Method and system for selecting manufacturing conditions for inhaled formulations

Abstract

The predictive modeling computing system includes a processor and a memory including a set of computer-executable instructions that, when executed by one or more processors, cause the predictive modeling computing system to receive a plurality of design parameters, determine a predicted median granularity, identify a predictive quadratic model, and display a visualized response surface. The computer-implemented method includes receiving a plurality of design parameters, determining a predicted median granularity, identifying a predictive quadratic model, and displaying a visualized response surface.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims priority and benefit of U.S. Provisional Application No. 63 / 130,063, filed on December 23, 2020, "Methods and Systems for Selecting Conditions for Making Inhalation Formulations", the entire disclosure of which is expressly incorporated herein by reference.

[0002] The present disclosure generally relates to the field of predictive modeling methods and systems for selecting optimal spray - drying processing conditions in particle processing, and more specifically, to predictive modeling techniques for selecting optimal spray - drying processing conditions for generating stable inhalable protein particles for dry - powder inhalation.

Background Art

[0003] Currently, the therapeutic drug market for protein formulations is growing rapidly. For example, the development and market launch of monoclonal antibodies (mAbs) are being expedited, and 18 new antibodies have been approved by the U.S. Food and Drug Administration (FDA) from 2018 to 2019. However, the administration of mAbs is currently limited to injection (e.g., subcutaneous or intravenous) routes, and there are no inhalable mAb products approved by the FDA. This current situation is largely due to the failure of existing particle - processing technologies to solve the practical problems required for the development and deployment of inhalable protein therapeutics such as mAbs.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The commercialization of inhalable mAb powders requires being appropriate in both processing and delivery. However, as shown by the failure of prior art to develop inhalable mAbs that achieve both aerosol performance and protein stability, the complexity and instability of mAbs pose a major barrier to the development of alternative administration routes including inhalation.

[0005] For example, conventional efforts have included analyzing powder properties (e.g., crystallinity) related to the protein stability of inhalable anti-immunoglobulin E (IgE) mAb powders produced using spray drying and spray freeze-drying. However, such techniques have failed to address the effects of processing and formulation conditions on mAb structure and aggregate formation. Another approach has been to evaluate the stability of spray-dried immunoglobulin G1 (IgG1) combined with various levels of mannitol, determining that a mannitol content of at least 20-30% provides stabilization. However, this has revealed poor aerosol performance of the resulting formulations.

[0006] Lung delivery of protein-based therapeutics such as mAbs is an attractive option for targeted therapy of lung diseases. To penetrate the deep airways of the lungs, droplets or particles (e.g., those released from delivery devices) require an aerodynamic diameter of less than 3-5 μm. However, to produce droplets (wherein "droplets" should be understood as referring to particles) with a suitable aerodynamic diameter to reach deep into the lungs, atomization used in conventional particle processing techniques requires load induction. Such load induction can induce structural changes in the droplet-forming proteins, droplet binding / aggregation, loss of therapeutic activity, and / or immunogenic responses. [Means for solving the problem]

[0007] In one embodiment, a predictive computing system for optimizing atomization settings during protein particle processing includes one or more processors and a memory containing a set of computer-executable instructions. When executed by one or more processors, these instructions cause the predictive computing system to receive a user-selected set of design parameters relating to a statistical design in the memory's design generation module. The memory may include further instructions that cause the predictive computing system to determine the median of the predicted granularity in the memory's suitability evaluation module at runtime, and to identify one or more predictive quadratic models in the memory's stability evaluation module by fitting each of the one or more response variables evaluated in statistical experiments corresponding to the statistical design. The memory may also include further instructions that cause the visualized response surfaces for each of the one or more predictive quadratic models to be displayed on the user's display device in the memory's visualization module at runtime.

[0008] In another embodiment, a computer-implemented method for determining the optimal formulation atomization settings in a protein particle processing process includes: receiving a user-selected set of set parameters relating to a statistical design in a set generation module of a predictive computing system; determining a predicted median particle size in a suitability evaluation module of the predictive computing system; identifying one or more predictive quadratic models by fitting each of one or more response variables evaluated in statistical experiments corresponding to the statistical design; and displaying a visualized response surface for each of the one or more predictive quadratic models on a user's display device in a visualization module of the predictive computing system. [Brief explanation of the drawing]

[0009] [Figure 1] This describes an exemplary predictive computing environment that performs a predictive modeling technique to select optimal spray drying conditions for generating stable, inhalable protein particles for a dry powder inhalation formulation, according to one embodiment. [Figure 2A] An exemplary particle processing method is shown according to one embodiment and scenario. [Figure 2B] An exemplary method for analyzing storage particles to perform stability evaluation is shown according to one embodiment and scenario. [Figure 3A] This shows a visual representation of an exemplary response surface for the predicted median particle size according to one embodiment and scenario. [Figure 3B] This provides a visual representation of an exemplary response surface for an increase in β-injection due to an increase in the relative area of ​​a dimer species, according to one embodiment and scenario. [Figure 3C] This shows a visual representation of an exemplary response surface of the increase in β-injection content when the parameters of solid content, atomization rate, and feed rate are varied, according to one embodiment and scenario. [Figure 3D] This shows a visual representation of an exemplary response surface for an increase in β-turn content according to one embodiment and scenario. [Figure 3E] This figure shows a visual representation of an exemplary response surface for the decrease in disordered content according to one embodiment and scenario. [Figure 4] This document presents an exemplary method for determining the optimal formulation atomization settings in protein particle processing. [Modes for carrying out the invention]

[0010] Multiple embodiments described herein relate particularly to techniques for selecting optimal spray drying conditions in particle processing, and more specifically to predictive modeling techniques for selecting optimal spray drying conditions for generating stable, inhalable protein particles (e.g., antibodies such as monoclonal antibodies (mAbs)) for dry powder inhalation formulations. The techniques further include methods and systems for particle processing and storage particle analysis.

[0011] Experiments have shown that direct administration of mAbs into the airways allows the drug to remain in the lungs for a longer period, resulting in less and slower transfer into the bloodstream. The target characteristics of the pharmacokinetic profile of inhalable mAbs are considered beneficial for the treatment of both chronic lung diseases (e.g., asthma, chronic obstructive pulmonary disease, lung cancer, etc.) and diseases that cause acute infections and inflammatory responses, particularly in the case of SARS-CoV-2, where antibody-based therapies are a promising approach.

