Apparatus for characterizing a drug delivery device or a subcomponent thereof

Abstract

Apparatus, systems, and methods are provided for characterizing the rear subassembly of an autoinjector (AI). Apparatus, systems, and methods are also provided for generating an injection time (IT) model for an autoinjector (AI). Autoinjectors (AI) (e.g., mechanical AI, spring-loaded AI, etc.), components for use within the AI ​​(e.g., pre-filled syringe (PFS), rear subassembly (RSA), etc.), and related methods can be used with mathematical modeling along with Design for Six Sigma (DFSS) and Design for Reliability and Manufacturability (DRM) to establish upstream controls that ensure the certainty of the injection time (IT) of a mechanical autoinjector (AI). For example, release and extrusion (BLE) notification limits, rear subassembly (RSA) area under the curve (AUC), RSA minimum force, etc. can be determined and used to select an RSA for any given PFS.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS Priority is claimed to U.S. Provisional Patent Application No. 63 / 254,294, filed October 11, 2021, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure relates generally to automatic drug product injectors. More specifically, the present disclosure relates to generating an injection time (IT) model for an autoinjector (AI) based on pre-filled syringe (PFS) data and rear sub-assembly (RSA) data. [Background technology]

[0003] Many drug products are manufactured and packaged, for example, in an autoinjector (AI). The AI ​​may include a prefilled syringe (PFS) and a rear subassembly (RSA) for providing a force to automatically expel the drug product (DP) from the PFS when the AI ​​is activated by a user.

[0004] Infusion times (IT) are often specified, for example, by the DP manufacturer. With known AIs, achieving consistent IT is often problematic. Selection of an RSA for a particular PFS is difficult at best. Thus, known AIs often fail to reliably achieve the specified infusion time.

[0005] There is also a need for an apparatus, system, and method for characterizing the rear subassembly of an autoinjector (AI).There is a need for an apparatus, system, and method for generating an injection time (IT) model of an autoinjector (AI). Summary of the Invention [Means for solving the problem]

[0006] A rear sub-assembly (RSA) output force characterization method may include providing a syringe-less (SyFR) rear sub-assembly (RSA) operating apparatus. The method may also include generating RSA output force profile data using the syringe-less (SyFR) rear sub-assembly (RSA) operating apparatus. The RSA output force profile data may be representative of a rear sub-assembly (RSA) output force profile. The method may further include generating an RSA output energy profile based on the RSA output force profile data.

[0007] In another embodiment, an apparatus for characterizing an output force of a drug delivery device or a rear subassembly of a drug delivery device may include a driver holder configured to secure the drug delivery device or the rear subassembly to the apparatus. The drug delivery device or the rear subassembly may include a drive member having a compressed position and an extended position. The apparatus may also include a force sensor configured to measure output force data associated with the drive member as the drive member moves from the compressed position to the extended position. The apparatus may further include an extrusion speed input configured to receive extrusion speed data. The extrusion speed data may be representative of a speed of the drive member. The apparatus may still further include a controller configured to receive the output force data and the extrusion speed input data and generate an output force profile based on the output force data. The controller may be further configured to generate an output energy profile based on the output force profile data.

[0008] In a further embodiment, a non-transitory computer readable medium storing computer readable instructions that, when executed by a processor, cause the processor to generate an injection time (IT) model for an autoinjector. Execution of the instructions can cause the processor to receive pre-filled syringe (PFS) release and extrusion resistance (BLER) data. The BLER data can be representative of an amount of force required to achieve each extrusion rate. Further execution of the instructions can cause the processor to receive rear sub-assembly (RSA) output force profile data. Further execution of the instructions can cause the processor to generate the injection time (IT) model for the autoinjector based on the BLER data and the RSA output force profile data.

[0009] The present disclosure will be more fully understood when taken in conjunction with the following description and the accompanying drawings, in which: Some of the drawings may be simplified by the omission of selected elements for the purpose of more clearly showing other elements. Such omission of elements in some of the drawings does not necessarily indicate the presence or absence of a particular element in any of the exemplary embodiments, unless expressly specified in the corresponding written description. Additionally, none of the drawings are necessarily drawn to scale. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 shows a block diagram of an exemplary injection time (IT) model for an autoinjector (AI). [Figure 2A] 1A-1D show various views of an exemplary autoinjector (AI). [Figure 2B] 1A-1D show various views of an exemplary autoinjector (AI). [Figure 2C] 1A-1D show various views of an exemplary autoinjector (AI). [Diagram 3] 1 illustrates an exemplary system for generating an IT model of an autoinjector (AI). [Figure 4] 1 illustrates an exemplary rear sub-assembly (RSA) test fixture. [Figure 5A] 1 illustrates an exemplary system for generating an IT model of an autoinjector (AI). [Figure 5B] 1 illustrates an exemplary system for generating an IT model of an autoinjector (AI). [Figure 5C] 1 illustrates an exemplary system for generating an IT model of an autoinjector (AI). [Figure 5D] 1 illustrates an exemplary system for generating an IT model of an autoinjector (AI). [Figure 5E] 1 illustrates an exemplary system for generating an IT model of an autoinjector (AI). [Figure 6] 1 shows an exemplary release and extrusion resistance (BLER) profile of a pre-filled syringe (PFS). [Figure 7] 1 illustrates an exemplary rear sub-assembly (RSA) output force profile. [Figure 8] 1 illustrates an exemplary force balance analysis used in the IT model. [Figure 9] 1 shows an exemplary hypothetical maximum pre-filled syringe (PFS) release and extrusion resistance (BLER) profile. [Figure 10] 1 shows an exemplary area under the curve (AUC) for minimum energy drug product extrusion. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Those skilled in the art will appreciate that elements in the figures are depicted for simplicity and clarity and are not necessarily drawn to scale. For example, the dimensions and / or relative positions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of the various embodiments of the present invention. Also, common but well-understood elements that are useful or necessary in commercially feasible embodiments are often not shown in order to lessen the clutter of the drawings of these various embodiments. Furthermore, it will be appreciated that certain acts and / or steps may be described or shown in a particular order of occurrence, but those skilled in the art will appreciate that such specificity with respect to the order is not actually required. Furthermore, it will be appreciated that certain acts and / or steps may be described or shown in a particular order of occurrence, but those skilled in the art will appreciate that such specificity with respect to the order is not actually required. It will also be appreciated that the terms and expressions used herein have the ordinary technical meaning that those skilled in the art would appreciate for such terms and expressions, unless a different specific meaning is otherwise expressly set forth herein.

[0012] Apparatus, systems and methods are provided for characterizing the rear subassembly of an autoinjector (AI). Apparatus, systems and methods are also provided for generating an injection time (IT) model of an autoinjector (AI).

