Smart inhaler systems and methods of use for targeted drug delivery

Abstract

Inhaler systems, and methods, for targeted drug delivery, including an inhaler comprising a body, a flow meter, a chamber, a nozzle, a processor, and a memory. The body defines a cavity, an intake orifice, and a mouthpiece orifice having a cross-section. The nozzle is in selective fluid communication with the chamber, is positionable within the cross-section, has an aperture having an adjustable diameter, and is configured to selectively release drugs from the chamber. The memory stores instructions that, when executed by the processor, cause the processor to: store a drug diameter and one or more targeted drug delivery regions; receive a profile including a peak flow rate and a duration; and determine a delivery strategy by providing the drug diameter, the one or more targeted regions, and the profile as inputs to a machine learning model. The delivery strategy may include aperture diameter, nozzle position, and release time.

Description

SMART INHALER SYSTEMS AND METHODS OF USE FOR TARGETED DRUG DELIVERYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to the provisional patent applications identified by US Serial No. 63 / 552,412, filed February 12, 2024, the entire contents of which are hereby expressly incorporated herein by reference.BACKGROUND

[0002] Recurrent Respiratory Papillomatosis (RRP) is a clinical syndrome with a viral origin that affects both adults and children, causing the development of papilloma in the respiratory system. It is a disease mainly caused by human papillomavirus (HPV) types 6 or 11, resulting in exophytic lesions throughout the human respiratory airways, most commonly the larynx and glottis. Although tumors resulting from RRP are mostly benign, the malignant possibility and fatality rate become much higher in children, accompanied by rapid exacerbation of the disease. The disease is also called Juvenile Onset RRP (JORRP), which is more aggressive than adult-onset RRP.

[0003] F inding an effective clinical therapy for JORRP is still challenging due to a high propensity for recurrence and unsolvable complications. Surgery using photoangiolytic lasers, including the 532 nm potassium titanyl phosphate (KTP) laser and the carbon dioxide (CO2) laser, are the most frequently used techniques. However, the recurrent nature of JORRP carries the risk of iatrogenic and general anesthesia consequences and thermal tissue damage from laser treatment. In this context, adjuvant antiviral medications have been prescribed over the past decade and researched for potential therapeutic uses. Several adjuvant medicines can be used to avoid blockage throughout the infection. The primary criteria for adjuvant therapy include more than four surgeries per year due to the growth of papillomata that compromise the airways or the spread of disease to multiple locations in the airway. Other antiviral drugs, such as acyclovir, cidofovir, and ribavirin, hold potential as additional adjuvant therapies to reduce treatment resistance. The goal of utilizing different vaccines is to both prevent and treat RRP. There is limited proof to show the effectiveness of these adjuvant methods, vaccines, and chemotherapy, and none entirely stop the growth of the tumors.

[0004] I nhalation therapy can also be used to deliver specific medications to the larynx and glottis areas to help control tumor growth and JORRP exacerbation. This may include the use of medications such as interferon, cidofovir, or 5-fluorouracil. However, conventional inhalation therapy will generate undesired deposition on healthy tissues through the pulmonary route,reducing the therapeutic effectiveness and leading to side effects. Such side effects can be complicated for children and adolescents to tolerate. Ineffective drug delivery can also result in the recurrence of JORRP tumors, which increases the need for ongoing treatment and the possibility of further complications. Therefore, targeted drug delivery (TDD) to the designated sites in the upper airway is necessary for JORRP treatment.

[0005] Computational fluid particle dynamics (CFPD) can be used to predict air-particle flow dynamics in lung airways. CFPD can be used to enhance the fundamental understanding of the complex mechanisms involved in respiratory drug delivery. Experimentally validated CFPD models can significantly reduce the time and expense required to develop new targeted delivery strategies, by providing high-resolution spatiotemporal distributions of pulmonary air-particle flow variables of interest. CFPD research efforts have been made to achieve pulmonary targeted drug delivery, such as what is referred to as the Controlled Particle Release and Targeting (CPRT) strategy. Specifically, by using the particle "backtracking" method, CPRT can link the particle deposition locations with their release positions at the mouth front to find the precise particle release coordinates to deliver the drug particles to designated lung sites. Research suggests a significant increase in particle delivery efficiency for the targeted region in lung airways using CPRT when compared with conventional inhalation therapy.

[0006] However, several factors make the targeted delivery to specific regions in the pulmonary route challenging. It is commonly observed that children and adult patients with serious lung conditions may face difficulty in producing enough inspiratory airflow to carry sufficiently high dose into designated lung sites, since a strong inhalation is required to fluidize the drug powders and produce an appropriate amount of therapeutic aerosols. In addition, it is quite challenging for physicians to comprehend how a patient with pulmonary diseases operates inhalers in practice when they are not in clinic. The inhaler misuse can be either unintentional or planned. In reality, healthcare professionals often overestimate patient adherence to their medications using correct patient-inhaler coordination. Therefore, inadequate inhalation practice and low adherence to the prescribed personalized treatment are two major factors in respiratory disease control failure among patients. This has consistently been linked to poor drug delivery efficiency, decreased living standard, and increased healthcare expenses. In other words, the intersubject variabilities and inconsistencies in breathing patterns (e.g., preferred inhalation flow rate) can significantly influence the drug delivery efficiency.

[0007] To address the issues of intersubject variabilities affecting delivery efficiency, the concept of "smart inhalers" has been introduced. These inhalers incorporate sensors to gatherdata on inhaler usage patterns and patient metrics, offering personalized feedback based on usage timing, inhalation technique, peak flow metrics, or survey outcomes. While some improvement in therapeutic effect is observed with smart inhaler use, the efficacy of drug delivery primarily hinges on the number of therapeutic particles delivered to the targeted sites. Previous smart inhalers merely collected patient data. However, there is a need for smart inhalers that can leverage this data to fine-tune the release and transport of aerosolized particles, aiming for precise drug delivery to specific regions while minimizing drug deposition on healthy tissues.

[0008] Therefore, there is a need for a smart inhaler, smart inhaler system, and methods of use, that integrate machine learning (ML) algorithm(s) enabled by CFPD to effectively target the diseased region for treatment with minimum particle deposition on healthy tissues.SUMMARY OF THE INVENTION

[0009] In one implementation, the present disclosure includes a non-transitory processor- readable medium storing processor-executable instructions that when executed by a processor cause the processor to: store data indicative of a drug particle diameter of a particle of a drug contained within a drug chamber disposed at least partially within an inner cavity of an inhaler and one or more targeted regions of an airway of a patient; receive, from a flow meter disposed within the inner cavity of the inhaler, a signal indicative of an inhalation profile corresponding to the patient, the inhalation profile including a peak inhalation flow rate and an inhalation duration of a test inhalation of the inhaler by the patient, the inhaler comprising an inhaler body, the flow meter, the drug chamber, a nozzle, and an actuator, the inhaler body defining the inner cavity, an air intake orifice in fluid communication with the inner cavity, and a mouthpiece orifice in fluid communication with the inner cavity, the flow meter disposed within the inner cavity between the air intake orifice and the mouthpiece orifice, the drug chamber disposed at least partially within the inner cavity, the nozzle disposed within the inner cavity proximal to the mouthpiece orifice, the mouthpiece orifice having a radial cross-sectional area, the nozzle in selective fluid communication with the drug chamber and configured to be positionable in at least a first direction within the radial cross-sectional area, the nozzle having a nozzle aperture having a nozzle aperture diameter configured to be adjustable, the actuator disposed within the inner cavity between the drug chamber and the nozzle and configured to selectively release the drug contained within the drug chamber to the nozzle, thereby placing the drug chamber and the nozzle in fluid communication; and determine a targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at a predetermined deposition fraction by providing the drug particle diameter, the oneor more targeted regions of the airway of the patient, and the inhalation profile as inputs to a machine learning model, the targeted delivery strategy including a target nozzle aperture diameter, a target nozzle position, and a target drug particle release time, wherein the target drug particle release time is a point in time during a treatment inhalation of the inhaler by the patient at which to cause the actuator to release the drug contained within the drug chamber to the nozzle, wherein the predetermined deposition fraction is a percentage of a total amount of the drug that is deposited at the one or more targeted regions of the airway of the patient.

