Systems and methods for automated, regulatory-compliant assays

Abstract

The present invention relates to methods and systems for automated regulatory compliant assays. Specifically, the present invention relates, in part, to fully automated methods and systems for performing GMP-compliant cell-based bioassays.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 438,391, filed January 11, 2023, which is incorporated herein by reference in its entirety.

[0002] The present invention relates to systems and methods for automated, regulatory-compliant assays. [Background technology]

[0003] Biotherapeutic discovery, development, production, and quality control testing require diverse, time-consuming assays performed by trained operators. Automation can be used to improve the efficiency and reliability of biotherapeutic assays, including semi-automated or fully automated assays. Automating assay steps allows for increased throughput, enabling laboratories to perform more assays in a given period of time and reducing project timelines and costs.

[0004] The requirements for how an assay is best performed, the type of sample used, and the results measured may depend on whether the assay is in, for example, the research and development stage, the quality control / assurance stage, or the manufacturing stage of therapeutic development. Performing assays in a manner that complies with regulations, such as Good Manufacturing Practice (GMP) guidelines, presents additional obstacles and challenges. To date, automation has been used in laboratories in the research and development stage of therapeutic development, but the challenges posed by meeting GMP compliance mean that no fully automated GMP-compliant assay methods or systems yet exist.

[0005] It can therefore be seen that a need exists for a regulatory compliant, fully automated method and system for performing biotherapeutic assays. Summary of the Invention

[0006] The present invention generally relates to methods for automated assays. Laboratory assays, such as cell-based bioassays, typically require extensive hands-on time and often exhibit high variability due, for example, to the use of live cells, high dilution volumes, and small pipetting volumes. This presents challenges for both GMP testing and assay investigations in laboratories and manufacturing facilities. To address assay variability and increase throughput at the R&D scale, various automation platforms have been developed. However, the investment in hardware and software setup and validation required to implement these technologies limits their incorporation into GMP environments. Here, we describe the development of an automated GMP-compliant assay that reduces variability from manual steps.

[0007] The present disclosure provides a method for performing an automated assay in a GMP-compliant manner. In some exemplary embodiments, the method includes: (a) a first component including a computer system that creates a protocol method; (b) a second component including an automated assay system that includes hardware capable of executing the protocol method created by and coupled to the first component; and (c) a third component coupled to the second component, the third component including a computer system that receives, creates, and maintains a GMP-compliant dataset detailing the protocol executed by the automated assay system.

[0008] The present disclosure also provides an automated GMP-compliant method for performing an assay. In some exemplary embodiments, the method includes developing a secure assay protocol for a GMP-compliant assay, storing the secure assay protocol to include any modifications and recording of associated usage data, where any modifications to the protocol are stored and tracked, communicating the secure assay protocol to a first secure computer system according to the assay protocol, subjecting at least one sample to the secure assay protocol executed by the first secure computer system, where the first secure computer system triggers automated operation of the secure assay protocol on the sample, collecting data related to the at least one sample subjected to the secure assay protocol, and generating a GMP-compliant dataset from the collected data, where the GMP-compliant dataset includes an audit trail of the dataset, identification of location data for at least one sample throughout execution of the secure assay protocol, and a record of any modifications to the secure assay protocol, software, and / or equipment controlled by the first secure computer system.

[0009] In one aspect, the assay is a bioassay.

[0010] In one embodiment, the protocol is optimized using data collected from at least one sample subjected to the protocol.

[0011] In one aspect, the protocol is password protected.

[0012] In one aspect, the protocol is subjected to quality control review 1-31 times per month, 1-10 times per month, 1-7 times per week, once per week, twice per week, or three times per week.

[0013] In one embodiment, the first secure computer system executes the protocol using scheduling software. In a particular embodiment, the scheduling software is Cellario.

[0014] In one embodiment, the automated operation of the protocol comprises a robotic arm. In a particular embodiment, the robotic arm is an ACell robotic arm.

[0015] In one embodiment, the automated operation of the protocol includes at least one liquid handler and / or reagent dispenser. In a particular embodiment, the at least one liquid handler and / or reagent dispenser is a Hamilton STARlet and / or Multidrop Combi reagent dispenser.

[0016] In one aspect, the at least one sample is selected from the group consisting of a cell culture medium, a harvested cell culture medium, a filtrate, a chromatography eluate, an active pharmaceutical ingredient, and a drug product.

[0017] In one embodiment, at least one sample comprises at least one therapeutic protein, the protein being selected from the group consisting of an antibody, a monoclonal antibody, a bispecific antibody, a fusion protein, an antibody-drug conjugate, a receptor, and an antibody fragment. In a particular embodiment, the at least one therapeutic protein is imdevimab or casirivimab. In another particular embodiment, the at least one therapeutic protein is dupilumab.

[0018] In one embodiment, collecting data comprises subjecting at least one sample to at least one measurement, in certain embodiments, the at least one measurement is selected from the group consisting of spectrophotometry, absorbance detection, ultraviolet detection, fluorescence detection, luminescence detection, radioactivity detection, Raman spectroscopy, mass spectrometry, biolayer interferometry, and surface plasmon resonance.

[0019] In one embodiment, the at least one sample is contained in a microplate.

[0020] In one embodiment, collecting the data comprises using at least one data analysis software. In a particular embodiment, the at least one data analysis software is SoftMax.

[0021] In one embodiment, the dataset comprises a unique identifier for at least one sample, hi another embodiment, the dataset comprises a unique identifier for a container containing said at least one sample.

[0022] In one embodiment, the method further includes generating a unique identifier for a container containing the at least one sample. In a particular embodiment, the unique identifier is a barcode. In a more particular embodiment, the barcode is generated using Sci-Print MP2+. In another particular embodiment, the container is labeled with the barcode using Sci-Print MP2+. In an additional particular embodiment, the method further includes scanning the barcode at a critical step of subjecting the at least one sample to a secure assay protocol to generate location data for the at least one sample.

[0023] In one aspect, the dataset is stored in a comma-separated value file. Alternatively, the dataset may be stored in any format acceptable to the U.S. Food and Drug Administration or an equivalent foreign agency.

[0024] In one embodiment, the assay is a cell-based assay, hi another embodiment, the assay involves isolation and / or purification of a therapeutic protein.

[0025] The present disclosure also provides an automated system for performing a GMP-compliant assay. In some embodiments, the system includes a secure computer system that stores a secure assay protocol for the GMP-compliant assay, the secure computer system being capable of collecting data associated with at least one sample subjected to the secure assay protocol and generating a GMP-compliant dataset, and at least one automated instrument that is capable of subjecting at least one sample to the secure assay protocol, the GMP-compliant dataset including an audit trail of the dataset, identification of location data for the at least one sample throughout execution of the secure assay protocol, and a record of any changes to the secure assay protocol, software, and / or instrument controlled by the secure computer system.

[0026] In one aspect, the assay is a bioassay.

[0027] In one embodiment, the protocol is created using Cellario.

[0028] In one embodiment, the protocol is optimized using data collected from at least one sample subjected to the protocol.

[0029] In one aspect, the protocol is password protected.

[0030] In one aspect, the protocol is subjected to quality control review 1-31 times per month, 1-10 times per month, 1-7 times per week, once per week, twice per week, or three times per week.

[0031] In one embodiment, the secure computer system uses scheduling software to trigger automated operations of at least one automated device. In a particular embodiment, the scheduling software is Cellario.

[0032] In one embodiment, the at least one automated instrument comprises a robotic arm. In a particular embodiment, the robotic arm is an ACell robotic arm.

