SYSTEM AND METHOD FOR SIMULATION OF MEDICAL DEVICES - Patent application

Abstract

Disclosed herein are apparatus and methods for generating and / or validating predicted temperature stress time creep coefficients, approving or rejecting medical devices, validating medical device designs using linear device models, and generating coefficients using thermal analysis.

Description

[Technical field]

[0001] Aspects of the present disclosure relate to designing and testing devices, e.g., medical devices, such as auto-injectors, based on simulation and / or degradation testing of device characteristics. More specifically, embodiments of the present disclosure relate to auto-injectors and methods for developing auto-injectors and associated components that meet quality and usage standards. [Background technology]

[0002] Various available medical devices, such as autoinjectors, include multiple parts. The entire medical device and / or one or more of the multiple parts are often required to meet quality or usage standards so that the medical device can operate in an acceptable manner over the duration of their individual lives. Deviations from the quality or usage standards may result in suboptimal performance or failure of the medical device. However, testing a medical device design in multiple iterations and updating the device and / or its parts to meet the quality or usage standards may not be possible due to time and / or cost constraints. Thus, an efficient method is needed to determine whether a medical device having a given design meets one or more quality or usage standards. Summary of the Invention

[0003] Methods and systems for analyzing medical devices and / or designs are disclosed herein. In one embodiment of the present disclosure, a method for determining accelerated test parameters for a medical device includes receiving raw creep coefficient data relating creep strain to stress duration and stress amount, factored by temperature range, generating prediction coefficients based on the raw creep coefficient data, and using the prediction coefficients to generate one or more of an accelerated test time, an accelerated stress, or an accelerated temperature.

[0004] The accelerated test time is generated based on the reference creep strain, the reference stress, and the accelerated temperature. The method includes generating an accelerated test creep strain based on an accelerated test of the medical device, the accelerated test being based on the accelerated temperature, the accelerated test time, and the accelerated stress, and outputting one of a medical device approval indication or a medical device rejection indication based on a comparison of the accelerated test creep strain and the reference creep strain. The approval indication approves the medical device, and the rejection indication rejects the medical device. The method further includes generating an accelerated test creep strain based on an accelerated test of the medical device, the accelerated test being based on the accelerated temperature, the accelerated test time, and the accelerated stress, and outputting one of a predicted coefficient approval indication or a predicted coefficient rejection indication based on a comparison of the accelerated test creep strain and the reference creep strain. The medical device is manufactured based on a medical device design corresponding to another medical device, and the raw creep coefficient data is based on the other medical device. The predicted coefficient is generated based on a three-dimensional (3D) interpolation of the raw creep strain data. Raw creep strain data is generated based on one of the simulated or experienced strains.

[0005] In another embodiment of the present disclosure, a method for validating a prediction coefficient of a medical device includes receiving raw creep strain data relating creep strain values ​​to stress duration and stress amount, with temperature range as a factor; generating a prediction coefficient configured to output an accelerated temperature, an accelerated time, and an accelerated stress based on a reference creep strain; receiving an accelerated temperature, an accelerated time, and an accelerated stress based on the reference creep strain; receiving an accelerated test creep strain of the medical device based on an accelerated test performed based on the accelerated temperature, the accelerated time, and the accelerated stress; and outputting one of an approval or rejection indication based on a comparison of the accelerated test creep strain and the reference creep strain.

[0006] According to the method, prediction coefficients are generated based on three-dimensional (3D) interpolation of raw creep strain data. A first creep strain for a first duration, a first stress amount, and a first temperature is different from a second creep strain for a first duration, a first stress amount, and a second temperature. The reference creep strain corresponds to a reference temperature, a reference time, and a reference stress. The reference temperature is an ambient temperature, the reference time is an expected storage life of the medical device, and the reference stress is an expected stress amount. An approval instruction approves the prediction coefficients and a rejection instruction rejects the prediction coefficients.

[0007] In another embodiment of the present disclosure, a method for validating a medical device design includes receiving a plurality of medical device relationships based on the medical device design, the plurality of medical device relationships corresponding to voltage, current, resistance, torque, speed, and force relationships of the medical device design and including a plurality of coefficients, generating a linear equipment model based on the plurality of medical device relationships, receiving simulated coefficient values ​​for each of the plurality of coefficients from a distribution function for the plurality of medical device relationships, generating a simulated power distribution for voltage, current, resistance, torque, speed, or force based on the simulated coefficient values ​​and the linear equipment model, comparing the simulated power distribution to a threshold power requirement, and outputting one of an approve or reject instruction based on comparing the simulated power distribution to the threshold power requirement.

[0008] According to the method, the distribution function is a normal distribution function or a mixture of normal distribution functions. Comparing the simulated power distribution to the threshold power requirement includes comparing a maximum distribution value to a maximum threshold power requirement, comparing a minimum distribution value to a minimum threshold power value, or comparing a peak distribution value to a peak threshold power value. The simulated power distribution is based on at least 10,000 simulations performed in less than one hour. The plurality of medical device relationships are based on a viscosity of a substance, the viscosity of the substance being calculated based on a substance temperature behavior determined based on a thermal analysis. A coefficient of the plurality of coefficients is generated based on a fluid path restriction analysis, the fluid path restriction analysis outputting a force acting as a factor of the determined viscosity and the determined velocity. [Brief description of the drawings]

[0009] [Figure 1A] FIG. 1 is a perspective view of an autoinjector according to an example of the present disclosure. [Figure 1B] FIG. 1B is a bottom view of the autoinjector of FIG. 1A according to an example of the present disclosure. [Figure 1C] FIG. 1B is a side view of the autoinjector of FIG. 1A according to an example of the present disclosure. [Diagram 2] FIG. 12 is an exploded view of the autoinjector of FIG. 11 according to an example of the present disclosure. [Figure 3A] FIG. 1 is a diagram for determining a creep strain profile according to an example of the present disclosure. [Figure 3B] 1 is a flow chart for generating a creep strain relationship according to an example of the present disclosure. [Figure 3C] FIG. 2 illustrates a chart of temperature-based creep coefficient taking into account time and stress according to an example of the present disclosure. [Figure 3D] FIG. 1 includes a chart of predictive temperature stress and time creep modulus according to an example of the present disclosure. [Figure 4] FIG. 1 is a diagram including parts of a linear equipment model according to an example of the present disclosure. [Figure 5A]FIG. 1 is a diagram including a system model according to an example of the present disclosure. [Figure 5B] FIG. 5B is a diagram including inputs to the system model of FIG. 5A according to an example of the present disclosure. [Figure 6] FIG. 5B includes output from the system model of FIG. 5A according to an example of the present disclosure. [Figure 7] FIG. 5B illustrates the role of the system model of FIG. 5A in the design process, according to an example of the present disclosure. [Figure 8A] FIG. 2 is a diagram of a structural assembly according to an example of the present disclosure. [Figure 8B] FIG. 2 is a diagram of a structural assembly according to an example of the present disclosure. [Figure 9A] FIG. 13 illustrates a plunger response according to an example of the present disclosure. [Figure 9B] FIG. 13 illustrates a plunger response according to an example of the present disclosure. [Figure 10] FIG. 1 illustrates a thermal analysis according to an example of the present disclosure. [Figure 11] FIG. 1 illustrates a fluid path restriction analysis according to an example of the present disclosure. [Figure 12] FIG. 1 illustrates a drop test analysis result according to an example of the present disclosure. [Figure 13A] FIG. 1 illustrates a leakage assessment according to an example of the present disclosure. [Figure 13B] FIG. 1 illustrates a physiological model according to an example of the present disclosure. [Figure 13C] FIG. 1 illustrates a needle coring according to an example of the present disclosure. [Figure 14] FIG. 1 illustrates a product life cycle according to an example of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various exemplary embodiments and, together with the description, serve to explain the principles of the disclosed embodiments. The drawings illustrate different aspects of the disclosure and, where appropriate, reference numerals indicating similar structures, components, materials, and / or elements in different drawings are similarly labeled. It is understood that various combinations of structures, components, and / or elements in the various embodiments other than those specifically shown are contemplated and are within the scope of the present disclosure.

[0011] Many embodiments are described and illustrated herein. The described apparatus and methods are not limited to any single aspect or embodiment thereof, nor to any combination and / or permutation of such aspects and / or embodiments. Moreover, each of the described aspects of the invention and / or its embodiments may be used alone or in combination with one or more of the other aspects and / or embodiments of the described invention. For the sake of brevity, certain permutations and combinations are not separately described and / or illustrated herein.

[0012] The accompanying drawing appendix showing various exemplary embodiments is included with the above description and drawings, which are incorporated in and constitute a part of this specification. Many embodiments are described and illustrated herein. The present disclosure is not limited to any single aspect or embodiment thereof, nor to any combination and / or permutation of such aspects and / or embodiments. Each of the aspects and / or embodiments of the present disclosure may be used alone or in combination with one or more of the other aspects and / or embodiments of the present disclosure. For the sake of brevity, many of these combinations and permutations are not described separately herein.

