Cell mixtures for validating cell processing systems

Abstract

Methods for validating use of a cell processing system are disclosed. The methods may include selecting first and second cell lines, combining the first and second cell lines to form a mixture at a ratio that corresponds to a target phenotype, storing the mixture in a cell bank, and using the mixture to validate a cell therapy manufacturing process or experiment performed with the cell processing system. The first cell line may comprise T-cells, such as Jurkat, Molt-3, Molt-4, or HuT78 cell lines, and the second cell line may comprise B-cells, such as NALM-6, Raji, Daudi, or Reh cell lines. The methods may enable consistent system validation using standardized cell populations that mimic patient phenotypes.

Description

Atty. Docket No.: CEES-027 / 01WO 337273-2173CELL MIXTURES FOR VALIDATING CELL PROCESSING SYSTEMSCROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 789,821 filed April 16, 2025, the content of which is incorporated herein by referenced in its entirety for all purposes.FIELD OF THE INVENTION

[0002] This invention generally relates to systems, methods, and compositions for validating automated cell processing systems, and more specifically through the use of standardized cell line mixtures designed to mimic patient phenotypes for consistent and reliable validation performance.BACKGROUND

[0003] Cell therapy manufacturing typically involves multiple processing steps such as cell enrichment, selection, activation, expansion, genetic modification, purification, and final formulation. These steps must be tightly controlled and validated to ensure the identity, purity, potency and safety of the final therapeutic product. Increasingly, these processes may be performed using automated systems designed to reduce operator variability and scale production. Such automated systems may need to be validated to ensure consistent performance when manufacturing therapies for diverse patient populations. However, validating these processes presents significant challenges, particularly in the context of autologous therapies, where starting materials are patient-specific and inherently variable. Validation studies often rely on peripheral blood or apheresis from healthy donors due to their consistency and accessibility, but they may not accurately recapitulate the phenotypic and functional characteristics of patient-derived materials. As a result, developing standardized validation approaches that account for this variability is crucial for improving the reliability and reproducibility of automated cell processing systems.

[0004] Furthermore, access to patient-derived starting materials, such as blood or apheresis material from individuals with cancer or autoimmune disease, may be limited due to ethical, logistical, and clinical constraints. The scarcity and variability of these materials complicates efforts to establish robust and reproducible validation strategies that are translatable to the patient and representative of real-world use. This is especially true because of the complex nature of manufacturing personalized cell therapies, where blood cells undergo a plurality ofAtty. Docket No.: CEES-027 / 01WO 337273-2173processing steps and where it is critical to ensure that system performance is consistent between healthy and patient samples.

[0005] As cell therapy developers introduce new equipment, both automated and manual, there is a growing need to demonstrate comparability and consistency between healthy donor and patient donor validation data. Without standardized approaches to account for biological variability, the risks associated with manufacturing failure, product inconsistency, and regulatory delay remain high. Access to a representative patient-like cell population is essential for consistent validation of cell processing systems. However, obtaining sufficient quantities of patient-derived cells for such studies is often challenging, expensive, and subject to variability. Therefore, there is a need for compositions and methods that enable consistent and reproducible system validation using standardized cell populations that mimic the characteristics of patient samples.

[0006] Accordingly, described herein are systems, methods, and cell compositions for evaluating, validating and de-risking automated cell therapy manufacturing processes in a way that accounts for donor-to-donor variability while minimizing reliance on scarce patient materials.SUMMARY

[0007] Methods and cell compositions for validating the performance of a cell processing system used in the manufacture of cell therapies are disclosed herein. In some variations, the method may include selecting two distinct (e.g., first and second) cell lines, combining the first and second cell lines in defined proportions to generate a mixed cell population that mimics a target cellular phenotype, and storing the mixture in a cell bank for future use. The mixture may be used to evaluate the performance of a cell therapy manufacturing process or system, including automated processing platforms, or to validate a cell therapy manufacturing experiment performed with the cell processing system.

[0008] In some variations, the first cell line may comprise T lymphocytes (T-cells). Suitable T-cell lines may comprise, but are not limited to, Jurkat, Molt-3, Molt-4, or HuT78 cell lines. In some variations, the second cell line may comprise B-cells. The B-cells may comprise NALM-6, Raji, Daudi, or Reh cell lines.

[0009] In some variations, the first cell line may comprise T-cells and the second cell line may comprise B-cells. The ratio of the first cell line to the second cell line may be at least about 2: 1. In some variations, the ratio may be about 1 :20 to about 1:1. The ratio may beAtty. Docket No.: CEES-027 / 01WO 337273-2173about 1 :4 or about 1 :3. In some variations, the mixture may correspond to a cellular profile representative of a healthy donor or a diseased patient population.

[0010] In some variations, the T-cells may comprise Jurkat cells and the B-cells may comprise NALM-6 or Raji cells. The method may further comprise comparing the ratio of T cells to B cells in the mixture to a predefined target ratio. The mixture may be qualified for use in validation studies when the ratio falls within a specified tolerance range of the target ratio. In some variations, when the observed ratio falls outside of the acceptable tolerance range, the method may further comprise adjusting the mixture by modifying the relative amounts of one or both cell lines to bring the ratio within the desired range.

[0011] In some variations, the method may further comprise selecting a third cell line and creating a second mixture comprising the first, second, and third cell lines in a defined second ratio. The third cell line may comprise natural killer (NK) cells. Suitable NK cell lines may comprise, but are not limited to, NK-92 or NKL cells. The second mixture may be formulated to simulate more complex immune cell compositions and may similarly be evaluated against a target ratio to ensure it falls within a predefined tolerance range prior to use in validation studies.

[0012] In some variations, the method may further comprise expanding cells of the first and second cell lines prior to forming a mixture. The expanded cells may be individually stored in a cell bank under defined storage conditions to preserve viability and functionality. In some variations, the mixture may be created using the expanded cells. Combining the first and second cell lines may comprise co-culturing the cells under suitable conditions to allow cell-to-cell interaction or phenotypic adaptation prior to use in validation studies.

[0013] In some variations, the method may further comprise calculating a number of cells required from the first and second cell lines to achieve the target phenotype. The calculation may account for viability, growth rates, or other parameters that affect the desired ratio.