[0013] Autoinjectors (AIs) (e.g., mechanical AIs, spring-loaded AIs, etc.), components for use within AIs (e.g., prefilled syringes (PFSs), rear subassemblies (RSAs), etc.), and related methods can be designed using mathematical modeling along with Design for Six Sigma (DFSS) and Design for Reliability and Manufacturability (DRM) to establish upstream controls that ensure certainty of injection time (IT) for mechanical autoinjectors (AIs). For example, release and extrusion (BLE) notification limits, rear subassembly (RSA) area under the curve (AUC), RSA minimum force, etc. can be determined and used to select an RSA for any given PFS.

[0014] To ensure the reliability of injection time (IT) performance of autoinjectors (AIs) (e.g., mechanical AIs, spring-loaded mechanical AIs, etc.), upstream controls (e.g., release and extrusion (BLE) notification limits, area under the curve (AUC), stall force, etc.) can be established. These controls can ensure the quality of the AI ​​and potentially improve the customer experience by bridging the gap between component specifications (e.g., PFS specifications, RSA specifications, etc.) and user specifications (e.g., injection time (IT), etc.). A sustainable and repeatable AI design framework that can be transferred to future drug products (DPs) and autoinjectors (AIs) is provided.

[0015] Referring to FIG. 1, a block diagram of an injection time (IT) model generation system 100 may include release and push-out (BLE) 105 notification limits 106, prefilled syringe (PFS) push-out force 107 data conversion, release and push-out resistance (BLER) 110 with IT model parameterization 111, rear subassembly 115 with component specifications 116, and RSA characterization profile data 117, which are combined as inputs 120 to injection time (IT) 125 with user specifications 126. The injection time (IT) model generation system 100 can predict the IT performance of the AI ​​using, for example, mathematical models as detailed herein. The model generation system 100 can predict the IT using empirical data of the prefilled syringe (PFS) and rear subassembly (RSA). Thus, the model generation system 100 can easily connect component (PFS and RSA) specifications to user specifications (IT). PFS resistance can be quantified using BLE and release and push-out resistance (BLER) forces. The mechanical performance of the RSA can be characterized using RSA output force measurements. Details regarding the mathematical model of the IT are included herein.

[0016] 2A-2C, an injection time (IT) model generation system 200a-c may include an autoinjector (AI) 230a-c (e.g., a single-use spring-loaded AI, etc.) having a pre-filled syringe (PFS) 235a-c and a rear subassembly (RSA) 240a-c (e.g., a mechanical RSA, a spring-loaded RSA, etc.).

[0017] 3, the autoinjector (AI) injection time (IT) model generation system 300 may include receiving release and push force data 331 of a prefilled syringe (PFS) 330 and rear subassembly (RSA) characterization data 341 from an RSA 340 and an RSA fixture 350. The autoinjector injection time (IT) model generation system 300 may also include a remote device 370 having an input device 328, a display device 374, and a printer 379. The display device 374 may include a user interface 375 configured to generate an injection time (IT) model of the autoinjector (AI), for example.

[0018] 4, an autoinjector (AI) injection time (IT) model generation system 400 may include a rear subassembly (RSA) fixture 450 (e.g., a syringe-less (SyFR) RSA test fixture, etc.) and a rear subassembly (RSA) 440. The RSA fixture 450 may include a base 451, a disc portion 452, a syringe driver holder 453, and an RSA load application portion 454. The RSA fixture may generate rear subassembly (RSA) characterization data from the load application portion 454 and the RSA 440.

[0019] The rear subassembly fixture 450 may be configured to characterize the output force of the drug delivery device or the rear subassembly of the drug delivery device. The fixture 450 may include a driver holder 453 configured to secure the drug delivery device or the rear subassembly to the fixture 450. The drug delivery device or the rear subassembly may include a drive member having a compressed position and an extended position. The apparatus may also include a force sensor 456 configured to measure output force data associated with the drive member as the drive member moves from the compressed position to the extended position. The fixture 450 may further include an extrusion rate sensor 457 configured to provide an extrusion rate of the drive member. The fixture 450 may still further include a controller 455 configured to receive the output force data from the sensor 456 and generate an output force profile based on the output force data. The controller 455 may further be configured to generate an output energy profile based on the output force profile data and the extrusion rate data.

[0020] 5A-5E, autoinjector injection time (IT) model generation systems 500a-e may include pre-filled syringe (PFS) and / or syringeless rear subassembly devices 550a-c in communication with remote devices (e.g., servers) 570a,d,e via network 580a. The pre-filled syringe (PFS) and / or syringeless rear subassembly devices 550a-c may be similar to, for example, the pre-filled syringe (PFS) and / or syringeless rear subassembly device 350 of FIG. 3. The remote devices 570a,d,e may be similar to, for example, the remote devices 120 of FIG.

[0021] The autoinjector injection time (IT) model generation system 500a-e can facilitate communication between the prefilled syringe (PFS) and / or syringeless rear subassembly devices 550a-c and remote devices 570a,d,e (e.g., remote servers, cloud-based resources, etc.) to provide, for example, RSA data and / or PFS data to the PFS and RSA database 576a.

[0022] For example, the autoinjector injection time (IT) model generation system 500a-e can obtain prefilled syringe data (e.g., prefilled syringe physical dimension data, prefilled syringe optical transmission data, prefilled syringe manufacturing data, etc.) from, for example, a user of the prefilled syringe (PFS) and / or syringe-less rear subassembly device 550a-c. Alternatively or additionally, although not shown in FIGS. 5A-5E, the syringe data and / or BLER data may be obtained automatically from a third party data source (e.g., a syringe manufacturer, a pharmaceutical manufacturer, etc.). As detailed herein, the autoinjector injection time (IT) model generation system 500a-e can automatically generate an injection time (IT) model (AI) of the autoinjector based, for example, on the BLER data and the RSA characterization data.

[0023] For clarity, only one pre-filled syringe (PFS) and / or syringe-less rear subassembly device 550a-c is shown in FIG. 5A. Although FIG. 5A shows only one pre-filled syringe (PFS) and / or syringe-less rear subassembly device 550a-c, it should be understood that any number of pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c can be supported. Each of the pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c can include a memory 551a and a processor 553a for storing and executing a module 552a. The module 552a stored in the memory 551a as a computer readable instruction set can be related to an application for automatically generating an IT model of AI.

[0024] As described in more detail herein, the module 552a may facilitate interactions between associated pre-filled syringe (PFS) and / or syringeless rear subassembly devices 550a-c and remote devices 570a,d,e. For example, the processor 553a may further execute the module 552a to facilitate communications between the remote devices 570a,d,e and the pre-filled syringe (PFS) and / or syringeless rear subassembly devices 550a-c via the network interface 556a, the communication link 581a, the network 580a, the remote device communication link 582a, and the remote device network interface 577a.

[0025] The pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c may include a user interface 554a along with a user input device, which may be any type of electronic display device, such as a touch screen display, a liquid crystal display (LCD), a light emitting diode (LED) display, a plasma display, a cathode ray tube (CRT) display, or any other type of known or suitable electronic display. The user interface 554a may present a user interface (e.g., any user interface 375, etc.) that shows a user interface for configuring the pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c to communicate with remote devices 570a,d,e.

