A stem cell culture medium development whole-process intelligent management system

Abstract

This invention provides an intelligent management system for the entire process of stem cell culture medium development, belonging to the field of intelligent management technology. This invention integrates artificial intelligence, big data analysis, and knowledge graph technologies, covering the entire process from demand analysis, intelligent formulation design and virtual simulation, small-scale culture verification and iterative optimization, pilot-scale process optimization, to quality compliance and traceability. Through a central intelligent decision engine coordinating various modules, this invention can significantly improve the efficiency of culture medium R&D, greatly reduce development costs, and ensure batch consistency and quality stability of the culture medium by reducing trial and error and real-time optimization. Simultaneously, it provides full-chain electronic traceability, fully meeting regulatory compliance requirements and effectively overcoming the problems of low efficiency, high cost, and batch instability in traditional R&D.

Description

Technical Field

[0001] This invention relates to the field of intelligent management, and in particular to an intelligent management system for the entire process of stem cell culture medium development. Background Technology

[0002] Stem cell technology is crucial in the biomedical field; however, the development and production of its culture media face numerous challenges. Traditional culture media development models rely heavily on experience, numerous repetitive experiments, and lengthy trial-and-error processes, resulting in low efficiency, lengthy cycles, and high costs. Existing methods generally lack systematic and intelligent management; data is isolated and difficult to integrate and utilize, hindering formulation prediction, iterative optimization, and knowledge accumulation. Furthermore, during the transition from small-scale to large-scale production, the lack of precise control and effective feedback mechanisms makes it difficult to guarantee batch-to-batch consistency. Simultaneously, inadequate quality control and traceability systems fail to meet increasingly stringent regulatory requirements. In particular, the lack of virtual simulation capabilities for initial formulations leads to significant resource waste on blind practical validation. These problems collectively impede the rapid development and large-scale application of novel, high-performance stem cell culture media. Summary of the Invention

[0003] To address the aforementioned problems in existing technologies, the present invention aims to provide an intelligent management system for the entire process of stem cell culture medium development, comprising:

[0004] The requirements analysis and target setting module is used to receive and process stem cell culture medium development targets, which include specific stem cell types, application scenarios, and expected culture medium performance indicators.

[0005] The formulation design and simulation module is used to intelligently screen and recommend suitable culture medium components from the culture medium component database based on the objectives determined by the demand analysis and target setting module, and to build an artificial intelligence prediction model to perform virtual simulation and effect prediction of the preliminary formulation.

[0006] The culture verification and data analysis module is used to interface with culture experimental equipment to collect real-time data on cell status, culture medium consumption, and products during small-scale stem cell culture, and to perform multi-dimensional analysis on the data to evaluate the culture effect of the preliminary formula.

[0007] The scale-up process optimization module is used to connect to the bioreactor control system after the culture medium formulation has been validated on a small scale. It enables real-time monitoring, data integration, and intelligent adjustment of the culture medium scale-up production process parameters to ensure the stability and consistency of the culture medium in large-scale production.

[0008] The quality compliance and traceability module is used to manage batch quality data of stem cell culture media, including batch test reports, quality control parameters and regulatory compliance documents, and provides full-chain traceability functionality.

[0009] The central intelligent decision engine interacts with and coordinates instructions with the demand analysis and target setting module, the formula design and simulation module, the culture verification and data analysis module, the scale-up process optimization module, and the quality compliance and traceability module. Based on historical data, real-time feedback, and preset rules, the central intelligent decision engine makes intelligent optimization decisions to guide the iteration and improvement of the entire stem cell culture medium development process.

[0010] Furthermore, the system implements a fully intelligent management method for the entire process of stem cell culture medium, including:

[0011] Step S1, Stem Cell Culture Medium Development Needs Analysis and Goal Setting: Through the needs analysis and goal setting module, the stem cell culture medium development goals input by the user are collected and integrated. The development goals include specific stem cell types, specific application scenarios, and detailed expected culture medium performance indicators. The expected culture medium performance indicators include cell proliferation rate, cell viability, expression of specific biomarkers, cell purity, cell differentiation potential, culture medium cost-effectiveness, and no animal-derived requirements.

[0012] Step S2, culture medium formulation design and component screening: Based on the development goals determined in step S1, the formulation design and simulation module uses an artificial intelligence prediction model to screen and generate a variety of potential initial culture medium formulations from the culture medium component database, and performs virtual simulation verification. The culture medium component database contains the physicochemical properties, biological functions, and verified synergistic or antagonistic effects of amino acids, vitamins, growth factors, cytokines, inorganic salts, lipids, and buffers.

[0013] Step S3, small-scale culture verification and iterative optimization: Through the culture verification and data analysis module, a small-scale stem cell culture experiment is performed, and the cell growth status, morphological characteristics, changes in metabolites, and culture medium consumption are monitored in real time during the culture process. Flow cytometry data, high-throughput sequencing data, and microscopic image data are collected as cell quality assessment parameters. The central intelligent decision engine compares the cell quality assessment parameters with the expected culture medium performance indicators, generates optimization suggestions, and guides the formulation design and simulation module to iterate the formulation.