[0013] In particular, for simplicity and clarity of description, certain aspects of the drawings show schematic structures and / or methods of construction of various embodiments. Descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring other features. Elements in the drawings are not necessarily drawn to scale, and dimensions of some features may be exaggerated relative to other elements to enhance understanding of the illustrative embodiments. For example, those skilled in the art will appreciate that cross-sectional views are not drawn to scale and should not be considered as representing proportional relationships between different components. Cross-sectional views are provided to facilitate explanation of various components of the illustrated assemblies and to show their relative positions to one another.

[0014] Reference will now be made in detail to examples of the present disclosure, as illustrated in the accompanying drawings. Where possible, the same reference numbers are used throughout the drawings to designate the same or similar parts. The embodiments of the present disclosure may be used with any type of fluid-containing product, such as liquid drug substances, liquid placebos, or other liquids that can be dispensed in dosage forms. In the following description, the terms "about," "approximately," "substantially," and the like, when used in describing numerical values, indicate a variation of + / - 10% of the value, unless otherwise specified.

[0015] As used herein, the terms "comprises," "comprising," "includes," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements may include other elements not expressly listed or inherent to such process, method, article, or apparatus, rather than including only those elements. The term "exemplary" is used in the sense of "example" rather than "ideal." In particular, an embodiment or implementation described herein as "example" or "exemplary" should not be construed as being preferred or advantageous over other embodiments or implementations, for example, but rather is intended to reflect or indicate that the embodiment is an "example" rather than an "ideal."

[0016] As used herein, the terms "distal" and "distally" refer to a location (or portion of an instrument) that is relatively closer to or toward a patient delivery site, and the terms "proximal" and "proximally" refer to a location (or portion of an instrument) that is relatively closer to or toward a user end opposite the distal location / portion of the instrument. Additionally, the terms "first," "second," and the like, used herein do not denote any order, quantity, or importance, but rather are used to distinguish one element, structure, step, or process from another. Additionally, the terms "a" and "an" used herein do not denote a limitation of quantity, but rather denote the presence of one or more of the referenced items.

[0017] As discussed above, existing autoinjectors include multiple parts. The entire autoinjector and / or each of the multiple parts of the autoinjector may need to meet quality or usage standards to ensure the usefulness of the autoinjector. Determining whether the autoinjector and / or its parts meet one or more quality or usage standards can be done by testing the physical autoinjector and / or its physical parts. However, such testing using a physical autoinjector may be prohibitively expensive and time consuming, as iterative improvements to the physical version of the autoinjector may require manufacturing a new autoinjector for each iteration. Alternatively, determining whether the autoinjector and / or its parts meet one or more quality or usage standards can be done by using an accelerated test chamber and / or by generating a simulation. However, existing techniques for accelerated testing do not consider parameters such as creep. In addition, existing techniques for generating simulations are often resource intensive and time consuming.

[0018] Thus, the present disclosure relates to various embodiments of simulation-based testing of medical devices (e.g., autoinjectors), which may be complete devices or parts of devices. Specifically, according to certain embodiments, creep coefficient data may be received and / or generated for a given device, part, and / or material. The raw creep coefficient data may include raw data including creep strain for a given device or part as a function of temperature, stress, and duration of stress. The creep strain may be expressed as a strain value and / or a relationship between creep strain and one or more of temperature, stress, and duration of stress. The raw creep strain data may be based, for example, on observed (e.g., using one or more sensors) or simulated creep strain experienced by the device or part for a given stress at a given temperature over a duration. The placement of the part within the autoinjector may result in, for example, a constant or varying stress being applied over time. For example, a spring in an autoinjector may be compressed and may stress various parts of the autoinjector. According to one implementation, the raw creep strain data may be used to generate relationships between creep strain and temperature, stress, and / or duration of stress.

[0019] The creep strain for the same stress duration and stress amount may be different for different temperatures. For example, different creep strain relationships may be identified for each of the first, second, and third temperatures and based on different durations and amounts of stress, respectively. Based on the multiple creep strain relationships, a predictive temperature stress time creep coefficient (e.g., predictive modulus) may be generated using interpolation (e.g., three-dimensional (3D)). The predictive temperature stress time creep coefficient may be used to output creep strain as a function of stress and time for multiple arbitrary temperatures. The predictive temperature stress time creep coefficient may be used to determine variables (e.g., temperature, stress amount, stress duration) for accelerated aging testing. For example, the predictive temperature stress time creep coefficient for a given equipment or part may identify a creep strain value for a given duration (e.g., 2 years) at a given temperature with a given amount of stress for a given equipment or part. The given amount of stress may correspond to an actual or theoretical amount of stress experienced by a given device or component.

[0020] The predicted temperature stress time creep coefficient may be used to identify variables (e.g., time, temperature, stress) that may be used in an accelerated test to observe the expected or experienced creep strain. The variables may be determined based on the selected creep strain value and / or one or more of temperature, time, and / or stress. Thus, an accelerated test may be performed using the variables (e.g., amount of time, temperature, and amount of stress) identified based on the predicted temperature stress time creep coefficient. The accelerated test may be used to determine the creep strain at the end of the accelerated test and compare the creep strain to the expected creep strain (e.g., the creep strain used to select the amount of time, temperature, and amount of stress for the accelerated test).

[0021] For example, a predicted temperature stress time creep coefficient may be generated based on interpolating raw creep coefficient data for a given part. The predicted temperature stress time creep coefficient may be used to identify an acceleration duration and test stress amount to produce a given creep strain at an accelerated test temperature (e.g., above ambient temperature) that may be used for the accelerated test. The acceleration duration may be less than the duration that the part experiences the given creep strain at, for example, ambient temperature.

[0022] Accelerated testing of a part may be performed at an accelerated test temperature for a specified duration using an aging chamber during which the part experiences a test stress amount. The aging chamber may be any applicable chamber that allows for accelerated aging testing by artificially manipulating a property (e.g., temperature). At the end of the accelerated test for the accelerated duration, the creep strain for the part may be measured and compared to a given creep strain. The comparison may be used to validate a predicted temperature stress time creep coefficient and / or to determine whether the observed creep strain of the part at the end of the test is within a given (e.g., expected) creep strain threshold range.

[0023] As applied herein, "creep" or "creep strain" is a material property that refers to the tendency of a solid material to move (e.g., slowly) or deform (e.g., permanently) under the influence of sustained mechanical stress. Creep strain can result from long-term exposure to stress that is less than the yield strength of the material. The severity of creep strain in a given material can increase as a factor of heat and / or time. For example, creep strain can be more increased when exposed to stress at a higher temperature compared to the same stress at a lower temperature (e.g., the effect of temperature can be greatest near the melting point of a given material). As another example, creep strain for a given material can be greater when exposed to the same stress for a longer period of time compared to a shorter period of time. Thus, the rate of deformation of a material due to creep strain can be a function of the material's properties, exposure time, exposure temperature, and applied structural load.

[0024] The creep threshold may be specific to a device (e.g., an autoinjector) and / or one or more parts of the device. The creep threshold for a device and / or one or more parts may be a creep strain value at or based on a level at which deformation of the device and / or one or more parts can no longer perform its given function.

[0025] According to certain embodiments, the simulation component may generate a linear equipment model of the medical equipment. The linear equipment model may be based on relationships between multiple parts of the medical equipment. The simulation component may generate the linear equipment model based on determined or sensed attributes of the medical equipment. For example, a sensor may be used to determine a relationship between the torque and axial force of a lead screw in the medical equipment. The simulation component may create the linear equipment model based on some or all of the determined or sensed attributes and their interrelationships.

[0026] Linear equipment models generated according to certain embodiments disclosed herein may include linear attributes to minimize simulation time. For example, the relationship between torque and axial force of a lead screw may be a linear attribute such that a change in axial force corresponds to a linear change in torque. Each linear attribute applied herein may have a change amount or "slope," generally referred to herein as a coefficient. For example, the coefficient of the torque to axial force ratio may be referred to as C2+C3, where this value indicates the slope of the linear attribute.

[0027] According to certain embodiments, the simulation component may generate a distribution of outputs (e.g., force, velocity, current torque, voltage, etc.) for the medical device and / or parts thereof. The distribution of outputs may be simulated outputs that the medical device or parts thereof may experience based on a set of inputs and may be generated using a linear equipment model. The inputs may include inputs for coefficients of the linear equipment model as well as distributions of other inputs (e.g., drug viscosity). For example, the linear equipment model may be used to simulate multiple potential force outputs of a given medical device. In a first case, a first distribution of coefficients may be applied to the linear equipment model along with a supply voltage, and the linear equipment model may output a distribution of multiple scalar values ​​(e.g., force values, velocity values, current values, torque values, voltage values, etc.) each based on an iterative simulation. The simulation may be run multiple times (e.g., about several thousand times, about tens of thousands times, about hundreds of thousands times, about 2 million times, about 1 million to about 4 million times, about 500,000 to about 5 million times, etc.) to output a distribution of output scalar values ​​corresponding to each output (e.g., force values, velocity values, current values, torque values, voltage values, etc.). The output may be used to determine whether a given design of a medical device meets one or more quality or usage standards. Alternatively or additionally, the output may be used to determine test parameters for a given medical device.

