Automated Batch Data Analysis

BACKGROUND ART

The present invention relates to a system and method for automated batch data analysis, for example, automatically identifying, extracting, and outputting certain information in relatable form along with context corresponding to the extracted information for analysis. The automated batch data analysis, for instance, may be performed in connection with pharmaceutical production.

Data processing involves collecting and processing volumes of data over a given period of time, including high volumes of data. For instance, in the life sciences industry, manufacturing execution systems (MES) may be employed to help manufacturers ensure bio-product quality and safety. This can be done by using the batch records to identify the conditions under which a product is being or was manufactured and/or verifying that these conditions are in accordance with various process requirements. An MES may be integrated with one or more digital control systems to collect manually entered data by operators, data from enterprise planning systems, data captured from plant-floor control systems, etc. while also verifying that the data is within expected ranges (and alerting operators if any abnormal situations arise). The consolidated data may then be entered into one or more batch record documents, which may be saved in a MES database. In this configuration, if an operator desires to retrieve and analyze a set of parameters captured from an external analytics system, the specific information related to the parameter must be “pulled out” from the vast amounts of information in the MES database.

In the aforementioned “pull” procedure, one of the disadvantages is that data or related information may be scattered across numerous tables in the MES database, and the ability to obtain certain kinds of information—especially on the fly—from the data, such as the above set of parameters, may be extremely difficult due to various limitations, such as system bandwidth limitations, processing that is resource-intensive in nature, and the overall quantity and disorganization of the information. Moreover, databases storing the batch records are typically built with the intent of executing information, and typically are not built to extract information in any relatable or useable form or any context associated with the information.

SUMMARY OF THE INVENTION

In accordance with one or more aspects of the disclosure, the invention is directed to systems and methods for automated batch data analysis. Certain information in the data may be tagged as information of interest. In at least that regard, the tagged information may be extracted or “pushed out” to a separate database for further analysis. The tagging procedure may be implemented during the building of the set of instructions for performing a particular task associated with an external analytics system associated with the collected data, for example, pH values of a titration process in a bioreactor tank. Moreover, the pushed out information may also be provided with contextual data or information that may give the user and/or operator some form of context with respect to the information that is pushed out.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2 illustrate example systems in accordance with one or more aspects of the invention.

FIG. 3 illustrates another system in accordance with one or more aspects of the invention.

FIG. 4 illustrates an example flow diagram of a recipe in accordance with one or more aspects of the invention.

FIG. 5 illustrates an example database table in accordance with one or more aspects of the invention.

FIG. 6 illustrates an example flow chart in accordance with one or more aspects of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The invention is directed to automated batch data analysis. In one example, a computing device of an MES may automatically identify, extract, and output one or more parameters associated with an MES recipe (e.g., a combination or a set of instructions for executing certain tasks associated with an external analytics system), for instance, to a database table, output file, etc., for further analysis. The MES, in one aspect, may be used to execute batch processes that include the implementation of one or more bioreactors and/or related equipment for producing biological products. The one or more parameters that are output, for example, may be parameters related to process conditions inside the tank that are of interest for subsequent analysis, such as the pH values of the bioreactor medium in the tank prior to, during, and after titration. In addition to the one or more parameters, further information or context related to the one or more parameters may also be provided, such as bioreactor tank identification information, plant location information, the exact times the pH measurements were taken, etc.

In accordance with examples of the disclosure, the one or more parameters to be extracted and output may be “tagged” prior to executing the MES recipe. As will be further discussed below, for example, when building the computer instruction set for carrying out the MES recipe, such as the above described titration process in the bioreactor tank, every parameter that is of interest for each of the instructions that make up the computer instruction set are tagged so that when a particular instruction is executed (e.g., titrate), the tagged parameter (e.g., pH of the bioreactor medium at the time of titration) is identified, extracted, and may be “pushed out” to a separate database, which may be further processed and analyzed.

As such, certain information in the data that is of interest is “tagged” prior to the collection of the information in the data so that the “tagged” information is automatically identified and extracted (e.g., “pushed”) to a separate database for further analysis. In at least that regard, one of the numerous advantages of the present disclosure is avoiding the slow, resource-intensive processing of information associated with the above-described “pull” procedure in conventional batch data processing methods. This advantage is gained, for instance, the extracted information (based on the tags) may be stored in local memory of a computing device, as opposed to executing database calls to retrieve the information across numerous tables in the MES database. Another advantage of the disclosure is that the tagged data and/or information is pushed out to a messaging queue or other data repository while the MES recipe is running, and thus, the extraction of data does not affect the recipe process.

FIG. 1 illustrates an example system 100 in accordance with one or more aspects of the invention. The system 100 may include one or more computing devices, e.g., computer 120, server computer 130, mobile computer 140, smartphone device 150, tablet computer 160, and storage device 170 connected to a network 190. For example, the computer 120 may be a desktop computer, which is intended for use by one or more users. The computer 120 includes various components associated with a desktop computer, such as one or more processors 102, memory 104, e.g., permanent or flash memory (which includes instructions 105 and data 106), one or more interfaces 108, and a display 110. In a further example, similar to the computer 120, the server computer 130 may include at least one processor, memory which also includes instructions and data, one or more interfaces, and/or a display (not shown). Moreover, the mobile computing device 140 may be a laptop (or any type of computer that is portable or mobile, such as an Ultrabook) and also include components similar to the computer 120 and/or server computer 130. The computer 120 may be configured to communicate with the server computer 130, the mobile computer 140, the smartphone device 150, the tablet computer 160 and/or the storage device 170 via the network 190. As shown in FIG. 1, the cascaded blocks associated with a particular component illustrate that more than one of those components may exist, which is only an example, and it may be understood that different components can be cascaded and that there may be numerous variations thereof.

The computer 120 may include a processor 102 (e.g., controller, which will be further discussed below), which instructs the various components of computer 120 to perform tasks based on the processing of certain information, such as instructions 105 and/or data 106 stored in the memory 104. For example, the processor 102 may be hardware that can be configured to perform one or more operations, e.g., adding, subtracting, multiplying, comparing, jumping from one program to another program, operating input and output, etc., and may be any standard processor, such as a central processing unit (CPU), or may be a dedicated processor, such as an application-specific integrated circuit (ASIC) or a field programmable gate array (FPGA) or an industrial process controller. Moreover, the processor 102 may have any suitable configuration and/or configuration of circuitry that processes information and/or instructs the components of computer 120. While one processor block is shown in FIG. 1, it may be understood that the computer 120 may also include multiple processors to individually or collectively perform tasks, as described above. In one or more embodiments, the computer 120 may be an industrial controller.

Memory 104, whether permanent or flash, may be any type of hardware configured to store information accessible by the processor 102, such as instructions 105 and data 106, which can be executed, retrieved, manipulated, and/or stored by the processor 102. It may be physically contained in the computer 120 or coupled to the computer 120. For example, memory 104 may be ROM, RAM, CD-ROM, hard drive, write-capable, read-only, etc.

Moreover, the instructions 105 stored in memory 104 may include any set of instructions that can be executed directly or indirectly by the processor 102. For example, the instructions 105 may be one or more “steps” associated with software that can be executed by the processor 102. The instructions 105 may be also transferred onto memory 104 in various way, e.g., from server computer 130 and/or storage device 170 via network 190. In addition, the data 106 stored in memory 104 may be retrieved, stored or modified by the processor 102, for example, in accordance with the instructions 105. In one aspect, the data 106 may be stored as a collection of data. For instance, although the invention is not limited by any particular data structure, the data 106 may be stored in registers, in a database as a table having multiple fields and records, such as an XML. The data 106 may be formatted in any computer readable format such as, but not limited to, ASCII, Extended Binary-Coded Decimal Interchange Code (EBCDIC), binary, Objectivity, SQL or other suitable database formats, etc. The data 106 may also be any information sufficient to identify the relevant data, such as text, codes, pointers, information used by one or more functions to calculate the data, etc. Similar to the instructions 105, the data 106 may also be transferred onto memory 104 from various components via network 190.

Interface 108 may be a particular device (such as a field-mounted instrument, processor-to-processor communication, keyboard, mouse, touch sensitive screen, camera, microphone, etc.), a connection or port or wirelessly that allows the reception of information and data, such as interactions from a user or information/data from various components via network 190. For instance, the interface 122 may include one or more input/output ports. The input/output ports may include any suitable type of data port, such as a digital control bus (Foundation™, ProfitbusDP™, DeviceNet™, Modbus IEEE RS-485, Modbus/IP, Serial IEEE RS-232, universal serial bus (USB) drive, zip drive, card reader, CD drive, DVD drive, etc.

