Medical instrument skin stimulation test data processing method based on artificial skin model

Abstract

The invention provides a medical instrument skin stimulation test data processing method based on an artificial skin model, and relates to the technical field of skin stimulation tests. Collecting the original OD value of each pore in the 96-pore plate under the wavelength of 570nm; calculating the survival rate and the standard deviation of the positive control group and the test sample group; verifying the data validity of the negative control group and the positive control group; and performing skin irritation judgment according to the average survival rate of the test sample group to obtain a skin irritation type. According to the method, the in-vitro skin model based on HTWSkinRHE is established, strict standardized culture conditions and grouping design are adopted, the batch-to-batch consistency and data comparability of the experimental model are remarkably improved, and the model can better meet more medical instrument requirements; the cell activity is detected by simulating a complete skin layered structure and combining an MTT method, and the judgment threshold value is highly matched with the in-vivo stimulation response.

Description

Technical Field

[0001] The present invention relates to the technical field of skin irritation tests, and in particular to a method for processing medical device skin irritation test data based on an artificial skin model. Background Art

[0002] With the rapid development of the global medical device industry, the demand for safety and biocompatibility evaluation is growing exponentially. According to data, the global medical device market will exceed $600 billion in 2023, with a compound annual growth rate of 5.4%. Implantable devices, patches, and other products that require long-term skin contact account for over 35% of this market. This rapid growth poses unprecedented challenges to traditional evaluation systems.

[0003] Currently, skin irritation tests for medical devices still mainly rely on animal experiments, but this method has multiple limitations: first, from an ethical perspective, approximately 500,000 experimental rabbits are used for medical device testing each year, triggering strong protests from animal welfare organizations; second, from an economic perspective, the cost of a single animal experiment is as high as US$15,000 to US$30,000, and the test cycle is as long as 4-6 weeks; more importantly, from a scientific perspective, the anatomical differences between animal and human skin lead to insufficient reliability in the extrapolation of results. Studies have shown that approximately 18% of clinical adverse reactions are not predicted by animal experiments.

[0004] The international regulatory landscape is undergoing profound changes. The EU Cosmetics Regulation (EC No. 1223 / 2009) has completely banned animal testing since 2013, and its successful implementation has provided a model for reform in the medical device sector. In 2024, the European Parliament passed the "Restriction of Animal Experimentation on Medical Devices Act," requiring a 40% reduction in animal testing within five years. This policy shift stems from both ethical considerations and breakthroughs in in vitro technology.

[0005] Significant technological progress has been made in in vitro surrogate models. However, the specificities of medical devices present unique challenges: material complexity (such as metal implants and polymer dressings) requires models with a broader spectrum of responsiveness; long-term contact (up to 30 days) requires extended culture system stability; and mechanical stress factors (such as dressing peeling) require the integration of dynamic mechanical stimulation modules. Existing models only achieve 60% to 75% of the sensitivity of animal experiments in these areas.

[0006] The gap between industry demand and technological bottlenecks is driving new research directions. In 2023, the ISO / TC194 working group launched a standard-setting project for "In vitro skin models for medical devices." This project emphasizes the need to develop: 3D skin models that incorporate immune cell co-cultures to assess chronic inflammatory responses; dynamic exposure systems that integrate microfluidics to simulate actual use conditions; and AI-based multi-parameter analysis algorithms to improve predictive accuracy.

[0007] Therefore, developing a specialized in vitro skin irritation evaluation method tailored to the characteristics of medical devices not only aligns with the international 3R principles (replacement, reduction, and optimization), but also meets the industry's urgent need for efficient and accurate evaluation technologies. This research holds significant scientific and innovative value, as well as socioeconomic benefits. This research is expected to drive fundamental changes in the medical device safety evaluation paradigm and provide key technical support for the modernization of the global regulatory system.

[0008] The general skin irritation evaluation of medical devices mainly relies on animal experiments and traditional in vitro models. However, these methods have significant defects in scientificity, ethics and applicability, including but not limited to the limitations of animal experiments, the difficulty of traditional in vitro models to adapt to the needs of medical devices, and insufficient sensitivity and predictive ability.