[0014] In some variations, the first and second cell lines may be selected based on one or more criteria, including but not limited to, cell identity, morphology, genetic profile, mycoplasma status, and absence of adventitious pathogens. In certain variations, storing the mixture may comprise cryopreserving the cell mixture under defined conditions suitable for long-term stability. The cell bank may comprise a plurality of fluid-containing devices, each containing the first or second cell lines. Each fluid-containing device may include, for example, a cryogenic vial or a cryopreservation-compatible cell storage bag.BRIEF DESCRIPTION OF THE DRAWINGSAtty. Docket No.: CEES-027 / 01WO 337273-2173

[0015] FIG. 1 is a block diagram of an exemplary variation of a cell processing system.

[0016] FIG. 2 is a flow diagram of an exemplary method for validating a cell processing system using a mixture of cell lines.

[0017] FIG. 3 illustrates an exemplary workflow for generating individual cell lines.

[0018] FIG. 4 illustrates an exemplary workflow for combining cell lines to create a working cell bank according to an exemplary variation.DETAILED DESCRIPTION

[0019] Disclosed herein are systems, methods, and compositions for validating cell processing systems, including automated manufacturing platforms used in the production of cell therapies. More specifically, the disclosure provides methods for creating standardized cell mixtures that mimic the phenotypic characteristics of healthy donor and / or patient-derived samples. These standardized mixtures may address the limitations associated with using biologically variable donor material for validation studies. In some variations, these cell mixtures may be utilized as reference material for validating automated cell processing workflows.

[0020] Cell therapies (e.g., gene editing) represent a rapidly advancing class of therapeutics that rely on the processing and manipulation of cellular materials to produce targeted treatments for various diseases. Unlike traditional pharmaceuticals with well-defined chemical compositions, cell therapies utilize living cellular components that exhibit inherent biological variability. This variability poses significant challenges for manufacturing process validation, particularly when scaling from early-stage research to clinical and commercial production. The standardized cell mixtures described herein offer a solution to these challenges by creating reproducible, well-characterized cellular compositions that closely mimic the relevant phenotypic and functional characteristics of patient-derived samples. These standardized mixtures may facilitate more robust and reliable validation of cell processing systems, thereby supporting the translation of cell therapy manufacturing processes validated with healthy donor material into patient-specific applications.Additionally, these mixtures may enable improved quality control practices and support regulatory compliance by serving as reference standards against which system performance may be measured.Atty. Docket No.: CEES-027 / 01WO 337273-2173I. Cell Processing Systems

[0021] Cell processing systems may be used to perform various manufacturing steps required for producing cell therapies. These steps may include, but are not limited to, cell enrichment, selection, activation, expansion, genetic modification, purification, formulation, and quality control testing. The systems may be partially or fully automated and may be configured to implement custom workflows tailored to specific cell therapy products or indications. In some variations, the systems may be capable of parallel processing of a plurality of cell products to increase throughput and operational efficiency. In some variations, the cell processing systems may comprise a workcell housing one or more instruments configured to interface with a disposable or reusable cartridge containing cells to perform one or more cell processing steps on the cells. In some variations, the system may be configured to process at least two cartridges simultaneously, allowing for concurrent manufacturing runs or experimental conditions.

[0022] In some variations, the systems described herein may comprise integrated storage infrastructure for maintaining a collection of cells and mixtures ("cell bank"). The storage infrastructure may comprise one or more containers, compartments, and / or other enclosures suitable for maintaining cryogenic temperatures required for long-term cell storage. For example, one or more storage compartments may be configured for long-term cell storage. Such compartments may be configured to maintain a temperature range of about -200°C to about 0°C, such as about -196°C to about -150°C. In some variations, one or more of the compartments may be configured to maintain temperatures of about -196°C to about -180°C for liquid phase nitrogen storage or about -180°C to about -150°C for vapor phase nitrogen storage. For intermediate storage, ultra-low temperature freezers may maintain temperatures of about -150°C to about -80°C. The storage infrastructure may comprise, for example, multitiered rack configurations to maximize storage capacity within a given footprint. In some variations, each stored sample may comprise a barcode, RFID tag, or other identifier to facilitate automated inventory tracking and retrieval of cells. The storage infrastructure may further comprise robotic retrieval systems configured to locate and access specific samples without manual intervention, thereby reducing temperature fluctuations and handling errors. Storage compartments may be organized into designated zones based on cell type, mixture ratio, or experimental purpose to streamline retrieval workflows. In some variations, the cell bank may utilize standardized container formats, such as uniform vial sizes or standardized bag dimensions, to optimize packing efficiency and simplify automated handling. The storageAtty. Docket No.: CEES-027 / 01WO 337273-2173infrastructure may be integrated with inventory management software that may track sample locations, freeze-thaw cycles, and usage history. In some variations, the cell bank may implement redundant storage protocols, wherein backup samples may be maintained in physically separate locations to mitigate risk of loss. Sample volumes may be optimized to store the minimum viable aliquot size needed for validation procedures, thereby maximizing the number of available storage slots while minimizing the space required for each cell line or mixture.

[0023] The systems described herein may comprise processing infrastructure configured to perform one or more manufacturing steps required for the production of cell therapies. In some variations, the processing infrastructure may comprise bioreactor modules configured for cell expansion, where cells are cultured under controlled conditions to achieve desired population sizes while maintaining phenotypic fidelity and viability. These bioreactor modules may incorporate features such as agitation systems, gas exchange membranes, and nutrient delivery mechanisms to optimize cell proliferation and metabolic activity. The processing infrastructure may also comprise electroporation modules configured to create temporary pores in cell membranes to enable intracellular delivery of genetic material. Such modules may be utilized for a variety of gene editing and transduction protocols, including but not limited to CRISPR-Cas9-mediated editing, viral vector transduction, or non-viral plasmid transfection. In some variations, the processing infrastructure may include magnetic selection modules that utilize antibody-conjugated magnetic beads to isolate specific cell populations based on surface marker expression. Additionally, the processing infrastructure may comprise counterflow centrifugal elutriation modules configured to separate cells based on size and density without the need for labels or markers.