The display 110 may be any suitable type of device capable of communicating data to a user. For example, the display 110 may be a liquid-crystal display (LCD) screen, a light emitting diode (LED) screen, a plasma screen, etc. The display 110 may provide to the user various types of information, such as visual representations of the software that can be executed by the computer 120 and various data, and the like, associated therewith.

According to one aspect, a user may input information and/or data using the interface 108. The interface 108 may be a graphical user interface (GUI) that is displayed to the user/operator on the display 110. By way of example, the GUI may be an operator interface (01) that displays processing units and data to a user/operator.

The server computer 130 may be rack mounted on a network equipment rack and/or located in a data center. In some examples, via the network 190, the server computer 130 may serve various requests associated with the programs executed on the computer 120, mobile computer 140, the smartphone device 150, the tablet computer 160, and/or the storage device 170. In further examples, the server computer 130 may be part of a plurality of server computers that support a back-end system (which may be “invisible” to users).

Mobile or portable computing devices, such as the mobile computer 140, the smartphone device 150, and tablet computer 160, may include similar components and functions to the computer 120 and/or server computer 130, e.g., one or more processors, memory, input/output capabilities, display, etc. and, by common Thin Client and Remote Desktop protocols, access display 110 and interface 108 present on the computer 120.

For example, the mobile computer 140 may be any type of device that is mobile or portable with computing capability and connectivity to a network. For example, the mobile computer 140 may be a laptop, an Ultrabook, smartphone, PDA, tablet computer, a wearable computing device, etc. The mobile computer 140 may also have one or more processors, memory, user interfaces, wired or wireless network connection hardware, and other types of components associated with a mobile computing device. Thus, the mobile computer 140 may be able to connect to network 190 via a wired or a wireless connection and communicate with other components connected to the network 190, such as server computer 130, storage device 170, etc.

The smartphone device 150 may be a mobile cellular phone with computing capability and network connectivity. For example, the smartphone 150 may include one or more processors, memory, one or more user interfaces, such as a QWERTY keypad, voice recognition, a camera, image sensors, a global positioning system (GPS), accelerator, temperature sensors, etc. Similar to the computer 120 and the server computer 130, the smartphone device 150 may be configured to execute computer instructions, applications, programs, and any set of instructions and data. Moreover, the tablet computer 160 may also include one or more processors (configured to execute computer instructions and/or applications), memory, one or more interfaces, a touchscreen display, sensors, microphone, camera, speakers, networking hardware (configured to connect to a network, such as network 190, via a wired or wireless connection), etc.

The storage device 170 may be configured to store a large quantity of data and may also be configured to transfer such data when requested or accessed by other components of network 190. For example, the storage device 170 may be a collection of storage components, such as ROM, RAM, hard-drives, solid-state drives, removable drives, network storage, virtual memory, multi-leveled cache, registers, CD, DVD, etc. In addition, the storage device 170 may be configured so other components of network 190, such as the computer 120 and/or server computer 130, can access and provide data to other components connected to the network 190. In some embodiments, a device such as the storage device 170 may be considered the MES database for storage of data related to batch processes and/or batch products.

The network 190 may be any suitable type of network, wired or wireless, configured to facilitate the transmission of data, instructions, etc. between one or more components of the network. For example, the network 190 may be a local area network (LAN) (e.g., Ethernet or other IEEE 802.03 LAN technologies), Wi-Fi (e.g., IEEE 802.11 standards), wide area network (WAN), virtual private network (VPN), global area network (GAN), or any combinations thereof. In this regard, the computer 120, server computer 130, mobile computer 140, smartphone device 150, and/or tablet computer 160 may connect to and communicate with one another via the network 190.

While the computer 120 may be a desktop computer in the above-described examples, computer 120 is not limited to just desktop computers, and any of the computers illustrated in FIG. 1 may be any device capable of processing data and/or instructions and transmitting and/or receiving data. Moreover, it will be understood by those of ordinary skill in the art that those components may actually include multiple processors, memories, instructions, data or displays that may or may not be stored within the same physical housing. For example, some or all of the instructions 105 and data 106 may be stored on removable media, or may be stored in a location physically remote from, yet still accessible by, the processor 102. And although the various components of FIG. 1 are connected to the network 190, it may be understood that the components may also be connected to each other, in any suitable combination.

FIG. 2 illustrates another example system 200 in accordance with aspects of the invention. In this example, the system 200 represents a manufacturing execution system (MES), and the various components depicted in FIG. 1, may be configured in such a manner to facilitate the control of the bioreactors and related equipment for the production of biological products, such as equipment 202 for fermentation and/or harvest, equipment 204 for microfiltration and purification (e.g., chromatography skid), equipment 206 for media preparation, such as Clean In Place (CIP) systems and System In Place (SIP) systems, equipment 208 for buffer preparation, and various field devices (e.g., sensors with transmitters, scales, switches, pumps, control valves, discrete valves, pumps with fixed-speed starters or variable frequency drives, agitators with variable frequency drives, discrete valves with limit switches). One or more computers, such as computer 120 of FIG. 1, may be dispersed throughout the system 200 and each computer may be dedicated to certain control and/or portions of the depicted system. Similarly, server computers, such as server computer 130 of FIG. 1, may also be physical or virtual and dispersed throughout the system 200 and dedicated to certain portions of the system to facilitate the communication of data and instructions. Moreover, it may be understood that the various system components facilitated by the MES may be for one or more chemical processes (e.g., components such as a chemical reactor, etc.) in addition to and/or alternative to components used for small molecule processes, such as the components depicted in FIG. 1.

While FIG. 2 illustrates a stand-alone MES, according to another embodiment of the invention, an MES may be implemented with a control system to operate as one system. By way of example, the system 200 of FIG. 2, which is an MES system, may be combined with a plant wide control system (PWCS) to operate as one system.

FIG. 3 illustrates a system 300, for example, incorporating an MES (e.g., system 200) illustrated in FIG. 2 and a PWCS in accordance with one or more aspects of the invention. As shown in FIG. 3, the Professional Plus Workstation (PRO) 302 may be the database for the system 300, the Batch Executive (EXEC) 304 stores, for instance, “recipe” information (which will be further discussed below) and may control batch processing, the Batch Historian (BHIST) 306 records and stores batch-related data from the system 300, the Continuous Historian (PI-PHIST) 308 records and stores continuous plant data from the system 300, each of the Terminal Servers (TS) 310, 312, and 314 may be a host for remote access sessions for thin client terminals, such as desktop computers and tablet computers, and each of the controllers 316, 318, and 320 is a system device that may execute and run algorithms and/or set of executable instructions used to control the various equipment and functionalities. Any one of the illustrated components in FIG. 3 may be (or correspond to) one or more of the computer 120, server computer 130, mobile computer 140, smartphone device 150, tablet computer 160, and the storage device 170. In embodiments, the controllers 316, 318, and 320, which may be hardware, implement one or more control modules, which may be software, to control one or more control loops, which control the various field devices of the system 300 illustrated in FIG. 3, such as various sensors, probes, actuators, pumps, agitators, monitors, etc., via the control modules.

FIG. 4 illustrates a flow diagram 400 of a titration recipe and parameter “tagging” in accordance with one or more aspects of the invention. By way of example, a “recipe” may be a combination or a set of instructions for executing certain tasks associated with an external analytics system. And each instruction of the instruction set can execute a step of a particular process. In embodiments, recipes may be uniquely built by users and/or operators from scratch for different types of processes and tasks. Alternatively, they can also be readily available or pre-programmed recipes. As will be described below, certain parameters may be tagged for extraction as the instructions of the recipe are executed. The tagging process may also be implemented from scratch by the users and/or operators. In FIG. 4, a recipe for performing titration for a bioreactor tank is shown. For instance, the bioreactor tank may be the tank for media preparation or the tank for buffer preparation in the PWCS shown in FIG. 2. Moreover, the titration recipe may be stored the Batch Executive (EXEC) 304, as described above in FIG. 3, and executed by one or more terminals, such as computer 120.

As illustrated in FIG. 4, the titration recipe may include a set of at least six different instructions. When the titration recipe is executed by one or more computing devices, instruction 402 allows a pH meter or sensor in the tank to be turned on (if the pH meter is not already turned on). Then, instruction 404 allows the measurement of the initial volume of titrant being used. At instruction 406, one or more pH values may be measured before the titrant is added to the medium in the bioreactor tank. At instruction 408, the titrant is added to the medium in the bioreactor tank. As titrant is added, at instruction 410, one or more pH values may be continuously measured. For example, a pH value may be incrementally measured at every “X” volume measurement of titrant that is added into the medium until all of the titrant has been added. Subsequently, instruction 412 allows the measurement of one or more pH values after the addition of the titrant into the medium. As described above and as will be understood by persons of ordinary skill in the art, the user and/or operator may build the titration recipe differently than the above described recipe, e.g., as simple or complex as the user and/or operator desires.