[0009] In order to solve the above-mentioned defects in the prior art, the present technical solution proposes a medical device skin irritation test data processing method based on an artificial skin model. Summary of the Invention

[0010] The present invention provides a medical device skin irritation test data processing method based on an artificial skin model, so as to solve the defects in the prior art.

[0011] In one aspect, the present invention provides a method for processing data from a medical device skin irritation test based on an artificial skin model, comprising: S1: Establishment of in vitro skin irritation model; S2: Based on the in vitro skin irritation model, the original OD value of each well in a 96-well plate at a wavelength of 570 nm was collected; each well contained a negative control group, a positive control group, and a test sample group; S3: Calculate the survival rate and standard deviation of the positive control group and the test sample group based on the original OD value; S4: Based on the preset acceptance criteria and the preset test substance data acceptance criteria, verify the data validity of the negative control group and the positive control group, and output the verification results; S5: Based on the verification results, skin irritation is determined according to the average survival rate of the test sample group to obtain the skin irritation type.

[0012] According to the method for processing data of medical device skin irritation test based on artificial skin model provided by the present invention, in step S1, the method of establishing an in vitro skin irritation model is as follows: establishing an in vitro skin model based on HTWSkinRHE, and irritating normal human keratinocytes of healthy volunteer donors at the air-liquid interface. An in vitro skin irritation model consisting of the basal, suprabasal, spinous, granular, and functional stratum corneum layers is established by culturing cells on a polycarbonate inert filter for 17 days.

[0013] According to the method for processing medical device skin irritation test data based on the artificial skin model provided by the present invention, in step S2, the step of collecting the original OD value includes: S2.1: Grouping is performed based on the in vitro skin irritation model, including a blank group, a negative control group, a positive control group, and a test sample group. Each group has three replicates and the grouping results are obtained; S2.2: Based on the grouping results, the treated epidermal tissues were transferred to a 24-well plate containing 300 μL MTT solution and incubated at 37°C, 5% CO2 for 3 hours. S2.3: After the incubation period, remove excess MTT solution from the bottom of the tissue and transfer the tissue to a 24-well plate containing 750 μL of isopropanol, ensuring that the tissue is completely immersed. Add an additional 750 μL of isopropanol to each well to cover the top of the tissue. Incubate at room temperature in the dark for 2 hours. S2.4: Use the tip of the forceps to pierce the tissue and the polycarbonate filter and pipette the solution three times to fully dissolve the formazan. S2.5: Take three 200 μL aliquots of the extract from each well and transfer them to the corresponding wells of a 96-well plate. Set up 6 wells with isopropanol solution as blank controls. Use a microplate reader to measure the OD value of each well at a wavelength of 570 nm and record the raw data.

[0014] According to the method for processing medical device skin irritation test data based on the artificial skin model provided by the present invention, in step S3, the step of calculating the survival rate includes: S3.1: Calculate the blank-corrected OD value of each group based on the original OD value of the blank group; S3.2: Based on the blank-corrected OD values ​​of each group, calculate the average of three replicate blank-corrected OD values ​​for each tissue in each group; S3.3: Based on the average of the blank-corrected OD values ​​of the negative control group, calculate the survival rate and standard deviation of the positive control group and the test sample group, respectively.

[0015] According to the data processing method for medical device skin irritation test based on the artificial skin model provided by the present invention, in step S3.1, the step of calculating the blank-corrected OD value of each group includes: S3.1.1: Calculate the average OD value based on the original OD value of the blank group and output the average OD value of the blank group; S3.1.2: Calculate the blank-corrected OD value of each group based on the average OD value of the blank group.

[0016] According to the data processing method for medical device skin irritation test based on the artificial skin model provided by the present invention, in step S3.2, the average value of the blank-corrected OD values ​​of each tissue in each group repeated three times is expressed as follows:

[0017] Where, ODSj is the average OD value of the jth tissue, j is the tissue number, k is the number of measurements, OD jk is the OD value of the kth repetition of the jth tissue.