[0024] The processing infrastructure may be equipped with one or more sensors for monitoring process parameters throughout the cell manufacturing workflow. These process parameters may include, but are not limited to, temperature, pH, dissolved oxygen, glucose concentration, lactate levels, carbon dioxide concentration, cell density, cell viability, growth rate, metabolic activity, nutrient consumption, waste production, agitation speed, gas flow rates, pressure, and fluid flow rates. These sensors may be integrated with a controller or software interface that enables real-time monitoring and automated adjustment of culture or process conditions to maintain optimal cell growth and functionality.

[0025] In some variations, the processing infrastructure may comprise or may be provided in one or more environmentally-controlled chambers, which may include HEPA filtration to maintain sterility throughout the manufacturing process and / or temperature / humidityAtty. Docket No.: CEES-027 / 01WO 337273-2173regulation. The processing infrastructure may further comprise fluidic systems including pumps, valves, and fluid conduits configured to facilitate the transfer of cells, reagents, and waste between different modules or instruments within the system. In some variations, the processing infrastructure may be configured to interface with disposable or reusable cartridges containing cells or reagents, and to execute predefined workflows on such cartridges. The modular design may allow for integration of additional instruments, or reconfigurations of unit operations to allow for flexibility depending on product requirements.

[0026] Additionally, the systems described herein may comprise analytical infrastructure configured to assess the quality and performance of cell mixtures at different stages of the manufacturing process. For example, this infrastructure may include flow cytometers configured to analyze cell surface markers, viability dyes, and intracellular proteins to assess cell identity, purity, and functional status. The flow cytometers may utilize fluorescent antibodies to detect specific cellular antigens and may be capable of analyzing multiple parameters simultaneously to provide comprehensive phenotypic profiling of cell populations. In some variations, the analytical infrastructure may comprise automated cell counters that utilize image analysis algorithms to determine cell concentration, viability, and morphological characteristics without operator intervention. As another example, the analytical infrastructure may comprise spectrophotometric and / or fluorometric analyzers for measuring metabolite concentrations, protein content, and nucleic acid purity. In some variations, the analytical infrastructure may comprise quantitative polymerase chain reaction (qPCR) systems for genetic analysis, including detection of transgene integration, vector copy number determination, and assessment of genetic stability. The analytical infrastructure may further include imaging systems such as fluorescence microscopes or high-content imaging platforms for visual assessment of cell morphology, aggregation, and functional responses. These imaging systems may be equipped with automated image analysis software to quantify cellular characteristics objectively and reproducibly.

[0027] The quality parameters monitored by these analytical instruments may include, but are not limited to, cell identity markers (e.g., CD3, CD4, CD8, CD19, CD56), cell purity, cell viability, cell count, cell morphology, proliferation rate, apoptosis levels, cytokine production, cytotoxicity, antigen recognition, genetic stability, transgene expression, vector copy number, sterility, mycoplasma status, endotoxin levels, residual processing reagents, and potency indicators specific to the therapeutic mechanism of action.Atty. Docket No.: CEES-027 / 01WO 337273-2173

[0028] In some variations, the systems herein may comprise a robot configured to transfer cells, reagents, and other materials between different components of the cell processing system. The robot may comprise an articulated arm with multiple degrees of freedom, enabling precise manipulation and positioning capabilities within the three-dimensional workspace of the system. In some variations, the robot may be equipped with specialized end effectors designed for handling different container types, such as vials, bags, tubes, plates, and cartridges. These end effectors may incorporate temperature control elements to maintain cell viability during transfer operations, particularly when moving cryopreserved materials. The robot may utilize machine vision systems with pattern recognition capabilities to identify and locate specific containers within storage compartments, processing modules, or analytical instruments. In some variations, the robot may operate within a controlled environment (e.g., an enclosure or room) to maintain sterility during transfer operations.

[0029] The robot may be configured to execute movement sequences for transferring materials between storage infrastructure, processing infrastructure, and analytical infrastructure. For example, the robot may be configured to retrieve specific cell line vials from the storage infrastructure, transfer them to a thawing station within the processing infrastructure, and subsequently deliver the thawed cells to a bioreactor module for expansion. The robot may also be programmed to collect samples from processing modules and deliver them to analytical instruments for quality control testing. In some variations, the robot may be configured to perform automated loading and unloading of centrifuges, incubators, or other processing equipment that requires precise positioning of multiple containers simultaneously. The robot may incorporate force feedback mechanisms to ensure gentle handling of delicate cellular materials and to detect and respond to unexpected obstructions or resistance during movement operations. In some variations, the robot may execute workflow protocols autonomously, operating continuously without human intervention for extended periods, thereby enhancing process consistency and reducing labor requirements.

[0030] Furthermore, the systems herein may comprise a controller for monitoring storage conditions within the cell bank and orchestrating system operations. The controller may be communicably coupled to a network of sensors distributed throughout the storage infrastructure to continuously monitor critical storage parameters. These storage parameters may comprise, for example, temperature, humidity, oxygen levels, carbon dioxide levels, particle concentration, light exposure, and / or liquid nitrogen levels. The controller may be configured to analyze sensor data and detect anomalous conditions (e.g., values outside ofAtty. Docket No.: CEES-027 / 01WO 337273-2173predefined acceptable ranges) that may compromise sample integrity, such as temperature and / or atmospheric changes. In some variations, the controller may be configured to trigger automated corrective actions in response to parameter deviations, such as activating backup cooling systems, initiating emergency liquid nitrogen filling procedures, or rerouting samples to alternative storage locations.

[0031] The controllers herein may comprise integrated computational hardware and software designed to manage, monitor, and control all aspects of cell processing operations. In some variations, a controller may comprise a processor, memory, communication device, input device, and display. These controller components may orchestrate the operation of the cell processing system, ensuring precise execution of manufacturing protocols while maintaining compliance with regulatory requirements for cell therapy production. The controller may additionally be configured to orchestrate validation protocols for the cell processing system using cell mixtures from the cell bank. For example, the controller may be programmed to implement validation workflows that assess multiple aspects of system performance using standardized cell mixtures that mimic healthy donor or patient phenotypes. These validation protocols may include automated execution of sample retrieval, thawing procedures, cell processing operations, and quality control analyses. The controller may coordinate the timing and sequencing of different processing steps to ensure optimal system throughput while maintaining process integrity. In some variations, the controller may be configured to compare system performance metrics obtained during validation runs against predetermined acceptance criteria and to generate detailed validation reports for regulatory documentation. The controller may maintain detailed records of all cell mixtures created, their composition, storage locations, usage history, and validation results, providing complete traceability. In some variations, the controller may analyze validation data across multiple runs to identify patterns and trends that may inform system optimization or predict potential process deviations. The controller may incorporate simulation capabilities that allow virtual testing of novel processing parameters or alternative workflows using historical data from previous validation runs, thereby accelerating the development and implementation of process improvements while minimizing the consumption of valuable cell materials. In some variations, the controller may be further programmed to implement the cellular model described herein, utilizing computational algorithms to determine optimal cell line combinations and ratios for mimicking specific patient-like population phenotypes.