FIG. 4 also shows an example of parameter “tagging” in accordance with aspects of the invention. By way of example, pH values of the medium in the bioreactor tank prior to, during, and after the titration process may be of interest for further analysis. In at least that regard, the users and/or operators that build the titration recipe may also implement into the one or more individual instructions “tags” for any parameters of interest.

In FIG. 4, for instance, there may be six different tags to extract various parameters associated with the titration process. Tag 1 may be implemented with instruction 406 and configured to extract a hundred consecutively measured pH values before titrant is added to the medium. Similarly, Tag 2 may be implemented with instruction 406, but may be configured to extract the remaining volume measurements of titrant at each of the hundred pH value measurements associated with Tag 1. As will be described below with respect to FIG. 5, the remaining volume of titrant should all be the same at each pH measurement since the titrant has not yet been added to the medium (e.g., the “add titrant” instruction has not yet been executed). Tag 3 may be implemented with instruction 410 and configured to extract measured pH values at “X” volume increments (e.g., 2 mL) of the titrant until all of the titrant has been added into the medium. For example, there may be a hundred increments, which may equate to a hundred pH readings. Tag 4 may also be implemented with instruction 410 and configured to extract the remaining volume measurements of the titrant at every pH reading. For example, the remaining volume at each pH measurement should continually decrease as more and more titrant is added to the medium. Tag 5, similar to Tag 1, extracts a hundred consecutively measured pH values after all of the titrant has been added to the medium in the bioreactor tank, but associated with instruction 412. Also associated with instruction 412 is Tag 6 configured to extract the remaining volume measurements of the titrant, all of which should be zero or approximately zero. It may be understood by those of ordinary skill in the art that not all of the above described tags 1-6 are necessary and that one or more of them may be selected in different combinations in additional embodiments.

Although FIG. 4 illustrates a titration recipe associated with a bioreactor tank, parameter tagging may be applied to different types of recipes associated with all sorts of components of a system, such as instructions for controlling sensors with transmitters, scales, switches, pumps, control valves, discrete valves, pumps with fixed-speed starters or variable frequency drives, agitators with variable frequency drives, discrete valves with limit switches, etc. Moreover, in a further example, data acquired for parameter tagging may not originate from the system component itself, such as the bioreactor tank, transmitters, scales, switches, etc., as set forth above, but there may be instances when an operator may perform a manual activity and enter the data into the system. For instance, data and/or results may originate from a benchtop test where the operator selects a result of either “pass” or “fail,” which may constitute as the tagging or identified for tagging in accordance with aspect(s) of the present invention.

FIG. 5 illustrates an example database table 500 in accordance with one or more aspects of the present invention. By way of example, the database table 500 may include all of the extracted or “pushed” parameters that have been previously tagged for extraction or “pushing out,” which may be output to the database table 500 during and/or after the execution of the titration recipe shown in FIG. 4. The local memory of the one or more computing devices, as illustrated in FIGS. 2 and/or 3 for instance, may store the database table 500. It may be understood that in embodiments, the database table 500 may also reside in a separate database for further data retrieval and analysis. In further embodiments, the parameters may be pushed out to a messaging queue for continued processing, which may then be collected and packaged into a particular format (e.g., database table 500, xml format, etc.). The parameters and the metadata associated with the parameters, as will be discussed further discussed below, automatically can be formatted in any fashion. For instance, FIG. 5 illustrates a table, but the extracted information may be organized and/or summarized in any suitable format. The parameters and/or the metadata, in accordance with one or more aspects of the disclosure, may be selectable (e.g., in real-time as the recipe is still being executed, after the aggregation of the data), as presented in the above-described format.

As shown in FIG. 5, the database table 500 may have three rows, each row containing the parameters corresponding to their respective instructions (e.g., instruction 406, instruction 410, instruction 412, etc.). Moreover, the database table may also include five columns: the first column specifies the specific instruction in the titration recipe illustrated in FIG. 4, the second column contains the extracted pH parameters for each of the instructions, the third column indicates the remaining volume measurement of the titrant for each of the instructions, the fourth column specifies the exact time the measurements were taken for each of the instructions, and the fifth column shows any relevant information, context, metadata, etc. associated with the measurements for each of the instructions, such as the identification information associated with the bioreactor tank, what type of system the tank is associated with (MES and/or combination system of MES and PWCS), where the tank is geographically located, etc. As described above, a user may be able to select any information in the database table 500, such as the “bioreactor tank 206” text, in which the computing device may display that the tank is currently part of the MES system 200, as illustrated in FIG. 2, and/or the user may also be able to select the actual parameters. As such, by selecting the extracted information, the user may be presented with relatable and important information (such as operator comments, notes, etc.) helpful for further analysis of the information.

Referring back to FIG. 4, when the pH and volume measurement parameters are extracted or pushed out by way of Tags 3 and 4 when instruction 410 is executed, the middle row (corresponding to instruction 410) may be populated with the same. As described above, for example, there may be a total of a hundred “X” volume measurements in the initial volume of the titrant being used. In other words, when “X” amount of the titrant is added to the medium at a time, then it will take a total of a hundred times to add all of the titrant to the medium. At each instance of adding “X” amount, a pH reading is taken using the pH meter or sensor, e.g., pH values 101 to 200 in FIG. 5 represent these readings. In the third column of the database table 500 of FIG. 5, there are a hundred values representing the remaining volume of titrant corresponding to each pH value. For instance, the remaining volume of titrant after adding the first “X” amount will be the initial volume minus “X,” which corresponds to pH Value 101. The exact time of each measurement is also recorded. In FIG. 5, each measurement is taken every second. Moreover, contextual information may be provided, such as the identification information associated with the bioreactor tank, the name of the control system the tank is associated with, where the tank is geographically located (e.g., that the tank is located at the “Location A” facility), etc., as described above. Other various parameters may similarly be populated in the database table 500, as above and as shown in FIG. 5.

As described above, information in the database table 500, along with other numerous types of information that may be extracted or pushed out by way of tagging in other recipes, may be stored in a separate database. In at least that regard, the information is easily accessible by users and/or operators for further analysis (e.g., examining and using the titration results for other types of requisite procedures in the bioreactor system, ensuring the titration results are within predetermined limits, etc.) and not scattered across different storage devices in the system database. Moreover, parameter tagging also allows for the unique identification and extraction of all parameters that are of interest with contextual information (e.g., metadata) that is useful and applicable, which is otherwise not possible in a pull procedure.

FIG. 6 illustrates a flow diagram 600 in accordance with one or more aspects of the present invention. The flow diagram 600 includes steps for identifying, extracting, and outputting certain information (e.g., parameters) in relatable form along with context corresponding to the extracted information based on the parameter tagging procedure described above. The steps of the flow diagram 600, for example, may be executed on one or more computing devices, such as computer 120 of FIG. 1.

At step 602, a computing device may execute a recipe for a component of an external analytics system, where the recipe may be the above-described titration recipe and the component may be the above-described bioreactor tank. When the recipe is executed, numerous types of information and data may be collected at step 604, of which some may be of interest. The computing device, at step 606, then determines which data from the collected data (e.g., data related to a batch process) are identified for extraction (or pushing out). The determination is based at least in part on the tagging process, which may have been already built into the recipe by a user and/or operator. Moreover, it may be understood that the determination may be made during or otherwise after the execution of the recipe.

At step 608, the data or parameters that are tagged as information of interest are subsequently extracted. Then, at step 610, the extracted data is output to a separate database and/or a messaging queue for transmittal to the separate database and/or middleware. In that regard, all the parameters, information, and/or data of interest to the user and/or operator all reside in one database, which is easily accessible at any time. As described above, the separate database may be stored in local memory of the computing device executing the recipe and/or the separate database of the system. At step 612, the aggregated data in the separate database may be accessed for further processing by any user and/or operator from any geographical location at any time. For example, an operator located in one geographical location may easily access the pH values prior to, during, and after titration of the medium in the bioreactor tank located in a differing geographical location in real-time.

The systems, devices, facilities, and/or methods described herein are suitable for use in and with culturing any desired cell line including prokaryotic and/or eukaryotic cell lines. Further, the systems, devices, facilities, and/or methods described herein allow for the production of eukaryotic cells, prokaryotic cells and/or products of the eukaryotic or prokaryotic cells, e.g., proteins, peptides, antibiotics, amino acids, nucleic acids (such as DNA or RNA), synthesized by the eukaryotic cells in a large-scale manner.

