Particle Diffusion Analysis in Mucus Barriers
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Solution Overview
Problem
Current methods for modeling particle diffusion through mucus layers are inaccurate due to assumptions of normal diffusion, which fail to account for sub-diffusive behavior and heterogeneity in mucus properties, leading to inconsistencies in drug delivery and dosing, especially in diseased states.
Innovation Solution
A method and system for data analysis and inference of particle diffusion that involves collecting experimental data, identifying stochastic diffusive processes, simulating particle movement using fractional Brownian motion models, and calculating passage time distributions across varying mucus thicknesses, accounting for heterogeneity and sub-diffusive behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional methods assume normal diffusion behavior for particles in mucus, then the modeling process is simple and computationally efficient, but the accuracy of diffusion coefficient determination deteriorates due to unquantifiable errors from ignoring sub-diffusive behavior and mucus heterogeneity
Solution Approach 1:
The patent changes the diffusion model parameters from normal diffusion assumptions to anomalous diffusion models ( fractional Brownian motion, continuous time random walk) that account for sub-diffusive behavior. This involves introducing new parameters like the anomalous diffusion exponent alpha and Hurst parameter to accurately describe particle transport in heterogeneous mucus environments
Solution Approach 2:
The patent uses computational simulations to create virtual copies of particle diffusion experiments. By simulating particle trajectories in silico with known ground truth parameters, the method validates and calibrates analytical approaches without requiring complex physical experiments, thereby improving measurement precision while controlling complexity
2Device complexity
If particle diffusion is modeled using a single diffusion coefficient, then the model is simple to implement, but it fails to capture the heterogeneity of mucus properties across different locations and disease states
Solution Approach 1:
The patent applies local quality by allowing diffusion parameters to vary spatially and temporally across different mucus regions. The model divides the mucus layer into discrete zones with locally optimized diffusion coefficients and anomalous diffusion exponents, capturing the heterogeneity in mucus composition, density, and flow properties at different locations and disease stages
Solution Approach 2:
The patent transitions from static diffusion coefficients to dynamic parameters that evolve over time and space. The model incorporates time-dependent diffusion coefficients and anomalous diffusion exponents that adapt to changing mucus conditions, such as disease progression, hydration changes, and inflammatory states, making the model versatile across different physiological and pathological conditions
3Device complexity
If MSD is assumed to scale linearly with time, then diffusion analysis is straightforward, but this assumption leads to inaccurate passage time predictions through mucus layers of varying thickness
Solution Approach 1:
The patent changes the temporal scaling relationship from linear (normal diffusion) to power-law scaling (anomalous diffusion). The mean squared displacement is modeled as MSD ~ t^alpha where alpha < 1 for sub-diffusive behavior. This parameter change fundamentally alters how passage times scale with mucus thickness, providing accurate predictions across varying layer thicknesses by capturing the memory effects and clogging phenomena in heterogeneous mucus
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides more accurate predictions of particle passage times through mucus layers, optimizing drug delivery by accounting for mucus heterogeneity and disease progression, thereby improving the effectiveness and efficiency of transmucosal drug delivery.
Implementation Method 1
The MSD of a particle undergoing Brownian motion scales linearly with time
Implementation Method 2
micron diameter particles in mucus exhibit sub-diffusive behavior, with a fractional power of lag time rather than scaling linearly in time
Data Source
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AI summary
Methods, systems, and computer readable media for data analysis and inference of particle diffusion in mucus barriers and generic permeable biomaterials are disclosed. According to one aspect, the subject matter described herein includes a method for data analysis and inference of particle diffusion in target materials, such as mucus barriers, or their simulants. The method includes collecting experimental data of observed particle movement through samples of a target material or simulant ("the target"), analyzing the collected data to determine the stochastic diffusive process that is being observed for particular particles in the particular sample, using one or more of the observed stochastic diffusive processes to simulate the diffusion of particles through layers of the target of various thicknesses, using the simulation results to determine how passage time scales according to thickness of the target, and verifying the simulation results.