# US 2024/0197166 A1 · Computational in silico model for cornea barrier

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2024/0197166 A1
- Document ID: US20240197166A1
- Status: Published application
- Kind: A1
- Application number: 18/542,002
- Filed: 2023-12-15
- Published: 2024-06-20
- Earliest claimed: 2022-12-15
- Assignees: CFD Research Corporation
- Applicants: CFD Research Corporation
- Inventors: Carrie Lynn German; Balabhaskar Prabhakarpandian; Ketan Harendrakumar Bhatt
- Web page: https://patentgrep.com/patent/US20240197166A1

## CPC

- A61B 3/0025: {characterised by electronic signal processing, e.g. eye models}
- G06T 7/0012: {Biomedical image inspection}
- G06T 15/00: Three-dimensional [3D] image rendering
- G09B 23/303: {specially adapted to simulate circulation of bodily fluids}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/50: for simulation or modelling of medical disorders
- G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro
- G06T 2207/30041: Eye; Retina; Ophthalmic
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

The method involves computationally modeling a cornea by simulating physical cornea models with computational in silico models. The simulated data is compared with actual data from the physical models, and the simulation is iterated until a match is achieved. The method may also include calibrating diffusion parameters for testing substances, and configuring the models for analyzing instances of fluid-induced shear and substance transport. The simulated data may be used to obtain corneal pharmacokinetic models, and the method may be used to obtain predictions for time-dependent concentrations of molecules in the eye or systemic circulation. The computational models may consider instances of convective-diffusive transport and carrier-mediated transport in calculations and determinations of transport characteristics and values of different testing substances.

## Claims

### Claim 1

A method of computationally modeling a cornea, comprising:
generating a 3D geometry computer model of an in vitro cornea device;
generating a mesh for the 3D geometry computer model;
generating governing equations for the 3D computer model of operational conditions occurring within or upon the in vitro cornea device;
generating volume and boundary conditions that characterize materials and/or conditions inside or at the boundary of the in vitro cornea device; and
processing the computer model using a multiscale multi-physics solver module to obtain an in silico model of the in vitro cornea device.

### Claim 2 (depends on 1)

The method of computationally modeling a cornea of claim 1, further comprising:
obtaining data from an in vitro cornea device, wherein the in vitro cornea device is a physical model of a cornea;
creating an in silico model of the in vitro cornea device; and
generating a physiology-based pharmacokinetic (PBPK) model of the cornea as the in silico cornea model.

### Claim 3 (depends on 1)

The method of computationally modeling a cornea of claim 1, further comprising:
applying a structured mesh to the 3D geometry computer model;
performing a grid independence test; and
determining the mesh for the 3D geometry computer model to be valid for the in vitro cornea device.

### Claim 4 (depends on 3)

The method of computationally modeling a cornea of claim 3, further comprising solving equations of fluid flow and species or heat transport of the 3D geometry computer model.

### Claim 5 (depends on 4)

The method of computationally modelling a cornea of claim 4, further comprising setting operating parameters of the in silico cornea model, which operating parameters are applied as boundary conditions.

### Claim 6 (depends on 5)

The method of computationally modelling a cornea of claim 5, wherein the in vitro cornea device comprises:
a tear flow chamber;
a stromal chamber adjacent to and porously coupled with the tear flow chamber;
an endothelial chamber adjacent to and porously coupled with the stromal;
an aqueous humor chamber adjacent to and porously coupled with the endothelial chamber;
wherein:
a first porous wall is positioned between the tear flow chamber and the stromal chamber, a second porous wall is positioned between the stromal chamber and the endothelial chamber, and a third porous wall is positioned between the endothelial chamber and the aqueous humor chamber,
the tear flow chamber includes epithelial cells;
the stromal chamber includes fibroblasts;
the endothelial chamber includes endothelial cells; and
the aqueous humor chamber includes a fluid;
which is configured as a microfluidic in vitro model of a cornea.

### Claim 7 (depends on 6)

The method of computationally modelling a cornea of claim 6, comprising the operating parameters as follows:
setting the fluid flow rate in the tear flow chamber;
setting the fluid flow rate in the aqueous humor chamber;
setting a test agent concentration as a constant for a given time period at an inlet of the tear flow chamber; and
setting atmospheric pressure at all remaining inlets and outlets of the in vitro cornea device.

### Claim 8 (depends on 6)

The method of computationally modelling a cornea of claim 6, further comprising setting fluid properties as volume conditions.

### Claim 9 (depends on 6)

The method of computationally modelling a cornea of claim 6, further comprising at least one of:
generating velocity, pressure, and/or shear profiles with the in silico cornea model; and
validating physiologically relevant sheer is achieved in the in vitro cornea device using the in silico cornea model.

### Claim 10 (depends on 6)

The method of computationally modelling a cornea of claim 6, further comprising at least one of:
generating a permeability index of one or more test agents for one or more flow rates in the in vitro cornea device;
generating a permeability index of the one or more test agents with the in silico cornea model;
comparing the permeability indices from the in vitro cornea device and the in silico cornea model;
calibrating or changing parameters or adding in additional transport/metabolic phenomena as needed to match the values of the permeability indices; and
validating the permeability index of the one or more test agents with the in vitro cornea model device and/or the in silico cornea model.

### Claim 11 (depends on 1)

The method of computationally modelling a cornea of claim 1, comprising fitting the in silico cornea model parameters to match outputs from the in silico cornea model with in vitro cornea device data.