[0018] According to the data processing method for medical device skin irritation test based on the artificial skin model provided by the present invention, in step S3.3, the formula for calculating the standard deviation is expressed as:

[0019] Where, OD S is the final representative value of the experimental group, and SD is the standard deviation within the group.

[0020] According to the method for processing medical device skin irritation test data based on the artificial skin model provided by the present invention, in step S4, the preset test substance data acceptance criteria are: The standard deviation within each batch is ≤18%; For a given test substance, if one batch predicts a different class of stimuli, the test substance must be retested in another batch; In this case, the results of all four batches will be considered as the final average and analyzed.

[0021] According to the method for processing data of a medical device skin irritation test based on an artificial skin model provided by the present invention, in step S4, the step of verifying the validity of the data of the negative control group and the positive control group includes: S4.1: Based on the historical database, if the mean OD value of the three tissues at 570 nm is ≥1.2, the data of the negative control group meet the acceptance criteria; if the standard deviation is ≤18%, the standard deviation is valid; S4.2: If the mean survival rate is <40% and the standard deviation is ≤18%, the data from the positive control group meet the acceptance criteria; S4.3: If the data from the negative control group and the data from the positive control group meet the above standard requirements, the data from all test substances in a batch are valid.

[0022] According to the data processing method for a medical device skin irritation test based on an artificial skin model provided by the present invention, in step S5, the skin irritation is determined as follows: if the average survival rate of at least one solvent extract is ≤50%, it is determined to be irritating; if the average survival rates of both solvent extracts are >50%, it is determined to be non-irritating.

[0023] The present invention provides a data processing method for a medical device skin irritation test based on an artificial skin model. By establishing an in vitro skin model based on HTWSkinRHE and adopting strictly standardized culture conditions and grouping design, the batch consistency and data comparability of the experimental model are significantly improved, and the use of this model can better adapt to more medical device needs. The three-well design combined with the strategy of averaging three OD value measurements effectively reduces the operating error and controls the standard deviation within a strict range of ≤18%. Through blank-corrected OD value calculation, survival rate formula determination and dynamic standard deviation monitoring, fully automated analysis from raw data to irritation determination is achieved. The preset double acceptance criteria can instantly identify invalid batches and avoid waste of resources. By simulating the complete skin layered structure and combining the MTT method to detect cell activity, its judgment threshold is highly consistent with the in vivo irritation response. The dual-solvent extract detection mechanism can identify irritants with different solubility characteristics, significantly reduce the risk of false negatives, and provide more comprehensive data support for medical device safety assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 The present invention provides a flowchart of a method for processing medical device skin irritation test data based on an artificial skin model. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0027] Example 1: The following combination Figure 1 The present invention describes a method for processing data of a medical device skin irritation test based on an artificial skin model.

[0028] like Figure 1 As shown, the method for processing medical device skin irritation test data based on an artificial skin model provided by an embodiment of the present invention includes: S1: Establish an in vitro skin stimulation model. The specific method is to establish an in vitro skin model based on HTWSkinRHE and use normal human keratinocytes from healthy volunteers to stimulate the skin at the air-liquid interface. After 17 days of culture on a polycarbonate inert filter, a three-dimensional HTWSkinRHE model was formed. The HTWSkinRHE (Hierarchical Temporal-WindowSkin Rheology Model) is a hierarchical, time-varying rheological model used to simulate the biomechanical properties of skin, primarily for applications in medical simulation, robotic haptics, and virtual reality. It comprises the primary basal, suprabasal, spinous, and granular layers, as well as a functional stratum corneum, similar to an in vivo model. Construction of this model strictly adheres to aseptic cell culture procedures to ensure the absence of microbial contamination, such as bacteria and fungi, in the culture environment to ensure model stability and the reliability of test results. During the culture process, cell morphology and growth were regularly observed under an inverted microscope to confirm the proper formation of each layer, particularly the integrity of the functional stratum corneum, which serves as a critical barrier against external stimuli. The model system should allow the direct application of polar (e.g., saline) and non-polar (e.g., sesame oil) extracts to the apical surface of the RHE construct. Cytotoxicity, as reflected by the MTT assay, is used to predict acute skin irritation from medical devices, a test system recommended in the standard.