[0026] The network interface 580a may be configured to facilitate communication between the pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c and the remote devices 570a,d,e via any wireless communication network 580a, including, for example, a wireless LAN, a MAN or WAN, WiFi, the Internet, or any combination thereof. Further, the pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c may be communicatively coupled to the remote devices 570a,d,e via any suitable communication system, such as any public or private communication network, including those using wireless communication structures, such as wireless communication networks including, for example, wireless LANs and WANs, satellite and cellular telephone communication systems, and the like. Pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c may, for example, transmit and store pre-filled syringe data and / or RSA data in, for example, remote devices 570a, d, e, memory 571a and / or remote PFS and RSA database 576a.

[0027] The pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c may include a PFS data source 531a and an RSA data source 541a. As described further herein, the pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c may be configured to, for example, cause a processor 543a to obtain PFS data 331 and / or RSA data 341.

[0028] The remote devices 450b,e may include a user interface 574a, a memory 571a, and a processor 573a for storing and executing the modules 572a, respectively. The modules 572a stored in the memory 571a as a set of computer readable instructions may facilitate applications related to automatically generating an AI IT model. The modules 572a may also facilitate communication between the remote devices 570a,d,e and the pre-filled syringe (PFS) and / or syringeless rear subassembly devices 550a-c via the network interface 577a and the network 580a and other functions and instructions.

[0029] The remote devices 570a, d, e may be communicatively coupled to the pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c. Although the pre-filled syringe (PFS) and / or syringe-less rear subassembly device 550a is shown in FIG. 5A as being communicatively coupled to the remote device 570a, it should be understood that the PFS and RSA database 576a may be located in a separate remote server (or any other suitable computing device) communicatively coupled to the remote devices 570a, d, e. Optionally, portions of the PFS and RSA database 576a may be associated with memory modules separate from one another, such as the memory 551a of the pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c.

[0030] The pre-filled syringe (PFS) and / or syringeless rear subassembly devices 550a-c may include a user interface generating module 552b, a pre-filled syringe data receiving module 553b, a BLER parameterization module 554b, an RSA syringeless data receiving module 555b, a PFS data transmitting module 556b, and an RSA data transmitting module 557b, which are stored in memory 551b, for example, as a set of computer readable instructions. In any event, modules 552b-557b may be similar to, for example, module 552a of FIG. 5A.

[0031] The method of operating the pre-filled syringe (PFS) and / or syringe-less rear subassembly devices 550a-c may be implemented by a first processor (e.g., processor 553a) executing at least a portion of modules 552b-557b, for example. In particular, processor 553a may execute user interface generation module 552b to cause processor 553a to generate, for example, user interface 375 (block 552e). The user interface may enable a user to input, for example, pre-filled syringe data and / or RSA data.

[0032] The processor 553a may execute a syringe data receiving module 553b to cause the processor 553a to receive prefilled syringe data, for example, from a prefilled syringe manufacturer, a drug manufacturer, etc. (block 553c). The processor 553a may execute a BLER parameterization module 554b to cause the processor 553a to parameterize syringe data, for example (block 554c). The processor 553a may execute an RSA syringeless data receiving module 555b to cause the processor 553a to receive RSA syringeless data, for example (block 555c). The processor 553a may execute a PFS data transmitting module 556b to cause the processor 553a to transmit PFS data, for example (block 556c). The processor 553a may execute an RSA data transmitting module 557b to cause the processor 553a to transmit RSA data, for example (block 556c).

[0033] The remote devices 570a, d, e may include a user interface generating module 572d, a syringe data receiving module 573d, an RSA data receiving module 574d, a PFS data parameterization module 575d, an RSA characterization module 576d, an injection time (IT) model generating module 577d, a release and extrusion (BLE) notification limit determining module 578d, a BLE resistance (BLER) force determining module 579d, an RSA area under the curve (AUC) data generating module 580d, and an RSA minimum force determining module 581d, which are stored in the memory 571d, for example, as a set of computer readable instructions. In any case, the modules 572d-581d may be similar to the module 552a of FIG. 5A, for example.

[0034] The method of operating the remote device 500e may be implemented by a processor (e.g., processor 573a) executing at least a portion of modules 572d-581d, for example. In particular, processor 573a may execute user interface generation module 572d to cause processor 573a to generate, for example, user interface 375 (block 572e).

[0035] Processor 573a may execute syringe data receiving module 573d to cause processor 573a to receive prefilled syringe data, for example, from a user via a user interface and / or from a third party prefilled syringe database (block 573e). Processor 573a may execute RSA data receiving module 574d to cause processor 573a to receive, for example, RSA data (block 574e).

[0036] The processor 573a may execute a PFS data parameterization module 575d to cause the processor 573a to, for example, parameterize the PFS data (block 575e). The processor 573a may execute an RSA characterization module 576d to cause the processor 573a to, for example, characterize the RSA (block 576e). The processor 573a may execute an infusion time (IT) model generation module 577d to cause the processor 573a to, for example, generate an IT model for the AI ​​(block 577e). The processor 573a may execute a release and extrusion (BLE) notification limit determination module 578d to cause the processor 573a to, for example, determine a release and extrusion (BLE) notification limit (block 578e).

[0037] The processor 573a may execute a BLE resistance (BLER) force determination module 579d to cause the processor 573a to determine, for example, a BLER force (block 579e). The processor 573a may execute an RSA area under the curve (AUC) data generation module 580d to cause the processor 573a to generate, for example, RSA AUC data (block 580e). The processor 573a may execute an RSA minimum force determination module 581d to cause the processor 573a to determine, for example, an RSA minimum force (block 581e).

[0038] Referring to FIG. 6, an injection time (IT) model generation system 600 can generate an IT model based on data used to generate a graphical representation 601 of the forces measured using the BLER method of the Repatha® PFS. This test method can characterize the injection behavior of a single PFS by performing tests over a range of speeds in a single syringe. The test speeds used can represent the speed profile that occurs during injection of, for example, a drug product (DP) via a spring-loaded mechanical autoinjector (AI). Full characterization of the resistance of the PFS at various speeds allows BLE notification limits to be defined, for example, by a mathematical model of injection time (IT). Furthermore, performing this (BLER) characterization on the PFS can provide deeper insight into how the PFS extrusion force relates to the AI ​​output force.

[0039] Utilizing the pre-filled syringe data collected with the BLER test method, a BLER profile (such as, for example, BLER profile 601 in FIG. 6) can be parameterized for use in a mathematical model of injection time (IT). A pre-filled syringe (PFS) BLER profile can be parameterized, for example, as a quadratic function of extrusion rate (such as the data shown in graph 601 in FIG. 6). A BLER force profile (F pfs )teeth,

number

[0040] In formula 1,

number

[0041] Equation 1 can be used within a mathematical model of IT to predict IT. The BLER method can provide a quantitative measurement of the amount of force required to extrude a drug product (DP) at various extrusion rates occurring throughout the total injection.