[0033] In one embodiment, the at least one automated instrument comprises at least one liquid handler and / or reagent dispenser. In certain embodiments, the at least one liquid handler and / or reagent dispenser is a Hamilton STARlet, a Multidrop Combi reagent dispenser, and / or a TEMPEST liquid handler.

[0034] In one aspect, the at least one sample is selected from the group consisting of a cell culture medium, a harvested cell culture medium, a filtrate, a chromatography eluate, an active pharmaceutical ingredient, and a drug product.

[0035] In one embodiment, at least one sample comprises at least one therapeutic protein, the protein being selected from the group consisting of an antibody, a monoclonal antibody, a bispecific antibody, a fusion protein, an antibody-drug conjugate, a receptor, and an antibody fragment. In a particular embodiment, the at least one therapeutic protein is imdevimab or casirivimab. In another particular embodiment, the at least one therapeutic protein is dupilumab.

[0036] In one embodiment, collecting data comprises subjecting at least one sample to at least one measurement, in certain embodiments, the at least one measurement is selected from the group consisting of spectrophotometry, ultraviolet detection, fluorescence detection, absorbance detection, luminescence detection, radioactivity detection, Raman spectroscopy, mass spectrometry, biolayer interferometry, and surface plasmon resonance.

[0037] In one embodiment, the at least one sample is contained in a microplate.

[0038] In one embodiment, collecting the data comprises using at least one data analysis software. In a particular embodiment, the at least one data analysis software is SoftMax.

[0039] In one embodiment, the dataset comprises a unique identifier for the at least one sample.

[0040] In one embodiment, the dataset includes a unique identifier for a container containing the at least one sample. In a particular embodiment, the unique identifier is a barcode. In a more particular embodiment, the barcode is generated using Sci-Print MP2. In another particular embodiment, the container is labeled with the barcode using Sci-Print MP2. In an additional particular embodiment, the automated operation includes scanning the barcode at each step of subjecting the at least one sample to a secure assay protocol to generate location data for the at least one sample.

[0041] In one aspect, the data set is stored in a comma separated value file.

[0042] In one embodiment, the assay is a cell-based assay, hi another embodiment, the assay involves isolation and / or purification of a therapeutic protein. [Brief explanation of the drawings]

[0043] [Figure 1] 1 illustrates a high-level diagram of three key components of the present disclosure, according to an exemplary embodiment.

[0044] [Figure 2] 1 depicts a high-level flowchart of a method for creating a GMP-compliant data set, according to an example embodiment.

[0045] [Figure 3] 1 illustrates a decision tree for when a bioassay protocol is updated and communicated to a secure computer system, according to an exemplary embodiment.

[0046] [Figure 4] 1 illustrates a workflow for tracking samples through an automated assay to generate a GMP-compliant data set, according to an exemplary embodiment.

[0047] [Figure 5] 1 illustrates a workflow for recording changes to equipment used in an automated assay to generate a GMP-compliant dataset, according to an exemplary embodiment.

[0048] [Figure 6] 1 shows a flowchart of assay automation coupled with data automation, according to an exemplary embodiment.

[0049] [Figure 7] 1 illustrates the setup of a fully automated assay system, according to an exemplary embodiment.

[0050] [Figure 8] 1 illustrates the workflow of a fully automated assay system, according to an exemplary embodiment.

[0051] [Figure 9] 1 illustrates the workflow of a semi-automated assay system, according to an exemplary embodiment.

[0052] [Figure 10] 1 shows a plate format for an automated assay, according to an exemplary embodiment.

[0053] [Figure 11A] 1 illustrates a method programmed on a liquid handler for an automated assay, according to an exemplary embodiment.

[0054] [Figure 11B] 1 illustrates code development on a liquid handler for automated assays, according to an exemplary embodiment.

[0055] [Figure 11C] 1 illustrates a 3D assay deck layout on a liquid handler for automated assays, according to an exemplary embodiment.

[0056] [Figure 11D] 1 illustrates a 2D assay deck layout on a liquid handler for automated assays, according to an exemplary embodiment.

[0057] [Figure 12] 1 illustrates the time savings between automated and manual assays according to exemplary embodiments.

[0058] [Figure 13A] 1 shows the reference standard unconstrained R2 for an automated anti-SARS-CoV-2 assay compared to a manual assay using imdevimab, according to an exemplary embodiment.

[0059] [Figure 13B] 1 shows the reference standard unconstrained R2 for an automated anti-SARS-CoV-2 assay compared to a manual assay using casirivimab, according to an exemplary embodiment.

[0060] [Figure 13C] 1 shows the reference standard max / min ratio for an automated anti-SARS-CoV-2 assay compared to a manual assay using imdevimab, according to an exemplary embodiment.

[0061] [Figure 13D] 1 shows the max / min ratio of the reference standard for an automated anti-SARS-CoV-2 assay compared to a manual assay using casirivimab, according to an exemplary embodiment.

[0062] [Figure 14A]1 shows the reportable efficacy of an automated anti-SARS-CoV-2 assay compared to a manual assay using imdevimab, according to an exemplary embodiment.

[0063] [Figure 14B] 1 shows the reportable efficacy of an automated anti-SARS-CoV-2 assay compared to a manual assay using casirivimab, according to an exemplary embodiment.

[0064] [Figure 15A] 1 shows an evaluation of positional bias of an automated anti-SARS-CoV-2 assay using imdevimab, according to an exemplary embodiment.

[0065] [Figure 15B] 1 shows an evaluation of positional bias of an automated anti-SARS-CoV-2 assay using casirivimab, according to an exemplary embodiment.

[0066] [Figure 16A] 1 shows the variability of an automated anti-SARS-CoV-2 assay compared to a manual assay using casirivimab, according to an exemplary embodiment.

[0067] [Figure 16B] 1 shows the variability of an automated anti-SARS-CoV-2 assay compared to a manual assay using imdevimab, according to an exemplary embodiment.

[0068] [Figure 17A] 1 shows the overall linearity of relative potency values ​​obtained by an automated anti-SARS-CoV-2 assay using imdevimab, according to an exemplary embodiment.

[0069] [Figure 17B] 1 shows the overall linearity of relative potency values ​​obtained by an automated anti-SARS-CoV-2 assay using casirivimab, according to an exemplary embodiment.

[0070] [Figure 18A] 1 shows a variance component analysis of an automated anti-SARS-CoV-2 assay using imdevimab, according to an exemplary embodiment.

[0071] [Figure 18B] 1 shows a variance component analysis of an automated anti-SARS-CoV-2 assay using casirivimab, according to an exemplary embodiment.

[0072] [Figure 19A] 1 shows a summary of side-by-side and linearity testing of an automated anti-SARS-CoV-2 assay using imdevimab, according to an exemplary embodiment.

[0073] [Figure 19B] 1 shows a summary of side-by-side and linearity testing of an automated anti-SARS-CoV-2 assay using casirivimab, according to an exemplary embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0074] Detailed Description

[0075] The processes of biotherapeutic product discovery, development, production, and quality control testing, for example, require a diverse, complex, and time-consuming series of assays to identify, characterize, and test biotherapeutic products. Each assay may involve the use of various reagents and one or more instruments, the complexity and sensitivity of which can present challenges for both manual and automated operation of the assays.

[0076] An exemplary class of assays includes bioassays, as used generally herein, which involve evaluating molecules or substances through biological methods, including the use of living cells. Bioassays provide important information regarding the safety and efficacy of biological or pharmaceutical products. This is necessary in the field of drug development to assess batch-to-batch consistency and the stability of drug production. Bioassays are commonly used in research, clinical, environmental, and industrial settings to detect or quantify the presence or amount of specific gene sequences, antigens, diseases, proteins, peptides, and / or pathogens. Bioassays can be used to identify organisms, including parasites, fungi, bacteria, and viruses, present in a host organism or sample. For example, bioassays can provide a quantification measure that can be used to calculate the extent of infection or disease and monitor disease status over time. Thus, bioassays can provide a quantification measure used to characterize the effectiveness or quality of therapeutic drugs.

