Methods for evaluating the effectiveness of aerosols for pulmonary drug delivery using inhaler devices, oral inhalation and / or nasal medications, and drug / device combination products.

Abstract

The present invention relates to a method for evaluating the effectiveness of an aerosol (3) for pulmonary drug delivery, as well as inhaler devices (2, 2a, 2b, 2c, 2d), orally inhaled drugs (1), and drug / device combination products (7). [Solution] An inhaler device (2, 2a, 2b, 2c, 2d) can generate an aerosol (3), which contains aerosol particles (30) containing an orally inhaled drug (1). The method includes the steps of providing a computational lung model (Lmod) and a computational particle transport and deposition model (Pmod), calculating the spatial particle deposition distribution (Ddist) of discrete particles (30') based on a determined aerosol value (Aval) of an aerosol parameter (Apar), calculating the efficacy value (Eval) of an efficacy parameter (Epar) based on the spatial particle deposition distribution (Ddist), and using the efficacy value (Eval) to automatically evaluate the efficacy of the aerosol (3) for lung drug delivery.

Description

[Technical Field]

[0001] The present invention relates to a method for evaluating the effectiveness of aerosols for pulmonary drug delivery. Furthermore, the present invention relates to inhalers and / or ventilators for administering orally inhaled and / or nasally administered drugs, orally inhaled and / or nasally administered drugs, and combination products of said drugs and said devices.

[0002] Furthermore, the present invention relates to the evaluation of the effectiveness of orally inhaled and / or nasal drugs and drug / device combination products for pulmonary drug delivery, as well as the performance evaluation of inhalers and / or ventilators for pulmonary drug delivery. The present invention also relates to a method for generating aerosols, a method for designing and / or manufacturing inhalers and / or ventilators, and a method for manufacturing orally inhaled and / or nasal drugs. [Background technology]

[0003] Lung delivery of drugs (pulmonary drug delivery) is a preferred and important route of drug administration. However, a major difficulty is that the amount of drug delivered to the site of action in the lungs is unpredictable and even difficult to measure retrospectively. The effectiveness of pulmonary drug delivery is primarily determined by the amount of drug delivered.

[0004] Different types of inhaler devices are typically used to administer different types of orally inhaled medications. Among other factors, the properties of the aerosols generated by the inhaler device significantly influence the process by which the aerosol particles are transported to the lungs, and consequently, the effectiveness of pulmonary drug delivery.

[0005] The amount of drug delivered locally is practically unpredictable. This is because aerosol transport and deposition depend on a variety of factors, from the design of the inhaler and inhalation pattern to the type of aerosol and even the patient's constitution. In lungs affected by diseases such as COPD and IPF, uniform air distribution is not observed; rather, strong local differences appear. Therefore, the amount of drug can vary greatly not only between subjects but also within the lung itself, depending on the lung morphology / physiological function and disease. Thus, there is a need for oral inhalation drugs, inhaler devices, and drug / device combination products that achieve predictable pulmonary drug delivery.

[0006] Currently, numerous drug delivery devices for delivering medications to the lungs are available on the market. The most common inhaler devices can be broadly categorized into three main types: Metered-dose inhalers (MDIs) include pressurized metered-dose inhalers (pMDIs) and breath-activated metered-dose inhalers (BAMDIs). Dry powder inhalers (DPIs) include single-dose dry powder inhalers, multi-unit dry powder inhalers, and multi-dose dry powder inhalers. Soft mist inhalers (SMIs) include vibrating mesh nebulizers (VMNs), jet nebulizers (JNs), and ultrasonic nebulizers.

[0007] Metered-dose inhalers (MDIs) are widely used to treat respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). Their ease of use, portability, and rapid relief of respiratory symptoms have made them a common choice. However, proper usage technique is essential for effective drug delivery, and some patients may find MDIs difficult to use correctly. Pressurized metered-dose inhalers (pMDIs) consist of a container holding a pressurized drug formulation and a mouthpiece for inhalation. When the patient presses the container, the drug is released regardless of whether they are inhaling or not. The effectiveness of drug delivery with MDIs can depend on the patient's ability to inhale at the appropriate time and coordinate their actions. In contrast, breath-activated metered-dose inhalers (BAMDIs) release the drug only when the patient is actively inhaling. This eliminates the need for the patient to coordinate their actions of releasing and inhaling the drug simultaneously.

[0008] A dry powder inhaler (DPI) is a medical device that delivers drugs directly to the lungs. Unlike a metered-dose inhaler (MDI) that delivers aerosolized drugs, a DPI delivers dry powder drugs. Furthermore, since a DPI releases the drug only during inhalation, it does not require the patient's cooperation and is easy to use. The disadvantage is that the drug dosage is not constant due to minute differences in each inhalation. Generally, three types of DPIs are available. A single-dose DPI encloses a single-dose of the drug in a pre-metered capsule. During use, this capsule is perforated, the drug is released, and the inhaler is discarded. A multiple-unit dose DPI contains multiple doses of the drug that are perforated in the same way as a single-use DPI. Such inhalers are discarded when all doses are consumed. A multi-dose DPI has a reservoir that stores the drug measured for each use. Furthermore, the drug can be replenished even when the inhaler is empty.

[0009] A soft mist inhaler (SMI), also called a nebulizer inhaler, uses a mechanical system to generate a mist that the patient inhales. Since the generation of the mist does not depend on the patient's inhalation, SMI is often used by patients who have difficulty using MDIs or DPIs. The disadvantages of SMI include the length of treatment time, a large and less portable device, the need for an external energy source, regular cleaning and maintenance, and the high cost of the device. Among soft mist inhalers, a vibrating mesh nebulizer (VMN) uses a vibrating mesh membrane to convert a liquid drug into a fine mist that can be inhaled. These are further classified into active vibration type and passive vibration type mesh nebulizers. A jet nebulizer (JN) uses a high-pressure gas flow over a liquid drug reservoir. Due to the high flow rate and the surface tension of the liquid, the liquid drug is converted into fine droplets. Droplets that are too large are deflected and further atomized by an air jet before being delivered from the nebulizer. Other types of jet nebulizers include an open vent nebulizer and a ventilator-assisted open vent nebulizer. An ultrasonic nebulizer generates high-frequency vibrations using a piezoelectric crystal to convert a liquid drug into a fine mist for the patient to inhale.

[0010] In addition to the three main categories, there are also other types of inhalers, such as rotary disk aerosol generators, spinning top aerosol generators, or centrifugal aerosol generators. These types use a high-speed rotating disk with grooves to generate aerosol particles from a drug reservoir. Due to the high rotational speed and its centrifugal force, the drug is deformed into a thin film, divided into fine droplets, and finally released from the rotating disk. This fine mist is carried by the airflow.

[0011] Due to the geometric complexity of the respiratory system, including the (human) lungs (hereinafter often simply referred to as the lungs), experimental studies using physical models of the lungs or parts thereof are only possible in highly simplified systems, and even in such cases, it is extremely difficult to conduct the experiments. The development of appropriate animal models is also difficult and involves ethical and other issues. In vivo imaging of aerosol inhalation and deposition in human subjects is possible using modern nuclear medicine imaging techniques such as radiation scintigraphy and three-dimensional SPECT imaging. However, these imaging methods are limited in spatial and temporal resolution due to body movement artifacts, depth-dependent spatial blurring, etc. Furthermore, their use in susceptible populations is often impossible or ethically problematic.

[0012] Computer modeling techniques for simulating the lung system of a specific patient have been considered as an alternative, and several computational methods for approximating particle transport in the human lung have been developed. However, existing models are significantly restricted by morphological truncation, i.e., not encompassing the entire airway tree and insufficient spatial resolution. Furthermore, existing models do not consider lung tissue, the rib cage, or the diaphragm.

[0013] Before discussing existing modeling techniques in detail, it is important to clarify specific terminology related to the spatial distribution and deposition of particles within the lung. In the relevant literature, the term "regional" deposition within the lung is commonly used, which typically includes distinctions between larger subdomains of the pulmonary system, such as the oropharyngeal region and the bronchial region, as well as distinctions between lung lobes and specific airway generations (branching levels of the lung airway tree). In contrast, the proposed modeling technique according to the present invention can achieve much higher resolution by predicting the precise trajectories of all particles both spatially and temporally. Therefore, we use the term "spatiotemporal" to refer to patterns in both space and time, and "regional" to refer to different compartments or subdomains within the pulmonary system.

[0014] However, known modeling techniques for lung drug delivery vary greatly depending on the specific application and purpose of the model. Historically, three main methods have been used for simulating drug delivery to the lungs. More specifically, the physical phenomena governing particle transport and deposition can be incorporated into computational models by deriving mathematical equations from empirical, empirical, or first-principles (i.e., physical laws governing particle transport within the lungs).

[0015] The so-called ICRP model (Non-Patent Literature 1) is an example of an empirical model. Essentially, this model consists of a series of mathematical filter functions with empirically determined deposition rates for different regions within the lung. Because the empirical model ignores the complex geometric structure of the lung, it cannot account for patient-specific lung and airway morphology or damage to lung tissue in specific subdomains.

[0016] Similarly, the trumpet model (see Non-Patent Documents 2-4) approximates the human airway system using a one-dimensional channel with a changing cross-section. Particle transport and deposition are described by differential equations in a one-dimensional channel that ignores all internal lung structure while considering the increase in airway volume between generations. While its conciseness and elegance are appealing, the trumpet model's coarse geometric simplifications hinder its applicability to individual patients.

[0017] In contrast, empirical models incorporate some information about the geometric structure of the airway tree. Therefore, these models are often also called morphometric models. Examples of this type of model include single-pathway (typical path) models (Non-Patent Literature 5) and multi-pathway models such as MPPD (Non-Patent Literature 6).

[0018] In these models, the calculation of airflow distribution within the lungs is not based on physical principles, but rather on the empirical assumption that airflow branching is proportional to the distal lung volume supplied by the branched airways. This assumption clearly breaks down in almost all cases where the airways and respiratory system are affected by disease and exhibit pathological abnormalities. Furthermore, these models are unsuitable for modeling patient-specific drug transport and deposition. Therefore, these models are only useful for studies aimed at a broad understanding of particle deposition phenomena in large cohorts of relatively healthy lungs, because they cannot identify specific particle deposition locations (e.g., hot spots) and cannot account for the complex flow patterns within the airway system. Another limitation of these models is that they were designed assuming simulations of atmospheric particle deposition and do not reflect the conditions of sprayed aerosol inhalation.

[0019] In contrast, there are physics-based computational fluid-particle dynamics (CFPD) models that use physics-based equations and actual or idealized patient-specific geometries.

[0020] Patent Document 1 describes a method in which the geometry of the first few generations of the airway is extracted from CT (computed tomography) scans, meshed, and then used as input for a CFD simulation of airflow in the first few generations (branching level) of the airway tree. Such models are essentially limited to larger generations of the conduction airway, typically the 6th to 7th generations. This is due to the resolution limitations of CT scans and the enormous computational cost required to perform a fully resolving 3D CFD simulation of the entire airway tree. This method omits most of the conduction and respiratory airways, as well as the surrounding tissues and structures of the lungs. A particular disadvantage is that two CT scans are required to estimate the exit boundary conditions at the distal end of the simulated portion of the airway tree. Using CT scans during complete inspiration and expiration, respectively, image processing techniques are used to calculate the lobe expansion over the respiratory cycle. This data is then used to adjust the outflow boundary conditions so that the lobe expansion in the model matches the measured lobe expansion. Furthermore, it is assumed that lobe expansion is uniform. In addition to requiring at least two CT scans, the method described in Patent Document 1 cannot handle structures beyond the lung lobes. This model does not include smaller peripheral airways and does not consider the alveolar region or the thoracic cage. This method is not predictive in the sense that it cannot extrapolate beyond the range measured by the two CT scans required for model calibration. Another drawback of Patent Document 1 is that it cannot model exhalation. Deposition statistics can only be estimated under the assumption that all inhaled particles are deposited. Deposition from central to peripheral is approximated by the total number of seed particles and the proportion of particles deposited in the first few generations. Furthermore, regional information beyond the lobe level and deposition statistics for higher generations cannot be calculated. Because a second CT scan is always required for model calibration, the application of this method is limited to scenarios where two CT scans are possible and cannot be applied to longitudinal studies where only routine data is available.

[0021] The concept of 3D CFD analysis can be extended by using morphological models of higher generations of airways, beyond the airway generations visualized by CT scans (Non-Patent Literature 7). However, resolving the structure and flow field in three dimensions results in computational costs that can only be handled by feasibility studies using very large-scale, high-performance computing systems. Extending this method to 3D CFPD simulations with added particle transport further increases the computational costs.

[0022] The method described in Patent Document 2 aims to determine the respiratory state and further optimize its treatment by means of 3D CFD simulation. The disclosed method is described as being able to determine the patient-specific lung dose as a function of patient-specific morphology, aerosol and device characteristics, and inhalation characteristics. However, the modeling method described in Patent Document 2 is identical to that described in Patent Document 1 and, naturally, has the same limitations.

[0023] Firstly, the lung structure model is limited to geometric information that can be extracted from high-resolution image data. Therefore, the airway model is limited to larger airways that can be identified by high-resolution CT scans. Secondly, lung tissue and other structures within the pleural cavity are not part of the model described in Patent Document 2. Therefore, the outflow boundary conditions in the 3D CFD simulation, i.e., the pressure at the exit of the bronchioles, must be adjusted to match the mass flow rate obtained from at least two CT images. The requirement of needing two or more volumetric images for model construction itself is a major drawback in that it requires special processing and examination protocols. These requirements expose patients to higher radiation doses compared to obtaining a single volumetric image used in clinical practice. Furthermore, since Patent Document 2 cannot simulate exhalation, the method described in that document can only approximate the effective lung dose by assuming that all particles transported beyond the glottis during inspiration are deposited in the lungs.

[0024] Because the model only includes the upper part of the airway tree and omits lung tissue and surrounding structures, the spatial resolution of the lung model and the resulting aerosol deposition pattern described in Patent Document 2 is very limited. Information beyond the lobe level cannot be obtained. Such a limited approach is also described in Non-Patent Document 8.

[0025] In summary, known approaches that rely on 3D CFD models do not cover the entire airway tree. The airway tree is either truncated before the respiratory region, or only a small portion of the airway pathways leading to the respiratory region are modeled. 3D CFD simulation of the entire airway tree is computationally intensive even for scientific applications using high-performance computers, and even more so for commercial applications.

[0026] Patent Document 3 discloses a method for determining patient-specific ventilation parameters for setting up a ventilation device as a means of ventilating a patient, based on modeling techniques for the fluid dynamics and structural mechanics of the lung. This document does not consider general particle transport or pulmonary drug delivery.

[0027] None of the methods described above are suitable for evaluating the effectiveness of pulmonary drug delivery throughout the lungs of human (or animal) subjects. Furthermore, known methods for predicting or evaluating pulmonary drug delivery suffer from low accuracy due to resolution limitations and overly simplified modeling techniques. In particular, the influence of inhaler devices on the properties of inhaled drug aerosols is not adequately considered in conventional techniques. [Prior art documents] [Patent Documents]

[0028] [Patent Document 1] European Patent Application Publication No. 2255843 [Patent Document 2] International Publication No. 2014 / 125059 [Patent Document 3] International Publication No. 2021 / 204931 [Patent Document 4] International Publication No. 2013 / 086486

Non-licensed literature

[0029] [Non-licensed document 1] The New ICRP Model for the Respiratory Tract. "Radiation Protection Dosimetry". 1994; 53(1-4):107-114. doi:10.1093 / rpd / 53.1-4.107 [Non-licensed document 2] Taulbee DB, Yu CP. "A theory of aerosol deposition in the human respiratory tract". Journal of Applied Physiology. 1975; 38(1):77-85. doi:10.1152 / jappl.1975.38.1.77, [Non-licensed document 3] Taulbee DB, Yu CP, Heyder J. "Aerosol transport in the human lung from analysis of single breaths". Journal of Applied Physiology. 1978; 44(5):803-812. doi:10.1152 / jappl.1978.44.5.803,

Non-licensed Document 4

Non-licensed Document 5

Non-Patent Document 6

Non-Patent Document 7

Non-Patent Document 8

[0030] An object of the present invention is to solve at least one of the problems described in the prior art. In particular, one object of the present invention is to evaluate the effectiveness of drug delivery throughout the lung, preferably with greater accuracy and / or in a shorter time.

[0031] A further object of the present invention is to make the evaluation of the effectiveness of pulmonary drug delivery available in technical applications such as performance evaluation of inhaler devices, evaluation of the efficacy and / or safety of oral inhalation and / or nasal drugs, particularly evaluation of the dosage of active ingredients in drugs, or evaluation of the effectiveness of drug / device combination products.

[0032] Another object of the present invention is to provide a method for designing and / or manufacturing an inhaler device for lung drug delivery having improved efficacy, and to provide such an inhaler device.

[0033] An object of the present invention is also to provide a method for operating an inhaler device to generate an aerosol having improved efficacy in pulmonary drug delivery.

[0034] A further object of the present invention is to provide a method for producing orally inhaled and / or nasal drugs with improved efficacy and / or safety, and to supply such drugs.

[0035] A further object of the present invention is to provide a drug / device combination product that combines an orally inhaled and / or nasal drug with an inhaler device for administering the orally inhaled and / or nasal drug, with improved efficacy and / or safety. [Means for solving the problem]

[0036] In particular, these challenges are addressed by the following method for evaluating the effectiveness of aerosols for pulmonary drug delivery. The aerosol comprises aerosol particles containing orally inhaled and / or nasal drugs. This method is A step of determining at least one aerosol value of at least one aerosol parameter that characterizes an aerosol, wherein the aerosol is generated by an inhaler device, and a step of determining the aerosol value, Preferably, the step of providing a computational lung model that represents the structure of the human respiratory system and the transient gas flow in the airways of the respiratory system, at least between the trachea and the lungs, particularly during inspiration and expiration, The computational lung model is preferably based on a discretized respiratory system structure derived from processed image data representing at least one human respiratory system, and preferably the discretized respiratory system structure is derived from processed image data of a single tomographic image of each of the at least one respiratory system. Steps include providing a computational lung model, The steps include providing a computational particle transport and deposition model that represents the transient transport of individual aerosol particles in the gas flow within the airways and the deposition of individual aerosol particles in the respiratory system, The steps include: calculating the spatial particle deposition distribution of multiple discrete particles deposited in a discretized respiratory system structure based on at least one aerosol value using a computational lung model and a computational particle transport and deposition model; A step of calculating at least one efficacy value of an efficacy parameter that indicates the effectiveness of aerosol-induced drug delivery to the lungs, based on the spatial particle deposition distribution, The process includes the steps of using efficacy values ​​to automatically evaluate the effectiveness of lung drug delivery, storing efficacy values ​​in a storage device, displaying efficacy values ​​using a display device, and / or transmitting efficacy values ​​for use in evaluating the effectiveness of lung drug delivery.

[0037] Efficacy values ​​may be used (exclusively) or made available for technical applications. Examples include (automatically) evaluating the performance of inhaler devices in pulmonary drug delivery, evaluating the efficacy and / or safety of oral inhalation and / or nasal drugs, particularly the dosage of the active ingredient, or evaluating the efficacy of drug / device combination products for pulmonary drug delivery.

[0038] Further technical applications of efficacy values ​​include the design and / or manufacture of inhaler devices for pulmonary drug delivery based on efficacy values, particularly the selection (setting / implementation) of inhaler design parameters; the operation of inhaler devices for pulmonary drug delivery based on efficacy values, particularly the setting of inhaler device operating parameters; the manufacture of oral inhalation and / or nasal drugs based on efficacy values; and the manufacture or provision of drug / device combination products of oral inhalation and / or nasal drugs and inhaler devices based on efficacy values.

[0039] Orally inhaled and / or nasally administered drugs within the scope of this invention refer to drugs intended for delivery to the lungs (pulmonary drug delivery). Typically, such drugs are administered to be inhaled through the mouth, but depending on the circumstances they may also be inhaled (partially) through both the mouth and nose (intentionally or unintentionally), or (intentionally or unintentionally) through the nose only. For example, a patient may use an inhalation mask, such as a nebulizer, to cover both the mouth and nose in order to inhale an aerosol of a drug typically referred to as an orally inhaled drug. To this extent, this invention refers to drugs that are primarily (exclusively) orally inhaled. Therefore, drugs administered to be inhaled through the nose with the intention of being delivered (primarily) to the nose (and not the lungs), in particular drugs intended to be absorbed into the nasal mucosa (e.g., nasal sprays), are not included in the definition of orally inhaled and / or nasally administered drugs within the meaning of this invention.

[0040] Furthermore, oral and / or nasal medications may be inhaled actively, i.e., by the individual's (healthy person or patient's) spontaneous breathing, or passively, i.e., using the patient's mechanical ventilation, particularly an artificial (mechanical) ventilator. Mixed forms of active and passive breathing, such as assisted spontaneous breathing, are also included. Thus, the term “inhaler device” refers to any device suitable for supplying aerosols for pulmonary drug delivery, regardless of whether the drug delivery to the lungs is by active or passive inhalation.

[0041] The efficacy of pulmonary drug delivery can be evaluated for drugs administered to treat lung diseases such as asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF), and is particularly applicable to the treatment of (local) tissue-level conditions such as emphysema and fibrosis, especially inflammation of lung tissue. Furthermore, it can be used to administer drugs for the treatment of pulmonary arterial hypertension (PAH), respiratory distress syndrome (RDS), or infections. The efficacy of pulmonary drug delivery can also be evaluated for cannabis as a drug.

[0042] The active ingredients in oral inhalation and / or nasal medications may include, but are not limited to, inhaled bronchodilators for the treatment of obstructive airway diseases such as asthma and COPD, anti-inflammatory products (including glucocorticoids for the treatment of inflammation of lung tissue), anti-infective agents for the treatment of infections, recombinant stardeoxyribonuclease (rhDNase) for the treatment of cystic fibrosis, mannitol for the treatment of bronchiectasis and cystic fibrosis, prostacyclin used for the treatment of pulmonary arterial hypertension (PAH), or pulmonary surfactants for the treatment of respiratory distress syndrome (RDS). The medication may also be intended for systemic therapy not limited to purely respiratory diseases and may contain active ingredients such as nicotine or roxapine. THC may also be an active ingredient, and oral inhalations may contain or be cannabis itself.