[0010] In another implementation, the present disclosure includes an inhaler, comprising: an inhaler body defining an inner cavity, an air intake orifice in fluid communication with the inner cavity, and a mouthpiece orifice in fluid communication with the inner cavity, the mouthpiece orifice having a radial cross-sectional area; a flow meter disposed within the inner cavity between the air intake orifice and the mouthpiece orifice; a drug chamber at least partially disposed within the inner cavity and containing a drug; a nozzle disposed within the inner cavity proximal to the mouthpiece orifice, the nozzle in selective fluid communication with the drug chamber and configured to be positionable in at least a first direction within the radial cross-sectional area, the nozzle having a nozzle aperture having a nozzle aperture diameter configured to be adjustable; an actuator disposed within the inner cavity between the drug chamber and the nozzle, the actuator configured to selectively release the drug contained within the drug chamber to the nozzle, thereby placing the drug chamber and the nozzle in fluid communication; a processor; and a non-transitory processor-readable medium storing processor-executable instructions that when executed by the processor cause the processor to: store data indicative of a drug particle diameter of a particle of the drug and one or more targeted regions of an airway of a patient; receive, from the flow meter, a signal indicative of an inhalation profile corresponding to the patient, the inhalation profile including a peak inhalation flow rate and an inhalation duration of a test inhalation of the inhaler by the patient; and determine a targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at a predetermined deposition fraction by providing the drug particle diameter, the one or more targeted regions of the airway of the patient, and the inhalation profile as inputs to a machine learning model, the targeted delivery strategy including a target nozzle aperture diameter, a target nozzle position, and a target drug particle release time, wherein the target drug particle release time is a point in time during a treatment inhalation of the inhaler by the patient at which to cause the actuatorto release the drug contained within the drug chamber to the nozzle, wherein the predetermined deposition fraction is a percentage of a total amountof the drug that is deposited at the one or more targeted regions of the airway of the patient.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate one or more implementations described herein and, together with the description, explain these implementations. The drawings are not intended to be drawn to scale, and certain features and certain views of the figures may be shown exaggerated, to scale, or in schematic in the interest of clarity and conciseness. Not every component may be labeled in every drawing. Like reference numerals in the figures may represent and refer to the same or similar element or function. In the drawings:

[0012] FIG. 1 is a diagrammatic view of an exemplary implementation of a system constructed in accordance with the present disclosure;

[0013] FIG. 2 is a diagrammatic view of an exemplary implementation of a controller shown in FIG. 1;

[0014] FIG. 3 is a diagrammatic view of an exemplary implementation of a user device shown in FIG. 1;

[0015] FIG. 4A is a perspective view of a patient illustrating airway regions of an airway of the patient in accordance with the present disclosure;

[0016] FIG. 4B is a schematic end view of an exemplary implementation of a mouthpiece orifice of an inhaler of the system shown in FIG. 1;

[0017] FIG. 4C is a schematic view of portions of an exemplary inhaler constructed in accordance with the present disclosure;

[0018] FIG. 5 is a diagrammatic view of an exemplary implementation of a method of using the system shown in FIG. 1;

[0019] FIG. 6A is a graph of comparative results from hypothetical simulated use of an exemplary method in accordance with the present disclosure; and

[0020] FIG. 6B is a graph of comparative results from hypothetical simulated use of an exemplary method in accordance with the present disclosure.DETAILED DESCRIPTION

[0021] The following detailed description refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements.

[0022] As used herein, the terms "comprises," "comprising," "includes," "including," "has," "having" or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, "or" refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by anyone of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

[0023] In addition, use of the "a" or "an" are employed to describe elements and components of the implementations herein. This is done merely for convenience and to give a general sense of the inventive concept. This description should be read to include one or more and the singular also includes the plural unless it is obvious that it is meant otherwise.

[0024] Further, use of the term "plurality" is meant to convey "more than one" unless expressly stated to the contrary.

[0025] As used herein, qualifiers like "substantially," "about," "approximately," and combinations and variations thereof, are intended to include not only the exact amount or value that they qualify, but also some slight deviations therefrom, which may be due to manufacturing tolerances, measurement error, wear and tear, stresses exerted on various parts, and combinations thereof, for example.

[0026] The use of the term "at least one" or "one or more" will be understood to include one as well as any quantity more than one. In addition, the use of the phrase "at least one of X, V, and Z" will be understood to include X alone, V alone, and Z alone, as well as any combination of X, V, and Z.

[0027] The use of ordinal number terminology (i.e., "first", "second", "third", "fourth", etc.) is solely forthe purpose of differentiating between two or more items and, unless explicitly stated otherwise, is not meant to imply any sequence or order or importance to one item over another or any order of addition.

[0028] Finally, as used herein any reference to "one implementation" or "an implementation" means that a particular element, feature, structure, or characteristic described in connection with the implementation is included in at least one implementation. The appearances of the phrase "in one implementation" in various places in the specification are not necessarily all referring to the same implementation.

[0029] Referring now to the drawings and in particular to FIG. 1, shown therein is an exemplary implementation of an inhaler system 10 (hereinafter, the "system 10") constructed in accordance with the present disclosure. The system 10 generally comprises an inhaler 14 and a user device 18. As described in more detail below, a user 20 may interact with the inhaler 14using the user device 18. The user device 18 may be connected to the inhaler 14 via a communication network 22. The communication network 22 may be the Internet and / or another network.

[0030] As schematically shown in FIG. 4C, the inhaler 14 may comprise an inhaler body 24 defining an inner cavity 26, an air intake orifice 30 in fluid communication with the inner cavity 26, and a mouthpiece orifice 34 in fluid communication with the inner cavity 26. As described in more detail below, the mouthpiece orifice 34 may have a radial cross-sectional area (shown in FIG. 4B).

[0031] The system 10 may include one or more flow meters 38. As described in more detail below, the flow meter 38 may be configured to measure a flow rate of air passing between the air intake orifice 30 and the mouthpiece orifice 34, indicated by dash-dotted arrows 40 shown in FIG. 1. In some implementations, the flow meter 38 may be disposed within the inner cavity 26 between the air intake orifice 30 and the mouthpiece orifice 34; however, it will be understood that the flow meter 38 may be positioned elsewhere in the system 10, along a path of airflow into, through, and / or out of the inhaler 14.

[0032] A drug chamber 42 may be at least partially disposed within the inner cavity 26. In some implementations, the drug chamber 42 may contain drug particles (shown in FIG. 4C). While the drug is described hereinafter as being in an aerosolized form, persons having ordinary skill in the art will understand that the drug chamber 42 may contain a drug in a liquid form. In some implementations, the drug chamber 42 may be configured to be removable from the inhaler 14 such that the user 20 may refill the drug chamber 42 or replace one implementation of the drug chamber 42 containing a particular drug with another implementation of the drug chamber 42 containing another particular drug.

[0033] In some implementations, optionally, an actuator46 may be disposed within the inner cavity 26 between the drug chamber 42 and the nozzle 50. The actuator 46 may be configured to selectively release the drug contained within the drug chamber 42 to a nozzle 50 disposed within the inner cavity 26.

[0034] The nozzle 50 may have an intake end 51 positioned proximate to the drug chamber 42 and an output end (shown in FIGS. 4B and 4C) positioned proximate to the mouthpiece orifice 34. Accordingly, the nozzle 50 may be in selective fluid communication with the drug chamber 42, and the actuator 46 releasing the drug contained within the drug chamber 42 may place the drug chamber 42 and the nozzle 50 in fluid communication. As described in more detail below, the output end of the nozzle 50 may be configured to be movably positionable within the radialcross-sectional area (shown in FIG. 4B) and may have a nozzle aperture diameter 124 (and / or aspect ratio) configured to be adjustable. In some implementations, one or more additional portions of the nozzle 50 in addition to the output end of the nozzle 50 may be configured to be moveable.

[0035] As shown in FIG. 1, the inhaler 14 may further configure a controller 54. In some implementations, the controller 54 may be disposed within the inner cavity 26. However, in other implementations, the controller 54 may be disposed outside of the inner cavity 26 and / or be positioned remotely from the inhaler 14. The controller 54 may communicate with the user device 18 using the communication network 22. As described in more detail below, the controller 54 may receive measurement signals from the flow meter 38 and may send control signals to the actuator 46 and the nozzle 50.