[0032] FIG. 1 is a block diagram of an illustrative variation of a cell processing system 100, which may comprise storage infrastructure 102, processing infrastructure 104, and analyticalAtty. Docket No.: CEES-027 / 01WO 337273-2173infrastructure 106. The storage infrastructure 102 may comprise a cell bank with one or more storage compartments may be configured for long-term cell storage. The processing infrastructure 104 may comprise at least one instrument configured to execute one or more steps of a cell processing workflow. In some variations, the at least one instrument may be configured to receive a cartridge containing the cells in order to perform the full or partial workflow. In some variations, the at least one instrument may comprise a first processing instrument and a second analytical instrument configured to perform one or more quality control assessments on cells during the workflow. Additionally, the system 100 may comprise a controller 120 that is communicably coupled to one or both of the storage and processing infrastructure 102 / 104. In some variations, the controller 120 may be configured to implement a cellular model configured to develop cell mixtures to mimic various patient population indications. The controller 120 may comprise one or more of a processor 122, memory 124, communication device 126, input device 128, and display 130. Each of these components of the controller 120 are described in further detail below.

[0033] The processor 122 may comprise one or more central processing units (CPUs), graphics processing units (GPUs), or application-specific integrated circuits (ASICs) configured to execute complex algorithms for cell processing workflow management, data analysis, and system control. In some variations, the processor 122 may utilize parallel processing capabilities to handle multiple computational tasks simultaneously, such as realtime process monitoring and quality control analysis.

[0034] The memory 124 may comprise various storage technologies including randomaccess memory (RAM) for active processing tasks and non-volatile storage such as solid-state drives (SSDs) or redundant array of independent disks (RAID) systems for long-term data retention. The memory 124 may be configured to store process workflows, standard operating procedures, validation protocols, equipment calibration data, audit trails, batch records, quality control results, and historical processing data. In some variations, the memory 124 may incorporate encryption technologies to protect proprietary manufacturing protocols and patient-related information in compliance with regulatory requirements.

[0035] The communication device 126 may comprise one or more wired and / or wireless interfaces to establish connectivity with all system components, external networks, and auxiliary systems. These interfaces may include ethemet, Wi-Fi, Bluetooth, USB, serial connections, and proprietary communication protocols optimized for reliable data transfer in manufacturing environments. The communication device 126 may incorporate cybersecurity measures such as firewalls, intrusion detection systems, and encrypted data transmission toAtty. Docket No.: CEES-027 / 01WO 337273-2173protect against unauthorized access to the cell processing system. The communication device 126 may also facilitate remote monitoring capabilities, allowing authorized personnel to access system status and performance metrics from off-site locations.

[0036] The input device 128 may comprise various interfaces for human operators to interact with the system, including touchscreens, keyboards, barcode scanners, RFID readers, and voice recognition systems. In some variations, the input device 128 may be configured with intuitive user interfaces that guide operators through complex procedures using step-by-step instructions, reducing the risk of human error.

[0037] The display 130 may comprise high-resolution screens for visualizing system status, process parameters, analytical results, and workflow progress. The display 130 may present information using customizable dashboards that prioritize critical data based on current processing operations and user preferences. In some variations, the display 130 may incorporate augmented reality (AR) capabilities to overlay digital information onto physical system components, assisting operators in identifying specific instruments, sample locations, or maintenance requirements. The display 130 may be configured to generate visual alerts for process deviations, equipment malfunctions, or quality control issues. For example, the display may utilize various text, symbols, color coding, and / or animation to indicate a type and / or severity of an alert.

[0038] Overall, these cell processing systems may require validation using healthy donor and / or patient cells to ensure consistent performance across different patient-like populations. However, this validation process may be challenged by variability between donor samples and limited availability of patient materials. Accordingly, the methods and cell compositions (mixtures) described herein below may provide standardized cell mixtures for system validation.II. Methods

[0039] The methods may be executed by the cell processing systems described above. These methods may address the challenges associated with validating cell processing systems using primary donor materials, which may exhibit high variability and limited availability. By utilizing established cell lines that may be expanded indefinitely while maintaining consistent characteristics, these methods may provide a more reliable and reproducible approach to system validation. The cell lines may be selected based on their ability to represent specific cellular components of healthy donor or patient samples, and may be combined in targeted ratios to mimic various phenotypes of interest. Accordingly, the following methods mayAtty. Docket No.: CEES-027 / 01WO 337273-2173comprise a comprehensive approach for developing standardized cell mixtures for validation of cell processing systems, including cell line selection, expansion, characterization, mixture creation, storage, and system validation.

[0040] The cell mixtures generated using the methods herein may provide advantages for validating cell processing systems. These advantages may include enhanced consistency and reproducibility compared to primary donor materials, improved accessibility without the logistical challenges associated with donor recruitment, and the ability to create standardized reference materials for systematic evaluation of system performance across different operational conditions or after equipment modifications.

[0041] The methods may be implemented using a development tool configured to develop cell mixtures that mimic desired phenotypes. This development tool may comprise computational algorithms and protocols for determining optimal cell line combinations and ratios based on desired phenotypic characteristics. This tool may be a trained tool comprising one or more models for analyzing phenotypic data from target populations, identifying optimal cell line combinations to represent specific phenotypes, predicting mixture behavior based on component characteristics, and establishing appropriate quality control parameters for different validation applications. In some variations, the tool may be retrained over time using feedback from validation studies, continuously improving its predictive capabilities and recommendations for future mixture designs.