[0043] Aerosols can be understood as a mixture of solid and / or liquid particles (suspended particles) and a carrier gas. The carrier gas consists of or can contain air and / or other gases, particularly oxygen and / or helium. The carrier gas may be a mixture of various gases, particularly oxygen and / or helium in the air. Aerosols containing solid particles can be produced from powder. Aerosols containing droplet-like liquid particles can be produced by atomizing a liquid.

[0044] Aerosol parameters are understood as parameters that characterize an aerosol, and preferably represent one or more of the physical properties of the aerosol, particularly the physical properties of the particles or carrier gas constituting the aerosol, or the physical properties of the aerosol (as a whole).

[0045] The evaluation of the effectiveness of pulmonary drug delivery means predicting, monitoring, and / or evaluating the effectiveness of orally inhaled and / or nasal drugs. These drugs may theoretically or potentially be administered to a (human or animal) body in the future, or have been administered to a (human or animal) body in the past. However, the methods according to the present invention are not therapeutic methods for the treatment of a human or animal body. The step of actually administering a drug to a human or animal is not included in these methods.

[0046] Efficacy parameters indicating the effectiveness of aerosols for pulmonary drug delivery can show how suitable an aerosol is for pulmonary drug delivery, particularly in terms of the amount of aerosol particles delivered (deposited) in the lungs and / or the area (such as location, section, and / or airway generation) in which the aerosol particles are deposited within the lungs (spatial particle deposition distribution), and especially with respect to the active ingredient of the drug contained in the aerosol particles. In particular, efficacy parameters can indicate the amount and / or ratio of drug (active ingredient) delivered to at least one location, preferably a specific area, within the lungs, such as location within the airway, left lung / right lung, lung lobe, or airway generation. Furthermore, efficacy parameters can indicate the amount and / or ratio of drug (active ingredient) that reaches the bloodstream and / or lung tissue through the lungs.

[0047] Processed image data representing at least one respiratory system is preferably obtained from imaging (tomography) applied to a (real) body in a healthy (human or animal) person or a patient (human or animal), particularly a patient with a lung disease, for a (human or animal) respiratory system, especially the lungs. Preferably, the discretized respiratory system structure is derived from processed image data of a single image (only), preferably by tomography, for each of at least one (real) respiratory system. In particular, a single (tomography) image preferably represents only one state (of the respiratory system, such as inspiration, expiration, or an intermediate state) at a single point in time. The processed image data can (exactly) represent one (real) respiratory system or multiple (averaged) (real) respiratory systems (preferably from different individuals). The processed image data can represent a (single) real respiratory system or a (single) virtual (averaged and / or modified) respiratory system. The processed image data may represent (part of) a healthy respiratory system and / or (part of) a pathological respiratory system.

[0048] Transient (time-dependent) gas flow in the airways refers specifically to the flow (laminar and / or turbulent) of gas (air and / or carrier gas) in the airways during inspiration and / or expiration. Particle transport specifically refers to the (passive) transport (motion) of (solid or liquid) aerosol particles in a gas flow, particularly due to the force of the gas flow acting on the particles.

[0049] A drug / device combination product can be understood as a pharmaceutical product comprising both a drug containing a specific dose of that drug and a medical device for administering that drug. In this disclosure, a drug / device combination product for pulmonary drug delivery includes an orally inhaled and / or nasal drug and an inhaler device for administering said orally inhaled and / or nasal drug. In particular, the combined efficacy of a drug / device combination product for pulmonary drug delivery depends on both the efficacy (and / or safety) of the drug and the performance of the inhaler device.

[0050] At least some, preferably all, of the steps can be performed using a computer (a computer implementation). In particular, method steps involving computation can be performed by at least one processor. Specifically, method steps providing a computation model can use at least one of a storage medium and / or database that provides the computation model.

[0051] This method has the advantage of being able to evaluate the effectiveness of drug delivery throughout the lung (whole). This is achieved by considering the transport and deposition of individual aerosol particles in the lung (whole) during inhalation and exhalation. Furthermore, the radiation dose can be reduced by using a single (tomographic) image of the individual's (patient's) respiratory system. This comprehensive approach improves the accuracy of the resulting spatial particle deposition distribution. As a result, more precise and reliable prediction and / or evaluation of drug delivery to the lung, particularly targeted drug delivery to specific areas of the lung, becomes possible. Local aerosol deposition and the amount of delivered drug can be accurately evaluated and / or predicted, especially for specific individuals (patients), at any site of action in the lung (whole).

[0052] In embodiments of this method, at least one aerosol parameter represents one or more of the following: particle size (preferably average particle size), particle size distribution, particle density, particle shape (preferably average particle shape), aerosol flow rate (preferably having time dependence, particularly relative to the respiratory cycle), aerosol velocity, carrier gas type, and aerosol carrier gas pressure. The aerosol parameter may constitute a set of several individual aerosol parameters that generally characterize the aerosol.

[0053] Typically, homogeneous aerosol parameters (particle size, particle density, particle volume, particle shape, particle velocity, etc.) within an aerosol are technically impossible to achieve in physical reality. In practice, aerosol parameters (such as particle size) typically follow a distribution (such as a size distribution, usually a log-normal distribution). To this extent, the values ​​of aerosol parameters (such as particle size) in an aerosol particle can be understood as the value of the particle with the highest probability within the distribution (such as a particle size distribution), or as a (narrow) range of the distribution around that highest probability value.

[0054] Particle size can be understood as the equivalent particle size of solid particles, such as those derived from liquid droplets (liquid) or powder. Particle size may be the (average) particle diameter of (particularly spherical) droplets, or the equivalent diameter of solid particles of different shapes, particularly those derived from powder. The particle size distribution is preferably a log-normal distribution and is preferably described by the aerodynamic median mass and / or geometric standard deviation. A log-normal distribution can be understood as a continuous probability distribution of a random variable (e.g., particle size) whose logarithm is normally distributed. For example, the particle size of an aerosol produced by a spraying device can be determined according to DIN EN 13544-1. Particle density is the (averaged) number of particles per unit volume of aerosol. Particle size (particle size distribution) and particle density are among the parameters most involved in determining particle transport in the lungs.

[0055] The aerosol flow rate may be an aerosol mass flow rate or an aerosol volume flow rate, and is preferably time-dependent with respect to the (human) respiratory cycle. The pressure of the carrier gas as an aerosol parameter may also be time-dependent. The aerosol flow velocity or particle (average) velocity can be defined at specific points along the aerosol flow path, preferably at the tracheal inlet and / or outlet of the inhaler device.

[0056] The carrier gas preferably contains air, helium, oxygen, or a mixture thereof. Aerosol parameters characterizing the aerosol (and its carrier gas) are the density, composition, and / or molecular weight of the carrier gas. The type of carrier gas in an aerosol affects particle transport within the lungs, particularly the relationship between the density of the carrier gas and the density of the aerosol particles.

[0057] In addition to the aerosol parameters mentioned above, further aerosol parameters include, but are not limited to, particle (surface) charge, particle hydrophobicity, and / or particle binding affinity. Aerosol parameters can indicate the amount of active ingredient of a drug contained in the particles, particularly the amount of active ingredient per unit particle volume.

[0058] In this embodiment of the method, at least one aerosol value is Measuring aerosol levels, Aerosol values ​​are obtained from a database containing data that shows the physical properties of aerosols. Obtaining or deriving aerosol values ​​from the specification data of inhaler devices. Calculating aerosol values ​​based on analytical relationships regarding the physical properties of aerosols, and / or Preferably, a computational inhaler device model is used to calculate aerosol values ​​as a result of computational simulation of the aerosol generation process and / or flow in the inhaler device. It is determined by [the following]. The aerosol values ​​of the aerosol parameters are measured (in experiments), retrieved (from a database), and / or calculated (in analytical or computational simulations) before performing a method to evaluate the effectiveness of aerosols for pulmonary drug delivery, and are possibly stored (buffered) and subsequently used as the aerosol values ​​for input to this method. Data retrieved from a database may be (pre-)measured data or pre-calculated data. The determined aerosol values ​​may be a combination of measured values, calculated values, and aerosol values ​​obtained by other methods.

[0059] In one embodiment, the method further includes the step of determining at least one respiratory value of respiratory parameters that characterize respiratory system respiration, particularly spontaneous respiration, assisted spontaneous respiration and / or mechanical respiration, wherein the respiratory value defines boundary conditions for a computational lung model. Preferably, the respiratory parameter is Preferably a time-dependent, preferably pressure difference between the pleural cavity and the trachea, Preferably, the gas flow rates of inhalation and / or exhalation are time-dependent, In particular, the minimum and / or maximum intrapulmonary pressure values ​​and respiratory cycle frequency values ​​in pressure-controlled mechanical ventilation, In particular, the amount of gas inspiratory and / or expiratory in volume-controlled mechanical ventilation and Includes one or more of the following.

[0060] Respiratory parameters may be respiratory curves that show the individual's (patient-specific) respiration over time. For example, these may be (individual) inspiratory and / or expiratory gas flow rates, (individual) lung volume, or (individual) lung pressure over time, preferably over at least one (complete) respiratory cycle (the time interval between inhalation and exhalation). Respiratory curves may refer to spontaneous respiration, assisted spontaneous respiration, and / or mechanical ventilation. Respiratory curves, in particular (a series of) respiratory values, can be used to calibrate a computational lung model, especially to determine all the boundary conditions required for the computational lung model. Preferably, in calibrating the computational lung model, the (measured) respiratory curves are matched (synchronized) with the lung state (e.g., complete inspiration or complete expiration) from which the tomographic images were created.

[0061] Respiratory values, particularly a series of respiratory values ​​representing a (discrete) respiratory curve, can preferably be derived (optionally with additional calculations and / or parameters) from a time-dependent respiratory curve of a measured individual (patient-specific) over a complete respiratory cycle. Respiratory values ​​can preferably be derived based on time-dependent, measured inspiratory and / or expiratory (respiratory cycle: inspiration and expiration) gas flow rates and / or individual (patient) lung volume. One or more respiratory values ​​of respiratory parameters can be determined or derived based on at least one measurement, for example, using a spirometer.

[0062] In a preferred embodiment, the respiratory value is derived based on time-dependent inspiratory and expiratory gas volume flow rates (v(t) [ml / s]) measured for the individual (patient) to which the respiratory system (on a tomographic image) belongs. Preferably, such measurement (e.g., measurement of the respiratory curve v(t)) is performed using a spirometer. Generally, a spirometer is a known medical device for measuring the volume of air (gas) inhaled and exhaled by the lungs. The respiratory value may be a tidal volume (e.g., in liters [l] or milliliters [ml]) representing the volume difference (ΔV [ml]) between the (fully) exhaled and (fully) inspiratory states of the lungs. (Tidal) lung volume can be derived from the measured inspiratory and / or expiratory gas flow rates, for example, by time integration. The respiratory value can be determined or derived based on pre-recorded (pre-recorded) measured respiratory data (patient-specific measured respiratory data) that, in particular, represent the individual's inspiratory and / or expiratory gas flow rates over a (complete) respiratory cycle. In another embodiment, the respiratory value can be derived based on at least the measured lung pressure (pressure curve p(t)), particularly the minimum lung pressure (full expiratory state) and the maximum lung pressure (full inspiratory state). The respiratory value can also be based on a combination of the measured gas volume flow rate and the measured lung pressure.

[0063] In particular, the pressure difference between the pleural cavity and the trachea is derived (calculated) based on measurements, especially the measured time-dependent inspiratory and / or expiratory gas flow rates and / or lung volume, and set as a boundary condition for the lung model. The derived (calculated) pressure difference may correspond to the measured gas flow rates. The respiratory values ​​characterizing mechanical ventilation can refer to pressure-controlled (application of pressure-time curves) or volume-controlled (application of flow-time curves) mechanical ventilation.

[0064] Preferably, a computational lung model can reproduce the gas flow in the airways during inspiration and expiration, based on respiratory values, and especially the (time-dependent) boundary conditions derived therefrom. In particular, it is preferable to achieve this based on a single (tomographic) image of the respiratory system, in combination with at least one (preferably measured) respiratory value that characterizes the respiration of the respiratory system.

[0065] In one embodiment of this method, the discretized respiratory system structure is based on spatial segmentation, which divides the structure of the respiratory system, preferably conductive airways, into a plurality of discrete, preferably three-dimensional, and more preferably tubular airway segments, and temporal discretization, preferably in time steps. Preferably, the gas flow velocity within at least a portion of the airway segments is constant in each time step.

[0066] The (three-dimensional) airway segments preferably include (tubular) airway walls (hollow cylinders), are preferably linear in the axial direction, and / or have axisymmetric cross-sections. The cross-section within each airway segment can be constant (straight cylindrical airway segments) or decrease (conical airway segments) in the downstream (inspiration) direction. Preferably, the discretized respiratory system structure represents the (whole) airway tree of the (human) lung, preferably including at least the (conducting) airways below the trachea, and optionally further including the oral cavity, pharynx, and / or laryngeal regions. Such a discretized respiratory system structure adequately models the anatomical structure of the airways of the (human) lung. Typically, in the medical literature, the airways of the human lung are divided into 23 generations. Within the range of conduction airways, each generation corresponds to the level of airway branching (i.e., n levels of airway branching correspond to n+1 airway generations).

[0067] Gas flow velocity can be modeled as a three-dimensional (3D flow) discretized velocity field in some airway segments (preferably lower-order airway generations, e.g., generation 0, 0-1, or 0-2). Other airway segments, particularly higher-order airways (e.g., generations higher than 0, 1, and 2), can be modeled as a dimensionally reduced, preferably zero-dimensional (0-dimensional flow, scalar velocity values) discretized velocity field. For lower-order airway generations (the first few generations), a computationally intensive 3D CFD simulation can be performed, while for higher-order airways (up to the final structurally decomposed airway generation), a computationally less intensive 0-dimensional simulation can be performed. In a preferred embodiment, the gas flow velocity in all airway segments within the discretized respiratory system structure is constant at each time step. Modeling (setting) the gas flow velocity within the airway segments as constant when calculating the spatial particle deposition distribution reduces computational cost.

[0068] In one embodiment of this method, the computational lung model represents the structure of the airway for at least six generations, preferably at least eight generations, more preferably at least ten generations, and even more preferably at least twelve generations. Preferably, the computational lung model further represents the oral cavity, pharynx, and / or larynx, and preferably further represents the alveoli of the lung. Preferably, the computational lung model represents transient airflow in the airway between the oral cavity and the lung, particularly between the oral cavity and the alveoli of the lung. Preferably, the computational lung model represents the closed volume of the airway, and / or the structural elasticity of the airway, particularly the structural elasticity of the airway wall and / or the structural elasticity of the lung tissue. Preferably, the computational lung model further represents the diaphragm and / or rib cage, which is achieved particularly by applying a volume-dependent force to the lung (which is a discretized respiratory system structure) as a boundary condition.

[0069] Computational lung models can decompose and / or model the airways of the lung (as a whole) by representing the elasticity (mechanical reaction force due to elasticity) of the airway walls and / or lung tissue. This means that the discretized airways encompass the closed volume through which aerosol particles are transported, thereby (automatically) also taking into account exhalation, particularly the exhalation of previously inhaled aerosol particles.

[0070] However, conventional computer-based lung models cannot account for the exhalation of aerosol particles. In particular, the computational lung model described in Patent Document 1 does not include narrower peripheral airways and alveolar regions because it discards airway generations with relatively low structural resolution. Therefore, this lung model represents the open volume of the airway (the state where the outflow is open in the inspiratory direction), and Patent Document 1 cannot account for the exhalation of previously inhaled aerosol particles. Inhaled particles that reach the final generation of the airway leave the computational domain and are "lost."

[0071] In one embodiment of this method, the computational lung model is based on individual discretized respiratory system structures derived from processed image data representing individual respiratory systems in healthy individuals or patients with lung disease. The computational lung model can represent the individual (patient-specific) respiratory system of a real individual (patient). In a preferred embodiment, the computational lung model is based on patient-specific discretized respiratory system structures derived from processed image data representing the respiratory system with individual lesions in a patient with lung disease. Such a lung model can be used to evaluate the effectiveness of lung drug delivery to that individual patient.

[0072] In another embodiment of this method, the computational lung model is based on processed image data, preferably discretized average discrete respiratory system structures derived from averaged processed image data, which preferably represent the average among multiple individual respiratory systems in at least one healthy individual and / or at least one patient with lung disease. Processed image data of multiple single (tomographic) images from different respiratory systems, i.e., different individuals, can be averaged using an image data averaging algorithm to obtain averaged processed image data. Then, based on the averaged processed image data, an average discretized respiratory system structure representing a (virtual) average respiratory system can be derived.

[0073] If a computational lung model represents an average healthy lung (derived from images of multiple different healthy lungs), the model can be used to evaluate the (potential) effectiveness of lung drug delivery in comparative studies of normal (average) healthy lungs. If a computational lung model represents an average diseased lung (derived from images of multiple diseased lungs), the model can be used to evaluate the (potential) effectiveness of lung drug delivery in representative studies of typical (average) diseased lungs. Such studies, for example, by applying this method to a digital cohort represented by the corresponding computational lung model, can significantly accelerate and / or reduce the cost of the medical approval process for orally inhaled and / or nasal drugs.

[0074] In one embodiment of this method, the computational lung model is based on a discretized respiratory system structure derived from processed image data representing at least one healthy respiratory system and at least one predetermined pathological image data pattern representing at least one region in the respiratory system, preferably a localized pathological change caused by a lung disease. The processed image data can represent at least one healthy respiratory system (or a portion thereof) and / or at least one pathological respiratory system (or a portion thereof). In particular, individual processed image data or averaged processed image data can be modified by an image data modification algorithm using at least one predetermined pathological image data pattern.

[0075] A defined pathological image data pattern represents a typical pathological change in a normal (healthy) lung (tomography) image that is identifiable as a pathological pattern in the (tomography) image, caused by a specific lung disease. Such pathological changes are, for example, localized changes in the airways (changes in airway diameter) or localized changes in lung tissue (histopathology), and may typically be observed in (tomography) images of patients with a specific lung disease.

[0076] A pathology image data pattern can be an instance (dataset) of (tomographic) image data representing a (local) cross-section of a (tomographic) image exhibiting a pathology pattern. Image data modification algorithms can modify, in particular, averaged image data using pathology image data patterns. For example, this modification can be done by adding pathology image data patterns as a data subset and / or by replacing a portion of the averaged image data with pathology image data patterns (data subset). A given pathology image data pattern can be obtained from a database containing a typical set of different pathology image data patterns, and is preferably collected for various different types of patients (e.g., lung disease, individual medical records, age, sex, habits, lung volume, previous treatments, other non-lung-specific medications).

[0077] Alternatively, the pathological image data pattern may be an instance (dataset) of structural data representing the pathological portion of the discretized respiratory system structure, extracted (as a data subset) from the average discretized respiratory system structure, which is derived from averaged image data representing the average of multiple respiratory systems from multiple patients suffering from a particular lung disease. Therefore, instead of modifying the (averaged) image data and deriving (locally) modified discretized respiratory system structures from it, it is also possible to (locally) modify the derived discretized respiratory system structure itself in accordance with (local) pathological changes.

[0078] If a computational lung model is modified to represent individual healthy lungs and based on an average lung model of diseased lungs, the model can be used to evaluate the (potential) effectiveness of lung drug delivery in a specific (healthy) individual when that individual develops the corresponding lung disease. If a computational lung model is modified to represent averaged individual healthy lungs and based on an average lung model of diseased lungs, the model can be used to evaluate the (potential) effectiveness of lung drug delivery in a typical healthy individual when that individual develops the corresponding lung disease. Such studies, for example by applying this method to a digital cohort of individuals represented by the corresponding computational lung model, could significantly accelerate and / or reduce the cost of the medical approval process for orally inhaled and / or nasal medications.

[0079] In one embodiment of this method, the computational particle transport and deposition model implements the Lagrangian method to track individual discrete particles transported in a gas flow. Specifically, it is based on modeling at least one physical force acting on each individual discrete particle, particularly gravity, flow resistance, buoyancy, and / or Brownian motion force. In particular, the transient motion (dynamic) and deposition of each individual discrete particle are tracked individually, preferably within each airway segment, preferably through inspiration and expiration. The implemented Lagrangian method is preferably a unidirectional coupled approach, meaning that the effect of transient gas flow in the airway on particles is considered, but the reverse is not. In particular, the direction of gravity in the computational particle transport and deposition model is set according to the spatial orientation of the respiratory system represented by the processed image data, preferably vertical or horizontal. The spatial orientation of the respiratory system preferably corresponds to the upright sitting (vertical) or supine (horizontal) posture of the individual (especially the patient). The effect of gravity in different directions on lung drug delivery can be evaluated on upright inhalation (and exhalation) compared to supine inhalation (and exhalation). Flow resistance is preferably calculated based on the density and / or viscosity of the aerosol's carrier gas (preferably air, helium, or oxygen). Buoyancy is preferably calculated based on the different densities of the aerosol particles and the carrier gas. Brownian motion is preferably calculated based on randomly generated velocity fluctuations, particularly when discrete particles are located within the alveolar region of the discretized respiratory system structure. The implemented Lagrangian method allows for tracking of individual aerosol particles throughout the airway tree and at any location within the lung tissue throughout inspiration and exhalation. Therefore, a more accurate, particularly local, spatial particle deposition distribution can be calculated, considering (all) individual particles within the computational domain.