[0012] However, as mentioned above, despite years of research and development to develop therapeutic drugs containing mAbs, efficiently producing inhalable mAbs on an industrial scale remains difficult because it requires a complete understanding of not only the action of mAbs, but also the degradation pathways and mechanisms related to their processing, storage, and delivery.

[0013] This technology advantageously provides a method for producing biopharmaceutical formulations in a dry, solid state, which offers the advantage of stabilization compared to storage in a liquid state. The ability to produce stable powdered mAbs offers several practical advantages. Firstly, in the case of inhalation-based delivery, the technology may include reconstituting the product produced using the technology (e.g., lyophilized cake) in a solution. The technology may include delivering the solution via a delivery system (e.g., a sprayer). In another embodiment, the technology may include loading a protein formulation (e.g., an antibody such as mAbs) into a dry powder inhaler (DPI) as an inhalable powder. Delivery of antibodies (e.g., mAbs) via a DPI is particularly advantageous. Compared to a sprayer, a DPI is more portable, does not require an external power source, and requires a shorter administration time. Thus, the technology advantageously simplifies administration and improves patient compliance.

[0014] This technology may be used in combination with spray drying and spray freeze-drying, and may include comparing the robustness of powders produced using the spray drying and spray freeze-drying particle processing techniques. The comparison may include performing load tests under typical storage conditions for DPI products. Those skilled in the art will understand that powders produced using spray drying and spray freeze-drying techniques have contrasting physicochemical properties, and therefore the long-term stability of unstable proteins, particularly mAbs, may differ. Thus, spray drying may be a suitable technology for several applications.

[0015] This technology involves investigating the instability of each aspect of particle processing (atomization, freeze-drying, and drying) to determine processing and formulation conditions suitable for the delivery of a wide range of therapeutic proteins, including but not limited to mAbs. In some embodiments, the instability generated using this technology can be compared to the baseline instability generated by freeze-drying, a technique conventionally used for the production of solid-state mAb pharmaceuticals. Anti-streptopidine IgG1 may be used as a model protein in some embodiments and scenarios. However, those skilled in the art will understand that this technology can be applied to the development of other protein-based therapeutics, including but not limited to inhalable vaccines and other therapeutic antibodies.

[0016] The present invention will be further illustrated by the following examples. These examples are for illustrative purposes only and are not intended to limit the scope of the invention in any way.

[0017] Exemplary Predictive Computing Environment Referring to FIG. 1, an exemplary predictive modeling computing environment 100 is shown for performing a predictive modeling technique to select optimal spray drying processing conditions to generate stable inhalable protein particles for dry powder inhalation formulations. The computing environment 100 includes a predictive computing system 102 that can include one or more computers that can each be implemented in, for example, a virtualized and / or cloud computing environment. The computing environment 100 further includes a particle processing analysis system 104 and a network 106 that communicatively couples the predictive computing system 102 and the particle processing analysis system 104. For example, without limitation, the network 106 can include one or more suitable wireless networks such as, for example, a 3G or 4G network, a WiFi network or other wireless local area network (WLAN), a satellite communication network, and / or a terrestrial microwave network. In some embodiments, the network 106 can also include one or more wired networks such as Ethernet.

[0018] The predictive computing system 102 can include a processor 110 and a memory 112. Although described in the singular, the processor 110 can include any suitable number of one or more types of processors (e.g., one or more central processing units (CPUs), graphics processing units (GPUs), cores, etc.). The memory 112 can include one or more types of memory (e.g., persistent memory, solid state memory, random access memory (RAM), etc.) and can store one or more modules 120. The one or more modules 114 can include, for example, a plan generation module 122, an experiment module 124, a stability evaluation module 126, and a visualization module 128.

[0019] When executed by the processor 110, the plan generation module 122 can include computer-executable instructions that can generate, modify, and execute one or more statistical plans (i.e., experimental designs, or "DoE"), such as factorial plans (e.g., general full factorial plans, two-level full factorial plans, Plackett-Burman plans, etc.), response surface plans (e.g., Box-Behnken plans, central composite plans, etc.), and / or randomization plans (e.g., Latin hypercube plans), but are not limited thereto. The computer-executable instructions including the plan generation module 122 can be described in one or more suitable programming languages (e.g., C, C++, R, Java, Python, JavaScript, LISP, etc.), and can include algorithms for executing the listed statistical plans, and additional instructions to assist an end user (e.g., engineer, scientist, programmer, quality assurance tester, statistician, etc.) in constructing, parameterizing, evaluating, and / or (e.g., by visualization) interpreting the experimental design and its output / results via one or more input / output devices.

[0020] In some embodiments, a dedicated package that can be used in combination with a suitable programming language, such as the rsm package described in R for fitting a linear model to the response surface components, can be included in or accessible from the plan generation module 122. The plan generation module 122 can include one or more run-time modules that execute the configured (i.e., parameterized) statistical plan, and / or further computer-executable instructions that save the parameterized plan and / or the results of one or more statistical plans to the memory 112 or another location (e.g., an electronic database). The settings and operations of the plan generation module 122 are described in more detail below.

[0021] In some embodiments, a computer application (not shown) may provide a wrapper or package of some of the functions associated with one or more modules 120. In particular, the application may include instructions that, when executed on a device, enable the device to use one or more modules 120. Specifically, after a user has set up one or more statistical plans, the application may package these plans and make them downloadable to one or more (potentially many) other devices, including mobile computing devices. The application may include, for example, mobile application programming instructions and / or software (e.g., an Android Package Kit (APK) file).

[0022] Experiment module 124 may include computer-executable instructions that, when executed, perform one or more experimental functions. Experimental functions may range from fully automated experimental steps (e.g., automated particle processing methods) to receiving data from manual experimental procedures performed by a user (e.g., a laboratory technician). Experiment module 124 may include computer-executable instructions that receive / acquire information from the particle processing and analysis system 104, such as experimental metadata (e.g., experimental series, protein identifiers, etc.). Experiment module 124 may also include instructions that access data within the particle processing and analysis system 104, such as the current state of the number of experimental variables, as well as instructions that control the particle processing and analysis system 104. For example, experiment module 124 may include instructions to operate dedicated hardware or software functions of the particle processing and analysis system 104 (e.g., X-ray powder diffraction (XRD) analysis). Generally, the experimental module 124 includes computer-executable instructions that perform one or more steps of the particle processing method 200 shown in Figure 2A and / or one or more steps of the stored particle analysis method 250 shown in Figure 2B.