[0042] In some variations, the methods may incorporate quality control measures throughout the process to ensure that the resulting cell mixtures consistently represent the target phenotypes. These quality control measures may include assessment of cell identity, viability, functionality, and ratio accuracy before and after storage. The methods may also comprise adjusting cell mixtures when quality control parameters fall outside of acceptable ranges. In some variations, one or more steps of the methods described below may be repeated, skipped, and / or replaced depending on specific validation requirements or constraints.

[0043] Furthermore, these methods may enable more comparable validation data across different processing runs, facilities, or equipment configurations, which may facilitate demonstrating process robustness and supporting regulatory submissions. By reducing reliance on variable donor samples, these methods may accelerate validation timelines, reduce costs associated with donor material acquisition, and ultimately facilitate more efficient development and implementation of cell processing systems for therapeutic applications.Atty. Docket No.: CEES-027 / 01WO 337273-2173

[0044] FIG. 2 is a flow diagram of an exemplary method for validating a cell processing system using a mixture of cell lines. The method 200 may provide a systematic approach to developing and utilizing cell mixtures for system validation. The method 200 may be implemented manually, or in some variations, may be partially or fully automated using the controller of the cell processing system.

[0045] The method 200 may first comprise selecting 202 first and second cell lines based on specific characteristics related to the target phenotype to be mimicked. The selection of cell lines may be based on multiple criteria to ensure suitability for validation purposes. These criteria may include cell identity (e.g., expression of characteristic surface markers such as CD3, CD4, CD8 for T-cells or CD19, CD20 for B-cells), cell morphology (e.g., size, shape, and structural features), genetic stability (e.g., consistent genotype over multiple passages), proliferation capacity (e.g., ability to expand to sufficient numbers), functional properties (e.g., cytokine production, cytotoxicity, or antibody secretion), and absence of contaminants (e.g., mycoplasma, bacteria, viruses, or other pathogens).

[0046] In some variations, the cell line selection may involve analyzing historical data on cell line performance in previous validation studies to identify lines that consistently represent desired phenotypic characteristics. The selection may also incorporate computational modeling to predict which cell line combinations may best represent specific donor or patient characteristics. In some variations, the selection may be performed by the development tool, which may analyze phenotypic data from target populations and recommend optimal cell line combinations.

[0047] For mimicking healthy donor phenotypes, the selection may involve choosing T-cell lines such as Jurkat, Molt-3, Molt-4, or HuT78, which may represent different T-cell subpopulations with varying functional characteristics. For B-cell components, the selection may involve choosing NALM-6, Raji, Daudi, or Reh cell lines, which may represent different stages of B-cell development or malignant transformations. In some variations, the selection may also include NK cell lines such as NK-92 or NKL to represent innate immune components.

[0048] For mimicking patient phenotypes associated with specific diseases, the selection may be tailored to reflect disease-specific cellular characteristics. For example, to mimic B-cell leukemia patients, the selection may involve choosing B-cell lines that represent leukemic phenotypes (e.g., Raji or NALM-6) along with appropriate proportions of T-cell lines to represent residual normal T-cells in these patients. For mimicking immunodeficiencyAtty. Docket No.: CEES-027 / 01WO 337273-2173disorders, the selection may focus on cell lines that represent the specific immune cell subsets affected by the disease.

[0049] Next, the method 200 may comprise combining 204 the first and second cell lines to form a mixture. The mixture may comprise a ratio of the cell lines that corresponds to a target phenotype to be used for system validation. The target phenotype may comprise a healthy donor phenotype or a patient phenotype associated with a specific disease state. The combining may be performed using precise cell counting and mixing techniques to achieve the desired ratio with high accuracy. In some variations, the combining may involve coculturing the cell lines to allow for cell-cell interactions that may better mimic in vivo conditions.

[0050] For mimicking healthy peripheral blood mononuclear cell (PBMC) compositions, the combining may involve creating mixtures with T-cell to B-cell ratios of approximately 2:1 to 7:1, reflecting the predominance of T-cells in healthy individuals. For example, Jurkat cells and NALM-6 cells may be combined in a 2:1 ratio to represent a simplified model of healthy PBMCs. In some variations, more complex mixtures may be created by including additional cell types, such as NK cells, typically at lower proportions (e.g., 5-15% of the total mixture) to better represent the full cellular composition of peripheral blood.

[0051] For mimicking disease states, the combining may involve creating mixtures with altered cell type ratios that reflect pathological conditions. For B-cell leukemias, the combining may involve using B-cell to T-cell ratios ranging from 1:1 to 20:1, depending on the disease stage and severity. For T-cell leukemias or lymphomas, the combining may involve using elevated proportions of T-cell lines. The exact ratios may be determined based on clinical data from actual patient samples, literature reports, or recommendations from the development tool.

[0052] In some variations, following the combining, the mixture may be assessed using one or more quality control tests to ensure the ratio is within an acceptable tolerance range of the target ratio. These quality control tests may include flow cytometry to quantify the proportions of each cell type based on specific markers, functional assays to verify that each cell type maintains its characteristic activities, and viability assessments to ensure that the mixing process does not adversely affect cell health. In some variations, the acceptable tolerance range may be within approximately 5% to 20% of the target ratio (e.g., within 10% of the target ratio), though narrower or wider tolerances may be applied depending on the specific validation requirements.Atty. Docket No.: CEES-027 / 01WO 337273-2173

[0053] If the quality control tests indicate that the actual ratio falls outside the acceptable tolerance range, the method 200 may comprise adjusting the mixture by adding additional cells from either the first or second cell line to achieve the target ratio. This adjustment process may be iterative, with repeated testing and adjustment until the desired ratio is achieved. In some variations, if repeated adjustments are unable to achieve the target ratio due to differential cell growth or survival during the mixing process, the method 200 may comprise re-evaluating the selection of cell lines or modifying the culturing conditions to improve compatibility.

[0054] The method 200 may additionally comprise storing 206 the mixture in a cell bank, such as a storage compartment of a cell processing system. The storing may involve cryopreservation of the mixture to enable long-term storage and retrieval for multiple validation studies. The cryopreservation process may involve controlled-rate freezing to minimize cell damage and maintain the established ratio of cell types. For example, the controlled-rate freezing may typically involve cooling at approximately 0.5°C to 2°C per minute from room temperature to around -80°C, though the exact cooling rate may be optimized for the specific cell mixture.

