Microfluidic devices and methods for designing and using microfluidic devices

Abstract

We provide a microfluidic platform that includes multiple biomimetic systems. [Solution] At least one of the platforms includes at least one inlet, a plurality of organ structures, each of which is sized relative to the other organ structures of the plurality of organ structures based on at least one predetermined human pharmacokinetic (PK) parameter, and a plurality of channels, each of which is in fluid communication with at least one other organ structure of the plurality of organ structures.

Description

[Technical Field]

[0001] [Claiming priority] This application claims priority to U.S. Patent Application No. 16 / 834,235, filed on 30 March 2020, the entire contents of which are incorporated herein by reference.

[0002] This disclosure generally relates to microfluidic devices. [Background technology]

[0003] Biomimetic systems (MPS) consist of a set of interconnected two- or three-dimensional cellular structures, often called organ-on-a-chip, tissue-chip, or in vitro organ structures. These structures are typically made from immortalized cell lines, primary cells derived from animals or humans, or organ-specific cells derived from naive cells, human embryonic stem cells, and induced pluripotent stem cells (iPSCs). Individually, each structure may be designed to replicate the structure and function of a human organ or organ region, with particular attention paid to the intracellular microenvironment and cellular heterogeneity. When these structures are combined to create an MPS, if drug delivery can be adequately modeled, they offer the potential to provide unprecedented physiological accuracy in in vitro studies of cell-cell, drug-cell, drug-drug, and organ-drug interactions.

[0004] Pharmacokinetics (PK) is a branch of pharmacology that aims to determine the fate of substances administered to living organisms. Typically, the substances under study can include any chemical xenobiotic, such as pharmaceuticals, insecticides, food additives, and cosmetics. In some cases, PK attempts to analyze chemical metabolism, absorption, metabolism, biodistribution, and / or excretion, seeking to discover the fate of a chemical substance from the moment it is administered until it is eliminated from the body. Generally, PK research can provide insights into how microorganisms process drugs.

[0005] Pharmacodynamics (PD) refers to the study of the biochemical and physiological effects of drugs (e.g., pharmaceuticals). These effects may include those that manifest in the bodies of animals (including humans), microorganisms, or combinations of microorganisms (e.g., infections). Pharmacodynamics places particular emphasis on dose-response relationships, i.e., the relationship between drug concentration and effect. Generally, PD research can provide insights into how drugs affect one or more diseases in microorganisms.

[0006] PK / PD modeling is a technique that combines two classical pharmacological principles: pharmacokinetics and pharmacodynamics. It integrates the components of pharmacokinetic and pharmacodynamic models into a set of mathematical formulas that enable the description of the time course of effect intensity in response to drug dose administration. [Overview of the project]

[0007] A method is provided in at least one aspect of this disclosure. The method comprises a step of analyzing the interaction between a molecular compound and each organ structure of a plurality of organ structures, where each organ structure of the plurality of organ structures corresponds to one organ species among a plurality of organ species. The method comprises a step of determining a plurality of concentration profiles based on the analysis, where each concentration profile corresponds to one organ structure among the plurality of organ structures. The method comprises a step of determining at least one pharmacokinetic (PK) parameter with respect to each organ species of the plurality of organ species and based on the concentration profile. The method comprises a step of determining at least one design parameter based on at least one PK parameter. The method comprises a step of designing a multi-organ structure platform based on at least one design parameter.

[0008] The step of determining at least one design parameter may include determining the relative size pattern between multiple organ structures based on at least one predetermined human PK parameter. The step of analyzing interactions may include determining at least one pre-design parameter for each of the multiple organ structures, where at least one pre-design parameter is determined based on the desired use of the multiple organ structures.

[0009] Multiple organ structures may include at least one of the following: gastrointestinal organ structures, liver organ structures, kidney organ structures, muscle organ structures, or adipose organ structures. Molecular compounds may include xenobiotics, and multiple concentration profiles may include multiple xenobiotic concentration profiles.

[0010] The step of determining at least one PK parameter may include a step of analyzing multiple concentration profiles using at least one ordinary differential equation. The at least one PK parameter may include at least one of clearance, permeability, and volume of distribution.

[0011] At least one design parameter may include at least one of the following: volume of at least one organ structure, surface area of ​​at least one organ structure, number of cells in at least one organ structure, arrangement of cells in at least one organ structure, flow pattern, volume of at least one channel, flow velocity, and flow segmentation value. The designed multi-organ structure platform may include four or more organ structures.

[0012] In one embodiment, a system is provided. The system comprises at least one inlet. The system comprises a plurality of organ structures, each of which is sized relative to the other organ structures of the plurality of organ structures based on at least one predetermined human pharmacokinetic (PK) parameter. The system comprises a plurality of flow channels, each of which fluidly connects one of the plurality of organ structures to at least one other organ structure of the plurality of organ structures.

[0013] Multiple channels may be configured to allow molecular compounds to flow through the system at a circulating flow velocity at which the molecular compounds are distributed at a threshold distribution velocity. Multiple organ structures may include at least one gastrointestinal organ structure, at least one liver organ structure, at least one kidney organ structure, and at least one of muscle organ structures or adipose organ structures. At least one organ structure of the multiple organ structures may include two membrane compartments separated by a porous membrane. Multiple organ structures may include at least four organ structures. Multiple organ structures may include at least one of gastrointestinal organ structures, liver organ structures, kidney organ structures, muscle organ structures, or adipose organ structures. Each of the multiple organ structures may include at least one of an apical compartment and a basal compartment.

[0014] At least a portion of the multiple channels may be configured to continuously circulate fluid through at least a portion of the multiple organ structures for at least a minute. The system may further include at least one second channel configured to fluidize at least one of the multiple organ structures and to facilitate the flow of fluid through at least one organ structure. The multiple organ structures may include gastrointestinal organ structures and kidney organ structures. At least one inlet may include a first inlet that fluidizees the gastrointestinal organ structure and a second inlet that fluidizees at least one of the multiple channels.

[0015] In one aspect, a method is provided. The method comprises the step of introducing a molecular compound into a microfluidic device having a plurality of organ structures. The method comprises flowing the molecular compound through the microfluidic device to allow the molecular compound to interact with the plurality of organ structures. The method comprises determining a plurality of concentration profiles based at least in part on the interacting step. The method comprises determining a plurality of first pharmacokinetic (PK) parameters including a first type based at least in part on the plurality of concentration profiles. The method comprises converting the plurality of first PK parameters into a plurality of second PK parameters including a second type.

[0016] The step of flowing the molecular compound through the microfluidic device may comprise distributing the molecular compound at a circulation flow rate at which the molecular compound is distributed at a threshold distribution rate.

[0017] The plurality of organ structures may comprise at least four organ structures. The plurality of organ structures may comprise at least one of a gastrointestinal tract organ structure, a liver organ structure, a kidney organ structure, a muscle organ structure, or an adipose organ structure. The plurality of organ structures may comprise at least one gastrointestinal tract organ structure, at least one liver organ structure, at least one kidney organ structure, and at least one of a muscle organ structure or an adipose organ structure. Each of the plurality of organ structures may comprise at least one of an apical compartment or a basolateral compartment.

[0018] The at least one pharmacokinetic parameter may comprise at least one of clearance, permeability, or volume of distribution. The step of determining the at least one pharmacokinetic parameter may comprise using at least one ordinary differential equation. The first type of PK parameters may comprise in vitro PK parameters, and the second type of PK parameters may comprise human PK parameters.

[0019] These and other aspects, features, and implementations can be expressed as methods, devices, systems, components, program products, as means or steps for carrying out functions, and in other ways.

[0020] These and other aspects, features, and implementations will become apparent from the following description, including the claims.

[0021] Implementations of the present disclosure can provide one or more of the following advantages. Compared with the prior art, implementations of the system described in the present disclosure are designed to better replicate in in vivo (e.g., human) biological systems that enable the study of organ-specific contributions to pharmacokinetics; they incorporate organ-organ crosstalk; they provide sufficient medium volume for frequent sample extraction to determine PK profiles; they facilitate medium recirculation, enabling the study of PK associated with both slow-clearance and fast-clearance drugs; they enable PK studies while each organ structure simultaneously contributes to the PK profile; they can be scaled based on individual MPS functions relevant to PK, and can incorporate appropriate mechanical cues such as shear stress to gastrointestinal tract and kidney organ structures. Compared with the prior art, implementations of the systems and methods described in the present disclosure provide in vitro results that are more accurately converted to predicted in vivo results; they facilitate the design of MPS platforms optimized for specific functions; and they can provide more rapid PK analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] [Figure 1] It is a block diagram showing an exemplary multi-MPS platform.

[0023] [Figure 2] It is a flowchart showing an exemplary method for designing a multi-MPS platform.

[0024] [Figure 3] It is a diagram showing an exemplary design of an MPS.

[0025] [Figure 4] This figure shows an example of how to determine a concentration profile.

[0026] [Figure 5] This figure shows an example of how to determine the PK parameter.

