Cell therapy manufacturing system

Abstract

The present disclosure relates to cell processing technology. As an example, a cell processing system can include a plurality of sample processing devices configured to process patient samples and a plurality of readers associated with the plurality of sample processing devices, respectively, wherein each reader is configured to read information from a tracking device associated with a respective patient sample. The system can also include a controller that uses information from the readers to provide an estimated completion time for a patient sample based on availability of the sample processing devices.

Description

Background Technology

[0001] The topics disclosed in this article relate to cell therapy techniques for optimally processing cells and delivering them to patients undergoing treatment.

[0002] In cellular immunotherapy, a patient's own blood, fluid, tissue, or cell samples are typically collected in a hospital / clinical setting and transferred to a central location for the manufacture of cell therapies derived from and / or based on the collected samples. The cell therapy product is then delivered back to the clinical setting for infusion into the same patient for autologous therapy or into different patients for non-autologous therapy. The production of cell therapy products can take several days, utilizing multiple dynamic resources at their specific biological response rates to achieve optimal assignment of one or more samples, and specific steps can have variable or unpredictable output times depending on the quality of the initial samples. Accordingly, because the processing time for each sample is highly variable for achieving the specified therapy quality (e.g., cell state and count), dynamic control of patient and care delivery resources is crucial for the follow-up administration of the manufactured cell therapy product to obtain the specified cell quantity and quality. Summary of the Invention

[0003] In one embodiment, a cell therapy manufacturing system is provided. The system includes a sample container configured to hold cell therapy samples; and a reader coexisting with a sample processing device or manufacturing location and configured to receive an identification signal from a tracking device coupled to the sample container. The system also includes a controller operatively coupled to the reader and configured to: access a sample processing timeline of a processing protocol associated with the identification signal upon reception of the identification signal; determine, at least in part, whether a deviation from the processing protocol has occurred, the deviation altering the sample processing timeline, based on the time of reception of the identification signal; provide one or more updated estimated completion times for the processing protocol; and transmit the updated estimated completion times of the sample processing timeline.

[0004] In another embodiment, a system is provided. The system includes a plurality of sample processing devices configured to process patient samples. The system also includes a plurality of readers associated with the plurality of sample processing devices, each reader configured to read information from a tracking device associated with a corresponding patient sample. The system further includes a controller comprising a processor configured to: receive a request to process a new patient sample according to a processing protocol; determine the availability of the plurality of sample processing devices based on signals from the plurality of readers; and provide an estimated completion time for the processing protocol based at least in part on the availability. For example, in one embodiment, the system may consider the clinical priority of samples when assigning resources. In another embodiment, the system may consider the availability or status of various resources (e.g., whether maintenance or downtime is scheduled). The system may also provide a hierarchically ordered sequence of activities to optimally achieve production capacity and meet turnaround time requirements while maintaining cell quality.

[0005] In another embodiment, a cell processing method is provided. The method includes the steps of: receiving a patient sample from a collection facility at a processing facility; tracking the patient sample in the processing facility using one or more tracking devices coupled to one or more sample processing containers; processing the patient sample using a plurality of sample processing devices to generate a processed patient sample; capturing identification information from the plurality of sample processing containers using a plurality of readers associated with respective sample processing devices; receiving data related to the patient sample from one or more sample processing devices; estimating a completion time for the patient sample based on the data and identification information; and providing the completion time to a remote facility.

[0006] In another embodiment, a cell processing tracking device is provided. The device includes: a sample processing container configured to contain a large number of patient samples; a sealable reservoir incorporated into or coupled to the sample processing container; and a tracking device encapsulated by a fluid-resistant membrane and deployed within the sealable reservoir, wherein the tracking device encapsulated by the fluid-resistant membrane is configured to be removed from the sealable reservoir by an operator for sterilization and reuse, wherein the tracking device stores identification information of the patient samples, the identification information being erased or rewritten when the tracking device is reused.

[0007] In another embodiment, a system is provided. The system includes a controller comprising a processor configured to: receive patient-related information having a clinical diagnosis; generate a request to process a sample of the patient according to a processing protocol; receive status information from a cell processing facility, the status information including available time for sample processing according to the processing protocol; receive information relating to the availability of one or more resources; and determine a sample acquisition time range that permits the transfer of the sample to the sample processing facility within a predetermined window corresponding to one of the available times of the availability of the one or more resources.

[0008] In another embodiment, a system is provided. The system includes a controller, the controller including a processor configured to: receive patient-related information having a clinical diagnosis; generate a request to process a sample of the patient according to a processing protocol; receive status information from a cell processing facility, the status information including a completion time for sample processing according to the processing protocol; receive information related to the availability of one or more resources; and determine a therapy implementation timeframe, the therapy implementation timeframe allowing the sample to be transferred to the therapy implementation facility within a predetermined window corresponding to an estimated completion time of the availability of the one or more resources.

[0009] In another embodiment, a system is provided. The system includes a plurality of sample processing devices configured to process patient samples; a plurality of readers associated with the plurality of sample processing devices, each reader configured to read information from a tracking device associated with a corresponding patient sample; and a controller including a processor configured to: receive a request to process a new patient sample according to a processing protocol; estimate the existence of potential bottlenecks in the manufacturing workflow based on the availability of one or more of the plurality of sample processing devices, the availability being based on signals from the plurality of readers; and update the number of sample processing devices at each step to avoid the bottlenecks.

[0010] In another embodiment, a system is provided. The system includes: a plurality of sample processing devices configured to process patient samples; a plurality of readers associated with the plurality of sample processing devices, wherein each reader is configured to read information from a tracking device associated with a corresponding patient sample; and a controller configured to: receive a request to process a new patient sample according to a processing protocol, receive cell count data from the processing devices while the patient sample is being processed, and estimate a completion time based on the cell count data and the availability of the next sample processing device in the sample processing workflow. Attached Figure Description

[0011] These and other features, aspects, and advantages of the invention will be better understood when the following detailed description is read with reference to the accompanying drawings, in which similar characters denote similar parts throughout the drawings, which include:

[0012] Figure 1 This is a schematic representation of cell therapy treatment according to embodiments of this disclosure;

[0013] Figure 2 This is a schematic representation of the interaction between the hospital's dispatch system and the cell processing facility's control system;

[0014] Figure 3 This is a schematic representation of the process of tracking intravenous-to-intravenous therapy;

[0015] Figure 4 This is a flowchart of a patient sample scheduling workflow according to an embodiment of the present disclosure;

[0016] Figure 5 This is an example of a vein-to-vein cell therapy process that can be modeled for use in producing asset utilization;

[0017] Figure 6 This is a schematic representation of a cell therapy manufacturing process according to embodiments of the present disclosure;

[0018] Figure 7 This is a flowchart of a dynamic scheduling system for cell therapy processes according to embodiments of the present disclosure;

[0019] Figure 8 This is a flowchart of a cell therapy process tracking method according to an embodiment of the present disclosure;

[0020] Figure 9 This is a schematic representation of an advanced cell therapy manufacturing process according to embodiments of the present disclosure;

[0021] Figure 10 This is a modeling analysis of patient dose levels of resources according to embodiments of this disclosure;

[0022] Figure 11 This is a block diagram of a cell therapy process tracking and control system according to embodiments of the present disclosure;

[0023] Figure 12 This is a schematic representation of an instrument control system according to an embodiment of the present disclosure;

[0024] Figure 13 This is an illustration of a tracking device assembly according to an embodiment of the present disclosure;

[0025] Figure 14 Examples of user interfaces for tracking patient samples during cell therapy procedures, according to embodiments of this disclosure; and

[0026] Figure 15 This is an example of a user interface for real-time tracking of resource availability during a cell therapy process, according to embodiments of this disclosure. Detailed Implementation

[0027] The disclosed embodiments can be used to facilitate the production of cell therapies to achieve a specified cell count or quality while minimizing turnaround time in a “vein-to-vein” workflow, including patient sample collection, cell therapy manufacturing, and delivery back to the patient. Individual patient samples can experience variability due to the unique biological characteristics of each individual patient sample and deviations in the biological processes during the manufacturing phase. For example, the initial patient starting material (often apheresis blood products) will vary in the number of target cells (e.g., T cells) used in autologous cell therapies. Additionally, despite the use of standardized protocols, growth rates and variations can occur from one donor sample to another during the manufacturing process. Standard operating procedures (SOPs) are developed during the manufacturing process to minimize deviations. However, these SOPs often do not address all the variability that can occur. For example, if cells grow more slowly than expected (as measured by cell counting assays), the SOP may need to be modified in real time to extend the duration of the cell expansion phase until the target cell count is achieved. This variability, if not addressed, can lead to lower quality cell products or, in the worst case, loss of the patient sample. For this reason, real-time detection of process variability and provision of operators with specific, identifiable, and tiered opportunities to modify protocols as needed and ensure successful manufacturing operations are achieved through automated control or directional I / O.