[0055] The cryopreservation media used for storing the cell mixtures may typically contain a basal medium supplemented with a cryoprotectant such as dimethyl sulfoxide (DMSO) at concentrations ranging from approximately 5% to 15%, along with protein components such as fetal bovine serum (FBS) at concentrations of approximately 20% to 90%. In some variations, serum-free cryopreservation media containing defined components may be used to reduce variability.

[0056] In some variations, the mixture may be stored at cryogenic temperatures, typically in the range of approximately -120°C to -196°C (e.g., -150°C to -196°C), to ensure long-term viability and stability. The specific temperature range may be achieved using liquid nitrogen storage systems, either in the liquid phase (-196°C) or vapor phase (-120°C to -180°C). The storing may also comprise documenting detailed information about the mixture, including its composition, date of creation, quality control results, and intended validation applications.

[0057] In some variations, the storing may involve dividing the mixture into multiple aliquots of standardized size, with each aliquot containing sufficient cells for a single validation study. This approach may minimize freeze-thaw cycles and maintain consistency across validation runs. The storing may also include creating backup aliquots stored in separate physical locations to mitigate the risk of loss due to equipment failure or other adverse events.Atty. Docket No.: CEES-027 / 01WO 337273-2173

[0058] Next, the method 200 may comprise validating 208 a cell processing system with the mixture. The validating may involve using the cell mixture as input material for a complete or partial cell processing workflow to assess system performance. The validation may evaluate numerous parameters that may be critical for cell processing outcomes. These parameters may include process efficiency (measured as cell recovery at each processing step, typically targeting recovery rates of about 60% to about 95%), cell viability throughout the workflow (typically targeting viability of about 70% to about 95% post-processing), preservation of cell functionality (assessed through appropriate functional assays specific to the cell types involved), achievement of target purity levels (typically within about 5% to about 15% of specified targets), and system reliability (measured as consistency across multiple validation runs with coefficient of variation typically targeted at less than about 15%).

[0059] Additional validation parameters may include assessment of sterility (absence of microbial contamination), endotoxin levels (typically targeting levels below approximately 0.5 to 5 EU / mL), residual processing reagent concentrations (below predefined safety thresholds), and process duration (within defined time limits to ensure cell quality). The specific parameters and their acceptance criteria may be determined based on the intended therapeutic application and regulatory requirements.

[0060] The validating may also involve comparing system performance using the standardized cell mixture against historical performance data with primary donor materials, when available. This comparison may establish whether the cell mixture provides a representative model for actual donor or patient samples in the context of specific processing operations. In some variations, the validating may involve performing the validation under various operational conditions to assess system robustness and identify potential failure modes or performance limitations.

[0061] In some variations, based on the validation results, the method 200 may involve adjusting processing parameters to optimize system performance for the specific cell mixture and, by extension, for the target donor or patient population it represents. These adjustments may include modifications to processing times (which may be increased or decreased by about 10% to about 30% from standard protocols), temperatures (which may be adjusted within about 2°C to about 5°C of standard settings), reagent concentrations (which may be adjusted by about 5% to about 25%), flow rates (which may be modified by about 10% to about 40%), or other operational parameters that influence processing outcomes.Atty. Docket No.: CEES-027 / 01WO 337273-2173

[0062] In some variations, iterative testing may be performed to determine an optimal parameter setting. The adjustment process may continue until all validation parameters meet predefined acceptance criteria.

[0063] FIG. 3 illustrates an exemplary workflow 300 for generating individual cell lines for use in creating standardized mixtures. This workflow may represent a systematic approach to establishing and characterizing cell lines that may serve as the foundation for mimicking target phenotypes.

[0064] As illustrated in FIG. 3, the workflow 300 may begin with carefully selecting and characterizing potential cell lines 302 based on their suitability for representing specific cellular components of the target phenotype. During this selection and characterization process, cell lines may be evaluated based on multiple criteria to ensure their ability to serve as reliable surrogates for the cell populations of interest. Cell identity may be verified through assessment of characteristic markers, such as CD3 expression for T-cell lines as shown in the example in FIG. 3. Genetic and phenotypic stability may be evaluated over multiple passages to ensure consistent performance over time. Screening for mycoplasma and pathogens may be conducted to verify the absence of contaminants that could affect cell behavior or compromise safety.

[0065] In some variations, the first cell line may comprise T-cells, such as Jurkat, Molt-3, Molt-4, or HuT78 cells. These may serve as immortalized surrogates for T-cells of a particular concentration. In some variations, the second cell line may comprise B-cells, such as NALM-6, Raji, Daudi, or Reh cells. In some variations, a third cell line may comprise NK cells, such as NK-92 or NKL cells.

[0066] Following selection, the workflow 300 may comprise expanding the selected cell lines and conduct quality control testing 304. During this expansion and quality control phase, the selected cell lines may be thawed and cultured under optimized conditions specific to each cell type. The cells may be expanded using seed vials that require cultivation in their respective optimal culture environments, ensuring maintenance of their characteristic properties throughout the expansion process. Throughout this expansion, multiple parameters may be monitored, including cell viability, growth rate, morphology, metabolic activity, and sterility, to ensure that the cells maintain their expected characteristics and remain free from contamination.

[0067] Quality control during this phase may involve multiple assessments to verify that the expanded cells meet predefined specifications. As indicated in the example portion of FIG. 3, the quality control release procedures may include verification of sterility and viability, whichAtty. Docket No.: CEES-027 / 01WO 337273-2173may be critical for ensuring the reliability of the expanded cells for subsequent processing. Additional quality control measures may include verification of identity markers, assessment of functional properties, and confirmation of growth characteristics consistent with the expected profile for each cell line.

[0068] After successful expansion and quality control, the method 300 may comprise generating a master cell bank 306. This banking phase may involve harvesting cells during their optimal growth phase, typically during exponential growth, to ensure uniformity and high viability. The harvested cells may be processed for cryopreservation using methods designed to maintain cellular integrity during freezing and long-term storage. The cryopreservation process may involve controlled-rate freezing and storage in liquid nitrogen at temperatures below -150°C to ensure long-term stability of the cell lines.