[0036] The communication network 22 may permit bi-directional communication of information and / or data between the inhaler 14 and the user device 18. The communication network 22 may interface with the inhaler 14 and the user device 18 in a variety of ways. For example, in some implementations, the communication network 22 may interface by optical and / or electronic interfaces, and / or may use a plurality of network topographies and / or protocols including, but not limited to, Ethernet, TCP / IP, circuit switched path, combinations thereof, and / or the like. For example, in some implementations, the communication network 22 may be implemented as the World Wide Web (or Internet), a local area network (LAN), a wide area network (WAN), a metropolitan network, a 4G network, a 5G network, a satellite network, a radio network, an optical network, a cable network, a public switch telephone network, an Ethernet network, combinations thereof, and / or the like, for example. Additionally, the communication network 22 may use a variety of network protocols to permit bi-directional interface and / or communication of data and / or information between the inhaler 14 and the user device 18.

[0037] Referring now to FIG. 2, shown therein is an exemplary implementation of the controller 54 shown in FIG. 1. The controller 54 may comprise one or more processors 58 (hereinafter, the "controller processor 58"), one or more communication devices 62 (hereinafter, the "controller communication device 62") capable of interfacing with the communication network 22, and one or more non-transitory processor-readable media 66 (hereinafter, the "controller memory 66") storing processor-executable instructions and / or one or more software applications 70 (hereinafter, the "software application 70"), one or more machine learning (ML) models 71 (hereinafter, the "ML model 71"), and one or more databases 72 (hereinafter, the"database 72"). The controller processor 58, the controller communication device 62, and the controller memory 66 may be connected via a controller path 74 such as a databus that permits communication among the components of the controller 54.

[0038] The controller processor 58 may be capable of interfacing and / or communicating with the user device 18 via the communication network 22 using the controller communication device 62. For example, the controller processor 58 may be capable of communicating via the communication network 22 by exchanging signals (e.g., analog, digital, optical, and / or the like) via one or more ports (e.g., physical or virtual ports) using a network protocol to interface and / or communicate with the user device 18.

[0039] In some implementations, the controller 54 may comprise one or more of the controller processor 58 working together, or independently, to execute processor-executable code stored on the controller memory 66. Each element of the controller 54 may be partially or completely network-based or cloud-based, and may or may not be located in a single physical location. The controller processor 58 may be implemented as a single processor or multiple processors working together, or independently, to execute the software application 70 as described herein. It is to be understood that in certain implementations using more than one of the controller processor 58, each may be located remotely from one another, located in the same location, or comprising a unitary multi-core processor. The controller processor 58 may be capable of reading and / or executing processor-executable code and / or capable of creating, manipulating, retrieving, altering, and / or storing data structures into the controller memory 66.

[0040] Exemplary implementations of the controller processor 58 may include, but are not limited to, a digital signal processor (DSP), a central processing unit (CPU), a field programmable gate array (FPGA), a graphics processing unit (GPU), a microprocessor, a multi-core processor, combinations, thereof, and / or the like, for example. The controller processor 58 may be capable of communicating with the controller memory 66 via the controller path 74.

[0041] In some implementations, the controller memory 66 may be located in the same physical location as the inhaler 14, and / or one or more of the controller memory 66 may be located remotely from the inhaler 14. For example, the controller memory 66 may be located remotely from the inhaler 14 and communicate with the controller processor 58 via the communication network 22. Additionally, when more than one of the controller memory 66 is used, a first one of the controller memory 66 may be located in the same physical location as the controller processor 58, and an additional one of the controller memory 66 may be located in a location physically remote from the controller processor 58. Additionally, the controller memory66 may be implemented as a "cloud" non-transitory computer readable medium (i.e., one or more of the controller memory 66 may be partially or completely based on or accessed using the communication network 22).

[0042] As described in more detail below, the software application 70 may, when executed, cause the controller processor 58 to perform one or more of the methods described herein. Additionally, the controller memory 66 may be implemented as a conventional non-transitory memory, such as for example, random access memory (RAM), CD-ROM, a hard drive, a solid-state drive, a flash drive, a memory card, a DVD-ROM, a disk, an optical drive, combinations thereof, and / orthe like, forexample. In some implementations, the software application 70 may be stored as a data structure, such as the database 72 and / or a data table, for example, or in non-data structure format such as in a non-compiled text file.

[0043] The ML model 71 may be trained with a plurality of training inputs (e.g., a plurality of drug particle diameters, a plurality of targeted airway regions (shown in FIG. 4A) of an airway (shown in FIG. 4A) of a patient (shown in FIG. 4A), a plurality of inhalation profiles, and / or a plurality of mouthpiece orifice aspect ratios) to determine a plurality of target outputs (e.g., a target nozzle aperture diameter, a target nozzle position, a target drug particle release time, and / or a target mouthpiece orifice aspect ratio) corresponding to a plurality of treatment inputs (e.g., a drug particle diameter, one or more targeted airway regions of the airway of the patient, and an inhalation profile). The ML model 71 may be trained using the training inputs to determine the target outputs based on the treatment inputs in order to reach a predetermined deposition fraction for each targeted region of the airway of the patient. As used herein, a "deposition fraction" refers to an amount of a drug that is deposited at the targeted region of the airway of the patient when compared with a total amount of the drug that is dispersed by the inhaler 14.

[0044] In some implementations, the ML model 71 may be or may include a decision tree (DT) model. In some such implementations, the ML model 71 may be or may include a classification and regression tree (CART) model. However, in some implementations, the ML model 71 may be or may include a support vector machine (SVM) model, a random forest (RF) model, or a gaussian process regression (GPR) model. In some implementations, the ML model 71 may be evaluated using one or more of mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2).

[0045] The database 72 may be a relational database or a non-relational database. Examples of such databases comprise, DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, MySQL, PostgreSQL, MongoDB, Apache Cassandra, and the like. It should be understood that theseexamples have been provided for the purposes of illustration only and should not be construed as limiting the presently disclosed inventive concepts. The database 72 may be centralized or distributed across multiple systems.

[0046] Referring now to FIG. 3, shown therein is an exemplary implementation of the user device 18 shown in FIG. 1. The user device 18 may comprise one or more input devices 78 (hereinafter, the "user input device 78"), one or more output devices 82 (hereinafter, the "user output device 82"), one or more processors 86 (hereinafter, the "user processor 86"), one or more communication devices 90 (hereinafter, the "user communication device 90") capable of interfacing with the communication network 22, and one or more non-transitory processor- readable medium 94 (hereinafter, the "user memory 94") storingthe software application 70, the ML model 71, and the database 72. The user input device 78, the user output device 82, the user processor 86, the user communication device 90, and the user memory 94 may be connected via a user path 102 such as a databus that permits communication among the components of the user device 18.

[0047] The user device 18 may include, but is not limited to, implementation as a personal computer, a cellulartelephone, a smart phone, a network-capable television set, a tablet, a laptop computer, a desktop computer, a network-capable handheld device, a server, a digital video recorder, a wearable network-capable device, and / or the like.

[0048] The user input device 78 may be capable of receiving information input from the user 20 and / or the user processor 86 and transmitting such information to other components of the user device 18 and / or the inhaler 14. The user input device 78 may include, but is not limited to, implementation as a keyboard, touchscreen, mouse, trackball, microphone, fingerprint reader, infrared port, slide-out keyboard, flip-out keyboard, cell phone, PDA, remote control, fax machine, wearable communication device, network interface, combinations thereof, and / or the like, for example.

[0049] The user output device 82 may be capable of outputting information in a form perceivable by the user 20 and / or the user processor 86. For example, implementations of the user output device 82 may include, but are not limited to, a computer monitor, a screen, a touchscreen, a speaker, a website, a television set, a smart phone, a PDA, a cell phone, a fax machine, a printer, a laptop computer, combinations thereof, and the like, for example.

