Dynamic Stimulation Drug Screening Platform
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Solution Overview
Problem
Current drug discovery efforts face challenges in identifying optimized drug combinations due to the complexity of cellular pathways and the discontinuity between in vitro and in vivo environments, leading to inefficiencies and safety issues in clinical trials.
Innovation Solution
A method involving dynamic stimulation of biological systems, measurement of time-varying responses, and fitting these responses into models to identify optimized drug combinations and dosages, allowing for personalized medicine approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible drug combinations are screened through in vitro tests, then the most desirable combination can be identified, but the labor and time required becomes enormous
Solution Approach 1:
The patent applies preliminary action by using in silico computational models to pre-screen and predict drug combination efficacy before conducting in vitro experiments. This allows the most promising combinations to be identified computationally, reducing the number of combinations that need to be tested physically, thereby significantly reducing screening time while maintaining identification accuracy.
Solution Approach 2:
The patent introduces an intermediary computational layer (in silico modeling) between theoretical drug combination space and physical experimentation. This intermediary model predicts which combinations are likely to be effective, allowing researchers to focus limited experimental resources on the most promising candidates rather than testing all possible combinations.
2Reliability
If in vitro successful drug combinations are applied in vivo with same dosage ratios, then the combination can be validated, but ADME issues cause discontinuity between cell line and animal results
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting drug dosage ratios and administration parameters based on predicted ADME characteristics for each specific patient. Rather than using fixed in vitro dosage ratios, the system modifies dosing parameters in silico to account for individual patient metabolism, absorption, and distribution characteristics, thereby improving the translation from in vitro to in vivo efficacy.
Solution Approach 2:
The patent performs preliminary ADME modeling and simulation before actual drug administration. By predicting how a specific patient's body will metabolize and distribute each drug in a combination, the system pre-optimizes dosage ratios to account for individual ADME variations, reducing the discontinuity between preclinical and clinical results.
3Productivity
If multiple drugs are tested at multiple concentrations, then optimized combinations can be identified, but the number of combinations increases exponentially
Solution Approach 1:
The patent applies segmentation by dividing the combinatorial optimization problem into separate computational modules: individual drug pharmacokinetic modeling, drug-drug interaction modeling, ADME prediction, and efficacy optimization. This segmentation allows each component to be modeled independently and then integrated, making the overall complex problem computationally tractable rather than requiring exhaustive testing of all combinations.
Solution Approach 2:
The patent uses parameter changes to reduce combinatorial complexity by continuously optimizing dosage ratios based on predicted efficacy and toxicity parameters. Rather than testing discrete concentration combinations, the system dynamically adjusts continuous dosage parameters in silico to identify optimal combinations, significantly reducing the effective search space.
Data Source
AI summary
A method includes: (1) applying stimulations to a system, wherein applying the stimulations includes modulating, over time, characteristics of the stimulations; (2) measuring a time-varying response of the system to the stimulations; (3) fitting the time-varying response of the system into a model of the system; and (4) using the model of the system, identifying an optimized combination of characteristics of the stimulations to yield a desired response of the system.