Computational Synergy Quantification for Multi-Compound Drug Formulations
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Solution Overview
Problem
Current systems lack the ability to effectively identify and quantify synergistic combinations of multiple chemical compounds for treating diseases or biological functions, as most pharmaceuticals are single compounds, and existing methods for combining compounds are derived through trial and error or brute force screening, without understanding the molecular interactions.
Innovation Solution
A system that takes an individual set of compounds, molecular pathway models, and biomarkers as input to determine additive, antagonistic, or synergistic effects, and quantify the synergistic effect by simulating compound sequences and dosages using a mathematical model to identify optimal combinations for biological systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial and error or brute force screening methods are used to identify compound combinations, then some synergistic combinations can be discovered, but the process is extremely time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary computational analysis by building in silico models of molecular pathways and compounds before actual experiments. This preliminary modeling predicts synergistic combinations, allowing researchers to prioritize only the most promising candidates for experimental validation, thereby dramatically reducing the time and resources needed for screening.
Solution Approach 2:
The system creates virtual copies of biological systems through mathematical models (in silico models) that replicate molecular pathway behaviors. These computational models serve as digital twins of actual biological systems, enabling virtual testing of compound combinations without requiring physical experiments for each screening iteration.
2Reliability
If comprehensive high throughput screening of all possible compound combinations is performed, then synergistic effects can be identified, but the complexity and cost of the system increases dramatically
Solution Approach 1:
The system replaces complex physical high-throughput screening machinery with computational models. Instead of using automated liquid handlers, robotic systems, and extensive laboratory infrastructure to physically test combinations, the system uses in silico mathematical models to predict synergistic effects, dramatically simplifying the physical infrastructure required.
Solution Approach 2:
The system performs preliminary computational filtering to identify only the most promising compound combinations before experimental validation. By pre-screening all possible combinations through mathematical models and selecting only top candidates for physical testing, the system avoids the need for comprehensive experimental screening of all combinations.
3Ease of manufacture
If ad hoc formulations of multiple compounds are created without molecular systems understanding, then product development is simplified, but the ability to achieve true synergistic effects is reduced
Solution Approach 1:
The system creates virtual models of molecular pathways and compound interactions to predict synergistic combinations. These computational models guide the formulation process by identifying specific compound combinations and dosage ratios likely to produce synergistic effects, providing a scientific basis for formulation development rather than relying on ad hoc approaches.
Solution Approach 2:
The system analyzes how changes in compound concentrations, ratios, and combinations affect biological outcomes through mathematical modeling. By understanding the quantitative relationships between formulation parameters and biological efficacy, the system can optimize formulations to achieve synergistic effects while maintaining manufacturability.
Data Source
AI summary
A process and a system are disclosed for determining if a combination of a set of chemical compounds has either a synergistic, additive, or antagonistic effect on a disease, e.g., osteoarthritis, or a biological function, e.g., oxidative stress. In one step of the process, for a particular ensemble of molecular pathway models representing the disease or biological function along with the particular set of biomarkers for that disease or biological function, computation is performed to quantify for a particular set of chemical compounds, their effects individually, sequentially, and in combination on the biomarkers. In another step of the process, comparison of these effects is performed to determine if the combinations of the chemical compounds behave synergistically, additively, or antagonistically. The end results are combinations of chemical compounds along with their dosage levels, which synergistically affect disease or biological function of interest.