[0050] It is to be understood that in some exemplary implementations, the user input device78 and the user output device 82 may be implemented as a single device, such as, for example, a touchscreen of a computer, a tablet, or a smartphone. It is to be further understood that, as usedherein, the term "user" is not limited to a human being, and may comprise a computer, a server, a website, a processor, a network interface, a human, a user terminal, a virtual computer, combinations thereof, and / or the like, for example.

[0051] The user input device 78 may transmit data to the user processor 86 and may be located in the same physical location as the user processor 86, or located remotely and / or partially or completely network-based. The user output device 82 may transmit information from the user processor 86 to the user 20. The user output device 82 may be located with the user processor 86, or located remotely and / or partially or completely network-based.

[0052] As referenced above, the user processor 86 may be capable of interfacing and / or communicating with the inhaler 14 via the communication network 22 using the user communication device 90. For example, the user processor 86 may be capable of communicating via the communication network 22 by exchanging signals (e.g., analog, digital, optical, and / or the like) via one or more ports (e.g., physical or virtual ports) using a network protocol to interface and / or communicate with the inhaler 14.

[0053] In some implementations, the user device 18 may comprise one or more of the user processor 86 working together, or independently, to execute processor-executable code stored on the user memory 94. Each element of the user device 18 may be partially or completely network-based or cloud-based, and may or may not be located in a single physical location. The user processor 86 may be implemented as a single processor or multiple processors working together, or independently, to execute the software application 70 as described herein. It is to be understood that in certain implementations using more than one of the user processor 86, each may be located remotely from one another, located in the same location, or comprising a unitary multi-core processor. The user processor 86 may be capable of reading and / or executing processor-executable code and / or capable of creating, manipulating, retrieving, altering, and / or storing data structures into the user memory 94.

[0054] Exemplary implementations of the user processor 86 may include, but are not limited to, a digital signal processor (DSP), a central processing unit (CPU), a field programmable gate array (FPGA), a graphics processing unit (GPU), a microprocessor, a multi-core processor, combinations, thereof, and / or the like, for example. The user processor 86 may be capable of communicating with the user memory 94 via the user path 102.

[0055] The user memory 94 may be implemented as a conventional non-transitory memory, such as for example, random access memory (RAM), CD-ROM, a hard drive, a solid-state drive, a flash drive, a memory card, a DVD-ROM, a disk, an optical drive, combinations thereof, and / orthe like, for example.

[0056] In some implementations, the user memory 94 may be located in the same physical location as the user device 18, and / or one or more of the user memory 94 may be located remotely from the user device 18. For example, the user memory 94 may be located remotely from the inhaler 14 and communicate with the controller processor 58 via the communication network 22. Additionally, when more than one of the user memory 94 is used, a first one of the user memory 94 may be located in the same physical location as the user processor 86, and an additional one of the user memory 94 may be located in a location physically remote from the user processor 86. Additionally, the user memory 94 may be implemented as a "cloud" non- transitory computer readable medium (i.e., one or more of the user memory 94 may be partially or completely based on or accessed using the communication network 22).

[0057] Referring now to FIG. 4A, shown therein is a patient 106 using an exemplary implementation of the inhaler 14 shown in FIG. 1. The patient 106 is shown as having an airway 110 including a plurality of airway regions, such as a first airway region 114a (hereinafter, the "upper right lobe 114a"), a second airway region 114b (hereinafter, the "upper left lobe 114b"), a third airway region 114c (hereinafter, the "lower left lobe 114c"), a fourth airway region 114d (hereinafter, the "middle right lobe 114d"), and a fifth airway region 114e (hereinafter, the "lower right lobe 114e") (collectively, the "airway regions 114") shown in FIG. 4A, for example. It will be understood by persons having ordinary skill in the art that the airway 110 includes other airway regions 114 that may be targeted for release of drugs from the drug chamber 42.

[0058] Referring now to FIG. 4B, shown therein is a schematic of an end view of the mouthpiece orifice 34. This schematic end view illustrates the position of an output end 52 of the nozzle 50 within the mouthpiece orifice 34, as well as the relationship of the position of the output end 52 of the nozzle 50 to the drug particles 43 in the drug chamber 42. The position of the output end 52 of the nozzle 50 within a radial cross-sectional area 118 of the mouthpiece orifice 34 may have a direct effect upon the locational deposition of the drug particles 43 within the airway 110 of the patient 106.

[0059] The radial cross-sectional area 118 of the mouthpiece orifice 34 is shown in FIG. 4B as having a plurality of mouthpiece regions, such as a first mouthpiece region 120a, a second mouthpiece region 120b, a third mouthpiece region 120c, a fourth mouthpiece region 120d, and a fifth mouthpiece region 120e (collectively, the "mouthpiece regions 120"), for example. As described in more detail below, each of the mouthpiece regions 120 may correspond to a particular one of the airway regions 114. That is, the position of the output end 52 of the nozzlein a particular mouthpiece region 120 may result in the drug particles 43 being deposited in a corresponding particular one or the airway regions 114.

[0060] As referenced above, the output end 52 of the nozzle 50 may be configured to be movably positionable within the radial cross-sectional area 118 of the mouthpiece orifice 34. In some implementations, the output end 52 of the nozzle 50 may have or be in contact with a nozzle-position mechanism that mechanically adjusts the position of the output end 52 of the nozzle 50. In some implementations, the output end 52 of the nozzle 50 is configured to be positionable within the radial cross-sectional area 118 of the mouthpiece orifice 34 in a first direction 126a. In some implementations, the output end 52 of the nozzle 50 is configured to be positionable within the radial cross-sectional area 118 of the mouthpiece orifice 34 in a second direction 126b. In some implementations, the output end 52 of the nozzle 50 is configured to be positionable within radial cross-sectional area 118 of the mouthpiece orifice 34 in the first direction 126a and the second direction 126b.

[0061] The output end 52 of the nozzle 50 may have a nozzle aperture diameter 124 (and / or aspect ratio) configured to be adjustable. In some implementations, the size of the nozzle aperture diameter 124 may be adjusted. In one nonexclusive example, the size of the nozzle aperture diameter 124 may be adjusted by restricting or removing restrictions from the output end 52 of the nozzle 50. In some implementations, the aspect ratio of the output end 52 of the nozzle 50 may be adjusted. In one nonexclusive example, the aspect ratio of the output end 52 of the nozzle 50 may be adjusted by squeezing or stretching the output end 52 of the nozzle 50. In another nonexclusive example, the size of the nozzle aperture diameter 124 and / orthe aspect ratio of the output end 52 may be adjusted by attaching or removing an attachment tothe output end 52, the attachment having a different diameter and / or aspect ratio than the output end 52 of the nozzle 50.

[0062] As shown in FIG. 4B, the radial cross-sectional area 118 of the mouthpiece orifice 34 may have a width 130a and a length 130b. As used herein, the "mouthpiece orifice aspect ratio" refers to a proportional relationship between the width 130a and the length 130b of the radial cross-sectional area 118 of the mouthpiece orifice 34. While the radial cross-sectional area 118 of the mouthpiece orifice 34 is depicted in FIG. 4B as being circular and having a mouthpiece orifice aspect ratio of 1:1, it will be understood by persons having ordinary skill in the art that the radial cross-sectional area 188 may have another shape, such as an ellipse or a polygon, and another mouthpiece orifice aspect ratio, such as 2:1, for example.

[0063] As described in more detail below, positioning the output end 52 of the nozzle 50within a particular one of the mouthpiece regions 120 may assist in targeting, for the release of drugs from the drug chamber 42, a particular one of the airway regions 114 corresponding to the particular one of the mouthpiece regions 120. For example, as shown in FIGS. 4A and 4B, positioning the nozzle 50 within the first mouthpiece region 120a may assist in targeting the upper right lobe 114a of the airway 110. Further, positioning the nozzle 50 within the second mouthpiece region 120b may assist in targeting the upper left lobe 114b, positioning the nozzle 50 within the third mouthpiece region 120c may assist in targeting the lower left lobe 114c, positioning the nozzle 50 within the fourth mouthpiece region 120d may assist in targeting the middle right lobe 114d, and positioning the nozzle 50 within the fifth mouthpiece region 120e may assist in targeting the lower right lobe 114e.