[0027] [Figure 6] This figure shows an exemplary model for determining design parameters.

[0028] [Figure 7] This flowchart illustrates an exemplary method for conducting PK research using a multi-MPS platform.

[0029] [Figure 8] This flowchart shows an exemplary model for converting in vitro PK parameters to in vivo PK parameters.

[0030] [Figure 9] This figure shows an exemplary computer system configured to run a machine learning model.

[0031] [Figure 10] This is a block diagram of an exemplary computer system used to provide computational functions associated with the algorithms, methods, functions, processes, flows, and procedures described herein. [Modes for carrying out the invention]

[0032] Investigating the pharmacokinetic (PK) properties of drugs can be particularly important during preclinical drug development, as it can facilitate decision-making regarding drug administration plans in early-stage clinical research. Animal species are often used to study the PK of compounds under development. However, such studies can be costly, considered unethical, and often fail to accurately capture the human phenotype. In vitro systems can be developed and utilized to investigate the absorption, distribution, metabolism, and excretion (ADME) of compounds. While these systems have been extremely useful in drug development, they are not without limitations, and there is a widespread recognition of the need for more physiologically realistic and better predictable in vitro models.

[0033] Multi-MPS (referred to as organ structures and organ-on-chip (OOC) in this specification) can offer numerous advantages in preclinical drug development. The term MPS encompasses a range of three-dimensional (3D), dynamically perfusated cell cultures with complex compositions (e.g., more than one cell type) and therefore can capture more features of human organ or tissue function compared to traditional static 2D cell cultures. Furthermore, the use of microfabricated biomimetic reactor platforms facilitates the in vitro reproduction of mechanical, fluid, spatial, and chemical stimuli and cues to which tissues may be exposed in vivo. To establish better pharmacological preclinical models that better replicate human physiology at a systemic level and translate more accurately into human outcomes, multi-MPS platforms can be designed to interconnect several MPSs that represent various organ aspects together, thus enabling organ-organ interactions and cross-signaling. In many cases, single- and multi-MPS platforms may be designed to mimic specific organ functions, microarchitectures, and organ-organ crosstalk relevant to the biological question at hand.

[0034] MPS has the potential to provide a means of preclinically exploring the PK properties of drugs. For such investigations, the integration of gastrointestinal and hepatic MPS may be important because these two organs can play a central role in the in vivo distribution and bioavailability of orally administered compounds (through processing such as intestinal permeability and hepatic metabolism). However, conventional MPS technologies may have the following limitations regarding their application to PK research: (1) they utilize materials that nonspecifically adsorb lipophilic compounds (e.g., polydimethylsiloxane (PDMS)); (2) they use relatively small culture volumes and cell numbers, which can negatively impact the collection of output biosignals and high-content measurements; (3) they do not allow continuous access to the MPS compartment for direct and frequent sample extraction of circulating drugs / metabolites, and therefore, data-rich quantitative PK profiles across all platform compartments are not always obtained; and (4) they are not typically coupled with mathematical modeling methodologies that resolve system-specific processes and parameters (e.g., flow rate, surface area) to biologically relevant parameters (e.g., intestinal permeability and intrinsic hepatic clearance, etc.), which may be important steps for subsequent in vitro-to-in vivo conversion.

[0035] Methods for resolving the aforementioned disadvantages can be provided using the systems and methods described herein. In some implementations, methods for designing a multi-MPS platform are described. The method may include the steps of: physically designing several individual MPSs; interacting a drug with each MPS to generate a concentration profile for each MPS; using the concentration profiles to estimate several PK parameters (e.g., coefficients related to gastrointestinal permeability, hepatic clearance, renal excretion, and volume of distribution, etc.) independently of the design specifications of the individual MPSs; using the PK parameters to determine design parameters for the multi-MPS platform; and designing the multi-MPS platform using the design parameters. The method may facilitate the optimization of the multi-MPS platform such that each MPS on the platform is designed relative to one another to reproduce a known human PK profile. The resulting MPS platform may also include channels that connect each MPS to at least one other MPS to facilitate continuous circulating flow of the drug between the MPSs. The method may design each channel to facilitate flow velocity, flow pattern, and flow splitting so that the platform as a whole is optimized to reproduce a known human PK profile. In some implementations, the resulting MPS platform includes a single-pass flow channel that facilitates the application of fluid shear stress to at least some of the MPS. The application of fluid shear stress to the MPS can stimulate cellular responses that may be important for endothelial cell function and atherosclerotic, for example, and can facilitate the differentiation of cells in cell culture.

[0036] In several implementations, methods for performing PK analysis using a multi-MPS platform are described. The method may include a step of flowing the drug through the platform to allow it to interact with each of the MPSs in the MPS platform. Samples of the MPS are collected at several time points, and the drug can be quantified for each sample, for example, using mass spectrometry. Based on the drug quantification, in-vitro PK profiles (concentration / time) can be generated. Based on these PK profiles, in vitro PK parameters (e.g., clearance and permeability) can be determined. The in-vitro PK parameters can be converted to human PK parameters using computer modeling, and in some implementations, machine learning.

[0037] In the following description, numerous specific details are included for illustrative purposes to provide a full understanding of this disclosure. However, it will become clear that this disclosure can be practiced even without these specific details. In other examples, well-known structures and devices are shown in block diagram form to avoid unnecessarily obscuring this disclosure.

[0038] In drawings, the specific arrangement or order of graphic elements, such as those representing devices, modules, instruction blocks, and data elements, is shown for ease of explanation. However, it should be understood by those skilled in the art that the specific ordering or arrangement of graphic elements in drawings is not intended to suggest that a particular order or sequence of processes, or separation of processes, is required. Furthermore, the inclusion of graphic elements in drawings is not intended to suggest that such elements are required in all embodiments, or that in some implementations, features represented by such elements may not be included in or combined with other elements.

[0039] Furthermore, where connections, relationships, or associations between two or more other graphic elements are shown in drawings using connecting elements such as solid or dashed lines or arrows, the absence of any such connecting element is not intended to suggest that such connections, relationships, or associations cannot exist. In other words, to avoid obscuring this disclosure, some connections, relationships, or associations between elements are not shown in the drawings. In addition, for ease of explanation, a single connecting element is used to represent multiple connections, relationships, or associations between elements. For example, if a connecting element represents the communication of signals, data, or instructions, it should be understood by those skilled in the art that such an element may, as necessary, represent one or more signal paths (e.g., buses) to affect the communication.

[0040] The implementation will now be described in detail, with examples illustrated in the attached drawings. Numerous specific details are provided in the following detailed description to ensure a thorough understanding of the various implementations described. However, it will be apparent to those skilled in the art that the various implementations described can be practiced without these specific details. In other examples, well-known methods, procedures, components, circuits, and networks are not described in detail so as not to unnecessarily obscure the nature of the implementation.

[0041] Hereafter, several features are described, each of which can be used independently of others or in any combination with other features. However, any individual feature may not address any of the problems discussed above, or may address only one of them. Some of the problems discussed above may not be fully addressed by any of the features described herein. Data that is given a heading but is related to a particular heading and not found in the section bearing that heading may be found elsewhere in this description.

[0042] Figure 1 is a block diagram of an exemplary multi-MPS platform 100. The platform 100 includes a fluid plate 101. In some implementations, the fluid plate 101 is at least partially constructed from polysulfone plastic, which may help minimize drug adsorption during use of the platform 100. In some implementations, the fluid plate 101 is at least partially constructed from one or more of polystyrene, polycarbonate, and cyclic olefin copolymers. The fluid plate 101 includes several MPS 110-140. In exemplary implementations, the several MPS 110-140 include gastrointestinal MPS 110, liver MPS 120, fat MPS 130, and kidney MPS 140. While certain organ structures are described in relation to exemplary implementations, other organ structures may be used in several implementations. In some implementations, the multiple MPS includes one or more muscle MPS and / or one or more skin MPS in addition to the MPS described in the exemplary implementation, or as a substitute for one or more of the MPS described in the exemplary implementation. Each of the multiple MPS 110-140 can be releasably attached to the fluid plate 101 (e.g., using the Transwell method) or integrated with the fluid plate 101.

[0043] Each of the multiple MPS 110–140 may include several cells (i.e., several cells and cell types) specific to the organ corresponding to its respective MPS. In some implementations, gastrointestinal MPS 110 corresponds to the esophagus, stomach, and / or pancreas and includes one or more cells 111 (i.e., cells and cell types) typically found in such organs, such as Caco2-BBe epithelial cells, mucin-producing goblet cells (HT29-MTX), and primary monocyte-derived dendritic cells. In some implementations, hepatic MPS 120 corresponds to the liver and includes one or more cells 121 typically found in the liver, such as hepatocytes, fibroblasts, Kupffer cells, hepatic sinusoidal endothelial cells (LSEC), hepatic stellate cells, and stromas. In some implementations, adipose MPS 130 corresponds to adipose organs and includes one or more cells 131 typically found in adipose organs, such as adipocytes. In some implementations, renal MPS 140 corresponds to the renal organ and includes one or more cells 141 that are typically found in the kidney, such as renal glomerular parietal cells, glomerular podocytes, renal proximal tubular brush margin cells, and collecting duct interstitial cells.

