Adverse Drug Risk Quantification Without Full Genotype Testing
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Solution Overview
Problem
Current healthcare systems lack cost-effective tools to assess and manage the genetic-based risks of adverse drug events, as obtaining genotype information for every patient is costly and often unavailable, leading to potential adverse drug reactions due to unknown genetic variations in drug metabolism.
Innovation Solution
A computing system that quantifies both known and unknown risks of adverse drug events by analyzing drug interactions, genetic factors, and patient-specific data, using matrices to display and manage risks, recommending additional testing when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If genotype information is obtained for every patient through DNA testing, then the precision of adverse drug event risk assessment is improved, but the cost of healthcare increases
Solution Approach 1:
The system changes the parameter of risk assessment from qualitative/unknown to quantitative/known by calculating probability values. It transforms the approach from requiring actual genotype data for every patient to using population-based probability parameters, thereby reducing cost while maintaining assessment capability
Solution Approach 2:
Instead of using expensive DNA testing for every patient, the system employs a cheaper alternative: probability calculations based on population data. This disposable-like approach uses statistical models rather than individualized genetic testing, reducing per-patient cost while providing sufficient risk assessment
2Quantity of substance
If genotype information is not obtained for patients, then the cost of healthcare is reduced, but the reliability of drug prescription safety decreases
Solution Approach 1:
The system performs preliminary risk assessment using probability calculations before actual genetic testing is performed. By calculating the probability of adverse events based on population data, it provides advance safety evaluation without requiring the actual genotype information, thus maintaining reliability while reducing cost
Solution Approach 2:
The system introduces probability calculations as an intermediary between the absence of genotype data and the need for safety assessment. This intermediary layer allows reliable risk evaluation to occur without direct access to individual genetic information, bridging the gap between cost constraints and safety requirements
3Loss of information
If comprehensive genetic testing is performed to identify all drug-gene interactions, then the completeness of risk information is improved, but the complexity of the healthcare system increases
Solution Approach 1:
The system segments the complex task of comprehensive genetic risk assessment into manageable probability calculations for individual drugs and genes. By breaking down the overall risk profile into discrete calculable components, it maintains information completeness while reducing system complexity through modular processing
Solution Approach 2:
The system replaces the mechanical complexity of actual genetic testing and analysis with computational probability calculations. Instead of physically testing and analyzing genetic material for every patient, it uses mathematical models to substitute and estimate risk, thereby reducing operational complexity while maintaining information completeness
Data Source
AI summary
Example methods of quantifying known and unknown risks of an adverse drug event in an individual based on various factors are disclosed. In some embodiments, factors include known drug-drug interactions and unknown phenotypes of cytochromes. Quantification may be based on severity of the adverse drug event/and or probability of occurrence in some embodiments. Example methods of displaying the quantified risk are also disclosed. In one embodiment, the risk of individuals is aggregated to display the risk of a population.