[0077] Assays may feature manual steps performed by an analyst, automated steps performed by a machine, or a combination thereof. Automation can improve the overall efficiency and reliability of a system or method and reduce the amount of analyst time and effort required. For example, automation of bioassays may allow liquid handling to be more consistent and eliminate operator-to-operator errors. Automation of assay workflows may also allow for increased throughput, allowing laboratories to perform more assays in a given period of time, thus reducing project timelines and costs. However, designing an automated system can present challenges based on the complexity and sensitivity of the tasks being performed and the need to integrate the functions of various different instruments.

[0078] Automation in laboratory settings often involves the use of computer systems, robotic systems, and / or components. Robotic systems and components are implemented in a variety of related industries. For example, robotic systems and components are commonly used in the manufacturing of consumer goods such as automobiles, electronics, pharmaceuticals, and biotechnology products. Robotic systems and components are often used in biotechnology, medical, and laboratory settings to automate certain steps in assay or bioassay processes.

[0079] Although automation is used in laboratory settings, for example, in the research and development phase of drug development, there are currently no automated systems capable of performing bioassays in a GMP-compliant manner.

[0080] Cell-based bioassays typically require extensive hands-on time and often exhibit high variability due to the use of live cells, high dilution volumes, and small pipette volumes, posing obstacles to both GMP routine testing and assay investigations in QC laboratories. To address bioassay variability and increase throughput in research and development (R&D) environments, various automated platforms have been developed. However, the investment in hardware qualification and software validation required to implement these technologies limits their incorporation into GMP environments. Compliance with GMP standards requires additional layers of oversight regarding protocols, systems, and software capable of integrating sample handling and modification, as well as complex physical processes into regulatory-compliant data collection and organizational processes.

[0081] Disclosed herein is the development of methods and systems for regulatory-compliant, fully automated assays. In some exemplary embodiments, the assays of the present invention are GMP-compliant automated bioassays using an integrated laboratory automation system (ILAS) platform, in which the majority of steps are automated, reducing variability from manual steps. Data from dilution linearity studies and side-by-side comparison studies of manual versus fully automated assays are presented. The results show that, in some exemplary embodiments, the fully automated methods achieve an 85% reduction in hands-on analyst time and a 95% reduction in analyst pipetting while demonstrating reliable dilution linearity and comparable performance to the manual method. Additionally, the failure rate was compared between the fully automated method development study and the manual method validation study. The results demonstrated that the failure rate of the fully automated method of the present invention was significantly reduced compared to that of the manual assay. Collectively, the results demonstrate that the fully automated assays of the present invention represent a viable alternative to manual assays in terms of assay performance in a GMP environment. Furthermore, the automated methods and systems of the present invention provide better reproducibility and reduced human error, and are reliable tools for assay and bioassay research.

[0082] Aspects of the present disclosure include systems and methods for pharmaceutical discovery, production, isolation, and / or analysis. According to certain embodiments, a fully automated assay system is provided. As shown in FIG. 1 , the fully automated assay system includes a first computer system 1.10. The fully automated assay system further includes a second module 1.20, which includes a secure computer system. The fully automated assay system further includes a third regulatory-compliant output system 1.30. The first and second modules can be connected via a secure computational connection, such that a secure assay protocol can be passed from the protocol generation module 1.10 to the secure computer system 1.20, allowing the secure computer system 1.20 to run the assay according to the protocol. The second module 1.20 and the third module 1.30 can also be connected via a secure computational connection, such that the secure computer system 1.20 can pass information related to any changes occurring in the second system to the third system, thus maintaining a record of those changes. In another aspect, the protocol can be transmitted via a secure connection.

[0083] Before the present systems and methods are described in more detail, it is to be understood that the disclosure is not limited to particular embodiments described, and the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0084] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative exemplary systems and methods are now described.

[0085] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has distinct components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present systems and methods. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0086] All publications and patents cited herein are incorporated by reference to the same extent as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated by reference herein to disclose and describe the methods and / or materials for which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the publication dates provided may be different from the actual publication dates, which may need to be independently confirmed.

[0087] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is understood that each intervening value between the upper and lower limits of that range, and any other stated or intervening value in the stated range, including the stated range endpoints, is encompassed by the systems and methods, to the tenth of the unit of the lower limit. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed by the systems and methods, subject to any specifically excluded limit in the stated range. Where the stated range includes either or both of the limits, ranges excluding either or both of those included limits are also included in the systems and methods.

[0088] Certain ranges are presented herein with numerical values ​​preceded by the term "about." The term "about" is used herein to provide literal support for the exact number preceding it, as well as a number that is near or approximately the number preceding the term. In determining whether a number is near or approximately a specifically recited number, the near or approximately unrecited number may be a number that, in the context in which it is presented, provides the substantial equivalent of the specifically recited number.

[0089] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude any optional element. Accordingly, this statement is intended to serve as a predicate for the use of such exclusive terminology, such as "solely," "only," and the like, in connection with the recitation of claim elements or the use of a "negative" limitation.

[0090] The term "a" should be understood to mean "at least one," and the terms "about" and "approximately" should be understood to allow for standard variation as understood by one of ordinary skill in the art, and when ranges are provided, the endpoints are included. As used herein, the terms "include," "includes," and "including" are meant to be open-ended and are understood to mean "comprise," "comprises," and "comprising," respectively.

[0091] As used herein, an assay is an investigative procedure for the qualitative assessment or quantitative measurement of the presence, amount, or functional activity of at least one target analyte, often used in laboratories for research in medicine, pharmacology, environmental biology, or molecular biology. The target analyte may be a drug, a biochemical, a cell in an organism, or an organic sample, and the measured entity may be the analyte. The purpose of an assay is to measure a property of the analyte in discrete units, such as molar concentration, density, functional activity, or the degree of some effect compared to a standard. Additionally, an assay may produce a qualitative result that can be interpreted by a skilled analyst.

[0092] The production of recombinant protein-based drug substances involves the development of several processes that adhere to guidelines set forth by the U.S. Food and Drug Administration (FDA), referred to as current good manufacturing practices (cGMP). As used and defined herein, cGMP-compliant refers to processes that adhere to FDA cGMP guidelines. In order to sell a pharmaceutical composition or drug product in the United States and elsewhere, it is necessary to produce the pharmaceutical composition or drug product in accordance with cGMP. Similarly, good manufacturing practices (GMP) relate to the GMP guidelines set forth by the FDA to ensure product quality and safety. For example, compliance with GMP guidelines requires ensuring that computerized systems are validated, ensuring that computer hardware and software are suitable to perform assigned tasks, providing controls to prevent unauthorized access to or alteration of data, providing records of data changes made, providing batch production records including the date, time of batch production, equipment used, and results of each critical step in batch production, and laboratory control records including complete data from all tests performed and comparisons with established acceptance criteria. In some exemplary embodiments, the present disclosure provides automated methods and systems for performing GMP-compliant assays.

[0093] As used herein, the term "data integrity" refers to the completeness, consistency, and accuracy of data collected by a system. As used herein, the term "GMP-compliant data integrity" refers to data that is attributable, legible, contemporaneously recorded as an original or true copy, and accurately maintained as specified in accordance with GMP standards.

[0094] As used herein, the term "metadata" refers to structured information that describes, explains, or otherwise facilitates the acquisition, use, or management of data.