[0014] Step S4, pilot-scale process verification and parameter optimization: After the initial formulation has passed the small-scale verification in step S3, the culture medium formulation is applied to a pilot-scale bioreactor for scale-up production via the scale-up process optimization module. The scale-up culture process parameters are monitored and intelligently adjusted in real time, including stirring speed, aeration rate, dissolved oxygen concentration, pH value, and temperature. The system integrates process analysis data reflecting the culture process status and product quality attributes, combines predictive models to assess the cell culture status and culture medium performance changes during scale-up, and provides process optimization schemes to ensure the stability and uniformity of the culture medium in the scale-up batches.

[0015] Step S5, Quality Control and Regulatory Compliance Management: Through the quality compliance and traceability module, comprehensive quality testing is conducted on batches of culture media that have completed pilot-scale amplification or formal production. The quality testing includes sterility testing, endotoxin testing, specific growth factor activity testing, and cell culture performance testing. The test results are compared with preset quality compliance standards. The module also records and manages batch information for all development, production, and quality control stages, enabling complete batch traceability of the culture media from raw materials to finished products, and generating batch reports and audit trail records that comply with Good Manufacturing Practices (GMP) requirements.

[0016] Furthermore, the stem cell culture medium development needs analysis and target setting in step S1 includes:

[0017] Step S101: Receive core performance indicators through the user interface and / or data import interface. The core performance indicators include target stem cell type, expected application scenario, target cell proliferation rate, cell viability, cell purity, and specific differentiation potential.

[0018] In step S102, the demand analysis and target setting module intelligently matches and retrieves the stem cell characteristic knowledge base inside the system based on the core performance indicators. The knowledge base contains data on stem cell growth requirements, metabolic characteristics, differentiation pathways, and the influence of known culture medium components.

[0019] In step S103, the central intelligent decision engine combines the knowledge base data and historical project data to prioritize and weight the core performance indicators, forming a multi-dimensional development target vector to guide subsequent recipe design.

[0020] Furthermore, the culture medium formulation design and component screening in step S2 include:

[0021] In step S201, based on the stem cell type and application scenario determined in step S1, the formulation design and simulation module screens out candidate components related to stem cell growth and function maintenance from the culture medium component database.

[0022] In step S202, the central intelligent decision engine invokes an artificial intelligence prediction model to generate multiple initial culture medium formulations with different component ratios and combinations based on the historical data and interaction patterns of the candidate components.

[0023] In step S203, the formulation design and simulation module performs virtual simulation of the initial culture medium formulation to predict its potential effects on the proliferation, differentiation, and functional expression of specific stem cell types in the expected application scenarios.

[0024] In step S204, the central intelligent decision engine performs a preliminary evaluation and ranking of the initial culture medium formulations based on the virtual simulation results, and recommends the top few optimal formulations for the next experimental verification.

[0025] Furthermore, the small-scale cultivation verification and iterative optimization in step S3 includes:

[0026] In step S301, the culture verification and data analysis module collects cell proliferation curves, cell morphology images, metabolite concentration changes, dissolved oxygen and pH values ​​in real time during small-scale culture experiments as culture environment parameters.

[0027] In step S302, the culture verification and data analysis module automatically processes and analyzes the collected data to extract key indicators such as cell growth rate, cell viability, and expression levels of specific proteins.

[0028] In step S303, the central intelligent decision engine intelligently compares the key indicators with the expected performance indicators of the culture medium to identify deficiencies or areas for optimization in the formulation.

[0029] In step S304, the central intelligent decision engine generates targeted formula optimization suggestions based on the comparison results. The formula optimization suggestions include adjusting the concentration of specific ingredients, replacing ingredients with poor effects, or adding new ingredients. The suggestions are then fed back to the formula design and simulation module to initiate the next round of iterative formula design and verification process.

[0030] Furthermore, the pilot-scale process verification and parameter optimization in step S4 include:

[0031] In step S401, the scale-up process optimization module receives sensor data from the bioreactor in real time. The sensor data includes dissolved oxygen probe data, pH sensor data, temperature sensor data, online cell density monitoring data, and online nutrient consumption analysis data.

[0032] In step S402, the system integration process analysis technology tool performs online processing and analysis on the real-time sensor data to dynamically evaluate the performance stability of the culture medium during the scale-up process and its impact on the cell culture state.

[0033] In step S403, the central intelligent decision engine intelligently adjusts the key process parameters of the bioreactor based on the process analysis technology data and the preset process control strategy. The key process parameters include stirring speed, aeration rate and feeding strategy, in order to maintain the optimal culture environment and ensure batch-to-batch consistency of the culture medium in the scale-up batches.

[0034] Furthermore, the quality control and regulatory compliance management in step S5 includes:

[0035] In step S501, the quality compliance and traceability module automatically records and manages key operational data and environmental parameters for all production stages, from raw material procurement, warehousing, culture medium preparation, dispensing, sterilization to final product batch testing.

[0036] Step S502: The module integrates batch quality test results, which include sterility test, endotoxin test, mycoplasma test, specific growth factor activity test, and cell culture performance verification report.

[0037] In step S503, the module intelligently compares the batch quality inspection results with preset quality standards and good manufacturing practice requirements, and automatically generates a compliance report and suggestions for handling non-conforming products.

[0038] Step S504: The module constructs a full-chain electronic traceability system based on batch number and production date identifier to fully trace the production history, raw material source, quality inspection records and flow of any batch of culture medium, in order to meet audit and regulatory requirements.