In one embodiment, the eukaryotic cells are mammalian cells. The mammalian cells can be for example human or rodent or bovine cell lines or cell strains. Examples of such cells, cell lines or cell strains include, for example, mouse myeloma (NSO)-cell lines, Chinese hamster ovary (CHO)-cell lines, HT1080, H9, HepG2, MCF7, MDBK Jurkat, NIH3T3, PC12, BHK (baby hamster kidney cell), VERO, SP2/0, YB2/0, Y0, C127, L cell, COS, e.g., COS1 and COST, QC1-3, HEK-293, VERO, PER.C6, HeLA, EB1, EB2, EB3, oncolytic or hybridoma-cell lines. Preferably the mammalian cells are CHO-cell lines. In one embodiment, the cell is a CHO cell. In one embodiment, the cell is a CHO-K1 cell, a CHO-K1 SV cell, a DG44 CHO cell, a DUXB11 CHO cell, a CHOS, a CHO GS knock-out cell, a CHO FUT8 GS knock-out cell, a CHOZN, or a CHO-derived cell. The CHO GS knock-out cell (e.g., GSKO cell) is, for example, a CHO-K1 SV GS knockout cell. The CHO FUT8 knockout cell is, for example, the Potelligent® CHOK1 SV (Lonza Biologics, Inc.).

In one embodiment, the eukaryotic cells are stem cells. The stem cells can be, for example, pluripotent stem cells, including embryonic stem cells (ESCs), adult stem cells, induced pluripotent stem cells (iPSCs), tissue specific stem cells (e.g., hematopoietic stem cells) and mesenchymal stem cells (MSCs).

In one embodiment, the eukaryotic cell is a lower eukaryotic cell. The lower eukaryotic cell can be, for example, a yeast cell. Examples of yeast cells include, for example, Pichia genus (e.g. Pichia pastoris, Pichia methanolica, Pichia kluyveri, and Pichia angusta), Komagataella genus (e.g. Komagataella pastoris, Komagataella pseudopastoris or Komagataella phaffii), Saccharomyces genus (e.g. Saccharomyces cerevisae, cerevisiae, Saccharomyces kluyveri, Saccharomyces uvarum), Kluyveromyces genus (e.g. Kluyveromyces lactis, Kluyveromyces marxianus), the Candida genus (e.g. Candida utilis, Candida cacaoi, Candida boidinii,), the Geotrichum genus (e.g. Geotrichum fermentans), Hansenula polymorpha, Yarrowia lipolytica, or Schizosaccharomyces pombe. Preferred is the species Pichia pastoris. Examples for Pichia pastoris strains are X33, GS115, KM71, KM71H; and CBS7435.

In one embodiment, the eukaryotic cell is a fungal cell. The fungal cell can be, for example, Aspergillus (such as A. niger, A. fumigatus, A. orzyae, A. nidula), Acremonium (such as A. thermophilum), Chaetomium (such as C. thermophilum), Chrysosporium (such as C. thermophile), Cordyceps (such as C. militaris), Corynascus, Ctenomyces, Fusarium (such as F. oxysporum), Glomerella (such as G. graminicola), Hypocrea (such as H. jecorina), Magnaporthe (such as M. orzyae), Myceliophthora (such as M. thermophile), Nectria (such as N. heamatococca), Neurospora (such as N. crassa), Penicillium, Sporotrichum (such as S. thermophile), Thielavia (such as T. terrestris, T. heterothallica), Trichoderma (such as T. reesei), or Verticillium (such as V. dahlia)).

In one embodiment, the eukaryotic cell is an insect cell (e.g., Sf9, Mimic™ Sf9, Sf21, High Five™ (BT1-TN-5B1-4), or BT1-Ea88 cells), an algae cell (e.g., of the genus Amphora, Bacillariophyceae, Dunaliella, Chlorella, Chlamydomonas, Cyanophyta (cyanobacteria), Nannochloropsis, Spirulina, or Ochromonas), or a plant cell (e.g., cells from monocotyledonous plants (such as maize, rice, wheat, or Setaria), or from a dicotyledonous plants (e.g., cassava, potato, soybean, tomato, tobacco, alfalfa, Physcomitrella patens or Arabidopsis).

Eukaryotic cells can also be avian cells, cell lines or cell strains, for example, EBx® cells, such as EB14, EB24, EB26, EB66, or EBv13.

In one embodiment, the prokaryotic cell is a Gram-positive cell such as Bacillus, Streptomyces Streptococcus, Staphylococcus or Lactobacillus. Examples of Bacillus include B. subtilis, B. amyloliquefaciens, B. licheniformis, B. natto, or B. megaterium. In some embodiments, the cell is B. subtilis, such as B. subtilis 3NA and B. subtilis 168. Bacillus is commercially available from the Bacillus Genetic Stock Center, Biological Sciences 556, 484 West 12^thAvenue, Columbus Ohio 43210-1214.

In one embodiment, the prokaryotic cell is a Gram-negative cell, such as Salmonella spp. or Escherichia coli, including e.g., TG1, TG2, W3110, DH1, DHB4, DH5a, HMS 174, HMS174 (DE3), NM533, C600, HB101, JM109, MC4100, XL1-Blue and Origami, as well as those derived from E. coli B-strains, such as BL-21 or BL21 (DE3), all of which are commercially available.

In some embodiments, the cell is a hepatocyte such as a human hepatocyte, animal hepatocyte, or a non-parenchymal cell. For example, the cell can be a plateable metabolism qualified human hepatocyte, a plateable induction qualified human hepatocyte, plateable Qualyst Transporter Certified™ human hepatocyte, suspension qualified human hepatocyte (including 10-donor and 20-donor pooled hepatocytes), human hepatic kupffer cells, human hepatic stellate cells, dog hepatocytes (including single and pooled Beagle hepatocytes), mouse hepatocytes (including CD-1 and C57BI/6 hepatocytes), rat hepatocytes (including Sprague-Dawley, Wistar Han, and Wistar hepatocytes), monkey hepatocytes (including Cynomolgus or Rhesus monkey hepatocytes), cat hepatocytes (including Domestic Shorthair hepatocytes), and rabbit hepatocytes (including New Zealand White hepatocytes). Example hepatocytes are commercially available from Triangle Research Labs, LLC, 6 Davis Drive Research Triangle Park, N.C., USA 27709.

In some embodiments, the cell is a differentiated form of any of the cells described herein. In other embodiments, the cell is a cell derived from any primary cell in culture. Suitable host cells are commercially available, for example, from culture collections such as the German Collection of Microorganisms and Cell Cultures GmbH (DSMZ) or the American Type Culture Collection (ATCC).

In some embodiments, the systems, devices, facilities, and/or methods described herein are suitable for culturing suspension cells or anchorage-dependent (adherent) cells. The systems, devices, facilities, and/or methods described herein can also be suitable for production operations configured for the production of pharmaceutical and biopharmaceutical products—such as polypeptide products, nucleic acid products (for example DNA or RNA), or cells and/or viruses such as those used in cellular and/or viral therapies. In other embodiments, the systems, devices, facilities, and/or methods described herein can be used for producing biosimilars.

In certain embodiments, the cultured cells express or produce a product, such as a recombinant therapeutic or diagnostic product. As described in more detail below, examples of products produced by cells include, but are not limited to, antibody molecules (e.g., monoclonal antibodies, bispecific antibodies), antibody mimetics (polypeptide molecules that bind specifically to antigens but that are not structurally related to antibodies such as e.g., DARPins, affibodies, adnectins, or IgNARs), fusion proteins (e.g., Fc fusion proteins, chimeric cytokines), other recombinant proteins (e.g., glycosylated proteins, enzymes, hormones), viral therapeutics (e.g., anti-cancer oncolytic viruses, viral vectors for gene therapy and viral immunotherapy), cell therapeutics (e.g., pluripotent stem cells, mesenchymal stem cells and adult stem cells), vaccines or lipid-encapsulated particles (e.g., exosomes, virus-like particles), RNA (such as e.g., siRNA) or DNA (such as e.g., plasmid DNA), antibiotics, peptides, amino acids, fatty acids or other useful biochemical intermediates or metabolites. For example, in some embodiments, molecules having a molecular weight of about 4000 daltons to greater than about 140,000 daltons can be produced. In other embodiments, these molecules can have a range of complexity and can include posttranslational modifications including glycosylation.