[0095] As used herein, the term "audit trail" refers to a secure, computer-generated, time-stamped electronic record that allows reconstruction of the sequence of events related to the creation, modification, or deletion of an electronic record. In some exemplary embodiments, the methods and systems of the present invention provide an automated audit trail that complies with GMP guidelines. Generating an audit trail may include, for example, automatically providing a unique label for each container of each sample, automatically reading the unique label at each step of the assay, and then automatically adding the time, date, location, and other status of the sample container to a data file for the generation of a GMP-compliant data set. Generating an audit trail may further include automatically storing data related to any changes to the equipment or software involved in the assay for the generation of a GMP-compliant data set. Entries added during the execution of an assay may be referred to as a run audit trail, while entries added before, after, or during the execution of an assay may be referred to as an external run audit trail. Audit trails can be tracked in a LIMS system.

[0096] As used herein, the term "GMP compliant backup" refers to a true copy of the original data generated from an assay that is securely maintained throughout the record retention period.

[0097] As used herein, the term "auto-save" refers to the process of automatically saving or storing data in long-term storage at the time of performance.

[0098] As used herein, a "sample" can be obtained from any step of a bioprocess, such as cell culture fluid (CCF), harvested cell culture fluid (HCCF), any step in downstream processing, drug substance (DS), or drug product (DP), including the final formulated product.

[0099] As used herein, the term "scheduling software" refers to software for scheduling the jobs of software and instruments used in an assay. The scheduling software may receive, store, and / or provide instructions for performing an assay, e.g., an assay protocol. The scheduling software may provide instructions for coordinating the jobs of, for example, a robotic arm, liquid handler, reagent dispenser, plate labeler, incubator, shaker, centrifuge, peeler, sealer, washer, heater, plate lid handler, barcode scanner, pipette, and / or detector. The scheduling software may further integrate software for performing the assay, e.g., protocol development or storage software, software for operating the instrument, and software for data collection and data analysis. In some exemplary embodiments, the assays of the present invention use Cellario scheduling software.

[0100] system Aspects of the present disclosure include sample analysis systems. The analysis systems can be adapted to perform a variety of analyses of interest, including hematology analyses, slide preparation and cell morphology analyses, erythrocyte sedimentation rate (ESR) analyses, coagulation analyses, real-time nucleic acid amplification analyses, immunoassay analyses, clinical chemistry analyses, and combinations thereof. In certain aspects, the analysis systems are automated, meaning that the system can perform the sample analysis and any necessary sample preparation steps without user intervention.

[0101] According to certain embodiments, the system of the present disclosure may function by developing a secure assay protocol, which is then saved in permanent storage. The secure assay protocol is then used by a sample testing system, which may be comprised of various instruments that perform the steps of the assay according to the protocol. At each step in the process, the protocol saved in permanent storage may be updated via automatic saving or saved manually by a user, with an audit trail of the protocol's execution, and any deviations from the original protocol being cataloged in permanent storage.

[0102] According to certain embodiments, the system of the present disclosure updates the assay protocol stored in permanent storage according to the decision tree. After the initial assay protocol is saved, if any changes occur to the protocol during the execution of the assay, the system creates a record of the changes and automatically saves them in a dataset stored in permanent storage.

[0103] According to certain embodiments, the secure computer system functions by first cataloging samples via barcodes. During assay execution, the system reads the barcodes at key steps in the protocol and saves their location in a permanently stored dataset along with a date and timestamp. This process repeats through key steps until the assay protocol is complete, after which a final GMP-compliant dataset is exported.

[0104] According to certain embodiments, during assay execution, the secure computer system creates an initial save of the assay protocol, including any necessary equipment specified by the assay protocol. During the protocol, the secure computer system can determine whether changes have been made to the equipment in the system. If changes have been made, a record of the changes is created and automatically saved to a dataset stored in permanent storage. This process repeats key steps until the assay protocol is complete, after which a final GMP-compliant dataset is exported.

[0105] According to certain embodiments, the secure computer system is an automated system for completing the assay.

[0106] According to certain embodiments, an automated assay system is designed to perform automated bioassays, which may be scalable and process entire sample specimens to generate results containing information about relevant parameters.

[0107] According to certain embodiments, the system can function as a separate automated assay system or as part of an integrated system (e.g., configured in a work cell) with one or more other such automated assay systems.

[0108] Figure 1 illustrates an automated GMP-compliant system according to one embodiment. In this configuration, a first module, including a secure computer system, creates a protocol to be executed as an assay. The protocol is then transmitted over a secure connection to a second module, which includes an automated assay system including the necessary hardware to execute the assay protocol. During execution of the protocol, the second module transmits information of its actions to a third module, which includes a computer system that receives the information, catalogs it, and creates a GMP-compliant data set.

[0109] According to certain embodiments, an assay protocol can be created via input from a user in a first secure computer system. Once the protocol is created, it is transmitted to an automated assay system and to a second secure computer system that stores it for GMP compliance.

[0110] According to certain embodiments, once a protocol is created and the initial protocol is stored, an automated assay is performed. A subject sample is input into the system and run through the automated assay system in the second module. During each step of the process, the secure assay system module communicates with a third module to catalog any deviations or changes made to the protocol from the original input protocol.

[0111] According to certain embodiments, at the end of a process in the automated assay system, a data set is created that includes the initial assay protocol and a record of any modifications developed throughout the operation of the automated assay system.

[0112] In some exemplary embodiments, the first secure computer system can include a personal computer (“PC”) on which an operator can design protocols for the execution of assays.

[0113] In some exemplary embodiments, transmitting the assay protocol may involve the execution of existing software code that, based on user input, passes a set of existing protocols to the automated assay system.

[0114] In some exemplary embodiments, the automated assay system includes laboratory instruments connected either directly or indirectly that can be used in series to carry out an assay protocol.

[0115] In some exemplary embodiments, the automated assay system includes at least one robotic arm to assist in the transfer of samples and plates between laboratory instruments.

[0116] In some exemplary embodiments, the automated assay system includes an automated pipette that automates the liquid handling of reagents and reactants according to an assay protocol.

[0117] In some exemplary embodiments, the automated assay system includes a barcode scanner to automate the verification of correct reagents and reactants in the assay protocol.

[0118] In some exemplary embodiments, the automated assay system includes a PlateOrient device that is used to automate the correct alignment of sample plates in laboratory equipment used according to an assay protocol.

[0119] In some exemplary embodiments, the automated assay system includes a LidValet that assists in the automated lidding and lid removal of plates according to the assay protocol.

[0120] In some exemplary embodiments, the automated assay system includes an automated liquid handling platform, such as a Hamilton Microlab STARlet, to ensure that assay liquid handling follows the assay protocol.

[0121] In some exemplary embodiments, the automated assay system includes a thermoshaker to mix samples, reagents, and intermediates according to the assay protocol.

[0122] In some exemplary embodiments, the automated assay system includes a centrifuge to mix samples, reagents, and intermediates according to the assay protocol.

[0123] In some exemplary embodiments, the automated assay system includes a thermal heat sealer for use in accordance with the assay protocol.

[0124] In some exemplary embodiments, the assay protocols are stored using GMP-compliant data integrity standards.

[0125] In some exemplary embodiments, the assay protocol contains all metadata generated by the system and is saved at the same time as any data saved in the protocol.

[0126] In some exemplary embodiments, the audit trail and all data may be stored simultaneously. The audit trail and all data may be stored, for example, in a database.

[0127] In some exemplary embodiments, the assay protocol is stored with a GMP-compliant backup.

[0128] In some exemplary embodiments, the assay protocol is automatically saved when any changes are made to the protocol. In some exemplary embodiments, the assay protocol can be saved by the user when any changes are made to the protocol.