[0039] Furthermore, the central intelligent decision-making engine further includes:

[0040] The data fusion and knowledge graph construction unit integrates heterogeneous data from all modules, including experimental data, literature data, market demand data, and regulatory data, and constructs a knowledge graph related to stem cell culture medium development. The machine learning and deep learning unit trains and optimizes multi-objective optimization algorithms, reinforcement learning models, and deep learning neural networks based on the knowledge graph and historical iteration data to learn the complex relationship between culture medium components and cell performance. The intelligent decision-making and iterative optimization unit predicts and optimizes culture medium formulations, culture process parameters, and quality control strategies from multiple dimensions based on the trained models, and generates intelligent decision suggestions to guide the automated execution or manual intervention of each module, achieving continuous improvement and efficiency enhancement of the culture medium development process.

[0041] Furthermore, the culture medium component database further includes: a basic component information database containing the names of various basic culture medium components, including amino acids, vitamins, growth factors, cytokines, inorganic salts, lipids, and buffers; the database also includes the CAS number, molecular structure, physicochemical properties, solubility, stability, and supplier information of the basic components; a biological function association database storing verified or predicted biological function association data between the basic components and the proliferation, differentiation, metabolism, gene expression, and maintenance of specific functions of different stem cell types; and a cost and compliance information database containing cost information, supply chain information, and information on whether each component complies with animal-free and Good Manufacturing Practice (GMP) regulations.

[0042] Furthermore, the central intelligent decision engine can specifically optimize the entire process of developing the culture medium according to preset stem cell culture conditions. The preset stem cell culture conditions include: specific stem cell types, for which the system performs efficient expansion optimization; specific culture environment requirements, including serum-free culture, chemically limited culture, or xenogeneic component-free culture; and specific application goals, including cell therapy for specific diseases, drug screening platforms, or gene therapy vector production. The optimization process includes adjusting the target weights of the demand analysis and target setting module, changing the component screening strategy of the formulation design and simulation module, modifying the evaluation parameter emphasis of the culture verification and data analysis module, and adjusting the process parameter adjustment logic of the scale-up process optimization module, so that the finally developed culture medium best meets the preset stem cell culture conditions.

[0043] Compared to existing technologies, the advantages of this invention are as follows: By integrating artificial intelligence, big data analysis, and automated control, this invention achieves intelligent and systematic management of the entire process of stem cell culture medium development. From demand analysis and virtual formulation simulation to experimental verification and iterative optimization, it effectively reduces blind trial and error, significantly accelerates the screening and development process of high-performance culture media, thereby greatly improving R&D efficiency and reducing development costs.

[0044] This invention significantly enhances the quality control and batch consistency of culture media, fully meeting regulatory compliance requirements. Real-time monitoring and intelligent adjustment of scale-up production process parameters ensure the stability and uniformity of large-scale production. Simultaneously, its quality compliance and traceability module enables end-to-end data recording and traceability from raw materials to the final product, providing a solid guarantee for product quality and enabling companies to efficiently handle regulatory audits, thereby enhancing market competitiveness and credibility. Attached Figure Description

[0045] Figure 1This is a schematic diagram of the system module structure of the present invention.

[0046] Figure 2 This is an exemplary flowchart of the intelligent management method for the entire system of the present invention.

[0047] Figure 3 This is an exemplary flowchart illustrating the steps involved in the requirements analysis and target setting of this invention.

[0048] Figure 4 This is an exemplary flowchart of the formulation design and ingredient screening process of the present invention.

[0049] Figure 5 This is an exemplary flowchart of the small-scale cultivation verification and iterative optimization steps of the present invention.

[0050] Figure 6 This is an exemplary flowchart of the pilot-scale process verification and parameter optimization of the present invention.

[0051] Figure 7 This is an exemplary flowchart of the quality control and regulatory compliance management steps of the present invention. Detailed Implementation

[0052] The present invention will be further described below with reference to specific embodiments.

[0053] This system mainly includes modules for requirements analysis and target setting, formulation design and simulation, culture validation and data analysis, scale-up process optimization, quality compliance and traceability, and a central intelligent decision engine as its core. These modules are tightly integrated and work collaboratively to achieve intelligent management of the entire process of stem cell culture medium development.

[0054] like Figure 1 The diagram shown is a schematic block diagram of the intelligent management system module for the entire process of stem cell culture medium development provided in this embodiment, including:

[0055] The requirements analysis and target setting module is used to receive and process stem cell culture medium development targets, which include specific stem cell types, application scenarios, and expected culture medium performance indicators.

[0056] The formulation design and simulation module is used to intelligently screen and recommend suitable culture medium components from the culture medium component database based on the objectives determined by the requirements analysis and target setting module, and to build an artificial intelligence prediction model to perform virtual simulation and effect prediction of the preliminary formulation.

[0057] The culture validation and data analysis module is used to interface with culture experimental equipment to collect real-time data on cell status, culture medium consumption, and products during small-scale stem cell culture, and to perform multi-dimensional analysis of the data to evaluate the culture effect of the initial formulation.

[0058] The scale-up process optimization module is used to connect to the bioreactor control system after the culture medium formulation has been validated on a small scale. It enables real-time monitoring, data integration, and intelligent adjustment of the culture medium scale-up production process parameters to ensure the stability and consistency of the culture medium in large-scale production.

[0059] The quality compliance and traceability module is used to manage batch quality data of stem cell culture media. Batch quality data includes batch test reports, quality control parameters and regulatory compliance documents, and provides full-chain traceability.