[0080] In one embodiment of this method, the discretized particle transport velocity field, particularly within the range of three-dimensional airway segments, has a higher spatial dimension than the discretized gas flow velocity field, particularly within the range of three-dimensional airway segments, preferably within the range of at least a portion of the discretized respiratory system structure, and preferably within airway segments belonging to at least the second generation, more preferably at least the third generation, of the airways represented by the computational lung model. Preferably, the three-dimensional (3D flow) discretized particle transport velocity field within the range of each three-dimensional airway segment is calculated (reconstructed) based on a constant (0-dimensional flow) discretized gas flow velocity within the range of each three-dimensional airway segment, at least within a portion of the discretized respiratory system structure, and preferably the entire discretized respiratory system. In particular, the discretized particle transport velocity field is used as input (unidirectional coupled approach) to the computational particle transport and deposition model. The discretized gas flow velocity field is obtained from the computational lung model. By applying dimensionality reduction methods to the gas velocity field, computational costs can be reduced, and computational resources, particularly those needed to improve the spatial resolution of the airway tree, can be saved. Overall, it becomes possible to track individual particles throughout the entire set of fundamental elements while sufficiently resolving transient gas flows.

[0081] In one embodiment of this method, a computational particle transport and deposition model tracks individual discrete particles within a spatially three-dimensional airway segment by applying a three-dimensional particle transport velocity vector as the particle velocity. Preferably, the particle transport velocity vector is calculated based on a predetermined three-dimensional velocity distribution across the entire cross-section of the airway segment, preferably a constant gas velocity, obtained from a computational lung model of the airway segment. The gas velocity field is preferably constant (spatially) within each airway segment and discontinuous in particular between adjacent airway segments. Preferably, a normalized velocity distribution corresponding to laminar flow (Poiseuille flow, particularly parabolic) or turbulent flow (particularly considering a turbulent boundary layer at the airway wall) is multiplied by the (constant) gas velocity.

[0082] In one embodiment, the method further includes the step of seeding individual discrete particles into a gas stream in the airway, wherein the step is Assigning a seed position to each individual discrete particle in the inflow cross-section of a discretized respiratory system structure, preferably in the inflow cross-section of the trachea, and / or Preferably, this includes assigning a seed time to each individual discrete particle based on a determined aerosol flow rate that is time-dependent and, in particular, relative to the respiratory cycle. The step of seeding individual discrete particles preferably further includes assigning one or more of the following to each individual discrete particle: seed velocity, seed acceleration, particle density, particle diameter, particle shape, particle mass, and drag coefficient. Preferably, the seed position is Preferably, based on measured, pre-calculated, and / or randomly generated aerosol particle distribution data, the statistical spatial distribution of aerosol particles in an aerosol is obtained or derived. In computational simulations of the aerosol generation process and / or flow, the particle positions of individual discrete particles in the inhaler device are preferably calculated using a computational inhaler device model, and / or This is determined based on measuring the spatial distribution of aerosol particles in an aerosol.

[0083] The seeding of individual discrete particles can be based on the condition of a uniform spatial distribution in the time-averaged inflow cross-section (of the trachea). The seeding time is preferably a seeding time step, which is preferably derived from a predetermined seeding frequency or obtained from a predetermined series of seeding time steps. The calculated seed positions can be the result of particle positions as output of a 3D CFPD simulation of at least one generation of (larger, i.e., upper) airways, preferably the trachea. In particular, the positions of individual discrete particles as the result of a 3D CFPD simulation of the first (lower) generation of airways are assigned as seed positions in the inflow cross-section of the second (higher) generation (preferably the next generation) of the (still relatively large) airways of the discretized respiratory system structure. Specifically, particle positions obtained as the result of a 3D CFPD simulation in the upper section of the (larger) airways are used as input for a 0D particle simulation in the (remaining) lower section of the (smaller) airways.

[0084] In one embodiment of this method, the computational particle transport and deposition model determines the path of individual discrete particles traversing the airway bifurcation, preferably during exhalation and / or inhalation, by assigning particles to one of the downstream airway segments based on evaluating at least one geometric bifurcation criterion. The geometric bifurcation criterion is preferably based on the geometric relationship between the outflow cross-sectional area of ​​the upstream airway segment and the inflow cross-sectional area of ​​the downstream airway segment. Preferably, the geometric bifurcation criterion is evaluated based on the position of the particles, particularly their radial and / or circumferential positions in the outflow cross-section of the upstream airway segment.

[0085] The airway bifurcation specifically comprises one upstream airway segment and two downstream airway segments. In the inspiratory direction of the respiratory system, the airway bifurcation comprises one upstream airway segment and two downstream airway segments branching off from it. In the expiratory direction of the respiratory system, the airway bifurcation comprises two upstream airway segments that merge into one downstream airway segment.

[0086] Evaluating (purely) geometric branching criteria requires only minimal computation time. Especially during exhalation (reverse branching), the path of particles crossing the branch is directed to a common downstream airway segment, making the use of geometric branching criteria sufficient. Only the radial and circumferential positions of the particles need to be determined based on the geometric criteria.

[0087] In one embodiment of this method, a computational particle transport and deposition model uses at least one pre-calculated airway bifurcation scenario to determine the path of individual discrete particles traversing the airway bifurcation, preferably during inspiration. Preferably, each of the pre-calculated airway bifurcation scenarios is based on an evaluation of a previously performed, preferably three-dimensional, high-dimensional simulation, regarding particle transport in the gas flow traversing the airway bifurcation. The at least one pre-calculated airway bifurcation scenario is preferably obtained from an airway bifurcation library that stores a plurality of different pre-calculated airway bifurcation scenarios.

[0088] A pre-calculated inspiratory airway bifurcation scenario may assign particles in the upstream airway segment of the airway bifurcation to one of the two downstream airway segments of the airway bifurcation, particularly to specific cross-sectional locations in the inflow cross-section of the downstream airway segment. A pre-calculated expiratory airway bifurcation scenario may assign discrete particles in one of the two upstream airway segments of the airway bifurcation to the downstream airway segment of the airway bifurcation, and assign those discrete particles to specific cross-sectional locations in the inflow cross-section of the downstream airway segment.

[0089] The method may include a step of interpolating between a plurality, preferably two, pre-calculated airway bifurcation scenarios. In particular, the flow paths of individual discrete particles traversing an airway bifurcation having a specific bifurcation shape, preferably a specific bifurcation angle, may be derived from interpolating at least two (preferably two) pre-calculated airway bifurcation scenarios having different bifurcation shapes, preferably different bifurcation angles. The interpolation of pre-calculated airway bifurcation scenarios is preferably based on interpolating at least two (preferably two) bifurcation shape parameters and corresponding interpolating at least two (preferably two) cross-sectional positions in the inflow cross-section of the downstream airway segment.

[0090] Pre-calculated bifurcation scenarios are preferred to be used instead of (purely) geometric bifurcation criteria, but can also be used in addition to them. Pre-calculated bifurcation scenarios can be based on relatively complex pre-calculated three-dimensional fluid flow / particle transport simulations (3D CFPD: 3D Computational Fluid-Particle Dynamics) traversing the bifurcation with high spatial and temporal resolution, and it is particularly preferable to consider turbulence depending on the specific (type) geometry of the bifurcation. By using pre-calculated bifurcation scenarios, it is possible to accurately calculate the spatial particle deposition distribution while using only a gas velocity field with reduced dimensions (0-dimensional flow). In particular, the flow paths of individual particles traversing the bifurcation during intake can be predicted with accuracy comparable to 3D CFD simulations while suppressing computational costs.

[0091] In one embodiment, preferably in the embodiment described above, the pre-calculated airway bifurcation scenario predicts the transport of discrete particles across the airway bifurcation based on the classified type of bifurcation shape of the airway bifurcation and / or based on the position of discrete particles in the upstream airway segment, preferably in the radial division and / or circumferential division of the upstream airway segment. Preferably, the pre-calculated airway bifurcation scenario assigns a cross-sectional position to the discrete particles in the inflow cross-section of the downstream airway segment.

[0092] In one embodiment, preferably in either of the two embodiments described above, it is preferable that a suitable pre-calculated airway bifurcation scenario for determining the flow path of individual discrete particles traversing a particular airway bifurcation is selected from an airway bifurcation library based on at least one or more of the following: the location of discrete particles (preferably radial and / or circumferential) in the upstream airway segment, at least one geometric parameter indicating the bifurcation shape of the airway bifurcation, the particle diameter of the discrete particles, the particle density in the region of the airway bifurcation, and the particle velocity upstream of the airway bifurcation.

[0093] The geometric parameters are preferably one or more of the following: the branching angle between the two downstream airway segments, the rotational orientation of one or more downstream segments with respect to the central axis of the upstream airway segment, and / or the flow deflection angle between the central axis of the upstream airway segment and the intermediate axis between the two downstream airway segments.

[0094] In one embodiment of this method, a computational particle transport and deposition model determines the deposition of discrete particles based on the evaluation of at least one deposition criterion. Here, the evaluation of the deposition criterion preferably includes at least one or more of the following: evaluating whether the calculated distance of the discrete particle to the airway wall of the airway segment is less than or equal to a predetermined deposition distance; evaluating whether the calculated particle velocity of the discrete particle is less than or equal to a predetermined minimum velocity; and / or evaluating whether the calculated collision angle between the flow path of the discrete particle and the airway wall of the airway segment exceeds a predetermined minimum angle (preferably at least 45 degrees, more preferably at least 60 degrees, and even more preferably at least 75 degrees). Optionally, the deposition criterion depends on the shape of the discrete particle and / or the attractive force between the discrete particle and the airway segment. Two or more of the described deposition criteria can be combined. The deposition criteria are used to determine whether, if, an individual particle deposits within the airway segment, and where and when it does. Calculating the locations of all deposited particles based on the evaluation of at least one deposition criterion forms the basis for calculating the spatial particle deposition distribution.

[0095] In one embodiment of this method, the step of determining at least one aerosol value uses a computational inhaler device model that represents the aerosol generation process and / or flow in the inhaler device. The inhaler device preferably includes a (dry) powder inhaler device, a metered-dose spray inhaler device, a soft mist inhaler device, and / or an inhaler device equipped with a ventilation tube.

[0096] The computational inhaler device model is, Aerosol particle size distribution, Aerosol particle density, Preferably, the pressure at the outlet from the mouthpiece and / or ventilation tube of the inhaler device, and preferably the pressure of the aerosol carrier gas, which is time-dependent with respect to the respiratory cycle. Preferably, the aerosol flow rate at the outflow portion from the mouthpiece and / or ventilation tube of the inhaler device, preferably the aerosol flow rate having time-dependent properties with respect to the respiratory cycle, and Preferably, the flow velocity of the aerosol at the outlet from the mouthpiece and / or ventilation tube of the inhaler device, preferably a flow velocity that is time-dependent with respect to the respiratory cycle. The computational inhaler device model represents one or more of the following, and preferably depends on at least one or more of the selected device design parameters, the set device operating parameters, and the respiratory values ​​of the respiratory parameters that affect the aerosol generation process and / or flow in the inhaler device. The computational inhaler device model can be based on a CFD (Computational Fluid Dynamics) simulation, preferably 3D CFD, based on a structural model of the geometric shape of the inhaler device (or its components). The structural model is preferably obtained from a CAD (Computer-Aided Design) model. The device design parameters can represent the dimensions of the inhaler device, particularly those that characterize the fluid region (or its components) of the inhaler device.

[0097] The respiratory values ​​of the respiratory parameters may be boundary conditions of the computational inhaler device model, preferably the inhalation gas flow rate and / or inhalation suction pressure defined in the outflow section of the computational inhaler device model, and preferably have a time dependence with respect to the respiratory cycle.

[0098] In one embodiment of this method, the step of determining at least one aerosol value uses a computational inhaler device model that represents the aerosol flow in the inhaler device. The computational inhaler device model preferably represents at least one component of the inhaler device characterized by at least one device design parameter. The device design parameter is The shape of the mouthpiece and / or ventilation tube of the inhaler device, The diameter and / or shape of the nozzle of the inhaler device, The flow path shape from the outlet of the aerosol generation and / or aerosol storage chamber to the outlet of the inhaler device (preferably the outlet of the mouthpiece and / or ventilation tube), and The volume and / or shape of the container (preferably for containing liquid oral inhalation and / or nasal medications) It is preferable to indicate one or more of these.

[0099] Device operating parameters can indicate the (adjustable) settings of the inhaler device. Inhaler operating parameters, in particular: Preferably, a time-dependent pressure in a container for containing an orally inhaled drug (preferably a container configured to be received by a metered-dose inhaler device); The mass flow rate and / or volume flow rate of the carrier gas supplied to the inhaler device; Preferably, the spray velocity of the liquid oral inhalant being ejected in a nebulizer device, such as a baffle in a soft mist inhaler device; The vibration frequency of a nebulizer device, preferably a soft mist inhaler device; and Nebulizer device rotation speed and / or groove shape One or more of these will be selected.

[0100] Preferably, at least one device design parameter, respiratory parameter, and / or inhaler operating parameter influences the aerosol generation process and / or flow in the inhaler device and is represented by a computational inhaler device model. The specific parameters represented by the computational inhaler device model depend on the inhaler device used (e.g., (dry) powder inhaler device, metered-dose inhaler device, soft mist inhaler device, and / or inhaler device including a ventilation tube).

[0101] In one embodiment of this method, the efficacy parameters are preferably one or more of the following: a local effective dose; preferably the density of locally deposited aerosol particles; and preferably the local concentration of the active ingredient of the oral inhalation and / or nasal agent. The effective dose may be the amount of the active ingredient delivered to the lungs. The local effective dose may be the amount of the active ingredient delivered to a specific area of ​​the respiratory system, particularly an area within the lungs (e.g., a specific location (area) in the airway or lung tissue, left lung or right lung, lung lobe and / or airway generation).

[0102] The (local) effective dose or concentration of the active ingredient can be determined based on at least the calculated spatial particle deposition distribution and a predetermined dose (concentration and / or volume) of the active ingredient contained in each deposited particle. The density of deposited aerosol particles can be determined based on the calculated spatial particle deposition distribution, in particular as the number of deposited aerosol particles per (lung) volume and / or per (internal) airway surface. In particular, particles that are inhaled but subsequently exhaled again are not part of the spatial particle deposition distribution.

[0103] Efficacy parameters, particularly the (topical) effective dose, may be equivalent to the efficacy parameters, particularly the equivalent (topical) effective dose, of similar (generic) drugs, especially in relation to the original drug. The described method for evaluating efficacy in pulmonary drug delivery can be used to evaluate whether similar products, particularly generic drugs, containing the same active ingredient, have equivalent pulmonary drug delivery efficacy to the original drug. Therefore, using this method can accelerate and / or simplify medical management procedures.

[0104] In one embodiment of this method, the aerosol particles contain a predetermined dose of the active ingredient of an orally inhaled and / or nasal drug. The efficacy parameter may represent the blood concentration of the active ingredient in the blood. Preferably, the calculation of at least one value of the blood concentration uses a computational absorption model that represents the absorption of the active ingredient contained in the aerosol particles into the bloodstream. Alternatively or in addition, the efficacy parameter may represent the tissue concentration of the active ingredient in the lung tissue. Preferably, the calculation of at least one value of the tissue concentration uses a computational absorption model that represents the absorption of the active ingredient contained in the aerosol particles into the lung tissue. The computational absorption model is preferably based on pharmacometric modeling, which is described in more detail below. Since some drugs are intended to be delivered to lung tissue via the blood (plasma) circulation and / or lungs, particularly to obtain systemic effects in the body, it is useful to evaluate efficacy based on the concentration of the active ingredient in the blood and / or lung tissue.

[0105] In one embodiment of this method, the efficacy parameter represents the preferably time-dependent spatial concentration distribution of the active ingredient of the orally inhaled and / or nasal drug in the lung tissue of the respiratory system and / or the circulatory system. Preferably, at least one efficacy value is at least one preferably time-dependent concentration value of the active ingredient in the lung tissue and / or the circulatory system. Preferably, a computational absorption model is used to calculate at least one efficacy value, representing the absorption of the active ingredient contained in aerosol particles deposited in the respiratory system into the lung tissue and / or the circulatory system. Preferably, the preferably time-dependent first concentration value of the active ingredient in the lung tissue in the first region of the discretized respiratory system structure is different from the preferably time-dependent second concentration value of the active ingredient in the lung tissue in the second region of the discretized respiratory system structure. Preferably, the preferably time-dependent first concentration value of the active ingredient in the circulatory system in the first region of the discretized respiratory system structure is different from the preferably time-dependent second concentration value of the active ingredient in the circulatory system in the second region of the discretized respiratory system structure. A higher concentration of the active ingredient typically indicates greater efficacy of pulmonary drug delivery. This concentration value can indicate the (local) effective dose of the active ingredient, and in some cases, whether the (local) toxic concentration has been reached. By obtaining the spatial concentration distribution of the active ingredient, the efficacy of local pulmonary drug delivery can be evaluated, particularly to specific areas of interest in the lungs, preferably to lesions in the lungs to be treated with the drug (lung disease), or to areas where the drug is effectively absorbed into the bloodstream and reaches lesions outside the lungs (non-lung disease).

[0106] In one embodiment of this method, the spatial particle deposition distribution includes, preferably, an averaged subdomain-specific particle deposition distribution in multiple subdomains of the discretized respiratory system structure, and at least one efficacy value of at least one efficacy parameter is determined depending on at least one of the subdomain-specific particle deposition distributions. Preferably, each subdomain represents: a healthy or pathological region of the respiratory system; and / or at least a portion of the airways (particularly a particular generation of the (conduction) airways) or at least a portion of the respiratory airways (particularly the alveoli); and / or a particular lobe of the lung; and / or a region of the respiratory system adjacent to (or within a defined radius of) a tumor; and / or a (pathological) region of the lung subject to surgery (e.g., lobectomy); and / or a branch of the respiratory system affected by an intrabronchial valve (for COPD patients). Multiple subdomains can collectively represent regions of the lung (respiratory system) or divisions of regions of the entire lung (respiratory system). Subdomain-specific particle deposition distributions represent the particle deposition distribution in each of the multiple subdomains of the discretized respiratory system structure. Subdomain-specific particle deposition distributions in a particular subdomain preferably refer to a specific region of the lung (e.g., a specific lobe, left / right lung, specific airway generation, etc.). This allows pulmonary drug delivery to a specific subdomain of interest within the lung to be evaluated based on subdomain-specific efficacy values. Subdomain-specific particle deposition distributions (Ddist) can be derived from the spatial distribution of the density of deposited aerosol particles (particle size) in different regions of the discretized respiratory system, preferably by averaging across subdomains. In particular, using averaged subdomain-specific particle deposition distributions can reduce the computational resource requirements by considering drug absorption with a reduced number of subdomains.

[0107] In one embodiment of this method, the computational absorption model represents the absorption of the active ingredient contained in aerosol particles deposited in the respiratory system into the lung tissue and / or blood circulation, based on the spatial particle deposition distribution and at least one absorption value of at least one absorption parameter. The computational absorption model is preferably based on pharmacokinetic modeling. In particular, in addition to the absorption of the active ingredient in the lung tissue, the computational absorption model can further represent at least one of the following: mucociliary clearance, dissolution of the active ingredient in the intrapulmonary coating fluid, and preferably the transfer of the active ingredient from the lung tissue to the blood circulation. The intrapulmonary coating fluid is preferably the mucus of the conduction tract or the alveolar coating fluid of the respiratory tract. Pharmacokinetic (PK) modeling is more accurate, particularly with respect to the location in the lungs where the drug absorption process takes place. Therefore, local predictions with high spatial resolution for efficacy parameters such as the concentration of the drug (active ingredient) in various regions of the lungs are possible, even in various regions within the lung tissue (before the drug enters the systemic blood circulation). According to PK modeling, experiments that require administering orally inhaled and / or nasal medications to patients or healthy individuals to determine the dose that effectively reaches blood circulation by taking blood samples can be avoided.

[0108] In a preferred embodiment, at least one efficacy value is calculated using a physiologically constructed population model (PSPM) according to Non-Patent Document 9.

[0109] In one embodiment, at least one efficacy value may include the amount or proportion of the active ingredient removed from the lung by mucociliary clearance. In a further embodiment, at least one efficacy value may be the local concentration C of the active ingredient in the intrapulmonary covering solution. flu It may also include. In another embodiment, at least one efficacy value is the local concentration C of the active ingredient in the intrapulmonary covering solution. flu , and / or local concentration C of the active ingredient in lung tissue (intrapulmonary tissue) tis It may also include. In other embodiments, at least one efficacy value is the local concentration C of the active ingredient in lung tissue (intrapulmonary tissue). tis, and / or local concentration C of the active ingredient in systemic blood circulation sys It may include.

[0110] At least one absorption value for at least one absorption parameter is preferably measured or obtained from a database. Absorption values ​​obtained from a database can particularly indicate the physical properties of aerosols, active ingredients, and / or lung tissue. At least one absorption value may be calculated based on the analytical relationship of the physical properties of the active ingredients of orally inhaled and / or nasal drugs, and / or aerosol particles. In some cases, absorption values ​​such as gas flow rate are from or obtained from a computational lung model.

[0111] Geometric absorption parameters can be derived from the discretized respiratory system structure. The value of at least one absorption parameter may be the value of at least one aerosol parameter (e.g., particle size, particle density). The value of at least one absorption value may be a function of generation depth, the anatomical lung entity (as shown in Figure 2), the actual spatial location, or any combination thereof.

[0112] Typical absorption parameters include geometric particle size / volume (distribution), particle density (distribution), absorption rate, (effective / apparent) permeability, partition coefficient (lung / plasma, lung / free plasma, tissue), free / non-ionized fraction, (saturated) solubility, dissolution rate, diffusion coefficient, blood / plasma ratio, epithelial layer (ELF) depth, tissue depth, perfusion, metabolic kinetics, transport kinetics, drug degradation rate, and tissue binding constant.