[0129] In one exemplary aspect, the present disclosure provides a non-transitory computer-readable medium storing instructions for causing a processor to execute a method for creating an assay protocol.

[0130] In one exemplary aspect, the present disclosure provides a non-transitory computer-readable medium storing instructions for a processor to perform a method for transmitting an assay protocol to a dataset in permanent storage.

[0131] In one exemplary aspect, the present disclosure provides a non-transitory computer-readable medium storing instructions for causing a processor to transmit an assay protocol to a secure assay automation system.

[0132] In one exemplary aspect, the present disclosure provides a non-transitory computer-readable medium storing instructions for causing a processor to transmit an assay protocol from a secure assay automation system to individual components within the secure assay automation system.

[0133] In one exemplary aspect, the present disclosure provides a non-transitory computer-readable medium storing instructions for causing a processor to determine whether a change has been made to a secure assay automation system, and if so, update an assay protocol with the change, and store the updated assay protocol in the non-transitory computer-readable medium. [Example]

[0134] Example 1. Design of a fully automated GMP-compliant assay system This disclosure describes the development of methods and systems for automated GMP-compliant assays. The systems of the present invention may be referred to as integrated laboratory automation systems (ILAS). The automated systems of the present invention may provide several features that enable the automation of GMP-compliant assays. One such feature is the generation of a run audit trail, which includes automatically recording assay details as they are performed. Another feature is the generation of an external run audit trail, which includes automatically recording changes made to the system outside of the assay run. A third feature is the tracking of samples moving through the system, for example, using a unique barcode for each sample plate. A fourth feature of the systems of the present invention is the integration of scheduling software with data recording and / or data analysis software to enable automated storage of data generated by the assay. A fifth feature includes the integration of scheduling software with individual instrument software, such as liquid handling and / or dispensing software, to enable security of instrument parameters. A sixth feature includes automated security of assay protocols, prevention of unauthorized changes to the protocols, and recording of authorized changes. A seventh feature is the integration of custom data files, e.g., spreadsheets or text files, corresponding to the samples, which can be automatically updated by the system and used to track the samples before, during, and after the automated assay.

[0135] In an exemplary embodiment, the scheduling software used to control the automated assay system is Cellario (HighRes Biosolutions), the equipment used to move plates through the system is an ACell robotic arm (HighRes Biosolutions), the equipment used for the majority of the automated liquid handling is a Hamilton STARlet (Hamilton), the software used to control the assay protocol, data collection, and data analysis is SoftMax (Molecular Devices), the reagent dispenser can be a Multidrop Combi reagent dispenser (ThermoFisher), the additional liquid handler or reagent dispenser can be a TEMPEST liquid handler / liquid dispenser (FORMULATRIX), the label printer used to label the sample plates is a Sci-Print MP2+ (Scinomix), and the data file used to track each sample plate is a comma-separated value (csv) file. The mechanical equipment used to execute the assay protocol can be referred to as hardware.

[0136] A general illustration of the methods and systems of the present invention is shown in Figure 1. A secure protocol is used to provide instructions for performing an automated assay, which generates GMP-compliant data as output.

[0137] 2 further illustrates an embodiment of the present invention. Methods and systems of the present invention may include developing a secure assay protocol for a GMP-compliant assay, communicating the secure assay protocol to a secure computer system in accordance with the assay protocol, subjecting samples to the secure assay protocol in an automated manner using the secure computer system, and generating a GMP-compliant dataset using data collected from the samples subjected to the secure assay protocol. The dataset may further include an audit trail of the dataset, identification of sample location data throughout the execution of the secure assay protocol, and a record of any changes to software and / or equipment controlled by the computer system.

[0138] Figure 3 further illustrates an embodiment of the present invention. Any changes to the measurement protocol are recorded and stored to document the changes. The stored assay protocol can be communicated to a secure computer system in accordance with the assay protocol.

[0139] An additional embodiment of the present invention is shown in Figure 4. Samples subjected to a secure assay protocol are tracked, for example, using a unique barcode attached to the container containing the sample, as part of generating a run audit trail. Whenever there is a change in sample condition or location, an automated system can read the sample barcode and store a record of the sample's location, date, and time. Upon completion of the protocol, these records can be incorporated into a GMP-compliant data set associated with the sample.

[0140] Figure 5 shows another embodiment of the present invention. Before or after subjecting a sample to a secure assay protocol (also called an assay run), changes to the equipment involved in the assay can be recorded in an audit trail that will contribute to a GMP-compliant data set upon completion of the protocol. This can be referred to as an external run audit trail.

[0141] A diagram of the automated features of the methods and systems of the present invention is shown in Figure 6. Examples of automated sample processing steps are described in detail. Examples of automated robotic processes include automated sample preparation, automated serial dilution using a Hamilton STARlet, automated reagent addition using a Hamilton STARlet, a Tempest Liquid Handler, and / or a Multidrop Combi reagent dispenser, automated incubation using HighRes Biosolutions and / or Liconic incubators, automated reagent addition using a Hamilton STARlet, a Tempest Liquid Handler STARlet, and / or a Multidrop Combi reagent dispenser, use of additional automated equipment such as shakers, sealers, peelers, and / or washers, automated plate reading, and automated robotic arms used, for example, to transport samples between integrated automated instruments. Automated software steps may include automated data collection software, automated record-keeping in an electronic notebook, and automated lab management software. Requirements that must be met to ensure data integrity include, for example, compliance with 21 CFR §11, security and access control, identification of data records, audit trails, and data backup and restoration.

[0142] Example 2. Side-by-side comparison of manual and automated assays for anti-SARS-CoV-2 neutralization To validate the automated GMP-compliant assay methods and systems of the present invention, an exemplary embodiment of the automated assay system of the present invention was developed to determine neutralization of SARS-CoV-2 by two anti-SARS-CoV-2 antibodies, casirivimab (also known as REGN10987) and imdevimab (also known as REGN10933). A side-by-side comparison study was conducted with the automated system (ILAS) compared to a manual assay for imdevimab and casirivimab.

[0143] The anti-SARS-CoV-2 neutralization assay is an in vitro cell-based assay developed to quantify the biological effect of the anti-SARS-CoV-2 antibodies imdevimab and casirivimab, specifically by neutralizing the SARS-CoV-2 spike protein and preventing viral entry into cells via the ACE2 receptor. The assay uses Vero cells, an adherent cell line of epithelial kidney cells that express the ACE2 receptor, a necessary component for SARS-CoV-2 viral entry and cell infection.

[0144] pVSV-Luc-SARS-CoV-2-S pseudoparticles are used to represent the SARS-CoV-2 virus. pVSV-Luc-SARS-CoV-2-S pseudoparticles are vesicular stomatitis virus (VSV) virions in which the VSV glycoprotein gene has been deleted and replaced with genes for the reporter proteins firefly luciferase (FLuc) and green fluorescent protein (GFP). These pVSV-Luc-G particles are pseudotyped with the SARS-CoV-2 spike protein. pVSV-Luc-SARS-CoV-2-S pseudoparticles are considered infectious but are limited to a single round of infection mediated by the spike protein. Background infectivity is measured using pVSV-Luc pseudoparticles pseudotyped without the spike protein.