[0060] The central intelligent decision engine interacts with and coordinates instructions with the demand analysis and target setting module, the formula design and simulation module, the culture verification and data analysis module, the scale-up process optimization module, and the quality compliance and traceability module. Based on historical data, real-time feedback, and preset rules, the central intelligent decision engine makes intelligent optimization decisions to guide the iteration and improvement of the entire stem cell culture medium development process.

[0061] like Figure 2 The diagram illustrates the intelligent management method for the entire process of stem cell culture medium implemented in this embodiment, including...

[0062] Step S1: Needs analysis and goal setting for stem cell culture medium development. Through the needs analysis and goal setting module, the stem cell culture medium development goals input by the user are collected and integrated. The development goals include specific stem cell types, specific application scenarios, and detailed expected culture medium performance indicators. The expected culture medium performance indicators include cell proliferation rate, cell viability, expression of specific biomarkers, cell purity, cell differentiation potential, cost-effectiveness of culture medium, and requirements for no animal origin.

[0063] like Figure 2 The diagram shown is an exemplary flowchart of step S1 of this embodiment, which involves the analysis of requirements and setting of targets for the development of stem cell culture medium.

[0064] Step S101 involves receiving core performance indicators via a user interface and / or data import interface. These indicators include the target stem cell type, expected application scenario, target cell proliferation rate, cell viability, cell purity, and specific differentiation potential. In one embodiment, the user can directly input the desired stem cell culture medium development target through the system's graphical user interface. The system also supports receiving external data via the data import interface. These core performance indicators form the basis for quantifying culture medium performance. For example, for induced pluripotent stem cells (iPSCs), it may be required that they achieve a cell purity of over 90% in a specific culture medium while maintaining high differentiation potential. The input of these detailed indicators provides clear targets for subsequent intelligent screening and simulation.

[0065] In step S102, the requirements analysis and target setting module intelligently matches and retrieves the stem cell characteristic knowledge base within the system based on core performance indicators. This knowledge base contains data on stem cell growth requirements, metabolic characteristics, differentiation pathways, and the effects of known culture medium components. In one embodiment, the requirements analysis and target setting module parses the core performance indicators received in S101 and intelligently matches and retrieves relevant information from the pre-built stem cell characteristic knowledge base within the system. This knowledge base is a structured database that stores detailed information on the growth requirements of various stem cell types, such as mesenchymal stem cells (MSCs), neural stem cells (NSCs), and hematopoietic stem cells (HSCs), including their metabolic characteristics under different culture environments, specific differentiation pathways and key regulatory factors, as well as data on the promoting or inhibiting effects of various known culture medium components on their proliferation, differentiation, or metabolism. Through this intelligent matching, the system can provide targeted, high-quality prior knowledge for subsequent formulation design.

[0066] In step S103, the central intelligent decision engine combines knowledge base data and historical project data to prioritize and weight core performance indicators, forming a multi-dimensional development target vector to guide subsequent recipe design.

[0067] Step S2, culture medium formulation design and component screening: Based on the development goals determined in step S1, the formulation design and simulation module uses an artificial intelligence prediction model to screen and generate a variety of potential initial culture medium formulations from the culture medium component database, and performs virtual simulation verification. The culture medium component database contains the physicochemical properties, biological functions, and verified synergistic or antagonistic effects of amino acids, vitamins, growth factors, cytokines, inorganic salts, lipids, and buffers.

[0068] like Figure 4 The diagram shown is an exemplary flowchart of step S2 of this embodiment, which involves the design of the culture medium formulation and the screening of its components.

[0069] In step S201, based on the stem cell type and application scenario determined in step S1, the formulation design and simulation module screens candidate components related to stem cell growth and functional maintenance from the culture medium component database. In one embodiment, the formulation design and simulation module intelligently screens highly relevant and promising candidate components from the system's built-in culture medium component database according to the specific stem cell type and application scenario. This culture medium component database is a comprehensive knowledge base that stores detailed information on various basic culture medium components, such as amino acids, vitamins, growth factors, cytokines, inorganic salts, lipids, and buffers. For each component, the database not only includes its basic physicochemical properties, molecular structure, solubility, stability, and supplier information, but also specifically records its biological function and data on verified synergistic or antagonistic effects with other components. For example, when the goal is to develop a culture medium for mesenchymal stem cells, the system automatically screens for growth factors or cytokines known to have a positive impact on MSC proliferation or differentiation.

[0070] In step S202, the central intelligent decision engine calls the artificial intelligence prediction model to generate multiple initial culture medium formulations with different component ratios and combinations based on the historical data and interaction patterns of the candidate components.

[0071] In step S203, the formulation design and simulation module performs virtual simulations of the initial culture medium formulation to predict its potential effects on the proliferation, differentiation, and functional expression of specific stem cell types in the intended application scenario. In one embodiment, the formulation design and simulation module utilizes its built-in computational model to perform insilico virtual simulations on each generated initial culture medium formulation. This simulation aims to predict the potential impact of the formulation on cell proliferation rate, differentiation efficiency, expression levels of specific biomarkers, and other functional expressions under specific stem cell types and intended application scenarios. These virtual simulations may be based on biological systems modeling, metabolic network analysis, or data-driven predictive models. They can provide preliminary performance assessments, identify promising formulations, and exclude obviously unsuitable formulations, thereby significantly reducing the cost and time of subsequent experimental validation.