[0028] Biovariability can also dynamically alter the demand for labor and equipment resources within a facility during the manufacturing process. Some process steps require certain resources over short periods, while others may require resources over several consecutive days (e.g., incubator space, bioreactors). The lack of the right resources when needed can impact cell quality and prolong overall turnaround time. Furthermore, this variability can affect a manufacturer's ability to receive additional patient samples for processing and / or accurately schedule the delivery of cell products to a clinical setting for therapeutic administration, or differentiate and accelerate therapies through processes used for prioritization.

[0029] This document provides a technique for minimizing overall manufacturing time while improving the quality of autologous cell therapy products, which in turn can increase the efficiency of autologous cell therapy manufacturing processes for one or more patients with homogeneous or stratified clinical priorities. The technique allocates production resources as a function of patient attributes (e.g., relative urgency), the expansion rate of one or more patient cell samples at a given process step, and the sequence of sample acquisition, processing, and delivery. In some embodiments, samples are scheduled for production as a function of available cell expansion production capacity and clinical outcomes required in the care plan. In another embodiment, samples are scheduled for production as a function of a bioprocess configuration that achieves a specified sample demand at a specified service level (which may optionally be provided as a performance-based service, where cell counts and / or turnaround times and / or costs are commercially guaranteed). In yet another embodiment, the technique allows dynamic process control to achieve cell rate, count, and quality targets. In still another embodiment, the technique ensures the tracking and safety of cell samples from vein to vein when samples are continuous or optionally partitioned. Sample return to patients is configured based on the rate and quality of cell production and the availability of clinical delivery resources. The disclosed embodiments permit the allocation of production resources as a function of patient attributes (such as relative urgency or another measure of designable importance), the expansion rate of one or more patient cell samples at a given process step, and the logistics sequence of sample acquisition, processing, and delivery.

[0030] While some embodiments of this disclosure pertain to autologous cell therapies (which involve the collection, manipulation, and reinsertion of a patient's own cells), applications of the disclosed techniques may include other patient-specific cell therapies (where a patient donor provides cells for a single but distinct patient recipient), or xenotransplantation of allogeneic cells, modified human cells, or non-human cells. Cell-based therapies contemplated for use in conjunction with the disclosed techniques may include those for organ or tissue regeneration, cancer treatment, blood disorders, immunotherapy, treatment of heart disease, or any other cell-based therapy.

[0031] Figure 1This is a schematic representation of autologous vein-to-vein cell therapy technology 10. A patient 12 requiring cell therapy is scheduled for sample collection 14 at a collection facility 16 (typically a hospital or outpatient treatment facility). The collected sample 18 (which may be blood, tissue, or another cell sample) is then provided to a cell therapy processing facility 20, where the collected sample 18 is appropriately processed via a cell processing protocol 22 to generate a cell therapy product 24. However, in some embodiments, the collected sample 18 is processed at a point-of-care facility coexisting with the collection facility 16. The manufactured cell therapy product 24 is then used in cell therapy treatment 26 for the patient 12. For example, the cell therapy product may be injected into the patient's vein. Before providing the cell therapy product 24 for use in cell therapy treatment 26, compliance with regulatory (e.g., FDA) quality and safety requirements may be evaluated for. Such requirements may include, for example, sterility, specific cell counts, or specific counts of viable cells (e.g., a minimum 70% cell viability). The patient may have other clinical activities 13 (e.g., chemotherapy), which may be co-managed with the cell therapy.

[0032] Figure 2 This is a block diagram of a system 50 for tracking the production of cell therapy from vein to vein. While system 50 is shown as having a hospital dispatch controller 54 associated with a hospital or another medical facility and configured to at least partially access remote cell processing facility controllers 60, certain functions of these individual components can be combined. That is, system 50 (including controllers 60 and 54) can be combined into a single control system. Alternatively, certain functions of controllers 54 and 60 can be distributed in a cloud computing environment. Accordingly, certain functions of controllers 54 and 60 of system 50 can be appropriately combined or exchanged. Furthermore, it should be understood that the hospital dispatch controller 54 can access and communicate with multiple cell processing facility control systems 60. Additionally, each cell processing facility controller 60 can receive samples from multiple hospitals (each with its own dedicated dispatch controller 54). System 50 can use various inputs and rule-based logic to schedule patients for patient sample acquisition (e.g., blood draw), schedule the transfer of patient samples to cell processing facilities, specify production plans and optionally provide sensitivity and / or turnaround time requests for cell therapy products based on patient samples, determine cell processing workflows, dynamically estimate or model the completion time of cell therapy product production, and specifically schedule care providers and physical assets to administer cell therapy products to patients.

[0033] Hospital resource information 62 (e.g., room availability), sample tracking information 64, care provider information 65, and patient information 66 in system 50 can be used as inputs to design process sequences, sizing, controlling, and transferring rules for individual patient samples. Cell processing facility controller 60 can also use operator information 70, sample tracking information 64, and resource information 72 to track the production of individual patient samples to determine vein-to-vein process capacity and movement rules, ensuring that key process indicators, such as service levels for a given (or test hypothesis) patient need or turnaround time, are met. In one embodiment, system 50 includes simulation logic 75, such as a simulation-based transfer function (e.g., discrete event simulation), which serves as control logic for system 50 to orchestrate cell expansion demand arrival patterns, allocate production machine assignments, predict machine durations based on the dynamic expansion rate of cells in the tracked process, modify production flow patterns as a function of optimal machine assignment and machine availability or reliability or relative effectiveness for a given cell sample, predict patient therapy delivery completion times, and schedule delivery to the clinic, such that expanded cells are procedurally loaded, transferred, and administered to patients with minimal loss of cell count and quality due to delays. System 50 facilitates improved control of cell quality for one or more varied patient samples. Simulation logic 75 may be part of one or both of hospital scheduling controller 54 or cell processing facility controller 60.

[0034] In one embodiment, a patient diagnosed with cancer and considered a candidate for cell therapy according to the disclosed technology is introduced into the system by a care provider. As described, care provider information 65 can be used as input to system 50. Measured clinical urgency is attributed to patient 76 attributes along with other attributes (e.g., name, biological and medical status, insurance, and expected logistics dates, which are part of patient information 66). The system acquires or accesses the physical status of resource information 62 (e.g., production assets, personnel, consumables, and equipment or space) to physically acquire samples and return processed batches. System 50 can create blank new patient objects 67 and write preferences and indicators into a structured classification of patient state objects. Patient objects are a collection of state engines and descriptors, as well as a basic autonomous agent in the simulation, which is given a care plan by the system simulator and optimizer. This autonomous agent logic is continuously updated by the master so that if the control system fails or is compromised, the patient object will have the latest plan and assignment information, such as what the optimized sample processing sequence is. These sequences include a deterministic "best assignment" (in terms of resources and time) as well as feasible and tiered alternatives. In addition to being written to the patient object, the image is also concurrently written to the system data archive by the system simulation logic 75. The simulation logic 75 is calculated and updated to anticipate patient arrival and treatment at a given time for each process step. The purpose of this feature is to ensure synchronization between presence control, the patient object, and its autonomous pairings (which are placed alongside samples, historical data, and local machine control). If any communication failure occurs, and if a cell sequencing step (which differs from the processing protocol) is attempted, the local machine control logic and the system itself will logically respond and issue a process alarm.

[0035] Simulation logic 75 designs tasks, resources, and operational strategies related to the core time flow of the system by creating entities and resources (which will be logically controlled by transition (workflow) logic), which is also the operational decision support strategy (which enables system control when the simulator transitions from its design to operational mode).

[0036] For example, a single patient 76 with patient information 66 is an entity in the analog logic. The system models and tracks a specific and unique patient 76 or several patients 76 (each of which may be distinct from the others or may be a subset of all patients but have attributes that make them a group of patients of particular interest). Examples of patients of particular interest are demographic descriptors (e.g., women aged 60-65 with a certain diagnosis, patients of "Doctor X", patients "Y" selected to use a cell production process or facility, or any other meaningful descriptor (that makes a subset or all patients members of a stratified, labeled, or control group)). Patient 76 may be referred to as a representative of a group, such as patient 76 being a patient type that represents other patients of similar types. Patient demand for the system is modeled as arrival patterns, where a specific patient 76 with its unique attributes is presented as a sequence of arrival times at points in time or as a sequence of arrival times for patients or patient types. Patients are characterized by demographic and clinical attributes (which may be used as inputs to analog logic 75).