[0044] In some implementations, one or more of the multiple MPS 110-140 include two or more compartments separated by a porous membrane. In an exemplary implementation, the gastrointestinal MPS 110 includes an apical compartment 110a and a lateral compartment 110b separated by a porous membrane 110c. The compartments 110a and 110b may approximate absorption and filtration functions, such as epithelial cells. As will be described later, a molecular compound (e.g., a buffer containing a drug) can be added to the gastrointestinal MPS 110 via the apical compartment 110a, where it interacts with one or more cells 110 (e.g., epithelial cells) that can be attached to the porous membrane 111, and then at least a portion of the molecular compound can be absorbed into the lateral compartment 110b, flow out of the gastrointestinal MPS 110, and begin circulating through the platform 110. In some implementations, the apical compartment 110a contains epithelial cells, and the basal compartment 110b contains immune cells (e.g., macrophages). In an exemplary implementation, the kidney MPS 140 includes an apical compartment 140a and a basal compartment 140b separated by a porous membrane 140c. These compartments 140a, 140b may be configured to replicate the clearance function of the kidney organ. The basal compartment 140b can receive molecular compounds as they circulate through the platform 100, and at least a portion of the molecular compounds (and culture medium) can move through the porous membrane 140c while interacting with one or more cells 141 that may be attached to the porous membrane 140c in the apical compartment 140a.

[0045] The fluid plate 101 includes a plurality of channels 160a to d. Each of the channels 160a to d is configured to fluidly communicate each of the plurality of MPS 110 to 140 with at least one other MPS from the plurality of MPS 110 to 140. In the example shown, the first channel 160a provides fluid communication between the renal MPS 140 and the gastrointestinal MPS 110, the second channel 160b provides fluid communication between the gastrointestinal MPS 110 and the hepatic MPS 120, the third channel 160c provides fluid communication between the hepatic MPS 120 and the fatty MPS 130, and the fourth channel 160d provides fluid communication between the fatty MPS 130 and the renal MPS 140. Each of the multiple flow paths 160a to 160d may include one or more pumps 161 that facilitate flow through the flow paths 160a to 160d and the MPS 110 to 140.

[0046] As shown, the multiple channels 160a-d are designed to provide a circulating flow of molecular compounds (e.g., drugs in the culture medium or buffer) in a culture medium or buffer between multiple MPS 110-140. In some implementations, the multiple channels 160a-d are designed so that the drug flows between the multiple MPS 110-140 at a predetermined system-level flow rate. In some implementations, the system-level flow rate represents the rate at which a portion of the drug sample flows from the gastrointestinal MPS 110 to the remaining MPS 120-140 and back to the gastrointestinal MPS 110. In some implementations, each of the multiple channels 160a-d is individually designed to have a different flow rate that, when combined, equals the system-level flow rate. In other words, through the first channel 160a, the culture medium or buffer (with or without a drug) can flow between the kidney MPS 140 and the gastrointestinal MPS 110 at a first flow rate, while through the second channel 160b, the drug sample can flow between the gastrointestinal MPS 110 and the liver MPS 120 at a second flow rate different from the first flow rate.

[0047] In some implementations, the fluid plate 101 includes single passage channels 170, 180 for allowing fluid to flow through one or more of the multiple MPS 110-140 in a non-circulating manner. In an exemplary implementation, the fluid plate 101 includes a first single passage channel 170 for allowing fluid to flow through the gastrointestinal MPS 110, and a second single passage channel 180 for allowing fluid to flow through the kidney MPS 140. The single passage channels 170, 180 can facilitate the application of fluid shear stress to the cells of the MPS, which may help stimulate cellular responses that are important for endothelial cell function and atherosclerotic, and cell differentiation during cell maturation, for example. Although described as non-circulating in the exemplary implementation, in some implementations, one or more of the passage channels 170, 180 may be configured to allow fluid to flow in a circulating manner through one or more of the multiple MPS 110-140.

[0048] In an exemplary implementation including one or more inlets 165a, 165b, the fluid plate 101 includes a first inlet 165a configured to receive a fluid sample (such as a drug sample), allowing the fluid sample to begin flowing through the platform 100 at the location of the first channel 160a. Insertion of the fluid sample through the first inlet 165a can replicate intravenous (IV) administration of a drug. The fluid plate 101 also includes a second inlet 165b configured to receive a fluid sample, allowing the fluid sample to begin flowing through the platform 100 into the gastrointestinal MPS 110. Insertion of the fluid sample through the second inlet 165b can replicate oral administration of a drug. The first inlet 165a is illustrated as being located between the renal MPS 140 and the gastrointestinal MPS 110, and the second inlet 165b is illustrated as being located on the gastrointestinal MPS 110, but other implementations are not limited thereto. In some implementations, the first inlet 165a is located between the liver MPS 120 and the fat MPS 130. Such implementations facilitate the differentiation of on-chip PK profiles for different types of drugs (e.g., drugs with fast clearance and drugs with slow clearance, drugs with fast permeation and drugs with slow permeation).

[0049] When constructing platform 100, each of the multiple channels 160a-160d and multiple MPS 110-140 may be designed relative to one another to maximize several desired functions (e.g., performing PK analysis). In some implementations, each of the multiple channels 160a-160d and multiple MPS 110-140 is designed according to one or more design parameters (e.g., culture medium volume, cell number by type, surface area, system-level flow velocity, flow pattern, and flow splitting) to maximize platform 100's ability to approximate human PK profiles. The step of determining one or more design parameters to maximize desired functions will be discussed later in this specification.

[0050] In some implementations, to operate platform 100, the platform is coupled to a pneumatic plate, where a pneumatic membrane can be used to separate platform 100 from the pneumatic plate. The pneumatic plate may include multiple outlets configured to distribute compressed air to small ports located beneath each of the pumps 161 on platform 100. The distribution of compressed air can operate the pumps 161, thereby allowing a fluid sample to flow through platform 100.

[0051] Figure 2 is a flowchart illustrating an exemplary method 200 for designing a multi-MPS platform. In several implementations, method 200 is used to design the MPS platform 100 described above with reference to Figure 1. The method comprises the steps of analyzing the interactions between molecular compounds and multiple organ structures (block 210), determining multiple concentration profiles (block 220), determining at least one PK parameter (block 230), determining at least one design parameter (block 240), and designing a multi-MPS platform (block 250).

[0052] In block 210, multiple MPSs are designed, each corresponding to a specific organ (e.g., liver, fat, skin, muscle, gastrointestinal tract, and kidney). In some implementations, a drug sample is introduced by flowing it through the MPS, allowing the drug sample to interact with each MPS for a predetermined period of time. Pre-design parameters may be determined to generate an initial design for each MPS. The initial design for each MPS may be based on maximizing tissue function (e.g., with respect to viability and PK-related functions).

[0053] Figure 3 shows exemplary designs 301–303 of the MPS. In the exemplary implementation, three different designs 301–303 of the hepatic MPS, design 1(301), design 2(302), and design 3(303), are initially developed. Design 1(301) involves planar hepatocyte culture (e.g., single culture or co-culture). Design 2(302) involves co-culture of hepatocytes and stromas having a physical membrane (which may be porous or non-porous) that separates the hepatocytes from the stromas. Design 3(303) involves co-culture of hepatocytes and stromas having micropatterning of small “islands” for hepatocyte adhesion. Exemplary co-culture cell types include hepatocytes, fibroblasts, Kupffer cells, liver cells, LSEC cells, and hepatic stellate cells. Several MPS configurations for each design (e.g., with respect to cell type, cell type ratio, and micropattern configuration) are experimentally tested to determine the maximum function with respect to PK function (e.g., hepatic MPS with the design / configuration combination that yields the highest clearance). As used herein, the design / configuration combination may also be called pre-design parameters. In some implementations, pre-design parameters that yield maximum PK function are selected for use. Examples of PK functions that may be used are absorption (e.g., for gastrointestinal MPS), volume of distribution (e.g., for adipose / cutaneous / muscle MPS), hepatic clearance (e.g., for hepatic MPS), and excretion (e.g., for renal MPS).

[0054] Referring again to Figure 2, in block 220, after the initial design of each MPS is selected, one or more samples of each MPS are collected over a predetermined period of time, and the drug concentration profile is determined for each sample.

[0055] Figure 4 shows an example of determining the concentration profile 401. In the exemplary implementation, a closed-loop perfusion method is used to interact a drug sample with a liver MPS 402 constructed using pre-design parameters similar to those of Design 2 discussed earlier with reference to Figure 3. Samples of MPS 402 are collected at several time points over a predetermined period of time, and for each time point, the drug concentration in the sample is quantified using mass spectrometry. Based on the quantified drug concentration, the drug concentration profile 401 is generated.