[0072] In step S204, the central intelligent decision engine performs a preliminary evaluation and ranking of the initial culture medium formulations based on the virtual simulation results, and recommends the top few optimal formulations for the next experimental verification.

[0073] Step S3, small-scale culture verification and iterative optimization: Through the culture verification and data analysis module, a small-scale stem cell culture experiment is performed. The growth status, morphological characteristics, changes in metabolites, and culture medium consumption of cells are monitored in real time during the culture process. Flow cytometry data, high-throughput sequencing data, and microscopic image data are collected as cell quality assessment parameters. The central intelligent decision engine compares the cell quality assessment parameters with the expected culture medium performance indicators, generates optimization suggestions, and guides the formulation design and simulation module to iterate the formulation.

[0074] like Figure 5 The diagram shown is an exemplary flowchart of step S3 of this embodiment, which includes small-scale culture verification and iterative optimization.

[0075] Step S301: The culture verification and data analysis module collects cell proliferation curves, cell morphology images, metabolite concentration changes, dissolved oxygen, and pH values ​​in real time during small-scale culture experiments as culture environment parameters. In one embodiment, the culture verification and data analysis module collects key data in real time by interfacing with automated or semi-automated culture experimental equipment, such as sensors in multi-well plate culture systems, small bioreactors, or cell culture incubators. This includes, but is not limited to: cell proliferation curves, which are usually automatically acquired through online cell counters or image analysis systems; cell morphology images, which are automatically captured by microscopic imaging systems or high-throughput cell imaging systems integrated in incubators; changes in the concentration of metabolites such as glucose and glutamine in the culture medium, as well as the accumulation of metabolites such as lactic acid and ammonium, which are usually obtained through online metabolic analyzers or offline analysis after periodic sampling; and dissolved oxygen (DO) and pH values, which are monitored in real time by optical sensors or probes integrated in the culture container.

[0076] In step S302, the culture validation and data analysis module automatically processes and analyzes the collected data, extracting key indicators such as cell growth rate, cell viability, and specific protein expression levels. This module utilizes built-in data processing algorithms and analysis tools to automatically process the massive amounts of data collected in real time. For example, it analyzes cell morphology images using image processing algorithms to assess cell viability and purity; and it extracts the expression levels of specific biomarkers or genes through flow cytometry and high-throughput sequencing data analysis. The key indicators extracted include: cell growth rate, calculated through proliferation curve fitting; cell viability, assessed through trypan blue staining or live / dead cell fluorescence staining combined with image analysis; and specific protein expression levels, analyzed through immunofluorescence or Western blotting to determine their expression on the cell membrane or within the cell.

[0077] In step S303, the central intelligent decision engine intelligently compares key indicators with expected culture medium performance indicators to identify deficiencies or areas for optimization in the formulation. The central intelligent decision engine receives the key cell quality assessment indicators extracted in S302 and intelligently compares them with the expected culture medium performance indicators set in S1. Through advanced data analysis and pattern recognition algorithms, the decision engine can automatically identify in which aspects the current culture medium formulation fails to meet expected goals and in which aspects there is potential for optimization. For example, if the cell proliferation rate is lower than expected, but certain metabolites are too high, the system may infer that there is a deficiency of certain nutrients in the culture medium or a problem with metabolic pathways.

[0078] In step S304, the central intelligent decision engine generates targeted formula optimization suggestions based on the comparison results. The formula optimization suggestions include adjusting the concentration of specific ingredients, replacing unsatisfactory ingredients, or adding new ingredients. These suggestions are then fed back to the formula design and simulation module to initiate the next round of iterative formula design and verification process.

[0079] Step S4, pilot-scale process verification and parameter optimization: After the initial formulation has passed the small-scale verification in step S3, the culture medium formulation is applied to a pilot-scale bioreactor for scale-up production through the scale-up process optimization module. The scale-up culture process parameters are monitored and intelligently adjusted in real time, including stirring speed, aeration rate, dissolved oxygen concentration, pH value, and temperature. The system integrates process analysis technology data reflecting the culture process status and product quality attributes, combines predictive models to evaluate the cell culture status and culture medium performance changes during the scale-up process, and provides process optimization schemes to ensure the stability and uniformity of the culture medium in the scale-up batches.

[0080] like Figure 6 The diagram shown is an exemplary flowchart of step S4 in this embodiment, which involves pilot-scale process verification and parameter optimization, including:

[0081] In step S401, the scale-up process optimization module receives real-time sensor data from the bioreactor. This sensor data includes dissolved oxygen probe data, pH sensor data, temperature sensor data, online cell density monitoring data, and online nutrient consumption analysis data. The scale-up process optimization module integrates with the pilot-scale bioreactor system to receive real-time data from its onboard sensors. These sensors include: a dissolved oxygen probe for measuring dissolved oxygen content in the culture medium; a pH sensor for monitoring the pH of the culture medium; a temperature sensor for maintaining a constant culture temperature; an online cell density monitoring device for monitoring cell number and viability; and an online nutrient analysis system for real-time analysis of nutrient consumption such as glucose and glutamine, and the production of metabolic products such as lactic acid and ammonium in the culture medium.