[0042] The rear sub-assembly (RSA) output force characterization method (e.g., Syringe-Free (SyFR) release test method, etc.) may collect quantitative information on the output force of the RSA, for example, in the form of area under the curve (AUC), minimum force, pre-activation force, and activation force in the presence of only the RSA of the AI. These measurements may be used to predict the injection time (IT) of the associated autoinjector (AI). The SyFR method may not constrain the movement of the actuator sleeve of the associated rear sub-assembly (RSA), and may allow for more accurate measurements by fully taking into account frictional forces that may occur during the movement of the plunger rod of the RSA. The SyFR method may be applied to all mechanical AIs.

[0043] Referring to FIG. 7, an injection time (IT) model generation system 700 may include an RSA output force profile 701 measured using a syringe-less test fixture 450 for an RSA of 2.9 kgf. The output force profile 701 of FIG. 7 collected using a SyFR test fixture and parameterized using Equation 2 may represent the force exerted by the plunger rod of the RSA on the plunger stopper as a function of plunger rod displacement. The interval defined between x1 and x2 is the region of interest during DP extrusion and is used when calculating IT. This interval represents the extension of the plunger rod and begins when the plunger rod exerts pressure on the plunger stopper and ends when the plunger stopper bottoms out on the cone region of the syringe (end of administration). The available energy (injection energy) indicated by the RSA to complete the injection is defined as the AUC within a set interval. The AUC may be calculated by integrating the output force profile over this interval. The AUC is a contributing factor to the injection performance of the AI ​​since it is strongly correlated with the IT. F sp (x)=K sp (L comp -x) expression 2

[0044] In formula 2, F sp (x) is the RSA force as a function of plunger rod displacement, K sp represents the RSA effective spring constant, and L comp represents the RSA effective compressed length.

[0045] The minimum output force over the injection interval shown in Figure 7 may represent the minimum force that the plunger rod exerts on the plunger stopper during drug product (DP) extrusion. The rear subassembly (RSA) may be configured to apply the minimum force, for example, to ensure that an associated autoinjector (AI) does not experience stall during full dose injection.

[0046] Mathematical modeling of the injection time (IT) can be performed by numerically solving a nonlinear ordinary differential equation (Equation 3) derived by quasi-steady force balance analysis for the plunger stopper of the PFS, as described in Figure 8. Equation 3 can be derived by equating Equation 1 and Equation 2. Note that the right hand side of the equation includes the constants previously described in this document. The IT model provides the amount of time it takes for the plunger stopper to move the required length in the syringe to push the entire amount of DP out of the PFS.

[0047] F pfs can be characterized, for example, using a BLER test method and a parameterization of a PFSBLER profile (such as profile 601 in FIG. 6).

[0048] F sp can be characterized, for example, using the RSA output force test method and a parameterization of the RSA output force profile (such as profile 701 in FIG. 7).

number

[0049] Referring to FIG. 8, an injection time (IT) model generation system 800 can perform a force balance analysis used for an IT model as shown by profile 801. A mathematical model of the IT is used to characterize the 99% lower tolerance interval (TI) of the RSA performance (characterized using the SyFR method and parameterized using the maximum BLER profile that allows the DP to be extruded with an IT of less than 15 seconds (when combined with the 99% lower TI of the RSA population (FIG. 7)). From this hypothetical BLER profile, the BLE (usually measured at an extrusion speed of 205 mm / min according to MET-406768) can be extracted and realized as the BLE notification limit of the DP PFS. Also, from this hypothetical maximum BLER profile, the maximum low-speed sliding force can be extracted. Graph 901 in FIG. 9 shows the implementation of this approach for a pre-filled syringe (PFS) (e.g., Repatha PFS, etc.) based on RSA data such as provided in FIG. 10. As shown by graph 1001, a BLE notification limit of 14 N and a maximum sliding force of 2.5 N, respectively, can be inferred.

[0050] With further reference to Figure 9, a hypothetical maximum Repatha BLER profile 901 (scaled up from Figure 6) is shown that when combined with the 99% lower bound TI population of the RSA profile (shown in Figure 7) allows for an IT of less than 15 seconds. The extracted BLE notification limits are marked with an asterisk. The extracted maximum low speed glide force is also marked with an asterisk.

[0051] To reach the desired injection time, a minimum area under the curve (AUC) is defined. Once the BLE notification limit is set, the minimum energy (AUC) required to inject the DP at a specified IT for any PFS that exhibits a BLE value smaller than the BLE notification limit can then be characterized using the IT model. Equation 4 is expressed as AUC, C1, C2, F f , and an expression of IT as a function of Δx(x1-x2) may be provided.

[0052] With further reference to FIG. 10, a characterization of the AUC required to extrude Repatha with an IT of less than 15 seconds is provided that the Repatha PFS exhibits a BLE value less than the BLE notification limit of 14N. Using the approach described herein, it is estimated that if a BLE value less than the BLE notification limit is combined with an RSA exhibiting an AUC greater than the physics-based limit derived using Equation 4 (e.g., 0.31 J for Repatha PFS (FIG. 10)), the injection time (IT) can be theoretically guaranteed to be less than the relevant IT specification (e.g., 15 seconds for Repatha PFS (FIG. 10)). Given that PFS BLE and AUC, an autoinjector (AI) design space can be identified to ensure that the relevant IT specification is met.

number

[0053] Figure 10 shows that the minimum energy (AUC) to extrude Repatha with IT less than 15 seconds is characterized as 0.31 J for all PFSs exhibiting BLE values ​​less than the BLE notification limit of 14 N. The IT model of the AI ​​may, for example, define the minimum force to avoid stalling the injection. The minimum force at the end of the injection interval (x2) may be inferred to be the minimum amount of force the RSA can provide. Using the approach described in section 2.d., the maximum slow sliding force is characterized as the amount of force required to prevent the plunger stopper from stalling during the injection. If the minimum force in Figure 10 is greater than the maximum sliding force in Figure 9, then avoidance of injection stall can be guaranteed. Thus, the maximum sliding force of the hypothetical maximum BLER can be set as the minimum force required by the RSA to avoid injection stall.

[0054] The above description describes various devices, assemblies, components, subsystems, and methods of use associated with drug delivery devices such as prefilled syringes. The devices, assemblies, components, subsystems, methods, or drug delivery devices (i.e., prefilled syringes) may also include or be used in conjunction with drugs, including but not limited to the drugs identified below, as well as their generic and biosimilar counterparts. As used herein, the term drug may be used interchangeably with other similar terms and may be used to refer to any type of pharmaceutical or therapeutic material, including traditional and non-traditional medicines, nutraceuticals, supplements, biological products, biologically active agents and compositions, large molecules, biosimilars, bioequivalents, therapeutic antibodies, polypeptides, proteins, small molecules, and generic drugs. Non-therapeutic injectable materials are also included. Drugs may be in liquid, lyophilized, or reconstituted form from a lyophilized form. The following list of exemplary drugs should not be considered exhaustive or limiting.

[0055] The drug is contained in a reservoir, for example, in a pre-filled syringe. In some cases, the reservoir is a primary container into which the drug is filled or pre-filled for treatment. The primary container may be a vial, a cartridge, or a pre-filled syringe.