[0037] In operation, the assumed duration estimates and resource availability can be replaced in real time with actual state information provided via various tracking interfaces, user inputs, sensors, etc., during the actual physical movement and processing of the samples, as well as in historical aggregations. For example, a tracking device physically coupled to the patient sample can provide wireless signals to track the patient sample in a cell processing facility such as that described herein. This information can be provided as input to system 50.

[0038] The real-time status information of the workflow is compared with the original plan and the dynamic prediction of the status in simulation logic 75. The prediction error with the current actual status and the possible error with the predicted status are calculated. The optimization algorithm adjusts the allocation of resources to optimally meet the plans and priorities of one or more samples, and if not, to meet the plans of the current patient and the concurrent needs of all other patients.

[0039] Controlling the timing of the cell expansion process in conjunction with other clinical activities (such as chemotherapy 13) can achieve clinical outcomes of high cell counts and minimal vein-to-vein delay resulting from cell therapy products. For example, a patient's in vitro blood cell count is typically managed via the administration of chemotherapy, and the reinsertion of expanded cells is ideally timed to a given level of blood cell count and condition. This technique synchronizes these two activities (chemotherapy 13 and manufacturing or processing 22) to minimize degradation or mistimed occurrences of the cell expansion process relative to the patient's physician-managed biological state.

[0040] In one embodiment, patient samples are obtained at a hospital, and a hospital dispatch controller 54 forecasts patients to enter a cell processing facility controller 60 for dispatching into the manufacturing process or processing 22. Figure 1The process involves future patient samples, ensuring capacity is maintained before those patient samples arrive at the processing facility. Processing is dynamically controlled as a function of the cells of a specific patient in response to the expansion process, in order to efficiently produce the necessary cell counts and quality relative to all other patient cells being processed.

[0041] Similarly, once processing begins, based on the cell processing system's characterization of cell expansion rates and machine assignment, predictions are made regarding the reinfusion of cell therapy into the patient. In the example embodiment, the hospital can then consume this information in the scheduling controller 54 to facilitate the scheduling of other clinical activities 13 and resource allocation for the patient. The system 50 can also substitute for the hospital scheduler in providing patient and resource scheduling when the hospital scheduler 54 is unable to perform probabilistic predictions or dynamic workflows. An example embodiment of this environment would involve the system publishing and consuming information via a web portal and dynamic data exchange (where patients and care providers can log in or receive alerts).

[0042] In some embodiments, system 50 forecasts the range of possible future realizations, and as these potential future realizations become actual measurable actions over time, the forecast intervals narrow, and asset allocation, machine and operational logistics control approach a deterministic state. The disclosed system 50 concurrently manages one or more patients (whose clinical care is at different stages). For example, patient 76 may be introduced into system 50, for example, without a plan, and scheduling may continue only if indicators of clinical urgency are present, which is then optimized, for example, by subtracting two weeks from the current time. The expected sample arrival of patients may be estimated, for example, by simulation logic 75, by subtracting 7 to 13 days from the current time, characterized by a Poisson distribution with an 11-day pattern. As time and events materialize in the physical world, scenario convergence and potential resource allocation choices are reduced.

[0043] This technology can evaluate dynamically optimized key performance indicators (KPIs) or protocol setpoints: safety (e.g., the right sample on the right machine); productivity (e.g., maximizing the number of patients served or cell quality for a specified interval); minimizing in-process inventory (cell batch); minimizing operating costs associated with energy, consumables, and labor; minimizing fulfillment errors (planned deviations from target shipment dates); and maximizing patient clinical metrics, patient stratification, or the total number of patients. Simulation logic can calculate or model inventory, operating costs, fulfillment, risk and reward, and clinical metrics for a given time period or for a given sample. Processing protocols can be designed with these setpoints in mind or as constraints on the logic, and can be dynamically modified to adhere to these setpoints.

[0044] In one embodiment, the system may compute one or more metrics to obtain key performance indicators (KPIs) with minimum individual or combined variance or combined weighted variance, making the solution robust to deviations from external forces (e.g., new patient arrivals, cell production rates, and machine reliability). For example, a scenario is generated for each sample-to-machine assignment and patient schedule duration (which is an optional option in the simulation logic). Each scenario for resource assignment is replicated, for example, 30 times, to capture the impact of deviations from, for example, probabilistic assumptions. For each scenario, the deviation from the plan, the target, or simply the natural deviation is calculated. The total number of metrics calculated and their deviations for each scenario are compared pairwise. For the metric seeking to be maximized, the point at which the highest KPI result with the lowest deviation is optimal and robust. For the metric whose value is expected to be minimized, optimality is the lowest value with the minimum deviation. Simulation and optimization explore patient logistics scheduling choices, machine assignment choices, and responses to changes in cell growth rates, production machine maintenance, shift scheduling, and other dynamic choices in the system.

[0045] In one embodiment, this technology facilitates machine sequencing of one or more patient samples by a sample processing device. The machines may be connected in parallel or in series. System 50 assigns patient samples to one or more sample processing devices by testing candidate assignments for one or more patients at one or more time intervals to find robust and optimal solutions. Optimization is replicated on a continuous basis, automatically optimizing the dynamic response as the device status, sample cell growth, or patient status changes. Accordingly, system 50 can use experience, assumptions, and / or real-time information in a model-building process to estimate the time to completion of a cell therapy product for any given patient sample.

[0046] Figure 3 It is compatible with the disclosed technology (e.g., System 50 (see...)). Figure 2 The illustrated workflow 100 is a schematic representation of an example workflow used in conjunction with other workflows. While the illustrated workflow 100 includes specific steps performed in conjunction with particular system components (which are used to process and / or schedule patient samples), it should be understood that the illustrated workflow 100 is exemplary and the disclosed techniques can be used in conjunction with other workflows. The workflow 100 can be implemented using a sequential framework to describe the state control of the cellular and physical systems. In some embodiments, the workflow 100 begins with a diagnosis 101 that the patient has cancer and concludes when the cell therapy delivery has produced a final clinical determination of process efficacy.

[0047] The patient is diagnosed with cancer using clinical diagnosis 101, which includes one or more diagnostic tests. The appropriateness measures for cell therapy are calculated, along with the general clinical workflow and timing for cell therapy administration, such as scheduling cell counts, timelines, and resources involved in cell therapy production 102. Scheduling can be performed by one or more controllers of system 50 (e.g., controller 54, controller 60) (which manage hospital and / or processing facility plans and resources). As described, the functionality of these controllers is part of system 50. Scheduling of cell therapy production 102 may also involve establishing or selecting processing protocols or production plans for patient samples, designed with the clinical requirements of the considered patients in mind.

[0048] A patient's scheduled blood sample 103 is drawn and assigned tracking information 104, and sent to a facility qualified to perform the cell expansion production process (where the sample is processed in a facility to manufacture cell therapy). For example, cells are expanded and processed using multiple resources in production process 105, designed to optimally achieve the specified service level and controlled to achieve cell counts and quality as specified by the production plan. While expanding cells and observing the bioproduction rate, workflow 100 tracks process 106, and, taking care of the patient's care plan into account, production continues until clinical re-infusion is scheduled by the system at scheduling step 108. The sample is sent from the laboratory in the logistics flow to be received 107 for use in administering patient therapy 109. The efficacy of the clinical cell therapy treatment is assessed, for example, via follow-up testing 110, and is adjusted, for example, as a completed process. In one embodiment, workflow 100 may be repeated based on assessments.

[0049] In some embodiments, system 50 (see Figure 2This system can assess whether a given patient has a type of cancer that is logistically treatable through the capacity of this cell therapy system. For feasibility determination, resources can be assigned, which may include space, assets, data, and professionals to conduct the assessment. For example, one such resource is a physician who must be available simultaneously with other necessary resources, such as, for example, an examination room, equipment, a patient, and some information. These dependencies are example logic code in a diagnostic and therapy scheduling entity—where each necessary resource exists for a certain time interval before the start of clinical activity, remains available for the expected duration of the clinical activity, and is then released for other activities. System 50 uses methods disclosed by Johnson in U.S. Patent Nos. 8027849, 8311850, 20090119126, and 20120010901 (which are incorporated herein by reference in their entirety for all purposes) to calculate resource availability over a time span, with the simulation logic employing an application protocol interface to invoke it as an object or as a service, which may be a user preference. Similarly, blood sample acquisition step 103 activates a task set to schedule clinical services and patients to extract blood for cell expansion purposes. In one embodiment, the system also transmits a clinical sample acquisition time target to align sample acquisition with a specific production capacity, allowing the system to control the process to achieve cell quality. Delays from sample acquisition to cell expansion can degrade cell counts, especially when cells are processed without freezing.