[0029] S2: Based on the in vitro skin irritation model, the original OD value of each well in a 96-well plate at a wavelength of 570 nm was collected. Each well contains a negative control group, a positive control group, and a test sample group.

[0030] The experimental equipment includes a vertical steam sterilizer, a vertical single-door double-layer constant-temperature shaking incubator, a CO2 incubator, a steel ruler, an electronic balance, a clean bench, a low-speed automatic balancing centrifuge, and a Multiskan™ Sky microplate reader. Equipped with a high-precision optical detection system, it accurately measures absorbance at various wavelengths and supports simultaneous multi-channel analysis, improving experimental efficiency. An inverted microscope (with imaging capabilities) facilitates observation of cell morphology, structural changes, and abnormalities in the HTWSkinRHE model, and allows for recording of relevant image data. The Multiskan™ Sky microplate reader is a high-performance, full-wavelength microplate reader, covering the UV-Vis-NIR spectral range of 200-1000nm, offering multiple detection modes including absorbance, fluorescence, luminescence, and time-resolved fluorescence. Its dual-grating monochromator and dual-beam design, coupled with 1nm high resolution and real-time reference correction, enables rapid high-throughput analysis of 6-384-well plates (a full-wavelength scan of a 96-well plate takes only 5 seconds). The device integrates an intelligent temperature control system (5-65°C) and HDR high dynamic range detection technology, and is suitable for molecular biology detection, drug screening, environmental monitoring and other fields.

[0031] Reagents include: maintenance culture medium, PBS (phosphate-buffered saline, with a stable pH, commonly used for washing cells or tissues to avoid osmotic damage), 0.9% sodium chloride injection (SC, a polar extractant with an osmotic pressure similar to that of human body fluids, simulating the environment of polar substances contacting the skin), pharmaceutical-grade sesame oil (SO, a non-polar extractant, simulating the conditions of non-polar substances contacting the skin. Pharmaceutical-grade standards ensure high purity and minimize interference with test results), 20% SDS, MTT, and isopropyl alcohol (as a solubilizer for formazan crystals; its purity must meet test requirements to ensure sufficient dissolution of formazan and accurate test results). SDS refers to sodium dodecyl sulfate, an anionic surfactant with cleaning, emulsification, and protein solubilization properties. It disrupts cell membranes and is used to extract intracellular proteins or nucleic acids. MTT refers to 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, a yellow, water-soluble dye commonly used in cell viability assays. Used to assess cell proliferation, survival, or toxicity. The principle is that succinate dehydrogenase, present in the mitochondria of living cells, reduces MTT to blue-purple formazan crystals, whereas dead cells lack this ability. Measuring the absorbance of formazan (usually around 490 nm) indirectly reflects the number of viable cells and is widely used in drug screening, cytotoxicity assays, and other applications.

[0032] The samples were extracted according to the ratio (sample: extract volume) shown in Table 1, and the control solution was prepared under the same conditions.

[0033] Table 1:

[0034] Before sample leaching, the inert container must be strictly sterilized, which can be done by high-pressure steam sterilization or ultraviolet irradiation. The sampling process must be completed in a clean workbench, and the sample must be accurately weighed using sterilized tools to ensure that the accuracy of the sampling volume meets the requirements of Table 1. During the extraction process, the temperature and oscillation frequency of the constant temperature oscillating box must be regularly checked to ensure that they are stable at the set values ​​to avoid fluctuations in environmental parameters that affect the extraction effect. The state of the leaching solution was observed before and after leaching. No particles or color changes were observed before and after extraction. After extraction, the extract was immediately used for testing. No pH adjustment, filtration, centrifugation, or dilution was performed on the extract before use. The color and pH of the extract did not change before and after use. The pH value was 5.5 after extraction. The state of the extract is shown in Table 2. Control samples were prepared under the same conditions.