### Claim 12 (depends on 1)

The method of computationally modelling a cornea of claim 1, comprising:
inputting parameters for one or more conditions and/or one or more test agents into the in silico cornea model; and
generating simulated cornea data for the one or more conditions and/or one or more test agents.

### Claim 13 (depends on 1)

The method of computationally modelling a cornea of claim 1, further comprising generating a simulation data file by:
selecting time step size and total number of time steps;
selecting the maximum number of iterations in which the solver should find the solution;
defining additional reactions to characterize convection, diffusion and elimination kinetics;
defining boundary conditions; and
defining volume conditions.

### Claim 14 (depends on 13)

The method of computationally modelling a cornea of claim 13, further comprising generating simulation results of the in silico cornea model.

### Claim 15 (depends on 14)

The method of computationally modelling a cornea of claim 14, further comprising solving equations that define the physics of the in silico cornea model by:
reading geometry and mesh data;
reading definitions and reactions from the simulation file; and
simultaneously solving the equations at each mesh node.

### Claim 16 (depends on 14)

The method of computationally modelling a cornea of claim 14, further comprising:
generating simulation data for the in vitro cornea device; and
outputting data files of concentration, pressure, fluid velocity, and shear rate for one or more test agents and one or more operating conditions at one or more locations in the in silico cornea model of the in vitro cornea device.

### Claim 17 (depends on 16)

The method of computationally modelling a cornea of claim 16, further comprising outputting simulated cornea data.

### Claim 18

A method of simulating a cornea, comprising:
providing an in silico cornea model that is based on an in vitro cornea device;
inputting test parameters for one of more test agents and/or one or more test conditions into the in silico cornea model; and
generating simulated cornea data for one or more test agents and/or one or more test conditions in a cornea.

### Claim 19 (depends on 18)

The method of simulating a cornea of claim 18, further comprising:
determining an experimental dose of a test agent to achieve a target dosing amount to cross the corneal barrier and reach the target tissue;
running at least one simulation using preliminary estimated values for the test agent;
validating the simulation with experimental data from the in vitro cornea device; and
generating additional simulated cornea data with the validated in silico cornea model.

### Claim 20 (depends on 18)

The method of simulating a cornea of claim 18, further comprising:
identifying one or more mechanisms for transport of a test agent and/or metabolism of the test agent in a cornea;
running a simulation for the test agent with the in silico cornea model using one or more basic transport definitions and/or one or more basic metabolism definitions to obtain simulated cornea data;
comparing the simulated cornea data with experimental data from the in vitro cornea device; and
determining whether or not the simulated cornea data matches the experimental data.

### Claim 21 (depends on 20)

The method of simulating a cornea of claim 20, when the simulated data matches the experimental data, the method further comprising:
providing a report with the simulated data validating the simulated mechanisms.

### Claim 22 (depends on 20)

The method of simulating a cornea of claim 20, when the simulated data does not match the experimental data, the method further comprising:
changing at least one input, transport parameter, operating parameter, or boundary condition; and
rerunning the simulation for the test agent.

### Claim 23 (depends on 18)

The method of simulating a cornea of claim 18, further comprising:
obtaining transport data using the in vitro cornea device; and
converting the in vitro transport data into synthetic in vivo transport data with the in silico cornea model.

### Claim 24 (depends on 18)

The method of simulating a cornea of claim 18, further comprising one or more of:
determining one or more of protein binding, ionization, lipophilicity and molecular weight of one or more test agents, and inputting the same into the computing system;
correlating ocular exposure to the test compound and toxicity to the eye;
modeling epithelial, stromal, or endothelial contribution to transport resistance;
evaluating mechanisms of corneal transport; or
performing a sensitivity analysis to predict which factors are likely to be rate limiting for transport and to identify factors that lead to corneal dysfunction and increased eye toxicity.

### Claim 25 (depends on 18)

The method of simulating a cornea of claim 18, wherein the in silico cornea model is used for mechanistic modeling of corneal transport, including paracellular, transcellular, and transporter mediated, and metabolism kinetics.

### Claim 26 (depends on 18)

The method of simulating a cornea of claim 18, wherein the in silico cornea model includes boundary conditions as follows:
i) fluid flow rates in tear flow and aqueous humor chambers set as constants at the inlets,
ii) compound concentration set as a constant at a tear flow chamber inlet, and
iii) atmospheric pressure set at all remaining inlets and outlets of the in vitro cornea device,
wherein fluid properties of the media are assigned as constant volume conditions, wherein the fluid properties optionally include density and viscosity.

### Claim 27 (depends on 18)

The method of simulating a cornea of claim 18, further comprising:
simulating the in vitro cornea device with the in silico cornea model;
comparing simulation data with experimental data from the in vitro cornea device; and
iterating the simulation parameters until simulation data matches the experimental data.

### Claim 28 (depends on 18)

The method of simulating a cornea of claim 18, comprising calibrating diffusion coefficients for one or more test agent to fit the simulation data of the in silico cornea model or the corneal PBPK model with the experimental data of the in vitro cornea device.

### Claim 29 (depends on 18)

The method of simulating a cornea of claim 18, wherein the in silico cornea model:
considers convective-diffusive transport, transporter mediated transport, metabolism, and combinations thereof; and/or
obtains data regarding specific transporters, metabolizing enzymes, and other parameters; and/or
predicts factors that are rate limiting steps for transfer of an agent across a corneal tissue.

### Claim 30 (depends on 1)

A hybrid cornea model system comprising:
a physical in vitro cornea device; and
an in silico cornea model as obtained in claim 1.