[0056] Referring again to Figure 2, in block 230, at least one PK parameter is determined based on the concentration profile. In some implementations, at least one PK parameter includes at least one PK parameter for each organ type represented by multiple MPSs. In other words, the PK coefficient can be determined independently of the MPS specifications (e.g., clearance per cell). A first-order differential equation can be used to determine the PK parameter.

[0057] Figure 5 shows an example of determining the PK parameter. As shown, the first-order differential equation is:

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[0058]

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[0059]

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[0060] Referring again to Figure 2, in block 240, the determined PK parameters are used to determine the design parameters for the MPS platform. In some implementations, the design parameters include the volume of culture medium for at least one MPS, the surface area for at least one MPS, the number of cells for each MPS, the volume of culture medium for at least one channel, the system-level flow velocity, the flow pattern, and / or flow splitting. In some implementations, to determine the design parameters, an ordinary differential equation can be derived using a connection diagram, and the ordinary differential equation can be used together with the determined PK parameters and a known human in vivo pharmacokinetic profile to optimize the design parameters so that the resulting MPS platform approximates a known "in vivo" pharmacokinetic profile.

[0061] Figure 6 shows an exemplary model 601 for determining design parameters. In the exemplary implementation, model 601 of the multi-MPS platform is shown, where the multi-MPS platform of model 601 includes gastrointestinal MPS 610, hepatic MPS 620, fatty MPS 630, and renal MPS 640. Systemic circulating flow velocity (Q 全身 Q represents the continuous distribution rate of cell culture medium and drug between MPSs. Flow splitting (i.e., renal flow velocity (Q)) for the apical and basal compartments of the renal MPS 640. 腎臓 ) Whole-body flow velocity (Q 全身 The ratio (P) can be based on the physiological ratio of renal blood flow rate (RBF) and glomerular filtration rate (GFR). The drug can be administered either to the systemic circulatory loop 650 (which may represent intravenous (IV) administration) or to the apical compartment of the gastrointestinal MPS 610 (which may represent oral administration). With respect to the latter, the permeability constant (P) of various drugs for transport through the monolayer of the gastrointestinal MPS 610, a typical gastrointestinal epithelial model, can be used.消化管 ) can be calculated from drug absorption experiments using an individual gastrointestinal MPS derived from either iPS cells or primary gastrointestinal cells, using a calculation model based on the principle of Fick's law. It can be assumed that the drug is metabolized by hepatocytes in liver MPS 620. For the parent drug metabolized by hepatocytes in vitro, the intrinsic drug clearance rate (Cl L ) can be obtained from individual liver MPS experiments using human hepatocytes and computational analysis (e.g., by applying first-order kinetics). It can be assumed that the drug is distributed and accumulated in slowly perfused organs such as adipose tissue, muscle, or skin tissue. The volume of distribution (V d ) parameter can be estimated from experiments using adipose MPS 630. Drugs that undergo glomerular filtration move into the kidney lumen and then are reabsorbed into the systemic circulation. Furthermore, drugs in the systemic circulation are secreted from the bloodstream into the lumen of the nephron. The permeation rate constants for reabsorption and secretion (P 腎臓 ) can be estimated in individual kidney MPS experiments and analyzed using a calculation model based on Fick's law. Drugs that enter the kidney lumen but are not completely reabsorbed into systemic circulation are typically excreted in urine. To repeat the excretion of the parent drug from the kidney MPS, a constant flow into the waste container (Q 老廃物 ) can be introduced to equal the kidney luminal flow (Q 腎臓 ). Since active filtration may not occur between the medium and the drug, the medium can be replenished at the same rate as the excretion rate (Q 老廃物 ) relative to the basal circulation (medium replenishment).

[0062] A mechanical calculation model can be implemented to describe absorption, distribution, metabolism, and excretion processes using ordinary differential equations for each of the platforms and based on the principle of mass conservation. Using these models, experimental pharmacokinetic data from individual MPS can be converted into pharmacokinetic parameters such as Cl L , P 腎臓 , P 消化管 , and V d . Further using multifunctional scaling techniques, the design parameters of interconnected MPS platforms (e.g., surface area of ​​gastrointestinal and renal MPS (A, respectively)) can be adjusted. 消化管 and A 腎臓 It can notify the number of cells in the liver and adipose MPS. Multifunctional scaling techniques can

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[0063] In the exemplary implementation, the objective includes two elements: (i) repeating clinically observed plasma concentration profiles (observation) of each drug in the training set, and (ii) using model calculations (prediction) of drug concentration profiles in systemic circulation on a multi-MPS platform. In vitro PK parameters (Cl) obtained from individual MPS experiments L , P 腎臓 , P 消化管 , and V d ) is a fixed parameter, and the design parameter (A) of the multi-MPS platform is a fixed parameter. 消化管 , A 腎臓 The design parameters (along with the number of hepatocytes and adipocytes) are implemented in a multifunction scaling model, and the design parameters can be estimated by minimizing the objective function. The training set of drugs can simultaneously contain several drug time-concentration profiles that will be used in the calculation. To explain the differences (observations) in drug dosage and bioavailability in vivo, the following: a.

number

[0064] In the formula, c(t) invivo This refers to the measured time-dependent drug plasma concentration, c(t) invivo ' is the normalized drug concentration, F is the bioavailability, D is the dose administered, V is the volume of distribution, and t is time.

[0065] For computational simulation (prediction), normalized drug concentration (e.g., 1 μg ml) -1 A unit concentration of ( ) may be administered to the platform, which may be equivalent to a dose of 1 μg administered for 1 ml of apical volume of gastrointestinal MPS. To compare the in vitro platform with the normalized in vivo time-based concentration profile, normalization of the same concept may be performed for the model concentration. a.

number

[0066] In the formula, V invitro This is the total culture medium volume of the platform, and D invitro This is the dose administered to the apical region of the gastrointestinal MPS ("oral administration"), F invitro This represents the fraction of the drug in systemic circulation. While different doses are administered in humans, this normalization method for both in vivo and in vitro time-concentration profiles allows for simultaneous comparison of various drugs at the same unit concentration. The differences in normalized concentrations can then be directly compared in the objective function.

[0067] In the exemplary implementation, the following design parameters are used: gastrointestinal, liver, adipose, and renal MPS compartment volume, gastrointestinal and renal MPS surface area, liver and adipose MPS cell count, and whole-body flow velocity (Q). 全身 ), as well as MPS specific flow velocity (e.g., Q 腎臓) can be investigated. To reduce the number of fitting parameters, the apical compartment volume can be fixed based on practical considerations such as the nutritional requirements of tissue culture obtained from previous experimental results. Prior to the parameter optimization algorithm, certain limitations on the range of design parameters to be fitted can be imposed, for example, based on experimental feasibility and practicality. In an exemplary implementation, the acceptable range for the total culture medium volume of the system includes 0.1 to 5 ml, and for filtered flow to the kidney, the acceptable range includes 1 to 10 ml per day.

[0068] Referring again to Figure 2, in block 250, the design parameters for each MPS are combined with the pre-design parameters to construct the MPS platform. Once constructed, the MPS platform can be verified by comparing the predictions obtained from block 240 with the results of experiments using the multi-MPS platform. If the results are within the threshold, the MPS can be verified as usable. If the results are not within the threshold, blocks 240-250 can be repeated as needed.

[0069] Figure 7 is a flowchart illustrating an exemplary method 700 for conducting PK research using a multi-MPS platform. The method comprises the steps of inserting a molecular compound into a microfluidic device (block 710), interacting the molecular compound with multiple organ structures (block 720), determining multiple concentration profiles (block 730), determining multiple first PK parameters (block 740), and converting the multiple first PK parameters into multiple second PK parameters (block 750).

[0070] In block 710, a molecular compound (e.g., a drug sample) is inserted into a microfluidic device such as the MPS platform 100 described above, with reference to Figure 1. The microfluidic device may include multiple organ structures. In some implementations, the microfluidic device has four or more organ structures. The multiple organ structures may include liver organ structures, kidney organ structures, muscle organ structures, and / or adipose organ structures. At least one of the multiple organ structures may include an apical membrane, a basolateral membrane, or both.

[0071] In block 720, the molecular compound is flowed through the microfluidic device so that it interacts with the organ structure of the microfluidic device (e.g., MPS). In some implementations, this includes a step of distributing the molecular compound at a circulating velocity at which the molecular compound is distributed at a threshold distribution velocity. The threshold distribution velocity can be determined using method 200 discussed earlier with reference to Figure 2 (e.g., when determining the design parameters of the MPS platform).

[0072] In block 730, samples are taken from each molecular structure at one or more time points during the time the molecular compound is flowing through the microfluidic device, and the concentration profile of each molecular structure is determined. In some implementations, mass spectrometry is performed on the samples to determine the concentration profiles.

[0073] In block 740, multiple in vitro PK parameters are determined for microfluidic devices based on the concentration profile, as previously described with reference to Figure 2. These multiple in vitro PK parameters may include clearance, absorption, distribution volume, and excretion. As previously shown, the in vitro PK parameters can be independent of the specifications of the organ structure. The in vitro PK parameters can be determined using the concentration profile with at least one ordinary differential equation.