[0035] Table 2 (extract state):

[0036] When observing the state of the extract, it is necessary to use the same observation angle and method under sufficient lighting conditions to ensure the objectivity of the results. For color description, refer to the standard colorimetric chart for judgment. For observation of clarity and particles, the extract can be placed in a transparent colorimetric tube and compared with a blank solution.

[0037] The steps for collecting raw OD values ​​include: S2.1: Grouping was performed based on the in vitro skin irritation model, including a blank group, a negative control group, a positive control group, and a test sample group. Each group had 3 replicates to obtain the grouping results. Setting replicates can reduce experimental errors and improve the reliability of the results. The setting of 3 replicates meets the general requirements of biological experiments. Preheat the maintenance culture medium at room temperature, mark a 6-well plate (3 replicates per group), including the test sample, positive control, solvent control, and negative control, and add 1 mL of preheated maintenance culture medium to the 6-well plate. Use sterile tweezers to transfer the tissue at an angle first. Be gentle during the transfer process to avoid damaging the tissue model. Observe whether there are bubbles under the 6-well plate. If there are bubbles, gently shake the plate to expel them to prevent the bubbles from affecting the contact between the tissue and the culture medium. Use a pipette under sterile conditions to dispense 100 μL (i.e., 200 μL / cm 2 ) with the corresponding test substance (three replicates per test substance), and a negative control (100 μL of PBS). Using a tip, gently spread the test substance evenly over the epidermal surface. Cover the plate and incubate the plate containing the treated HTWSkinRHE tissue for 24 hours at 37°C, 5% CO2, and 95% humidity. See Table 3 for test and control samples.

[0038] Table 3:

[0039] All sample and control information in Table 3 must be accurately recorded, including batch number, storage conditions, etc., for traceability. Descriptions of the properties and color of the test samples must be objective and truthful to ensure test reproducibility.

[0040] S2.2: Prepare a 24-well plate and add 300 μL of MTT solution (dilute the MTT stock solution in maintenance medium to a final concentration of 1 mg / mL). Remove the 6-well plate from the incubator and remove the epidermal tissue using sterile forceps. Rinse thoroughly 25 times with 1 mL of PBS at a distance of 5-8 cm from the tissue to remove any residual test substance from the epidermal surface. After the final rinse, dry the bottom of the tissue on sterile absorbent paper or gauze for 1-2 seconds. Gently wipe the stratum corneum with a double-ended sterile cotton swab (5-6 strokes per end). The rinsed tissue can be placed in a 24-well plate containing 300 μL of maintenance medium. After all tissues have been rinsed, remove any excess medium from the bottom of the tissue with absorbent paper and tilt the plate into the 24-well plate containing the MTT solution. Check for bubbles. Cover and incubate at 37°C, 5% CO2, and 95% humidity for 3 hours.

[0041] S2.3: Add 750 μL of isopropanol to a 24-well plate. After incubation, remove any excess MTT solution from the bottom of the tissues and, using sterile forceps, transfer the treated tissues to the plate containing the isopropanol solution. Add 750 μL of isopropanol solution on top of each tissue, ensuring that the tissue is completely covered with the isopropanol solution. Cover the wells, place in a resealable plastic bag, and incubate at room temperature for 2 hours (± 5 minutes) with gentle agitation (approximately 150 rpm) to extract the formazan.

[0042] S2.4: After the incubation period, remove the plate from the plastic bag. Use isopropanol solution as a blank (replicate 6 times). Pierce the tissue and polycarbonate filter with the tip of a forceps and dissolve the formazan evenly by pipetting up and down 3 times.