[0113] In one embodiment of this method, preferably in the embodiment described above, at least one absorption parameter is Saturation solubility C of the active ingredient of an orally inhaled and / or nasal medication in an intrapulmonary covering solution s , The dissolution rate k of the active ingredient of an orally inhaled and / or nasal medication in the intrapulmonary lining fluid. diss Preferably, local maximum dissolution rate, The effective permeability Papp of the airway wall to the active ingredient of an oral inhalation and / or nasal drug The tissue-free plasma partition coefficient K indicating the distribution between the concentration of the active ingredient released in the lung tissue and the concentration of the active ingredient in the plasma pu,tis , The tissue-plasma partition coefficient K indicating the distribution between the concentration of the active ingredient in the lung tissue and the concentration of the active ingredient in the plasma p,tis indicates one or more of them.

[0114] Further parameters include particle density (preferably average particle density), particle diameter (preferably average particle diameter), airway radius r br , diffusion coefficient D, airway surface area SA, local perfusion / blood flow Q, or blood / plasma ratio BP in the lung.

[0115] In one embodiment of the method, at least one absorption value of at least one absorption parameter, particularly the saturation solubility C s , dissolution rate k diss , effective permeability P app , tissue-free plasma partition coefficient K pu,tis and / or tissue-plasma partition coefficient K p,tis The values are determined according to the predetermined pathological changes in at least one region of the respiratory system caused by the lung disease of the lung. The values of the absorption parameters may depend on the pathological conditions of the lung. For example, asthma causes thickening of the airway wall, mucociliary clearance is affected by lung diseases, and fibrotic lung tissue may have changed absorption characteristics. Thereby, a more accurate, particularly patient-specific evaluation of the effectiveness of pulmonary drug delivery becomes possible.

[0116] In one embodiment of this method, at least one absorption value of at least one absorption parameter is determined according to the spatial particle deposition distribution. The spatial particle deposition distribution preferably includes subdomain-specific particle deposition distributions in multiple subdomains of the discretized respiratory system structure. In particular, each subdomain represents: a healthy or pathological region of the respiratory system; and / or at least a portion of the conduction airway, particularly a specific generation of the airway or at least a portion of the alveoli; and / or a specific lobe of the lung. Preferably, at least one absorption value is determined according to the subdomains of the discretized respiratory system structure. As a result, the first absorption value of the absorption parameter in the first subdomain may differ from the second absorption value of the absorption parameter in the second subdomain. The value of the absorption parameter (e.g., dissolution rate) may vary depending on particle deposition (e.g., within the conduction airway region or within the alveolar region), particularly depending on the airway generation.

[0117] At least one absorption value may be obtained from in-vitro analysis (in-vitro experiment), particularly in-vitro measurements, especially those performed in a laboratory. For example, measurements of solubility and / or dissolvability, in-vitro tests, etc. In-vitro measurements may use actual or artificial human or animal lung tissue. At least one absorption value may also be obtained from a database that stores values ​​of absorption parameters obtained in vivo using one or more of the following: bronchial absorption, bronchial brushing, mucosal biopsy, bronchoalveolar lavage (BAL), or blood sampling. However, the methods according to the present invention do not involve any surgical procedures, and in particular are not methods for treating the body of a human or animal by surgical or therapeutic means.

[0118] In one embodiment of this method, at least one absorption value is determined by in vitro measurement, particularly using a microfluidic lung-on-chip device. Such a device mimics the mechanical and biochemical behavior of the (human) lung within a microfluidic device (lung-on-chip) using tissue engineering. By applying simulated airflow and / or blood flow, parameters related to the absorption process involving lung tissue can be measured. Lung-on-chip devices are generally known. Patent Document 4 discloses a technical embodiment of a microfluidic lung-on-chip device. Those skilled in the art can use a microfluidic lung-on-chip device to in vitro measure absorption values ​​of absorption parameters and evaluate the computational absorption model. In vitro measurement of absorption parameters has the advantage that it does not require the actual administration of drugs to patients or healthy individuals to determine the relevant absorption parameters. Furthermore, various parameters can be accurately measured under defined laboratory conditions.

[0119] In one embodiment, this method further: The steps include determining a predetermined minimum effectiveness value for an effectiveness parameter; A step of using at least one determined aerosol value as a starting value; A step of adapting aerosol values, preferably performed in an iterative loop in which at least some of the steps of any of the above methods are repeatedly performed until the calculated effectiveness value is equal to or greater than a predetermined minimum effectiveness value; The steps include storing the adapted aerosol values ​​in a storage device, outputting the adapted aerosol values ​​to a display device, and / or providing the adapted aerosol values ​​as optimized aerosol values ​​for aerosol parameters in use to evaluate efficacy for pulmonary drug delivery.

[0120] This method is repeatable and preferably controlled by an optimization algorithm. The ability to obtain optimized aerosol values ​​is a notable advantage of the method described herein, as it allows for the use of improved (optimized) effectiveness values ​​in the technical applications described herein.

[0121] In one embodiment of this method, at least one aerosol value is, in particular, when at least one efficacy value is greater than or equal to a predetermined minimum efficacy value of the efficacy parameter, and preferably less than a predetermined maximum efficacy value. A step of generating an aerosol containing aerosol particles containing an orally inhaled and / or nasal drug, wherein the aerosol is characterized by an aerosol value, and preferably using an inhaler device, A step of setting the operating parameters of an inhaler device so that the inhaler device generates an aerosol characterized by an aerosol value, and The process of designing and / or manufacturing an inhaler device configured to produce an aerosol characterized by an aerosol value, and A process for manufacturing orally inhaled and / or nasal drugs, preferably characterized by an aerosol value in which the aerosol parameter is particle size, average particle size, and / or particle size distribution, and preferably in the form of a dry powder, used in one or more of the processes for manufacturing orally inhaled and / or nasal drugs.

[0122] A predetermined minimum efficacy value can define a lower threshold for the (desired) efficacy of pulmonary drug delivery, in particular, such that a (positive and / or desirable) medical effect of the active ingredient is known or observed above that value (or no medical effect is known or observed below that value). A predetermined maximum efficacy value can define an upper threshold for the (desired) efficacy of pulmonary drug delivery, in particular, such that a (negative and / or undesirable) effect of the active ingredient, in particular (strong or undesirable) side effects or toxic effects, is known or observed above that value. Therefore, by using this method for the technical applications described, it is possible to provide aerosols, inhaler devices, and / or drug formulations having the desired efficacy in pulmonary drug delivery, particularly for causing or promoting the desired medical effect.

[0123] In one embodiment of this method, at least one effectiveness value is, in particular, greater than a predetermined minimum effectiveness value of the effectiveness parameter, and preferably less than a predetermined maximum effectiveness value. A process for evaluating the performance of an inhaler device in lung drug delivery, In particular, we evaluated the impact of the settings of the device operating parameters and / or design parameters of the inhaler device on the performance of pulmonary drug delivery. The inhaler device is configured such that, when operating with predetermined settings of the device operating parameters, it generates an aerosol for pulmonary drug delivery in a state characterized by the aerosol value of the aerosol parameter. The process of evaluating the performance of an inhaler device. A step of evaluating the efficacy and / or safety of oral inhalation and / or nasal agents in pulmonary drug delivery, particularly regarding the dosage of the active ingredient of the oral inhalation and / or nasal agent, Oral inhalation and / or nasal medications are preferably prepared to be administered using an inhaler device in the form of an aerosol characterized by the aerosol value of the aerosol parameter. A process for evaluating the efficacy and / or safety of orally inhaled and / or nasal drugs, A step in evaluating the efficacy of drug / device combination products of oral inhalation and / or nasal drugs and inhaler devices in pulmonary drug delivery, Oral inhalation and / or nasal medications are prepared to be administered using an inhaler device, particularly when operated with predetermined settings of the device operating parameters, in the form of an aerosol characterized by an aerosol value. The inhaler device is configured to generate an aerosol characterized by the aerosol value of the aerosol parameter, particularly when operating with predetermined settings of the device operating parameters. It is used in one or more steps in the process of evaluating the efficacy of drug / device combination products.

[0124] As described above, using this method for the described technical applications makes it possible to design and / or operate inhaler devices, manufacture drug products, or provide drug / device combination products with desired efficacy in pulmonary drug delivery. In particular, it makes it possible to induce or promote desired medical effects. Specifically, this method can be used to select appropriate combinations of orally inhaled and / or nasal drugs and inhaler devices as suitable (efficient and / or safe) pairs for drug / device combination products.

[0125] Another potential application is that the efficacy and / or safety evaluation methods described could be used to expedite and simplify the approval process by healthcare regulatory bodies for medical devices and drugs.

[0126] The present invention also relates to a method for evaluating the performance of an inhaler device in pulmonary drug delivery, wherein the inhaler device is configured to generate an aerosol containing aerosol particles containing orally inhaled and / or nasal drugs. The performance of the inhaler device in pulmonary drug delivery is evaluated based on the effectiveness of the aerosol for pulmonary drug delivery as evaluated according to any embodiment of the method described above. Preferably, the performance of the inhaler is determined based on the effectiveness of the aerosol generated by the inhaler in pulmonary drug delivery, depending on the design and / or operating parameters of the device. The effects and advantages of this method are similar to at least one of the methods described above.

[0127] The present invention also relates to a method for evaluating the efficacy and / or safety of orally inhaled and / or nasal agents in pulmonary drug delivery, wherein the orally inhaled and / or nasal agents are prepared to be administered in aerosol form, preferably using an inhaler device. The efficacy of the orally inhaled and / or nasal agents in pulmonary drug delivery is evaluated according to the efficacy of the aerosol in pulmonary drug delivery as evaluated according to any embodiment of the methods described above. The efficacy and / or safety of the agent can be evaluated by comparing it with predetermined minimum and / or maximum efficacy values. The effects and advantages of this method are similar to at least one of the methods described above.

[0128] The present invention also relates to a method for evaluating the efficacy of a drug / device combination product of an orally inhaled and / or nasal drug and an inhaler device in pulmonary drug delivery, wherein the inhaler device is configured to generate an aerosol containing aerosol particles containing the orally inhaled and / or nasal drug, and the orally inhaled and / or nasal drug is prepared to be administered in aerosol form using the inhaler device. The efficacy of the drug / device combination product in pulmonary drug delivery is evaluated based on the efficacy of the aerosol for pulmonary drug delivery as evaluated according to any embodiment of the methods described above. The performance of the inhaler is preferably determined based on the efficacy of the aerosol generated by the inhaler for pulmonary drug delivery, depending on the device design and / or operating parameters. The efficacy and / or safety of the drug product can be evaluated in comparison to predetermined minimum and / or maximum efficacy values. The effects and advantages of this method are similar to at least one of the methods described above.

[0129] At least one problem according to the present invention is solved by a computer program product. The program product includes instructions that cause a computer to perform at least some (preferably all) of the steps of the method described above when the program is executed by the computer. The computer program is executable on a distributed computer system consisting of a plurality of computers and / or servers connected via a wireless and / or wired network.

[0130] At least one problem of the present invention is solved by a computer-readable medium. The computer-readable medium stores instructions that, when executed by a computer, implement at least some (preferably all) of the steps of the method for evaluating the effectiveness of aerosols for lung drug delivery described above.

[0131] The present invention also relates to a method for generating an aerosol containing aerosol particles containing orally inhaled and / or nasal drugs using an inhaler device. A step of carrying out the method described in any of the above items, A step of providing an orally inhaled and / or nasal drug, in particular, a step of providing an orally inhaled and / or nasal drug when, based on efficacy evaluation by the method described above, at least one efficacy value is greater than or equal to a predetermined minimum efficacy value of the efficacy parameter, A step of providing an inhaler device, in particular, a step of providing an oral inhalation and / or nasal drug when, based on an efficacy evaluation by the method described above, at least one efficacy value is greater than or equal to a predetermined minimum efficacy value of the efficacy parameter, A process of supplying an orally inhaled and / or nasal drug to an inhaler device, A step of operating an inhaler device, preferably by setting the device operating parameters of the inhaler device based on the effectiveness evaluation by the method described above, and operating the inhaler device so that the inhaler device generates an aerosol characterized by the aerosol value of the aerosol parameter, particularly when at least one effectiveness value is greater than or equal to a predetermined minimum effectiveness value of the effectiveness parameter.

[0132] The effects and advantages of this method for generating aerosols are the same as those of the method described above. Furthermore, the generated aerosols can be used for testing inhaler devices (in the testing process, preferably as part of the design process). In particular, this can be done by analyzing the properties of the aerosol (preferably of aerosol particles) generated by the inhaler device, preferably by measuring at least one aerosol value of the aerosol parameters, and more preferably by comparing the determined aerosol value (used as input values ​​for simulation) with the measured aerosol value. The inhaler device under test is preferably a (dry) powder inhaler device, a metered-dose inhaler device, a nebulizer (soft mist) inhaler device, or an inhaler device equipped with a ventilation tube.

[0133] The present invention also relates to a method for designing and / or manufacturing an inhaler device configured to generate an aerosol containing aerosol particles containing a pharmaceutical product for oral inhalation and / or intranasal administration. A step of selecting at least one value of a device design parameter that characterizes an inhaler device, A step of carrying out the aforementioned method, preferably a step of determining at least one aerosol value, which involves carrying out the aforementioned method using a computational inhaler device model representing the aerosol flow in the inhaler device, A step of designing and / or manufacturing an inhaler device, which includes a step of designing and / or manufacturing the inhaler device such that it achieves a selected value of a device design parameter, in particular based on an effectiveness evaluation according to any of the preceding items, in particular when at least one effectiveness value is greater than or equal to a predetermined minimum effectiveness value of the effectiveness parameter.

[0134] A computational inhaler device model can represent at least one component of an inhaler device characterized by at least one device design parameter. The device design parameter in this method can be any one or more device design parameters described above with respect to the computational inhaler device model. The effects and advantages of this method are the same as those of at least one of the methods described above.

[0135] The present invention also relates to an inhaler device for administering orally inhaled and / or nasal medications, which is designed and / or manufactured by performing the steps of the aforementioned methods for designing and / or manufacturing inhalers and / or ventilators. The effects and advantages of this inhaler device are similar to those of at least one of the aforementioned methods.

[0136] In one embodiment, the inhaler device includes a ventilation tube for administering orally inhaled and / or nasal medications to a patient under mechanical ventilation. The ventilation tube preferably includes a first inlet for an aerosol flow and a second inlet for a ventilation gas flow generated by the ventilation device. Preferably, the ventilation tube further includes a common outlet for the aerosol flow and the ventilation gas (air) flow. The aerosol flow generated by the nebulizer device is supplied to (combined with) the ventilation gas flow generated by the ventilation device and together supplied to the patient via the ventilation tube. Such an inhaler device has the advantage of being able to administer aerosols of orally inhaled and / or nasal medications to mechanically ventilated patients who cannot use types of inhaler devices that require active breathing.

[0137] The present invention also relates to a method for producing a pharmaceutical formulation for oral inhalation and / or intranasal administration, preferably in the form of a dry powder. A step of carrying out the method described in any of the preceding items, preferably a step of carrying out the method, wherein the particle size characterizing the dry powder is selected as the determined aerosol value, A step of manufacturing an orally inhaled and / or nasal drug, preferably in the form of a dry powder, and particularly based on an efficacy evaluation of the aerosol characterized by an aerosol value determined according to any of the preceding items, the step of manufacturing an orally inhaled and / or nasal drug so that the orally inhaled and / or nasal drug is administered using an inhaler device in the form of an aerosol characterized by an aerosol value of an aerosol parameter, in which case at least one efficacy value is greater than or equal to a predetermined minimum efficacy value of the efficacy parameter.

[0138] Preferably, the particle size characterizing the dry powder corresponds to the (average) particle size of the solid particles of the dry powder. The (average) particle size of the dry powder is preferably less than 10 μm, more preferably 1 to 5 μm, and even more preferably 1 to 3 μm or 3 to 5 μm. Generally, particles of 1 to 3 μm tend to exert a systemic therapeutic effect in the body. This is because the relatively small particles also deposit in the alveolar region of the lungs and are preferably further carried into the bloodstream. Particles of 3 to 5 μm preferably have a local therapeutic effect in the lungs because the relatively large particles deposit (mainly) in the (conducting) airways and are less likely to reach the alveolar region.

[0139] The present invention also relates to orally inhaled and / or nasal drugs, particularly dried powders, obtained by carrying out the steps of a method for producing orally inhaled and / or nasal drugs. The effects and benefits of such drugs are similar to those of at least one of the methods described above.

[0140] The present invention also relates to a drug / device combination product comprising an orally inhaled and / or nasal drug and an inhaler device for administering the orally inhaled and / or nasal drug, wherein the drug / device combination product includes the aforementioned orally inhaled and / or nasal drug and / or the aforementioned inhaler device according to the present invention. The effects and advantages of such a drug / device combination product are similar to those of at least one of the methods described above. [Brief explanation of the drawing]

[0141] The present invention will be described in the context of further embodiments shown in the drawings.

[0142] [Figure 1] This diagram illustrates a method for evaluating pulmonary drug delivery. [Figure 2] A schematic diagram of the human respiratory system is shown. [Figure 3] Figure 1 shows a schematic diagram of the computational particle transport and deposition model used in the method described therein. [Figure 4] Detailed schematic diagrams of the computational particle transport and deposition models and the computational absorption models used in the method described in Figure 1 are shown. [Figure 5] A schematic diagram of a computational particle transport model that implements the Lagrangian method used in the method shown in Figure 1 is presented. [Figure 6] Figure 1 shows a schematic diagram of particle transport at the airway bifurcation during inspiration and expiration, as used in the method described in Figure 1. [Figure 7] Figure 6 shows a schematic diagram of an example of an airway branching library used for particle transport. [Figure 8] Figure 1 shows a schematic diagram of the computational particle deposition model used in the method described in Figure 1. [Figure 9] Figure 1 shows a schematic diagram of the pharmacological model used to determine the drug effect in the method described in Figure 1. [Figure 10a] A schematic diagram showing a pressurized metered-dose inhaler device and a corresponding computational inhaler device model is provided. [Figure 10b] A schematic diagram of a respiratory-operated metered-dose inhaler device and a corresponding computational inhaler device model is shown. [Figure 10c] A schematic diagram of a dry powder inhaler device and a corresponding computational inhaler device model is shown. [Figure 10d] A schematic diagram of a vibrating mesh nebulizer inhaler device and a corresponding computational inhaler device model is shown. [Figure 10e] A schematic diagram of a jet nebulizer inhaler device and its corresponding computational inhaler device model is shown. [Figure 10f] A schematic diagram of an ultrasonic nebulizer inhaler device and a corresponding computer-based inhaler device model is shown. [Figure 10g] A schematic diagram of a nebulizer inhaler device with a ventilation tube and a corresponding computational inhaler device model is shown. [Figure 11] This diagram illustrates various methods used to evaluate pulmonary drug delivery. [Figure 12] This diagram illustrates the absorption of the active ingredients of orally inhaled and / or nasal medications based on pharmacokinetic modeling. [Figure 13] A schematic diagram of a method for evaluating pulmonary drug delivery by determining the concentration of the active ingredient in lung tissue is shown. [Modes for carrying out the invention]

[0143] In the following explanation, elements having the same or similar effect will be given the same reference sign. In general, reference numbers with an apostrophe (') (e.g., 4', 40', 30') refer to discretized (modeled / calculated) quantities, while the corresponding reference numbers without an apostrophe (e.g., 4, 40, 30) refer to the corresponding (real) physical quantities.

[0144] The method shown in Figure 1 is suitable for evaluating the effectiveness of aerosol 3 (see, for example, Figure 2) for pulmonary drug delivery. Here, aerosol 3 comprises aerosol particles 30 containing orally inhaled and / or nasal drug 1. The (real) aerosol particles 30 are represented by discrete (modeled) aerosol particles 30'.

[0145] A (single) patient-specific tomographic image Img of the respiratory system 4 of a human individual is obtained by applying imaging techniques, such as performing a (single) CT scan on a specific respiratory system 4. Other imaging techniques besides computed tomography (CT), particularly magnetic resonance imaging (MRI), ultrasound, X-ray, or electrical impedance imaging (EIT), can also be used. The (human) individual may be healthy or a patient with lung disease. The tomographic image Img is processed using a segmentation process known in the prior art to generate processed image data representing the respiratory system 4. Next, a discretized respiratory system structure 4' is derived from the processed image data. A computational lung model Lmod (lung model) is generated and calibrated based on the discretized respiratory system structure 4'. Appropriate boundary conditions for the lung model Lmod are derived based on the respiratory value Rval of the respiratory parameter Rpar, which characterizes the respiration of a human individual. More details of the lung model Lmod are described later with reference to Figure 2.

[0146] The discretized respiratory system structure 4' is based on spatially segmenting the structure of the airway 40 of the respiratory system 4 into multiple discrete spatially three-dimensional (tubular) airway segments 40'. The discrete airway segments 40' can be so-called finite elements in FEM discretization (FEM: finite element method). In the next step, the transient gas flow Gvel within the airway 40 modeled by the multiple discrete airway segments 40' is calculated, preferably as a zero-dimensional flow, in a dimensionality-reduced flow simulation.

[0147] Next, using a unidirectional coupled approach based on the transient gas flow velocity Gvel, the spatial particle deposition distribution Ddist is calculated using the computational particle transport and deposition model Pmod (particle transport and deposition model) for multiple individual discrete particles 30' deposited in the airway 40, particularly in the lungs 42. Inhalation and exhalation of individual particles 30' are considered by the particle transport and deposition model Pmod. Each individual discrete particle 30' is seeded in the computational domain and tracked individually according to the Lagrangian method through the entire airway tree of the discretized respiratory system structure 4'.

[0148] Multiple discrete particles 30', individually tracked through the airway segment 40', represent aerosol particles 30 of an aerosol 3 containing the active ingredient of an orally inhaled and / or nasal drug 1, particularly preferably administered for the treatment of a lung disease. The orally inhaled and / or nasal drug 1 is preferably but not limited to being intended for the treatment of a lung disease and may particularly include cannabis (THC as the active ingredient).