[0074] In block 750, in vitro PK parameters are used to determine in vivo PK parameters. In some implementations, physiological pharmacokinetic (PBPK) and / or quantitative systems pharmacology (QSP) models are used to convert in vitro PK parameters to in vivo PK parameters. QSP models can be developed based on a wealth of biological and (patho)physiological data, as well as information on target and drug properties (e.g., human pathophysiology, biochemistry, cell biology, genomics, in vivo data, clinical data, target properties, drug properties, and pharmacology). Quantitative information derived from MPS experiments then provides the QSP model with values ​​or ranges for specific parameters. MPS results may provide biology-specific parameters to define a hypothetical patient biology for simulating drug activity that may be specifically related to drug activity or originate from other sources. In some implementations, QSP / PBPK models may include scaling factors to scale in vitro PK parameters to in vivo PK parameters. For example, since an average human liver can contain 3,000,000,000 liver cells, while a liver MPS can only contain 100,000 liver cells, scaling in vitro parameters to human specifications can be beneficial. In some implementations, one or more machine learning models are used to determine an empirical scaling factor (ESF) to scale in vitro PK parameters to in vivo PK parameters. As described later, the ESF can be determined by a machine learning model using the molecular structure of the drug used in the experiment, based on known results from past experiments using drugs with similar molecular structures.

[0075] Figure 8 is a flowchart of an exemplary model 800 for converting in vitro PK parameters to in vivo PK parameters. Using the human physiological pharmacokinetic (PBPK) model 800 exemplified in Figure 8, which can be obtained based on the principle of conservation of mass, in vitro PK results can be converted to human in vivo profiles. In an exemplary implementation, PK parameters obtained from in vitro MPS (e.g., hepatic MPS, renal MPS, adipose MPS, and gastrointestinal MPS) can be used as input parameters 810 in the corresponding components of the human PBPK model 800. In other words, intrinsic clearance values ​​can be obtained, for example, from hepatic MPS of an MPS platform and used as input parameters for the liver component of the PBPK model 800. Parameters can be adjusted based on differences between in vitro and in vivo physiology (e.g., differences in cell number and enzyme activity) and drug-specific parameters (e.g., biochemical properties). With respect to physiological differences, parameters can be directly scaled. For example, scaling can be obtained based on the number of hepatocytes in hepatic MPS and the number of hepatocytes in human liver. For drug-specific parameters, empirical scaling factors (ESFs) can be used. ESF parameters for novel molecular entities can be estimated using machine learning algorithms, as described later. The scaled in vitro parameters can then be used in the PBPK model 800 to convert the results of the in vitro MPS into an in vivo human PK profile 820. This approach can be applied to several PK parameters, such as permeability parameters in the gastrointestinal tract and kidneys, hepatic clearance in the liver, and volume of distribution in fat.

[0076] Figure 9 shows an exemplary computer system 900 configured to run a machine learning model. Generally, the computer system 900 is configured to process data showing molecular structure and determine ESF for scaling in vitro PK parameters to in vivo PK parameters. System 900 includes a computer processor 910. The computer processor 910 includes computer-readable memory 911 and computer-readable instructions 912. System 900 also includes a machine learning system 950. The machine learning system 950 includes a machine learning model 920. The machine learning model 920 may be separated from or integrated with the computer processor 910.

[0077] The computer-readable medium 911 (or computer-readable memory) may include any data storage technology type suitable for the local technical environment, including but not limited to semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory, removable memory, disk memory, flash memory, dynamic random access memory (DRAM), static random access memory (SRAM), and electrically erasable programmable read-only memory (EEPROM). In one embodiment, the computer-readable medium 911 includes code segments having executable instructions.

[0078] In some implementations, the computer processor 910 includes a general-purpose processor. In some implementations, the computer processor 910 includes a central processing unit (CPU). In some implementations, the computer processor 910 includes at least one application-specific integrated circuit (ASIC). The computer processor 910 may also include a general-purpose programmable microprocessor, a graphics processing unit, a dedicated programmable microprocessor, a digital signal processor (DSP), a programmable logic array (PLA), a field-programmable gate array (FPGA), dedicated electronic circuitry, or a combination thereof. The computer processor 910 is configured to execute program code means such as computer executable instructions 912 and to execute executable logic including a machine learning model 920.

[0079] The computer processor 910 is configured to receive data, for example, that shows the molecular structure of a drug. The data can be obtained by one or more means, such as wireless communication with a database, optical fiber communication, USB, and CD-ROM.

[0080] The machine learning model 920 is capable of processing data to determine the ESF. In some implementations, the machine learning model 920 is trained to determine the ESF using a dataset that includes molecular properties of several drugs (e.g., chemical structure and / or biochemical properties), in vitro PK parameters of the drugs determined using the MPS platform, and known human in vivo PK parameters of the drugs. The machine learning model 920 can determine a scaling factor between the in vivo and in vitro PK parameters for each drug and relate the scaling factor to the molecular properties of the drug. Therefore, when a novel drug is introduced into the machine learning model 920, it can determine a scaling factor for the in vitro PK properties of the drug obtained using the MPS platform based on the molecular properties of the drug.

[0081] The machine learning system 950 can train the machine learning model 920 by applying machine learning techniques. As part of training the machine learning model 920, the machine learning system 950 forms a training set of input data by identifying a positive training set of input data items that are determined to have the target characteristic, and in some embodiments, a negative training set of input data items that lack the target characteristic.

[0082] The machine learning system 950 extracts feature values ​​from the input data of the training set, where features are variables that are thought to potentially relate to whether or not an input data item has a property to which it is associated. An ordered list of features for the input data is referred to herein as the feature vector for the input data. In one embodiment, the machine learning system 950 applies dimensionality reduction (e.g., by linear discriminant analysis (LDA) or principal component analysis (PCA)) to reduce the amount of data in the feature vector for the input data to a smaller, more representative set of data.

[0083] In some implementations, the machine learning system 950 uses supervised machine learning to train the machine learning model 920 using positive and negative training set feature vectors that serve as input. Various machine learning techniques may be used in various embodiments, such as linear support vector machines (linear SVM), Boost for other algorithms (e.g., AdaBoost), neural networks, logistic regression, naive Bayes, memory-based learning, random forests, bagging trees, decision trees, boosted trees, or booststamps. When applied to the feature vectors extracted from the input data items, the machine learning model 920 outputs an indication of whether the input data items have the property of interest, such as a Boolean pass / fail estimate or a scalar value representing a probability.

[0084] In some embodiments, the validation set is formed from additional input data, in addition to that of the training set, whose presence or absence of the target characteristic has already been determined. The machine learning system 950 applies the trained machine learning model 920 to the validation set data to quantify the accuracy of the machine learning model 920. Common metrics applied in accuracy measurement include accurate = TP / (TP+FP) and recall = TP / (TP+FN), where accurate is the number (TP or true positives) that the machine learning model correctly predicted out of the total number of predictions (TP+FP or false positives) it made, and recall is the number (TP) that the machine learning model correctly predicted out of the total number of input data items (TP+FN or missed detections) that had the target characteristic. The F-score (F-score = 2 × PR / (P+R)) unifies accurate and recall into a single measure. In one embodiment, the machine learning module iteratively retrains the machine learning model until a stopping condition occurs, such as an indication of an accurate measurement that the model is sufficiently accurate, or the number of training sessions performed.

[0085] In some implementations, the machine learning model 920 is a convolutional neural network (CNN). A CNN can be constructed based on the assumption that its inputs correspond to image pixel data of an image, or other data containing features at multiple spatial locations. For example, the set of inputs can form a multidimensional data structure representing the color features of an exemplary digital image, such as a tensor (e.g., a biological image of a biological tissue). In some implementations, the inputs to the CNN correspond to various other types of data, such as data from various devices and sensors of a vehicle, point cloud data, audio data containing several features or raw speech at each of multiple time stages, or various types of one-dimensional or multidimensional data. The convolutional layers of the CNN can process the inputs to transform the image features represented by the inputs of the data structure. For example, the inputs are processed by performing a dot product operation with the input data along a given dimension of the data structure and a set of parameters for the convolutional layers.

[0086] The stage of performing computations for a convolutional layer may include applying one or more sets of kernels to the input portion of the data structure. The way a CNN performs computations may be based on specific characteristics for each layer of an exemplary multilayer neural network or deep neural network that supports the workload of a deep neural network. A deep neural network may include one or more convolutional towers (or layers) along with other computational layers. In particular, with respect to exemplary computer vision applications, these convolutional towers often account for the majority of the inference computations performed. The convolutional layers of a CNN may have a set of artificial neurons arranged in three dimensions: width, height, and depth. The depth dimension corresponds to the third dimension of the input or activation volume and can represent each color channel of the image. For example, an input image may form the input volume of data (e.g., activations), and the volume has dimensions 32x32x3 (width, height, and depth, respectively). The three depth dimensions may correspond to the RGB color channels: red (R), green (G), and blue (B).