[0082] Step S402 involves integrating process analysis tools to process and analyze real-time sensor data online, dynamically evaluating the performance stability of the culture medium during scale-up and its impact on cell culture status. These tools receive the massive amounts of sensor data collected in S401 in real time and process and analyze it online. This may involve advanced algorithms such as data cleaning, filtering, trend analysis, anomaly detection, and pattern recognition. Through these analyses, the system can dynamically assess the performance stability of the culture medium during scale-up production, for example, whether its ability to maintain normal cell metabolism and provide nutrients declines over time. Simultaneously, the system also assesses the impact of changes in culture medium performance on cell culture status, such as whether it leads to cell proliferation arrest, decreased viability, or abnormal differentiation.

[0083] In step S403, the central intelligent decision engine intelligently adjusts the key process parameters of the bioreactor based on process analysis technology data and preset process control strategies. The key process parameters include stirring speed, aeration rate and feeding strategy to maintain the optimal culture environment and ensure batch-to-batch consistency of the culture medium in scale-up batches.

[0084] Step S5, Quality Control and Regulatory Compliance Management: Through the quality compliance and traceability module, comprehensive quality testing is conducted on batches of culture media that have completed pilot-scale amplification or formal production. Quality testing includes sterility testing, endotoxin testing, specific growth factor activity testing, and cell culture performance testing. The test results are compared with preset quality compliance standards. The module also records and manages batch information for all development, production, and quality control stages, enabling complete batch traceability of culture media from raw materials to finished products, and generating batch reports and audit trail records that comply with Good Manufacturing Practices (GMP) requirements.

[0085] like Figure 7 The diagram shown is an exemplary flowchart of quality control and regulatory compliance management in step S5 of this embodiment, including:

[0086] In step S501, the quality compliance and traceability module automatically records and manages key operational data and environmental parameters for all production stages, from raw material procurement, warehousing, culture medium preparation, dispensing, sterilization to final product batch testing.

[0087] Step S502: The module integrates batch quality test results, which include sterility test, endotoxin test, mycoplasma test, specific growth factor activity test, and cell culture performance verification report.

[0088] In step S503, the module intelligently compares the batch quality inspection results with the preset quality standards and good manufacturing practice requirements, and automatically generates a compliance report and suggestions for handling non-conforming products.

[0089] Step S504: The module constructs a full-chain electronic traceability system based on batch number and production date identifiers to fully trace the production history, raw material source, quality inspection records and flow of any batch of culture medium, in order to meet audit and regulatory requirements.

[0090] The central intelligent decision-making engine further includes:

[0091] The data fusion and knowledge graph construction unit integrates heterogeneous data from all modules, including experimental data, literature data, market demand data, and regulatory data, and constructs a knowledge graph related to stem cell culture medium development. The machine learning and deep learning unit trains and optimizes multi-objective optimization algorithms, reinforcement learning models, and deep learning neural networks based on the knowledge graph and historical iteration data to learn the complex relationship between culture medium components and cell performance. The intelligent decision-making and iterative optimization unit predicts and optimizes culture medium formulations, culture process parameters, and quality control strategies from multiple dimensions based on the trained models, and generates intelligent decision suggestions to guide the automated execution or manual intervention of each module, thereby achieving continuous improvement and efficiency enhancement of the culture medium development process.

[0092] The culture medium composition database further includes: a basic component information database containing the names of various basic culture medium components, including amino acids, vitamins, growth factors, cytokines, inorganic salts, lipids, and buffers. The database also includes the CAS number, molecular structure, physicochemical properties, solubility, stability, and supplier information of the basic components; a biological function association database storing verified or predicted biological function association data between basic components and the proliferation, differentiation, metabolism, gene expression, and maintenance of specific functions of different stem cell types; and a cost and compliance information database containing cost information, supply chain information, and information on whether each component complies with animal-free and Good Manufacturing Practice (GMP) regulations.

[0093] In one embodiment, the culture medium component database is the core data source for formulation design and screening in this system; it is a hierarchical, multi-dimensional dataset. It further includes a basic component information database, which stores detailed names and classifications of various basic culture medium components, such as amino acids, vitamins, growth factors, cytokines, inorganic salts, lipids, and buffers. For each basic component, the database also includes its unique CAS number, detailed molecular structure, key physicochemical properties such as pH, solubility, stability, and storage conditions, as well as supplier information, ensuring transparency and traceability of component origin. Furthermore, a biological function association database is another important component of this database, storing verified or predicted biological function associations between the basic components and different stem cell types in proliferation, differentiation, metabolism, gene expression, and maintenance of specific functions.

[0094] In this embodiment, the central intelligent decision engine can specifically optimize the entire process of culture medium development based on preset stem cell culture conditions. The preset stem cell culture conditions include: specific stem cell types, with the system performing efficient expansion optimization for specific stem cell types; specific culture environment requirements, including serum-free culture, chemically limited culture, or xenogeneic component-free culture; and specific application goals, including cell therapy for specific diseases, drug screening platforms, or gene therapy vector production. The optimization process includes adjusting the target weights of the demand analysis and target setting module, changing the component screening strategy of the formulation design and simulation module, modifying the evaluation parameter emphasis of the culture validation and data analysis module, and adjusting the process parameter adjustment logic of the scale-up process optimization module, so that the finally developed culture medium best meets the preset stem cell culture conditions.