[0149] At least one aerosol value Aval of at least one aerosol parameter Apar that characterizes aerosol 3 is preferably measured and obtained from specification data or a database of the inhaler devices 2, 2a-d that generate aerosol 3, and determined. The aerosol value Aval can also be calculated based on analytical relationships relating to the physical properties of aerosol 3, or as a result of computational simulation of the generation process and / or flow of aerosol 3 in the inhaler devices 2, 2a-d. A computational inhaler device model Dmod can be used for such simulations (see Figure 3, and especially Figures 10a-g). Typical aerosol parameters include (mean) particle size, preferably (mean) particle size distribution, particle density, and / or aerosol flow rate (mass flow rate or volumetric flow rate), which are preferably time-dependent with respect to the respiratory cycle of an individual human (patient-specific).

[0150] In the post-processing step, at least one efficacy value Eval of the efficacy parameter Epar is calculated based on the spatial particle deposition distribution Ddist. In the example in Figure 1, the efficacy parameter Epar represents a group of various parameters including the particle deposition rate in the target area of ​​the lung (Eval=12%), the average particle diameter [μm] in the target area (Eval=2.2μm), the standard deviation of particle diameter in the target area [μm] (Eval=1.5μm), and the amount of absorbed drug [mg] in the target area (Eval=0.01mg). Various types of efficacy values ​​Eval and other specific examples will be described later with reference to the examples.

[0151] The efficacy value Eval can be used to automatically evaluate the effectiveness of pulmonary drug delivery. The efficacy value Eval can be stored, displayed, and / or transmitted for further use in various technical applications. In particular, the efficacy value Eval can be used to evaluate the efficacy and / or safety of orally inhaled and / or nasal drugs 1, or to evaluate the performance of inhaler devices 2, 2a-d for pulmonary drug delivery (e.g., during the design or testing process of inhaler devices 2, 2a-d).

[0152] Oral inhalation medications may include examples of active ingredients described below. Currently, many different formulations of oral inhalation medications are approved by regulatory authorities for oral inhalation. Many existing end-consumer medications are based on different active ingredients for different diseases. These active ingredients can be classified into the following main groups according to their primary pharmacological group: Inhaled bronchodilators are used to treat obstructive airway diseases such as asthma and COPD. These subgroups of inhaled bronchodilators include β2 adrenergic receptor agonists (e.g., active ingredients epinephrine, albuterol, terbutaline, revalbuterol, fenoterol, aformoterol, formoterol, indacaterol, olodaterol, and salmeterol) and muscarinic acetylcholine receptor antagonists (e.g., active ingredients ipratropium, tiotropium, glycopyrronium, aclidinium, umeclidinium, and rebefenacin); anti-inflammatory products include glucocorticoids for treating inflammation of lung tissue, for example, in asthma. Active ingredients include beclomethasone dipropionate, budesonide, ciclesonide, flunisolide, fluticasone furoate, fluticasone propionic acid, and mometasone furoate. Furthermore, combination products that combine the aforementioned active ingredients to treat COPD and asthma are also included; anti-infective drugs are used for infections such as tuberculosis, influenza, pneumonia, measles, and severe acute respiratory syndrome. Active ingredients include tobramycin, aztreonamlysinate, levofloxacin, colismethate sodium, amikacin, ribavirin, and pentamidine; recombinant star deoxyribonuclease (rhDNase) is a glycosylated 260-amino acid protein used to treat cystic fibrosis; mannitol is a non-ionic sugar alcohol used to increase the periciliary fluid layer in dry powder form. This drug induces coughing, thereby clearing the airways in bronchiectasis and cystic fibrosis; prostacyclin is used for pulmonary arterial hypertension (PAH) and contains iloprost and treprostinil as active ingredients; pulmonary surfactants are administered by oral inhalation, particularly to treat respiratory distress syndrome (RDS) in infants.Pulmonary surfactants are extracted from animal lungs or synthesized; systemic therapeutic drugs are used to treat systemic diseases, not limited to purely respiratory diseases. Active ingredients may include nicotine, roxapine, levodopa (for the treatment of Parkinson's disease), or insulin (for the treatment of diabetes).

[0153] Figure 2 shows the structure of the human lung. Below, we will use this figure to explain the computational lung model Lmod in more detail.

[0154] Typically, in medical literature, the airways of the human lung are divided into 23 generations (see Figure 3), comprising the trachea 46 (0th generation), bronchi 47 including the main bronchus (1st generation), lobar bronchi (2nd generation), segmental bronchi (3rd and 4th generations), and segmental subbronchus (5th to 11th generations), bronchioles 48 (12th to 19th generations) including the terminal bronchioles (16th generation) and respiratory bronchioles (17th to 19th generations), and alveoli 49 including the alveolar ducts (20th to 22nd generations) and alveolar sacs (23rd generation). Usually, the 1st to 16th generations are classified as conduction airways, and the 17th to 23rd generations are classified as respiratory airways. Within the scope of conduction airways, each generation corresponds to a level of airway branching. The lungs 42 are located within the thoracic cavity 8 and above the diaphragm 43.

[0155] More specifically, the (human) airway 40 is divided into the trachea 46 and the bronchial system, and the bronchial system is divided into the right and left main bronchi (main bronchi), each supplying oxygen to one of the two lungs. Each bronchial trunk further branches into smaller bronchi (secondary bronchi): the right main bronchus usually divides into three main branches, supplying the usual three lobes of the right lung. The left main bronchus usually divides into two main branches, supplying the usual two lobes of the left lung. These five main branches form the so-called lobar bronchi, which further branch into segmental bronchi, and then into increasingly thinner branches (generations). Through approximately 22 stages of branching (i.e., 23 generations), a system of extensively branched bronchial trees is thus formed.

[0156] This system of 47 bronchi can be obtained from CT image datasets and can be converted or segmented into a 3D structure dataset using, for example, an artificial intelligence-based image recognition algorithm. This makes it possible to provide a lung model that constitutes the model shape of the lung.

[0157] As the bronchi narrow, their internal structure becomes simpler and more narrow. The bronchioles, the thinnest branches of the bronchi, have an inner diameter of less than 1 mm. Therefore, CT resolution is insufficient to spatially resolve and represent these structures. Relatively lower-order airways are segmented directly from CT data, while higher-order airways are generated using a space-filling algorithm, as described, for example, in Non-Patent Document 10. Airways (trachea and bronchial trees) are generated recursively from generations where segmentation from CT data becomes impossible, or earlier generations, and this continues until the peripheral airways reach a length termination criterion (e.g., 1.2 mm), a radius termination criterion (e.g., 0.2 mm), or a generation termination criterion (e.g., Ngen=17). The radius scaling from juvenile to parent branch at the left-right branching of the bronchial tree is 0.876 and 0.686, respectively, as is known from morphological studies of the human body. Radial scaling, airway direction, and airway length are adaptable to functions of CT data that are spatially assigned to them in order to map the heterogeneity of the lung. Lower-order respiratory tracts segmented based on CT data are connected to higher-order respiratory tracts generated using a space-filling algorithm. At the ends of the generated airway tree, so-called terminal units are created to model the remainder of the airway system, which can consist of alveoli 49, alveolar ducts, alveolar sacs, and / or parts of bronchioles 48. These terminal units together form what is referred to herein as lung tissue 6.

[0158] In the following, a lung model is understood as a digital (i.e., computer-implemented) human lung model suitable for simulating the physiological function of the human lung. This may be a patient-specific lung model Lmod based on the patient's CT data. Alternatively, the lung model Lmod may be based on an evaluation of CT data from a patient group, or generally on an evaluation of lung data from a database. A patient-specific lung model Lmod can be calibrated for the patient, for example, by using the patient's actual respiratory (volume) curve to calibrate the lung model.

[0159] The output variables of a simulation based on the lung model Lmod include pressure P and flow rate Q in each airway segment 40', but may also include lung tissue strain and / or surface activators on the alveolar surface.

[0160] The lung model Lmod considers (i) an airway 40 consisting of a trachea 46 and bronchi 47 and bronchioles 48, (ii) terminal units, and preferably (iii) terminal unit interactions considering viscoelastic junctions between adjacent terminal units. When inhalation occurs, the lung volume increases compared to when it does not, extending the alveoli 49. In this case, adjacent alveoli 49 are connected in their expansion by lung tissue 6 that connects them to each other.

[0161] The terminal cluster connecting elements model the interactions between adjacent terminal units and between terminal units and respiratory ducts. These terminal cluster connecting elements connect mutually influencing terminal units (and respiratory ducts) in pairs or groups. This interaction arises from volume competition between adjacent alveoli / alveolar sacs within the lung tissue 6. The resulting mutual influence is realized by additional forces on the terminal units (and respiratory ducts) connected to the terminal unit connecting elements. This allows multiple terminal units to be stretched even when pressure is applied only to subpleural terminal units. In other words, the pleural pressure boundary condition applies only to terminal units that are actually adjacent to the pleural space.

[0162] The lung model Lmod reflects the three-dimensional geometric structure of the patient's lungs. For this purpose, a dataset (discrete respiratory system structure 4') that represents the three-dimensional structural shape of the patient's lungs is loaded into the lung model Lmod as an input variable.

[0163] In one embodiment, instead of the above, the dataset may be averaged across the structural shapes of multiple patients. For example, an averaged structural dataset (averaged discrete respiratory system structure 4') can be generated in patients with a specific pre-existing lung disease and loaded into a lung model Lmod to evaluate the effectiveness of drug delivery to that particular lung.

[0164] The lung model Lmod employs an approach that expresses the pressure difference ΔP along the airway segment 40' as a linearly dependent variable of the flow rate Q and flow resistance R through the respiratory duct channel. That is, ΔP = Q * R.

[0165] The details and mathematical explanation of the lung model Lmod are clearly stated in Patent Document 3 and the documents cited therein.

[0166] The Lmod lung model represents a complete human lung, complete with elastic airway walls 50 and lung (alveolar) tissue 6. Therefore, the Lmod lung model also reproduces the expiratory phenomenon by considering the elastic reaction force associated with lung compression during exhalation.

[0167] Figure 3 shows, in more detail, the spatial particle deposition distribution Ddist calculated based on the computational particle transport and deposition model Pmod.

[0168] The computational inhaler device model Dmod represents the aerosol flow in the mouthpiece 20 (discretized mouthpiece 20') of inhaler devices 2, 2a-c. The computational oral cavity model Mmod then reproduces the aerosol flow in the regions of the oral cavity 41, pharynx 44, and larynx 45. Dmod and Mmod can be based on a CFD model that considers particle transport, preferably a three-dimensional CFD model.

[0169] This method includes the step of seeding individual discrete particles 30' into the gas flow in the airway 40. Seeding particles can be understood as the initialization of (new) particles input into the computational domain. As will be discussed later, it is important that particle transport is represented and solved in three-dimensional space. At discrete time points, individual discrete particles 30' are seeded by initializing at least one Lagrangian particle 30' with corresponding position, velocity, acceleration, density, mass, drag coefficient, radius or shape information and corresponding dimensions in the lung model domain (at least one location therein), preferably in the inflow cross-section of the trachea 46. The number of particles seeded, as well as their individual properties and the timing of seeding, can be determined in several different ways to match the actual aerosol properties at the seeding location. For example, the values ​​may be obtained from one (or any combination) of the following: a list of directly specified values, measured values ​​obtained from experiments, values ​​determined as a result of other computer models and simulations (e.g., including aerosol generation processes and / or flows and / or upper airways), values ​​determined as solutions to analytical models (e.g., including aerosol generation processes and / or flows and / or upper airways), and randomly generated values ​​that satisfy the statistical distribution obtained by any of the above options.

[0170] For illustrative purposes, two specific use cases will be described in more detail. Given a time-dependent aerosol mass fraction, a time-dependent particle size distribution (e.g., a log-normal distribution using the diameter of the median mass and the geometric standard deviation, experimentally measured for an inhaler), a constant particle density, and the spherical shape of the particles, a collection of discrete particles 30' is generated by randomly assigning diameters extracted from the log-normal distribution and then assigning / calculating the remaining properties such as density, mass, and drag coefficient. Particle generation for the discrete time interval currently under consideration is stopped as soon as the target aerosol mass is achieved in that time interval. The specific seed position of each particle within the cross-section of the tracheal inlet (i.e., the inlet) is randomly selected based on the assumption that particles are evenly distributed per unit cross-sectional area.

[0171] Figure 4 shows the particle transport and deposition of aerosol particles 30 at the alveolar level 49 using the computational particle transport and deposition model Pmod. The aerosol particles 30 contain a predetermined dose of the active ingredient of the orally inhaled and / or nasal drug 1, which can be absorbed into the blood circulation 5 in the alveoli 49. The computational absorption model Amod is evaluated to consider the absorption into the blood circulation 5 of the active ingredient contained in the aerosol particles 30 deposited in the alveoli 49, i.e., in the terminal units comprising the alveoli 49. The computational absorption model Amod is preferably based on pharmacological modeling, particularly a first-order reaction, the Michaelis-Menten reaction, or a parallel model of a first-order reaction and the Michaelis-Menten reaction. Pharmacological modeling allows for the evaluation of drug concentration and drug effect at any time point (e.g., in plasma). The efficacy parameter Epar can be an index indicating the blood concentration (e.g., [mg / ml]) of the active ingredient in the blood.

[0172] Figure 5 shows how three-dimensional particle transport within the airway is represented by a zero-dimensional fluid field. This allows for reduced computational load by conserving computational resources while considering the high spatial resolution of the airway tree. A key advantage of this method is achieved by utilizing three-dimensional particle dynamics in the Lagrangian form to model aerosol transport through airway 40. The fluid field information necessary to calculate the coupling forces for discrete particles 30' (e.g., drag Df, buoyancy Bf, gravity Gf, and optionally Brownian motion BMf) is obtained by reconstructing the three-dimensional fluid field from a dimensionally reduced fluid field solution. The computational particle transport and deposition model Pmod implements the Lagrangian method to track individual discrete particles 30' transported by the gas flow Gvel. The Lagrangian form is a unidirectional coupled approach, meaning that the effect of transient gas flow within the airway on particles is considered, but the reverse is not.

[0173] The reconstructed velocity distribution Velprof is based on the assumption of a radially symmetric, parabolic velocity distribution in the cylindrical airway segment 40' (see Figure 5). Given the radius of the airway segment and the volumetric flow rate and pressure at the inlet and outlet from the 0-dimensional fluid solution, the velocity and pressure (as well as their gradients and other derivations) at any point in the 3-dimensional domain can be calculated.

[0174] Alternatively, dimensionality reduction from 3 dimensions to 0 dimensions and vice versa can be achieved by using other assumptions, such as conical airways or different velocity distributions. However, the concept of dimensionality reduction of the fluid field, solving the problem of the reduced dimensions, and reconstructing the fluid field in all dimensions are similarly maintained for the purpose of calculating the forces acting on the transported particles.

[0175] Figure 6 illustrates the method for particle movement between individual airway segments 40', 40a', 40b', and 40c'. Because the 0-dimensional fluid field is discontinuous at the boundaries of the individual airway segments 40', 40a', 40b', and 40c', this specialized method is necessary for the movement of discrete particles 30' across airway bifurcations. When simulating discrete particles 30' in a 0-dimensional fluid consisting of individual airway segments 40' during inhalation, movement begins when the particles flow out of the upstream airway segment 40a'. Discrete particles can be simulated using a purely geometric method (geometric bifurcation criterion) or the bifurcation library Lib. bifurc Using additional information obtained from the interpolation method, the particles are moved to either the downstream airway segment 40b' or 40c'. Similarly, during exhalation, when particles are expelled from either the downstream airway segment 40b' or 40c', movement in the reverse direction occurs.

[0176] Figure 7 shows the airway bifurcation library Lib, which stores multiple pre-calculated airway bifurcation scenarios. bifurc This shows the creation process of the airway bifurcation library Lib. bifurc This can be used for the movement of discrete particles 30' across airway bifurcations. This airway bifurcation library Lib bifurcThis includes pre-calculated particle transport information obtained from numerous pre-calculated 3D CFPD simulations. These 3D CFPD simulations include various different flow modes, bifurcation geometry, and aerosol parameters Apar. This generates different pre-calculated airway bifurcation scenarios (Bifurc1, Bifurc2, Bifurc3, ... Bifurc). The pre-calculated bifurcation scenarios can be based on relatively complex pre-calculated 3D simulations of bifurcation crossings with high spatial and temporal resolution, and preferably can also take into account turbulence in different geometries of various bifurcations. The pre-calculated bifurcation scenarios are preferentially used for more complex bifurcation scenarios in the inspiratory flow direction, while purely geometric bifurcation criteria can be used in the (reverse) expiratory flow direction.

[0177] Figure 8 is an illustrative diagram of two typical particle deposition mechanisms that occur throughout the lung region.

[0178] According to the first deposition mechanism, a deposition model based on different deposition criteria is evaluated for each individual particle 30. This deposition model is preferably a submodel (subroutine) of the computational particle transport and deposition model Pmod. These deposition criteria include comparing the distance value Wval between the individual particle 30' and the airway wall 50' of the airway segment 40' with a predefined deposition distance Dval. If the distance value Wval is smaller than the deposition distance Dval, deposition occurs. Further deposition criteria may include the current particle velocity Pvel, the collision angle between the discrete particle 30' and the airway segment 40', the shape of the discrete particle 30', or the attractive force between the discrete particle 30' and the airway segment 40'.

[0179] The second deposition mechanism occurs when discrete particles 30' reach the end of the terminal airway segment 40'. These particles 30' deposit in the associated terminal unit, which represents an individual alveolar 49 or a larger area within the lung.

[0180] The spatial particle deposition distribution Ddist is calculated based on all deposited particles within the range of a discretized respiratory system structure 4', which comprises an airway segment 40' representing the airway 40 and terminal units representing the alveoli 49.

[0181] Figure 5 (Figure 9) shows a pharmacological model for calculating drug blood (plasma) concentration as an example of determining drug efficacy. The results of particle transport and deposition simulations using Pmod, particularly the spatial particle deposition distribution Ddist, can be used as input to subsequent pharmacological models to evaluate drug exposure, i.e., drug concentration at any given time (e.g., in plasma), and drug effect. In particular, by using a pharmacokinetic (PK) model, it is possible to correlate locally deposited drug amounts with the time course of drug concentrations measured in the body (usually in plasma).

[0182] The method described uses a compartmentalized PK model comprising an input model and a kinetic model. The input model describes the time course over which the drug moves from the deposition region or subdomain to the drug measurement side (e.g., into plasma). The kinetic model, on the other hand, describes the time course over which the drug is distributed in the body, metabolized, and excreted. A preferred modification is schematically shown in Figure 9.

[0183] Based on the calculated spatial deposition distribution Ddist in the lung, the doses Dsub1, Dsub2, ... Dsubn of the active ingredient for any subdomain Lungsubd1, Lungsubd2, ... Lungsubdn in the lung model can be calculated. These subdomains may be based on the spatial segmentation of the discretized structure of the airway system 4' (e.g., peripheral and central regions) or on airway generation (e.g., conduction and respiratory airways). Absorption in each lung subdomain Lungsubd1, Lungsubd2, ... Lungsubdn or compartment can be modeled by the kinetics of a primary reaction, the Michaelis-Menten reaction, or a primary reaction with the Michaelis-Menten reaction added in parallel. Similarly, systemic kinetics can be modeled by one or more compartments, namely the central compartment Centcomp and peripheral compartments Pcomp1, Pcomp2, as shown in Figure 9. Mucociliary clearance Muccl can be modeled by one or more independent primary removal processes.

[0184] The parameter CL of a PK model, consisting of at least one absorption half-life (tMuccl, tsub1, tsub2) and one or more clearances (CLdcomp1, CLdcomp2), can be calculated using state-of-the-art methods for population PK models (as outlined in Non-Patent Literature 11). This calculation preferably uses measurement data regarding the time course of the drug's plasma concentration.

[0185] Figures 10a to 10g show various inhaler devices 2, 2a to d capable of generating an aerosol 3 containing an orally inhaled and / or nasal drug 1 containing the active ingredient of the drug. The aerosol 3 contains a carrier gas 31 and aerosol particles 30 that can be inhaled by a person. Figures 10a and 10b show a metered-dose inhaler 2a, Figure 10c shows a dry powder inhaler 2a, Figures 10d to 10f show nebulizer inhaler devices, and Figure 10g shows a nebulizer device 2d with a ventilation tube 24 for mechanical ventilation.

[0186] The generation process and flow of aerosol 3 in each inhaler device 2, 2a-d (inhaler) are represented by the corresponding computational inhaler device model Dmod. The value Aval of the aerosol parameter Apar, which characterizes the aerosol 3 generated in each inhaler device 2, 2a-d, can be determined by simulating the generation and flow of aerosol 3 using the computational inhaler device model Dmod. For example, the aerosol flow in inhaler devices 2, 2a-d can be calculated using 3D CFD simulation on a discretized region of the shape of inhaler device 2 and its components (e.g., obtained from a CAD model). The calculated aerosol value Aval is then used as input to the computational particle transport and deposition model Pmod to determine the effectiveness of aerosol 3 in pulmonary drug delivery.

[0187] The computed inhaler device model Dmod may depend on one or more of the inhaler design parameters Ddpar and / or inhaler operating parameters Dopar. These may vary depending on the type of inhaler device 2, 2a to d. The respiratory value Rval, which represents respiratory parameters Rpar such as the gas flow rate of inhalation and / or the suction pressure of inhalation, can be defined as a boundary condition for the computed inhaler device model Dmod at the outflow portion of the inhaler device 2, i.e., the outflow opening of the mouthpiece 20, i.e., the discretized mouthpiece 20' of model Dmod.