[0145] For the anti-SARS-CoV-2 assay, adherent Vero cells are seeded and incubated overnight. On the second day of the assay, serial dilutions of anti-SARS-CoV-2 antibodies are incubated with a fixed amount of pVSV-Luc-SARS-CoV-2-S pseudoparticles. The anti-SARS-CoV-2 antibody / pseudoparticle complexes are then added to the seeded Vero cells and incubated overnight. During this incubation, unneutralized pseudoparticles infect the cells via the ACE2 receptor, activating the luciferase reporter in the Vero cells and resulting in luciferase expression. After incubation, ONE-Glo is added to the plate wells to measure luciferase expression. Higher concentrations of anti-SARS-CoV-2 products result in lower luminescence signals due to higher neutralization of the spike protein and subsequent lower binding to the ACE2 receptor. pVSV-Luc pseudoparticles lacking the spike protein are used as a negative control, and pVSV-Luc-SARS-CoV-2-S pseudoparticles not incubated with anti-SARS-CoV-2 antibodies are used as a positive control. All pseudoparticles used herein may also be referred to as virus-like particles (VLPs).

[0146] The anti-SARS-CoV-2 neutralization assay was automated, with most of the steps for Days 1 and 2 performed on an Integrated Laboratory Automation System (ILAS) platform. An exemplary workflow overview of the automated anti-SARS-CoV-2 neutralization assay, showing an exemplary instrument integrated into the automated workflow using the methods and systems of the present invention, is shown in Figure 7. An exemplary workflow of the automated method over the course of Days 1, 2, and 3 is shown in Figure 8. Solid green boxes highlight the automated steps on the ILAS. The assay workflow is further illustrated in Figure 9. The plate format of the assay is shown in Figure 10. The layout of the automated method using Hamilton STARlet is shown in Figure 11. The benefit of the automated method in reducing operator time is shown in Figure 12.

[0147] This study provided experimental evidence of potency measurements with reliable assay performance for a fully automated anti-SARS-CoV-2 neutralization assay. The samples used included a 122 mg / mL imdevimab sample and a 120 mg / mL casirivimab sample. For the anti-SARS-CoV-2 neutralization assay, the cell seeding step on Day 1 and most of the steps on Day 2 (including most of the predilution steps) were automated according to the method and system of the present invention. Only the cell harvest on Day 1, the initial step of sample predilution on Day 2, and the pVSV-Luc-SARS-CoV-2 pseudoparticle-related preparation on Day 3, and the One-Glo preparation on Day 3 were performed manually by the analyst.

[0148] To demonstrate the effectiveness of the fully automated assay of the present invention compared with assays performed manually by analysts, side-by-side comparison studies evaluated factors such as assay validity parameters, such as system suitability and parallelism, assay performance characteristics, such as precision and intermediate precision, and null rate, among others. Each side-by-side comparison assay included six plates, including three plates for the automated assay and three plates for the manual assay. Plates were used for both the reference standard (RS) and test article (TA) positions for the imdevimab or casirivimab studies, respectively. Finally, analysts read all six plates and generated results. Individual sample preparations were performed for the RS and TA positions on each plate. The validity criteria established for the manual assay were used to assess the validity of the fully automated assay. One-way ANOVA analysis or equivalence tests were used to assess whether the fully automated anti-SARS-CoV-2 neutralization assay and the manual assay were comparable or substantially equivalent.

[0149] RS UnlimitedR 2Three key parameters were used to assess system suitability to ensure that the method was performed in a suitable system: RS max / min ratio, RS max / min ratio, and positive / negative control ratio. The max / min ratio is the ratio between the maximum and minimum mean signals of the reference standard. The positive / negative control ratio is the ratio between the mean positive control signal and the mean negative control signal. These criteria were considered requirements for an assay to be considered valid.

[0150] As shown for imdevimab in Figure 13A and casirivimab in Figure 13B, paired manual assays showed RS unconstrained R 2 was in the range of 0.99 to 1.00, whereas RS unconstrained R 2 was consistently 1.00 for all fully automated assays for both test antibodies. These results demonstrated that the automated assays had similar or better system suitability compared to the manual assays.

[0151] One-way ANOVA analysis showed no significant difference in the RS max / min ratio between the fully automated and manual assays, as shown for imdevimab in Figure 13C and casirivimab in Figure 13D, suggesting that the assay background noise from assays performed on the ILAS was comparable to assays performed manually.

[0152] Furthermore, the geometric mean (or geometric average) of the positive / negative control ratios in the fully automated assay was 504.3 for imdevimab and 433.0 for casirivimab. These data met the acceptance criteria of ≥ 15. The geometric mean is calculated as follows:

number

[0153] In conclusion, these results demonstrated that the automated assay performed in a manner consistent with or better than the manual assay.

[0154] RS IC50 values ​​were also compared between the automated and manual assays using equivalence tests. IC50, or half-maximal inhibitory concentration, is a measure of a substance's effectiveness in inhibiting a specific biological or biochemical function. In this assay, this quantitative measure indicates the amount of anti-SARS-CoV-2 antibody required to inhibit the binding of pseudoparticle spike proteins to the ACE2 receptor on Vero cells. IC50 represents the concentration of drug required for 50% inhibition of binding.

[0155] Practical difference threshold settings of -2.000 ng / mL to 2.000 ng / mL for imdevimab and -1.650 ng / mL to 1.650 ng / mL for casirivimab were selected based on analyst variability. The 95% confidence interval (CI) of the RS IC50 difference between the manual and automated assays from Student's t-test was used to demonstrate equivalence. The 95% CI of the difference must fall within the practical difference thresholds to indicate equivalence.

[0156] The 95% CIs of the manual-automated differences in RS IC50 and the threshold for substantial difference are shown in Table 1. The 95% CIs of the differences in RS IC50 between the manual and automated assays all fell within the range of substantial difference. These data suggest that the RS IC50s measured between the manual and automated assays are comparable. [Table 1]

[0157] As a prerequisite for calculating relative potency, parallelism parameters were assessed to ensure similarity between the TA and RS. If the RS and TA preparations are similar and the assay response is plotted against concentration on a logarithmic scale, the resulting constrained TA curve should be identical to the RS curve but horizontally shifted by an amount equivalent to the logarithm of the relative potency estimate. If the two curves are not sufficiently similar, an accurate relative potency cannot be determined, and therefore, its use as a comparison between the TA and RS is no longer meaningful.

[0158] To evaluate whether a fully automated anti-SARS-CoV-2 neutralization assay could demonstrate the similarity of TA to RS, the parameters used for parallelism assessment from the automated assay (i.e., UAR, SR, and A*B ratio) were evaluated against the criteria established from the manual assay. The upper asymptote ratio (UAR, or A ratio) is the ratio between the TA upper asymptote and the RS upper asymptote. The slope ratio (SR, or B ratio) is the ratio between the slope of the TA response curve and the slope of the RS response curve. The A*B ratio is the upper asymptote ratio × slope ratio between TA and RS, and is equal to UAR (A ratio) × SR (B ratio).

[0159] The mean UAR, SR, and A*B ratio and their 95% / 95% acceptance intervals (95 / 95 TI) were calculated for both the fully automated and manual assays and are summarized in Table 2. The mean UAR, A*B ratio, and SR values ​​and 95 / 95 TI for the fully automated assay all met the acceptance criteria established by the manual assay. This indicates that the fully automated anti-SARS-CoV-2 neutralization assay was able to release a product that met the parallelism criteria established from the manual assay. Furthermore, although the low TI (0.73) of the 95% TI for the A*B ratio in the manual assay was lower than the acceptance criteria (0.75–1.30), the 95 / 95 TI from the side-by-side fully automated assay (0.80–1.21) was well within the acceptance criteria. This suggests that the performance from the automated assay is more consistent than that from the manual assay. [Table 2]

[0160] To compare the effectiveness of the fully automated assay versus the manual assay, the reportable relative potency (%RP) was assessed. The % relative potency (or PLA potency) of each TA was calculated from the quotient of the IC50 values ​​determined from the parallel line analysis (PLA) curves as follows: PLA potency = Constrained IC50(RS) / Constrained IC50(TA) Plate % relative potency = PLA potency * 100%

[0161] The geometric mean of the three plate relative potencies is the reportable relative potency (%RP). For each plate, two position-specific %RPs and an overall %RP across all TA positions were generated. The overall %RPs were compared between the automated and manual assays, followed by a position-specific %RP comparison for position bias assessment.