[0069] In some variations, the master cell bank may be organized with comprehensive tracking information, including documentation of passage number, number of vials created, and storage location details. This may enable precise inventory management and ensure traceability of each cell line throughout subsequent processing and application.

[0070] Finally, the method 300 may comprise post-bank validating and management 308. Here, representative samples from the master cell bank may be thawed and subjected to testing to verify that the cryopreservation process has not adversely affected critical cell properties. As shown in the example portion of FIG. 3, this validation may occur after a specified storage period. The post-banking validation may involve one or more tests to verify that the cells maintain their expected characteristics after thawing. These tests may include viability assessment, identity verification, functional analysis, and growth characteristic evaluation to ensure that the banked cells remain suitable for their intended application in creating standardized mixtures.

[0071] FIG. 4 illustrates an exemplary method 400 for combining cell lines to create a working cell bank according to an exemplary variation. This method may represent a systematic approach to creating, characterizing, and storing cell mixtures that mimic target phenotypes for validation purposes.

[0072] As shown in FIG. 4, the method 400 may begin with determining the desired cellular composition 402 based on the target phenotype to be mimicked. For example, a number of cells needed from each cell line to achieve the desired cellular composition for healthy or diseased indications may be calculated. The calculation may account for the specific cellular distribution patterns characteristic of the target phenotype, translating these patterns into precise mixing ratios for the available cell lines. As illustrated in the example portion of FIG.Atty. Docket No.: CEES-027 / 01WO 337273-21734, this may involve defining specific target percentages for each cellular component, such as CD3+ cells at 20% and CD 19+ cells at 60% for a particular target indication (labeled as "Target Indication: A" in the figure). These target percentages may be derived from analysis of actual donor or patient samples, published literature on cellular distributions in specific conditions, or recommendations from the development tool based on computational modeling of the target phenotype.

[0073] Following composition determination, the method 400 may comprise assembling the cell mixture and conducting quality control testing 404. In particular, the selected cell lines may be thawed and, if necessary, expanded in their respective optimal culture conditions to generate target quantities for mixture creation. The expanded cells may then be combined according to the calculated ratios to create a mixture representing the target phenotype.

[0074] Once combined, the mixture may undergo comprehensive quality control testing to verify achievement of the target composition within acceptable tolerance ranges. As shown in the example in FIG. 4, the quality control release procedures may include verification that the measured percentages of each cell type align with the target values within defined tolerance limits. For instance, the example indicates measured values of CD3+ at 19.6% ± 2.5% and CD19+ at 59.2% ± 3.0%, demonstrating that the mixture falls within acceptable ranges of the target composition (e.g., 20% and 60%, respectively).

[0075] In some variations, if quality control testing reveals that the actual ratio falls outside the acceptable tolerance range, the mixture may be adjusted by modifying the amount of one or more component cell lines. For instance, if the ratio deviates from the target by more than about 5% to about 20% (e.g., more than about 10%), additional cells from the underrepresented cell line(s) may be added to rebalance the mixture. These adjustments may be made using cells from the individual master cell banks, preferably using cells with generation numbers close to those in the original mixture (e.g., within about two to five generations) to maintain consistency in cellular properties.

[0076] Next, the method 400 may comprise creating a working cell bank 406. During this banking phase, the verified cell mixture may be processed for cryopreservation to enable long-term storage and consistent access for validation studies. The cry opreservation process may be optimized specifically for the mixed cell population, accounting for any unique requirements arising from the combination of different cell types. As shown in the example portion of FIG. 4, the working cell bank may be organized with tracking information similar to the master cell bank, including passage number, number of vials, and storage location details (marked as "TBD" in the figure, indicating that these would be determined duringAtty. Docket No.: CEES-027 / 01WO 337273-2173actual implementation). This organizational structure may ensure traceability and accessibility of the standardized mixtures for future validation applications.

[0077] In creating the working cell bank, consideration may be given to the potential for differential expansion rates between the component cell lines. Each working cell bank aliquot may contain sufficient cells to support at least one complete validation study without requiring post-thaw expansion, thereby avoiding potential shifts in the established ratio that might occur during extended culture of the mixture.

[0078] Finally, the method 400 may comprise post-bank validation and management 408. During this step, representative samples from the working cell bank may be thawed and subjected to comprehensive testing to verify maintenance of the target composition and functional characteristics after cryopreservation. As illustrated in the example in FIG. 4, the post-thaw quality control release may include assessment of viability and verification of marker expression (CD3+ and CD 19+) to confirm that the mixture maintains its intended composition and cellular properties.

[0079] This post-banking validation may ensure that the cryopreserved mixtures will perform consistently when used for system validation. The validation protocols may include provisions for monitoring the stability of the banked mixtures over time and may establish criteria for replacement or supplementation of the working cell bank as needed to maintain reliable validation capabilities.

[0080] The standardized cell mixtures generated through the methods illustrated in FIG. 3 and FIG. 4 may be implemented in various ways to address specific validation requirements for cell processing systems.

[0081] In some variations, the methods may involve combining T-cells and B-cells in specific ratios to form cell mixtures that mimic desired phenotypes. To model healthy peripheral blood, T-cells may generally outnumber B-cells, often in ratios ranging from approximately 2: 1 to 7: 1. For example, Jurkat cells and NALM-6 or Raji cells may be combined in a 2: 1 ratio to create a simplified model of healthy peripheral blood mononuclear cells.

[0082] For conditions where B-cells may be more dominant, such as certain types of leukemias, the ratio may be adjusted to approximately 1:1 in early or mild disease states, with B-cell contribution potentially increasing to ratios as high as 1 :20 or greater in advanced disease states. The specific ratio for a given application may depend on the particular disease being modeled and may be determined based on clinical data, literature reports, or recommendations from the development tool.Atty. Docket No.: CEES-027 / 01WO 337273-2173

[0083] In some variations, the methods may involve generating more complex mixtures by incorporating additional cell types beyond T-cells and B-cells. For example, NK cell lines such as NK-92 or NKL may be included to better represent the innate immune component of blood samples. These mixtures may more accurately reflect the cellular diversity found in actual donor or patient samples, which may enhance the predictive value of validation runs with these mixtures.