[0188] Figure 10a shows a metered-dose inhalation device 2b (inhaler), specifically a pressurized metered-dose inhalation inhaler (pMDI). The inhaler 2b comprises a container 21 and an actuator 27. The container 21 contains a substance containing an orally inhaled drug 1 (liquid) and a propellant 26, which is under pressurization. Furthermore, the container 21 is provided with a valve 29. The actuator 27 comprises an actuator seat 28, a nozzle 22, and a mouthpiece 20. When the container 21 (cartridge) is inserted into the actuator 27, the valve 29 is inserted into the actuator seat 28. As the container 21 is further pushed toward the actuator 27, the valve 29 is pressed against the actuator seat 28, thereby activating the valve 29, which releases all or part of the drug 1 (medicine) in the form of aerosol particles 30 through the nozzle 22. The aerosol particles 30 are delivered from the actuator 27 through the mouthpiece 20.

[0189] Figure 10b shows a metered-dose inhalation device 2b (inhaler), specifically a respiratory-operated metered-dose inhalation inhaler (BAMDI). The inhaler 2b comprises a container 21 and an actuator 27. The container 21 contains a substance containing an oral inhalation drug 1 (liquid) and a propellant 26, which is under pressurization. The container 21 is provided with a valve 29. The actuator 27 comprises a lever 60, a first spring 61, a second spring 62, a vane 63, an operating mechanism 64, an actuator seat 28, a nozzle 22, and a mouthpiece 20. When the lever 60 is in the ready-to-operate state, the first spring 61 is pulled, pushing the container 21 into the operating mechanism 64. When negative pressure, particularly an inhalation pressure Rval, is applied to the mouthpiece 20, the vane 63 is pulled in the direction of the mouthpiece 20, thereby activating the operating mechanism 64. When the actuation mechanism 64 is activated, the valve 29 is pressed against the actuator seat 28, which activates the valve 29, causing at least a portion of the drug 1 to be released from the nozzle 22 in the form of aerosol particles 30. The aerosol particles 30 are delivered from the actuator 27 through the mouthpiece 20. While the actuation mechanism 64 is operating, the second spring 62 is under tension. When the application of negative pressure to the mouthpiece 20 ends, the second spring 62 releases the actuation mechanism 64, and the release of aerosol particles 30 stops.

[0190] By changing several device design parameters Ddpar of the metered-dose inhaler device 2b, drug delivery to specific areas of the lungs can be improved. For example, the diameter and / or shape of the nozzle 22, and / or the distance from the nozzle 22 to the mouthpiece 20, affect the particle size distribution. The pressure inside the container 21 is a device operating parameter Dopar that affects the ejection rate of drug 1.

[0191] Figure 10c shows a dry powder inhaler device 2a (DPI), which can be configured as a single-dose DPI, a multi-unit DPI, or a multi-dose DPI. The inhaler 2a comprises a container 21 and an actuator 27. The container 21 contains an orally inhaled drug 1 in the form of a dry powder. The pressurized container 21 can also contain a propellant 26. The actuator 27 comprises an actuator 71, a spring 61, a metering mechanism 70, and a mouthpiece 20. When the actuator cap 71 is pressed, the metering mechanism 70 is activated, and at least a portion of the drug 1 is released from the container 1 as solid aerosol particles 30 and sprayed through the mouthpiece 20.

[0192] The effectiveness of the dry powder inhaler device 2a in pulmonary drug delivery can be influenced by adjusting the particle size of the pre-loaded dry powder. The shape of the mouthpiece 20 is a device design parameter Ddpar that can affect the flow rate of the aerosol 3, and this also affects the velocity of the particles 30 during inhalation.

[0193] Figure 10d shows a nebulizer inhaler device 2c (inhaler), specifically a vibrating mesh nebulizer (VMN). The inhaler 2c can be configured as an actively vibrating mesh nebulizer or a passively vibrating mesh nebulizer. The inhaler 2c comprises a reservoir (here, container 21) filled with an orally inhaled medication 1, a mesh 80 (or membrane), and a mouthpiece 20. The mesh 80 can be connected to a power source (not shown). When the mesh 80 vibrates, a portion of the medication 1 is atomized and released from the mouthpiece 20 as a result of particles 30 forming droplets. The mouthpiece 20 can be formed as an inhalation mask through which the patient can inhale through the mouth and / or nose.

[0194] Figure 10e shows another type of nebulizer inhaler device 2c (inhaler), specifically a jet nebulizer (JN). The inhaler 2c comprises an aerosol generation chamber 23 containing a liquid oral inhalation drug 1 and a mouthpiece 20. A gas source (not shown) for the carrier gas 31 forms a high-pressure gas jet 82 that flows into the aerosol generation chamber 23 from the bottom, generating droplet-shaped aerosol particles 30. The aerosol particles 30 are released from the aerosol generation chamber 23 together with the carrier gas 31 and pass through the mouthpiece 20 as a flow of aerosol 3. The inhaler is equipped with a baffle 81 to divert larger aerosol particles, thereby generating an aerosol 3 consisting of relatively small particles (droplets) 30.

[0195] Figure 10f shows another type of nebulizer inhaler device 2c (inhaler), specifically an ultrasonic nebulizer (UN). The inhaler 2c comprises an aerosol generation chamber 23 containing an orally inhaled drug 1 (medicine) and a mouthpiece 20. When a vibrating component 83 vibrates, at least a portion of the drug 1 is sprayed into aerosol particles 30. The vibrating component may include or consist of a piezoelectric crystal. The aerosol particles 30 are released from the generation chamber 23 through the mouthpiece 20. The inhaler 2c may further include a baffle 81 to deflect larger aerosol particles 30 and prevent leakage from the generation chamber 23.

[0196] The effectiveness of nebulizer inhaler devices 2c, also known as soft mist inhalers (SMIs), in lung drug delivery can be improved by adapting, and especially optimizing, specific device operating parameters (Dopar, SCU), such as the operating frequency of the vibrating mesh nebulizer (VMN) or ultrasonic nebulizer (UM). Furthermore, in the design process of the inhaler device 2c, appropriately selecting device design parameters Ddpar, such as the shape of the mouthpiece 20 and / or baffle 81 and the dimensions of the aerosol generation chamber 23, can influence the aerosol parameter Apar and improve the effectiveness of the inhaler 2c in lung drug delivery. The surface tension and / or viscosity of certain liquid drugs 1 may also affect the aerosol generation (spraying) process, and consequently affect the properties of the resulting aerosol 3, particularly the size (diameter) of the particles 30.

[0197] The nebulizer inhaler device 2c may further comprise an aerosol storage chamber and an inhalation valve and / or an exhalation valve. The aerosol storage chamber is capable of collecting and temporarily storing the volume of aerosol already generated while the patient exhales. The exhalation valve may be integrated into the mouthpiece 20.

[0198] In a rotating disc inhaler (not shown), drug delivery efficiency may be influenced by setting the rotation speed of the disc as the device operating parameter Dopar, and / or selecting an appropriate groove shape on the rotating disc as the device design parameter Ddpar.

[0199] Figure 10g shows an inhaler device 2d with a ventilation tube (24) for administering oral inhalation and / or nasal medication to a patient on a ventilator. The ventilation tube has a first inlet for an aerosol flow and a second inlet for a ventilation gas (air or oxygen) flow generated by a ventilation device (not shown). The ventilation tube 24 has a common outlet 24c for the mixed aerosol / ventilation flow. The aerosol 3 is generated by a nebulizer device 25. The nebulizer device 25 may operate according to the aerosol generation principle of either the nebulizer inhaler device 2c or any other type of nebulizer described above. The ventilation gas flow takes in the aerosol from the outlet of the nebulizer device 25. The settings of the ventilation device that determine the ventilation gas flow are device operating parameters that affect the aerosol flow at the common outlet. The nebulizer device 25 is incorporated into the ventilation tube 24, which is inserted into the patient's respiratory system for mechanical ventilation. The aerosol flows into the patient's respiratory system from the outlet of the ventilation tube. Such an inhaler device 2d allows for the administration of oral inhalation and / or nasal medication 1 to patients who are on a ventilator and are unable to breathe (inhale) actively.

[0200] Figure 11 illustrates various methods, including method steps, for evaluating pulmonary drug delivery, particularly according to the method shown in Figure 1. Similar to the method described with respect to Figure 1, an inhaler device 2 is used to generate an aerosol 3 from an orally inhaled drug 1 (e.g., a liquid or dry powder to be atomized). The aerosol 3 consists of liquid (liquid particulate matter) or solid (powder particles) aerosol particles 30 and a carrier gas 31. The aerosol parameter Apar represents the physical properties of the aerosol 3. A tomographic image Img (e.g., CT scan) of the respiratory system 4 of an individual (human), including the lungs, forms the basis for the computational lung model Lmod. The respiratory value Rvar of the respiratory parameter Rpar, which characterizes the respiratory behavior (respiratory characteristics) of the respiratory system 4, is derived from measurements of the individual's (patient's) respiratory curve. The respiratory curve is a time curve of the inspiratory and expiratory air volumes during a complete respiratory cycle of the individual (patient). Measurements are preferably performed using a spirometer. Depending on the structure of the inhaler device 2, the computational device model Dmod is derived. Dmod is parameterized, i.e., it depends on the device design parameter Ddpar and / or the device operation parameter Dopar. The computational device model Dmod is used to simulate the generation and / or flow of aerosol 3, which contains individual discrete aerosol particles 30'.

[0201] The aerosol value Aval of the aerosol parameter Apar is determined based on a simulation of the inhaler device 2 using the computational device model Dmod. Alternatively (see dashed arrow Aavl in Figure 11), instead of, or in addition to, modeling the aerosol flow in the inhaler device 2, one or more aerosol values ​​Aavl can be determined by other means, for example, by measuring the aerosol parameter Aapr of aerosol 3. For example, optical and / or aerodynamic methods are preferably applied to measure the particle size or particle size distribution of aerosol 3. For example, a laser aerosol spectrometer or an aerodynamic particle size analyzer can be used. The aerosol value Aval can also be obtained from a database containing data showing the physical properties of aerosol 3, or from the specification data of the inhaler device 2 and the type of aerosol 3 it generates (particularly depending on the settings of specific inhaler operating parameters).

[0202] Based on the gas flow velocity Gvel in the discretized airway 40' calculated using the lung model Lmod, the computational particle transport and deposition model Pmod calculates the spatial particle deposition distribution Ddist, which is preferably the local spatial density of aerosol particles deposited in different regions of the respiratory system 4 (particularly the lungs). Based on the spatial particle deposition distribution Ddist, one or more values ​​Eval of the efficacy parameter Epar are calculated. The efficacy parameter Epar preferably represents the locally effective dose of drug 1, for example, the blood concentration of the active ingredient of drug 1 in the blood circulation 5, or the tissue concentration in the lung tissue 6. Such a blood concentration Eval can be calculated based on Ddist using an additional absorption model Amod. A predetermined minimum efficacy value Eval_min can define a lower threshold for desired efficacy in pulmonary drug delivery, above which the desired medical effect of the active ingredient is observed. A predetermined maximum efficacy value Eval_max can define an upper threshold for desired efficacy in pulmonary drug delivery, above which undesirable effects, particularly undesirable side effects or toxic effects, are observed. Preferably, with respect to the aerosol value Aval suitable for pulmonary drug delivery, Eval is located between Eval_min and Eval_max.

[0203] The efficacy of oral inhaled drug 1 in pulmonary drug delivery, particularly the efficacy of the active ingredient contained in oral inhaled drug 1, is evaluated automatically, for example, using a computer, based on an efficacy value Eval. In particular, Eval can be stored in a storage device (not shown), displayed using a display device (not shown), and / or transmitted via a transmitting device (not shown), for example by mobile communication, particularly in local or remote computer networks, for use or further processing in another location.

[0204] In one embodiment, the efficacy value Eval can be used to produce an orally inhaled drug 1 by, for example, producing a dry powder having an average particle size corresponding to Aval when the value of Aval is suitable for pulmonary drug delivery based on the evaluation of Eval. The particle size characterizing the dry powder corresponds to the average particle size of the solid particles in the dry powder of drug 1. The formulation of drug 1 may contain additives in addition to the active ingredient.

[0205] In yet another embodiment, efficacy evaluation can be used in the manufacture, design, and / or operation of the inhaler device 2 so that it produces an aerosol 3 having an aerosol parameter Apar corresponding to Aval, in particular by appropriately selecting design parameters (e.g., nozzle diameter) so that the inhaler device 2 produces an aerosol 3 characterized by the Aval value.

[0206] In addition to the advantages of the method described above, this method is also applicable to the evaluation of drug / device combination products 7, which combine an orally inhaled drug 1 with inhaler devices 2, 2a-d. An example of a drug / device combination product 7 is a nebulizer inhaler device 2c equipped with a container 21 pre-filled with liquid orally inhaled drug 1. Drug 1 is atomized by the inhaler device 2c to produce an aerosol 3 with a desired particle size optimal for pulmonary drug delivery, according to an evaluation based on Eval. In particular, in the early stages of development and regulatory approval procedures for novel drug / device combination products, the method shown in Figure 11 can provide reproducible digital evidence that a novel drug / device combination product 7 based on the same active ingredient as an approved drug will result in the same effective pulmonary dose and a similar spatial aerosol particle deposition distribution. For example, by defining a digital patient cohort using multiple different patient-specific lung models Lmod and then conducting a computer study according to the described method, digital biomarkers can be calculated to predict or optimize target effects, dose ranges, or population limitations.

[0207] If the described methods are approved by regulatory authorities, they can be applied to late-stage clinical trials and provide supplementary or auxiliary information in the regulatory approval of drug products. Furthermore, given a candidate group of inhaler devices 2, the optimal inhaler device 2 can be selected in combination with a given drug 1 to achieve drug delivery to a specific lung target area. Information for modifying a given inhaler device 2 or associated inhalation protocol can also be provided.

[0208] Figure 12 shows the absorption of the active ingredient 32 of an orally inhaled and / or nasal drug 1 based on pharmacokinetic modeling. The detailed diagram of the airway wall 50 illustrates the pharmacokinetic absorption model that forms the basis of the computational absorption model Amod. While other absorption models, such as the pharmacometric model mentioned earlier (see Figure 9), are based on empirical rules, the pharmacokinetic absorption model models the physical laws of pharmacokinetics (PK) to explain the process by which the active ingredient 32 (pharmaceutical active ingredient: API) contained in aerosol particles 30 deposited in a portion of the airway 40 passes through the airway wall 50 and then enters the blood circulation 5.

[0209] The airway wall 50 shown in Figure 12 comprises the following layers: intrapulmonary lining fluid 51 (mucus for conduction airways or alveolar lining fluid in the alveolar space), lung tissue (intrapulmonary tissue) 6 (alveolar tissue or conduction airway tissue), and (systemic) blood circulation 6 (plasma). The computational absorption model Amod models how aerosol particles 30 deposited in the respiratory system 4, and the active ingredients 32 (molecules) contained therein, are absorbed in the lung tissue 6 and (systemic) blood circulation 5. The computational absorption model Amod can model the process by which the deposited particles 30 are transported within the range of the airway tree to the oral-pharyngeal region by mucociliary clearance 52. The computational absorption model (Amod) shown in Figure 12 represents the processes of mucociliary clearance 52, dissolution of the active ingredients 53 in the intrapulmonary lining fluid 51, absorption of the active ingredients 54 in the lung tissue 6, and transfer of the active ingredients 55 from the lung tissue 6 to the blood circulation 5. The lung tissue 6 can be further divided into one or more sublayers, including the epithelium, interstitium, basement membrane, and endothelium. The computational absorption model Amod can also model the absorption of active ingredients from the oral-pharyngeal region via the gastrointestinal tract into the systemic blood circulation 5. In the computational absorption model Amod, the airway 40 is represented by the airway segment 40' of the discretized respiratory system structure 4' and the discretized airway wall 50', and the absorption process is based on the dose of active ingredient 32 contained in the deposited discretized particles 30'.

[0210] The computational absorption model Amod of this embodiment calculates at least one efficacy value Eval of at least one efficacy parameter Epar based on the particle deposition distribution Ddist and at least one absorption value Sval. For example, the efficacy parameter Epar may be the spatial concentration distribution Cdist of the active ingredient 32 of the orally inhaled drug 1 in the lung tissue 6, and preferably has time dependence. One or more efficacy values ​​Eval, Eval1, Eval2 may preferably be time-dependent local concentration values ​​Cval1, Cval2, Cval3 of the active ingredient 32 in a specific region of the lung tissue 6, and C tis It is also written as follows. Alternatively or in addition, the efficacy parameter Epar and its efficacy value may preferably indicate the time-dependent (local) concentration of the active ingredient 32 in the blood circulation 5.

[0211] The following describes a pharmacokinetic model of various physical side processes involved in the absorption of the active ingredient in the lungs, as realized in one embodiment of the computational absorption model Amod. The computational absorption model Amod represents mucociliary clearance 52, dissolution 53 of the active ingredient 32 (contained in solid / liquid aerosol particles 30) in the intrapulmonary coating fluid 51, absorption 54 of the active ingredient 32 in the lung tissue 6, and transfer 55 of the active ingredient 32 from the lung tissue 6 to the blood circulation 5. This embodiment is based on a physiologically structured population model (PSPM) in accordance with Non-Patent Literature 9. The effectiveness value Eval of the effectiveness parameter Epar is calculated based on the spatial particle deposition distribution Ddist and the absorption values ​​Sval, Sval1, and Sval2 of at least one absorption parameter Spar. The geometric absorption parameter Spar can be derived from the discretized respiratory system structure 4'. Other absorption parameters Spar can be obtained from measured values ​​and / or databases.

[0212] The spatial particle deposition distribution Ddist may comprise, preferably averaged, subdomain-specific particle deposition distributions (i.e., local spatial particle deposition distributions) in multiple subdomains Lungsubd1, Lungsubd2, ..., Lungsubdn of the discretized respiratory system structure 4'. The effectiveness values ​​Eval, Eval1, Eval2 of one or more effectiveness parameters Epar are determined according to the particle deposition distribution of at least one subdomain. Different subdomain-specific particle deposition distributions Ddist can be determined and used for each subdomain. The subdomain-specific particle deposition distribution Ddist can be derived from the density spatial distribution of aerosol particles (particle size) deposited in different regions of the discretized respiratory system 4', preferably by averaging across the subdomain (this is also referred to as the "contracted" particle deposition distribution). The subdomain-specific (local) particle deposition distribution is derived from the (global) particle deposition distribution Ddist obtained based on Pmod (see the method described in Figure 1).

[0213] In a preferred embodiment, each subdomain Lungsubd1, Lungsubd2, ..., Lungsubdn represents a specific generation (generation depth x) of the (conduction) airway 40, particularly a specific generation of airway segment 40', or down to the alveolar region of the alveolus 49. For example, typically, the (average) deposited discretized particles 30' in the lower-order airway generation subdomains have a larger particle size, while the (average) deposited discretized particles 30' in the higher-order airway generation subdomains have a smaller particle size. Other subdomain-specific particle deposition distributions Ddist can be derived for subdomains belonging to specific lobes of the lung 42 or to healthy or pathological regions of the discretized respiratory system 4'.

[0214] In a particularly preferred embodiment, the reduced particle deposition distribution Ddist in the subdomain of the conduction airway 40' includes the generation depth x (i.e., the airway generation in which the particle is located) and the geometric particle volume s. The reduced particle deposition distribution Ddist in the subdomain of the alveolar region includes the (averaged) geometric particle volume s of the particles 30' deposited therein.

[0215] In a preferred embodiment of the pharmacokinetic computational absorption model Amod, the mucociliary clearance process is modeled according to the following equation:

[0216]

number

[0217] In this embodiment, the process of dissolving the active ingredient 32 into the intrapulmonary covering solution 51 53 is modeled according to the following equation.

[0218]

number

[0219] In this embodiment, the process of absorption 54 from the intrapulmonary coating liquid 51 to the lung tissue (intrapulmonary tissue) 6 is modeled according to the following formula.

[0220]

Equation

[0221] In this embodiment, the process of absorption 55 of the active ingredient from the lung tissue 6 to the systemic blood circulation 5 is expressed according to the following model.

[0222] [Number] In the above formula, local perfusion / blood flow Q (usually in units of L / h), blood / plasma ratio BP in the lung (usually unitless), local concentration C of the active ingredient 32 in the lung tissue 6 tis (usually M = mol / L, μM = 10 -6 mol / L or nM = 10 -9 mol / L units), lung / plasma partition coefficient K p,tis (usually unitless), and local concentration C of the active ingredient 32 in the systemic blood circulation 5 sys (usually M = mol / L, μM = 10 -6 mol / L or nM = 10 -9 mol / L units) are used. Here, "local" means being in one or more of the alveolar region, the conducting airway region, or within a specific generation depth x. In this preferred embodiment, at least one absorption parameter Spar includes the local perfusion / blood flow Q, the blood / plasma ratio BP in the lung, and the lung / plasma partition coefficient K p,tis At least one effectiveness parameter Epar can include the local concentration C of the active ingredient 32 in the lung tissue 6 tis (also denoted as Cval1, Cval2, Cval3), and / or the local concentration C of the active ingredient 32 in the systemic blood circulation 5 sys can be included.

[0223] In other embodiments, the computational absorption model Amod can also model the drug absorption process directly on the overall (global) spatial particle deposition distribution Ddist, i.e., without using the averaged (reduced) subdomain-specific particle deposition distribution.

[0224] Figure 13 shows a method for evaluating the effectiveness of aerosol 3 for pulmonary drug delivery using the computational absorption model Amod. The particle deposition distribution Ddist is obtained using the method described in Figure 1 (where "..." in Figure 13 indicates steps in Figure 1 described above). At least one absorption value Sval of at least one absorption parameter Spar, which characterizes the absorption properties of aerosol 3 in the lung 42, is determined and used when determining at least one value Eval, Eval1, Eval2 of one or more effectiveness parameters Epar. In the embodiment shown in Figure 13, the effectiveness parameter Epar is the time-dependent spatial concentration distribution Cdist(t) of the active ingredient 32 of the orally inhaled and / or nasal drug 1 in lung tissue 6. The effectiveness values ​​Eval, Eval1, Eval2 are the time-dependent concentration values ​​Cval1(t), Cval2(t), Cval3(t) of the active ingredient (32) in different subdomains Lungsubd1, Lungsubd2, and Lungsubd3 of the lung tissue 6. The first time-dependent concentration value Cval1(t) for the active ingredient 32 in the lung tissue 6 in the first region (Lungsubd1) of the discretized respiratory system structure 4' is different from the second time-dependent concentration value Cval2(t) for the active ingredient 32 in the lung tissue 6 in the second region (Lungsubd2) of the discretized respiratory system structure 4'.