[0056] In some embodiments, the reservoir of the drug delivery device may be loaded with or the device may be used with colony stimulating factors such as granulocyte colony stimulating factor (G-CSF). Such G-CSF formulations include, but are not limited to, Neulasta® (pegfilgrastim, PEGylated filgrastim, PEGylated G-CSF, PEGylated hu-Met-G-CSF) and Neupogen® (filgrastim, G-CSF, hu-MetG-CSF), UDENYCA® (pegfilgrastim-cbqv), Ziextenzo® (LA-EP2006; pegfilgrastim-bmez) or FULPHILA (pegfilgrastim-bmez).

[0057] In other embodiments, the drug delivery device may contain or be used with an erythropoietin stimulating agent (ESA), which may be in liquid or lyophilized form. An ESA is any molecule that stimulates erythropoietin. In some embodiments, an ESA is an erythropoietin stimulating protein. As used herein, "erythropoietin stimulating protein" refers to any protein that directly or indirectly causes activation of the erythropoietin receptor, for example, by binding to the receptor and causing receptor dimerization. Erythropoietin stimulating proteins include erythropoietin and its variants, analogs, or derivatives that bind to and activate the erythropoietin receptor; antibodies that bind to and activate the erythropoietin receptor; or peptides that bind to and activate the erythropoietin receptor. Erythropoietin stimulating proteins include Epogen® (epoetin alfa), Aranesp® (darbepoetin alfa), Dynepo® (epoetin delta), Mircera® (methoxypolyethylene glycol-epoetin beta), Hematide®, MRK-2578, INS-22, Retacrit® (epoetin zeta), Neorecormon® (epoetin beta), Silapo® (epoetin zeta), Binocrit® (epoetin alfa), Epoetin alpha, epoetin beta, epoetin iota, epoetin omega, epoetin delta, epoetin zeta, epoetin theta, and epoetin delta, PEGylated erythropoietin, carbamylated erythropoietin, and molecules or variants or analogs thereof.