[0028] self-injector The simulations disclosed herein may be based on an autoinjector or parts of an autoinjector. The embodiments of the present disclosure may be used in addition to and / or in combination with aspects of International Application No. PCT / US2018 / 031077, which is incorporated herein by reference in its entirety. International Application No. PCT / US2018 / 031077 discloses an example of an autoinjector 2, which is also shown in FIGS. 1A-2 herein. It is also contemplated that the simulations contemplated herein may be performed based on other suitable autoinjectors. The autoinjector 2 may include a housing 3 having a tissue-engaging surface (e.g., a bottom surface) 4, and a needle may be deployed through the tissue-engaging surface 4 and retracted via an opening 6, as shown in FIG. 1B. An activation switch 1409 may be disposed on the tissue-engaging surface 4, as shown in FIGS. 1B and 1C, and may be configured to activate the autoinjector 2 or otherwise place the autoinjector 2 in a "ready" mode. A touch sensor 1410 may also be disposed on the tissue engaging surface 4 and configured to help the controller of the autoinjector 2 determine whether the autoinjector 2 is placed on the user's skin (indicating that the autoinjector should fire or otherwise deploy the needle) or whether the activation switch 1409 has been improperly triggered (indicating that operation of the autoinjector 2 should be stopped). A connection port 13 may be disposed on the tissue engaging surface 4 to facilitate programming of the autoinjector 2. The autoinjector 2 may have any suitable dimensions suitable for portability and allowing self-application by a user.

[0029] 2, a shuttle 340 (e.g., a shuttle actuator) can be configured to move the driver 320 through the deployment gear. The shuttle 340 can be coupled to a resilient member (e.g., a spring).

[0030] An adhesive patch 12 may be coupled to the tissue engaging surface 4 to help secure the autoinjector 2 to a user's body (eg, skin). Creep As disclosed herein, a medical device, such as the autoinjector 2, may experience creep strain as a result of one or more stresses applied to the medical device. The stresses may be internal stresses (e.g., stresses applied by the shuttle 340) such as those applied by a spring, a part on another part, a plunger, etc. A technique for identifying the creep strain profile of a given device or part is shown in FIG. 3A. Creep strain predictions may be used to build time-hardening models, applied to stress fields on preloaded parts, and used to predict material (e.g., plastic) deflections of features, etc. Creep strains on a physical device may be analyzed to determine trend lines based on different strain values, as shown in chart 302. Chart 302 plots the separate creep strains measured for a given part (e.g., shuttle 340) shown in image 304 at five separate varying strain levels. Each of the five trend lines in chart 302 may be determined by applying each of the five separate strain levels for the same duration. The amount of strain corresponding to a given trend line may be plotted using Equation 1 (labeled 302A).

[0031]

number

[0032] As disclosed above in Equation 1, the creep strain ε · is determined based on a first coefficient C1, a second coefficient C2, a time t, and a third coefficient C3. The coefficients C1, C2, and C3 may be coefficients as further described herein.

[0033] The creep strain level for a part at a given time or over time may be shown superimposed on the part. The example provided in image 304 shows strain points (e.g., creep strain plotted on chart 302) superimposed on the part that correspond to the creep strain plotted on chart 302. In image 304, distinct amounts of creep strain experienced in distinct regions of shuttle 340 are shown by distinct shading.

[0034] The creep strain plotted on the chart 302 based on the creep strain sensed at the distinct strain levels over time may be used to generate a creep strain profile 306 for the part shown in the image 304. The creep strain profile 306 may indicate displacement over time and correspond to creep strains that the part shown in the image 304 may experience over time based on strains applied to the part when it is used within the autoinjector 2 at ambient conditions. The creep strain profile 306 may be used to determine whether the part shown in the image 304 meets usage and / or quality criteria, either independently or as a part of the autoinjector 2.

[0035] According to another implementation of the disclosed subject matter, a predicted temperature stress time creep coefficient may be generated for an instrument or part, such as, for example, an autoinjector. FIG. 3B illustrates a process 307 for generating a predicted temperature stress time creep coefficient that may be used for accelerated aging testing. One or more aspects of the process 307 may be implemented using one or more of a computer, processor, memory, etc., as further disclosed herein. At 307A of the process 307, creep coefficient data (e.g., raw creep strain data) may be received by a computer, processor, memory, etc. The creep coefficient data may include creep strain values ​​related to the duration (e.g., time) and amount of stress for multiple temperatures. FIG. 3C illustrates a chart 308 having multiple creep strain planes 310A, 310B, and 310C. Creep strain surfaces 310A, 310B, and 310C are visual representations of creep strain for a given equipment or part, factored with stress duration and stress amount, as temperatures (e.g., T1, T2, and T3) change. According to one implementation, raw creep strain data can be predicted (e.g., using simulation) or generated (e.g., based on sensed creep strain) for an equipment or part based on the techniques disclosed herein. Creep strain surfaces 310A, 310B, and 310C each show a visual representation of raw creep strain data over time and across distinct stress amounts. The raw creep strain data can be used to determine creep strains factored with time and stress for distinct temperatures (e.g., T1, T2, and T3). For example, the visual representation shown by surface 310A illustrates the amount of creep strain a given part may experience at various distinct stress levels based on the amount of time each stress level is applied to the part at temperature T1.

[0036] At 307B of process 307, an interpolation (e.g., 3D interpolation) may be performed on the raw creep strain data for the given equipment or part to generate a predicted temperature stress time creep coefficient based on the given equipment or part. The predicted temperature stress time creep coefficient generated based on the given equipment or part is used to determine creep strain in terms of a particular stress amount, duration of stress, and temperature (e.g., as visually represented using surface 310i) for the given equipment or part or one or more other equipment or parts (e.g., parts similar to the given equipment or part).

[0037] According to one implementation, the interpolation performed in 307B of process 307 may be trilinear interpolation. Trilinear interpolation may be a multivariate interpolation on a 3D regular grid. Trilinear interpolation may linearly approximate the value of a function at midpoints within a local axis-aligned cuboid (e.g., x, y, z coordinates on chart 308) using function data on the grid points. The predicted temperature stress time creep coefficients generated using the interpolation performed in 307B may be used to estimate the temperature stress time creep coefficients for any applicable temperature T i A creep strain as a function of stress and time for a given temperature may be identified. Thus, the generated predicted temperature stress time creep coefficient may be used to output a creep strain based on temperature, amount of time, and stress. Alternatively, the generated predicted temperature stress time creep coefficient may be used to output a temperature required to reach a given creep strain based on a given amount of time and a given amount of stress. Alternatively, the generated predicted temperature stress time creep coefficient may be used to output a time required to reach a given creep strain based on a given amount of stress at a given temperature.

[0038] A medical device, such as the autoinjector 2, and / or one or more parts of the medical device may be tested for degradation. Degradation may include material degradation as well as creep strain degradation. Material degradation (e.g., the amount that plastics degrade) may occur, for example, by the processes of corrosion and oxidation in wet and dry environments, respectively. Material degradation may be tested to identify the effect of water vapor on materials over time. Material degradation may be tested using an accelerated aging chamber. Accelerated aging tests may be performed to accelerate the normal aging process of a given device, part, or material using deteriorating conditions such as heat, humidity, oxygen, sunlight, vibration, etc. Accelerated aging tests may be used to help determine the long-term effects of expected stress levels in a shorter time. Material degradation based on the amount of time spent in an accelerated aging chamber may be correlated to the amount of time at ambient conditions based on Arrhenius relationships that map ambient conditions to accelerated conditions. However, no such relationships exist for creep strain degradation.

[0039] At 307C of the process 307, variables may be identified for accelerated aging testing of a given equipment or part. The variables may be temperature, time amount, and / or strain amount (e.g., to mimic creep strain over a given period of time, given stress, and / or ambient temperature). FIG. 3D shows a chart 312 for identifying variables according to the techniques disclosed herein. Chart 312 is a visual representation of a predicted temperature stress time creep coefficient generated at 307B based on an interpolation of the raw creep coefficient data received at 307A. The predicted temperature stress time creep coefficient generated at 307B and shown in FIG. 3D is generated based on identifying accelerated test variables (e.g., temperature, time, stress) to estimate the creep coefficient at a reference temperature T REF The creep strain surface 314 can be used to simulate creep strain at T REF 1 is a visual representation of the predicted temperature stress time creep coefficient for T REF may correspond to the ambient temperature or to the temperature associated with the use and / or storage of a given device or component. As shown in FIG. 3D, tSHELF may correspond, for example, to the expected shelf life of a given device or component. SHELF It will be understood that may be any applicable duration. The target point 314A may correspond to a reference creep strain, for example, a temperature T REF , the target storage life time t for a given equipment or component having a predicted amount of stress corresponding to point 314A. SHELF The expected amount of stress may be determined, for example, based on creep strain sensors that detect the stresses experienced by the equipment or component during storage or operation.