[0064] Referring now to FIG. 5, shown therein is an exemplary implementation of a method 200 of using the system 10 shown in FIG. 1. As shown in FIG. 5, the method 200 generally comprises the steps of: receiving data indicative of a drug particle diameter (i.e., a diameter of a drug particle 43 of the drug contained within the drug chamber 42) and one or more targeted airway regions (i.e., one or more of the airway regions 114 targeted for the delivery of the drug contained in the drug chamber 42) of an airway 110 of a patient 106 (step 204); receiving (e.g., from the flow meter 38) a measurement signal indicative of an inhalation profile corresponding to the patient 106 (step 208); and determining a targeted delivery strategy for delivering the drug contained in the drug chamber 42 to the one or more targeted airway regions of the airway 110 of the patient 106 at a predetermined deposition fraction (i.e., a percentage of the total amount of the drug delivered to the airway 110 of the patient 106 that is deposited at the one or more targeted airway regions of the airway 110 of the patient 106) by providing the drug particle diameter, the one or more targeted airway regions of the airway 110 of the patient 106, and the inhalation profile as inputs to the ML model 71 (step 212).

[0065] The step of receiving the data indicative of the drug particle diameter and the one or more targeted airway regions of the airway 110 of the patient 106 (step 204) may include, for example, receiving the data stored in the database 72. In some implementations, the data may be received from the user 20.

[0066] The inhalation profile may include a peak inhalation flow rate (i.e., a peak flow rate measured by the flow meter 38 during a test inhalation of the inhaler 14 by the patient 106) and an inhalation duration (i.e., a duration of the test inhalation of the inhaler 14 by the patient 106). In some implementations, the step of receiving the measurement signal indicative of the inhalation profile corresponding to the patient 106 (step 208) is further defined as determining,based on the test inhalation of the inhaler 14 by the patient 106, the peak inhalation flow rate and the inhalation duration. In other implementations, the step of receiving the measurement signal indicative of the inhalation profile corresponding to the patient 106 (step 208) is further defined as determining, based on a plurality of test inhalation of the inhaler 14 by the patient 106, an average peak inhalation flow rate and an average inhalation duration. In such implementations, the inhalation profile may include the average peak inhalation flow rate and the average inhalation duration.

[0067] The method 200 may further comprise the step of receiving, from the ML model 71, an output of the determined targeted delivery strategy.

[0068] In some implementations, the targeted delivery strategy includes a target nozzle aperture diameter, a target nozzle position, and a target drug particle release time (i.e., a point in time during a treatment inhalation of the inhaler 14 by the patient 106 at which to cause the actuator 46 to release the drug contained within the drug chamber 42 to the nozzle 50).

[0069] The target nozzle position may include a first target nozzle coordinate corresponding to a target position of the output end 52 of the nozzle 50 within the radial cross-sectional area 118 of the mouthpiece orifice 34 in the first direction 126a and a second target nozzle coordinate corresponding to a target position of the nozzle within the radial cross-sectional area 118 of the mouthpiece orifice 34 in the second direction 126b.

[0070] In some implementations, the method 200 may further comprise, prior to the step of determining the targeted delivery strategy (step 212), storing data indicative of the mouthpiece orifice aspect ratio of the mouthpiece orifice 34 of the inhaler 14. In such implementations, the step of determining the targeted delivery strategy (step 212) is further defined as determining the targeted delivery strategy for delivering the drug contained in the drug chamber 42 to the one or more targeted airway regions of the airway 110 of the patient 106 by providing the drug particle diameter, the one or more targeted airway regions of the airway 110 of the patient 106, the inhalation profile, and the mouthpiece orifice aspect ratio as inputs to the ML model 71 (step 212). In other implementations, the targeted delivery strategy further includes a target mouthpiece orifice aspect ratio; that is, in other implementations, the mouthpiece orifice aspect ratio is an output of the ML model 71.

[0071] In some implementations, the method 200 may further comprise sending, to the nozzle 50, one or more control signals to adjust one or more of: (i) the nozzle aperture diameter 124 of the nozzle aperture 122 to the target nozzle aperture diameter; and (ii) the position of the output end 52 of the nozzle 50 within the radial cross-sectional area 118 of the mouthpiece orifice34 to the target nozzle position. In some implementations, the method 200 may further comprise sending, to the nozzle 50, one or more control signals to adjust one or more of: (i) the nozzle aspect ratio of the output end 52 of the nozzle 50; and (ii) the position of the output end 52 of the nozzle 50 within the radial cross-sectional area 118 of the mouthpiece orifice 34 to the target nozzle position.

[0072] In some implementations, the method 200 may further comprise: determining that the treatment inhalation of the inhaler 14 by the patient 106 has begun, such as by receiving (e.g., from the flow meter 38) measurement signals indicative of a flow rate above a predetermined threshold; and, responsive to the determination that the treatment inhalation of the inhaler 14 by the patient 106 has begun, subsequent to the target drug particle release time elapsing, causing the actuator 46 to release the drug contained within the drug chamber 42 to the nozzle 50.

[0073] A hypothetical example of the system 10 in use will now be provided.

[0074] The patient 106 may obtain the inhaler 14 and may position the mouthpiece orifice 34 in the mouth of the patient 106. The patient 106 may take one or more test inhalations. The flow meter 38 may measure a flow rate of air and / or a duration of the flow of air passing between the air intake orifice 30 and the mouthpiece orifice 34. The controller 54 may receive data from the flow meter 38 indicative of the flow rate of air and / or the duration of the flow of air and may determine the inhalation profile for the patient 106 from the data from the flow meter 38 from the one or more test inhalations, which may include a peak inhalation flow rate and an inhalation duration.

[0075] In some implementations, the controller 54 may determine, based on the test inhalation of the inhaler 14 by the patient 106, the peak inhalation flow rate and the inhalation duration. In some implementations, the controller 54 may determine, based on a plurality of test inhalations of the inhaler 14 by the patient 106, an average peak inhalation flow rate and an average inhalation duration. The inhalation profile may include the average peak inhalation flow rate and the average inhalation duration.

[0076] The controller 54 of the inhaler 14 may receive or retrieve information indicative of one or more targeted airway regions of the airway 110 of the patient 106. The controller 54 of the inhaler 14 may receive or retrieve the drug particle diameter of the drug particles 43 in the drug chamber 42.

[0077] The controller 54 may provide the inhalation profile of the patient 106, the drug particle diameter, and the one or more targeted airway regions of the airway 110 of the patient106 as inputs to the ML model 71.

[0078] The controller 54 may receive, from the ML model 71, an output of the determined targeted delivery strategy for the inhaler 14 for the patient 106 for delivering the drug particles 43 contained within the drug chamber 42 to the one or more targeted airway regions of the airway 110 of the patient 106 at a predetermined deposition fraction (i.e., a percentage of the total amount of the drug delivered to the airway 110 of the patient 106 that is deposited at the one or more targeted airway regions of the airway 110 of the patient 106).

[0079] The controller 54 may implement the targeted delivery strategy for the inhaler 14, such as by controlling the diameter and / or aspect ratio of the output end 52 of the nozzle 50, the position of the output end 52 of the nozzle 50 within the radial cross-sectional area 118 of the mouthpiece orifice 34, and the release time for the drug particles 43 contained within the drug chamber 42 during the treatment inhalation of the patient 106.

[0080] In a simulated hypothetical use, the method 200 resulted in an improvement in drug deposition fractions to targeted areas: (i) in comparison to conventional injection (shown in FIG. 6A); and (ii) in comparison to the use of computational fluid particle dynamics alone (shown in FIG. 6B).

[0081] In a simulated hypothetical use, the Transition Shear-Stress Transport (SST) model was employed to predict the transitional flow patterns from laminar to turbulence in the geometry from the mouth to the trachea induced by the mouth inlet velocity conditions. As a part of data preparation for a machine learning (ML) model in a simulated hypothetical use, a total of 315 computational fluid particle dynamics (CFPD) simulations were conducted with different variables of peak inhalation flow rate and particle aerodynamic diameter (which are patient-specific and medication-specific information depending on patient-inhaler coordination and on drug type / characteristics), to find the optimal nozzle diameter and nozzle location (x, y, z) of an inhaler to maximize drug deposition from the inhaler in a diseased site (for example, the larynx and / or the glottis). Peak inhalation flow rate and particle aerodynamic diameter were used as ML model inputs. A Classification and Regression Trees (CART) machine learning model was used for the simulation and results compared to conventional inhalation therapy and conventional targeted delivery strategy. In the simulated hypothetical, the machine learning model was trained and used to output a desired nozzle diameter and location (x, y, z) of the inhaler for targeted drug delivery to desired locations based on patient-specific and medication-specific inputs (such as peak inhalation flow rate and drug particle diameter). The machine learning model outperformed conventional methods for drug delivery to the targeted areas.