[0058] Particularly exemplary proteins include the specific proteins listed below, including fusions, fragments, analogs, variants or derivatives thereof: OPGL-specific antibodies (also referred to as RANKL-specific antibodies, peptibodies, etc.), peptibodies, related proteins, etc., including fully humanized and human OPGL-specific antibodies, particularly fully humanized monoclonal antibodies; myostatin-binding proteins, peptibodies, related proteins, etc., including myostatin-specific peptibodies; IL-4 receptor-specific antibodies, peptibodies, related proteins, etc., which particularly inhibit activities mediated by binding of IL-4 and / or IL-13 to its receptor; Interleukin 1-receptor 1 ("IL1-R1") specific antibodies, peptibodies, related proteins, etc.; Ang2 specific antibodies, peptibodies, related proteins, etc.; NGF specific antibodies, peptibodies, related proteins, etc.; CD22 specific antibodies, peptibodies, related proteins, etc., in particular a dimer of human-mouse monoclonal hLL2 gamma chain disulfide bound to human-mouse monoclonal hLL2 kappa chain, e.g., epratuzumab (CAS Registry Number 501423-23-0). human CD22-specific antibodies, including but not limited to, humanized and fully human monoclonal antibodies, including but not limited to, human CD22-specific IgG antibodies, particularly including but not limited to, human CD22-specific fully humanized antibodies of HuMax; IGF-1 receptor-specific antibodies, peptibodies and related proteins, including but not limited to, anti-IGF-1R antibodies; B-7-related protein 1-specific antibodies, peptibodies, related proteins, including but not limited to, those that inhibit the interaction of B7RP-1 with its natural receptor, ICOS, on activated T cells, including but not limited to, a B7RP-specific fully human monoclonal IgG2 antibody, including but not limited to, a fully human IgG2 monoclonal antibody that binds to an epitope in the first immunoglobulin-like domain of B7RP-1; IL-15 specific antibodies, such as humanized monoclonal antibodies, peptibodies, related proteins, and the like, including, but not limited to, IL-15 antibodies and related proteins;IFN gamma specific antibodies, including, but not limited to, human IFN gamma specific antibodies, including, but not limited to, fully human anti-IFN gamma antibodies; TALL-1 specific antibodies, peptibodies, related proteins, and the like, as well as other TALL specific binding proteins; parathyroid hormone ("PTH") specific antibodies, peptibodies, related proteins, and the like; thrombopoietin receptor ("TPO-R") specific antibodies, peptibodies, related proteins, and the like; fully human monoclonal antibodies that neutralize hepatocyte growth factor / scatter factor (HGF / SF) hepatocyte growth factor ("HGF") specific antibodies, peptibodies, related proteins, etc., including those that target the HGF / SF:c-Met axis (HGF / SF:c-Met), such as monoclonal antibodies; TRAIL-R2 specific antibodies, peptibodies, related proteins, etc.; activin A specific antibodies, peptibodies, proteins, etc.; TGF-beta specific antibodies, peptibodies, related proteins, etc.; amyloid-beta protein specific antibodies, peptibodies, related proteins, etc.; proteins that bind to c-Kit and / or other stem cell factor receptors, including, but not limited to, including, but not limited to, c-Kit specific antibodies, peptibodies, related proteins, etc.; OX40L specific antibodies, peptibodies, related proteins, etc., including, but not limited to, proteins that bind OX40L and / or other ligands of the OX40 receptor; Activase® (alteplase, tPA); Aranesp® (darbepoetin alfa), erythropoietin [30-asparagine, 32-threonine, 87-valine, 88-asparagine, 90-threonine], darbepoetin alfa, stimulating de novo erythropoiesis Protein (NESP); Epogen® (epoetin alfa or erythropoietin); GLP-1, Avonex® (interferon beta-1a); Bexxar® (tositumomab, an anti-CD22 monoclonal antibody); Betaseron® (interferon-beta); Campath® (alemtuzumab, an anti-CD52 monoclonal antibody); Dynepo® (epoetin delta); Velcade® (bortezomib); MLN0002 (anti-α4β7 mAb);MLN1202 (anti-CCR2 chemokine receptor mAb); Enbrel® (etanercept, TNF receptor / Fc fusion protein, TNF blocker); Eprex® (epoetin alfa); Erbitux® (cetuximab, anti-EGFR / HER1 / c-ErbB-1); Genotropin® (somatropin, human growth hormone); Herceptin® (trastuzumab, anti-HER2 / neu(erbB2) receptor mAb); Kanjinti™ (trastuzumab-anns) anti-HER2 monoclonal antibody, a biosimilar of Herceptin® or another product containing trastuzumab for the treatment of breast or gastric cancer; Humatrope® (somatropin, human growth hormone); Humira® (adalimumab ;Vectibix® (panitumumab), Xgeva® (denosumab), Prolia® (denosumab), immunoglobulin G2 human monoclonal antibody against RANK ligand, Enbrel® (etanercept, TNF receptor / Fc fusion protein, TNF blocker), Nplate® (romiplostim), rilotumumab, ganitumab, conatumumab, brodalumab, insulin in solution; Infergen® (interferon alfacon-1); Natrecor® (nesiritide; recombinant human B-type natriuretic peptide (hBNP); Kineret® (anakinra); Leukine® (sargamostim, rhuGM-CSF); LymphoCide® (epratuzumab, anti-CD22 mAb); Benlysta™ (lymphostat B, belimumab, anti-BlyS mAb); Metalyse® (tenecteplase, t-PA analog); Mircera® (methoxypolyethylene glycol-epoetin beta); Mylotarg® (gemtuzumab ozogamicin); Raptiva® (efalizumab); Cimzia® (certolizumab pegol, CDP 870); Soliris™ (eculizumab); pexelizumab (anti-complement C5); Numax® (MEDI-524);Lucentis® (ranibizumab); Panorex® (17-1A, edrecolomab); Trabio® (lerdelimumab); TheraCim hR3 (nimotuzumab); Omnitarg (pertuzumab, 2C4); Osidem® (IDM-1); OvaRex® (B43.13); Nuvion® (vigilizumab); cantuzumab mertansine (huC242-DM1); NeoRecormon® (epoetin beta); Neumega® (oprelvekin, human interleukin-11); Orthoclone OKT3® (muromonab-CD3, anti-CD3 monoclonal antibody); Procrit® (epoetin alfa); Remicade® (infliximab, anti-TNFα monoclonal antibody); Reopro® (abciximab, anti-GP lIb / Ilia receptor monoclonal antibody); Actemra® (anti-IL6 receptor mAb); Avastin® (bevacizumab), HuMax-CD4 (zanolimumab); Mvasi™ (bevacizumab-awwb); Rituxan® (rituximab, anti-CD20 mAb);Tarceva® (erlotinib);Roferon-A®-(interferon alpha-2a);Simulect® (basiliximab);Prexige® (lumiracoxib);Synagis® (palivizumab);145c7-CHO (anti-IL15 antibody, see U.S. Pat. No. 7,153,507);Tysabri® (natalizumab, anti-α4 integrin mAb);Valortim® (MDX-1303, anti-B. anthracis protective antigen mAb);ABthrax®;Xolair® (omalizumab);ETI211 (anti-MRSA mAb);IL-1 trap (Fc portion of human IgG1 and extracellular domain of both IL-1 receptor components (type I receptor and receptor accessory protein));VEGF trap (IgG1 Ig domain of VEGFR1 fused to Fc; Zenapax® (daclizumab);Zenapax® (daclizumab, anti-IL-2Rα mAb); Zevalin® (ibritumomab tiuxetan); Zetia® (ezetimibe); Orencia® (atacicept, TACI-Ig); anti-CD80 monoclonal antibody (galiximab); anti-CD23 mAb (lumiliximab); BR2-Fc (huBR3 / huFc fusion protein, soluble BAFF antagonist); CNTO 148 (golimumab, anti-TNFα mAb); HGS-ETR1 (mapatuzumab; human anti-TRAIL receptor-1 mAb); HuMax-CD20 (ocrelizumab, anti-CD20 human mAb); HuMax-EGFR (zalutumumab); M200 (volociximab, anti-α5β1 integrin mAb); MDX-010 (ipilimumab, anti-CTLA-4 mAb and VEGFR-1 (IMC-18F1); anti-BR3 mAb; anti-C. difficile toxin A and toxin BC mAbs MDX-066 (CDA-1) and MDX-1388); anti-CD22 dsFv-PE38 conjugate (CAT-3888 and CAT-8015); anti-CD25 mAb (HuMax-TAC); anti-CD3 mAb (NI-0401); adecatumumab; anti-CD30 mAb (MDX-060); MDX-1333 (anti-IFNAR); anti-CD38 mAb (HuMax CD38); anti-CD40L mAb; anti-Cripto mAb; anti-CTGF idiopathic pulmonary fibrosis stage 1 fibrogen (FG-3019); anti-CTLA4 mAb; anti-eotaxin 1 mAb (CAT-213); anti-FGF8 mAb; anti-ganglioside GD2 mAb; anti-ganglioside GM2 mAb; Anti-GDF-8 human mAb (MYO-029); Anti-GM-CSF receptor mAb (CAM-3001); Anti-HepC mAb (HuMax HepC); Anti-IFNα mAb (MEDI-545, MDX-198); Anti-IGF1R mAb; Anti-IGF-1R mAb (HuMax-Inflam); Anti-IL12 mAb(ABT-874); Anti-IL12 / IL23 mAb(CNTO 1275); Anti-IL13 mAb (CAT-354); anti-IL2Ra mAb (HuMax-TAC); anti-IL5 receptor mAb; anti-integrin receptor mAb (MDX-018, CNTO 95);Anti-IP10 ulcerative colitis mAb (MDX-1100); BMS-66513; anti-mannos receptor / hCGβ mAb (MDX-1307); anti-methelin dsFv-PE38 conjugate (CAT-5001); anti-PD1 mAb (MDX-1106(ONO-4538)); anti-PDGFRα antibody (IMC-3G3); anti-TGFβ mAb (GC-1008); anti-TRAIL receptor-2 mAb (HGS-ETR2); anti-TWEA; K mAb; anti-VEGFR / Flt-1 mAb; and anti-ZP3 mAb (HuMax-ZP3).