[0162] To compare whether the reportable potencies obtained from the fully automated and manual assays were comparable, an equivalence test was performed with equivalence bounds of 90-110% based on intrinsic assay variability to compare the manual assay to the automated assay, as shown for imdevimab in Figure 14A and casirivimab in Figure 14B. Based on the data, the potency measurements from the automated and manual assays are substantially comparable for both imdevimab and casirivimab.

[0163] As shown in Table 3, the geometric mean reportable potency for imdevimab was 96%, and the geometric mean reportable potency for casirivimab was 105%. All reportable potencies were within the empirical 80-125% tolerance range used for assay validation. Furthermore, the 95% CI for the reportable potency with each molecule was well within the 80-125% tolerance range. Therefore, the automated anti-SARS-CoV-2 neutralization assay provides good reportable potency. [Table 3]

[0164] Next, to assess whether the automated assay exhibited positional bias, the %RP of each TA position from the automated assay was evaluated and compared. One-way ANOVA analysis demonstrated that there was no significant difference (p>0.05) in potency measurements between the two TA positions in the automated assay using imdevimab and casirivimab. This indicates that the automated assay does not have positional bias, as shown in Figure 15A for imdevimab and Figure 15B for casirivimab.

[0165] To assess whether the fully automated assay performed as accurately as or more accurately than the manual assay, a Brown-Forsythe test was performed to compare the variability of the assays. The results demonstrated that there was no significant difference (p>0.05) in the variance comparison between the manual and fully automated assays using both imdevimab (shown in Figure 16A) and casirivimab (shown in Figure 16B). This suggested that the fully automated assay was as accurate as the manual assay for potency measurements.

[0166] Additionally, the intermediate precision of the automated and manual assays was quantitatively calculated for both molecules (using the percent geometric coefficient of variation, or %GCV), as shown in Table 4. The intermediate precision of the fully automated assay ranged from 4% to 7%, with an upper 95% CI ranging from 8% to 14%. This is well within the empirical acceptance criteria of ≤30% used in assay validation, indicating that the automated assay has well-controlled assay variance and is suitable for use. [Table 4]

[0167] In the side-by-side comparison study, assay failure rates were also monitored as part of the assay performance evaluation. The failure rate was calculated by dividing the amount of invalid TA dose-response curves by the total (valid and invalid) TA curves. Table 5 summarizes the failure rates of the automated and manual assays for both the molecules imdevimab and casirivimab. Neither the fully automated nor the manual assay failed, indicating good and stable overall performance of the automated assay. [Table 5]

[0168] In the above side-by-side comparison studies, system suitability, parallelism (or sample suitability), potency measurements, and assay null rates were evaluated using either one-way ANOVA analysis or equivalence tests and showed that the automated assays were as good as or better than those performed manually by analysts.

[0169] In conclusion, the automated GMP-compliant assay method and system of the present invention, when used to perform an exemplary anti-SARS-CoV-2 neutralization assay, proved to be comparable to a paired manual assay.

[0170] Example 3. Linearity of an automated GMP-compliant assay To further validate the automated GMP-compliant assay method and system of the present invention, a linearity study of the automated assay for anti-SARS-CoV-2 neutralization by imdevimab and casirivimab was performed.

[0171] The automated assay was performed at three target potency levels: 50%, 100%, and 160%, which were prepared by diluting imdevimab or casirivimab in assay medium on the day the assay was performed. For each molecule, two potency levels were run as two TAs on each plate. Three analysts ran two sets of plates for imdevimab, with two separate dilution preparations made for each potency level across three plates. Two analysts ran three sets of plates for casirivimab, with two separate dilution preparations made for each potency level across three plates. This resulted in 12 potency measurements (i.e., four reportable potency values) generated at each level of imdevimab and casirivimab. Each assay was performed using fully automated steps on the ILAS, with the exception of cell harvesting on day 1, initial sample preparation, VLP, positive and negative VLP control preparation on day 2, and One-Glo preparation on day 3. From this linearity test, the precision and intermediate precision of the assay were assessed.

[0172] As described above, the targeted 50%, 100%, and 160% potency levels were tested by three analysts for imdevimab and two analysts for casirivimab. Precision (% recovery) at each potency level was determined by comparing the geometric mean of the measured relative potency values ​​of all assays performed at each target potency level to the expected %RP. Specifically, precision (% recovery) was calculated by dividing the observed % geometric mean relative potency (%GMRP) by the expected %RP and multiplying by 100. Precision data are summarized in Table 6. [Table 6]

[0173] As shown in Table 6, the mean accuracy at each target potency level using imdevimab ranged from 100% to 104%, with an overall accuracy of 101%. The mean accuracy at each target potency level using casirivimab ranged from 97% to 104%, with an overall accuracy of 100%. The accuracy of these tests was well within the acceptable range of 80% to 125% used for assay validation. Furthermore, the 95% CIs for the mean and overall accuracy at each potency level using both imdevimab and casirivimab were within the acceptable range of 80% to 125%. Therefore, the accuracy of the fully automated anti-SARS-CoV-2 neutralization assay was within acceptable limits.

[0174] Linearity was determined using three potency levels. Overall linearity was obtained by determining the linear fit of the mean log-transformed relative potency values ​​at each potency level across all analysts, as shown in Figure 17A for imdevimab and Figure 17B for casirivimab. The mean R of all data 2The values ​​were 1.0000 for imdevimab and 0.9966 for casirivimab, which were greater than the acceptance criterion of 0.98 used in assay validation. This indicates a good linear fit between the data generated from the automated method and the model. For the overall linearity plot with imdevimab, the y-intercept was -0.127022 and the slope was 1.0333788. For the overall linearity plot with casirivimab, the y-intercept was -0.068272 and the slope was 1.0172479.

[0175] The %GCV was calculated for four values ​​of each potency level from all validated assays. The intermediate precision for all potency levels from the assays using imdevimab ranged from 7% to 8%, with an overall precision of 8%, with a 95% CI ranging from 20% to 27%, and an overall upper 95% CI of 15%. The intermediate precision for all potency levels from the assays using casirivimab ranged from 4% to 7%, with an overall precision of 7%, with a 95% CI ranging from 12% to 23%, and an overall upper 95% CI of 12%. All intermediate precisions met the assay validation acceptance criteria of ≤30%, as shown in Tables 7 and 8, indicating that the variance of the automated assay was within the normal range of assay variability. [Table 7] [Table 8]

[0176] Additionally, variance component analysis was performed to estimate sources of variance, with assay day and analyst analyzed for assays using either imdevimab or casirivimab, as shown in Figures 18A and 18B, respectively. Log-transformed %RP shows a relatively consistent spread with similar standard deviations across analysts and assay days. The variance components for the assay using imdevimab indicated that 8.1% and 0% of the variability were attributable to assay day and analyst, respectively. The variance components for the assay using casirivimab indicated that 2.9% and 0% of the variability were attributable to assay day and analyst, respectively. These data suggest that neither analyst nor assay day were major contributors to overall variability.

[0177] A summary of side-by-side and linearity studies using the automated GMP-compliant method and system of the present invention is provided for imdevimab in Figure 19A and casirivimab in Figure 19B. In conclusion, the automated GMP-compliant assay method and system of the present invention achieved satisfactory precision and accuracy when used to perform the exemplary anti-SARS-CoV-2 neutralization assay.