[0082] The foregoing description provides illustration and description, but is not intended to be exhaustive or to limit the inventive concepts to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the methodologies set forth in the present disclosure.

[0083] Even though particular combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure. In fact, many of these features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each dependent claim listed below may directly depend on only one other claim, the disclosure includes each dependent claim in combination with every other claim in the claim set.

[0084] No element, act, or instruction used in the present application should be construed as critical or essential to the invention unless explicitly described as such outside of the preferred implementation. Further, the phrase "based on" is intended to mean "based, at least in part, on" unless explicitly stated otherwise.

[0085] The following are exemplary, non-exclusive, numbered embodiments of inventive concepts disclosed herein:

[0086] In an exemplary embodiment 1, a non-transitory processor-readable medium storing processor-executable instructions that when executed by a processor cause the processor to:

[0087] receive data indicative of a drug particle diameter of a particle of a drug contained within a drug chamber disposed at least partially within an inner cavity of an inhaler and one or more targeted regions of an airway of a patient;

[0088] receive, from a flow meter disposed within the inner cavity of the inhaler, a signal indicative of an inhalation profile corresponding to the patient, the inhalation profile including a peak inhalation flow rate and an inhalation duration of a test inhalation of the inhaler by the patient, the inhaler comprising an inhaler body, the flow meter, the drug chamber, a nozzle, and an actuator, the inhaler body definingthe innercavity, an air intake orifice in fluid communication with the inner cavity, and a mouthpiece orifice in fluid communication with the inner cavity, the flow meter disposed within the inner cavity between the air intake orifice and the mouthpiece orifice, the drug chamber disposed at least partially within the inner cavity, the nozzle disposed within the inner cavity proximal to the mouthpiece orifice, the mouthpiece orifice having a radial cross-sectional area, the nozzle in selective fluid communication with the drug chamber and configured to be positionable in at least a first direction within the radial cross-sectional area, the nozzle having a nozzle aperture having a nozzle aperture diameter configured to be adjustable,the actuator disposed within the inner cavity between the drug chamber and the nozzle and configured to selectively release the drug contained within the drug chamber to the nozzle, thereby placing the drug chamber and the nozzle in fluid communication; and

[0089] determine a targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at a predetermined deposition fraction by providing the drug particle diameter, the one or more targeted regions of the airway of the patient, and the inhalation profile as inputs to a machine learning model, the targeted delivery strategy including a target nozzle aperture diameter, a target nozzle position, and a target drug particle release time, wherein the target drug particle release time is a point in time during a treatment inhalation of the inhaler by the patient at which to cause the actuator to release the drug contained within the drug chamber to the nozzle, wherein the predetermined deposition fraction is a percentage of a total amount of the drug that is deposited at the one or more targeted regions of the airway of the patient.

[0090] In an exemplary embodiment 2, the non-transitory processor-readable medium of exemplary embodiment 1, wherein the signal is a first signal, and wherein the processorexecutable instructions when executed by the processor further cause the processor to send, to the nozzle, one or more second signals to adjust one or more of the nozzle aperture diameter of the nozzle aperture to the target nozzle aperture diameter and a position of the nozzle within the radial cross-sectional area to the target nozzle position.

[0091] In an exemplary embodiment 3, the non-transitory processor-readable medium of exemplary embodiment 2, wherein the processor-executable instructions when executed by the processor further cause the processor to:

[0092] determine that the treatment inhalation of the inhaler by the patient has begun; and

[0093] responsive to the determination that the treatment inhalation of the inhaler by the patient has begun, subsequent to the target drug particle release time elapsing, cause the actuator to release the drug contained within the drug chamber to the nozzle.

[0094] In an exemplary embodiment 4, the non-transitory processor-readable medium of any one of exemplary embodiments 1-3, wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, and a corresponding target drug particle release time for a given drug particle diameter, one or more given targeted regions of the airway of the patient, and a given inhalation profile in order to reach the predetermineddeposition fraction for the one or more targeted regions of the airway of the patient.

[0095] In an exemplary embodiment 5, the non-transitory processor-readable medium of any one of exemplary embodiments 1-4, wherein the machine learning model is a classification and regression tree model.

[0096] In an exemplary embodiment 6, the non-transitory processor-readable medium of any one of exemplary embodiments 1-5, wherein the data is first data, and the processor-executable instructions when executed by the processor further cause the processor to:

[0097] prior to determining the targeted delivery strategy, store second data indicative of a mouthpiece orifice aspect ratio of the mouthpiece orifice of the inhaler;

[0098] wherein determining the targeted delivery strategy is further defined as determining the targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at the predetermined deposition fraction by providing the drug particle diameter, the one or more targeted regions of the airway of the patient, the inhalation profile, and the mouthpiece orifice aspect ratio as the inputs to the machine learning model; and

[0099] wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, and a corresponding target drug particle release time for a given drug particle diameter, one or more given targeted regions of the airway of the patient, a given inhalation profile, and a given mouthpiece orifice aspect ratio in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

[0100] In an exemplary embodiment 7, the non-transitory processor-readable medium of any one of exemplary embodiments 1-6, wherein the targeted delivery strategy further includes a target mouthpiece orifice aspect ratio, and wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, a corresponding target drug particle release time, and a corresponding target mouthpiece orifice aspect ratio for a given drug particle diameter, one or more given targeted regions of the airway of the patient, and a given inhalation profile in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

[0101] In an exemplary embodiment 8, the non-transitory processor-readable medium of any one of exemplary embodiments 1-7, wherein the step of receiving the signal is further defined as determining, based on the test inhalation of the inhaler by the patient, the peak inhalation flow rate and the inhalation duration of the test inhalation.

[0102] In an exemplary embodiment 9, the non-transitory processor-readable medium of exemplary embodiment 8, wherein the step of determining the peak inhalation flow rate and the inhalation duration of the test inhalation is further defined as determining, based on a plurality of test inhalations of the inhaler by the patient, an average peak inhalation flow rate and an average inhalation duration, wherein the inhalation profile includes the average peak inhalation flow rate and the average inhalation duration.

[0103] In an exemplary embodiment 10, the non-transitory processor-readable medium of any one of exemplary embodiments 1-9, wherein the nozzle is further configured to be positionable in at least the first direction and a second direction within the radial cross-sectional area, and wherein the target nozzle position includes a first target nozzle coordinate corresponding to a target position of the nozzle within the radial cross-sectional area in the first direction and a second target nozzle coordinate corresponding to the target position of the nozzle within the radial cross-sectional area in the second direction.

[0104] In an exemplary embodiment 11, an inhaler, comprising:

[0105] an inhaler body defining an inner cavity, an air intake orifice in fluid communication with the inner cavity, and a mouthpiece orifice in fluid communication with the inner cavity, the mouthpiece orifice having a radial cross-sectional area;

[0106] a flow meter disposed within the inner cavity between the air intake orifice and the mouthpiece orifice;

[0107] a drug chamber at least partially disposed within the inner cavity and containing a drug;

[0108] a nozzle disposed within the inner cavity proximal to the mouthpiece orifice, the nozzle in selective fluid communication with the drug chamber and configured to be positionable in at least a first direction within the radial cross-sectional area, the nozzle having a nozzle aperture having a nozzle aperture diameter configured to be adjustable;

[0109] an actuator disposed within the inner cavity between the drug chamber and the nozzle, the actuator configured to selectively release the drug contained within the drug chamber to the nozzle, thereby placing the drug chamber and the nozzle in fluid communication;

[0110] a processor; and

[0111] a non-transitory processor-readable medium storing processor-executable instructions that when executed by the processor cause the processor to:

[0112] receive data indicative of a drug particle diameter of a particle of the drug and one or more targeted regions of an airway of a patient;

[0113] receive, from the flow meter, a signal indicative of an inhalation profile corresponding to the patient, the inhalation profile including a peak inhalation flow rate and an inhalation duration of a test inhalation of the inhaler by the patient; and

[0114] determine a targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at a predetermined deposition fraction by providing the drug particle diameter, the one or more targeted regions of the airway of the patient, and the inhalation profile as inputs to a machine learning model, the targeted delivery strategy including a target nozzle aperture diameter, a target nozzle position, and a target drug particle release time, wherein the target drug particle release time is a point in time during a treatment inhalation of the inhaler by the patient at which to cause the actuator to release the drug contained within the drug chamber to the nozzle, wherein the predetermined deposition fraction is a percentage of a total amount of the drug that is deposited at the one or more targeted regions of the airway of the patient.