[0059] In some embodiments, the drug delivery device may contain or be used in conjunction with a sclerostin antibody, such as, but not limited to, romosozumab, brosozumab, BPS 804 (Novartis), Evenity™ (romosozumab-aqqg), another product containing romosozumab for the treatment of postmenopausal osteoporosis and / or fracture healing, and in other embodiments, a monoclonal antibody (IgG) that binds to human proprotein convertase subtilisin / kexin type 9 (PCSK9). Such PCSK9-specific antibodies include, but are not limited to, Repatha® (evolocumab) and Praluent® (alirocumab). In other embodiments, the drug delivery device may contain or be used in conjunction with rilotumumab, bixalomer, trebananib, ganitumab, conatumumab, motesanib diphosphate, brodalumab, vidupiprant, or panitumumab. In some embodiments, the reservoir of the drug delivery device may be loaded with, or the device may be used with, IMLYGIC® (talimogene laherparepvec) or another oncolytic HSV for the treatment of melanoma or other cancers, including but not limited to, OncoVEXGALV / CD; OrienX010; G207, 1716; NV1020; NV12023; NV1034; and NV1042. In some embodiments, the drug delivery device may contain, or be used with, an endogenous tissue inhibitor of metalloproteinases (TIMP), such as but not limited to, TIMP-3. In some embodiments, the drug delivery device may contain, or be used with, Aimovig® (erenumab-aooe), anti-human CGRP-R (calcitonin gene-related peptide type 1 receptor), or another product containing erenumab for the treatment of migraines. Antagonistic antibodies to the human calcitonin gene-related peptide (CGRP) receptor, such as, but not limited to, erenumab, and bispecific antibody molecules targeting the CGRP receptor and other headache targets, may also be delivered using the drug delivery devices of the present disclosure.In addition, bispecific T cell engager (BiTE®) antibodies, such as but not limited to BLINCYTO® (blinatumomab), can be used in or with the drug delivery device of the present disclosure. In some embodiments, the drug delivery device can contain or be used with an APJ large molecule agonist, such as but not limited to apelin or an analogue thereof. In some embodiments, a therapeutically effective amount of anti-thymic stromal lymphopoietin (TSLP) or a TSLP receptor antibody is used in or with the drug delivery device of the present disclosure. In some embodiments, the drug delivery device can contain or be used with Avsola™ (infliximab-axxq), an anti-TNF alpha monoclonal antibody, a biosimilar of Remicade® (infliximab) (Janssen Biotech, Inc.), or another product containing infliximab for the treatment of autoimmune diseases. In some embodiments, the drug delivery device may contain or be used in conjunction with Kyprolis® (carfilzomib), (2S)-N-((S)-1-((S)-4-methyl-1-((R)-2-methyloxiran-2-yl)-1-oxopentan-2-ylcarbamoyl)-2-phenylethyl)-2-((S)-2-(2-morpholinoacetamido)-4-phenylbutanamido)-4-methylpentanamide, or another product containing carfilzomib for the treatment of multiple myeloma. In some embodiments, the drug delivery device may contain or be used in conjunction with Otezla® (apremilast), N-[2-[(1S)-1-(3-ethoxy-4-methoxyphenyl)-2-(methylsulfonyl)ethyl]-2,3-dihydro-1,3-dioxo-1H-isoindol-4-yl]acetamide, or another product containing apremilast for the treatment of various inflammatory diseases.In some embodiments, the drug delivery device may contain or be used in conjunction with Parsabiv™ (etelcalcetide HCl, KAI-4169) or another product containing etelcalcetide HCl for the treatment of secondary hyperparathyroidism (sHPT), such as in patients with chronic kidney disease (KD) on hemodialysis. In some embodiments, the drug delivery device may contain or be used in conjunction with ABP 798 (rituximab), a biosimilar candidate of Rituxan® / MabThera™, or another product containing an anti-CD20 monoclonal antibody. In some embodiments, the drug delivery device may contain or be used in conjunction with a VEGF antagonist, such as a non-antibody VEGF antagonist, and / or a VEGF trap, such as aflibercept (Ig domain 2 from VEGFR1 and Ig domain 3 from VEGFR2 fused to the Fc domain of IgG1). In some embodiments, the drug delivery device may contain or be used with ABP 959 (eculizumab), a biosimilar candidate of Soliris®, or another product containing a monoclonal antibody that specifically binds to complement protein C5. In some embodiments, the drug delivery device may contain or be used with rogivafusp alfa (formerly AMG 570), a new bispecific antibody-peptide conjugate that simultaneously blocks ICOSL and BAFF activity. In some embodiments, the drug delivery device may contain or be used with omecamtib mecarbil, a small molecule selective cardiac myosin activator or myotrope that directly targets the contractile machinery of the heart, or another product containing a small molecule selective cardiac myosin activator. In some embodiments, the drug delivery device may contain sotorasib (formerly known as AMG 510), a KRAS. G12C Small molecule inhibitors or KRAS G12CThe drug delivery device may contain or be used with another product containing a small molecule inhibitor. In some embodiments, the drug delivery device may contain or be used with another product containing tezepelumab, a human monoclonal antibody that inhibits the action of thymic stromal lymphopoietin (TSLP), or a human monoclonal antibody that inhibits the action of TSLP. In some embodiments, the drug delivery device may contain or be used with another product containing AMG 714, a human monoclonal antibody that binds to interleukin-15 (IL-15), or a human monoclonal antibody that binds to interleukin-15 (IL-15). In some embodiments, the drug delivery device may contain or be used with another product containing AMG 890, a small interfering RNA (siRNA) that reduces lipoprotein (a), also known as Lp(a), or a small interfering RNA (siRNA) that reduces lipoprotein (a). In some embodiments, the drug delivery device may contain or be used with ABP 654 (human IgG1 kappa antibody), a biosimilar candidate for Stelara®, or another product that contains a human IgG1 kappa antibody and / or binds to the p40 subunit of the human cytokines interleukin (IL)-12 and IL-23. In some embodiments, the drug delivery device may contain or be used with Amjevita™ or Amgevita™ (formerly ABP 501) (mab anti-TNF human IgG1), a biosimilar candidate for Humira®, or another product that contains a human mab anti-TNF human IgG1. In some embodiments, the drug delivery device may contain or be used with AMG 160 or another product that contains a half-life extended (HLE) anti-prostate specific membrane antigen (PSMA) x anti-CD3 BiTE® (bispecific T cell engager) construct. In some embodiments, the drug delivery device may contain or be used in conjunction with another product containing AMG 119 or delta-like ligand 3 (DLL3) CAR T (chimeric antigen receptor T cell) cell therapy.In some embodiments, the drug delivery device may contain or be used with another product containing AMG 119 or delta-like ligand 3 (DLL3) CAR T (chimeric antigen receptor T cell) cell therapy. In some embodiments, the drug delivery device may contain or be used with another product containing AMG 133 or a gastric inhibitory polypeptide receptor (GIPR) antagonist and a GLP-1R agonist. In some embodiments, the drug delivery device may contain or be used with another product containing AMG 171 or a growth differentiation factor 15 (GDF15) analog. In some embodiments, the drug delivery device may contain or be used with another product containing AMG 176 or a small molecule inhibitor of myeloid cell leukemia 1 (MCL-1). In some embodiments, the drug delivery device may contain or be used with another product containing AMG 199 or a half-life extended (HLE) bispecific T cell engager construct (BiTE®). In some embodiments, the drug delivery device may contain or be used in conjunction with AMG 256 or another product containing an anti-PD-1 x IL21 mutein and / or an IL-21 receptor agonist designed to selectively activate the interleukin 21 (IL-21) pathway in programmed cell death-1 (PD-1) positive cells. In some embodiments, the drug delivery device may contain or be used in conjunction with AMG 330 or another product containing an anti-CD33 x anti-CD3 BiTE® (bispecific T cell engager) construct. In some embodiments, the drug delivery device may contain or be used in conjunction with AMG 404 or another product containing a human anti-programmed cell death-1 (PD-1) monoclonal antibody being investigated as a treatment for patients with solid tumors. In some embodiments, the drug delivery device may house or be used in conjunction with another product containing AMG 427 or a half-life extended (HLE) anti-fms-like tyrosine kinase 3 (FLT3) x anti-CD3 BiTE® (bispecific T cell engager) construct.In some embodiments, the drug delivery device may contain or be used with AMG 430 or another product containing an anti-Jagged-1 monoclonal antibody. In some embodiments, the drug delivery device may contain or be used with AMG 506 or another product containing a multispecific FAPx4-1BB targeted DARPin® biologic being investigated as a treatment for solid tumors. In some embodiments, the drug delivery device may contain or be used with AMG 509 or another product containing a bivalent T cell engager and designed using XmAb® 2+1 technology. In some embodiments, the drug delivery device may contain or be used with AMG 562 or another product containing a half-life extended (HLE) CD19xCD3 BiTE® (bispecific T cell engager) construct. In some embodiments, the drug delivery device may contain or be used with efavalukin alpha (previously AMG 592) or another product containing an IL-2 mutein Fc fusion protein. In some embodiments. In some embodiments, the drug delivery device may contain or be used with another product containing AMG 596 or CD3 x epidermal growth factor receptor vIII (EGFRvIII) BiTE® (bispecific T cell engager) molecules. In some embodiments, the drug delivery device may contain or be used with another product containing AMG 673 or a half-life extended (HLE) anti-CD33 x anti-CD3 BiTE® (bispecific T cell engager) construct. In some embodiments, the drug delivery device may contain or be used with another product containing AMG 701 or a half-life extended (HLE) anti-B cell maturation antigen (BCMA) x anti-CD3 BiTE® (bispecific T cell engager) construct. In some embodiments, the drug delivery device may contain or be used with AMG 757 or a half-life extended (HLE) anti-delta-like ligand 3 (DLL3) x anti-CD3 BiTE® (bispecific T cell engager) construct. In some embodiments, the drug delivery device may house or be used in conjunction with AMG 910 or another product containing the half-life extended (HLE) epithelial cell tight junction component protein claudin 18.2 x CD3 BiTE® (bispecific T cell engager) construct.