[0087] Generally, the layers of a CNN are configured to transform a 3D input volume (input) into a neuronal-activated multidimensional output volume (activation). For example, a 32x32x3 3D ​​input structure holds the raw pixel values ​​of an exemplary image, which in this case is an image with width 32, height 32, and three color channels R, G, and B. The convolutional layers of the CNN in the machine learning model 920 compute the outputs of neurons that can be connected to local regions in the input volume. Each neuron in the convolutional layer can be spatially connected only to local regions in the input volume, but can be connected up to the maximum depth of the input volume (e.g., all color channels). For a set of neurons in the convolutional layer, the layer computes the dot product of the parameters (weights) for the neuron and a specific region in the input volume to which the neuron is connected. This computation can result in a volume such as 32×32×12, where 12 corresponds to the number of kernels used for the computation. The connection of a neuron to the input of a region can have a spatial range along the depth axis equal to the depth of the input volume. The spatial range corresponds to the spatial dimensions of the kernel (e.g., x and y dimensions).

[0088] A set of kernels may have spatial features that include width and height and extend through the depth of the input volume. Each set of kernels for a layer is applied to one or more sets of inputs given to the layer. In other words, with respect to each kernel or set of kernels, the machine learning model 920 can superimpose a multidimensionally representable kernel onto a first part of a layer input (e.g., it forms an input volume or input tensor) that can be represented multidimensionally. For example, a set of kernels for the first layer of a CNN may have a size of 5 × 5 × 3 × 16, where the size corresponds to a width of 5 pixels, a height of 5 pixels, a depth of 3 corresponding to the color channels of the input volume to which the kernel is applied, and an output dimension of 16 corresponding to the number of output channels. In this context, the set of kernels contains 16 kernels, so that the output of the convolution has a depth dimension of 16.

[0089] Next, the machine learning model 920 can compute the dot product from the overlapping elements. For example, the machine learning model 920 can convolve (or slide) each kernel across the width and height of the input volume and compute the dot product of the entries between the kernel and the input for the position or region of the image. Each output value in the convolution output is the result of the dot product of the kernel and several sets of inputs from an exemplary input tensor. The dot product can produce a convolution output corresponding to a single-layer input, for example, an activation element having an upper-left position in the overlapping multidimensional space. As discussed above, neurons in the convolution layer can be coupled to a region of the input volume containing multiple inputs. The machine learning model 920 can convolve each kernel over each input of the input volume. The machine learning model 920 can perform this convolution operation, for example, by moving (or sliding) each kernel over each input in the region.

[0090] The machine learning model 920 can move each kernel over the input of a region based on a stride value for a given convolutional layer. For example, if the stride is set to 1, the machine learning model 920 can then move the kernel over the region by 1 pixel (or input) at a time. Similarly, if the stride is 2, the machine learning model 920 can then move the kernel over the region by 2 pixels at a time. Thus, the kernel may be shifted based on the stride value for the layer, and the machine learning model 920 can repeat this process until the input for the region has the corresponding dot product. Related to the stride value is the skip value. The skip value can identify one or more sets of inputs (2x2) that are skipped when the input is loaded for processing in the neural network layer within the region of the input volume. In some implementations, the input volume of pixels for an image may be "padding" with zeros, for example, near the border region of the image. Zero padding is used to control the spatial size of the output volume.

[0091] As discussed earlier, the convolutional layers of a CNN are configured to transform a 3D input volume (input region) into a multidimensional output volume of neuronal activations. For example, when a kernel is convolved onto the width and height of the input volume, the machine learning model 920 can generate a multidimensional activation map containing the results of convolving the kernel at one or more spatial positions based on stride values. In some cases, increasing the stride value produces a spatially smaller output volume of activations. In some implementations, the activations may be applied to the output of the convolution before the output is sent to the next layer of the CNN.

[0092] An exemplary convolutional layer may have one or more control parameters for the layer that describe the layer's characteristics. For example, the control parameters may include the number of kernels K, the spatial range of the kernels F, the stride (or skip) S, and the amount of zero padding P. The numerical values ​​for these parameters, the input to the layer, and the parameter values ​​for the kernels for the layer shape the computations that occur in the layer and the size of the output volume for the layer. In some implementations, the spatial size of the output volume is computed as a function of the input volume size W using the formula (W - F + 2P) / S + 1. For example, the input tensor may represent a pixel input volume of size [227 × 227 × 3]. A convolutional layer in a CNN may have a spatial range value F = 11, a stride value S = 4, and no zero padding (P = 0). The machine learning model 920 performs the calculation for the layer using the above formula and a layer kernel quantity of K=96, and the calculation results in a convolutional layer output volume of size [55×55×96], where 55 is obtained from [(227-11+0) / 4+1=55].

[0093] The computations for the convolutional layers of a CNN, or other layers (e.g., dot product calculations), involve using the computing units of the machine learning model 920's hardware circuitry to perform mathematical operations, such as multiplication and addition. The design of the hardware circuitry may limit the system's ability to fully utilize the circuit's computing cells when performing computations for the neural network layers.

[0094] Figure 10 is a block diagram of an exemplary computer system 1000 used to provide computing capabilities associated with the algorithms, methods, functions, processes, flows, and procedures described herein (such as Method 200, as mentioned above with reference to Figure 2), according to some implementations of this disclosure. The exemplary computer 1002 is intended to encompass any computing device, including a server, desktop computer, laptop / notebook computer, wireless data port, smartphone, personal data assistant (PDA), tablet computing device, or one or more processors within such devices, including physical instances, virtual instances, or both. Computer 1002 may include input devices such as keypads, keyboards, and touchscreens that can accept user information. Computer 1002 may also include output devices that can transmit information associated with the operation of computer 1002. The information may include digital data, visual data, audio information, or a combination of information. The information may be presented in a graphical user interface (UI or GUI).

[0095] Computer 1002 can function in a client, network component, server, database, persistence, or computer system component to perform the subject matter described in this disclosure. The illustrated computer 1002 is communicably connected to network 1030. In some implementations, one or more components of computer 1002 may be configured to operate in a variety of environments, including cloud computing-based environments, local environments, global environments, and combinations of environments.

[0096] At a high level, computer 1002 is an electronic computing device capable of receiving, transmitting, processing, storing, and managing data and information associated with the subject being described. According to some implementations, computer 1002 may also include, or be communicably connected to, an application server, mail server, web server, cache server, streaming data server, or a combination of servers.

[0097] Computer 1002 can receive requests from client applications via the network 1030 (for example, running on another computer 1002). Computer 1002 can respond to received requests by processing them using software applications. Requests can also be sent to computer 1002 from internal users (for example, from a command console), external (or third) parties, automation applications, entities, individuals, systems, and computers.

[0098] Each component of computer 1002 can communicate using the system bus 1003. In some implementations, any or all components of computer 1002, including hardware or software components, can be connected to each other or to interface 1004 (or a combination of both) via the system bus 1003. The interface can be an application programming interface (API) 1012, a service layer 1013, or a combination of API 1012 and service layer 1013. API 1012 can include specifications for routines, data structures, and object classes. API 1012 may be computer language independent or computer language dependent. API 1012 may refer to a complete interface, a single function, or a set of APIs.

[0099] Service layer 1013 can provide software services to computer 1002 and other components (whether or not they are exemplified) that are communicatively connected to computer 1002. The functionality of computer 1002 may be accessible to all service users utilizing this service layer. Software services, such as those provided by service layer 1013, may provide defined functionality that is reusable through defined interfaces. For example, the interface may be software written in a language that provides data in Java®, C++, or Extensible Markup Language (XML) format. Although exemplified as an integrated component of computer 1002, in alternative implementations, API 1012 or service layer 1013 may be standalone components in relation to other components of computer 1002 and other components communicatively connected to computer 1002. Furthermore, any or all parts of API 1012 or service layer 1013 may be implemented as a child or submodule of another software module, an enterprise application, or a hardware module without departing from the scope of this disclosure.

[0100] Computer 1002 includes interface 1004. Although illustrated as a single interface 1004 in Figure 10, two or more interfaces 1004 may be used depending on specific needs, desires, or the specific implementation and described functionality of computer 1002. Interface 1004 can be used by computer 1002 to communicate with other systems (whether illustrated or not) connected to network 1030 in a distributed environment. Generally, interface 1004 may include, or be implemented using, logic encoded in software or hardware (or a combination of software and hardware) capable of communicating with network 1030. More specifically, interface 1004 may include software that supports one or more communication protocols associated with the communication. Thus, network 1030 or interface hardware may be capable of communicating physical signals inside and outside the illustrated computer 1002.

[0101] Computer 1002 includes a processor 1005. Although illustrated as a single processor 1005 in Figure 10, two or more processors 1005 can be used depending on specific needs, preferences, or specific implementations and functions described for computer 1002. Generally, the processor 1005 can execute instructions and manipulate data to perform operations of computer 1002, including operations using algorithms, methods, functions, processes, flows, and procedures as described in this disclosure.