Claims

1. 1. An automated GMP-compliant method for performing an assay, comprising: Developing an assay protocol for a GMP-compliant assay; storing said assay protocol including any changes and recording of associated usage data, wherein any changes to said protocol are stored and tracked; communicating said assay protocol to a first secure computer system in accordance with said assay protocol; subjecting at least one sample to the assay protocol executed by the first computer system, the first computer system causing automated operation of the assay protocol on the sample; collecting data relating to the at least one sample subjected to the assay protocol as part of the automated operation of the assay protocol on the sample; generating a GMP-compliant dataset from the data collected as part of the automated operation of the assay protocol for the samples, the GMP-compliant dataset including an audit trail of the dataset, identification of location data of the at least one sample throughout execution of the assay protocol, and a record of any changes to the assay protocol, software, and / or equipment controlled by the first computer system.

2. The method of claim 1 , wherein the assay is a bioassay.

3. 10. The method of claim 1, wherein the protocol is optimized using data collected from at least one sample subjected to the protocol.

4. The method of claim 1 , wherein the protocol is password protected.

5. 10. The method of claim 1, wherein the protocol is subjected to quality control review 1 to 31 times per month, 1 to 10 times per month, 1 to 7 times per week, once per week, twice per week, or three times per week.

6. The method of claim 1 , wherein the first computer system executes the protocol using scheduling software.

7. The method of claim 6 , wherein the scheduling software is Cellario.

8. The method of claim 1 , wherein the automated operation of the protocol comprises a robotic arm.

9. The method of claim 8 , wherein the robotic arm is an ACell robotic arm.

10. The method of claim 1 , wherein the automated operation of the protocol comprises at least one liquid handler and / or reagent dispenser.

11. The method of claim 10, wherein the at least one liquid handler and / or reagent dispenser is a Hamilton STARlet and / or Multidrop Combi reagent dispenser.

12. 10. The method of claim 1, wherein the at least one sample is selected from the group consisting of a cell culture medium, a harvested cell culture medium, a filtrate, a chromatography eluate, an active pharmaceutical ingredient, and a drug product.

13. 10. The method of claim 1, wherein the at least one sample comprises at least one therapeutic protein, the protein being selected from the group consisting of an antibody, a monoclonal antibody, a bispecific antibody, a fusion protein, an antibody-drug conjugate, a receptor, and an antibody fragment.

14. 14. The method of claim 13, wherein the at least one therapeutic protein is imdevimab or casirivimab.

15. 14. The method of claim 13, wherein the at least one therapeutic protein is dupilumab.

16. The method of claim 1 , wherein collecting data comprises subjecting the at least one sample to at least one measurement.

17. 17. The method of claim 16, wherein the at least one measurement is selected from the group consisting of spectrophotometry, ultraviolet detection, fluorescence detection, luminescence detection, radioactivity detection, Raman spectroscopy, mass spectrometry, biolayer interferometry, surface plasmon resonance, and absorbance detection.

18. The method of claim 1 , wherein the at least one sample is contained in a microplate.

19. The method of claim 1 , wherein collecting data comprises using at least one data analysis software.

20. 20. The method of claim 19, wherein the at least one data analysis software is SoftMax.

21. 10. The method of claim 1, wherein the dataset comprises a unique identifier for the at least one sample.

22. The method of claim 1 , wherein the dataset includes a unique identifier for a container containing the at least one sample.

23. The method of claim 1 , further comprising generating a unique identifier for a container containing the at least one sample.

24. 24. The method of claim 23, wherein the unique identifier is a barcode.

25. 25. The method of claim 24, wherein the barcode is generated using Sci-Print MP2+.

26. 25. The method of claim 24, wherein the container is labeled with the barcode using Sci-Print MP2+.

27. 25. The method of claim 24, further comprising scanning the barcode at key steps of subjecting the at least one sample to the assay protocol to generate location data for the at least one sample.

28. The method of claim 1 , wherein the assay is a cell-based assay.

29. 1. An automated system for performing GMP-compliant assays, comprising: a computer system that stores an assay protocol for a GMP-compliant assay, the computer system being capable of collecting data relating to at least one sample that is subjected to the assay protocol and generating a GMP-compliant data set; at least one automated instrument capable of subjecting at least one sample to an assay protocol, wherein the computer system causes automated operation of the at least one automated instrument; An automated system wherein the GMP-compliant data set includes an audit trail of the data set, identification of location data of the at least one sample throughout execution of the assay protocol, and a record of any changes to the assay protocol, software, and / or equipment controlled by a secure computer system.

30. 30. The system of claim 29, wherein the assay is a bioassay.

31. 30. The system of claim 29, wherein the protocol is created using Cellario.

32. 30. The system of claim 29, wherein the protocol is optimized using data collected from at least one sample subjected to the protocol.

33. 30. The system of claim 29, wherein the protocol is password protected.

34. 30. The system of claim 29, wherein the protocol is subject to quality control review 1 to 31 times per month, 1 to 10 times per month, 1 to 7 times per week, once per week, twice per week, or three times per week.

35. 30. The system of claim 29, wherein the computer system uses scheduling software to trigger automated operation of the at least one automated device.

36. 36. The system of claim 35, wherein the scheduling software is Cellario.

37. 30. The system of claim 29, wherein the at least one automated instrument comprises a robotic arm.

38. 38. The system of claim 37, wherein the robotic arm is an ACell robotic arm.

39. 30. The system of claim 29, wherein the at least one automated instrument comprises at least one liquid handler and / or reagent dispenser.

40. 40. The system of claim 39, wherein the at least one liquid handler and / or reagent dispenser is a Hamilton STARlet and / or Multidrop Combi reagent dispenser.

41. 30. The system of claim 29, wherein the at least one sample is selected from the group consisting of a cell culture medium, a harvested cell culture medium, a filtrate, a chromatography eluate, an active pharmaceutical ingredient, and a drug product.

42. 30. The system of claim 29, wherein the at least one sample comprises at least one therapeutic protein, the protein being selected from the group consisting of an antibody, a monoclonal antibody, a bispecific antibody, a fusion protein, an antibody-drug conjugate, a receptor, and an antibody fragment.

43. 43. The system of claim 42, wherein the at least one therapeutic protein is imdevimab or casirivimab.

44. 43. The system of claim 42, wherein the at least one therapeutic protein is dupilumab.

45. 30. The system of claim 29, wherein collecting data comprises subjecting the at least one sample to at least one measurement.

46. 46. ​​The system of claim 45, wherein the at least one measurement is selected from the group consisting of spectrophotometry, ultraviolet detection, fluorescence detection, luminescence detection, radioactivity detection, Raman spectroscopy, mass spectrometry, biolayer interferometry, surface plasmon resonance, and absorbance detection.

47. 30. The system of claim 29, wherein the at least one sample is contained in a microplate.

48. 30. The system of claim 29, wherein collecting data includes using at least one data analysis software.

49. 49. The system of claim 48, wherein the at least one data analysis software is SoftMax.

50. 30. The system of claim 29, wherein the dataset comprises a unique identifier for the at least one sample.

51. 30. The system of claim 29, wherein the dataset comprises a unique identifier for a container containing the at least one sample.

52. 52. The system of claim 51, wherein the unique identifier is a barcode.

53. 53. The system of claim 52, wherein the barcode is generated using Sci-Print MP2+.

54. 53. The system of claim 52, wherein the container is labeled with the barcode using Sci-Print MP2+.

55. 53. The system of claim 52, wherein the automated operation comprises scanning the barcode at each step of subjecting the at least one sample to the assay protocol to generate location data for the at least one sample.

56. 30. The system of claim 29, wherein the assay is a cell-based assay.

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