In embodiments, the protein is, e.g., BOTOX, Myobloc, Neurobloc, Dysport (or other serotypes of botulinum neurotoxins), alglucosidase alpha, daptomycin, YH-16, choriogonadotropin alpha, filgrastim, cetrorelix, interleukin-2, aldesleukin, teceleulin, denileukin diftitox, interferon alpha-n3 (injection), interferon alpha-nl, DL-8234, interferon, Suntory (gamma-la), interferon gamma, thymosin alpha 1, tasonermin, DigiFab, ViperaTAb, EchiTAb, CroFab, nesiritide, abatacept, alefacept, Rebif, eptoterminalfa, teriparatide (osteoporosis), calcitonin injectable (bone disease), calcitonin (nasal, osteoporosis), etanercept, hemoglobin glutamer 250 (bovine), drotrecogin alpha, collagenase, carperitide, recombinant human epidermal growth factor (topical gel, wound healing), DWP401, darbepoetin alpha, epoetin omega, epoetin beta, epoetin alpha, desirudin, lepirudin, bivalirudin, nonacog alpha, Mononine, eptacog alpha (activated), recombinant Factor VIII+VWF, Recombinate, recombinant Factor VIII, Factor VIII (recombinant), Alphnmate, octocog alpha, Factor VIII, palifermin, Indikinase, tenecteplase, alteplase, pamiteplase, reteplase, nateplase, monteplase, follitropin alpha, rFSH, hpFSH, micafungin, pegfilgrastim, lenograstim, nartograstim, sermorelin, glucagon, exenatide, pramlintide, iniglucerase, galsulfase, Leucotropin, molgramostirn, triptorelin acetate, histrelin (subcutaneous implant, Hydron), deslorelin, histrelin, nafarelin, leuprolide sustained release depot (ATRIGEL), leuprolide implant (DUROS), goserelin, Eutropin, KP-102 program, somatropin, mecasermin (growth failure), enlfavirtide, Org-33408, insulin glargine, insulin glulisine, insulin (inhaled), insulin lispro, insulin deternir, insulin (buccal, RapidMist), mecasermin rinfabate, anakinra, celmoleukin, 99 mTc-apcitide injection, myelopid, Betaseron, glatiramer acetate, Gepon, sargramostim, oprelvekin, human leukocyte-derived alpha interferons, Bilive, insulin (recombinant), recombinant human insulin, insulin aspart, mecasenin, Roferon-A, interferon-alpha 2, Alfaferone, interferon alfacon-1, interferon alpha, Avonex′ recombinant human luteinizing hormone, dornase alpha, trafermin, ziconotide, taltirelin, diboterminalfa, atosiban, becaplermin, eptifibatide, Zemaira, CTC-111, Shanvac-B, HPV vaccine (quadrivalent), octreotide, lanreotide, ancestirn, agalsidase beta, agalsidase alpha, laronidase, prezatide copper acetate (topical gel), rasburicase, ranibizumab, Actimmune, PEG-Intron, Tricomin, recombinant house dust mite allergy desensitization injection, recombinant human parathyroid hormone (PTH) 1-84 (sc, osteoporosis), epoetin delta, transgenic antithrombin III, Granditropin, Vitrase, recombinant insulin, interferon-alpha (oral lozenge), GEM-21S, vapreotide, idursulfase, omnapatrilat, recombinant serum albumin, certolizumab pegol, glucarpidase, human recombinant C1 esterase inhibitor (angioedema), lanoteplase, recombinant human growth hormone, enfuvirtide (needle-free injection, Biojector 2000), VGV-1, interferon (alpha), lucinactant, aviptadil (inhaled, pulmonary disease), icatibant, ecallantide, omiganan, Aurograb, pexigananacetate, ADI-PEG-20, LDI-200, degarelix, cintredelinbesudotox, Favld, MDX-1379, ISAtx-247, liraglutide, teriparatide (osteoporosis), tifacogin, AA4500, T4N5 liposome lotion, catumaxomab, DWP413, ART-123, Chrysalin, desmoteplase, amediplase, corifollitropinalpha, TH-9507, teduglutide, Diamyd, DWP-412, growth hormone (sustained release injection), recombinant G-CSF, insulin (inhaled, AIR), insulin (inhaled, Technosphere), insulin (inhaled, AERx), RGN-303, DiaPep277, interferon beta (hepatitis C viral infection (HCV)), interferon alpha-n3 (oral), belatacept, transdermal insulin patches, AMG-531, MBP-8298, Xerecept, opebacan, AIDSVAX, GV-1001, LymphoScan, ranpirnase, Lipoxysan, lusupultide, MP52 (beta-tricalciumphosphate carrier, bone regeneration), melanoma vaccine, sipuleucel-T, CTP-37, Insegia, vitespen, human thrombin (frozen, surgical bleeding), thrombin, TransMID, alfimeprase, Puricase, terlipressin (intravenous, hepatorenal syndrome), EUR-1008M, recombinant FGF-I (injectable, vascular disease), BDM-E, rotigaptide, ETC-216, P-113, MBI-594AN, duramycin (inhaled, cystic fibrosis), SCV-07, OPI-45, Endostatin, Angiostatin, ABT-510, Bowman Birk Inhibitor Concentrate, XMP-629, 99 mTc-Hynic-Annexin V, kahalalide F, CTCE-9908, teverelix (extended release), ozarelix, rornidepsin, BAY-504798, interleukin4, PRX-321, Pepscan, iboctadekin, rhlactoferrin, TRU-015, IL-21, ATN-161, cilengitide, Albuferon, Biphasix, IRX-2, omega interferon, PCK-3145, CAP-232, pasireotide, huN901-DMI, ovarian cancer immunotherapeutic vaccine, SB-249553, Oncovax-CL, OncoVax-P, BLP-25, CerVax-16, multi-epitope peptide melanoma vaccine (MART-1, gp100, tyrosinase), nemifitide, rAAT (inhaled), rAAT (dermatological), CGRP (inhaled, asthma), pegsunercept, thymosinbeta4, plitidepsin, GTP-200, ramoplanin, GRASPA, OBI-1, AC-100, salmon calcitonin (oral, eligen), calcitonin (oral, osteoporosis), examorelin, capromorelin, Cardeva, velafermin, 131I-TM-601, KK-220, T-10, ularitide, depelestat, hematide, Chrysalin (topical), rNAPc2, recombinant Factor V111 (PEGylated liposomal), bFGF, PEGylated recombinant staphylokinase variant, V-10153, SonoLysis Prolyse, NeuroVax, CZEN-002, islet cell neogenesis therapy, rGLP-1, BIM-51077, LY-548806, exenatide (controlled release, Medisorb), AVE-0010, GA-GCB, avorelin, ACM-9604, linaclotid eacetate, CETi-1, Hemospan, VAL (injectable), fast-acting insulin (injectable, Viadel), intranasal insulin, insulin (inhaled), insulin (oral, eligen), recombinant methionyl human leptin, pitrakinra subcutancous injection, eczema), pitrakinra (inhaled dry powder, asthma), Multikine, RG-1068, MM-093, NBI-6024, AT-001, PI-0824, Org-39141, Cpn10 (autoimmune diseases/inflammation), talactoferrin (topical), rEV-131 (ophthalmic), rEV-131 (respiratory disease), oral recombinant human insulin (diabetes), RPI-78M, oprelvekin (oral), CYT-99007 CTLA4-Ig, DTY-001, valategrast, interferon alpha-n3 (topical), IRX-3, RDP-58, Tauferon, bile salt stimulated lipase, Merispase, alaline phosphatase, EP-2104R, Melanotan-II, bremelanotide, ATL-104, recombinant human microplasmin, AX-200, SEMAX, ACV-1, Xen-2174, CJC-1008, dynorphin A, SI-6603, LAB GHRH, AER-002, BGC-728, malaria vaccine (virosomes, PeviPRO), ALTU-135, parvovirus B19 vaccine, influenza vaccine (recombinant neuraminidase), malaria/HBV vaccine, anthrax vaccine, Vacc-5q, Vacc-4x, HIV vaccine (oral), HPV vaccine, Tat Toxoid, YSPSL, CHS-13340, PTH(1-34) liposomal cream (Novasome), Ostabolin-C, PTH analog (topical, psoriasis), MBRI-93.02, MTB72F vaccine (tuberculosis), MVA-Ag85A vaccine (tuberculosis), FARA04, BA-210, recombinant plague FIV vaccine, AG-702, OxSODrol, rBetV1, Der-p1/Der-p2/Der-p7 allergen-targeting vaccine (dust mite allergy), PR1 peptide antigen (leukemia), mutant ras vaccine, HPV-16 E7 lipopeptide vaccine, labyrinthin vaccine (adenocarcinoma), CIVIL vaccine, WT1-peptide vaccine (cancer), IDD-5, CDX-110, Pentrys, Norelin, CytoFab, P-9808, VT-111, icrocaptide, telbermin (dermatological, diabetic foot ulcer), rupintrivir, reticulose, rGRF, HA, alpha-galactosidase A, ACE-011, ALTU-140, CGX-1160, angiotensin therapeutic vaccine, D-4F, ETC-642, APP-018, rhMBL, SCV-07 (oral, tuberculosis), DRF-7295, ABT-828, ErbB2-specific immunotoxin (anticancer), DT3SSIL-3, TST-10088, PRO-1762, Combotox, cholecystokinin-B/gastrin-receptor binding peptides, 111In-hEGF, AE-37, trasnizumab-DM1, Antagonist G, IL-12 (recombinant), PM-02734, IMP-321, rhIGF-BP3, BLX-883, CUV-1647 (topical), L-19 based radioimmunotherapeutics (cancer), Re-188-P-2045, AMG-386, DC/1540/KLH vaccine (cancer), VX-001, AVE-9633, AC-9301, NY-ESO-1 vaccine (peptides), NA17.A2 peptides, melanoma vaccine (pulsed antigen therapeutic), prostate cancer vaccine, CBP-501, recombinant human lactoferrin (dry eye), FX-06, AP-214, WAP-8294A (injectable), ACP-HIP, SUN-11031, peptide YY [3-36] (obesity, intranasal), FGLL, atacicept, BR3-Fc, BN-003, BA-058, human parathyroid hormone 1-34 (nasal, osteoporosis), F-18-CCR1, AT-1100 (celiac disease/diabetes), JPD-003, PTH(7-34) liposomal cream (Novasome), duramycin (ophthalmic, dry eye), CAB-2, CTCE-0214, GlycoPEGylated erythropoietin, EPO-Fc, CNTO-528, AMG-114, JR-013, Factor XIII, aminocandin, PN-951, 716155, SUN-E7001, TH-0318, BAY-73-7977, teverelix (immediate release), EP-51216, hGH (controlled release, Biosphere), OGP-I, sifuvirtide, TV4710, ALG-889, Org-41259, rhCC10, F-991, thymopentin (pulmonary diseases), r(m)CRP, hepatoselective insulin, subalin, L19-IL-2 fusion protein, elafin, NMK-150, ALTU-139, EN-122004, rhTPO, thrombopoietin receptor agonist (thrombocytopenic disorders), AL-108, AL-208, nerve growth factor antagonists (pain), SLV-317, CGX-1007, INNO-105, oral teriparatide (eligen), GEM-OS1, AC-162352, PRX-302, LFn-p24 fusion vaccine (Therapore), EP-1043, S pneumoniae pediatric vaccine, malaria vaccine, Neisseria meningitidis Group B vaccine, neonatal group B streptococcal vaccine, anthrax vaccine, HCV vaccine (gpE1+gpE2+MF-59), otitis media therapy, HCV vaccine (core antigen+ISCOMATRIX), hPTH(1-34) (transdermal, ViaDerm), 768974, SYN-101, PGN-0052, aviscumnine, BIM-23190, tuberculosis vaccine, multi-epitope tyrosinase peptide, cancer vaccine, enkastim, APC-8024, GI-5005, ACC-001, TTS-CD3, vascular-targeted TNF (solid tumors), desmopressin (buccal controlled-release), onercept, and TP-9201.