[0023] The stability evaluation module 126 includes computer-executable instructions to evaluate the stability of a target protein under varying atomization conditions, according to multiple response variables determined by the plan generation module 122 and using experimental data provided by the experiment module 124. For example, the stability evaluation module may include instructions to predict the median particle size according to the following formula.

number

[0024] Specifically, the stability evaluation module 126 can use the above formula in several embodiments to predict the suitability of atomization conditions, as evaluated by an atomization statistical model (e.g., the Box-Behnken DoE model), in the production of inhalable spray-dried particles and / or inhalable spray-freeze-dried particles.

[0025] In some embodiments, the stability evaluation module 126 includes instructions to fit one or more quadratic models to each individual response variable of the atomization statistical model by considering increases and decreases in the protein response variable with respect to the atomization statistical model rather than the response variable of the untreated protein. The stability evaluation module 126 may include instructions to consider all decreases in the identification factor as equivalent to zero when only increases are considered, and / or to consider all increases as equivalent to zero when only decreases in the factor are considered. The stability evaluation module 126 may include instructions to consider all independent variables of the statistical model and to evaluate the relationship between the mean response of a certain number (e.g., 15) untreated samples and pairwise comparisons of untreated and treated samples in a given run. The stability evaluation module 126 may include further instructions to remove terms that are not important from the model.

[0026] The stability evaluation module 126 may include further instructions to determine whether a given statistical model is suitable for the prediction purpose based on F-tests and dissuitability tests. For example, the stability evaluation module 126 may include instructions requiring that the F-test be significant (i.e., P<0.1) and / or that the dissuitability test be insignificant (i.e., P>0.1). Furthermore, for these models deemed suitable for the prediction purpose, the stability evaluation module 126 may use a desirability function to optimize the model and determine the optimal atomization settings to achieve inhalable particles with limited impact on protein stability.

number

number

[0027] The stability evaluation module 126 may include instructions for generating one or more predictive quadratic equations corresponding to, for example, predicted particle size, SEC-HPLC HMW peak area versus average untreated control increase, β-strand content versus untreated pair-per-control increase, β-turn content versus untreated pair-per-control increase, and disorder content versus untreated pair-per-control decrease. One or more predictive quadratic equations can be visualized via the visualization module 128 (e.g., within the response surface).

[0028] The visualization module 128 can generate one or more visual representations of the output of the stability evaluation module 126. For example, the visualization module 128 may include computer executable instructions that generate one or more response surfaces corresponding to each quadratic equation, the response surfaces including indices of response variables that play a role in influencing the predicted particle size and the protein structure change after atomization. Visualization will be discussed in detail below. The visualization module 128 may also include instructions that generate one or more visual representations and display them on an input / output device (e.g., an input / output device of the modeling computing system 102, a user's mobile device, etc.).

[0029] The predictive computing system 102 may also include an input / output (I / O) device 130. The I / O device 130 includes a display device and an input device, which may correspond to, for example, a computer video display and hardware peripherals (e.g., a keyboard, mouse, etc.). In some embodiments, the display device and the input device may be combined with or integrated with a single hardware device such as a touchscreen. Generally, the I / O device 130 allows one or more users to interact with the functions provided by module 120 by (i) providing input to module 120 and (ii) perceiving the output of module 120, or both.

[0030] The predictive computing system 102 may further include a network interface (NIC) 140 that enables the predictive computing system 102 to send and receive network traffic (e.g., Internet Protocol packets) over the network 106. The predictive computing system 102 may also include an electronic database that stores information about the operation of module 120 and / or particle processing analysis system 104.

[0031] The particle processing and analysis system 104 includes a set of functions for processing particles, as discussed herein with reference to Figures 2A and 2B. For example, the particle processing and analysis system 104 may include hardware and software for performing tasks such as composing / compounding one or more formulations, spraying, spray drying and / or spray freeze-drying of the composed / compounded one or more formulations, generating powder blends, and performing spectroscopic analysis. The particle processing and analysis system 104 may include a particle data acquisition module (not shown) that collects and formats data relating to particle processing analysis and transmits it to the predictive computing system 102 via the network 106.

[0032] Those skilled in the art will understand that in several embodiments, the topology of the computing environment 100 can be adjusted or further simplified. For example, in one embodiment, the predictive computing system 102 and the particle processing and analysis system 104 may correspond to the same physical server. In yet another embodiment, some or all of the functions provided by the predictive computing system 102 (e.g., the plan generation module 122) may be implemented as an application accessible to a user of the environment 100 within a mobile computing device (not shown). In yet another embodiment, a user of the particle processing and analysis system 104 may use the mobile computing device as an input / output device while conducting experiments enabled by module 124.

[0033] In operation, the user can prepare or select the composition and / or formulation of the drug by, for example, using a particle processing method as described below. The user accesses the predictive computing system 102 to set up at least one statistical experiment. For example, the user sets up a Box-Behnken design of experiment (DoE) with three levels and three factors to evaluate the stability of a protein (e.g., IgG1) under varying atomization conditions. The user can access the input / output functions of the predictive computing system 102 to establish the statistical experiment. The user can select a design space that represents the atomization conditions likely to be encountered in particle processing of inhalable biopharmaceutical powders. For example, the user may develop a design such as the following: [Table 1]

[0034] Response factors evaluated by atomized DoE may include, for example, percentage changes in oligomer species, changes in the Z-mean, changes in secondary structure content, changes in the fusion endothermic peak, and the predicted median particle size. Each of these response factors can be measured using storage particle analysis methods as described later.

[0035] The user accesses the plan generation module 122 and sets the plan parameters as shown in the table above. In some embodiments, once the user has finished setting the experimental parameters and is ready to proceed, the user can select a display in the graphical user interface of the predictive computing system 102 and have the stability evaluation module 126 generate a quadratic model fitted to each response variable and determine whether the quadratic model is predictive, including, in some embodiments, whether additional desirability function optimization is performed as described above. In some embodiments, the user can select a different display and have the visualization module 128 generate one or more visual representations corresponding to the predictable and desirable model, which may be advantageous in helping the user understand the significance of the generated model. Those skilled in the art will understand that in some cases, little or no user input is required when module 120 generates the predictive model. In other words, the process of setting the statistical model / plan parameters and performing the stability evaluation can be partially or fully automated.

