3D Digital Drug Twin for Release Rate Prediction
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Solution Overview
Problem
Conventional methods for evaluating and predicting the release rate of active pharmaceutical ingredients from controlled release drug formulations are time-consuming, resource-intensive, and face challenges in establishing reliable correlations between in-vitro and in-vivo results, with limitations in objectivity and accuracy due to variability in experimental conditions and apparatus performance.
Innovation Solution
An image-based method combining experimental data with numerical simulation, using imaging techniques like CT or FIB-SEM to reconstruct a three-dimensional digital representation of the drug release system, and solving partial differential equations to predict the release rate, allowing for faster and more accurate computation of release profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional in-vitro and in-vivo experiments are conducted to evaluate release rate, then reliable release rate data can be obtained, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent creates a digital twin (three-dimensional digital representation) of the drug release system that copies the physical structure and properties. This virtual model can be simulated repeatedly without physical experimentation, enabling rapid evaluation of release rate under different conditions without the time constraints of actual in-vitro and in-vivo experiments.
Solution Approach 2:
The patent replaces physical experimental measurements with computational simulations. Instead of conducting actual release experiments using dissolution apparatuses or in-vivo animal studies, the system uses numerical methods to solve partial differential equations that model drug release mechanisms, substituting mechanical/physical experimentation with computational analysis.
2Measurement precision
If conventional experimental methods are used for release rate evaluation, then release rate can be measured, but objectivity and accuracy are limited due to variability in experimental conditions and apparatus performance
Solution Approach 1:
The patent systematically varies multiple parameters in the computational model (such as diffusion coefficients, pore size distributions, polymer degradation rates) to explore the sensitivity of release rate predictions. This allows the system to identify which parameters most significantly affect release characteristics, improving both accuracy and reliability of predictions while reducing dependence on specific experimental conditions.
Solution Approach 2:
The patent incorporates feedback mechanisms by comparing computational predictions with available experimental data and iteratively refining the digital twin model. The model can be validated against in-vitro release data and adjusted to better predict in-vivo performance, creating a feedback loop that improves reliability of the release rate evaluation.
3Productivity
If sophisticated controlled release designs with complex release control mechanisms are implemented, then improved drug delivery performance can be achieved, but the complexity of evaluating and predicting release rate increases
Solution Approach 1:
The patent divides the complex drug release system into distinct functional segments or zones within the three-dimensional digital representation. Each segment can be modeled and simulated independently using appropriate physical principles, then the results are integrated to predict overall release performance. This segmentation approach makes evaluation of complex multi-mechanism release systems more manageable and computationally efficient.
Solution Approach 2:
The patent transitions from two-dimensional or zero-dimensional simplified models to three-dimensional digital representations that capture the full spatial complexity of controlled release systems. By imaging and modeling the complete three-dimensional microstructure, the system can accurately predict release behavior of complex designs without oversimplifying the geometry or architecture of the release control mechanisms.
4Measurement precision
If a three-dimensional digital representation is constructed from imaging data, then accurate release rate prediction can be achieved, but computational resources and processing time increase
Solution Approach 1:
The patent extracts only the critical structural and functional information from the complete three-dimensional imaging data that is necessary for predicting release rate. Rather than using all imaging data equally, the system identifies and extracts key features such as pore connectivity, surface area, and phase distributions, creating a simplified but accurate digital twin that reduces computational requirements while maintaining prediction accuracy.
Data Source
AI summary
A method for computing the release rate of a controlled release drug and medical device using a combination of imaging data and computational physics is described. The method employs a three-dimensional digital representation of a drug sample, derived from two-dimensional or three-dimensional imaging, which captures the drug active pharmaceutical ingredient (API), excipients, and porosity with distinctive contrasts. Direct numerical simulations are conducted on the three-dimensional digital representation to derive effective transport properties of the API going through a porous matrix or membrane. Drug release rate can be predicted more efficiently than laboratory-based methods. When there is strong heterogeneity presented in the drug, a further method is described that engages imaging and release simulations at multiple scales. Computerized systems and programs for performing the methods are also described.