In some embodiments, the polypeptide is adalimumab (HUMIRA®), infliximab (REMICADE™), rituximab (RITUXAN™/MAB THERA™) etanercept (ENBREL™) bevacizumab (AVASTIN™), trastuzumab (HERCEPTIN™), pegrilgrastim (NEULASTA™), or any other suitable polypeptide including biosimilars and biobetters.

Other suitable polypeptides for use are those listed below and described in Table 1 of U.S. Patent Publication No. 2016/0097074:

TABLE 1

Protein Product
Reference Listed Drug

interferon gamma-1b
Actimmune ®

alteplase; tissue plasminogen activator
Activase ®/Cathflo ®

Recombinant antihemophilic factor
Advate

human albumin
Albutein ®

Laronidase
Aldurazyme ®

Interferon alfa-N3, human leukocyte
Alferon N ®

derived

human antihemophilic factor
Alphanate ®

virus-filtered human coagulation
AlphaNine ® SD

factor IX

Alefacept; recombinant, dimeric fusion
Amevive ®

protein LFA3-Ig

Bivalirudin
Angiomax ®

darbepoetin alfa
Aranesp ™

Bevacizumab
Avastin ™

interferon beta-1a; recombinant
Avonex ®

coagulation factor IX
BeneFix ™

Interferon beta-1b
Betaseron ®

Tositumomab
BEXXAR ®

antihemophilic factor
Bioclate ™

human growth hormone
BioTropin ™

botulinum toxin type A
BOTOX ®

Alemtuzumab
Campath ®

acritumomab; technetium-99 labeled
CEA-Scan ®

alglucerase; modified form of beta-
Ceredase ®

glucocerebrosidase

imiglucerase; recombinant form of beta-
Cerezyme ®

glucocerebrosidase

crotalidae polyvalent immune Fab, ovine
CroFab ™

digoxin immune fab [ovine]
DigiFab ™

Rasburicase
Elitek ®

Etanercept
ENBREL ®

epoietin alfa
Epogen ®

Cetuximab
Erbitux ™

algasidase beta
Fabrazyme ®

Urofollitropin
Fertinex ™

Protein Product
Reference Listed Drug

follitropin beta
Follistim ™

Teriparatide
FORTEO ®

human somatropin
GenoTropin ®

Glucagon
GlucaGen ®

follitropin alfa
Gonal-F ®

antihemophilic factor
Helixate ®

Antihemophilic Factor; Factor XIII
HEMOFIL

adefovir dipivoxil
Hepsera ™

Trastuzumab
Herceptin ®

Insulin
Humalog ®

antihemophilic factor/von Willebrand
Humate-P ®

factor complex-human

Somatotropin
Humatrope ®

Adalimumab
HUMIRA ™

human insulin
Humulin ®

recombinant human hyaluronidase
Hylenex ™

interferon alfacon-1
Infergen ®

eptifibatide
Integrilin ™

alpha-interferon
Intron A ®

Palifermin
Kepivance

Anakinra
Kineret ™

antihemophilic factor
Kogenate ® FS

insulin glargine
Lantus ®

granulocyte macrophage colony-
Leukine ®/Leukine ®

stimulating factor
Liquid

lutropin alfa for injection
Luveris

OspA lipoprotein
LYMErix ™

Ranibizumab
LUCENTIS ®

gemtuzumab ozogamicin
Mylotarg ™

Galsulfase
Naglazyme ™

Nesiritide
Natrecor ®

Pegfilgrastim
Neulasta ™

Oprelvekin
Neumega ®

Filgrastim
Neupogen ®

Protein Product
Reference Listed Drug

Fanolesomab
NeutroSpec ™

(formerly LeuTech ®)

somatropin [rDNA]
Norditropin ®/

Norditropin Nordiflex ®

Mitoxantrone
Novantrone ®

insulin; zinc suspension;
Novolin L ®

insulin; isophane suspension
Novolin N ®

insulin, regular;
Novolin R ®

Insulin
Novolin ®

coagulation factor VIIa
NovoSeven ®

Somatropin
Nutropin ®

immunoglobulin intravenous
Octagam ®

PEG-L-asparaginase
Oncaspar ®

abatacept, fully human soluable
Orencia ™

fusion protein

muromomab-CD3
Orthoclone OKT3 ®

high-molecular weight hyaluronan
Orthovisc ®

human chorionic gonadotropin
Ovidrel ®

live attenuated Bacillus Calmette-Guerin
Pacis ®

peginterferon alfa-2a
Pegasys ®

pegylated version of interferon alfa-2b
PEG-Intron ™

Abarelix (injectable suspension);
Plenaxis ™

gonadotropin-releasing hormone

antagonist

epoietin alfa
Procrit ®

Aldesleukin
Proleukin, IL-2 ®

Somatrem
Protropin ®

dornase alfa
Pulmozyme ®

Efalizumab; selective, reversible T-cell
RAPTIVA ™

blocker

combination of ribavirin and alpha
Rebetron ™

interferon

Interferon beta 1a
Rebif ®

antihemophilic factor
Recombinate ® rAHF/

antihemophilic factor
ReFacto ®

Lepirudin
Refludan ®

Protein Product
Reference Listed Drug

Infliximab
REMICADE ®

Abciximab
ReoPro ™

Reteplase
Retavase ™

Rituxima
Rituxan ™

interferon alfa-2^a
Roferon-A ®

Somatropin
Saizen ®

synthetic porcine secretin
SecreFlo ™

Basiliximab
Simulect ®

Eculizumab
SOURIS (R)

Pegvisomant
SOMAVERT ®

Palivizumab; recombinantly produced,
Synagis ™

humanized mAb

thyrotropin alfa
Thyrogen ®

Tenecteplase
TNKase ™

Natalizumab
TYSABRI ®

human immune globulin intravenous
Venoglobulin-S ®

5% and 10% solutions

interferon alfa-n1, lymphoblastoid
Wellferon ®

drotrecogin alfa
Xigris ™

Omalizumab; recombinant DNA-derived
Xolair ®

humanized monoclonal

antibody targeting immunoglobulin-E

Daclizumab
Zenapax ®

ibritumomab tiuxetan
Zevalin ™

Somatotropin
Zorbtive ™ (Serostim ®)

In other embodiments, the polypeptide is a hormone, blood clotting/coagulation factor, cytokine/growth factor, antibody molecule, fusion protein, protein vaccine, or peptide, these and other exemplary products are shown in Table 2.