[0060] The drug delivery devices, assemblies, components, subsystems and methods have been described in terms of exemplary embodiments, but are not limited thereto. The detailed description should be interpreted merely as an example, and does not describe all possible embodiments of the present disclosure. Many alternative embodiments can be implemented using either current technology or technology developed after the filing date of this patent, but such embodiments still fall within the scope of the claims that define the invention disclosed herein.

[0061] The detailed description should be construed as merely illustrative and does not describe all possible embodiments of the present disclosure. Many alternative embodiments can be implemented using either current technology or technology developed after the filing date of this patent, but such embodiments still fall within the scope of the claims that define the invention disclosed herein. Those skilled in the art will understand that various modifications, alterations and combinations can be made to the above-mentioned embodiments without departing from the spirit and scope of the invention disclosed herein, and that such modifications, alterations and combinations are construed as being within the concept of the present invention.

Claims

1. A rear sub-assembly (RSA) output force characteristic evaluation method, comprising: providing a syringe-less (SyFR) rear sub-assembly (RSA) operating device; generating RSA output force profile data using the Syringe-Free (SyFR) rear sub-assembly (RSA) operating device, the RSA output force profile data representing a rear sub-assembly (RSA) output force profile; and generating an RSA output energy profile based on the RSA output force profile data; A method comprising:

2. The RSA output force profile data as a function of plunger rod displacement x is F ｓｐ (x)=K ｓｐ (L ｃｏｍｐ -x) is represented by In the ceremony, K ｓｐ represents the RSA effective spring constant, and L ｃｏｍｐ The method of claim 1 , wherein: denotes the RSA effective compression length.

3. The method of claim 1 or 2, wherein the rear sub-assembly (RSA) output force profile data represents an area under the curve (AUC).

4. The method of claim 1 or 2, wherein the rear sub-assembly (RSA) output force profile data represents a minimum force.

5. The method of claim 1 or 2, wherein the rear sub-assembly (RSA) output force profile data represents pre-actuation forces.

6. The method of claim 1 or 2, wherein the rear sub-assembly (RSA) output force profile data represents an actuation force.

7. 1. An apparatus for characterizing an output force of a drug delivery device or a rear subassembly of a drug delivery device, comprising: a driver holder configured to secure the drug delivery device or the rear subassembly to the apparatus, the drug delivery device or the rear subassembly including a drive member having a compressed position and an extended position; a force sensor configured to measure output force data associated with the drive member as the drive member moves from the compressed position to the extended position; an extrusion speed input configured to receive extrusion speed data, the extrusion speed data representing a speed of the drive member; and a controller configured to receive the output force data and the extrusion rate input data and generate an output force profile based on the output force data, the controller further configured to generate an output energy profile based on the output force profile data; 1. An apparatus comprising:

8. The rear subassembly (RSA) characterization apparatus of claim 7 configured as a syringe-less (SyFR) rear subassembly fixture.

9. 9. The rear subassembly (RSA) characterization apparatus of claim 7 or 8, configured to characterize a rear subassembly (RSA), wherein an RSA output force profile is proportional to a mechanical load, and wherein the controller is further configured to generate RSA characterization data based on a series of mechanical loads sequentially applied to the rear subassembly through various RSA plunger movements.

10. The rear subassembly (RSA) characterization device of claim 7 or 8, further comprising a rear subassembly (RSA) base.

11. The rear sub-assembly (RSA) characterization apparatus of claim 7 or 8, further comprising a disk portion configured to impart an inertial force to the RSA.

12. 1. A non-transitory computer-readable medium storing computer-readable instructions that, when executed by a processor, cause the processor to generate an injection time (IT) model for an autoinjector (AI), wherein execution of the instructions causes the processor to: receiving pre-filled syringe (PFS) release and extrusion resistance (BLER) data, the BLER data representing the amount of force required to achieve each extrusion rate; receiving rear sub-assembly (RSA) output force profile data; generating an injection time (IT) model for the autoinjector based on the BLER data and the RSA output force profile data; Non-transitory computer-readable medium.

13. 13. The non-transitory computer-readable medium of claim 12, wherein the BLER data represents the amount of force required to expel a drug product (DP) through a full dose injection.

14. Further execution of the instructions causes the processor to: [Equation 1] (In the formula, [Equation 2] represents the extrusion speed, and C １ represents the second-order attenuation coefficient of the PFS, and C ２ represents the first-order attenuation coefficient of the PFS, F ｆ represents the low-speed sliding force of the PFS, and C ２ indicates the non-Newtonian behavior of the drug product (DP) within the PFS) Parameterizing the pre-filled syringe (PFS) release and extrusion resistance (BLER) data based on The non-transitory computer-readable medium of claim 12.

15. C ２ 15. The non-transitory computer-readable medium of claim 14, wherein shear thickening behavior of the DP is inferred if .

16. C ２ 16. The non-transitory computer-readable medium of claim 14 or 15, wherein shear thinning behavior of the DP is inferred when is less than zero.

17. C ２ 16. The non-transitory computer-readable medium of claim 14 or 15, wherein Newtonian behavior of the DP is inferred when ∇ ...

18. F ｆ represents the low-speed sliding force of the PFS.

19. F ｆ 16. The non-transitory computer-readable medium of claim 14 or 15, wherein ≡ ...

20. F ｆ 16. The non-transitory computer-readable medium of claim 14 or 15, wherein: is an amount of force that avoids stalling of the AI.

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