[0095] Those skilled in the art will understand that the above embodiments are merely exemplary, and various modifications and equivalent substitutions can be made without departing from the spirit and scope of the invention. For example, specific feature point algorithms, optimizer selection, distortion model details, etc., can be adjusted according to actual needs. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. An intelligent management system for the entire process of stem cell culture medium development, characterized in that: include, The requirements analysis and target setting module is used to receive and process stem cell culture medium development targets, which include specific stem cell types, application scenarios, and expected culture medium performance indicators. The formulation design and simulation module is used to intelligently screen and recommend suitable culture medium components from the culture medium component database based on the objectives determined by the demand analysis and target setting module, and to build an artificial intelligence prediction model to perform virtual simulation and effect prediction of the preliminary formulation. The culture verification and data analysis module is used to interface with culture experimental equipment to collect real-time data on cell status, culture medium consumption and products during small-scale stem cell culture, and to perform multi-dimensional analysis on the data to evaluate the culture effect of the preliminary formula. The scale-up process optimization module is used to connect to the bioreactor control system after the culture medium formulation has been validated on a small scale, and to monitor, integrate, and intelligently adjust the process parameters of the culture medium scale-up production in real time. The quality compliance and traceability module is used to manage batch quality data of stem cell culture media. The batch quality data includes batch test reports, quality control parameters and regulatory compliance documents, and provides full-chain traceability functionality. The central intelligent decision engine interacts with the demand analysis and target setting module, the formula design and simulation module, the culture verification and data analysis module, the scale-up process optimization module, and the quality compliance and traceability module for data exchange and command coordination. Based on historical data, real-time feedback, and preset rules, the central intelligent decision engine makes intelligent optimization decisions to guide the iteration and improvement of the entire process of stem cell culture medium development. The central intelligent decision-making engine further includes: The data fusion and knowledge graph construction unit integrates heterogeneous data from all modules, including experimental data, literature data, market demand data, and regulatory data, and constructs a knowledge graph related to stem cell culture medium development. The machine learning and deep learning unit trains and optimizes multi-objective optimization algorithms, reinforcement learning models, and deep learning neural networks based on the knowledge graph and historical iteration data to learn the complex relationship between culture medium components and cell performance. The intelligent decision-making and iterative optimization unit predicts and optimizes culture medium formulations, culture process parameters, and quality control strategies from multiple dimensions based on the trained models, and generates intelligent decision suggestions to guide the automated execution or manual intervention of each module.

2. The intelligent management system for the entire process of stem cell culture medium development according to claim 1, characterized in that: The system implements a fully intelligent management method for stem cell culture media, including: Step S1: Needs analysis and target setting for stem cell culture medium development. Through the needs analysis and target setting module, the stem cell culture medium development goals input by the user are collected and integrated. The development goals include specific stem cell types, specific application scenarios, and detailed expected culture medium performance indicators. The expected culture medium performance indicators include cell proliferation rate, cell viability, expression of specific biomarkers, cell purity, cell differentiation potential, cost-effectiveness of the culture medium, and requirements for no animal origin. Step S2, culture medium formulation design and component screening: Based on the development goals determined in step S1, the formulation design and simulation module uses an artificial intelligence prediction model to screen and generate a variety of potential initial culture medium formulations from the culture medium component database, and performs virtual simulation verification. The culture medium component database contains the physicochemical properties, biological functions, and verified synergistic or antagonistic effects of amino acids, vitamins, growth factors, cytokines, inorganic salts, lipids, and buffers. Step S3, small-scale culture verification and iterative optimization: Through the culture verification and data analysis module, a small-scale stem cell culture experiment is performed, and the cell growth status, morphological characteristics, changes in metabolites, and culture medium consumption are monitored in real time during the culture process. Flow cytometry data, high-throughput sequencing data, and microscopic image data are collected as cell quality assessment parameters. The central intelligent decision engine compares the cell quality assessment parameters with the expected culture medium performance indicators, generates optimization suggestions, and guides the formulation design and simulation module to iterate the formulation. Step S4, pilot-scale process verification and parameter optimization: After the initial formulation passes the small-scale verification in step S3, the culture medium formulation is applied to a pilot-scale bioreactor for scale-up production via the scale-up process optimization module. The scale-up culture process parameters are monitored and intelligently adjusted in real time, including stirring speed, aeration rate, dissolved oxygen concentration, pH value, and temperature. The system integrates process analysis data reflecting the culture process status and product quality attributes, combines predictive models to assess the cell culture status and culture medium performance changes during scale-up, and provides process optimization schemes to ensure the stability and uniformity of the culture medium in the scale-up batches. Step S5, Quality Control and Regulatory Compliance Management: Through the quality compliance and traceability module, comprehensive quality testing is conducted on batches of culture media that have completed pilot-scale amplification or formal production. The quality testing includes sterility testing, endotoxin testing, specific growth factor activity testing, and cell culture performance testing. The test results are compared with preset quality compliance standards. The module also records and manages batch information for all development, production, and quality control stages, enabling complete batch traceability of the culture media from raw materials to finished products, and generating batch reports and audit trail records that comply with Good Manufacturing Practices (GMP) requirements.

3. The intelligent management system for the entire process of stem cell culture medium development according to claim 2, characterized in that: The stem cell culture medium development requirements analysis and target setting in step S1 include: Step S101: Receive core performance indicators through the user interface and / or data import interface. The core performance indicators include target stem cell type, expected application scenario, target cell proliferation rate, cell viability, cell purity, and specific differentiation potential. Step S102: Based on the core performance indicators, the demand analysis and target setting module intelligently matches and retrieves the stem cell characteristic knowledge base inside the system. The knowledge base contains data on the growth requirements, metabolic characteristics, differentiation pathways, and the influence of known culture medium components on stem cells. In step S103, the central intelligent decision engine combines the knowledge base data and historical project data to prioritize and weight the core performance indicators, forming a multi-dimensional development target vector to guide subsequent recipe design.