[0050] Figure 4 This is a flowchart 120 of a vein-to-vein patient sample scheduling workflow, in which the system (e.g., system 50) can also assess the capacity of each production component and the temporal relationship between them and the production capacity of the amplified cell samples. This ensures that capacity and temporal availability are available at a specified point in time for assigning a given sample to production. Sample extraction and transfer are timed so that the sample arrives at the production line in the future, timed to the time period during which the production equipment is ready to receive it.

[0051] Additionally, the expanded cell production results are used to re-administer cell therapy to patients. Based on the descriptive properties of the cell organism and the actual production status of the laboratory relative to the current sample and all other samples processed and scheduled for processing, simulations (e.g., generated via simulation logic 75, see...) are performed. Figure 2Initially, the predicted probability duration is used for expansion to reach the target cell count. As the actual rate of expansion is observed, the formal assumptions used in the simulator are updated using actual cell count and quality status information. The simulated predicted sample, benefiting from the actual changes in cell expansion rates from the current sample and all other samples, is ready to be re-implemented back to the patient's estimated treatment completion time, and this prediction or model, along with an assessment of the availability of new samples, is provided to the system in step 124. Clinical implementation control of scheduling patients, clinical facilities, and resources schedules samples for production based on availability in the facility (e.g., the first available intake date) (step 126), and schedules sample acquisition (step 128) and transfer to the facility based on a production prediction or model (step 130), for example, scheduling may be based on a production date where there is a minimum time loss from production expansion completion to cell insertion. The simulation logic considers logical protocols, asset assignment rules, movement strategies, movement activities, and dependencies on necessary resources.

[0052] In simulation mode, assumptions are made about task duration, resource availability, demand conditions, movement response, and assignment logic based on prior observations and the control logic designed into the system. Key process indicators such as sample production capacity, turnaround time, asset utilization, and cell quantity and quality are estimated using simulation algorithms.

[0053] Once the patient sample has arrived at the cell processing facility, the estimated processing completion time is dynamically updated based on any changes in the state of the sample itself and / or the facility (step 132). For example, the cell expansion duration may vary. Additional variability can be introduced into cell acquisition, expansion, and delivery to produce a specified number of cells and to deliver them with minimal loss for reinsertion into the patient. The third interval is the vein-to-vein duration.

[0054] Task and resource consumption are optimally controlled using a constraint-based optimization construct. Task duration is probabilistic and interdependent with physical resource limits and the state of the electromechanical-biological system. Task duration prediction is generated through discrete event simulation (which calculates scenarios prior to actual activity). The constraint-based approach derives the critical paths for each allocation of resources and patients. Replication is performed to characterize biases arising from prediction errors, external factors (such as, for example, sample growth rate, machine reliability, and operator proficiency). If the duration of any of the three specified intervals exceeds the expected amount of time to obtain a given cell expansion count, dynamic asset and resource allocation is tested, and the production capacity time for one or more samples is calculated. The calculated production capacity time is compared to the expected time, and the planning variance is derived for each scenario of allocation, configuration, and operation selection. The scenario with the lowest mean cycle time and bias among the available options is considered optimal and robust. In one embodiment, full factorial simulation-optimization-classification and grading can be performed on one or more CPUs, while in another embodiment, stochastic optimization can be sought using an objective, and in yet another embodiment, reduction or scenario rationalization can be performed.

[0055] The analytical process for dynamic control of the system's temporal and physical states begins with the identification of feasible resources to be applied to the cell acquisition, transport, and expansion processes. The durations of probabilistic tasks in those processes are derived from simulation in one embodiment and from statistical regression in another. These durations are then consumed by a constraint-based algorithm with priority-based logic, and the critical path is calculated for each scenario and its replication. Scenario and replication outcomes are then graded and classified according to the deviation from the target set of control points relative to the outcome and risk. Risk (deviation from the target or the absolute value of the deviation) can be used with a single quality metric or normalized and aggregated across multiple quality metrics.

[0056] Treatment appointments are then scheduled according to a set of assignment and control decision setpoints (which most robustly achieve one or more quality metrics) (step 134). This plan informs the hospital operations management system and assigns cell expansion machine capacity prior to actual patient cell growth activity. Once cell expansion activity begins, cell growth and machine status are measured, and machine assignment and logistics control are updated relative to that actual growth rate. Those corrections made to resource assignments and machine operational controls used to achieve the expected status are implemented via a system simulator using forward potential paths with actual status information from a re-initialized model. The optimal new machine control point, resource or patient schedule is calculated, and the control system is analyzed and then recalculated in the next time increment to seek better control of the system state as measured and compared to the expected state.

[0057] Figure 5 This is an example of a vein-to-vein process that can be modeled for production asset utilization. The process begins at start point 172, triggering a sequence of events corresponding to cell sample acquisition (which, in an exemplary embodiment, requires the concurrent presence of patient 173, room 174, and healthcare provider 176, each of whom must have specific availability for the time period required to prepare and acquire sample 180). If any of the three necessary persons or assets is unavailable when the task is scheduled, the critical path time for sample acquisition increases, thus delaying sample acquisition. Scheduled availability and task duration are characterized probabilistically.

[0058] Upon acquiring a patient's cell sample 180, cells are transferred 182 to reach 184, where a cell expansion process 186 will occur. Cell quality can be defined, for example, by target cell concentration or the number of viable cells, and may degrade over time depending on cell sample handling. If cells are acquired but then delayed in transfer 182 or in processing upon arrival, the cells are not improved and may actually degrade. Therefore, values ​​are controlled in the processing such that upon arrival, cells are processed with minimal delay on a specific machine (which has been sequenced to become an available resource in the correct physical and chemical state for initiating cell expansion). Similarly, if transfer 182 is to be delayed, for example due to predictable logistical factors (e.g., traffic, transport resource availability, weather), the cell sample acquisition time is controlled to account for those delays, such as taking sample extraction at a later time.

[0059] At the start of cell processing 186, machine settings, protocols, and multiple sequences of the machine are dynamically controlled by the disclosed invention to achieve processing capacity, machine status, and cell expansion rate in order to minimize production time, maximize cell quality, and utilize production resources for more than one patient as needed, optimally allocating assets to concurrently achieve these goals to produce cells. An example cell expansion process may have two parallel steps 186 and 187, which, upon completion, may proceed to either or both of two other processes 3A and 3B (188, 189), as routed and dynamically assigned by controller 190, to be processed until completion. Control of process and machine assignment is provided by a system controller (e.g., controller 60, see...). Figure 2(It tracks all cell expansion activities and resources in the production environment) to orchestrate. In this example, it determines the routing and prepares two sample processing units associated with processes 3A and 3B. The system simulator allows the local routing controller 190 to locally optimize the route, for example, when processes 3A and B have the same duration and quality but one is occupied and therefore the local routing decision will assign another machine or process sequence. However, global sequencing between machines can result in excellent overall facility productivity or clinical performance. Examples would be when process steps 3A and B are orchestrated on different machines and are more effective for certain cell types or cell therapy products in different ways or have different processing capabilities or limitations for interacting with various cell types or therapy products. The system simulator and optimizer, simulating potential paths and interactions with machines, as well as priorities and expansion rates or quality, guide samples to a given path in the set of simulated scenarios that improve global outcomes (186, 187, 188, 189). Assignment control also attempts to reschedule to a certain completion time from the original plan in response to changes in the medical status of one or more patients. This thus rescheduling and routing other interdependent resources. The delivery time of the cell therapy product 192 produced can also be dynamically adjusted to control the expected changes in cell quality for one or more patients and to be fed back to process control assignment.

[0060] Cell delivery time is a function of the duration of processing of cell therapy product 192 and the delivery time of processing 193 (which varies due to control signals or due to deviations in processing rate or machine or resource failure). The expansion process may be dynamically modified, or patient cell delivery may be rescheduled to be completed at a given time for one or more patients(s). Implementation of cell therapy product 192 to patient 173 (or, in other embodiments, different patients in non-autologous examples) involves scheduling qualified providers 195 and room 194 for implementation of cell therapy product 192.