[0036] Once a predictive model is generated, the user can begin experiments to test the atomization settings corresponding to the model and their impact on the stability of these models. For example, size exclusion chromatography and dynamic light scattering can be used to evaluate protein aggregation before and after atomization.

[0037] The ability to apply a statistical design rather than having users explore atomization conditions ad-hoc is advantageous, resulting in more stable and accurate predictive models. By avoiding exhaustive exploration, this technique can be used when formulation quantities are limited. Further avoidance of exhaustive exploration can advantageously save time and critical resources. Moreover, this technique allows users to perform direct comparisons and draw conclusions regarding the behavior of different proteins in response to different atomization conditions (e.g., IgG1-SD and IgG1-SFD).

[0038] Exemplary particle processing method Refer to Figure 2A, which shows an exemplary particle processing method 200. As a non-limiting example, one or more steps of the particle processing method 200 may be performed by the experimental module 124 of the predictive computing system 102 shown in Figure 1.

[0039] The particle processing method 200 may include constructing / compounding one or more formulations (block 202). In some embodiments, the formulations may correspond to immunoglobulin formulations (e.g., IgG1). For example, in some embodiments, a suitable IgG1 formulation having optimal physicochemical properties for protein stability and aerosol performance may be a powder composition containing 58.8% protein, 39.2% sucrose, 1.74% histidine, and 0.24% polysorbate 80.

[0040] Multiple compositions / formulated formulations may correspond to different weight / volume concentrations. For example, the first formulation may correspond to a liquid-supplied formulation using the above-described IgG1 composition at 10% w / v. The second formulation may be formulated at 5.5% w / v, and the third at 1% w / v. Formulation may involve identifying a solids content level that has an optimal balance between high protein stability and a small predictive geometric particle size distribution (PSD). The final pH of the formulation may be, for example, 6.3. The above examples are simplified for illustrative purposes, and it should be understood that formulations may be formulated at any appropriate weight / volume concentration and contain any appropriate composition with an appropriate pH.

[0041] The particle processing method 200 may include atomizing one or more formulations composed / formulated in block 202 (block 204). For example, particle processing method 202 may include atomizing IgG1 using a two-fluid pneumatic nozzle having a 0.7 mm nozzle and a 1.5 mm nozzle cap, with a cleaning needle removed from the nozzle to prevent negative pressure generation from disturbing the flow rate. Atomization in block 204 may include using compressed nitrogen as the atomizing gas. The atomizing air flow rate may be set using a flow meter located at the nozzle outlet. The supply flow rate may be set using a syringe pump. In some embodiments, atomization may include atomizing the formulation into a 50 mL glass vial set at a fixed distance below the nozzle.

[0042] The particle processing method 200 may include performing spray drying and / or spray freeze-drying (block 206) to produce one or more individual powders. For example, in one embodiment, the particle processing method 200 may perform spray drying using a BUCHI B-290 spray dryer equipped with a dehumidifier attachment having a suction rate set to 100% and an inlet temperature set to 130°C.

[0043] In one embodiment, the particle processing method 200 can produce a formulation slurry by performing spray freezing into liquid nitrogen. After freezing, the particle processing method 200 may include pouring the liquid nitrogen formulation slurry into one or more glass vials loaded into a loosely covered and pre-cooled shelf freeze-dryer (e.g., VirTis BenchTop lyophilizer). In some embodiments, the particle processing method 200 performs freeze-drying according to a publicly disclosed method familiar to those skilled in the art.

[0044] In some embodiments, the particle processing method 200 may include generating experimental controls relating to either or both of the freeze-drying spray drying process and the spray freeze-drying treatment. For example, continuing this example, in some embodiments, the particle processing method 200 may freeze-dry an IgG1 formulation (e.g., IgG1-Lyo) using the same settings as the spray freeze-drying treatment. After the secondary drying treatment, the particle processing method 200 may include filling the freeze-drying chamber with nitrogen gas, completely closing the vial using compressed air in the chamber (e.g., using a rubber stopper), and sealing the vial with an aluminum cap.

[0045] In some embodiments, the particle processing method 200 may include generating a powder blend to evaluate its long-term effects on crystallinity and protein stability. Continuing in the above examples, the particle processing method 200 may include generating an IgG1-lactose powder blend using a prepared spray-dried and spray-freeze-dried powder and a dry powder inhalation excipient (e.g., Lactohale 100 lactose monohydrate). The particle processing method 200 may include blending the resulting 10% w / w formulation with lactose (equivalent to 5-6% or less of IgG1), combining the powders using a geometric dilution process, then mixing for a set period of time (e.g., 2 hours) (e.g., using a Turbula mixer), and subsequently grinding (e.g., using a mortar and pestle) until a coefficient of variation (%CV) of less than 5% is obtained for IgG1.

[0046] The particle processing method 200 may include storing one or more of each powder (block 208). For example, continuing in the above example, the particle processing method 200 may include storing the IgG1 powder at 25°C / 60%RH and 40°C / 75%RH for 30 days in accordance with the provisions of the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) for intermediate and accelerated stabilization conditions. For example, the particle processing method 200 may include filling IgG1-SD, IgG1-SFD-lac, and IgG1-SD-lac into size capsules (e.g., #3 HPMC inhalation grade capsules) with a target weight of 20 mg, and filling IgG1-SFD into a target weight of 5 mg based on a lower density powder. Following encapsulation, the particle processing method 200 may include placing the capsules in a freeze-dryer at 100 mTorr for 8 hours to remove water adsorbed during the encapsulation process, and placing the capsules into HDPE vials sealed in foil bags without a desiccant. The particle processing method 200 may include storing IgG1-Lyo powder in a rubber-stoppered vial in a foil bag with a desiccant. As a further control, the method may include storing bulk Ig1-SFD and IgG1-SD powders in a similar manner to IgG1-Lyo.

[0047] Exemplary method for processing stored particles Figure 2B shows a storage particle analysis method 250 according to one embodiment. The storage particle analysis method 250 may include extracting physicochemical features of the powder, testing for structural integrity (e.g., IgG1 structural integrity), and / or performing aerosol performance analysis compared to baseline metrics after a suitable time (e.g., 30 days). As a non-limiting example, the storage particle analysis method 250 may be performed by the stability evaluation module 126 of the predictive computing system 102 shown in Figure 1.