TABLE 2

Therapeutic

Product type
Product
Trade Name

Hormone
Erythropoietin, Epoein-α
Epogen, Procrit

Darbepoetin-α
Aranesp

Growth hormone (GH),
Genotropin, Humatrope,

somatotropin
Norditropin, NovIVitropin,

Human follicle-stimulating
Nutropin, Omnitrope,

hormone (FSH)
Protropin, Siazen,

Human chorionic
Serostim, Valtropin

gonadotropin
Gonal-F, Follistim

Lutropin-α
Ovidrel

Glucagon
Luveris

Growth hormone releasing
GlcaGen

hormone (GHRH)
Geref

Secretin
ChiRhoStim (human

Thyroid stimulating
peptide),

hormone (TSH),
SecreFlo

thyrotropin
(porcine peptide)

Thyrogen

Blood
Factor VIIa
NovoSeven

Clotting/
Factor VIII
Bioclate, Helixate,

Coagulation
Factor IX
Kogenate, Recombinate,

Factors
Antithrombin III (AT-III)
ReFacto

Protein C concentrate
Benefix

Thrombate III

Ceprotin

Cytokine/
Type I alpha-interferon
Infergen

Growth factor
Interferon-αn3 (IFNαn3)
Alferon N

Interferon-β1a (rIFN-β)
Avonex, Rebif

Interferon-β1b (rIFN-β)
Betaseron

Interferon-γ1b (IFN γ)
Actimmune

Aldesleukin (interleukin
Proleukin

2(IL2), epidermal
Kepivance

theymocyte activating
Regranex

factor; ETAF
Anril, Kineret

Palifermin (keratinocyte

growth factor; KGF)

Becaplemin (platelet-

derived growth factor;

PDGF)

Anakinra (recombinant IL

1 antagonist)

Antibody
Bevacizumab (VEGFA
Avastin

molecules
mAb)
Erbitux

Cetuximab (EGFR mAb)
Vectibix

Panitumumab (EGFR mAb)
Campath

Alemtuzumab (CD52 mAb)
Rituxan

Rituximab (CD20 chimeric
Herceptin

Ab)
Orencia

Trastuzumab (HER2/Neu
Humira

mAb)
Enbrel

Abatacept (CTLA Ab/Fc
Remicade

fusion)
Amevive

Adalimumab (TNFα mAb)
Raptiva

Etanercept (TNF
Tysabri

receptor/Fc fusion)
Soliris

Infliximab (TNFα chimeric
Orthoclone, OKT3

mAb)

Alefacept (CD2 fusion

protein)

Efalizumab (CD11a mAb)

Natalizumab (integrin α4

subunit mAb)

Eculizumab (C5mAb)

Muromonab-CD3

Other:
Insulin
Humulin, Novolin

Fusion
Hepatitis B surface
Engerix, Recombivax HB

proteins/
antigen (HBsAg)
Gardasil

Protein
HPV vaccine
LYMErix

vaccines/
OspA
Rhophylac

Peptides
Anti-Rhesus(Rh)
Fuzeon

immunoglobulin G
QMONOS

Enfuvirtide

Spider silk, e.g., fibrion

In embodiments, the protein is multispecific protein, e.g., a bispecific antibody as shown in Table 3.

TABLE 3

Bispecific Formats

Name (other

names,

Proposed

Diseases (or

sponsoring
BsAb

mechanisms
Development
healthy

organizations)
format
Targets
of action
stages
volunteers)

Catumaxomab
BsIgG:
CD3,
Retargeting of T
Approved in
Malignant ascites

(Removab ®,
Triomab
EpCAM
cells to tumor, Fc
EU
in EpCAM

Fresenius Biotech,

mediated effector

positive tumors

Trion Pharma,

functions

Neopharm)

Ertumaxomab
BsIgG:
CD3, HER2
Retargeting of T
Phase I/II
Advanced solid

(Neovii Biotech,
Triomab

cells to tumor

tumors

Fresenius Biotech)

Blinatumomab
BiTE
CD3, CD19
Retargeting of T
Approved in
Precursor B-cell

(Blincyto ®, AMG

cells to tumor
USA
ALL

103, MT 103,

Phase II and
ALL

MEDI 538,

III
DLBCL

Amgen)

Phase II
NHL

Phase I

REGN1979
BsAb
CD3, CD20

(Regeneron)

Solitomab (AMG
BiTE
CD3,
Retargeting of T
Phase I
Solid tumors

110, MT110,

EpCAM
cells to tumor

Amgen)

MEDI 565 (AMG
BiTE
CD3, CEA
Retargeting of T
Phase I
Gastrointestinal

211, MedImmune,

cells to tumor

adenocancinoma

Amgen)

RO6958688
BsAb
CD3, CEA

(Roche)

BAY2010112
BiTE
CD3, PSMA
Retargeting of T
Phase I
Prostate cancer

(AMG 212, Bayer;

cells to tumor

Amgen)

MGD006
DART
CD3, CD123
Retargeting of T
Phase I
AML

(Macrogenics)

cells to tumor

MGD007
DART
CD3, gpA33
Retargeting of T
Phase I
Colorectal cancer

(Macrogenics)

cells to tumor

MGD011
DART
CD19, CD3

(Macrogenics)

SCORPION
BsAb
CD3, CD19
Retargeting of T

(Emergent

cells to tumor

Biosolutions,

Trubion)

AFM11 (Affimed
TandAb
CD3, CD19
Retargeting of T
Phase I
NHL and ALL

Therapeutics)

cells to tumor

AFM12 (Affimed
TandAb
CD19, CD16
Retargeting of NK

Therapeutics)

cells to tumor

cells

AFM13 (Affimed
TandAb
CD30,
Retargeting of NK
Phase II
Hodgkin's

Therapeutics)

CD16A
cells to tumor

Lymphoma

cells

GD2 (Barbara Ann
T cells
CD3, GD2
Retargeting of T
Phase I/II
Neuroblastoma

Karmanos Cancer
preloaded

cells to tumor

and

Institute)
with BsAb

osteosarcoma

pGD2 (Barbara
T cells
CD3, Her2
Retargeting of T
Phase II
Metastatic breast

Ann Karmanos
preloaded

cells to tumor

cancer

Cancer Institute)
with BsAb

EGFRBi-armed
T cells
CD3, EGFR
Autologous
Phase I
Lung and other

autologous
preloaded

activated T cells

solid tumors

activated T cells
with BsAb

to EGFR-positive

(Roger Williams

tumor

Medical Center)

Anti-EGFR-armed
T cells
CD3, EGFR
Autologous
Phase I
Colon and

activated T-cells
preloaded

activated T cells

pancreatic

(Barbara Ann
with BsAb

to EGFR-positive

cancers

Karmanos Cancer

tumor

Institute)

rM28 (University
Tandem
CD28,
Retargeting of T
Phase II
Metastatic

Hospital TUML übingen)
scEv
MAPG
cells to tumor

melanoma

IMCgp100
ImmTAC
CD3, peptide
Retargeting of T
Phase I/II
Metastatic

(Immunocore)

MHC
cells to tumor

melanoma

DT2219ARL
2 scFv
CD19, CD22
Targeting of
Phase I
B cell leukemia

(NCI, University of
linked to

protein toxin to

or lymphoma

Minnesota)
diphtheria

tumor

toxin

XmAb5871
BsAb
CD19,

(Xencor)

CD32b

NI-1701
BsAb
CD47,

(NovImmune)