[0043] S2.5: Transfer three 200 μL aliquots of the extract from each well to the corresponding wells of a 96-well plate. Perform the transfer accurately to avoid cross-contamination and ensure consistent liquid volume in each well. Set up six additional wells containing isopropanol as a blank control. Measure the OD value of each well using a microplate reader at a wavelength of 570 nm. Calibrate the reader before measurement to ensure it is in proper working order. Record all instrument settings and raw data during the measurement.

[0044] S3: Based on the original OD values, calculate the survival rate and standard deviation of the positive control group and the test sample group.

[0045] The steps to calculate survival rate include: S3.1: Calculate the blank-corrected OD value of each group based on the original OD value of the blank group. The steps include: S3.1.1: Calculate the average OD value based on the original OD value of the blank group and output the average OD value of the blank group.

[0046] S3.1.2: According to the average OD value of the blank group blank, calculate the blank-corrected OD value of the group.

[0047] PBS-treated negative control (NC): Calculate blank-corrected OD NC =OD NCraw –OD blank .

[0048] SDS-treated positive control (PC): Calculate blank-corrected OD PC =OD PCraw –OD blank .

[0049] Test sample group (TT): Calculate the blank correction value OD TT =OD TTraw –OD blank .

[0050] The purpose of blank correction is to eliminate the influence of the isopropanol solution itself on the absorbance, so that the OD value of each group can more truly reflect the amount of formazan, that is, the number of living cells.

[0051] Table 4 is the experimental calculation results of the blank-corrected OD value.

[0052] Table 4:

[0053] S3.2: Based on the blank-corrected OD values ​​of each group, calculate the average of the three replicate blank-corrected OD values ​​for each tissue in each group. The formula is:

[0054] Where, OD Sj is the average OD value of the jth tissue, j is the tissue number, k is the number of measurements, OD jk is the OD value of the kth repetition of the jth tissue.

[0055] Table 5 is the experimental calculation result table of the average OD value.

[0056] Table 5:

[0057] By calculating the average of three replicates for each tissue, the random error of a single measurement was reduced and the reliability of the data was improved.

[0058] S3.3: Based on the average of the blank-corrected OD values ​​of the negative control group, calculate the survival rate and standard deviation of the positive control group and the test sample group, respectively.

[0059] The survival rate of the positive control group was calculated as:

[0060]

[0061] Where, PC% is the survival rate of the positive control group, PC% av is the average survival rate of all tissues in the positive control group, and N is the number of all tissues in the positive control group.

[0062] The survival rate of the test sample group is calculated as follows:

[0063]

[0064] Among them, TT% is the survival rate of the test sample group, TT% av is the average survival rate of all tissues in the experimental sample group, and M is the number of all tissues in the experimental sample group.

[0065] The formula for calculating standard deviation is:

[0066] Where, OD S is the average OD value of multiple replicates of the experimental group, SD is the standard deviation within the group, and 3 represents 3 replicates for each tissue.

[0067] Standard deviation is used to measure the degree of dispersion of data. The smaller the standard deviation, the better the repeatability of the data and the more reliable the test results.

[0068] Table 6 is the experimental calculation result table of survival rate.

[0069] Table 6:

[0070] S4: Based on the preset acceptance criteria and the preset test substance data acceptance criteria, verify the data validity of the negative control group and the positive control group, and output the verification results.

[0071] In step S4, the acceptance criteria for the test substance data are preset as follows: Interassay mean viability was calculated from three independent assays or runs using the intraassay tissue mean (3 tissues per run), with a standard deviation within each batch ≤18%.

[0072] For a given test substance, if one batch predicts a different class of stimulus, the test substance must be retested in another batch.

[0073] In this case, the results from all four batches will be considered the final average for analysis. This standard is established to ensure the stability and reliability of the irritation determination results for the test substance and to avoid misjudgments due to accidental errors in a single test. When inconsistent results occur between batches, the accuracy of the results can be improved by adding more batches and combining the results from multiple batches.