4. The intelligent management system for the entire process of stem cell culture medium development according to claim 2, characterized in that: The culture medium formulation design and component screening in step S2 include: Step S201: Based on the stem cell type and application scenario determined in step S1, the formulation design and simulation module screens out candidate components related to stem cell growth and function maintenance from the culture medium component database. In step S202, the central intelligent decision engine calls the artificial intelligence prediction model to generate multiple initial culture medium formulations with different component ratios and combinations based on the historical data and interaction patterns of the candidate components. Step S203: The formulation design and simulation module performs virtual simulation of the initial culture medium formulation to predict its potential effects on the proliferation, differentiation, and functional expression of specific stem cell types in the expected application scenarios. In step S204, the central intelligent decision engine performs a preliminary evaluation and ranking of the initial culture medium formulations based on the virtual simulation results, and recommends the top few optimal formulations for the next experimental verification.

5. The intelligent management system for the entire process of stem cell culture medium development according to claim 2, characterized in that: The small-scale cultivation verification and iterative optimization in step S3 includes: Step S301: The culture verification and data analysis module collects cell proliferation curves, cell morphology images, metabolite concentration changes, dissolved oxygen and pH values ​​in real time during small-scale culture experiments as culture environment parameters. In step S302, the culture verification and data analysis module automatically processes and analyzes the collected data to extract key indicators such as cell growth rate, cell viability, and expression level of specific proteins. Step S303: The central intelligent decision engine intelligently compares the key indicators with the expected culture medium performance indicators to identify deficiencies or areas for optimization in the formulation. In step S304, the central intelligent decision engine generates targeted formula optimization suggestions based on the comparison results. The formula optimization suggestions include adjusting the concentration of specific ingredients, replacing ingredients with poor effects, or adding new ingredients. The suggestions are then fed back to the formula design and simulation module to initiate the next round of iterative formula design and verification process.

6. The intelligent management system for the entire process of stem cell culture medium development according to claim 2, characterized in that: The pilot-scale amplification process verification and parameter optimization in step S4 includes: In step S401, the scale-up process optimization module receives sensor data from the bioreactor in real time. The sensor data includes dissolved oxygen probe data, pH sensor data, temperature sensor data, online cell density monitoring data, and online nutrient consumption analysis data. Step S402: The system integrates process analysis technology tools to process and analyze the real-time sensor data online, and dynamically evaluate the performance stability of the culture medium and its impact on the cell culture state during the scale-up process. In step S403, the central intelligent decision engine intelligently adjusts the key process parameters of the bioreactor based on the process analysis technology data and the preset process control strategy. The key process parameters include stirring speed, aeration rate and feeding strategy, in order to maintain the optimal culture environment and ensure batch-to-batch consistency of the culture medium in the scale-up batches.

7. The intelligent management system for the entire process of stem cell culture medium development according to claim 2, characterized in that: The quality control and regulatory compliance management in step S5 includes: Step S501: The quality compliance and traceability module automatically records and manages key operational data and environmental parameters for all production processes, from raw material procurement, warehousing, culture medium preparation, dispensing, sterilization to final product batch testing. Step S502, the module integrates batch quality test results, which include sterility test, endotoxin test, mycoplasma test, specific growth factor activity test and cell culture performance verification report; Step S503: The module intelligently compares the batch quality inspection results with preset quality standards and good manufacturing practice requirements, and automatically generates a compliance report and suggestions for handling non-conforming products. Step S504: The module constructs a full-chain electronic traceability system based on batch number and production date identifier to fully trace the production history, raw material source, quality inspection records and flow of any batch of culture medium, in order to meet audit and regulatory requirements.

8. The intelligent management system for the entire process of stem cell culture medium development according to claim 1, characterized in that: The culture medium component database further includes: a basic component information database containing the names of various basic culture medium components, including amino acids, vitamins, growth factors, cytokines, inorganic salts, lipids, and buffers; the database also includes the CAS number, molecular structure, physicochemical properties, solubility, stability, and supplier information of the basic components; a biological function association database storing verified or predicted biological function association data between the basic components and the proliferation, differentiation, metabolism, gene expression, and maintenance of specific functions of different stem cell types; and a cost and compliance information database containing cost information, supply chain information, and information on whether each component complies with animal-free and Good Manufacturing Practice (GMP) regulations.

9. The intelligent management system for the entire process of stem cell culture medium development according to claim 3, characterized in that: The central intelligent decision engine can specifically optimize the entire process of developing the culture medium according to preset stem cell culture conditions. The preset stem cell culture conditions include: specific stem cell types, for which the system performs efficient expansion optimization; specific culture environment requirements, including serum-free culture, chemically limited culture, or xenogeneic component-free culture; and specific application goals, including cell therapy for specific diseases, drug screening platforms, or gene therapy vector production. The optimization process includes adjusting the target weights of the demand analysis and target setting module, changing the component screening strategy of the formulation design and simulation module, modifying the evaluation parameter emphasis of the culture verification and data analysis module, and adjusting the process parameter adjustment logic of the scale-up process optimization module, so that the finally developed culture medium best meets the preset stem cell culture conditions.

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