[0115] In an exemplary embodiment 12, the inhaler of exemplary embodiment 11, wherein the signal is a first signal, and wherein the processor-executable instructions when executed by the processor further cause the processor to send, to the nozzle, one or more second signals to adjust one or more of the nozzle aperture diameter of the nozzle aperture to the target nozzle aperture diameter and a position of the nozzle within the radial cross-sectional area to the target nozzle position.

[0116] In an exemplary embodiment 13, the inhaler of exemplary embodiment 12, wherein the processor-executable instructions when executed by the processor further cause the processor to:

[0117] determine that the treatment inhalation of the inhaler by the patient has begun; and

[0118] responsive to the determination that the treatment inhalation of the inhaler by the patient has begun, subsequent to the target drug particle release time elapsing, cause the actuator to release the drug contained within the drug chamber to the nozzle.

[0119] In an exemplary embodiment 14, the inhaler of any one of exemplary embodiments11 -13, wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality ofinhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, and a corresponding target drug particle release time for a given drug particle diameter, one or more given targeted regions of the airway of the patient, and a given inhalation profile in orderto reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

[0120] In an exemplary embodiment 15, the inhaler of any one of exemplary embodiments 11-14, wherein the machine learning model is a classification and regression tree model.

[0121] In an exemplary embodiment 16, the inhaler of any one of exemplary embodiments 11-15, wherein the data is first data, and the processor-executable instructions when executed by the processor further cause the processor to:

[0122] prior to determining the targeted delivery strategy, store second data indicative of a mouthpiece orifice aspect ratio of the mouthpiece orifice of the inhaler;

[0123] wherein determining the targeted delivery strategy is further defined as determining the targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at the predetermined deposition fraction by providing the drug particle diameter, the one or more targeted regions of the airway of the patient, the inhalation profile, and the mouthpiece orifice aspect ratio as the inputs to the machine learning model; and

[0124] wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, and a corresponding target drug particle release time for a given drug particle diameter, one or more given targeted regions of the airway of the patient, a given inhalation profile, and a given mouthpiece orifice aspect ratio in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

[0125] In an exemplary embodiment 17, the inhaler of any one of exemplary embodiments 11-16, wherein the targeted delivery strategy further includes a target mouthpiece orifice aspect ratio, and wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, a corresponding target drug particle release time, and a corresponding target mouthpiece orifice aspect ratio for a given drug particle diameter, one ormore given targeted regions of the airway of the patient, and a given inhalation profile in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

[0126] In an exemplary embodiment 18, the inhaler of any one of exemplary embodiments 11-17, wherein the step of receiving the signal is further defined as determining, based on the test inhalation of the inhaler by the patient, the peak inhalation flow rate and the inhalation duration of the test inhalation.

[0127] In an exemplary embodiment 19, the inhaler of exemplary embodiment 18, wherein the step of determining the peak inhalation flow rate and the inhalation duration of the test inhalation is further defined as determining, based on a plurality of test inhalations of the inhaler by the patient, an average peak inhalation flow rate and an average inhalation duration, wherein the inhalation profile includes the average peak inhalation flow rate and the average inhalation duration.

[0128] In an exemplary embodiment 20, the inhaler of any one of exemplary embodiments 11-19, wherein the nozzle is further configured to be positionable in at least the first direction and a second direction within the radial cross-sectional area, and wherein the target nozzle position includes a first target nozzle coordinate corresponding to a target position of the nozzle within the radial cross-sectional area in the first direction and a second target nozzle coordinate corresponding to the target position of the nozzle within the radial cross-sectional area in the second direction.

[0129] In an exemplary embodiment 21, a method may comprise: measuring, a flow meter of an inhaler, a flow rate of air and / or a duration of the flow of air passing between an air intake orifice and a mouthpiece orifice of the inhaler; receiving, with a controller of the inhaler, data from the flow meter indicative of the flow rate of air and / or the duration of the flow of air; determining, with the controller of the inhaler, an inhalation profile from the data from the flow meter, which may include a peak inhalation flow rate and an inhalation duration.

[0130] In some embodiments, the inhalation profile may comprise the peak inhalation flow rate and the inhalation duration and / or an average peak inhalation flow rate and / or an average inhalation duration.

[0131] The method may comprise receiving, with the controller of the inhaler, information indicative of one or more targeted airway regions of an airway of a patient. The method may comprise receiving, with the controller of the inhaler, drug particle diameter of drug particles in the inhaler.

[0132] The method may comprise providing, with the controller, the inhalation profile, the drug particle diameter, and the one or more targeted airway regions of the airway 110 of the patient 106 as inputs to a Machine Learning model.

[0133] The method may comprise receiving, with the controller, from the Machine Learning model, an output of a determined targeted delivery strategy for the inhaler for the patient for delivering the drug particles contained within the inhaler to the one or more targeted airway regions of the airway of the patient at a predetermined deposition fraction, wherein the deposition fraction is a percentage of the total amount of the drug delivered to the airway of the patient that is deposited at the one or more targeted airway regions of the airway of the patient.

[0134] The method may comprise adjusting, with the controller, a diameter and / or aspect ratio of an output end of a nozzle of the inhaler, the position of the output end of the nozzle within a radial cross-sectional area of a mouthpiece orifice of the inhaler, and / orthe release time for the drug particles contained within the inhaler, so as to implement the targeted delivery strategy received from the Machine Learning model.

Claims

What is claimed is:

1. A non-transitory processor-readable medium storing processor-executable instructions that when executed by a processor cause the processor to: receive data indicative of a drug particle diameter of a particle of a drug contained within a drug chamber disposed at least partially within an inner cavity of an inhaler and one or more targeted regions of an airway of a patient; receive, from a flow meter disposed within the inner cavity of the inhaler, a signal indicative of an inhalation profile corresponding to the patient, the inhalation profile including a peak inhalation flow rate and an inhalation duration of a test inhalation of the inhaler by the patient, the inhaler comprising an inhaler body, the flow meter, the drug chamber, a nozzle, and an actuator, the inhaler body defining the inner cavity, an air intake orifice in fluid communication with the inner cavity, and a mouthpiece orifice in fluid communication with the inner cavity, the flow meter disposed within the inner cavity between the air intake orifice and the mouthpiece orifice, the drug chamber disposed at least partially within the inner cavity, the nozzle disposed within the inner cavity proximal to the mouthpiece orifice, the mouthpiece orifice having a radial cross-sectional area, the nozzle in selective fluid communication with the drug chamber and configured to be positionable in at least a first direction within the radial cross-sectional area, the nozzle having a nozzle aperture having a nozzle aperture diameter configured to be adjustable, the actuator disposed within the inner cavity between the drug chamber and the nozzle and configured to selectively release the drug contained within the drug chamber to the nozzle, thereby placing the drug chamber and the nozzle in fluid communication; and determine a targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at a predetermined deposition fraction by providing the drug particle diameter, the one or more targeted regions of the airway of the patient, and the inhalation profile as inputs to a machine learning model, the targeted delivery strategy including a target nozzle aperture diameter, a target nozzle position, and a target drug particle release time, wherein the target drug particle release time is a point in time during a treatment inhalation of the inhaler by the patient at which to cause the actuator to release the drug contained within the drug chamber to thenozzle, wherein the predetermined deposition fraction is a percentage of a total amount of the drug that is deposited at the one or more targeted regions of the airway of the patient.