[0040] As described herein, accelerated testing can be used to determine whether a given equipment or part meets creep strain quality criteria. The equipment or part tested using accelerated testing can be the same equipment or part for which the creep coefficient is received at 307A in FIG. 3C and based thereon, a predicted temperature stress time creep coefficient is generated at 307B. Alternatively, the equipment or part tested using accelerated testing can be similar to the equipment or part for which the creep coefficient is received at 307A in FIG. 3C. For example, the equipment or part tested using accelerated testing can be an equipment manufactured based on the same design as the equipment or part for which the creep coefficient is received at 307A in FIG. 3C. The creep strain experienced using accelerated testing can be compared to the expected creep strain (e.g., the creep strain at reference point 314A).

[0041] As described, the accelerated testing may be performed by receiving raw creep coefficient data (e.g., as shown in FIG. 3C ) for the equipment or component. A predicted temperature stress time creep coefficient (e.g., as visually represented in FIG. 3D ) may be generated in 307B. The predicted temperature stress time creep coefficient generated in 307B may be used to generate a reference point (e.g., a reference temperature T REF Based on the predicted temperature stress time creep coefficient reference point 314A) for the accelerated temperature, accelerated time, and / or accelerated stress may be output.

[0042] The accelerated test may be performed using an accelerated temperature, accelerated time, and accelerated stress determined using the predicted temperature stress time creep coefficient. The accelerated test may be performed under the accelerated temperature and accelerated stress, and the accelerated test creep strain experienced by a given device or component may be determined after the accelerated time. The accelerated test creep strain may be determined at a temperature (e.g., a reference temperature T REF The accelerated test creep strain may be compared to a baseline creep strain (at reference point 314A of the predicted temperature stress time creep coefficient for the designed equipment or part). If the accelerated test creep strain is within the threshold creep strain amount of the baseline creep strain, the equipment or part may be approved and / or the predicted temperature stress time creep coefficient may be certified. For example, an approval instruction may be generated approving the equipment or part or approving the predicted temperature stress time creep coefficient. If the accelerated test creep strain is not within the threshold creep amount of the baseline creep strain, the equipment or part may be rejected and / or the predicted temperature stress time creep coefficient may not be certified. For example, a rejection instruction may be generated to reject the equipment or part or to reject the predicted temperature stress time creep coefficient. The approved equipment or part may be approved for use by, for example, a user or provider. The certified predicted temperature stress time creep coefficient may be used to predict creep strain for the designed equipment or part. In accordance with the instructions, an accelerated test to verify the predicted temperature stress time creep coefficient may be performed using the part. The part may be a simple part when compared to one or more other parts or equipment having multiple parts. The part may have limited stress points or stress point changes so that the predicted temperature stress time creep coefficients may be validated with a limited number of external variables.

[0043] Further disclosed herein is the identification of one or more variables using a predicted temperature stress time creep coefficient. As shown in FIG. 3D, the creep strain at the reference point 314A is T i The Ti creep strain surface 313 may correspond to an acceleration point 313A on the creep strain surface 313. The Ti creep strain surface 313 may correspond to an acceleration point 313A on the creep strain surface 313 at any temperature T i The creep strain at the reference point 314A can be a visual representation of the predicted temperature stress time creep coefficient versus time tSHELF A time shorter than t AGE At the acceleration temperature T i , can be modeled in terms of the amount of stress shown by the line at acceleration point 313A.

[0044] As an example, reference point 314A is a 3-year shelf life t SHELF At target point 314A, the amount of creep strain may be based on the amount of stress indicated by the stress lines at target point 314A. To mimic the creep strain at reference point 314A, a predicted temperature stress time creep coefficient may be used to estimate the creep strain at a 3 month accelerated time t AGE The amount of creep strain at acceleration point 313A at time t SHELF Temperature T at REF The creep strain at the accelerated temperature T i Therefore, the acceleration temperature T i The creep strain surface 313 at t is an acceleration point 313A (e.g., 3 months time t) that mimics the creep strain at the reference point 314A. AGE Based on the temperature, stress, and time variables depicted using creep strain surface 313, a given device or part may be identified and predicted to have a creep strain (at temperature T i At time t AGE (e.g., 3 months). According to another example, the acceleration time t AGE can be output by the predicted temperature stress time creep coefficient shown in FIG. AGE is a reference creep strain at reference point 314A, a reference stress shown at target point 314A, and a given temperature T i For example, the acceleration chamber may output a temperature based on the acceleration temperature T iTo mimic the conditions at reference point 314A, the predicted temperature stress time creep coefficient of FIG. 3D is used to calculate the reference stress and accelerated temperature T i , the acceleration time t required to mimic the conditions at reference point 314A. AGE can be identified.

[0045] In 307D of the process 307, an aging test may be performed based on the variables identified for the accelerated aging test. The aging test may be performed for a period of time t AGE Over the period, the temperature T i and placing a given article in an accelerated aging chamber set to a stress level determined based on the lines at acceleration point 313A and / or reference point 314A. According to one example, a first medical device may be used to determine a predicted temperature stress time creep coefficient as described at 307A-307C. At 307D, a second medical device may be aging tested based on the predicted temperature stress time creep coefficient determined using the first medical device. In this example, the first medical device may be a controlled medical device and the second medical device may be manufactured based on the design of the first medical device. The second medical device may be tested using the aging tests disclosed herein to ensure it meets the parameters established using the first medical device. Alternatively or additionally, one or more variables for the aging test may be determined using the reference stress (e.g., a calculated stress or stress field) and the predicted temperature stress time creep coefficient.

[0046] Linear Equipment Models for Improved Simulation According to embodiments of the disclosed subject matter, multiple simulations may be generated using a linear equipment model. The simulation component may generate multiple simulations and may include or be one or more of a processor, a controller, a microcontroller, a memory, and the like. The simulation component may generate a simulation based on a linear equipment model of the equipment (e.g., the autoinjector 2). The linear equipment model may be generated by or provided to the simulation component. The linear equipment model may be based on relationships between multiple parts of the equipment.

[0047] The multiple simulations may be based on attributes of the device defined based on relationships between parts of the device. The multiple simulations may output how a physical version of the device will behave (e.g., how long the injection duration is when using the device with a drug having a given viscosity). The multiple simulations may enable a design of experiment (DOE) to generate multiple outputs based on several separate variables. The multiple simulations may output a distribution of one or more scalar values ​​associated with the device. The distribution may be compared to one or more usage or quality criteria and / or used to test the device to ensure device operation across the distribution. For example, as further described herein, the simulation may output a distribution of the amount of force experienced by the autoinjector. The distribution of the amount of force may be determined using a simulation performed based on relationships between parts of the device, such as the battery, electronic control, motor / gearbox, lead screw, cartridge, plunger, and fluid path. The distribution may range from a lower threshold to an upper threshold. Thus, the lower or upper thresholds may be compared to quality and / or usage standards to ensure that the attribute (e.g., force) experienced by the autoinjector is within an acceptable range. Alternatively or additionally, a physical version of the autoinjector may be tested under lower or upper thresholds of an attribute (e.g., upper threshold force) to ensure suitability (e.g., durability) at the lower or upper threshold of the attribute.

[0048] As shown in FIGS. 1A-1C, the autoinjector 2 may include multiple parts. One or more of the parts may interact with one or more other parts of the autoinjector 2. The interactions may define multiple relationships between the parts. For example, the shuttle 340 may move based on a force applied by a motor that uses a gearbox to transmit the force. The motor may be driven using a battery and electronic control components. The voltage for the motor may be provided by the battery via electronic control such that the motor and gearbox operate at a given motor current to generate a given motor torque. Thus, the voltage and current provided by the battery and electronic control components may be related to the motor speed and torque.

[0049] FIG. 4 illustrates a number of relationships 412A, 412B, 412C, 412D that together form a linear equipment model 402 for an autoinjector (e.g., autoinjector 2). Although a linear equipment model (e.g., linear equipment model 402) is illustrated herein, it will be understood that a linear equipment model may refer to a linearized model (e.g., of a nonlinear system), a reduced model, and / or a quasi-linear equipment model / quasi-steady state model that may be applied in accordance with the techniques disclosed herein. For example, a linearized model may be generated by creating a linear approximation of a nonlinear system. A quasi-steady state model may be used to approximate the behavior of a non-stationary system by applying discrete points in time. A linear equipment model may predict a study state parameter based on one or more coefficients as disclosed. A linear equipment model may be computationally efficient such that large-scale design of experimental simulations may be performed (e.g., using Monte Carlo simulations) with limited computational resources. For example, more than 10,000 simulations may be performed in less than one hour (e.g., using commercially available computing equipment). As another example, over 2 million simulations can be performed in less than an hour (e.g., using commercially available computing equipment). As another example, approximately 100,000 simulations can be performed in less than 30 seconds.