[0061] Figure 6 This is a schematic representation of a workflow 200 for patient samples 201 (e.g., blood samples provided by cancer patients) processed by a specific sample processing device. One or more patient samples 201 may arrive at the system at a certain rate (which is a function of a quantity estimate or a predefined plan). Arrival can also occur randomly based on the number of arrivals and / or the timing of arrival. If the arrival rate is faster than the rate at which these entities are processed, the total time spent in the system increases.

[0062] Sample 201 arrives according to its arrival mode 202 or as determined by controller 60 ( Figure 2The sample 201 is processed through workflow 200 in a certain order, guided by the process flow. At each process step, the sample 201 may wait in a queue before being processed, until resources are ready to process the entity. The workflow may include probabilistic routing as shown in 206 and 207.

[0063] At each process step, there may be multiple sub-steps 209, each requiring one or more different resources 204, 205 and taking different amounts of time. Resources 205 may include various devices, including, as non-limiting examples, erythrocyte sedimentation rate (ESR) brown-yellow layer isolators, shakers, biosafety rooms (BSCs), bioreactors, samples, cell counters, thawing devices, centrifuges, magnetic particle separators, and / or incubators. In example 209, the amount of time an entity spends at a process step is affected by the availability of a particular resource(s) and the amount of time spent utilizing those resources. For example, if the operator (204) is unavailable but the incubator is available in 209, sample 201 must wait until both resources are available. In one embodiment, the availability of resources 204, 205 is affected by the number of resources assigned to the overall process, their operating conditions (e.g., operating time and / or unavailability due to damage or maintenance), and the demand for those resources by other entities in the system at different process steps while the request is being made. Additionally, as provided herein, the workflow may include steps for providing information related to the sample as it is processed. For example, the steps may include cell counting.

[0064] The progress of the sample through workflow 200 can be affected by deviations in arrival rate, processing rate, resource availability, processing time, and the current state of the system. These dependencies and deviations introduce additional latency. Discrete event simulation captures these complexities. For example, as... Figure 7As shown in the flowchart, method 200, as provided herein, may include the step of accessing a sample processing timeline for a specific patient sample (step 224). The sample processing timeline may be based on a production schedule established when or before the sample arrives at the cell processing facility. The sample processing timeline, as provided herein, may be an estimate or forecast model of the sample's completion time based on an empirical or historical estimate of the specific production schedule. The sample processing timeline may be determined via system 50, for example, using simulation logic 75 residing on one or more controllers (e.g., controller 54, controller 60). Forecasts may also take into account the estimated resource availability of the scheduled sample based on the concurrent production of other samples in the facility. When a sample is determined to be different from the timeline (step 226) (which may be determined from real-time tracking of samples, processing steps, and operator resources in the production process, as well as global tracking of other samples and their allocated resources), method 220 updates its estimated completion time (step 228). Thus, the estimated completion time is dynamic to allow for more precise scheduling of patient therapies. It should be understood that some changes in the production process may be timeline-neutral and therefore do not cause any updates to the estimated completion time. For example, a sample may be redirected to a different sample processing unit than initially specified in the production plan for the sample. However, this change may not affect the timeline.

[0065] Processing protocol and / or timeline deviations may include previous or subsequent estimated completion times, changes in the sequence of one or more steps, changes in the duration of one or more steps, the addition of one or more steps, the removal of one or more steps, changes in device assignment, or changes in estimated ingestion dates. In some embodiments, deviations may be triggered by events involving other patient samples. For example, the lack of availability of a device may have downstream effects on other samples. In another embodiment, deviations may be triggered by characteristics of the sample itself, such as cell count, viability, presence of biomarkers, etc. For example, a cell count below a threshold may be associated with a deviation in the timeline allowing for longer expansion cycles. A cell count above a threshold may cause a deviation in the timeline allowing for shorter expansion cycles. In one embodiment, a cell count above a threshold allows for early withdrawal from the associated processing step. However, such withdrawal may depend on downstream device availability. Accordingly, the decision to withdraw a sample or complete a processing step early and proceed to the next step cannot be implemented if the simulation logic does not determine that a potential change in the processing protocol will result in improved sample quality and / or faster completion time.

[0066] In another embodiment, such as Figure 8As shown in the flowchart, the timeline can be updated based on real-time data characterizing the samples generated during the process. Samples are received by the cell processing facility (step 252) and placed into the process flow. In some embodiments, sample quality can be assessed (254) upon receipt to determine the extent, if any, sample degradation during transport from the sample acquisition site. Based on the cell quality assessment, the estimated process completion time can be updated. For example, the system can access data representing the average total process time for samples with similar quality attributes (e.g., cell count, cell viability, sample size) to update the estimated process completion time (if different). In one embodiment, low cell counts or viability may be associated with a longer overall process due to increased incubation or expansion time.

[0067] During the production process, patient samples may be processed by a series of sample processing devices. In one embodiment, cells may be expanded in a cell expansion device (step 256), and the cells in the resulting processed sample may be counted (step 258). The cell count data may be provided to the system as input to analog logic to estimate the process completion time and as part of a decision to move the sample to the next sequential step of the process (step 260). For example, if the cell count is below a threshold, the sample continues to expand, which may result in a longer overall process and a later completion time than expected. That is, expansion (step 256) continues, and another cell count (step 258) is performed after expansion. These steps may be repeated until the expected cell count is reached. If the cell count is above a threshold, the sample may be moved to the next step, which may result in a process of expected length or shorter and an unchanged or earlier completion time. Based on the cell count results, the system (e.g., system 50, see...) Figure 2 The estimated completion time for patient samples can be updated (step 262).

[0068] The integration of methods used to design, operate, and control vein-to-vein cell therapy production capacity systems balances the principles of the Theory of Constraints (TOC), a fundamental "physics" that drives complex production capacity systems to generate consistent revenue through the sale of their products. According to the TOC, there are system constraints or control points that limit production capacity. Typically, this control point is a process step or region with the longest cycle time. This is analogous to a chain connected by multiple links. The "weakest link" on the chain defines the overall strength of the chain. Increasing the strength of the weakest link does not necessarily increase the overall strength of the chain. Alternatively, making the weakest link stronger (i.e., making the slowest process faster) will increase the total strength of the chain (i.e., will increase the system's production capacity), which can make another link the next bottleneck (i.e., the next slowest process becomes the new bottleneck or constraint).

[0069] As an example, if we consider Figure 9In the advanced cell therapy process illustrated, incubation step 272 is the slowest step in the chain of process steps defined by 270, 272, 273, and 274. Since system productivity is limited by this step, ensuring the highest utilization of incubation resources is crucial. Any loss of incubation resource capacity will reduce system productivity or profitability.

[0070] Therefore, according to the TOC principle, processes and operations should be designed as follows: incubation resources should never be insufficient to prevent incubator 272 from lacking supply 275, thus avoiding idle / incomplete utilization of valuable resources. Avoiding blockages 276 due to insufficient space or resources in subsequent process steps 273 is also a key design and operational strategy for incubation resources. For example, if a patient sample has finished using the incubator and must be moved to a bioreactor, and if no bioreactor is available, it may continue to occupy valuable incubator space 278 when no longer needed. If another sample from 270 is available and ready for incubation, and if no other incubator is available, incubator resource capacity will be wasted, thus reducing the chance of gain. TOC is also a driving force for predicting operational efficiency of bottlenecks based on current conditions. In cell therapy manufacturing, patient samples arrive at the facility as new samples enhanced by a cell expansion system. When fresh samples arrive, preparation 270 is required and they are immediately placed in the incubator 271. If the incubator is unavailable, other measures must be taken, which may affect the quality of the patient sample, which in turn may affect the efficacy of the treatment. Therefore, in one embodiment, a “just-in-time” logistics system is facilitated by controlling patients and hospitals at the optimal time of sample acquisition based on predicted incubator space availability.

[0071] Discrete event simulation can be used for capacity planning to determine resource levels and layout implications based on system dynamics and regulatory requirements. Figure 10 This is an example of scenario analysis based on simulation.