[0102] Computer 1002 also includes a database 1006 that can hold data for computer 1002 and other components (whether illustrated or not) connected to network 1030. For example, database 1006 may be an in-memory, conventional, or data-storing database consistent with this disclosure. In some implementations, database 1006 may be a combination of two or more different database types (e.g., a hybrid in-memory and conventional database) depending on specific needs, preferences, or the specific implementation and described functionality of computer 1002. Although illustrated as a single database 1006 in Figure 10, two or more databases (the same, different, or a combination of types) may be used depending on specific needs, preferences, or the specific implementation and described functionality of computer 1002. Although database 1006 is illustrated as an internal component of computer 1002, in alternative implementations, database 1006 may be external to computer 1002.

[0103] Computer 1002 also includes memory 1007 that can hold data for computer 1002 or any combination of components connected to network 1030 (whether or not illustrated). Memory 1007 can store any data consistent with this disclosure. In some implementations, memory 1007 may be a combination of two or more different types of memory (e.g., a combination of semiconductor and magnetic storage) depending on specific needs, preferences, or the specific implementation and described functionality of computer 1002. Although illustrated as a single memory 1007 in Figure 10, two or more memory 1007s (same, different, or combinations of types) may be used depending on specific needs, preferences, or the specific implementation and described functionality of computer 1002. Although memory 1007 is illustrated as an internal component of computer 1002, in alternative implementations, memory 1007 may be external to computer 1002.

[0104] Application 1008 may be an algorithmic software engine that provides functionality according to specific needs, desires, or specific implementations and described functions of computer 1002. For example, application 1008 can function as one or more components, modules, or applications. Furthermore, although illustrated as a single application 1008, application 1008 may be implemented as multiple applications 1008 on computer 1002. In addition, although illustrated as being internal to computer 1002, in alternative implementations, application 1008 may be external to computer 1002.

[0105] The computer 1002 may also include a power supply 1014. The power supply 1014 may include a rechargeable or non-rechargeable battery that can be configured to be either user-replaceable or non-user-replaceable. In some implementations, the power supply 1014 may include power conversion and management circuitry that includes recharging, standby, and power management functions. In some implementations, the power supply 1014 may include a power plug that allows the computer 1002 to be plugged into a wall outlet or power source, for example, to supply power to the computer 1002 or to recharge the rechargeable battery.

[0106] Any number of computers 1002 exist, either associated with or outside a computer system including computer 1002, and each computer 1002 can communicate via network 1030. Furthermore, the terms “client,” “user,” and other appropriate terms may be used interchangeably as needed without departing from the scope of this disclosure. In addition, this disclosure intends that many users may use one computer 1002, and one user may use multiple computers 1002.

[0107] Implementations of the subject matter and functional operations described herein may be implemented in digital electronic circuits, in tangible computer software or firmware including the structures disclosed herein and their structural equivalents, or combinations thereof, or in computer hardware. Software implementations of the subject matter described may be implemented as one or more computer programs. Each computer program may include one or more modules of computer program instructions encoded in a tangible, non-temporary, computer-readable computer storage medium for execution by a data processing device or for controlling the operation of a data processing device. Alternatively or additionally, program instructions may be encoded in / on artificially generated propagating signals. For example, the signals may be mechanically generated electrical, optical, or electromagnetic signals produced to encode information for transmission to a suitable receiving device for execution by a data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random-access or sequential-access memory device, or a combination of computer storage media.

[0108] The terms “data processing device,” “computer,” and “electronic computer device” (or equivalents as understood by those skilled in the art) refer to data processing hardware. For example, a data processing device may encompass all types of devices, machines, and equipment for processing data, including, for example, a programmable processor, a computer, or multiple processors or computers. A device may also include dedicated logic circuits, including, for example, a central processing unit (CPU), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some implementations, a data processing device or dedicated logic circuit (or a combination of a data processing device and a dedicated logic circuit) may be hardware-based or software-based (or a combination of both). A device may optionally include code that creates an execution environment for computer programs, such as processor firmware, a protocol stack, a database management system, an operating system, or code that constitutes a combination of execution environments. This disclosure intends to describe the use of a data processing device with or without a conventional operating system, such as Linux®, Unix®, Windows®, Mac OS®, Android®, or iOS®.

[0109] Computer programs, also called or described as programs, software, software applications, modules, software modules, scripts, or code, can be written in any form of programming language. Programming languages ​​can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as standalone programs, modules, components, subroutines, or units for use in a computing environment. Computer programs may, but may not, correspond to files in a file system. Programs may be stored in a single file assigned to the program in question, or in a portion of a file containing one or more scripts stored in a set of coordinate files containing one or more modules, subprograms, or portions of code, within other programs or data, such as markup language documents. Computer programs can be deployed for execution on one computer, or on multiple computers distributed across multiple sites interconnected by a communication network, for example, located at one site. While parts of a program illustrated in various diagrams may be shown as separate modules implementing various features and functions through different objects, methods, or processes, a program can instead contain multiple submodules, third-party services, components, and libraries. Conversely, the features and functions of various components can be combined into a single component as needed. Thresholds used to make computational decisions can be determined statically, dynamically, or both statically and dynamically.

[0110] The methods, processes, or logic flows described herein may be performed by one or more programmable computers that perform functions by executing one or more computer programs to operate with respect to input data and produce outputs. The methods, processes, or logic flows may also be performed by dedicated logic circuits, e.g., CPUs, FPGAs, or ASICs, and the devices may also be implemented as dedicated logic circuits, e.g., CPUs, FPGAs, or ASICs.

[0111] A computer suitable for running computer programs can be based on one or more general-purpose and dedicated microprocessors and other types of CPUs. The elements of a computer are a CPU for executing or running instructions, and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from memory (and write data to memory). A computer can also include or be operationally connected to one or more mass storage devices for storing data. In some implementations, a computer can send and receive data to and from mass storage devices, including, for example, magnetic, magneto-optical, or optical disks. Furthermore, a computer can be embedded in another device, such as a mobile phone, personal digital assistant (PDA), portable audio or video player, game console, Global Positioning System (GPS) receiver, or portable storage device such as a Universal Serial Bus (USB) flash drive.

[0112] Computer-readable media (temporary or non-temporary as needed) suitable for storing computer program instructions and data can include all forms of fixed / non-fixed and volatile / non-volatile memory, media, and memory devices. Computer-readable media can include semiconductor memory devices such as random access memory (RAM), read-only memory (ROM), phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer-readable media can also include magnetic devices such as tapes, cartridges, cassettes, and internal / removable disks. Computer-readable media can also include magneto-optical disks and optical memory devices, as well as technologies such as digital video discs (DVD), CD-ROM, DVD+ / -R, DVD-RAM, DVD-ROM, HD-DVD, and Blu-ray. Memory can store a variety of objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. The types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and criteria. Furthermore, memory can include logs, policies, security, or access data, and reporting files. The processor and memory can be complemented by or integrated into dedicated logic circuits.

[0113] Implementations of the subject matter described herein may be implemented on a computer having a display device for providing user interaction, including displaying information to the user (and receiving input from the user). Types of display devices may include, for example, cathode ray tubes (CRTs), liquid crystal displays (LCDs), light-emitting diodes (LEDs), and plasma displays. Display devices may include keyboards and pointing devices, including, for example, mice, trackballs, or trackpads. User input may also be provided to the computer by using a touchscreen, such as the surface of a pressure-sensitive tablet computer, or a multi-touchscreen using capacitive or electrical sensing. Other types of devices may be used to provide user interaction, including receiving user feedback, including sensory feedback, such as visual feedback, auditory feedback, or haptic feedback. Input from the user may be received in the form of acoustic, speech, or haptic input. In addition, the computer may interact with the user by sending documents to and receiving documents from devices used by the user. For example, the computer may send a web page to a web browser on the user's client device in response to a request received from a web browser.

[0114] The term “graphical user interface” or “GUI” can be used in the singular or plural form to describe one or more graphical user interfaces, and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including but not limited to web browsers, touchscreens, or command-line interfaces (CLIs), that processes information and efficiently presents the results to the user. Generally, a GUI can include several user interface (UI) elements, some or all of which are associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements may be related to or represent the functionality of the web browser.

[0115] Implementations of the subject matter described herein may be implemented in a computing system that includes backend components (e.g., as a data server) or middleware components (e.g., an application server). Furthermore, the computing system may include frontend components, such as client computers having one or both of a graphical user interface or a web browser, thereby allowing a user to interact with the computer. Components of the system may be interconnected in a communication network by any form or medium (or combination of data communications) of wired or wireless digital data communication. Examples of communication networks include local area networks (LANs), wireless access networks (RANs), metropolitan area networks (MANs), wide area networks (WANs), World Wide Interoperability for Microwave Access (WiMAX®), wireless local area networks (WLANs) (e.g., using 802.11a / b / g / n or 802.20, or a combination of protocols), all or part of the Internet, or any other communication systems (or combinations of communication networks) at one or more locations. A network can communicate with, for example, Internet Protocol (IP) packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses.