[0050] Each of the relationships 412A, 412B, 412C, 412D characterizes how the corresponding component operates. For example, relationship 412A defines the voltages and currents associated with the battery and electronic controls of the autoinjector. Relationship 412A is shown below in Equation 2 and Equation 3.

[0051]

number

[0052] The chart showing relationship 412B defines the motor speed taking into account the motor current and motor torque of the motor and gearbox of the autoinjector. As shown, since relationship 412B defines the motor speed and motor torque of the motor and gearbox, the motor voltage V of Equation 2 for the battery and electronic control of the autoinjector can be calculated by mtr is related to the relationship 412B (e.g., the motor speed is related to the motor voltage V mtr In addition, the motor torque of the motor and gearbox of relationship 412B depends on the motor current (A mtr ). The motor torque of the motor and gearbox in relationship 412B is related to the torque of the lead screw in relationship 412C. The axial force of the lead screw in relationship 412C is related to the fluid forces of the cartridge, plunger, and fluid path in relationship 412D. The fluid forces of the cartridge, plunger, and fluid path in relationship 412D are affected by the motor speed of the motor and gearbox in relationship 412B. Thus, each of the relationships 412A, 412B, 412C, 412D of the linear equipment model 402 are related to each other and model the operation of the autoinjector.

[0053] The linear relationships 412A, 412B, 412C, 412D of the linear equipment model 402 may model the corresponding autoinjector in sufficient detail and may allow simulations based on the linear equipment model 402 to be run at a faster rate as compared to simulations run on non-linear equipment models. By running faster simulations using the linear equipment model 402, the amount of time required to determine the operation of the autoinjector and performance metrics of the autoinjector may be reduced. Additionally, by running simulations faster, decisions regarding how to control the autoinjector using an electronic controller may also be determined faster and / or a larger population may be studied (e.g., using Monte Carlo analysis).

[0054] As shown in the linear equipment model 402, each of the relationships 412B, 412C, 412D may be based on coefficients C1-C7. Coefficients C1-C7 may correspond to the amount of change or "slope" of the linear attributes of the autoinjector. Coefficient C1 may be determined based on a combination of conditions such as the needle's limiting amount through which fluid is forced and / or the pitch of the lead screw. C2+C3 may correspond to the torque of the lead screw. C4 may be the slope of the relationship between motor current and motor torque. C5 may be the y-intercept corresponding to the no-load torque, and thus C5 is the amount of current required for the autoinjector motor to rotate with no load applied to the shaft (i.e., overcoming internal friction, electrical resistance, etc.). C6 may be the slope of the relationship between motor torque and motor speed. C7 may be the no-load speed scaled by the input voltage.

[0055] According to one example, a simulation generated based on the linear equipment model 402 may be determined by generating a matrix based on the relationships 412A, 412B, 412C, 412D. The matrix may then be inverted to provide the simulation components. Multiple simulations may be generated based on varying variables, such as one or more of the coefficients C1-C7, as further disclosed herein.

[0056] FIG. 5A illustrates a system model structure that applies the linear equipment model 402 of FIG. 4. The linear equipment model 402 may receive multiple inputs including a supply voltage 502, a rate control loop output 510, a current limit loop output 512, a drug viscosity 503, and a component coefficient 504. The supply voltage 502 may be a voltage output from a battery (e.g., a battery corresponding to relationship 412A) or other voltage generator. The multiple component coefficients 504 may correspond to coefficients C1-C7 described in connection with FIG. 4. The rate control loop output 510 and the current limit loop output 512 may be used based on a function of a random combination of the inputs, as described further herein. For example, if the scalar output exceeds a threshold, one or both of the rate control loop output 510 and the current limit loop output 512 may be used to decrease either the rate or the current through the respective loop to maintain a given scalar output within a threshold. Thus, the speed control loop output 510 and the current limit loop output 512 may be used to adjust the supply voltage 502 when the output 508 is outside of a given threshold. The current measurement precision 506 may also be input randomized to account for variance in the current measurements, which may result in a more realistic distribution of the output 508, as described further herein.

[0057] A set of part coefficients 504 may be applied to the linear equipment model 402 for each simulation to be generated. The part coefficients 504 used for each given simulation may be selected using Monte Carlo simulation, where the Monte Carlo simulation outputs a value for each coefficient (i.e., for coefficients C1-C7). Monte Carlo simulation may be a computerized technique that considers distribution probabilities in decision making. The value of each given coefficient (i.e., coefficients C1-C7) for each simulation may be selected from a distribution of potential values ​​for the corresponding coefficient. A probability distribution function may be used by the Monte Carlo simulation to select a random value for the coefficient. The distribution function for a given coefficient may be determined based on the variance of that coefficient in the autoinjector (e.g., variance due to material properties, manufacturing variations, use, etc.). A random value may be selected from a weighted distribution curve for each given coefficient. For example, FIG. 5B shows motor parameters 402A for the motor of an autoinjector for which a simulation is generated. The motor parameters may correspond to coefficients C4 and C5 in the linear equipment model 402. The top left chart shows a distribution of outputs that may correspond to C4, for example, such that a Monte Carlo simulation may provide a value for coefficient C4 based on the probability curve shown in the top left chart for parameters 402 A. Thus, a Monte Carlo simulation may provide a value for coefficient C4 that is more likely to be found toward the center of the top left distribution chart than toward the edges of the top left distribution chart.

[0058] Each simulation of the multiple simulations may use one or more distinct values ​​for each component coefficient 504. For example, numerous combinations of coefficient values ​​for component coefficients 504 may be provided to the linear equipment model 402, ranging from about 10,000 simulations to about 2 million or more simulations.

[0059] The linear equipment model 402 may also receive a value of drug viscosity 503 output by a Monte Carlo simulation based on a distribution curve of possible drug viscosities. The distribution curve of drug viscosity may be determined based at least in part on drug properties at distinct temperatures. Additional inputs to the linear equipment model 402 may include thread friction as shown in distribution 402B of FIG. 5B, as well as a battery voltage input, a fluid path resistance input, and a current measurement input 402C of FIG. 5B. The thread friction shown in distribution 402B may correspond to coefficients C2 and C3 of the linear equipment model 402. The fluid path resistance may correspond to coefficient C1 of the linear equipment model 402.

[0060] The output 508 of the linear machine model 402 for each simulation may include scalar values, such as force values, velocity values, current values, torque values, voltage values, etc. For example, FIG. 6 shows two scalar value outputs including a force output distribution 602 and a velocity output distribution 604. Each of the two scalar value output distributions may be output by the linear machine model 402 as shown in FIG. 4 and FIG. 5A. The force output distribution 602 and the velocity output distribution 604 are based on approximately 2 million simulations, as indicated by N=2e6 in FIG. 6. As shown in FIG. 6, the force output distribution 602 may range from a minimum force value 602A to a maximum force value 602B.

[0061] As an example, the force output distribution 602 may correspond to a force exhibited by an autoinjector on which the linear equipment model 402 is based (e.g., based on the force applied when using the autoinjector's plunger). The peak of the force output distribution 602 may be 126 N (Newtons). Such a peak value of 126 N may be exhibited by a conventional probability model. However, according to the techniques disclosed herein, the force output distribution 602 may be provided and may exhibit a maximum force value 602B of, for example, 200 N. According to one implementation, the maximum or minimum distribution value (e.g., maximum force value 602B) may be a value that exceeds a probability threshold (e.g., such that the maximum or minimum value does not correspond to a value with a small or infinitesimal probability). The probability threshold may be a value or may be determined based on the number of deviations from the mean value or using a different statistical variation other than the standard deviation. Thus, one or more parts of the autoinjector may be designed to withstand a force of up to 200 N instead of a peak force value of 126 N. As another example, the rate output distribution 604 may represent a distribution of potential rates at which medication may be injected via the autoinjector. Accordingly, one or more tests may be performed to ensure that the range of rates from a minimum rate to a maximum rate represented by the rate output distribution 604 are acceptable rates for use with the autoinjector.

[0062] According to implementations of the disclosed subject matter, one or more power distributions (e.g., power distribution 602, power distribution 604, etc.) may be used to approve a device design or a part design. The power distribution (e.g., force power distribution 602) may be compared to one or more threshold powers (e.g., maximum, minimum, average, etc.). For example, the model 402 may be used to generate a device model based on the coefficients and based on the design of the autoinjector. The device model may be used to generate one or more power distributions (e.g., power distribution 602, power distribution 604, etc.).