2. The non-transitory processor-readable medium of claim 1, wherein the signal is a first signal, and wherein the processor-executable instructions when executed by the processor further cause the processor to send, to the nozzle, one or more second signals to adjust one or more of the nozzle aperture diameter of the nozzle aperture to the target nozzle aperture diameter and a position of the nozzle within the radial cross-sectional area to the target nozzle position.

3. The non-transitory processor-readable medium of claim 2, wherein the processorexecutable instructions when executed by the processor further cause the processor to: determine that the treatment inhalation of the inhaler by the patient has begun; and responsive to the determination that the treatment inhalation of the inhaler by the patient has begun, subsequent to the target drug particle release time elapsing, cause the actuator to release the drug contained within the drug chamber to the nozzle.

4. The non-transitory processor-readable medium of claim 1, wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, and a corresponding target drug particle release time for a given drug particle diameter, one or more given targeted regions of the airway of the patient, and a given inhalation profile in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

5. The non-transitory processor-readable medium of claim 1, wherein the machine learning model is a classification and regression tree model.

6. The non-transitory processor-readable medium of claim 1, wherein the data is first data, and the processor-executable instructions when executed by the processor further cause theprocessor to: prior to determining the targeted delivery strategy, store second data indicative of a mouthpiece orifice aspect ratio of the mouthpiece orifice of the inhaler; wherein determining the targeted delivery strategy is further defined as determining the targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at the predetermined deposition fraction by providing the drug particle diameter, the one or more targeted regions of the airway of the patient, the inhalation profile, and the mouthpiece orifice aspect ratio as the inputs to the machine learning model; and wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, and a corresponding target drug particle release time for a given drug particle diameter, one or more given targeted regions of the airway of the patient, a given inhalation profile, and a given mouthpiece orifice aspect ratio in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

7. The non-transitory processor-readable medium of claim 1, wherein the targeted delivery strategy further includes a target mouthpiece orifice aspect ratio, and wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, a corresponding target drug particle release time, and a corresponding target mouthpiece orifice aspect ratio for a given drug particle diameter, one or more given targeted regions of the airway of the patient, and a given inhalation profile in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

8. The non-transitory processor-readable medium of claim 1, wherein the step of receiving the signal is further defined as determining, based on the test inhalation of the inhaler by the patient, the peak inhalation flow rate and the inhalation duration of the test inhalation.

9. The non-transitory processor-readable medium of claim 8, wherein the step of determining the peak inhalation flow rate and the inhalation duration of the test inhalation is further defined as determining, based on a plurality of test inhalations of the inhaler by the patient, an average peak inhalation flow rate and an average inhalation duration, wherein the inhalation profile includes the average peak inhalation flow rate and the average inhalation duration.

10. The non-transitory processor-readable medium of claim 1, wherein the nozzle is further configured to be positionable in at least the first direction and a second direction within the radial cross-sectional area, and wherein the target nozzle position includes a first target nozzle coordinate corresponding to a target position of the nozzle within the radial cross-sectional area in the first direction and a second target nozzle coordinate corresponding to the target position of the nozzle within the radial cross-sectional area in the second direction.

11. An inhaler, comprising: an inhaler body defining an inner cavity, an air intake orifice in fluid communication with the inner cavity, and a mouthpiece orifice in fluid communication with the inner cavity, the mouthpiece orifice having a radial cross-sectional area; a flow meter disposed within the inner cavity between the air intake orifice and the mouthpiece orifice; a drug chamber at least partially disposed within the inner cavity and containing a drug; a nozzle disposed within the inner cavity proximal to the mouthpiece orifice, the nozzle in selective fluid communication with the drug chamber and configured to be positionable in at least a first direction within the radial cross-sectional area, the nozzle having a nozzle aperture having a nozzle aperture diameter configured to be adjustable; an actuator disposed within the inner cavity between the drug chamber and the nozzle, the actuator configured to selectively release the drug contained within the drug chamber to the nozzle, thereby placing the drug chamber and the nozzle in fluid communication; a processor; and a non-transitory processor-readable medium storing processor-executable instructionsthat when executed by the processor cause the processor to: receive data indicative of a drug particle diameter of a particle of the drug and one or more targeted regions of an airway of a patient; receive, from the flow meter, a signal indicative of an inhalation profile corresponding to the patient, the inhalation profile including a peak inhalation flow rate and an inhalation duration of a test inhalation of the inhaler by the patient; and determine a targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at a predetermined deposition fraction by providing the drug particle diameter, the one or more targeted regions of the airway of the patient, and the inhalation profile as inputs to a machine learning model, the targeted delivery strategy including a target nozzle aperture diameter, a target nozzle position, and a target drug particle release time, wherein the target drug particle release time is a point in time during a treatment inhalation of the inhaler by the patient at which to cause the actuator to release the drug contained within the drug chamberto the nozzle, wherein the predetermined deposition fraction is a percentage of a total amount of the drug that is deposited at the one or more targeted regions of the airway of the patient.

12. The inhaler of claim 11, wherein the signal is a first signal, and wherein the processorexecutable instructions when executed by the processor further cause the processor to send, to the nozzle, one or more second signals to adjust one or more of the nozzle aperture diameter of the nozzle aperture to the target nozzle aperture diameter and a position of the nozzle within the radial cross-sectional area to the target nozzle position.

13. The inhaler of claim 12, wherein the processor-executable instructions when executed by the processor further cause the processor to: determine that the treatment inhalation of the inhaler by the patient has begun; and responsive to the determination that the treatment inhalation of the inhaler by the patient has begun, subsequent to the target drug particle release time elapsing, cause the actuator to release the drug contained within the drug chamber to thenozzle.

14. The inhaler of claim 11, wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, and a corresponding target drug particle release time for a given drug particle diameter, one or more given targeted regions of the airway of the patient, and a given inhalation profile in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

15. The inhaler of claim 11, wherein the machine learning model is a classification and regression tree model.

16. The inhaler of claim 11, wherein the data is first data, and the processor-executable instructions when executed by the processor further cause the processor to: prior to determining the targeted delivery strategy, store second data indicative of a mouthpiece orifice aspect ratio of the mouthpiece orifice of the inhaler; wherein determining the targeted delivery strategy is further defined as determining the targeted delivery strategy for delivering the drug contained within the drug chamber to the one or more targeted regions of the airway of the patient at the predetermined deposition fraction by providing the drug particle diameter, the one or more targeted regions of the airway of the patient, the inhalation profile, and the mouthpiece orifice aspect ratio as the inputs to the machine learning model; and wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, and a corresponding target drug particle release time for a given drug particle diameter, one or more given targeted regions of the airway of the patient, a given inhalation profile, and a given mouthpiece orifice aspect ratio in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

17. The inhaler of claim 11, wherein the targeted delivery strategy further includes a target mouthpiece orifice aspect ratio, and wherein the machine learning model is trained with a plurality of drug particle diameters, a plurality of targeted regions of a plurality of airways of a plurality of patients, and a plurality of inhalation profiles to determine a corresponding target nozzle aperture diameter, a corresponding target nozzle position, a corresponding target drug particle release time, and a corresponding target mouthpiece orifice aspect ratio for a given drug particle diameter, one or more given targeted regions of the airway of the patient, and a given inhalation profile in order to reach the predetermined deposition fraction for the one or more targeted regions of the airway of the patient.

18. The inhaler of claim 11, wherein the step of receiving the signal is further defined as determining, based on the test inhalation of the inhaler by the patient, the peak inhalation flow rate and the inhalation duration of the test inhalation.

19. The inhaler of claim 18, wherein the step of determining the peak inhalation flow rate and the inhalation duration of the test inhalation is further defined as determining, based on a plurality of test inhalations of the inhaler by the patient, an average peak inhalation flow rate and an average inhalation duration, wherein the inhalation profile includes the average peak inhalation flow rate and the average inhalation duration.

20. The inhaler of claim 11, wherein the nozzle is further configured to be positionable in at least the first direction and a second direction within the radial cross-sectional area, and wherein the target nozzle position includes a first target nozzle coordinate corresponding to a target position of the nozzle within the radial cross-sectional area in the first direction and a second target nozzle coordinate corresponding to the target position of the nozzle within the radial cross- sectional area in the second direction.

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