[0048] The storage particle analysis method 250 may include analyzing the structure and / or aggregation of the stored powder in block 208 of Figure 2 (block 252). For example, the particle analysis method 250 can examine untreated and treated mAbs (e.g., IgG1) for aggregation, secondary structure, and tertiary structure changes. The storage particle analysis method 250 may quantify the presence of soluble aggregates using size exclusion chromatography (SEC-HPLC) and previously published methods familiar to those skilled in the art. The storage particle analysis method 250 can determine the types of oligomers present in the sample by referring to SEC curves using proteins of various molecular weights. In some embodiments, the storage particle analysis method 250 may use dynamic light scattering as an alternative / complementary technique to SEC. In some embodiments, the Malvern Zetasizer can be used to automatically find the optimal setting for a sample using a certain number of size measurements (e.g., 3) per sample.

[0049] The storage particle analysis method 250 can detect the presence of insoluble aggregates in block 252. For example, the storage particle analysis method 250 can measure the turbidity of IgG1 powder dissolved in 100 mM sodium phosphate and 250 mM sodium chloride buffer at wavelengths of 340 nm to 360 nm and 690 nm, and the powder is dissolved to a predetermined concentration (e.g., 0.6 mg / mL protein).

[0050] In the case of atomization experiments, storage particle analysis method 250 allows for the evaluation of changes in the secondary structure of liquid proteins using circular dichroism (CD) spectroscopy in block 252.

[0051] Storage particle analysis method 250 may include evaluating changes in the tertiary structure of proteins using differential scanning calorimetry (DSC) in block 252. The onset and midpoint of the endothermic peak can be determined using software. Changes in the tertiary structure of proteins can also be monitored by observing the fluorescence emission maxima of the sample.

[0052] In some embodiments, the storage particle analysis method 250 may include evaluating the particle size distribution (PSD) of the storage powder (e.g., IgG1-SD and IgG1-SFD) at baseline and after storage using a laser diffraction unit coupled to a dry powder disperser (e.g., RODOS) (block 254). The storage particle analysis method 250 can determine the effect of storage on powder dispersibility by performing PSD measurements at multiple bar dispersion pressures (e.g., 0.2 bar and 4 bar). The storage particle analysis method 250 can calculate the final PSD by averaging time slices in each plume corresponding to optical densities from 5% to 25%.

[0053] The storage particle analysis method 250 may include, for example, examining the morphology of the powder (e.g., IgG1 powder) at a baseline using a scanning electron microscope (SEM), such as a Zeiss Supra 40VP SEM. The storage particle analysis method 250 may also include placing the sample on an aluminum stub using double-sided carbon tape and sputter coating it with 15 nm platinum / palladium (Pt / Pd) in an argon environment using a sputter coater (e.g., a Cressington sputter coater 208HR).

[0054] The storage particle analysis method 250 may include performing X-ray powder diffraction (XRD) analysis to determine storage-induced changes in powder crystallinity (block 256). For example, the storage particle analysis method 250 may include scanning the sample in continuous mode from 5 to 45°C at a speed of 1° / min and a step size of 0.025° using a Riguku MiniFlexII equipped with a radiation source generated at 40kV and 15mA. The storage particle analysis method 250 may also include measuring the change in glass transition temperature using modulated DSC.

[0055] The storage particle analysis method 250 may include evaluating the aerosol performance of the powder (e.g., IgG1) at baseline and after storage using a cascade impactor (e.g., next-generation impactor) and a high-resistance DPI (e.g., RS01 single-dose DPI) (block 258).

[0056] The storage particle analysis method 250 may include determining differences between powders produced using two or more of the following methods: freeze-drying, spray-drying, or spray freeze-drying, at baseline and after a certain storage period (e.g., 30 days) under statistically significant intermediate and / or accelerated stability conditions (block 260). For example, the storage particle analysis method 250 may include analysis of variance (ANOVA) by Tukey post-hoc analysis and calculation of alpha 0.05.

[0057] Generally, this technology may include analyzing atomization conditions / parameters identified as optimal by the planning generation module 122 using one or more steps of the particle processing method 200 and / or one or more steps of the storage particle analysis method 250. For example, in one embodiment, the effects of atomization drying and atomization freeze-drying on the structure and aggregation of IgG1 can be evaluated using one or more steps of the particle processing method 200 and the storage particle analysis method 250.

[0058] Visualization of an exemplary response surface Figures 3A to 3E show visual representations of exemplary response surfaces corresponding to predictive quadratic equations describing desirable atomization settings. As described above, this technique does not rely on guesswork or interpolation to determine the optimal settings.

[0059] Rather, the visualization of surface responses, as shown in Figures 3A-3E, can help users discover optimal values. Therefore, even when analyzing a protein that the user is completely unfamiliar with, visualization as shown in Figures 3A-3E allows for immediate determination of how sensitive the protein is to parameter processing, and whether protein stabilization is easy or difficult. Thus, the visualization function of this technology advantageously provides researchers with rapid and intuitive clues about the possible behaviors of novel formulations. This saves energy and resources by helping users avoid selecting formulations that are likely to delay or interrupt projects, or by helping them select more appropriate formulations.

[0060] Those skilled in the art will understand that many formulation attributes, such as excessive or insufficient viscosity, inadequate surface tension, or excessive or insufficient protein loading, can render a formulation unsuitable. Visualization of response surfaces helps users avoid costly errors by providing visual guidance to such unsuitable compounds / formulations.

[0061] An exemplary method for determining the optimal atomization setting. Refer to Figure 4, which shows an exemplary method 400 for determining the optimal formulation atomization settings in a protein particle processing process.

[0062] Method 400 may include receiving a user-selected set of design parameters for a statistical design in a design generation module of a predictive computing system (block 402). For example, the design generation module of the predictive computing system may correspond to the design generation module 122 of the predictive computing system 102 in Figure 1. User selection may be enabled, for example, by a graphical user interface displayed on the input / output device 130 in Figure 1. User selection may include one or more selections to formulate a statistical design, such as a Box-Behnken DoE experiment, which includes parameters. In some embodiments, the parameters, or design space, may be selected to represent atomization conditions typically used in particle processing of inhalable biopharmaceutical powders. If the statistical design is a Box-Behnken experimental design, the evaluated response variables may include one or more of the following: a change in the proportion of oligomer species, a change in the Z mean, a change in secondary structure content, a change in the fusion endothermic peak, or the predicted median particle size.