[0063] The output distribution generated by the equipment model may be compared to one or more threshold outputs. If the output of the equipment model is within the boundaries established by the one or more threshold outputs (e.g., within range, lower, higher, etc.), the autoinjector design may be approved. If the output of the equipment model is outside the boundaries established by the one or more threshold outputs (e.g., outside range, lower, higher, etc.), the autoinjector design may be rejected. Thus, using the techniques disclosed herein, a probability output of an equipment design or part design may be determined based on a linear equipment model. The probability output may be compared to the threshold outputs to approve or reject the equipment design or part design.

[0064] According to one implementation, the power distribution generated by the equipment model base can be applied to the design. The power distribution can be a normal distribution or one or more mixture distributions. Thus, the minimum and / or maximum power distributions can be used to update the part design or equipment design to operate based on the minimum and / or maximum power. For example, the force power distribution 602 can correspond to the forces that the equipment part can act on. Thus, the equipment design can be updated to withstand the range of forces in the power distribution, including the minimum force, the peak force, and / or the maximum force.

[0065] Alternatively or additionally, the equipment design or part design may be modified based on one or more power distributions. The power distributions may be provided as feedback to the equipment or part and design updates may be generated based on one or more target threshold powers. For example, a target threshold force peak for the part may be between 140 and 160 Newtons. A peak output force from a force output distribution (e.g., power distribution 602) from a first part design may exhibit a force peak of 180 Newtons. Thus, the force peak of 180 Newtons may be provided as feedback to the first part design. The first part design may be updated (e.g., shape, material, function, etc.) based on the feedback and an updated distribution may be calculated. The updated distribution may exhibit a force peak of approximately 144 Newtons. Thus, a feedback loop may be terminated based on an updated distribution having a peak at 144 Newtons (i.e., between the range of target threshold force peaks of approximately 140 and 160 Newtons). The equipment model may be used to adjust control parameters that may affect the force distribution. For example, one or more coefficients of the equipment model may be adjusted, which may result in a modified force output distribution.

[0066] Figure 7 illustrates the role of the system module of Figure 5A in the design process. As shown in Figure 7, a system model 714 corresponding to the system model of Figure 5A may receive control parameters 702, motor performance 704 (e.g., based on testing), parameters of a thermal model 706 such as drug temperature, viscosity (e.g., based on testing and / or simulation), thread friction 708 (e.g., based on testing and / or simulation), plunger friction 710 (e.g., based on testing and / or simulation), fluid path resistance 712 (e.g., based on testing and / or simulation), etc. The system model 714 may output performance metrics 716 (e.g., injection rate, motor stall margin, etc.), control strategy evaluation 718 (e.g., speed control loop output 510, current limit loop output 512, etc.), structural assembly output 720 (e.g., mechanical output including deformation, stiffness, strain), and plunger response output 722 (e.g., mechanical output including sealing pressure, sliding force, deformation, stiffness, strain).

[0067] 8A-8B show diagrams of simulated structural assemblies according to embodiments disclosed herein. Diagrams 802, 804, 806, 808 may be generated using a finite element method, in which the geometry of a given part (e.g., the autoinjector of diagram 802) is divided into smaller sections, and stress and displacement fields are projected onto the part based on numerically determining the stress and displacement for each smaller section. Such parts may be, but are not limited to, structural assemblies, snaps, threads, fluid paths (e.g., fluid path bends), and the like. The attributes (e.g., stress, displacement, and the like) of a given part may be based on elements such as plasticity, nonlinear contact, hyperelastic materials, and the like. Diagram 802 shows an autoinjector with stresses projected for each subsection, all of which are combined to make up the autoinjector. Diagrams 8A-8B may be generated based on the output of linear equipment model 402 of FIGS. 4 and 5A. For example, diagram 802 in FIG. 8A shows the effect of a maximum force value 602B of 200N.

[0068] As shown in FIG. 8A , the highest stress on the autoinjector of diagram 802 may be experienced in section 802A, which is shaded to indicate higher stress. Similarly, the most stressed part of diagram 804 may be section 804A. Diagram 804 may be generated based on the forces applied when assembling the autoinjector of diagram 804 (e.g., based on coupling a glass cartridge to a holder such that a force is generated when the two parts are snapped together). Similarly, the most stressed part of diagram 806 may be section 806A. Diagram 806 may be generated based on a screw that pushes the plunger of the autoinjector. Similarly, the most stressed part of diagram 808 may be section 808A. Diagram 808 may be generated based on the forces applied when assembling the autoinjector of diagram 808 (e.g., similar to assembling the parts of diagram 804).

[0069] 9A and 9B show diagrams of a simulated structural assembly according to an embodiment disclosed herein. The diagrams shown in FIG. 9A and 9B can be used to evaluate contact (e.g., sealing) pressure, characterize rod-plunger interaction, verify peak strain, predict drag (e.g., resistance to glass) as a function of fluid pressure for a system model, and / or the like. Diagram 900 can be generated using the finite element method disclosed with reference to FIG. 8A and 8B. Diagram 900 shows the sealing pressure of a rubber part on a glass part. If the sealing pressure is lower than the fluid pressure of the corresponding fluid, the fluid may leak as a result of the pressure difference. The maximum force value 602B of 200N in FIG. 6 can be used to generate the sealing pressure shown in diagram 900. Thus, tests can be performed to determine whether leakage is likely to occur based on either the minimum force value 602A or the maximum force value 602B.

[0070] Figure 9B includes a chart 902 showing the effect of pushing the plunger with a force of 200N (i.e., maximum force value 602B of Figure 6). The force on the plunger may pull the parts towards each other or away from each other. As shown in Figure 9B, a force of 200N may cause the part on the right to push towards the part on the left. The maximum force is shown at 902A. To maintain operation of the parts shown in Figure 9B, a test may be performed to determine if the push exceeds a threshold amount of push.

[0071] The viscosity of a substance (e.g., a drug) may be a function of the temperature of the substance. According to one implementation of the disclosed subject matter, free convection thermal analysis and / or forced convection thermal analysis may be used to determine the behavior of the substance temperature. For example, such thermal analysis may be used to determine the rate at which the temperature of the substance changes (e.g., when moved from a cold environment to a warm environment). Thus, the viscosity of the substance (e.g., a drug) may be estimated using thermal analysis. The estimation may be based on the rate at which the temperature changes when the substance is moved from a first environment to a second environment. The temperature of a medical device or part containing the substance, and / or the movement of the medical device, part, or substance from a first environment at one temperature to a second environment at a different temperature may change the viscosity of the substance. Such viscosity variations may affect the design of the device or part. As an example, a relatively high material viscosity (e.g., as a result of a relatively low temperature) may increase the mechanical load on an instrument or component, shorten the injection time of an injection, change the probability that a control loop will trip (e.g., higher viscosity may increase the probability of reaching a current limit), or the like.

[0072] The viscosity of the substance may be an input to the linear instrument model. Alternatively, one or more temperatures may be input to the linear instrument model, and the linear instrument model may determine the viscosity of the substance based on the one or more temperatures. The power distribution output by the linear instrument model may be further based on the viscosity of the substance, and / or the viscosity of the substance factored with time and / or temperature. For example, the power distribution of the instrument or part may be adjusted over a period of time based on the change in the viscosity of the substance over a period of time. The change in the viscosity of the substance may be based on the temperature change over that period of time (e.g., after moving the drug from a cold environment to a room temperature environment). The power distribution of the instrument or part may change based on the temperature and / or the viscosity of the individual substance. For example, the force output distribution 602 of FIG. 6 may be modified based on the range of potential temperatures and / or viscosities of the individual substances. The force output distribution 602 may have a higher range or peak and / or a lower range or peak based on, for example, the temperature and / or the viscosity of the individual substances. The design of the instrument and / or part may be approved and / or rejected based on the modified force output distribution 602.

[0073] According to one implementation, thermal analysis may be used to determine the temperature behavior of a substance (e.g., a drug). Temperature behavior based on thermal analysis may be used to estimate the viscosity of the substance (e.g., over a period of time, such as when the substance is moved from a first environment to a second environment where the temperature changes). Thermal analysis may include, for example, free convection analysis, forced convection analysis, etc. FIG. 10 shows a free convection analysis 1010 corresponding to drug temperature 1012 over time. The free convection analysis 1010 may be used to evaluate temperature transients during warm-up of the device (e.g., when moved from a refrigerated environment to room temperature). Drug temperature 1012 determined based on the free convection analysis 1010 over a period of time may inform drug viscosity. The free convection analysis 1010 may be based on the amount of ambient air circulation, and the analysis shown via the free convection analysis 1010 may provide a greater understanding of sensitivity based on ambient air circulation at or around a given device or part. The free convection analysis 1010 may be based at least in part on a simulation based on airflow generated based on temperature differences between the equipment or part and ambient conditions. The free convection analysis 1010 may be used, for example, when there are no external influences that move air across a temperature gradient introduced by the given equipment or part itself. According to the example shown in FIG. 10, the drug temperature 1012 may be input into a linear equipment model (e.g., linear equipment model 402). Alternatively or additionally, the drug temperature 1012 may be provided as a control loop output 510 since it may be related to the velocity of the drug.

