computer-implemented method configured to control an automated process for elution product recovery having improved consistency and yield, Receiving initialization data including a column loading mass and an initial recovery ratio corresponding to one or more elution products via one or more processors, Instantiating a communication link to a process control or automation system via the one or more processors, Determining an estimated total peak area and an area recovery criterion corresponding to the one or more elution products via the one or more processors, Initiating, via the communication link, the recovery of the one or more elution products in the process control or automation system; Reading, via the communication link, one or more data values from the process control or automation system; Receiving, from a peak area estimation model that analyzes the one or more data values, a ratio of the recovered curve area under the curve corresponding to the recovery amount of the one or more elution products with respect to the recovery criterion; A method including, based on the ratio of the recovered curve area under the curve with respect to the recovery criterion, stopping, via the communication link, the recovery of the one or more elution products in the process control or automation system.

9. Obtaining control of the process control or automation system via the communication link; Releasing control of the process control or automation system via the communication link; Syntax-analyzing the one or more data values from the process control or automation system via the communication link to convert them into respective formatted data values; Switching an outlet valve of the process control or automation system via the communication link; Checking the position of the outlet valve of the process control or automation system via the communication link; Switching an inlet valve of the process control or automation system via the communication link; Checking the position of the inlet valve of the process control or automation system via the communication link; or Discarding the communication link via the communication link The computer-implemented method according to claim 8, further comprising.

10. The computer-implemented method according to claim 8 or 9, wherein the communication link is an Open Platform Communications (OPC) communication link.

11. The computer-implemented method according to claim 9, further comprising determining the estimated total peak area and the area recovery criterion corresponding to the one or more elution products using linear regression of a calibration curve specific to the one or more elution products.

12. The computer-implemented method according to claim 9, wherein the one or more data values read from the process control or automation system include one or both of (i) ultraviolet absorbance and (ii) cumulative volume.

13. The computer-implemented method according to claim 12, wherein the process control or automation system is at least one of (i) an AKTA pure microsystem, or (ii) an Emerson DeltaV automation system.

14. The computer-implemented method according to any one of claims 8-9 or 11-13, further comprising causing visualization of the recovery of the one or more elution products to be displayed on a graphical user interface of a user device, the visualization including display of a recovery start percentage and a recovery end percentage.

15. If executed, cause a computer to receive initialization data including a column load mass and an initial recovery ratio corresponding to one or more elution products; instantiate a communication link to a process control or automation system; determine an estimated total peak area and an area recovery criterion corresponding to the one or more elution products; cause the process control or automation system to start the recovery of the one or more elution products via the communication link; cause the process control or automation system to read out one or more data values via the communication link; calculate, via a peak area estimation model that analyzes the one or more data values, a ratio of the recovered curve under area corresponding to the recovery amount of the one or more elution products to a recovery criterion; based on the ratio of the recovered curve under area to the recovery criterion, cause the process control or automation system to stop the recovery of the one or more elution products via the communication link A non-transitory computer-readable medium comprising program instructions for causing the above.

16. If executed, cause a computer to acquire control of the process control or automation system via the communication link; release control of the process control or automation system via the communication link; parse the one or more data values from the process control or automation system via the communication link and convert them into respective formatted data values; switch an outlet valve of the process control or automation system via the communication link; check an outlet valve position of the process control or automation system via the communication link; switch an inlet valve of the process control or automation system via the communication link; Checking the inlet valve position of the process control or automation system via the communication link, or Discarding the communication link via the communication link The program instructions to be performed are further included, The non-transitory computer-readable medium according to claim 15.

17. The non-transitory computer-readable medium according to claim 15 or 16, wherein the communication link is an open platform communication (OPC) communication link.

18. If executed, cause the computer to Using the linear regression of the calibration curve specific to the one or more elution products, determine the estimated total peak area and area recovery criteria corresponding to the one or more elution products The program instructions are further included, and the non-transitory computer-readable medium according to claim 16.

19. The non-transitory computer-readable medium according to claim 16, wherein the one or more data values read from the process control or automation system include one or both of (i) ultraviolet absorbance and (ii) cumulative volume.

20. The non-transitory computer-readable medium according to any one of claims 15 to 16 or 18 to 19, wherein the process control or automation system is at least one of (i) an AKTA pure microsystem or (ii) an Emerson DeltaV automation system.

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