[0063] Method 400 may include determining the median predicted particle size in the suitability evaluation module of the predictive computing system (block 404). The suitability evaluation module of the predictive computing system may correspond to the suitability evaluation module 126 of the predictive computing system 102. The median predicted particle size can be determined using the equation described above. The median predicted particle size is determined by analyzing each droplet size, weight fraction, and dry particle size. Specifically, the droplet size obtained as a result of each atomization setting can be determined using laser diffraction and used in the calculation of the median predicted particle size according to the following equation.

number

[0064] Method 400 may include identifying one or more predictive quadratic models by fitting each of one or more response variables evaluated in a statistical experiment corresponding to a statistical design (block 406). For example, quadratic equations may be fitted to predictive median particle size, SEC-HPLC dimer relative peak area versus mean untreated control increase, etc. As a result of these fittings, where the absence of F-test p-values ​​and goodness-of-fit p-values ​​is significant and non-significant, respectively, a given model is identified as predictive. In some embodiments, the predictive computing system 102 can access experimental data via the experimental module 124 and / or particle processing analysis system 104, for example, to compare treated and untreated proteins or to acquire storage particle data.

[0065] In some cases, method 400 may include applying a desirability function to further optimize the predictive properties of the quadratic model that was deemed predictive in block 406. As described above, the equation used to determine the optimal atomization conditions is as follows:

number

[0066] Method 400 may include, in the visualization module of the predictive computing system, causing a visual representation of the response surface for each of one or more predictive quadratic models to be displayed on the user's display device (block 408). For example, several predictive quadratic equations are shown in Figures 3A to 3D. The visualization module 128 of the predictive computing system 102 may generate and / or display such visual representations on the input / output device 130 of the predictive computing system 102, the display device of a remote web client, the user's mobile device, etc. In some embodiments, the visualization module 128 may transmit the generated visual representations (e.g., to a database 150 for storage via email).

[0067] In general, the protein on which Method 400 acts may be any suitable protein, including, but not limited to, antibodies (e.g., monoclonal antibodies). Generally, the particle processing of Method 400 may be configured to produce an inhalable biopharmaceutical powder and may be configured to deliver the inhalable biopharmaceutical powder via (i) a sprayer and / or a dry powder inhaler.

[0068] In some embodiments, Method 400 may include initiating subsequent experiments using a predictive quadratic model to control atomization settings in a particle processing analysis system, such as the particle processing analysis system 104 in Figure 1. For example, the subsequent experiments may vary one or more atomization settings (e.g., based on the output of a desirability function). Method 400 may compare the results of the subsequent experiments with the results of statistical experiments corresponding to a statistical design.

[0069] In some embodiments, Method 400 may include receiving corresponding experimental data generated by a particle processing method from a particle processing analysis system, wherein the experimental data corresponds to one or more evaluated response variables. For example, the particle processing analysis system 104 can automatically transmit data describing the flow rate of a flow meter to a predictive computing system. Method 400 may further include receiving corresponding experimental data generated by a storage particle analysis method from the particle processing analysis system, wherein the experimental data corresponds to one or more evaluated response variables. For example, data describing circular dichroism spectroscopy used to evaluate changes in the secondary structure of proteins may be received / acquired by the predictive computing system 102 in Figure 1 and / or transmitted by the particle processing analysis system 104 in Figure 1.

[0070] Additional considerations All references cited herein, including patents, patent applications, publications, etc., are incorporated herein by reference in their entirety.

[0071] Furthermore, when describing a range of values, it should be understood that this disclosure takes into account the individual values ​​found within that range. For example, “cell aggregate size of approximately 20 nm to approximately 200 nm” could be, but not limited to, 40 nm, 60 nm, 100 nm, and any value between these. All ranges described herein include the endpoint of the range. However, this specification also takes into account cases where the lower and / or higher endpoints of the same range are excluded.

[0072] Furthermore, unless a term is explicitly defined in this Patent by the phrase "The term '____' used herein is defined to mean ~" or similar wording, there is no intention to explicitly or implicitly limit the meaning of that term beyond its declarative or ordinary meaning, and such term should not be interpreted as having a limited scope based on declarations made in any chapter of this Patent (excluding the wording of the claims). This is done solely for the purpose of clarity, so as long as any term used in the claims at the end of this Specification is referred to in a manner consistent with a single meaning within this Specification, it is not intended to implicitly or separately limit such terminology in the claims to a single meaning. Finally, unless an element of a claim is defined by a reference to the word “means” and a function is defined without reference to any structure, no claim is intended to be interpreted under the application of 35 U.S. SC § 112(f). The systems and methods described herein are aimed at improving computer functionality and are intended to improve the functionality of conventional computers.

[0073] Throughout this specification, the term “set” means a set having one or more elements, but not an empty set, unless otherwise explicitly defined.

[0074] Throughout this specification, multiple instances may implement components, operations, or structures described as a single instance. While individual operations of one or more methods are illustrated and described as separate operations, one or more of these operations may be performed simultaneously, and they do not need to be performed in the order illustrated. Structures and functions shown as separate elements in the examples may be implemented as a combined structure or element. Similarly, structures and functions shown as single elements may be implemented as separate elements. These and other variations, modifications, additions, and improvements are within the scope of this specification.

[0075] Furthermore, certain embodiments described herein include logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (codes implemented on a non-temporary, tangible, machine-readable medium) or hardware. In hardware, routines, etc., are tangible devices capable of performing specific operations and may be configured or arranged in a particular manner. In examples of embodiments, one or more computer systems (e.g., standalone, client, or server computer systems) or one or more modules of a computer system (e.g., a processor or a group of processors) may be configured as modules that operate to perform specific operations described herein by software (e.g., an application or application portion).

[0076] In various embodiments, modules may be mechanically or electronically implemented. Therefore, the term “module” should be understood to encompass tangible entities, whether physically configured, permanently configured (e.g., wired), or temporarily configured (e.g., programmed), to operate in a particular manner or to perform specific operations as described herein. In consideration of embodiments where multiple modules are temporarily configured (e.g., programmed), each module does not need to be configured or instantiated at any given time. For example, if a module includes a general-purpose processor configured with software, the general-purpose processor may be configured as different modules at different times. The software may accordingly include processors to configure, for example, one module at one time and different modules at different times.