CD19

MM-111
BsAb
ErbB2,

(Merrimack)

ErbB3

MM-141
BsAb
IGF-1R,

(Merrimack)

ErbB3

NA (Merus)
BsAb
HER2,

HER3

NA (Merus)
BsAb
CD3,

CLEC12A

NA (Merus)
BsAb
EGFR,

HER3

NA (Merus)
BsAb
PD1,

undisclosed

NA (Merus)
BsAb
CD3,

undisclosed

Duligotuzumab
DAF
EGFR,
Blockade of 2
Phase I and II
Head and neck

(MEHD7945A,

HER3
receptors, ADCC
Phase II
cancer

Genentech, Roche)

Colorectal cancer

LY3164530 (Eli
Not
EGFR, MET
Blockade of 2
Phase I
Advanced or

Lily)
disclosed

receptors

metastatic cancer

MM-111
HSA body
HER2,
Blockade of 2
Phase II
Gastric and

(Merrimack

HER3
receptors
Phase I
esophageal

Pharmaceuticals)

cancers

Breast cancer

MM-141,
IgG-scEv
IGF-1R,
Blockade of 2
Phase I
Advanced solid

(Merrimack

HER3
receptors

tumors

Pharmaceuticals)

RG7221
CrossMab
Ang2, VEGF
Blockade of 2
Phase I
Solid tumors

(RO5520985,

A
proangiogenics

Roche)

RG7716 (Roche)
CrossMab
Ang2, VEGF
Blockade of 2
Phase I
Wet AMD

A
proangiogenics

OMP-305B83
BsAb
DLL4/VEGF

(OncoMed)

TF2
Dock and
CEA, HSG
Pretargeting
Phase II
Colorectal,

(Immunomedics)
lock

tumor for PET or

breast and lung

radioimaging

cancers

ABT-981
DVD-Ig
IL-1α, IL-1β
Blockade of 2
Phase II
Osteoarthritis

(AbbVie)

proinflammatory

cytokines

ABT-122
DVD-Ig
TNF, IL-17A
Blockade of 2
Phase II
Rheumatoid

(AbbVie)

proinflammatory

arthritis

cytokines

COVA322
IgG-
TNF, IL17A
Blockade of 2
Phase I/II
Plaque psoriasis

fynomer

proinflammatory

cytokines

SAR156597
Tetravalent
IL-13, IL-4
Blockade of 2
Phase I
Idiopathic

bispecific

proinflammatory

pulmonary

(Sanofi)
tandem IgG

cytokines

fibrosis

GSK2434735
Dual-
IL-13, IL-4
Blockade of 2
Phase I
(Healthy

(GSK)
targeting

proinflammatory

volunteers)

domain

cytokines

Ozoralizumab
Nanobody
TNF, HSA
Blockade of
Phase II
Rheumatoid

(ATN103, Ablynx)

proinflammatory

arthritis

cytokine, binds to

HSA to increase

half-life

ALX-0761 (Merck
Nanobody
IL-17A/F,
Blockade of 2
Phase I
(Healthy

Serono, Ablynx)

HSA
proinflammatory

volunteers)

cytokines, binds

to HSA to

increase half-life

ALX-0061
Nanobody
IL-6R, HSA
Blockade of
Phase I/II
Rheumatoid

(AbbVie, Ablynx;

proinflammatory

arthritis

cytokine, binds to

HSA to increase

half-life

ALX-0141
Nanobody
RANKL,
Blockade of bone
Phase I
Postmenopausal

(Ablynx,

HSA
resorption binds

bone loss

Eddingpharm)

to HSA to

increase half-life

RG6013/ACE910
ART-Ig
Factor IXa,
Plasma
Phase II
Hemophilia

(Chugai, Roche)

factor X
coagulation

In embodiments and unless stated otherwise herein, the systems, devices, facilities, and/or methods described herein can also include any suitable unit operation and/or equipment not otherwise mentioned, such as operations and/or equipment for separation, purification, and isolation of such products. Any suitable facility and environment can be used, such as traditional stick-built facilities, modular, mobile and temporary facilities, or any other suitable construction, facility, and/or layout. For example, in some embodiments modular clean-rooms can be used. Additionally and unless otherwise stated, the devices, systems, and methods described herein can be housed and/or performed in a single location or facility or alternatively be housed and/or performed at separate or multiple locations and/or facilities.

Moreover and unless stated otherwise herein, the systems, devices, facilities, and/or methods can include any suitable reactor(s) including but not limited to stirred tank, airlift, fiber, microfiber, hollow fiber, ceramic matrix, fluidized bed, fixed bed, and/or spouted bed bioreactors. As used herein, “reactor” can include a fermentor or fermentation unit, or any other reaction vessel and the term “reactor” is used interchangeably with “fermentor.” For example, in some aspects, an example bioreactor unit can perform one or more, or all, of the following: feeding of nutrients and/or carbon sources, injection of suitable gas (e.g., oxygen), inlet and outlet flow of fermentation or cell culture medium, separation of gas and liquid phases, maintenance of temperature, maintenance of oxygen and CO2 levels, maintenance of pH level, agitation (e.g., stirring), and/or cleaning/sterilizing. Example reactor units, such as a fermentation unit, may contain multiple reactors within the unit, for example the unit can have 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, or 100, or more bioreactors in each unit and/or a facility may contain multiple units having a single or multiple reactors within the facility. In various embodiments, the bioreactor can be suitable for batch, semi fed-batch, fed-batch, perfusion, and/or a continuous fermentation processes. Any suitable reactor diameter can be used. In embodiments, the bioreactor can have a volume between about 100 mL and about 50,000 L. Non-limiting examples include a volume of 100 mL, 250 mL, 500 mL, 750 mL, 1 liter, 2 liters, 3 liters, 4 liters, 5 liters, 6 liters, 7 liters, 8 liters, 9 liters, 10 liters, 15 liters, 20 liters, 25 liters, 30 liters, 40 liters, 50 liters, 60 liters, 70 liters, 80 liters, 90 liters, 100 liters, 150 liters, 200 liters, 250 liters, 300 liters, 350 liters, 400 liters, 450 liters, 500 liters, 550 liters, 600 liters, 650 liters, 700 liters, 750 liters, 800 liters, 850 liters, 900 liters, 950 liters, 1000 liters, 1500 liters, 2000 liters, 2500 liters, 3000 liters, 3500 liters, 4000 liters, 4500 liters, 5000 liters, 6000 liters, 7000 liters, 8000 liters, 9000 liters, 10,000 liters, 15,000 liters, 20,000 liters, and/or 50,000 liters. Additionally, suitable reactors can be multi-use, single-use, disposable, or non-disposable and can be formed of any suitable material including metal alloys such as stainless steel (e.g., 316 L or any other suitable stainless steel) and Inconel, plastics, and/or glass.

Unless stated otherwise herein, the systems, devices, facilities, and/or methods can include any desired volume or production capacity including but not limited to bench-scale, pilot-scale, and full production scale capacities.

By way of non-limiting examples and without limitation, U.S. Patent Publication Nos. 2012/0077429; 2011/0312087; 2009/0305626; and U.S. Pat. Nos. 9,388,373; 8,771,635; 8,298,054; 7,629,167; and 5,656,491, which are hereby incorporated by reference in their entirety, describe example facilities, equipment, and/or systems that may be suitable.

The embodiments, aspects, and/or examples described in the disclosure are advantageous in various ways. For example, tagging all of the parameters of interest when a particular recipe is built allows for the tagged parameters to be “pushed” to a messaging queue, one or more separate databases designated for such data, and/or middleware, rather than a user and/or operator “pulling” the parameters from various storage devices scattered across the system database. In at least that regard, the information is easily, conveniently, and quickly accessible from any geographical location at any time in real-time (and not at a later time). Moreover, information is pushed out with various contextual data, metadata, such as external analytics system information and other types of contextual data, allowing the information to be in more relatable form and also allowing user and/or operator to better perform analysis on the information, such as, selecting and analyzing the data presented to the user. In addition, the extracted information may reside in local memory of the computing device executing the recipe, which makes it unnecessary to perform database calls.

The foregoing disclosure has been set forth merely to illustrate the invention and is not intended to be limiting. Since modifications of the disclosed embodiments incorporating the spirit and substance of the invention may occur to persons skilled in the art, the invention should be construed to include everything within the scope of the appended claims and equivalents thereof. Although the disclosure uses terminology and acronyms that may not be familiar to the layperson, those skilled in the art will be familiar with the terminology and acronyms used herein.

Automated Batch Data Analysis

Information

Publication Number

Date Filed

Date Published

Inventors

Original Assignees

CPC

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Abstract

Description

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