[0072] Figure 281 shows the results from a set of simulation experiments conducted to determine critical resources 283 and quantities 287 based on different annual patient dose levels 286. Based on the analysis, resource levels were adjusted to achieve the annual quantity target without unnecessary delays caused by insufficient resource levels 288 and 284. This analysis can inform decision-makers (e.g., manufacturing teams) about layout and space requirements based on equipment and personnel levels. For example, it might be possible to achieve an annual sample unit production capacity of 1000 units / year using a single cleanroom. However, if the target is to achieve a production level of 5000 units, the same cleanroom would need to be replicated 5 times. In Figure 281, it is assumed that there are no regulatory restrictions on the maximum number of open samples in the cleanroom. In Figure 282, similar simulation experiments were conducted with additional constraints 289 and 290 to limit the number of open samples to a maximum of 6. Based on this regulatory constraint and for the given assumptions in the model, the maximum quantity that the cleanroom can control is 200, with the resource levels estimated in 285. This result could have a significant impact on the number of cleanrooms and the resource requirements of the facilities.

[0073] Certain embodiments of this disclosure facilitate the tracking of patient samples as they are processed to generate cell therapy products. For example, sample tracking information can be used as input to simulation logic (which estimates process completion time, determines production schedules based on resource availability, etc.). For instance, patient samples throughout a processing facility can be tracked to a specific processing unit. Such units can be indicated as unavailable based on the receipt of tracking information. That is, by associating specific samples with specific resources, the cell processing facility can assess overall resource availability. Furthermore, the tracking information can allow the system to track the location of samples throughout the facility.

[0074] In some embodiments, the disclosed technology uses a control system 300, which is implemented as follows: Figure 11 The block diagram shows controller 304. The controller could be a hospital scheduling controller 54 or a cell processing facility controller 60 (see [link]). Figure 2 As part of the cell processing facility, controller 304 is implemented as cell processing facility controller 60, which communicates with device 328 and other components of the cell processing facility. However, it should be understood that this is merely an example, and the hardware components of controller 304 may also exist in or be implemented as controller 54. Furthermore, in some embodiments, controller 304 may be implemented on one or more sample processing devices.

[0075] Controller 304 may include processor 306, which may include one or more processing means and memory 308 storing instructions executable by processor 306. Memory 308 may include one or more tangible, non-transitory machine-readable media. For example, such machine-readable media may include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage devices, magnetic disk storage devices or other magnetic storage devices, or any other medium capable of carrying or storing intended program code in the form of machine-executable instructions or data structures and accessible by processor 306 or by any programmed general-purpose or special-purpose computer or other machine having a processor. Controller 304 may also include communication circuitry 314 and / or input and output circuitry 312 to facilitate communication with other components of system 300. Furthermore, controller 304 may include display 310, which provides a graphical user interface for operator interaction.

[0076] The reader 320 is configured to read information via receiver or transceiver hardware 352. The reader 320 can coexist with sample processing apparatus 328 and any sample in sample processing container 330 operated by sample processing apparatus 328. Alternatively or additionally, the reader can coexist with sample processing stations or workspaces (which do not include sample processing apparatus 328). The central processing unit 372 of the reader 320 can execute instructions stored in memory 374. Furthermore, in some embodiments, the reader 320 can be implemented as an edge device. For example, when implemented as an edge device, the reader 320 can provide an entry point to a network and may include hardware circuitry such as routers, routing switches, integrated access devices (IADs), multiplexers, and various metropolitan area network (MAN) and wide area network (WAN) access devices. The reader 320 may include onboard input / output circuitry 376, communication circuitry 378, and a display 380 providing a graphical user interface for operator interaction. The reader 320 can execute routines to convert received signals (which use a specific protocol, such as the RFID protocol) into the HTTP protocol before transmitting them to the controller 304.

[0077] In the illustrated embodiment, system 300 includes one or more readers 320 that read identification information from controller 304 and then transmit the identification information to controller 304. Additionally, readers 320 may read information from a coexisting sample processing device 328 (which may be an incubation device, culture device, purification device, separation device, storage device, etc.). This information may include device identification and parameters (e.g., operating parameters) and device location within the manufacturing facility. Identification signals may be associated with unique patient samples in the controller and may reference identification information (e.g., patient or sample quantity or other information) to associate sample processing containers and encapsulated samples with specific patients. For example, identification signals may include unique identifiers associated with patient / sample information (and, for example, processing protocols associated with or assigned to patients) in the controller's database. In such embodiments, identification signals associate identifiers with appropriate information stored in the memory of controller 304.

[0078] In another embodiment, a separate reader 320 coexists with the sample processing device 328 to provide sample processing device information without having to read information from the sample processing device 328 each time the device is used. That is, because the sample processing device 328 remains the same even if the patient and / or operator changes, the sample processing device information can be stored in the memory of the device 328 and / or reader 320 for transmission to the controller 304 along with operator or patient sample information. Although in the illustrated embodiment, the controller 304 is implemented as a separate device from the reader 320 and sample processing device 328, it should be understood that certain functionalities of the controller 304 may be additionally or alternatively incorporated into the sample processing device 328 and / or reader 320. For example, the sample processing device 328 and / or reader 320 may include a processor, memory, I / O interface, display, communication circuitry, etc. The reader 320 may also read information from one or more sample processing containers 330.

[0079] Sample information (e.g., sample-related data) determined from the sample processing device 328 via appropriate monitoring or sensing circuitry can also be provided to the controller 304 via a network connection or through the reader 320. For example, when scheduling samples for a counting step, the device 328 can count the cells in the sample, and the device 328 can store cell count values ​​or related information in its onboard memory. These values ​​or information can be provided to the controller 304 along with sample identification information for use in analog logic to determine if any deviations have occurred in the processing protocol. In one embodiment, the values ​​or information can be bundled with information read by the reader 320. In another embodiment, the controller receives the values ​​or information from the reader 320 or the device 328 and associates them with the sample (which is determined to be in the same location based on the identification signal read by the reader 320). Although this example is discussed in the context of cell counting, it should be understood that other data characterizing the processed sample can be provided by the sample processing device 328.

[0080] Figure 12This is a schematic diagram of a workstation or work area in a sample processing facility. A work area represents an area used to complete steps in the cell processing workflow as provided herein. One or more readers 320 read identification information associated with an operator 326, for example via an operator tag card 327, and which then transmits the identification information to a controller 304. Additionally, readers 320 may read information from a coexisting sample processing apparatus 328 (which may be an incubation apparatus, culture apparatus, purification apparatus, separation apparatus, storage apparatus, etc.). During sample processing, the sample is transferred to an appropriate sample processing container 330 for use with the sample processing apparatus 328. Readers 320 read information from a tracking device 340 (shown as an RFID tag, such as tracking device 340a) on the sample processing container and, in some embodiments, from a tracking device 340b on the sample processing apparatus. Information from the sample processing container 330 may include patient identification of the patient sample. Information may also include identification information or specifications of the sample processing container 330. In one embodiment, the sample processing container includes its own markings or tags with container identification information readable by the reader 320. However, additionally or alternatively, this information may be provided via tracking device 340a. Information from sample processing device 328 may include device identification and / or operating parameters. Reader 320 may also capture information from tracking device 340c on operator tag card 327. Information read by reader 320 from tracking device 340 is sent to controller 304 for confirmation of a patient-associated workflow being performed (which may be indicated via reader 320). Controller 304 may be located in the work area and, in some embodiments, may include its own dedicated reader 320 to prevent operators from having to enter the cleanroom where the samples are located using tag cards. In other embodiments, controller 304 is located away from the work area. Signals from reader 320 are provided to controller 304 and can be used to estimate the completion time for one or more samples. In some embodiments, reader 320 may be integrated into sample processing device 328.

[0081] Cell production samples and facilities prioritize asset management to ensure safe operation and reduce overall operating costs. Sample asset management may include readers as described herein, as well as autonomous active control and interactive tags that maintain safe handling of cell samples in the event of a main control system failure or disconnection.

[0082] Multiple sample containers are frequently moved from one location to another (e.g., from different collection sites) and handled by various machines, where some uncertainty arises regarding the current location of a particular container at any given time. The likelihood of a container being misplaced, placed on the wrong machine, or mixed with other samples increases when an operator moves containers from one location to another or regroups containers to access a specific container.

[0083] The process of reading and communicating with a tracking device generally involves bringing the tracking device close to the sensor. The tracking device can include an active RFID tag operable to emit RF signals (or alternatively, a pulse beacon) or a passive RFID tag that is irradiated by the radio frequency field of an RFID sensor (at which point it transmits a signal back to the RFID sensor). Besides radio frequency, other types of tracking technologies can use communication media including light (e.g., the frequency, pattern, or intensity of light), infrared, electromagnetic, ultrasonic, etc., or combinations thereof.