[0077] Modules can provide information to other modules or receive information from other modules. Therefore, the aforementioned modules can be considered to be communicatively coupled. When multiple such modules exist simultaneously, communication can be achieved through signal transmission connecting the modules (e.g., via appropriate circuits and buses). In multiple embodiments where multiple modules are configured or instantiated at different times, communication between such modules can be achieved, for example, through the storage and retrieval of information into and from a memory structure accessible to multiple modules. For example, one module may perform an operation and store the output of that operation in a memory device to which the module is communicatively coupled. Further modules can then access the memory device, retrieve the stored output, and process it. Modules can also initiate communication with input or output devices to manipulate resources (e.g., sets of information).

[0078] Various operations of the exemplary methods described herein may be performed, at least in part, by one or more processors that are temporarily or permanently configured (e.g., by software) to perform the relevant operations. Whether temporarily or permanently configured, such processors may include processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may include processor-implemented modules in some exemplary embodiments.

[0079] Similarly, any methods or routines described herein may be processor-implemented at least partially. For example, at least part of the operation of a method may be performed by one or more processors or processor-implemented modules. The execution of a particular operation may reside not only within a single machine but also distributed across one or more processors deployed across multiple machines. In some exemplary embodiments, one or more processors may be located in a single geographical location (e.g., a home environment, an office environment, or a server farm), while in other embodiments, processors may be distributed across multiple locations.

[0080] The specific performance characteristics of an operation may be distributed among one or more processors and may reside not only within a single machine but also deployed across multiple machines. In some exemplary embodiments, one or more processors or processor-implemented modules may be located in a single geographical location (e.g., a home environment, an office environment, or a server farm). In other exemplary embodiments, one or more processors or processor-implemented modules may be distributed across multiple geographical locations.

[0081] Unless otherwise specified, discussions using terms such as “processing,” “computing,” “calculating,” “determining,” “presenting,” and “displaying” in this specification may refer to the operation or processing of a machine (e.g., a computer) that manipulates or transforms data represented as physical (electronic, magnetic, or optical) quantities in one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other mechanical elements that receive, store, transmit, or display information. Some embodiments may be described using the expressions “combined” and “connected” along with their derivatives. For example, some embodiments may be described using the term “combined” to indicate that two or more elements are in direct physical or electrical contact. However, the term “combined” may also mean that two or more elements cooperate or interact with each other even if they are not in direct contact with each other. These multiple embodiments are not limited to the context described above.

[0082] In this specification, any reference to “one embodiment” or “a certain embodiment” means that a particular element, feature, structure, or characteristic described in relation to an embodiment may be included in at least one embodiment. The phrase “in one embodiment” appearing in various places in this specification does not necessarily refer to the same embodiment. Furthermore, the articles “a” or “an” are used to describe the elements and components of the embodiments in this specification. This is merely for convenience and to give a general meaning to the description. This specification and subsequent claims should be interpreted as including one or at least one unless it becomes clear that they have a different meaning, and both singular and plural.

[0083] As used herein, the terms “include,” “contain,” “inclusive,” “contains,” “has,” “has,” or any variation thereof shall mean non-exclusive inclusion. For example, a process, method, article, or apparatus containing a list of elements is not necessarily limited to those elements and may include other elements not expressly enumerated or specific to such process, method, article, or apparatus. Furthermore, unless expressly declared otherwise, “or” means inclusive “or” and not exclusive “or.” For example, condition A or B is satisfied by any one of the following: A is true (or exists) and B is false (or does not exist), A is false (or does not exist) and B is true (or exists), and both A and B are true (or exist).

[0084] The above detailed descriptions should be interpreted as illustrative only, and not all possible embodiments are described, as it would be impractical, if not impossible, to describe all possible embodiments. Many alternative embodiments can be realized using either the current art or art developed after the filing date of this application. Those skilled in the art will understand, upon reading this disclosure, further additional alternative structural and functional designs for implementing the systems and methods disclosed herein through the principles disclosed herein. Accordingly, while specific embodiments and applications have been illustrated and described, it should be understood that the embodiments disclosed are not limited to the structures and components disclosed herein themselves. Various modifications, changes, and variations that will be obvious to those skilled in the art can be made to the configuration, operation, and details of the methods and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims.

[0085] Certain features, structures, or properties of any particular embodiment may be combined with one or more other embodiments in any suitable manner and in any suitable combination, including the use of selected features without corresponding use of other features. Furthermore, many modifications may be made to adapt particular applications, situations, or materials to the essential scope and spirit of the invention. Other variations and modifications of the embodiments of the invention described and illustrated herein are possible in light of the teachings herein and should be understood as being part of the spirit and scope of the invention.

[0086] While preferred embodiments of the present invention have been described, it should be understood that the present invention is not limited thereto and can be modified without departing from the present invention. The scope of the present invention is defined by the appended claims, and all devices included in the meaning of the claims are to be encompassed literally or equivalently. Accordingly, it should be understood that the above detailed description is intended to be illustrative rather than limiting, and the spirit and scope of the present invention are defined by the following claims, including all equivalents.

Claims

1. A predictive modeling computing system for optimizing atomization settings of inhalable biopharmaceutical powders containing proteins during protein particle processing, One or more processors, Memory containing a set of computer-executable instructions, The instruction includes, and when the instruction is executed by one or more processors, the predictive modeling computing system receives The memory's plan generation module receives a set of user-selected plan parameters relating to a statistical experiment corresponding to a statistical plan, wherein the plan parameters define atomization conditions for optimizing atomization settings for particles of an inhalable biopharmaceutical powder containing a protein during protein particle processing, and the statistical experiment corresponding to the statistical plan evaluates the stability of the protein under the atomization conditions. In the memory suitability evaluation module, the steps include determining the predicted median particle size of the biopharmaceutical powder in order to predict the suitability of the atomization conditions for producing the inhalable biopharmaceutical powder containing protein, The memory stability evaluation module includes a step of identifying one or more predictive quadratic models by fitting one or more response variables evaluated in the statistical experiment corresponding to the predictive quadratic models, wherein the one or more response variables include the predicted median particle size, and the one or more predictive quadratic models predict the effect of changes in the design parameters on the one or more response variables, and identify atomization conditions that achieve an inhalable particle size and maintain protein stability. The memory visualization module includes the step of displaying the visualized response surface on the user's display device for each of the one or more predictive quadratic models, A predictive modeling computing system that performs this task.