[0225] In the first application of this method, the effectiveness of pulmonary drug delivery can be evaluated (predicted) for patients with lung diseases affecting lung tissue, based on the spatial concentration distribution Cdist of the active ingredient 32 in lung tissue 6. This is because lung tissue 6 is the site where the drug should be delivered to obtain the greatest therapeutic effect. In the second application of this method, the effectiveness of pulmonary drug delivery can be evaluated (predicted) for patients with diseases affecting body parts other than the lungs (i.e., not lung diseases), based on the spatial concentration distribution Cdist of the active ingredient 32 in the blood circulation 5. This is because drug administration via the lungs is a suitable (efficient) method for drug delivery to the body (e.g., more efficient than injection or transdermal therapy). In the third application of this method, the spatial concentration distribution Cdist of the active ingredient 32 can be used to evaluate (predict or monitor) the toxicity level (concentration peak) of the drug in the lungs, particularly lung tissue 6 and / or blood circulation 5.

[0226] In one embodiment, the effectiveness value Eval may be a function of at least one other (post-processed) effectiveness value. Typical functions include the area under the curve (AUC) of the time-resolved concentrations (e.g., Cval1(t), Cval2(t), Cval3(t)) (within a defined time interval; usually in nM·h units), and the maximum value of the time-resolved concentration (usually M=mol / L, μM=10⁻¹⁰). -6 mol / L or nM = 10 -9 (in mol / L units), time T to reach maximum concentration after administration of drug 1. max These include (usually in minutes), peak sharpness at time-resolved concentrations (ratio of maximum concentration to AUC), and (time-resolved) pulmonary retention rate of the active ingredient (usually in percent). The efficacy value Eval may be dose-normalized (divided by the mass of the administered dose).

[0227] The absorption value Sval of the efficacy parameter Spar may be further calculated by taking into account additional properties of the lung model Lmod. Examples of properties of the lung model Lmod that can be included in the calculation of at least one efficacy value Eval include the (local) volume of the intrapulmonary covering fluid 51, the (local) volume of the lung tissue 6, and / or the (local) volume of the blood, for converting the concentration values ​​per unit volume of the active ingredient in any layer of the airway wall 50 to absolute (molar) mass. The absorption value Sval may also be determined based on simulation results including (but not limited to) spatially dependent values ​​such as pressure P, blood flow velocity Q, strain of the lung tissue 6, and / or surface activators on the alveolar surface. At least one absorption value Sval may also be determined based on spatially dependent results of a blood flow simulation.

[0228] Generally, at least one efficacy parameter Epar may be: the global, local, or functional concentration of the active ingredient 32; the global, local, or functional area under the curve (AUC) of the active ingredient concentration; the global, local, or functional peak sharpness of the concentration (ratio of maximum concentration / AUC); the global, local, or functional intrapulmonary retention volume of the active ingredient; or the global, local, or functional pulmonary selectivity of the active ingredient 32 (ratio of intrapulmonary concentration / systemic concentration). In this context, "global" refers to the entire lung, "local" refers to the parameter value in a given spatial region of interest (AOI) (subdomain of interest) within the lung 42, and "functional" refers to the parameter value in a functional unit (FU) of the lung. A functional unit FU can be understood as a subdomain of the lung 42 having a specific function. The AOI may be one or more of the left / right lung, selected lobes of the lung 42, regions classified as healthy / pathological, central / peripheral regions of the lung 42, one or more anatomical lung elements shown in Figure 2, or any combination thereof. For example, the functional unit (FU) may be one or more of the airway 40, intrapulmonary lining fluid 51, mucus, alveolar lining fluid, lung tissue 6, epithelium, interstitium, basement membrane, endothelium, systemic blood circulation 5, plasma, or any combination thereof. At least one efficacy value Eval may be used to evaluate the efficacy and / or toxicity of the active ingredient 32.

[0229] The computational absorption model Amod may further consider at least one absorption value Spar, which is a function of the spatial location of particles 30' absorbed within the lung. The absorption value Sval is determined based on the spatial particle deposition distribution Ddist, in particular, the subdomain-specific particle deposition distribution in multiple subdomains Lungsubd1, Lungsubd2, ..., Lungsubdn of the discretized respiratory system structure 4'. The absorption value Sval may also be determined based on the spatially dependent pathological state of the lung. The pathological state of the lung can be understood as a predetermined pathological change in at least one region of the respiratory system 4 caused by a lung disease in that lung. For example, the pathological state may result from emphysema or fibrosis in a specific pathological region of the patient's lung. Examples of pathological lung states that may affect the absorption process include airway 40 narrowing due to asthma, decreased mucociliary clearance in the diseased airway, or changes in blood flow in the diseased region.

[0230] The computational absorption model Amod is applicable to modeling the chemical and / or biological dissolution of aerosol 3 containing one or more intermediate substances. Amod is applicable to modeling the dissolution and absorption of lipid nanoparticles (LNPs). The absorption model Amod can model the perfusion mechanism within the lungs.

[0231] Examples of efficacy values ​​Eval for different aerosol values ​​Aval and respiratory values ​​Rval, obtained according to the described method based on different patient-specific lung models Lmod, are summarized in the tables of Examples 1 and 2 below.

[0232] Example 1 In the study of Example 1, the aerodynamic mass median diameter of particle 30 of aerosol 3 was selected as the aerosol parameter Apar, and two different aerosol values ​​Aval (3 μm and 6 μm, see left column) were used. Based on the calculated spatial particle deposition distribution Ddist, the effectiveness value Eval (see right column) of the effectiveness parameter Epar was calculated.

[0233] Ratio of aerosol mass deposited in lung tissue to inhaled aerosol mass (Epar):

[0234] [Table 1]

[0235] Ratio of aerosol mass deposited in conducting airways to inhaled aerosol mass (Epar):

[0236] [Table 2]

[0237] Ratio of aerosol mass deposited in each airway generation to the total deposited aerosol mass (Epar). According to the following table, it is deduced that the remainder of the deposited aerosol mass has migrated to airway generations higher than 16, particularly to the level of alveoli 49, and / or to the blood circulation 5.

[0238] [Table 3]

[0239] Ratio of aerosol mass deposited in the lung to inhaled aerosol mass (Epar):

[0240] [Table 4]

[0241] Ratio of deposition between central and peripheral regions (Epar). Here, "central" refers to the central region of the lung, and "peripheral" refers to the lung region located around the outer boundary of the central region. Regarding the structure of the airways, the central region is mainly composed of the lower generations of the conducting airways (i.e., relatively large airways), while the peripheral region is mainly composed of the remaining higher generations (i.e., smaller airways):

[0242] [Table 5]​

[0243] The ratio of aerosol mass deposited per lung lobe to total aerosol mass (Epar), for example, the amount deposited in the upper lobe (upper left) of the left lung (42):

[0244] [Table 6]

[0245] Mean deposition mass concentration in the lungs [μg / ml] (Epar):

[0246] [Table 7]

[0247] Example 2 In the study of Example 2, lung tidal volume [ml] was selected as the respiratory parameter Rpar, and two different respiratory values ​​Rval (600 ml and 1000 ml) were used. Based on the spatial particle deposition distribution Ddist calculated for a specific aerosol parameter Apar and aerosol value Aval (e.g., Apar: aerodynamic mass median diameter, Aval: 3 μm), the effectiveness value Eval (see right column) of the effectiveness parameter Epar was calculated according to the two different respiratory values ​​Rval (see left column).

[0248] The ratio of the amount of aerosol deposited in lung tissue to the amount of aerosol inhaled (Epar):

[0249] [Table 8]

[0250] Ratio of aerosol mass deposited in the conduction tract to inhaled aerosol mass (Epar):

[0251] [Table 9]

[0252] Ratio of aerosol mass deposited per airway generation to total deposited aerosol mass (Epar):

[0253] [Table 10]

[0254] The ratio of the amount of aerosol deposited in the lungs to the amount of aerosol inhaled (Epar):

[0255] [Table 11]

[0256] Ratio of deposition between the central and peripheral regions (Epar):

[0257] [Table 12]

[0258] Ratio of aerosol mass deposited per lung lobe to total aerosol mass (Epar):

[0259] [Table 13]

[0260] Mean deposition mass concentration in the lungs [μg / ml] (Epar):

[0261] [Table 14]

[0262] The above experiment demonstrated that the effectiveness of pulmonary drug delivery can be accurately evaluated, particularly locally throughout the entire lung. Furthermore, the effectiveness of drug delivery to specific, predetermined target areas in the lung can also be accurately evaluated. [Explanation of symbols]

[0263] 1. Medications 2. Inhaler device 2a Dry powder inhaler device 2b Metered-dose inhalation device 2c Nebulizer inhaler device (soft mist inhaler device) Inhaler device with 2D ventilation tube 3 Aerosol 4 Respiratory system 4' Discretized structure of the respiratory system 5 Blood circulation 6 lung tissue 7. Inhalers / Medication Combination Products 8. Thorax 20, 20' mouthpiece 21 Container 22 nozzles 23 Aerosol Generation Room 24 Ventilation tubes 24a 1st entrance 24b 2nd entrance 24c exit 25 Nebulizer device 26 Propellant 27 Actuators 28 Actuator seat 29 valves 30 Aerosol particles 30' discrete particles 31 Carrier gas 32 Active Ingredients 40 Airways 40' Airway segment 40a' Upstream airway segment 40b', 40c' Downstream airway segments 41 Oral cavity 42 Lungs 43. Diaphragm 44 Pharynx 45 Larynx 46. ​​Trachea 47 bronchi 48 Bronchioles 49 Alveoli 50, 50' Airway wall 51 Intrapulmonary covering solution 52 Mucociliary clearance 53 Dissolution of active ingredients in intrapulmonary covering solution 54 Absorption of active ingredients in lung tissue 55 Transfer of active ingredients into the bloodstream 60 Lever 61, 62 springs 63 Bane 64 Operating mechanism 70 Measuring mechanism 71 Actuator Cap 80 mesh 81 Baffle 82 Gas injection 83 Vibration components IMG tomographic image Apar Aerosol Parameters Epar efficacy parameters Dopar Inhaler Operating Parameters Ddpar Inhaler Design Parameters Rpar respiratory parameters Aval Aerosol Parameter Aerosol Value Eval Efficacy parameter effectiveness value Eval1 First Efficacy Value Eval2 Second Efficacy Value Eval_min: Minimum value of the effectiveness parameter Eval_max: Maximum value of the effectiveness parameter Rval: Respiratory value of respiratory parameter Ddist spatial particle deposition distribution Dval Deposition distance Wval wall distance value Gvel gas flow rate Pvel particle transport velocity vector Velprof velocity distribution Gf Gravity Df Flow resistance force (drag force) Bf buoyancy BMf Brownian motion Mmod Computational Oral Model Lmod Computational Lung Model Pmod Computational Particle Transport and Deposition Model Amod Absorption Model Dmod Computational Inhaler Device Model Lib Bifurc Airway bifurcation library Spar absorption parameters Sval absorption value Sval1 First Absorption Value Sval2 Second Absorption Value Cdist: Spatial concentration distribution of the active ingredient in the lungs Cval1: First concentration value of the active ingredient in the lungs Cval2: Second concentration value of the active ingredient in the lungs Cval3: Third concentration value of the active ingredient in the lungs Bifurc1, Bifurc2, Bifurc3, …Bifurc r Pre-calculated airway bifurcation scenarios Dsub1, Dsub2, ..., Dsubn Dosage of the active ingredient Lungsubd1, Lungsubd2, ... Lungsubdn pulmonary subdomains CentCOMP Central Compartment Pcomp1, Pcomp2 Peripheral compartments Muccl Mucociliary Clearance tMuccl, tsub1, tsub2 absorption half-life CLdcomp1, CLdcomp2, CL clearance

Claims

1. A method for evaluating the effectiveness of an aerosol (3) for pulmonary drug delivery, wherein the aerosol (3) comprises aerosol particles (30) containing an orally inhaled and / or nasal drug (1), A step of determining at least one aerosol value (Aval) of at least one aerosol parameter (Apar) that characterizes the aerosol (3), wherein the aerosol (3) is generated by an inhaler device (2, 2a, 2b, 2c, 2d), Preferably, the step of providing a computational lung model (Lmod) that represents the structure of the human respiratory system (4) and the transient gas flow in the airway (40) between at least the trachea (46) and lungs (42) of the respiratory system (4), particularly during inspiration and expiration. The computational lung model (Lmod) is preferably based on a discretized respiratory system structure (4') derived from processed image data representing at least one human respiratory system (4), and preferably the discretized respiratory system structure (4') is derived from processed image data of each single tomographic image (Img) of the at least one respiratory system (4). The steps include providing the computational lung model (Lmod), The steps include providing a computational particle transport and deposition model (Pmod) that represents the transient transport of individual aerosol particles (30) in the gas flow within the airway (40) and the deposition of individual aerosol particles (30) in the respiratory system (4), Using the computational lung model (Lmod) and the computational particle transport and deposition model (Pmod), the step of calculating the spatial particle deposition distribution (Ddist) of a plurality of discrete particles (30') deposited in the discrete respiratory system structure (4') based on the at least one aerosol value (Aval), A step of calculating at least one efficacy value (Eval) of an efficacy parameter (Epar) indicating the efficacy of the aerosol (3) in lung drug delivery based on the spatial particle deposition distribution (Ddist), The steps of using the efficacy value (Eval) to automatically evaluate the effectiveness of lung drug delivery, storing the efficacy value (Eval) in a storage device, displaying the efficacy value (Eval) using a display device, and / or transmitting the efficacy value (Eval) for use in evaluating the effectiveness of lung drug delivery. A method that includes this.

2. The aforementioned at least one aerosol parameter (Apar) is: Particle size, preferably average particle size, Particle size distribution and, Particle density and, Particle shape, preferably average particle shape, In particular, an aerosol flow rate that preferably has a time dependence relative to the respiratory cycle, The flow velocity of the aerosol and, The type of carrier gas, The pressure of the carrier gas of the aerosol and The method according to claim 1, wherein one or more of the following are represented.

3. The aforementioned at least one aerosol value (Aval) is, To measure the aerosol value (Aval), Obtaining the aerosol value (Aval) from a database containing data indicating the physical properties of the aerosol (3), Obtaining or deriving the aerosol value (Aavl) from the specification data of the inhaler device (2, 2a, 2b, 2c, 2d), Calculating the aerosol value (Aval) based on the analytical relationship of the physical properties of the aerosol (3), and / or Preferably, the aerosol value (Aval) is calculated using a computational inhaler device model (Dmod), particularly in the inhaler devices (2, 2a, 2b, 2c, 2d), as a result of computational simulation of the generation process and / or flow of the aerosol (3). The method according to claim 1 or 2, as determined by...

4. Furthermore, the process includes determining at least one respiratory value (Rval) of respiratory parameters (Rpar) that characterize the respiration of the respiratory system (4), particularly spontaneous respiration, assisted spontaneous respiration, and / or mechanical respiration, wherein the respiratory value (Rval) defines the boundary conditions of the computational lung model (Lmod). The aforementioned respiratory parameter (Rpar) is: Preferably a time-dependent, preferably pressure difference between the pleural cavity and the trachea (46), Preferably, the gas flow rates for inhalation and / or exhalation are time-dependent, In particular, the minimum and / or maximum intrapulmonary pressure values ​​and respiratory cycle frequency values ​​in pressure-controlled mechanical ventilation, In particular, the amount of gas inspiratory and / or expiratory in volume-controlled mechanical ventilation and The method according to any one of claims 1 to 3, preferably comprising one or more of the above.

5. The discretized respiratory system structure (4') is based on spatial segmentation, which divides the structure of the preferably conductive airway (40) in the respiratory system (4) into a plurality of discrete, preferably three-dimensional, more preferably tubular airway segments (40', 40a', 40b', 40c'), and preferably on time discretization in time steps. Preferably, the gas flow velocity (Gvel) in at least a portion of the airway segments (40', 40a', 40b', 40c') is constant at each time step, according to any one of claims 1 to 4.

6. The computational lung model (Lmod) represents at least six generations, preferably at least eight generations, more preferably at least ten generations, and even more preferably at least twelve generations of the structure of the airway (40), preferably further representing the oral cavity (41), pharynx (44) and / or larynx (45), and preferably further representing the alveoli (49) of the lung (42), The computational lung model (Lmod) preferably represents the closed volume of the airway (40), and / or The method according to any one of claims 1 to 5, wherein the computational lung model (Lmod) preferably represents the structural elasticity of the airway (40), particularly the structural elasticity of the airway wall (50) and / or the structural elasticity of the alveolar tissue.

7. The aforementioned computational lung model (Lmod) is: Preferably, individual discretized respiratory system structures (4') derived from processed image data representing individual respiratory systems (4) in healthy individuals or patients with lung disease, or Preferably, the mean discretized respiratory system structure (4') is derived from averaged processed image data, which preferably represents the mean among a plurality of individual respiratory systems (4) in at least one healthy person and / or at least one patient with lung disease. The method according to any one of claims 1 to 6, based on the above.

8. The method according to any one of claims 1 to 7, wherein the computational lung model (Lmod) is based on a discretized respiratory system structure (4') derived from processed image data representing at least one healthy respiratory system (4) and at least one predetermined pathological image data pattern representing at least one region in the respiratory system (4) that is caused by a lung disease, preferably a localized pathological change.

9. The computational particle transport and deposition model (Pmod) implements the Lagrangian method to track individual discrete particles (30') transported in the gas flow, and is based in particular on modeling at least one physical force acting on each of the individual discrete particles (30'), in particular gravity (Gf), flow resistance (Df), buoyancy (Bf), and / or Brownian motion force (BMf). In particular, the method according to any one of claims 1 to 8, wherein the direction of gravity (Gf) in the computational particle transport and deposition model (Pmod) is set according to the spatial direction, preferably vertical or horizontal, of the respiratory system (4) represented by the processed image data.

10. In particular, the velocity field of particle transport discretized within the three-dimensional airway segments (40', 40a', 40b', 40c') has a higher spatial dimension than the velocity field of gas flow discretized within the three-dimensional airway segments (40', 40a', 40b', 40c'), Preferably, the method according to any one of claims 1 to 9, wherein the velocity field of three-dimensional discretized particle transport within the range of three-dimensional spatial airway segments (40', 40a', 40b', 40c') is calculated based on a constant discretized gas velocity (Gvel) within the range of three-dimensional spatial airway segments (40', 40a', 40b', 40c'), and at least within the range of a portion of the discretized respiratory system structure (4'), preferably within the range of airway segments (40', 40a', 40b', 40c') belonging to at least the second generation, more preferably at least the third generation, of the airway (40) represented by the computational lung model (Lmod).

11. The computational particle transport and deposition model (Pmod) tracks individual discrete particles (30') within the range of three-dimensional airway segments (40', 40a', 40b', 40c') by preferably applying a three-dimensional particle transport velocity vector (Pvel) as the velocity of the particles (30'). The method according to any one of claims 1 to 10, preferably the particle transport velocity vector (Pvel) is calculated based on a preferably constant gas flow velocity (Gvel) and a predetermined three-dimensional velocity distribution (Velprof) over the entire cross-section of the airway segment (40', 40a', 40b'), obtained from the computational lung model (Lmod) in the airway segment (40', 40a', 40b').

12. Furthermore, the process includes the step of seeding individual discrete particles (30') into the gas flow in the airway (40), wherein the step is In the inflow cross-section of the discretized respiratory system structure (4'), preferably in the inflow cross-section of the trachea (46), a seed position is assigned to each individual discrete particle (30'), and / or Preferably, a seed time is assigned to each individual discrete particle (30') based on a determined aerosol flow rate that is time-dependent and particularly relative to the respiratory cycle. Includes, The step of seeding individual discrete particles (30') further includes assigning one or more of the following to each individual discrete particle (30'): seed velocity, seed acceleration, particle density, particle diameter, particle shape, particle mass, and drag coefficient. Preferably, the seed position is Preferably, the statistical spatial distribution of aerosol particles (30) in the aerosol (3) is obtained or derived based on measured, pre-calculated, and / or randomly generated aerosol particle distribution data. In the computational simulation of the generation process and / or flow of the aerosol (3), the particle positions of the individual discrete particles (30') in the inhaler device (2, 2a, 2b, 2c, 2d) are preferably calculated using a computational inhaler device model (Dmod), and / or To measure the spatial distribution of aerosol particles (30) in the aerosol (3) The method according to any one of claims 1 to 11, determined based on [the specified method].

13. The computational particle transport and deposition model (Pmod) preferably determines the path of individual discrete particles (30') traversing the airway bifurcations (40a', 40b', 40c') during exhalation and / or inhalation by assigning the particles (30') to one of the downstream airway segments (40b', 40c') based on an evaluation of at least one geometric bifurcation criterion. Preferably, the geometric branching criterion is based on a geometric relationship between the outflow cross-sectional area of ​​the upstream airway segment (40a') and the inflow cross-sectional area of ​​the downstream airway segments (40b', 40c'), according to any one of claims 1 to 12.

14. The aforementioned computational particle transport and deposition model (Pmod) is based on at least one pre-calculated airway bifurcation scenario (Bifurc １ Bifurc ２ Bifurc ３ ,...Bifurc ｒ Using this method, preferably the path of individual discrete particles (30') traversing the airway branching points (40a', 40b', 40c') during inhalation is determined. Preferably, the pre-calculated airway bifurcation scenario (Bifurc １ Bifurc ２ Bifurc ３ ,...Bifurc ｒ Each of these is based on an evaluation of a preferably three-dimensional high-dimensional simulation that has been performed at least once in the past with respect to particle transport in the gas flow traversing the airway bifurcation (40a', 40b', 40c'), Preferably, the at least one pre-calculated airway bifurcation scenario (Bifurc １ , Bifurc ２ , Bifurc ３ , … Bifurc ｒ ) is preferably obtained from an airway bifurcation library (Lib Ｂｉｆｕｒｃ ) that stores a plurality of pre-calculated airway bifurcation scenarios (Bifurc １ , Bifurc ２ , Bifurc ３ , … Bifurc ｒ ) that are preferably different from each other, according to any one of claims 1 to 13.