[0116] A computing system can include clients and servers. Clients and servers can generally be remote from each other and typically interact via a communication network. The relationship between a client and a server can arise from computer programs running on each computer that have a client-server relationship.

[0117] A cluster file system can be any file system type that is accessible from multiple servers for reading and updating. Locking of the exchange file system may be performed at the application layer, so locking or consistency tracking may not be necessary. Furthermore, Unicode data files may differ from non-Unicode data files.

[0118] While this specification includes many specific implementation details, these should not be interpreted as limitations on the scope of what is claimable, but rather as descriptions of features that may be specific to a particular implementation. Some features described herein in the context of separate implementations may also be implemented in combination or in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented in multiple implementations, separately or in any preferred partial combination. Furthermore, while the aforementioned features are described as acting in a particular combination and may even be initially claimed as such, one or more features from the claimed combination may, in some cases, be removed from the combination, and the claimed combination may cover a partial combination or a variation of a partial combination.

[0119] In the foregoing description, embodiments of the present invention have been described with reference to numerous specific details that may vary from implementation to implementation. Therefore, the description and drawings should be considered illustrative rather than restrictive. The sole and exclusive indicator of the scope of the present invention, and what the applicant intends to be the scope of the present invention, is the literal and equivalent scope of a set of claims arising from this application in any particular form that gives rise to such claims, including any subsequent amendments. Any definitions explicitly stated herein with respect to terms included in such claims shall determine the meaning of such terms as used in the claims. In addition, where the terms “further comprising” or “further including” are used in the foregoing description or in the following claims, what follows this phrase may be an additional stage or entity, or a sub-stage / sub-entity of a stage or entity described previously.

[0120] We have described a specific implementation of the subject matter. Other implementations, modifications, and rearrangements of the described implementation are within the scope of the following claims, as will be apparent to those skilled in the art. While the operations are shown in a specific order in the drawings or claims, this should not be understood as requiring that such operations be performed in a specific order or sequence as shown, or that all exemplified operations be performed to achieve a desired result (some operations may be considered optional). In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and appropriate and may be performed.

[0121] Furthermore, the separation or integration of various system modules and components in the aforementioned implementations should not be understood as requiring such separation or integration in all implementations. Rather, the described program components and systems can generally be integrated together into a single software product, or packaged into multiple software products.

[0122] Therefore, the exemplary implementations described above do not define or limit this disclosure. Other changes, substitutions, and modifications are also possible without departing from the intent and scope of this disclosure.

[0123] Furthermore, any claimed implementation is considered applicable to at least a computer implementation method, a non-temporary computer-readable medium storing computer-readable instructions for performing the computer implementation method, and a computer system comprising computer memory or instructions stored in the non-temporary computer-readable medium interoperably coupled with a hardware processor configured to perform the computer implementation method.

[0124] Multiple embodiments of these systems and methods have been described. Nevertheless, it will be understood by those skilled in the art that various modifications can be made without departing from the spirit or scope of this disclosure.

Claims

1. A step of analyzing the interaction between a molecular compound and each organ structure of a plurality of organ structures, wherein each organ structure of the plurality of organ structures corresponds to one organ type among a plurality of organ types, A step in which, based on the above analysis, a plurality of concentration profiles are determined, wherein each of the plurality of concentration profiles corresponds to one of the plurality of organ structures, A step of determining at least one pharmacokinetic (PK) parameter for each of the aforementioned plurality of organ types, and based on the plurality of concentration profiles, A step of determining at least one design parameter based on the aforementioned at least one PK parameter, The step of designing a multi-organ structure platform based on at least one of the aforementioned design parameters and A method for providing this.

2. The method according to claim 1, wherein the step of determining at least one design parameter includes the step of determining the relative size pattern between the plurality of organ structures based on at least one predetermined human PK parameter.

3. The method according to claim 1 or 2, wherein the step of analyzing interactions includes determining at least one pre-design parameter for each of the multiple organ structures, the at least one pre-design parameter being determined based on the desired use of the multiple organ structures.

4. The method according to any one of claims 1 to 3, wherein the plurality of organ structures include at least one of gastrointestinal organ structures, liver organ structures, kidney organ structures, muscle organ structures, or fatty organ structures.

5. The method according to any one of claims 1 to 4, wherein the molecular compound includes a xenobiotic substance and the plurality of concentration profiles include a plurality of drug concentration profiles.

6. The method according to any one of claims 1 to 5, wherein the step of determining at least one PK parameter comprises the step of analyzing the plurality of concentration profiles using at least one ordinary differential equation.

7. The method according to any one of claims 1 to 6, wherein the at least one PK parameter includes at least one of clearance, permeability, and distribution volume.

8. The method according to any one of claims 1 to 7, wherein the at least one design parameter includes at least one of the volume of at least one organ structure, the surface area of ​​at least one organ structure, the number of cells in at least one organ structure, the arrangement of cells in at least one organ structure, the flow pattern, the volume of at least one channel, the flow velocity, and the flow division value.

9. The method according to any one of claims 1 to 8, wherein the designed multi-organ structure platform comprises four or more organ structures.

10. At least one inlet, A plurality of organ structures, wherein each organ structure of the plurality of organ structures is sized relative to the other organ structures of the plurality of organ structures based on at least one predetermined human pharmacokinetic (PK) parameter, A plurality of flow channels, each of the plurality of flow channels, which allows one of the plurality of organ structures to be in fluid communication with at least one other organ structure among the plurality of organ structures. A system equipped with these features.

11. The system according to claim 10, wherein the plurality of channels are configured to cause molecular compounds to flow through the system at a circulating flow velocity such that the molecular compounds are distributed at a threshold distribution velocity.

12. The system according to claim 10 or 11, wherein the plurality of organ structures include at least one gastrointestinal organ structure, at least one liver organ structure, at least one kidney organ structure, and at least one of a muscle organ structure or a fatty organ structure.

13. The system according to any one of claims 10 to 12, wherein at least one of the plurality of organ structures includes two membrane compartments separated by a porous membrane.

14. The system according to any one of claims 10 to 13, wherein the plurality of organ structures include at least four organ structures.

15. The system according to any one of claims 10 to 14, wherein the plurality of organ structures include at least one of gastrointestinal organ structures, liver organ structures, kidney organ structures, muscle organ structures, or fatty organ structures.

16. The system according to any one of claims 10 to 15, wherein each of the plurality of organ structures includes at least one of an apical compartment and a lateral bottom compartment.

17. The system according to any one of claims 10 to 16, wherein at least a portion of the plurality of flow channels is configured to continuously circulate fluid through the plurality of organ structures for at least a fraction of minutes.

18. The system according to any one of claims 10 to 17, further comprising at least one second flow path configured to be in fluid communication with at least one of the plurality of organ structures and to facilitate the flow of fluid through the at least one organ structure.

19. The aforementioned plurality of organ structures include gastrointestinal organ structures and kidney organ structures. The at least one inlet includes a first inlet that is in fluid communication with the gastrointestinal organ structure and a second inlet that is in fluid communication with at least one of the plurality of flow paths. The system according to any one of claims 10 to 18.

20. The steps include inserting molecular compounds into a microfluidic device having multiple organ structures, The steps include: flowing the molecular compound through the microfluidic device to allow the molecular compound to interact with the plurality of organ structures; A step of determining multiple concentration profiles, at least partially based on the interaction step, A step of determining a plurality of first pharmacokinetic (PK) parameters, including a first type, based at least partially on the plurality of concentration profiles, The steps include converting the plurality of first PK parameters into a plurality of second PK parameters, including a second type. A method for providing this.

21. The method according to claim 20, wherein the step of flowing the molecular compound through the microfluidic device includes the step of distributing the molecular compound at a circulating flow velocity such that the molecular compound is distributed at a threshold distribution velocity.

22. The method according to claim 20 or 21, wherein the plurality of organ structures include at least four organ structures.

23. The method according to any one of claims 20 to 22, wherein the plurality of organ structures include at least one of gastrointestinal organ structures, liver organ structures, kidney organ structures, muscle organ structures, or fatty organ structures.

24. The method according to any one of claims 20 to 23, wherein the plurality of organ structures include at least one gastrointestinal organ structure, at least one liver organ structure, at least one kidney organ structure, and at least one of a muscle organ structure or a fatty organ structure.

25. The method according to any one of claims 20 to 24, wherein each of the plurality of organ structures includes at least one of an apical compartment or a lateral compartment.

26. The method according to any one of claims 20 to 25, wherein the at least one pharmacokinetic parameter includes at least one of clearance, permeability, or volume of distribution.

27. The method according to any one of claims 20 to 26, wherein the step of determining at least one pharmacokinetic parameter includes the step of using at least one ordinary differential equation.

28. The method according to any one of claims 20 to 27, wherein the first type of PK parameter includes an in vitro PK parameter, and the second type of PK parameter includes a human PK parameter.

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