2. When the memory is executed, the predictive modeling computing system will Further instructions apply a desirability function to further optimize the aforementioned predictive quadratic model, The predictive modeling computing system according to claim 1, including the following:

3. The predictive modeling computing system according to claim 1 or 2, wherein the protein is an antibody.

4. The predictive modeling computing system according to any one of claims 1 to 3, wherein the predictive modeling computing system is configured to control a particle processing and analysis system to produce the inhalable biopharmaceutical powder.

5. The predictive modeling computing system according to claim 4, wherein when the memory is executed, the predictive modeling computing system controls a sprayer and / or a dry powder inhaler to administer an inhalable biopharmaceutical powder.

6. In order to compare the protein contained in an inhalable biopharmaceutical powder manufactured using the optimal atomization setting with the protein contained in an inhalable biopharmaceutical powder manufactured without the optimal atomization setting, the evaluated response variable is Changes in the proportion of oligomer species quantified by size exclusion chromatography (SEC-HPLC), Change in the Z-mean quantified by dynamic light scattering, Changes in secondary structure content quantified by circular dichroism (CD) spectroscopy, Changes in the fusion endothermic peak quantified by differential scanning calorimetry (DSC), or Predicted median granularity, A predictive modeling computing system according to any one of claims 1 to 5, comprising one or more of the above.

7. The aforementioned predicted median granularity is, [Math 1] A predictive modeling computing system according to any one of claims 1 to 6, which is determined by analyzing the size, weight fraction, and dry particle size of each droplet based on the above.

8. When the memory is executed, the predictive modeling computing system will The steps include: applying a desirability function to the predictive quadratic model, minimizing the predicted median particle size, and determining atomization settings that change the stability of the protein; In a particle processing analysis system, the steps include: setting the supply flow rate, atomizing air flow rate, and solid content to a value determined by the desirability function, thereby initiating a subsequent experiment using the predictive quadratic model to control the atomization settings; A step of receiving experimental data generated by the subsequent experiment from the particle processing analysis system, wherein the experimental data corresponds to one or more response variables measured with the inhalable biopharmaceutical powder produced with the defined atomization settings, A step of comparing the results of the subsequent experiment with the results of the statistical experiment corresponding to the statistical design, A predictive modeling computing system according to any one of claims 1 to 7, including further instructions to perform the following:

9. When the memory is executed, the predictive modeling computing system will A step of receiving experimental data from a particle processing analysis system, which is generated by a particle processing method and corresponds to one or more evaluated response variables. A predictive modeling computing system according to any one of claims 1 to 8, including further instructions to perform the following:

10. When the memory is executed, the predictive modeling computing system will A step of receiving experimental data from a particle processing analysis system, which is generated by a stored particle analysis method and corresponds to one or more evaluated response variables. A predictive modeling computing system according to any one of claims 1 to 9, including further instructions to perform the following:

11. A computer-implemented method for determining the optimal formulation atomization settings in a protein particle processing process, A step in a plan generation module of a predictive computing system is to receive a user-selected set of set parameters relating to a statistical experiment corresponding to a statistical plan, wherein the set parameters define atomization conditions for optimizing atomization settings for particles of an inhalable biopharmaceutical powder containing a protein during protein particle processing, and the statistical experiment corresponding to the statistical plan evaluates the stability of the protein under the atomization conditions. In the suitability evaluation module of the predictive computing system, the steps include determining the predicted median particle size of the biopharmaceutical powder in order to predict the suitability of the atomization conditions for producing the inhalable biopharmaceutical powder containing protein, A step of identifying one or more predictive quadratic models by fitting one or more response variables evaluated in statistical experiments corresponding to predictive quadratic models, wherein the one or more response variables include the predicted median particle size, and the one or more predictive quadratic models predict the effect of changes in the design parameters on the one or more response variables, and identify atomization conditions that achieve inhalable particle size and maintain protein stability. In the visualization module of the predictive computing system, the steps include displaying the visualized response surface on the user's display device for each of the one or more predictive quadratic models, A method that includes this.

12. Further optimizing the predictive quadratic model by applying a desirability function, The computer-implemented method according to claim 11, further comprising:

13. The computer-implemented method according to claim 11 or 12, wherein the protein is an antibody.

14. The computer-implemented method according to any one of claims 11 to 13, wherein the particle processing is configured to produce an inhalable biopharmaceutical powder.

15. The inhalable biopharmaceutical powder is delivered via (i) a sprayer and (ii) a dry powder inhaler, or both. The computer-implemented method according to claim 14, further comprising:

16. The aforementioned statistical design is a Box-Behnken design, and in order to compare the proteins contained in inhalable biopharmaceutical powders produced using optimal atomization settings with the proteins contained in inhalable biopharmaceutical powders produced without using optimal atomization settings, the evaluated response variable is: Changes in the proportion of oligomer species quantified by size exclusion chromatography (SEC-HPLC), Change in the Z-mean quantified by dynamic light scattering, Changes in secondary structure content quantified by circular dichroism (CD) spectroscopy, Changes in the fusion endothermic peak quantified by differential scanning calorimetry (DSC), or Predicted median granularity, A computer-implemented method according to any one of claims 11 to 15, comprising one or more of the above.

17. The aforementioned predicted median granularity is, [Math 2] A computer-implemented method according to any one of claims 11 to 16, which is determined by analyzing the size, weight fraction, and dry particle size of each droplet based on the above.

18. A step of determining atomization settings that apply a desirability function to the predictive quadratic model, minimize the predicted median particle size, and change the stability of the protein, In a particle processing analysis system, the steps include: setting the supply flow rate, atomizing air flow rate, and solid content to a value determined by the desirability function, thereby initiating a subsequent experiment using the predictive quadratic model to control the atomization settings; A step of receiving experimental data generated by the subsequent experiment from the particle processing analysis system, wherein the experimental data corresponds to one or more response variables measured with the inhalable biopharmaceutical powder produced with the defined atomization settings, A step of comparing the results of the subsequent experiment with the results of the statistical experiment corresponding to the statistical design, A computer-implemented method according to any one of claims 11 to 17, further comprising:

19. Receiving experimental data from a particle processing analysis system, which is generated by a particle processing method and corresponds to one or more evaluated response variables. A computer-implemented method according to any one of claims 11 to 18, further comprising:

20. Receiving experimental data from a particle processing analysis system, which is generated by a stored particle analysis method and corresponds to one or more evaluated response variables. A computer-implemented method according to any one of claims 11 to 19, further comprising:

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