[0074] In step S4, the steps of verifying the validity of the data of the negative control group and the positive control group include: S4.1: Based on the historical database, if the mean OD value of the three tissues at 570 nm is ≥1.2, the NC data meet the acceptance criteria. If the standard deviation is ≤18%, the standard deviation is considered valid. A mean OD value of ≥1.2 for the negative control group indicates good cell viability and a healthy model system. A standard deviation of ≤18% indicates good reproducibility of the negative control data and stable experimental conditions.

[0075] S4.2: PC data meet the acceptance criteria if the mean viability (expressed as a percentage of NC) is <40% and the standard deviation is ≤18%. A mean viability of the positive control <40% indicates that the positive control substance (20% SDS) effectively reduces cell viability and exhibits significant irritation, consistent with its intended role as a positive control. A standard deviation of ≤18% indicates that the positive control data are reproducible and can be used to verify the sensitivity of the assay system.

[0076] S4.3: If both the negative and positive control data meet the above criteria, all test substance data within a batch are considered valid. Only when both the negative and positive control data meet the acceptance criteria can the entire test system be considered stable, reliable, and sensitive. Only then can the test substance data obtained have reference value and be used in subsequent irritation determinations.

[0077] S5: Based on the verification results, the skin irritation is determined according to the average survival rate of the test sample group to obtain the skin irritation type. The way to determine skin irritation is: if the average survival rate of at least one solvent extract is ≤50%, it is determined to be irritating. If the average survival rates of both solvent extracts are >50%, it is determined to be non-irritating. By comparing the relationship between the average survival rate of the test sample group in polar and non-polar solvent environments and the threshold of 50%, the acute irritation effect of the medical device on the skin can be predicted more accurately. When the average survival rate of at least one solvent extract is ≤50%, it indicates that the medical device may cause skin irritation under the corresponding contact environment; when the average survival rate of both solvent extracts is >50%, it means that the medical device is less irritating to the skin under these two common contact environments and can be determined to be non-irritating.

[0078] In summary, the data processing method for the medical device skin irritation test based on the artificial skin model provided by the present invention establishes an in vitro skin model based on HTWSkinRHE, adopts strictly standardized culture conditions and group design, significantly improves the batch consistency and data comparability of the experimental model, and the use of this model can better adapt to more medical device needs. The three-well design combined with the strategy of averaging three OD value measurements effectively reduces the operating error and controls the standard deviation within a strict range of ≤18%. Through blank-corrected OD value calculation, survival rate formula determination and dynamic standard deviation monitoring, fully automated analysis from raw data to irritation determination is achieved. The preset double acceptance criteria can instantly identify invalid batches and avoid waste of resources. By simulating the complete skin layered structure and combining the MTT method to detect cell activity, its judgment threshold is highly consistent with the in vivo irritation response. The dual-solvent extract detection mechanism can identify irritants with different solubility characteristics, significantly reduce the risk of false negatives, and provide more comprehensive data support for medical device safety assessment.

[0079] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for processing data from a medical device skin irritation test based on an artificial skin model, characterized in that: include: S1: Establishment of in vitro skin irritation model; S2: Based on the in vitro skin irritation model, the original OD value of each well in a 96-well plate at a wavelength of 570 nm is collected; each well includes a negative control group, a positive control group, and a test sample group; S3: Calculate the survival rate and standard deviation of the positive control group and the test sample group according to the original OD value; S4: Verifying the data validity of the negative control group and the positive control group based on the preset acceptance criteria and the preset test substance data acceptance criteria, and outputting the verification results; S5: Based on the verification result, skin irritation is determined according to the average survival rate of the test sample group to obtain the skin irritation type.

2. The method for processing medical device skin irritation test data based on an artificial skin model according to claim 1, wherein: In step S1, the in vitro skin stimulation model is established by: establishing an in vitro skin model based on HTWSkinRHE, and stimulating normal human keratinocytes from healthy volunteers at the air-liquid interface. An in vitro skin irritation model consisting of the basal, suprabasal, spinous, granular, and functional stratum corneum layers is established by culturing cells on a polycarbonate inert filter for 17 days.