[0074] According to one implementation, the sensed temperature value may be provided as an input to an equipment or component, or a controller used to control it. The temperature value may be sensed using a temperature sensor that is internal or external to the given equipment or component. The sensed temperature value may be used to adjust a control parameter for the equipment or component.

[0075] According to one implementation, the coefficient C1 in FIG. 4 may be determined based on a combination of conditions such as the restriction of the needle through which the fluid is forced and / or the pitch of the lead screw. FIG. 11 shows an analysis of the fluid path restriction 1110 to determine the coefficient C1 based on the slope of the resulting chart 1112 based on the force applied factored by viscosity and plunger speed and / or flow rate. As shown in the exemplary chart 1112, the analysis of the fluid path restriction 1110 may be used to predict the plunger reaction force as a function of the fluid viscosity and the plunger travel speed. The plunger force may be a function of the needle geometry, drug viscosity, and / or plunger speed. The analysis of the fluid path restriction 1110 may be a simulation based on the device or parts thereof and may be a driver for the injection time of the device.

[0076] The output distribution of the instrument or component may vary based on a coefficient C1 determined based on an analysis of the fluid path restriction 1110. For example, the force output distribution 602 of FIG. 6 may be modified based on a slope (C1) identified based on the simulated forces of the instrument or component factoring in viscosity and velocity. The force output distribution 602 may have a higher range or peak and / or a lower range or peak based on the slope (C1), for example. The design of the instrument and / or component may be approved and / or rejected based on the modified force output distribution 602.

[0077] FIG. 12 shows simulated drop test results 1210, 1212, 1214 with acceleration as a factor versus time as shown in chart 1216. The drop test simulation used to predict the drop test results 1210, 1212, 1214 can be used to predict the acceleration and / or deformation over time of a given device or part. Such predictions can be used to extract forces between parts, stresses within parts, and / or strains of parts. Forces on the device or parts thereof can be predicted using the drop test results 1210, 1212, 1214. The drop test results 1210, 1212, 1214 can be generated for impacts based on acceleration over time as shown in chart 1216. The devices shown in the drop test results 1210, 1212, 1214 can be approved or modified based on the results.

[0078] FIG. 13A illustrates a leak assessment 1310 of a part or equipment according to one implementation of the disclosed subject matter. For example, the leak assessment may be for a Leur fitting used in the equipment or part. The leak assessment may be performed to simulate potential leaks of the part or equipment. Such predictions may be based on performing an iterative analysis to determine whether fluid pressure overcomes contact pressure between two or more surfaces. For example, fluid pressure penetration may be used to assess the risk of leakage in slip fittings designed for a design part. The results of the leak assessment may be used to modify attributes of fittings such as slip fittings.

[0079] 13B illustrates physiological modeling of a part or device. A quantitative physiological model may be a mathematical representation that approximates the behavior of a physiological system (e.g., a body, a body part, tissue 1314, etc.). The physiological model 1312 may describe a physiological system without using mathematics. The model may be applied to the dermis 1318, the fat layer 1316, and / or the tissue 1314. The physiological model 1312 may be used to simulate the physiology of the body (e.g., of the model tissue 1314) to predict its response to the injection of a fluid.

[0080] FIG. 13C illustrates a needle coring simulation 1320. The needle coring simulation 1320 may be generated using Arbitrary Lagrangian-Eulerian (ALE) and / or Smoothed Particle Galerkin (SPG) techniques to refine needle tip geometry, e.g., to reduce coring risk. The needle coring simulation 1320 may be used as a finite element model of needle coring, e.g., using ALE and / or SPG techniques. As applied herein, needle coring may be the removal of material from a closure by a needle (e.g., as the needle pierces the closure). Such a core may be, for example, elongated in shape. The needle coring simulation 1320 may be used to simulate needle performance and reduce coring in a designed needle to be used with an instrument or part. One or more simulations may be used to refine needle geometry and / or closure material selection.

[0081] 14 illustrates a product lifecycle 1400 implemented for simulation-driven development in accordance with the techniques disclosed herein. The product lifecycle 1400 can be used to generate better and / or fewer physical iterations of an equipment or product, can be used for focused design ideation, provides virtual prototyping, increases design optimality, enhances design characterization, supports the constraint process, improves root cause analysis, accelerates redesign, and supports change.

[0082] As shown in FIG. 14 , the product life cycle 1400 may include discovery and ideation at 1402, followed by invention and prototyping 1404. Invention and prototyping 1404 may include iterations of design ideation 1406, virtual prototyping 1408, and design optimization 1411. Invention and prototyping 1404 may be performed using techniques disclosed herein. For example, the linear equipment model 402 may be used for virtual prototyping 1408 and design optimization 1411. Pre-clinical testing may be performed at 1412, followed by clinical testing at 1414. Determination of limitations 1416 may be based on predictions of success 1418 and / or predictions of failure 1420. Product launch 1422 may be complemented by post-market surveillance 1424. Post-market surveillance may include identifying root causes 1428 to enable redesign 1426.

[0083] Any suitable system infrastructure may be deployed to generate the simulations and / or data disclosed herein. Any of the disclosed systems or methods may be executed or performed by a computing system. Although not required, aspects of the disclosure are described in the context of computer-executable instructions, such as routines executed by data processing devices, such as server computers, wireless devices, and / or personal computers. Those skilled in the art will appreciate that aspects of the disclosure may be implemented with other communication, data processing, or computer system configurations, including: Internet appliances, handheld devices (including personal digital assistants ("PDAs")), wearable computers, cellular or mobile phones of any kind (including voice over IP ("VoIP") phones), dumb terminals, media players, gaming devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, set-top boxes, network PCs, minicomputers, mainframe computers, and the like. Indeed, the terms "computer," "server," and the like are used interchangeably throughout this specification and refer to any of the above devices and systems, as well as any data processors.

[0084] Aspects of the present disclosure may be embodied in special purpose computers and / or data processors specifically programmed, configured and / or constructed to execute one or more of the computer-executable instructions detailed herein. Although aspects of the present disclosure, such as certain functions, are described as being executed exclusively on a single device, the present disclosure may also be practiced in a distributed environment in which functions or modules are shared among different processing devices linked via a communication network, such as a local area network ("LAN"), a wide area network ("WAN"), and / or the Internet. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0085] Aspects of the present disclosure may be stored and / or distributed on non-transitory computer-readable media, including magnetically or optically readable computer disks, hardwired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memories, biological memories, or other data storage media. Alternatively, computer-executable instructions, data structures, screen displays, and other data under aspects of the present disclosure may be distributed over a period of time over the Internet and / or other networks (including wireless networks) on propagating signals on a propagating medium (e.g., electromagnetic wave(s), sound waves, etc.), and / or provided over any analog or digital network (packet-switched, circuit-switched, or other manner).

[0086] The features listed above are described in the context of specific embodiments, however, as will be appreciated by those skilled in the art, the features and aspects of each embodiment may be combined, added to, subtracted from, etc., other embodiments in any manner suitable to aid in the controlled preparation and / or delivery of a pharmaceutical agent.

[0087] The present specification discloses the following items: Item 1. A method for determining accelerated test parameters for a medical device, comprising: receiving raw creep modulus data relating creep strain to duration and amount of stress as a factor of temperature range; generating predicted coefficients based on raw creep coefficient data; and using the prediction coefficients to generate one or more of an accelerated test time, an accelerated stress, or an accelerated temperature.

[0088] Item 2. The method according to item 1, wherein an accelerated test time is generated based on a reference creep strain, a reference stress, and an accelerated temperature. Item 3. A step of generating an accelerated test creep strain based on an accelerated test of the medical device, the accelerated test being performed based on an accelerated temperature, an accelerated test time, and an accelerated stress; and outputting one of a medical device approval instruction or a medical device rejection instruction based on comparing the accelerated test creep strain and the reference creep strain.

[0089] Item 4. The method of item 3, wherein the approval instruction approves the medical device and the rejection instruction rejects the medical device. Item 5. A step of generating an accelerated test creep strain based on an accelerated test of the medical device, the accelerated test being based on an accelerated temperature, an accelerated test time, and an accelerated stress; and outputting one of a prediction coefficient approval indication or a prediction coefficient rejection indication based on comparing the accelerated test creep strain and the reference creep strain.

[0090] Item 6. The method of item 1, wherein the medical device is manufactured based on a medical device design that corresponds to another medical device, and the raw creep coefficient data is based on the other medical device. Item 7. The method of item 1, wherein the prediction coefficients are generated based on three-dimensional (3D) interpolation of the raw creep strain data.