[0084] In some variations, the methods herein may involve co-culturing the selected cell lines rather than simply mixing them after separate expansion. This co-culture approach may allow for cellular interactions that more closely mimic the in vivo environment, potentially leading to phenotypic adaptations that better represent actual donor or patient samples. The specific co-culture conditions may be optimized based on the particular cell lines involved and the target phenotype to be represented.

[0085] Throughout this application, the term “about” is used to indicate that a value includes the inherent variation of error for the device or the method being employed to determine the value, or the variation that exists among the samples being measured. Unless otherwise stated or otherwise evident from the context, the term “about” means within 10% above or below the reported numerical value (except where such number would exceed 100% of a possible value or go below 0%). When used in conjunction with a range or series of values, the term “about” applies to the endpoints of the range or each of the values enumerated in the series, unless otherwise indicated. As used in this application, the terms “about” and “approximately” are used as equivalents.

[0086] While embodiments of the present invention have been shown and described herein, those skilled in the art will understand that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.III. ExamplesExample 1: Creation of a T-cell and B-cell Mixture to Mimic Healthy Donor PhenotypeAtty. Docket No.: CEES-027 / 01WO 337273-2173

[0087] Jurkat cells (T-cell line) and NALM-6 cells (B-cell line) were selected based on their established characteristics and suitability for the intended application. Both cell lines were expanded in their optimal culture conditions until sufficient numbers were obtained. The expanded cells were then combined in a ratio of 2:1 (Jurkat) to mimic a healthy donor phenotype.

[0088] The mixture was subjected to quality control testing to ensure that the actual ratio was within ±10% of the target ratio. The mixture was then cryopreserved and stored in a working cell bank. Representative vials from the working cell bank were tested for post-thaw viability and maintenance of the desired ratio.

[0089] The cell mixture was used as the starting material for validating an automated cell processing system. The performance of the system with the cell mixture was comparable to its performance with actual healthy donor samples, demonstrating the utility of the cell mixture for validation purposes.Example 2: Creation of a T-cell and B-cell Mixture to Mimic a Diseased Patient Phenotype

[0090] Jurkat cells and Raji cells (B-cell line) were selected and expanded as described in Example 1. The expanded cells were combined in a ratio of 1 :4 (Jurkat) to mimic a specific diseased patient phenotype where B cells are more dominant.

[0091] The mixture was subjected to quality control testing, cryopreserved, and stored in a working cell bank as described in Example 1. The cell mixture was used to validate the performance of an automated cell processing system for a specific therapeutic application targeting the disease represented by the mixture.Example 3: Incorporation of NK Cells to Create a More Complex Mixture

[0092] Jurkat cells, NALM-6 cells, and NK-92 cells (NK cell line) were selected and expanded as described in Example 1. The expanded cells were combined in a specific ratio to create a more complex mixture that better represented a particular disease state.

[0093] The mixture was processed and used for validation as described in Examples 1 and 2, demonstrating the versatility of the approach for creating customized cell mixtures that mimic various patient phenotypes.

Claims

Atty. Docket No.: CEES-027 / 01WO 337273-2173CLAIMSWhat is claimed is:

1. A method for validating use of a cell processing system, comprising:selecting first and second cell lines;combining the first and second cell lines to form a mixture at a ratio that corresponds to a target phenotype;storing the mixture in a cell bank; andusing the mixture to validate a cell therapy manufacturing process or experiment performed with the cell processing system.

2. The method of claim 1, wherein the first cell line comprises T-cells.

3. The method of claim 2, wherein the T-cells comprise Jurkat, Molt-3, Molt-4, or HuT78 cell lines.

4. The method of claim 1, wherein the second cell line comprises B-cells.

5. The method of claim 4, wherein the B-cells comprise NALM-6, Raji, Daudi, or Reh cell lines.

6. The method of claim 1, wherein the first cell line comprises T-cells, the second cell line comprises B-cells.

7. The method of claim 6, wherein the ratio of the first cell line to the second cell line is at least about 2:1.

8. The method of claim 6, wherein the ratio of the first cell line to the second cell line is about 1 :20 to about 1:1.

9. The method of claim 8, wherein the ratio of the first cell line to the second cell line is about 1:4.Atty. Docket No.: CEES-027 / 01WO 337273-217310. The method of claim 8, wherein the ratio of the first cell line to the second cell line is about 1:3.

11. The method of claim 6, wherein the T-cells comprise Jurkat cells and the B-cells comprise NALM-6 or Raji cells.

12. The method of claim 1 further comprising comparing the ratio to a target ratio.

13. The method of claim 12, wherein the mixture is used for validation when the ratio is within a tolerance range of the target ratio.

14. The method of claim 12, wherein, when the ratio falls outside of the tolerance range of the target ratio, the method further comprises adjusting the mixture by modifying an amount of at least one of the first and second cell lines.

15. The method of claim 1 further comprising selecting a third cell line and creating a second mixture of the first, second, and third cell lines in a second ratio.

16. The method of claim 15, wherein the third cell line comprises NK cells.

17. The method of claim 16, wherein the NK cells comprise NK-92 or NKL cells.

18. The method of claim 1 further comprising expanding cells of the first and second cells lines and creating the mixture using the expanded cells.

19. The method of claim 18 further comprising individually storing the expanded cells of the first and second cell lines in the cell bank.

20. The method of claim 1, wherein combining the first and second cell lines comprises co-culturing the first and second cell lines.

21. The method of claim 1 further comprising calculating a number of cells needed from the first and second cell lines to correspond to the target phenotype.Atty. Docket No.: CEES-027 / 01WO 337273-217322. The method of claim 1, wherein the target phenotype comprises a healthy donor phenotype.

23. The method of claim 1, wherein the target phenotype comprises a diseased patient phenotype.

24. The method of claim 1, wherein the first and second cell lines are selected based on one or more of cell identity, morphology, genetics, mycoplasma, and pathogens.

25. The method of claim 1, wherein storing the mixture comprises cry opreserving the mixture.

26. The method of claim 1, wherein the cell bank comprises a plurality fluid devices, each containing at the first or second cell lines.

27. The method of claim 1, wherein each fluid device comprises a vial or a bag.

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