15. Pre-calculated airway bifurcation scenario (Bifurc １ Bifurc ２ Bifurc ３ ,...Bifurc ｒ The method according to any one of claims 1 to 14, preferably claim 14, wherein the transport of the discrete particles (30') across the airway bifurcations (40a', 40b', 40c') is predicted based on the classified type of branching shape of the airway bifurcations (40a', 40b', 40c') and / or based on the position of the discrete particles (30') in the upstream airway segment (40a'), preferably in the radial division and / or circumferential division of the upstream airway segment (40a').

16. A suitable pre-calculated airway bifurcation scenario (Bifurc) is used to determine the path of individual discrete particles (30') traversing specific airway bifurcations (40a', 40b', 40c'). １ Bifurc ２ Bifurc ３ ,...Bifurc ｒ Preferably, the airway bifurcation library (Lib Ｂｉｆｕｒｃ )from, The position of the discrete particle (30') in the upstream airway segment (40a'), preferably in the radial and / or circumferential direction, At least one geometric parameter indicating the branching shape of the airway bifurcation (40a', 40b', 40c'), The particle size of the discrete particle (30'), The particle density in the region of the aforementioned airway bifurcation (40a', 40b', 40c'), and Particle velocities upstream of the aforementioned airway bifurcation (40a', 40b', 40c') A method according to any one of claims 1 to 15, preferably claim 14 or 15, selected based on at least one or more of the above.

17. The aforementioned computational particle transport and deposition model (Pmod) determines the deposition of discrete particles (30') based on the evaluation of at least one deposition criterion. The evaluation of the aforementioned deposition criteria is as follows: To evaluate whether the calculated distance value (Wdist) of the discrete particles (30') to the airway wall (50') of the airway segments (40', 40a', 40b', 40c') is less than or equal to a predetermined deposition distance (Dval), Evaluate whether the calculated particle velocity (Pvel) of the discrete particles (30') is less than or equal to a predetermined minimum velocity, and / or The method involves evaluating whether the calculated collision angle between the flow path of the discrete particles (30') and the airway wall (50') of the airway segments (40', 40a', 40b', 40c') exceeds a predetermined minimum angle, preferably at least 45 degrees, more preferably at least 60 degrees, and even more preferably at least 75 degrees. The method according to any one of claims 1 to 16, preferably comprising at least one or more of the above.

18. The step of determining the at least one aerosol value (Aval) is performed using a computational inhaler device model (Dmod) that represents the generation process and / or flow of aerosol (3) in the inhaler device (2, 2a, 2b, 2c, 2d), The inhaler devices (2, 2a, 2b, 2c, 2d) preferably include a dry powder inhaler device (2a), a metered-dose inhaler device (2b), a nebulizer inhaler device (2c), and / or an inhaler device (2d) equipped with a ventilation tube (24). The aforementioned computational inhaler device model (Dmod) is: The particle size distribution of the aerosol, The particle density of the aerosol, Preferably, the pressure of the aerosol carrier gas at the outlet from the mouthpiece (20) and / or ventilation tube (24) of the inhaler device (2, 2a, 2b, 2c, 2d), which is preferably time-dependent with respect to the respiratory cycle. Preferably, the aerosol flow rate at the outlet from the mouthpiece (20) and / or ventilation tube (24) of the inhaler device (2, 2a, 2b, 2c, 2d), which is preferably time-dependent with respect to the respiratory cycle, and Preferably, the flow rate of the aerosol at the outlet from the mouthpiece (20) and / or ventilation tube (24) of the inhaler device (2, 2a, 2b, 2c, 2d), which is preferably time-dependent with respect to the respiratory cycle. It represents one or more of these, Preferably, the method according to any one of claims 1 to 17, depending on at least one or more of selected device design parameters (Ddpar), set device operating parameters (Dopar), and respiratory values ​​(Rval) of respiratory parameters (Rpar) that affect the generation process and / or flow of the aerosol (3) in the inhaler device (2, 2a, 2b, 2c, 2d).

19. The step of determining the at least one aerosol value (Aval) is performed using a computational inhaler device model (Dmod) that represents the flow of the aerosol (3) in the inhaler device (2, 2a, 2b, 2c, 2d), The computational inhaler device model (Dmod) preferably represents at least one component of the inhaler device (2, 2a, 2b, 2c, 2d) characterized by at least one device design parameter (Ddpar), The aforementioned device design parameter (Ddpar) is: The shape of the mouthpiece (20) and / or ventilation tube (24) of the inhaler device (2, 2a, 2b, 2c, 2d), The diameter and / or shape of the nozzle (22) of the inhalation device (2, 2a, 2b, 2c, 2d), The flow path shape from the outlet of the aerosol generation and / or aerosol storage chamber (23) to the outlet of the inhaler device (2, 2a, 2b, 2c, 2d), preferably to the outlet of the mouthpiece (20) and / or ventilation tube (24), and Preferably the volume and / or shape of the container (21) for containing the liquid oral inhalation and / or nasal drug (1). The method according to any one of claims 1 to 18, wherein preferably one or more of the above are represented.

20. The aforementioned effectiveness parameter (Epar) is: Preferably a local effective dose, Preferably, the concentration of locally deposited aerosol particles (30), and Preferably, the concentration of the active ingredient of the oral inhalation and / or nasal drug (1) in a localized area. The method according to any one of claims 1 to 19, wherein one or more of the above are represented.

21. The aerosol particles (30) contain a predetermined dose of the active ingredient of the oral inhalation and / or nasal drug (1), Preferably, the efficacy parameter (Epar) represents the blood concentration of the active ingredient in the blood, and the calculation of at least one value (Eval) of the blood concentration preferably uses a computational absorption model (Amod) that represents the absorption of the active ingredient contained in the aerosol particles (30) into the blood circulation (5), and / or Preferably, the efficacy parameter (Epar) represents the tissue concentration of the active ingredient in lung tissue, and the calculation of at least one value (Eval) of the tissue concentration preferably uses a computational absorption model (Amod) that represents the absorption of the active ingredient contained in the aerosol particles (30) into lung tissue (6), according to any one of claims 1 to 20.

22. The efficacy parameter (Epar) indicates the spatial concentration distribution (Cdist) of the active ingredient (32) of the oral inhalation and / or nasal drug (1) in the lung tissue (6) and / or blood circulation (5) of the respiratory system (4), preferably having a time-dependent nature. The method according to any one of claims 1 to 21, preferably claim 20 or 21, wherein the at least one efficacy value (Eval) is preferably at least one of preferably time-dependent concentration values ​​(Cval1, Cval2, Cval3) of the active ingredient (32) in lung tissue (6) and / or blood circulation (5).

23. The spatial particle deposition distribution (Ddist) includes, preferably, an averaged subdomain-specific particle deposition distribution in a plurality of subdomains (Lungsubd1, Lungsubd2, ..., Lungsubdn) of the discretized respiratory system structure (4'), The at least one effectiveness value (Eval, Eval1, Eval2) of the at least one effectiveness parameter (Epar) is determined according to at least one of the subdomain-specific particle deposition distributions. Preferably, each subdomain (Lungsubd1, Lungsubd2, ..., Lungsubdn) is: The healthy area or pathological area of ​​the respiratory system (4), and / or At least a portion of the airway (40), in particular at least a portion of a particular generation of the airway (40), or at least a portion of the alveoli (49), and / or A specific lobe of the lung (42) A method according to any one of claims 1 to 22, preferably any one of claims 20 to 22, representing the above.

24. The computational absorption model (Amod) represents the absorption of the active ingredient (32) contained in the aerosol particles (30) deposited in the respiratory system (4) into the lung tissue (6) and / or blood circulation (5), based on the spatial particle deposition distribution (Ddist) and at least one absorption value (Sval, Sval1, Sval2) of at least one absorption parameter (Spar). The method according to any one of claims 1 to 23, preferably any one of claims 20 to 23, wherein the computational absorption model (Amod) is preferably based on pharmacokinetic modeling.

25. At least one absorption parameter (Spar) is, The saturation solubility C of the active ingredient of the oral inhalation and / or nasal drug (1) in the intrapulmonary covering solution (51) ｓ , The dissolution rate k of the active ingredient of the oral inhalation and / or nasal drug (1) in the intrapulmonary covering solution (51) ｄｉｓｓ Preferably, local maximum dissolution rate, The effective permeability P of the airway wall (50) to the active ingredient of the oral inhalation and / or nasal drug (1) ａｐｐ , The tissue-free plasma partition coefficient K indicates the distribution of the concentration of the active ingredient released in lung tissue to the concentration of the active ingredient in plasma. ｐｕ，ｔｉｓ , The tissue-plasma partition coefficient K indicates the distribution of the active ingredient's concentration between lung tissue and plasma. ｐ，ｔｉｓ A method according to any one of claims 1 to 24, preferably claim 24, relating to one or more of the above.

26. At least one absorption parameter (Spar) and at least one absorption value (Sval, Sval1, Sval2), in particular the saturated solubility C ｓ , dissolution rate k ｄｉｓｓ Effective transmittance P ａｐｐ Tissue free plasma distribution coefficient K ｐｕ，ｔｉｓ and / or tissue-plasma distribution coefficient K ｐ，ｔｉｓ The method according to any one of claims 1 to 25, preferably claim 24 or 25, wherein the value of is determined according to a predetermined pathological change in at least one region of the respiratory system (4) caused by a lung disease of the lung.

27. At least one absorption parameter (Spar) and at least one absorption value (Sval, Sval1, Sval2) are determined according to the spatial particle deposition distribution (Ddist), The spatial particle deposition distribution (Ddist) preferably includes subdomain-specific particle deposition distributions in multiple subdomains (Lungsubd1, Lungsubd2, ..., Lungsubdn) of the discretized respiratory system structure (4'), Preferably, each subdomain (Lungsubd1, Lungsubd2, ..., Lungsubdn) is: The healthy area or pathological area of ​​the respiratory system (4), and / or At least a portion of the conductive airway (40), particularly a particular generation of the airway (40), or at least a portion of the respiratory airway (40), particularly a portion of the alveoli (49), and / or A method according to any one of claims 1 to 26, preferably any one of claims 24 to 26, which represents a specific lobe of the lung (42).

28. The method according to any one of claims 1 to 27, preferably any one of claims 24 to 27, wherein at least one absorption value (Sval, Sval1, Sval2) is determined by in vitro measurement, particularly using a microfluidic lung-on-chip device.

29. moreover, The steps include determining a predetermined minimum effectiveness value (Eval_min) for the effectiveness parameter (Epar), A step of using at least one determined aerosol value (Aval) as a starting value, A step of applying the aerosol value (Aval), preferably performed in an iterative loop in which at least some of the steps of the method according to any one of claims 1 to 28 are repeatedly performed until the calculated effectiveness value (Eval) is equal to or greater than the predetermined minimum effectiveness value (Eval_min), The steps include: storing the adapted aerosol value (Aval) in a storage device, outputting the adapted aerosol value (Aval) to a display device, and / or providing the adapted aerosol value (Aval) as an optimized aerosol value (Aval) of the aerosol parameter (Apar) in use for evaluating the effectiveness of lung drug delivery; The method according to any one of claims 1 to 28, including

30. The at least one aerosol value (Aval) is, in particular, when the at least one effectiveness value (Eval) is greater than or equal to a predetermined minimum effectiveness value (Eval_min) of the effectiveness parameter (Epar), and preferably less than a predetermined maximum effectiveness value (Eval_max) of the effectiveness parameter (Epar), A step of generating an aerosol (3) comprising aerosol particles (30) containing an orally inhaled and / or nasal drug (1), wherein the aerosol (3) is characterized by the aerosol value (Aavl), and preferably an inhaler device (2, 2a, 2b, 2c, 2d) is used for generating the aerosol (3). A step of setting the device operating parameter (Dopar) of the inhaler device (2, 2a, 2b, 2c, 2d) such that the inhaler device (2, 2a, 2b, 2c, 2d) generates an aerosol (3) characterized by the aerosol value (Aval), A process of designing and / or manufacturing an inhaler device (2, 2a, 2b, 2c, 2d) configured to generate an aerosol (3) characterized by the aerosol value (Aval), and A step of producing an oral inhalation and / or nasal drug (1), wherein the aerosol parameter (Apar) is characterized by an aerosol value (Aval) which is the particle size, average particle size, and / or particle size distribution, and the step of producing the oral inhalation and / or nasal drug (1) in the form of a dry powder. Used in one or more of the processes The method according to any one of claims 1 to 29.

31. The at least one effectiveness value (Eval) is, in particular, greater than a predetermined minimum effectiveness value (Eval_min) of the effectiveness parameter (Epar), and preferably less than a predetermined maximum effectiveness value (Eval_max), A process for evaluating the performance of inhaler devices (2, 2a, 2b, 2c, 2d) in lung drug delivery, In particular, the effect of setting the device operating parameters (Dopar) and / or device design parameters (Ddpar) of the inhaler devices (2, 2a, 2b, 2c, 2d) on the performance of lung drug delivery was evaluated. The inhaler devices (2, 2a, 2b, 2c, 2d) are configured such that, when operated with a predetermined setting of the device operating parameter (Dopar), they generate an aerosol (3) for lung drug delivery in a state characterized by the aerosol value (Aval) of the aerosol parameter (Apar). A step of evaluating the performance of the inhaler device (2, 2a, 2b, 2c, 2d), A step of evaluating the efficacy and / or safety of the dosage of an oral inhalation and / or nasal agent (1) in pulmonary drug delivery, particularly the dosage of the active ingredient of the oral inhalation and / or nasal agent (1), The oral inhalation and / or nasal drug (1) is preferably prepared to be administered in the form of an aerosol (3) characterized by the aerosol value (Aval) of the aerosol parameter (Apar) using an inhaler device (2, 2a, 2b, 2c, 2d). A step of evaluating the efficacy and / or safety of the oral inhalation and / or nasal drug (1), and A step to evaluate the effectiveness of a drug / device combination product (7) consisting of an orally inhaled and / or nasal drug (1) and an inhaler device (2, 2a, 2b, 2c, 2d) in pulmonary drug delivery, The oral inhalation and / or nasal drug (1) is prepared to be administered in the form of an aerosol (3) characterized by the aerosol value (Aval) when operated using the inhaler device (2, 2a, 2b, 2c, 2d), particularly with predetermined settings of the device operating parameter (Dopar). The inhaler devices (2, 2a, 2b, 2c, 2d) are configured to generate an aerosol (3) characterized by the aerosol value (Aal) of the aerosol parameter (Apar) when operated with a predetermined setting of the device operating parameter (Dopar). Steps to evaluate the effectiveness of the drug / device combination product (7) The method according to any one of claims 1 to 30, used in one or more of the steps.

32. A method for evaluating the performance of inhaler devices (2, 2a, 2b, 2c, 2d) in pulmonary drug delivery, The inhaler device (2, 2a, 2b, 2c, 2d) is configured to generate an aerosol (3) containing aerosol particles (30) that contain an orally inhaled and / or nasal drug (1), The performance of the inhaler device (2, 2a, 2b, 2c, 2d) in lung drug delivery is evaluated according to the effectiveness of the aerosol (3) for lung drug delivery when evaluated according to the method of any one of claims 1 to 31.

33. A method for evaluating the efficacy and / or safety of oral inhalation and / or nasal agents (1) in pulmonary drug delivery, The oral inhalation and / or nasal drug (1) is preferably prepared to be administered in the form of an aerosol (3) using an inhaler device (2, 2a, 2b, 2c, 2d), The effectiveness of the oral inhalation and / or nasal drug (1) in pulmonary drug delivery is evaluated according to the effectiveness of the aerosol (3) in pulmonary drug delivery as evaluated according to the method of any one of claims 1 to 32.

34. A method for evaluating the effectiveness of a drug / device combination product (7) consisting of an orally inhaled and / or nasal drug (1) and an inhaler device (2, 2a, 2b, 2c, 2d) in pulmonary drug delivery, The inhaler device (2, 2a, 2b, 2c, 2d) is configured to generate an aerosol (3) containing aerosol particles (30) that contain the oral inhalation and / or nasal drug (1), The oral inhalation and / or nasal drug (1) is prepared to be administered in the form of an aerosol (3) using the inhaler device (2, 2a, 2b, 2c, 2d), The effectiveness of the drug / device combination product (7) in pulmonary drug delivery is evaluated according to the effectiveness of the aerosol (3) for pulmonary drug delivery as evaluated according to the method of any one of claims 1 to 33.

35. A computer program product that includes instructions causing the computer to perform at least some steps in the method according to any one of claims 1 to 34 when the program is executed by the computer.

36. A computer-readable medium for storing instructions that, when executed by a computer, implement at least some steps in the method according to any one of claims 1 to 34.

37. A method for generating an aerosol (3) containing an orally inhaled and / or nasal drug (1) aerosol particles (30) using an inhaler device (2, 2a, 2b, 2c, 2d), A step of carrying out the method according to any one of claims 1 to 34, A step of providing an oral inhalation and / or nasal drug (1), wherein, based on an efficacy evaluation by any one of claims 1 to 34, the oral inhalation and / or nasal drug (1) is provided when, in particular, at least one efficacy value (Eavl) is equal to or greater than a predetermined minimum efficacy value (Eavl_min) of the efficacy parameter (Epar), A step of providing an inhaler device (2, 2a, 2b, 2c, 2d), in particular, based on an effectiveness evaluation by the method of any one of claims 1 to 34, the step of providing the inhaler device (2, 2a, 2b, 2c, 2d) in particular when at least one effectiveness value (Eaval) is equal to or greater than a predetermined minimum effectiveness value (Eaval_min) of the effectiveness parameter (Epar), A step of supplying the oral inhalation and / or nasal drug (1) to the inhaler device (2, 2a, 2b, 2c, 2d), A step of operating the inhaler device (2, 2a, 2b, 2c, 2d), preferably by setting the device operating parameter (Dopar) of the inhaler device (2, 2a, 2b, 2c, 2d) based on an effectiveness evaluation by the method of any one of claims 1 to 34, wherein the inhaler device (2, 2a, 2b, 2c, 2d) is operated such that, in particular when the at least one effectiveness value (Eval) is greater than or equal to a predetermined minimum effectiveness value (Eval_min) of the effectiveness parameter (Epar), the inhaler device (2, 2a, 2b, 2c, 2d) generates an aerosol (3) characterized by the aerosol value (Aval) of the aerosol parameter (Apar). Methods that include...

38. A method for designing and / or manufacturing an inhaler device (2, 2a, 2b, 2c, 2d) configured to generate an aerosol (3) containing an orally inhaled and / or nasal drug (1) aerosol particles (30), A step of selecting at least one value of a device design parameter (Ddpar) that characterizes an inhaler device (2, 2a, 2b, 2c, 2d), A step of carrying out the method according to any one of claims 1 to 34, preferably claim 19, A step of designing and / or manufacturing an inhaler device (2, 2a, 2b, 2c, 2d), in particular based on an effectiveness evaluation by any one of the methods in paragraphs 1 to 34 above, the step of designing and / or manufacturing the inhaler device (2, 2a, 2b, 2c, 2d) such that the inhaler device (2, 2a, 2b, 2c, 2d) implements a selected value of the device design parameter (Ddpar) when, in particular, the at least one effectiveness value (Eavl) is greater than or equal to a predetermined minimum effectiveness value (Eavl_min) of the effectiveness parameter (Epar). A method that includes this.

39. Inhaler devices (2, 2a, 2b, 2c, 2d) for administering an oral inhalation and / or nasal drug (1), An inhaler device (2, 2a, 2b, 2c, 2d) designed and / or manufactured by performing each step of the method according to claim 38.

40. The device is equipped with a ventilation tube (24) for administering an oral inhalation and / or nasal drug (1) to a patient under mechanical ventilation, The ventilation tube (24) preferably has a first inlet (24a) for the aerosol flow and a second inlet (24b) for the ventilation gas flow generated by the ventilation device, and preferably further has a common outlet (24c) for the aerosol flow and the ventilation gas flow. The inhaler device (2, 2d) according to claim 39, comprising:

41. A method for producing an oral inhalation and / or nasal drug (1), preferably in the form of a dry powder, A step of carrying out the method according to any one of claims 1 to 34, wherein preferably the particle size characterizing the dried powder is selected as the determined aerosol value (Aavl), A step of manufacturing an oral inhalation and / or nasal drug (1), wherein the oral inhalation and / or nasal drug (1) is administered using an inhaler device (2, 2a, 2b, 2c, 2d) in the form of an aerosol (3) characterized by the aerosol value (Aval) determined according to the method of any one of claims 1 to 34, preferably in the form of a dry powder, and particularly when the at least one efficacy value (Eval) is greater than or equal to a predetermined minimum efficacy value (Eavl_min) of the efficacy parameter (Epar), the oral inhalation and / or nasal drug (1) is administered using an inhaler device (2, 2a, 2b, 2c, 2d) in the form of an aerosol (3) characterized by the aerosol value (Aval) of the aerosol parameter (Apar). A method that includes this.

42. An orally inhaled and / or nasal drug (1), particularly a dry powder, obtained by performing each step of the method according to claim 41.

43. A drug / device combination product (7) comprising an orally inhaled and / or nasal drug (1) and an inhaler device (2, 2a, 2b, 2c, 2d) for administering the orally inhaled and / or nasal drug (1), the drug / device combination product (7) comprising the orally inhaled and / or nasal drug (1) according to claim 42 and / or the inhaler device (2, 2a, 2b, 2c, 2d) according to claim 39 or 40.

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