[0091] Item 8. The method of item 1, wherein the raw creep strain data is generated based on one of simulated strain or experienced strain. Item 9. A method for verifying the predictive coefficients of a medical device, comprising: receiving raw creep strain data relating creep strain values ​​to stress duration and stress amount, factored by temperature range; generating a prediction coefficient, the prediction coefficient being configured to output an accelerating temperature, an accelerating time, and an accelerating stress based on the reference creep strain; receiving an acceleration temperature, an acceleration time, and an acceleration stress based on a reference creep strain; receiving an accelerated test creep strain for the medical device based on an accelerated test conducted based on an accelerated temperature, an accelerated time, and an accelerated stress; and outputting one of an approval or a rejection indication based on comparing the accelerated test creep strain to the reference creep strain.

[0092] Item 10. The method of item 9, wherein the prediction coefficients are generated based on three-dimensional (3D) interpolation of raw creep strain data. Item 11. The method of item 9, wherein the first creep strain for the first duration, the first stress amount, and the first temperature is different from the second creep strain for the first duration, the first stress amount, and the second temperature.

[0093] Item 12. The method according to item 9, wherein the reference creep strain corresponds to a reference temperature, a reference time, and a reference stress. Item 13. The method according to Item 12, wherein the reference temperature is an ambient temperature, the reference time is an expected storage life of the medical device, and the reference stress is an expected amount of stress.

[0094] Item 14. The method of item 9, wherein an approval instruction approves the prediction coefficients and a rejection instruction rejects the prediction coefficients. Item 15. A method for validating a medical device design, comprising: receiving a plurality of medical device relationships based on the medical device design, the plurality of medical device relationships corresponding to voltage, current, resistance, torque, speed, and force relationships for the medical device design and including a plurality of coefficients; generating a linear device model based on the plurality of medical device relationships; receiving simulated coefficient values ​​for each of a plurality of coefficients from a distribution function for a plurality of medical device relationships; generating a simulated output distribution in terms of voltage, current, resistance, torque, speed, or force based on the simulated coefficient values ​​and the linear equipment model; comparing the simulated power distribution to a threshold power requirement; and outputting one of an approval instruction or a rejection instruction based on comparing the simulated output distribution to a threshold output requirement.

[0095] Item 16. The method according to Item 15, wherein the distribution function is a normal distribution function or a mixture of normal distribution functions. Item 17. The step of comparing the simulated power distribution to the threshold power requirement comprises: comparing the maximum distribution value to a maximum threshold output requirement; Comparing the minimum distribution value to a minimum threshold output value; or Item 16. The method of item 15, comprising comparing the peak distribution value to a peak threshold output value.

[0096] Item 18. The method of item 15, wherein the simulated power distribution is based on at least 10,000 simulations performed in less than one hour. Item 19. The method according to Item 15, wherein the plurality of medical device relationships are based on a viscosity of a substance, and the viscosity of the substance is calculated based on a substance temperature behavior determined based on a thermal analysis.

[0097] Item 20. The method according to item 15, wherein one coefficient of the plurality of coefficients is generated based on a fluid path restriction analysis, the fluid path restriction analysis outputting a force that is factored in the determined viscosity and the determined velocity.

[0098] Although several items are presented herein, multiple variations of such items, and combinations of elements from one or more items, are possible and are contemplated as being within the scope of the present disclosure. Moreover, those skilled in the art will appreciate that the concepts underlying the present disclosure may be readily used as a basis for the designing of other apparatuses, methods, and systems for carrying out several purposes of the present disclosure.

Claims

1. 1. A method for validating a medical device, comprising: receiving raw creep coefficient data for a first medical device, the raw creep coefficient data including creep strains corresponding to multiple stress durations and multiple stress amounts for a temperature range; generating prediction coefficients for the first medical device based on the raw creep coefficient data, the prediction coefficients being generated based on a three-dimensional (3D) interpolation of raw creep strain data; using the prediction coefficients to generate an accelerated test time, an accelerated stress, or an accelerated temperature; conducting a first accelerated test of a second medical device based on the accelerated test time, the accelerated stress, or the accelerated temperature; generating a first accelerated test creep strain based on the first accelerated test of the second medical device; and outputting one of an approval instruction for a second medical device or an indication to reject the second medical device based on comparing the first accelerated test creep strain to a reference creep strain associated with the first medical device.

2. The method of claim 1 , wherein the accelerated test time is generated based on the reference creep strain, the reference stress, and the accelerated temperature.

3. The method of claim 1 or 2, wherein the approval instruction approves the second medical device and the rejection instruction rejects the second medical device.

4. generating a second accelerated test creep strain based on a second accelerated test of the first medical device, the second accelerated test being based on the accelerated temperature, the accelerated test time, and the accelerated stress; and outputting one of a prediction coefficient approval indication or a prediction coefficient rejection indication based on comparing the second accelerated test creep strain and the reference creep strain.

5. a. the second medical device is manufactured based on a medical device design corresponding to the first medical device, and the raw creep coefficient data is based on the first medical device; and / or 5. The method of claim 1, wherein the raw creep strain data is generated based on one of simulated strain or experienced strain.

6. The prediction coefficients are configured to output the accelerated temperature, the accelerated test time, and the accelerated stress based on the reference creep strain, and the method further comprises: receiving the accelerated temperature, the accelerated test time, and the accelerated stress based on the reference creep strain; receiving the first accelerated test creep strain for the second medical device based on the first accelerated test conducted based on the accelerated temperature, the accelerated test time, and the accelerated stress; 2. The method of claim 1, further comprising the step of: outputting one of an approval or a rejection indication for the prediction coefficients based on comparing the first accelerated test creep strain and the reference creep strain. a first creep strain for a first duration, a first stress amount, and a first temperature is different from a second creep strain for the first duration, the first stress amount, and a second temperature; and / or 6. The method of claim 5, wherein the reference creep strain corresponds to a reference temperature, a reference time, and a reference stress.

8. 1. A method for validating a predictive coefficient of a medical device, comprising: receiving raw creep strain data relating creep strain values ​​to stress duration and stress amount, factored by temperature range; generating prediction coefficients, the prediction coefficients being configured to output an accelerating temperature, an accelerating time, and an accelerating stress based on a reference creep strain; receiving the acceleration temperature, the acceleration time, and the acceleration stress based on the reference creep strain; receiving an accelerated test creep strain for the medical device based on an accelerated test conducted based on the accelerated temperature, the accelerated time, and the accelerated stress; and outputting one of an approval or a rejection indication based on comparing the accelerated test creep strain to the reference creep strain.

9. The method of claim 8 , wherein the prediction coefficients are generated based on three-dimensional (3D) interpolation of the raw creep strain data.

10. 9. The method of claim 8, wherein a first creep strain for a first duration, a first stress amount, and a first temperature is different from a second creep strain for the first duration, the first stress amount, and a second temperature.

11. The method of claim 8 , wherein the reference creep strain corresponds to a reference temperature, a reference time, and a reference stress.

12. 12. The method of claim 11, wherein the reference temperature is an ambient temperature, the reference time is an expected shelf life of the medical device, and the reference stress is an expected amount of stress.

13. The method of claim 8 , wherein the approval instruction approves the prediction coefficients and the rejection instruction rejects the prediction coefficients.

14. 1. A method for validating a medical device design, comprising: receiving a plurality of medical device relationships based on the medical device design, the plurality of medical device relationships corresponding to voltage, current, resistance, torque, speed, and force relationships for the medical device design and including a plurality of coefficients; generating a linear equipment model based on the plurality of medical equipment relationships; receiving simulated coefficient values ​​for each of the plurality of coefficients from a distribution function for the plurality of medical device relationships; generating a simulated output distribution in terms of voltage, current, resistance, torque, speed, or force based on the simulated coefficient values ​​and the linear machine model; comparing the simulated power distribution to a threshold power requirement; and outputting one of an approval or a rejection indication based on comparing the simulated power distribution to the threshold power requirement.

15. The method of claim 14 , wherein the distribution function is a normal distribution function or a mixture of normal distribution functions.

16. Comparing the simulated power distribution to the threshold power requirement comprises: comparing the maximum distribution value to a maximum threshold output requirement; Comparing the minimum distribution value to a minimum threshold output value; or The method of claim 14 including comparing the peak distribution value to a peak threshold power value.

17. 15. The method of claim 14, wherein the simulated power distribution is based on at least 10,000 simulations performed in less than one hour.

18. 15. The method of claim 14, wherein the plurality of medical device relationships are based on a viscosity of a substance, the viscosity of the substance being calculated based on a substance temperature behavior determined based on a thermal analysis.

19. 15. The method of claim 14, wherein a coefficient of the plurality of coefficients is generated based on a fluid path restriction analysis, the fluid path restriction analysis outputting a force applied factored by a determined viscosity and a determined velocity.

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