[0084] Some tracking technologies exhibit low durability and often require significant replacement costs. For example, tracking technologies are relatively expensive due to their active communication, local processing, and interactive displays. Reuse increases the likelihood of failures associated with cleaning or disinfection. Another reason for the increased likelihood of tracking technology failures can be associated with exposure to other samples, bacteria, dirt, or other contaminants. Improved durability is needed to reduce the likelihood of failures due to maintenance, cleaning, disinfection, or sterilization. Furthermore, maintenance and downtime can be scheduled during estimated intervals in resource usage.

[0085] This article provides tracking devices and assemblies, in which the highest-cost part of the protection device has protective elements to facilitate reuse. Figure 13 An example of a tracking device 340 is shown, including a first encapsulation film 420 configured to receive a tag 405. The first encapsulation film 420 may be formed of a waterproof or airtight material (e.g., polyethylene, polystyrene, etc.). The material composition of the first encapsulation film 420 may be transparent, allowing a person to visually see the identification of the portable device encapsulated therein, and to read its screen and / or interact with it via capacitive coupling or pressure contact. The first encapsulation film 420 may generally be configured to completely encapsulate the device and prevent penetration and / or be impermeable to fluids and dirt. The first encapsulation film 420 may be configured to isolate the tracking tag from exposure to continuous mechanical and fluid immersion contact, and to prevent penetration of preservatives, disinfectants, and soaps. The first encapsulation film 420 may also be configured to isolate the tracking tag from exposure to or prevent penetration of plasma gases, ultraviolet light, or radiation exposure.

[0086] The material composition of the first encapsulation film 420 can be transparent to various tracking technology media (such as optical identification, barcode, radio frequency, infrared, ultraviolet light, etc.) to allow the tag 405 to communicate with a remote tracking station of the tracking system (such as a transmitter, receiver, or transceiver or a combination thereof).

[0087] Embodiments of the first encapsulation film 420 may be operable to shrink or reduce in size in response to a threshold application of energy (e.g., a threshold heat from a flow of hot air blown by a hair dryer, a threshold frequency or intensity of light), allowing the film to seal the portable device therein from exposure to fluids (e.g., water, steam, air). The encapsulation of the first encapsulation film 420 may be designed to prevent dirt or bacteria from penetrating to reach the worn tracking tag 405 on the object 410. Embodiments of the encapsulation film 420 may be applied to various embodiments, including shrink packaging with an hermetically sealed or mechanically permeable seal achieved by adhesive encapsulation and packaging. The first encapsulation film 420 may comprise one or more layers.

[0088] Embodiments of the encapsulation film 420 may include visual indicators or electronically formatted status indicators stored and retrieved from a non-transitory storage medium, representing the remaining time period of the disinfection status of the tracking device. Embodiments of the encapsulation film 420 may, alone or in combination with the above-described, include stored and retrieved visual indicators or electronically formatted status indicators representing the current measure of chemical penetration of any disinfectant (which has contacted or permeated the encapsulation film 420 or the tag 405 encapsulated therein). In one embodiment, a small display (such as a liquid crystal driven, for example, via a local processor (which calculates models and alerts)) provides the measurements, alarms, status information, and optical codes (which are then visible through the encapsulation film).

[0089] Tracking device 340 ( Figure 13 It may also include a bag having a strap or attachment 440 configured to receive a first sealing film 420 and a bag 405 encapsulated therein. The strap 440 may be configured to receive or mechanically fasten an attachment of the bag or container to the object 410 to support location tracking and tag 405. One embodiment of the strap 440 may include a tubular form made of a material operable to surround or receive an attachment of the object 410.

[0090] The material construction of the belt 440 may also include a sleeve having a reservoir for receiving the tracking device 340, such as a pouch or pocket portion 450 configured with a flap portion 455. The pocket portion 450 may include an opening end 460 defining a space within the pocket portion 450, generally configured to receive a first sealing film 420 and the tracking tag 405 encapsulated therein. The pocket portion 450 may be integrally formed with the belt 440 or attached to the belt 440 by mechanical fastening means (e.g., Velcro, adhesive, plug connectors, etc.) or by thermal fusion or chemical bonding. The flap portion 455 may be integrally formed and attached to the pocket portion 450 by mechanical fastening means (e.g., Velcro, adhesive, plug connectors, etc.) or by thermal fusion or chemical bonding. The flap portion 455 may generally be configured to cover the opening end 460 to secure the tracking tag 405 within the pocket portion 450 of the belt 440.

[0091] The pocket portion 450 and flap portion 455 may be composed of a material that reduces in size or shrinks in response to the application of received threshold energy (such as the frequency or intensity of light, the increased temperature of hot air from a hair dryer, etc.) so as to seal the pocket portion 450 in a manner that prevents dirt or debris or splashed fluids (such as blood, water) from seeping into the pocket portion 450.

[0092] The strap 440 can be constructed in a manner similar to that of an application responsive to energy (such as the blowing of hot air from a hair dryer or similar device) to tighten or reduce its size around a person's appendage. Furthermore, the material composition of the strap or pocket portion and the flap portion can generally be transparent to allow the identification of the tracking tag 405 enclosed therein to be generally visible to a person.

[0093] Embodiments of the strap 440 and / or pocket portion 450 may be integrally formed with or independent of the film. The strap 440 or pocket portion 450 may also be attached to the encapsulation device via mechanical fastening devices (e.g., adhesives, buckles, clamps, Velcro, etc.) 465. The strap 440 may be composed of materials similar to those used in the encapsulation device to shrink or reduce its size in a manner similar to that of energy threshold applications (e.g., threshold frequency or wavelength or intensity of light, threshold temperature of hot air blown from a blower). In another embodiment, the strap 440 may include one or more slings secured around a person's appendage by mechanical means 470 (e.g., adhesives, clamps, buckles, Velcro, etc.).

[0094] Embodiments of tracking tag 405 may include an antenna, receiver, transmitter, or transceiver, or a combination thereof, configured to communicate in a known manner via a tracking technology medium (e.g., optical identification, barcode, radio frequency, infrared, ultraviolet light, etc.). Tag 405 may be used in conjunction with fixed sensors to track the location of an object and other anticipated parameters of the object relative to predetermined landmarks or areas. Tag 405 may be a passive tag that transmits a signal having an identifier of the person or asset wearing the tag in response to receiving a first signal from a fixed tracking system. Tag 405 may also be an active tag that transmits a signal having a sample identifier on a continuous or periodic basis.

[0095] The tag 405 included in the first encapsulation film 420 and / or the strip 440 may include an energy source 475 that powers the tag 405. The energy source 475 may be a battery, an energy harvesting technology operable to convert motion, vibration, solar energy, thermal energy, radio frequency energy, etc., into electrical energy to power the tracking tag and / or other sensors used in conjunction with it. In one embodiment, the tag 405 may be powered by a sample processing device in use. For example, when the device is a shaker, the shaking motion may power the tag 405. Thus, when the device is not operating, no power is supplied to the tag 405. Consequently, no sample tracking information is received. The first encapsulation film 420 and / or the strip 440 may also be configured to receive paper or other printable media for printing an identification code or name of the object wearing the tracking device 340. The strip 440 may be marked with a color pattern or a combination thereof to provide visual identification. The strip 440 may be marked to enable registration with an optical scanner (not shown). The strip 440 may be dynamically changed by an internal or external triggering device to indicate a change in state. The tape 440 and the first encapsulation film 420 may include at least a transparent, defined window space 480 for transmitting passive RFID, infrared, or optical signals, which may be continuous or triggered internally or externally. The diameter of the tape 440 may be variable to allow attachment to various types of containers or bags 410 (including physical devices and disposable items). Embodiments of the tape 440 may include mechanical fastening devices to prevent re-expansion after shrink-fit assembly.

[0096] The tracking device 340 may be configured to receive and protect other types of electronic devices 405 that are susceptible to increased probability of malfunction or improper operation due to exposure to disinfectants, bodily fluids, or debris. These devices 405 (e.g., sensors) include: devices for recording or measuring sound, blood glucose levels, saturated oxygen content, temperature, blood pressure, light, electrical conductivity, motion or vibration, RF signals, optical signals, infrared signals; devices for creating an electric field on the skin surface; devices for dispensing medication (e.g., via pumps, flow control, skin absorption, etc.); devices for recording ultrasound; output devices (e.g., LCD screens); devices such as position tracking sensors (electromagnetic sensors); and devices including electrodes to detect and store bioelectric potential signals (e.g., pulses, electrocardiograms, etc.) generated by the human body.

[0097] The general construction of an embodiment of the tracking device 340 has been described. The following is a general description of the method of operating the tracking device assembly 340 described above.