3. The method for processing medical device skin irritation test data based on an artificial skin model according to claim 1, wherein: In step S2, the step of collecting the original OD value includes: S2.1: Perform grouping based on the in vitro skin irritation model, including a blank group, a negative control group, a positive control group, and a test sample group, with three replicates in each group, to obtain grouping results; S2.2: Based on the grouping results, the treated epidermal tissues were transferred to a 24-well plate containing 300 μL MTT solution and incubated at 37°C, 5% CO2 for 3 hours; S2.3: After the incubation period, remove excess MTT solution from the bottom of the tissue and transfer the tissue to a 24-well plate containing 750 μL of isopropanol, ensuring that the tissue is completely immersed. Add an additional 750 μL of isopropanol to each well to cover the top of the tissue. Incubate at room temperature in the dark for 2 hours. S2.4: Use the tip of the forceps to pierce the tissue and the polycarbonate filter and pipette the solution three times to fully dissolve the formazan. S2.5: Take three 200 μL aliquots of the extract from each well and transfer them to the corresponding wells of a 96-well plate. Set up 6 wells with isopropanol solution as blank controls. Use a microplate reader to measure the OD value of each well at a wavelength of 570 nm and record the raw data.

4. The method for processing medical device skin irritation test data based on an artificial skin model according to claim 3, wherein: In step S3, the step of calculating the survival rate includes: S3.1: Calculate the blank-corrected OD value of each group based on the original OD value of the blank group; S3.2: Based on the blank-corrected OD values ​​of each group, calculate the average of three replicate blank-corrected OD values ​​for each tissue in each group; S3.3: Based on the average of the blank-corrected OD value of the negative control group, calculate the survival rate and standard deviation of the positive control group and the test sample group, respectively.

5. The method for processing medical device skin irritation test data based on an artificial skin model according to claim 4, characterized in that: In step S3.1, the steps of calculating the blank-corrected OD value of each group include: S3.1.1: Calculate the average OD value based on the original OD value of the blank group and output the average OD value of the blank group; S3.1.2: Calculate the blank-corrected OD value of the group according to the average OD value of the blank group.

6. The method for processing medical device skin irritation test data based on an artificial skin model according to claim 4, characterized in that: In step S3.2, the formula for the average of the blank-corrected OD values ​​of three replicates for each tissue in each group is expressed as: Where, OD Sj is the average OD value of the jth tissue, j is the tissue number, k is the number of measurements, OD jk is the OD value of the kth repetition of the jth tissue.

7. The method for processing medical device skin irritation test data based on an artificial skin model according to claim 6, characterized in that: In step S3.3, the formula for calculating the standard deviation is expressed as: Where, OD S is the final representative value of the experimental group, and SD is the standard deviation within the group.

8. The method for processing medical device skin irritation test data based on an artificial skin model according to claim 1, wherein: In step S4, the acceptance criteria for the test substance data are preset as follows: The standard deviation within each batch is ≤18%; For a given test substance, if one batch predicts a different class of stimuli, the test substance must be retested in another batch; In this case, the results of all four batches will be considered as the final average and analyzed.

9. The method for processing medical device skin irritation test data based on an artificial skin model according to claim 1, wherein: In step S4, the step of verifying the validity of the data of the negative control group and the positive control group includes: S4.1: Based on the historical database, if the mean OD value of the three tissues at 570 nm is ≥1.2, the data of the negative control group meet the acceptance criteria; if the standard deviation is ≤18%, the standard deviation is valid; S4.2: If the mean survival rate is <40% and the standard deviation is ≤18%, the data of the positive control group meet the acceptance criteria; S4.3: If the data from the negative control group and the data from the positive control group meet the criteria of steps S4.1-S4.2, then the data from all test substances in a batch are valid.

10. The method for processing medical device skin irritation test data based on an artificial skin model according to claim 9, characterized in that: In step S5, the skin irritation is determined as follows: if the average survival rate of at least one solvent extract is ≤50%, it is determined to be irritating; if the average survival rates of both solvent extracts are >50%, it is determined to be non-irritating.

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