[0098] This method can include encapsulating a tag 405 within a tracking device 340 such that cells, dirt, and bacterial contamination generally do not penetrate the device 340 and come into contact with the tag 405. Embodiments of the tracking device 340 may be reduced in size or shrunk to wrap around an electronic device 420. For ease of illustration, it is assumed that the electronic device 420 includes a tracking or tracking and control device 405. The method of encapsulating the tag 405 can include immersing the tag 405 in a solvent-impermeable housing (wherein the housing is soft or rigid) to form an encapsulation film 420 around the device 405. The encapsulation film 420 can include the ability to incorporate straps or other mechanical connections 470. The encapsulation film 420 may be made of a material that is chemically resistant to penetration by disinfectant solvents (which are used for disinfection in healthcare or clinical settings).

[0099] The method may also include providing a pocket portion 450 for the strap 440 to receive the sealing film 420 and the encapsulated electronics 405. Embodiments of the strap 440 may comprise a tubular form of plastic material configured to receive a sample container or human appendage that is also tracked and controlled. The pocket portion 450 may be integrally formed and attached to the strap 440 either by mechanical connection (e.g., adhesive, Velcro, buckle, etc.). A flap portion 455 may be coupled to generally cover the pocket portion 450. The flap portion 455 may be folded over the opening by inserting the sealing film 420 and the encapsulated tag 405 into the open end 460 of the pocket portion 450 to encapsulate the sealing film 420 and the bag 405 within the pocket portion 450 of the strap 440. The material composition of the strap 440 and the attached pocket portion 450 can be generally similar to that of the sealing film 420, such that the application of energy (e.g., heat, chemicals, etc.) can generally fuse the flap portion 455 and the pocket portion 450 to seal the sealing film 420 and the label 450, preventing exposure to fluids (e.g., water, vapor, disinfectant chemicals, etc.) or other contaminants. The material composition of the sealing film 420 and the pocket portion 450 of the strap 440 can be such that the identification of the label 405 enclosed therein is visually perceptible. By inserting the appendage 465 into the strap 440, the application of energy as described above can cause the strap to shrink or contract in a close manner around the appendage 465 to prevent it from shifting. Clinicians or technicians can visually identify and store the label 405 along with the identification of the object 410 receiving the strap 440, so that it can be operated to track and store the movement of the object 410 treated or diagnosed in the facility.

[0100] Upon termination of sample and / or person tracking, removal of the tracking device 340 may include cutting the tape 440 to release or remove it from the accessory 465 or object 410. The tape 440 itself may be disposable, while the encapsulation film 420 and the encapsulation label 405 may be retained, sterilized, and reused. Sterilization of the encapsulation film 420 and the encapsulation label 405 may include cutting the pocket portion 450 of the tape 440 to remove the encapsulation film 420 and the encapsulation label 405 without disturbing the construction of the encapsulation film 420 around the label 405, and applying a disinfectant or another cleaning solution to the encapsulation film 420 and the encapsulation label 405. Applying a disinfectant or another cleaning solution may include wiping or immersing the encapsulation film 420 and the encapsulation label 405 in the disinfectant or cleaning solution. For example, the encapsulation film 420 and the encapsulation label 405 may be fully immersed in a disinfectant (e.g., CIDEXTM) for a threshold time period. The encapsulation film 420 prevents disinfectant from penetrating and coming into contact with the encapsulation label 405 (which would otherwise increase the likelihood of corrosion or other damage to the label 405), and thereby prevents the label 405 from being reused by another person. The encapsulation film 420 and the encapsulation label 405 are also capable of sterilization by immersion in water vapor for a threshold time period. Reuse may also include rewriting or erasing existing identification data stored in the label 405.

[0101] Electronic devices, including their displays, batteries, and sensors, may be temperature-sensitive. In its preferred embodiment, the present invention is designed and applied as a system with anticipated use cases. For example, the thermal mass within the encapsulation film 420 surrounding device 405 can be designed such that the sterilization duration absorbs heat energy, allowing the electronic device and its associated components to remain below their threshold temperature damage point through heat absorption in the thermal mass. The design density of the thermal mass is selectable as a function of distance from the film 420 and the encapsulation device 405 to allow the film 420 to reach and maintain the necessary temperature, while the device 405 also maintains its design temperature range. After sterilization, the label 405 encapsulated in the encapsulation film 420 can be used in conjunction with another object 410 in a manner similar to that described above.

[0102] The disclosed embodiments can also be used in conjunction with one or more operator interfaces. Figure 14 and Figure 15 This is an example user interface display, which can be used to validate the workflow for tracking patient samples. The display shown is compatible with system 50 (see [link]). Figure 2 This is used to schedule patients and track patient samples. For example, in... Figure 14The system can track new samples 801 and display information such as sample arrival date 802 and estimated completion time 803. Other display screens in the user interface provide a workflow overview, sample tracking, real-time process updates, etc. The timing and quantity of future samples 801 can be determined by using the current status of the samples, the status of equipment and human resources, and availability to initialize the simulation model and simulate future states. Figure 15 This is an example showing the current asset utilization 901, including which samples occupy which assets 902, 903, and the long-term utilization of each asset for capacity planning and preventative maintenance 905.

[0103] The technical effects of this invention include improved sample tracking and production capacity in cell therapy manufacturing. The disclosed technology facilitates improved utilization of resources for cell therapy product manufacturing with less downtime. Furthermore, control of processing equipment can depend on adherence to previous steps in the workflow. Such technologies can be used to improve the production capacity and quality of cell therapy manufacturing. Additionally, the disclosed technology can improve patient scheduling for cell therapy patients and the utilization of hospital and provider resources.

[0104] This written description uses examples to illustrate certain embodiments (including optimal modes) and also enables those skilled in the art to practice the invention, including making and using any apparatus or system, and performing any combination methods. The scope of this disclosure is defined by the claims and may include other examples that will occur to those skilled in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that are exactly the same as the literal language of the claims, or if they comprise equivalent structural elements that have a non-substantially different literal language from the claims.

Claims

1. A cell therapy manufacturing system, comprising: Sample container configured to store cell therapy samples; A reader, which is located in conjunction with a sample processing device or manufacturing location, and is configured to receive an identification signal from a tracking device coupled to the sample container; as well as A controller, operatively coupled to the reader, and configured to: Access the sample processing timeline of the processing protocol associated with the identification signal when the identification signal is received; Whether a deviation from the sample processing timeline of the processing protocol has occurred is determined at least in part based on the time of receipt of the identification signal. One or more updated estimated processing completion times are provided for the sample processing timeline of the processing protocol; as well as The updated estimated processing completion time is transmitted over the sample processing timeline. The controller is configured to be at least partially based on: The identification or location of the manufacturing area and the sample containers, and the duration of the sample containers at each manufacturing area, or The data collected by the sample processing device, To determine whether a deviation from the sample processing timeline has occurred.

2. The system as claimed in claim 1, wherein, The one or more updated estimated processing completion times include multiple estimated processing completion times for corresponding multiple potential processing protocols, and wherein the controller is configured to select one of the potential processing protocols based on the corresponding estimated processing completion times.

3. The system as described in claim 1, wherein, The data is a cell count.

4. The system as described in claim 3, wherein, The controller is configured to provide a later, updated estimated processing completion time based on cell counts falling below a threshold.

5. The system as described in claim 3, wherein, The controller is configured to provide an earlier updated estimated processing completion time based on cell counts exceeding a threshold.

6. The system as claimed in claim 1, wherein, The controller is configured to update the sample processing timeline to have larger time blocks for the sample processing device based on the following: the lack of availability of the next sample processing device in the workflow of the cell therapy sample.

7. The system as claimed in claim 1, wherein, The controller is configured to mark the sample processing device as unusable for another cell therapy sample when the sample container is within range of the receiver.

8. The system of claim 1, wherein, The sample processing device simultaneously makes multiple cell therapy samples available for processing, and the controller is configured to mark the processing slot of the sample processing device as unavailable when the sample container is within range of the reader.

9. The system as claimed in claim 1, wherein, The motion of the sample processing device is configured to power the tracking device, and the controller is configured to determine that the sample processing device is in operation based on the reception of the identification signal.

10. The system of claim 1, wherein, The controller is configured to assess the quality of the cell therapy sample based on the deviation.

11. The system of claim 1, wherein, The controller is configured to take the sample processing timeline and the updated estimated processing completion time as input to provide an estimated processing completion time for similar cell therapy samples.

12. The system of claim 11, wherein, The similar cell therapy sample has a similar cell count or percentage of activity to the cell therapy sample.

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