Kinase Mutation Matching for Cancer Drug Resistance Prediction
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Solution Overview
Problem
Current methods are inadequate for predicting patient response to cancer therapeutics, monitoring resistance development, and streamlining drug development, leading to high attrition rates and costly clinical trials.
Innovation Solution
A proprietary crystal structure library and pattern matching algorithm are used to identify kinase mutations, predict small molecule inhibitor specificity, and design new drug candidates, enabling personalized treatment regimens and monitoring resistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional small molecule drug discovery methodology is used, then drug candidates can be developed through clinical trials, but the process is expensive, time-consuming, and has high attrition rates
Solution Approach 1:
The patent performs preliminary computational screening and virtual docking of drug candidates against mutant kinase structures before actual clinical trials. This preliminary action identifies promising candidates early, avoiding the need to proceed with all potential drugs through expensive and time-consuming clinical trial phases.
Solution Approach 2:
The patent creates virtual copies of kinase structures with mutations and uses computational models to simulate drug-kinase interactions. These digital copies allow researchers to test thousands of drug candidates in silico, replacing the need for physical testing in early development stages.
2Reliability
If traditional drug discovery process is followed, then drugs can be validated in patients, but massive expenditures occur before efficacy determination
Solution Approach 1:
The system performs preliminary computational validation of drug candidates against mutant kinase structures before any clinical investment. By pre-screening candidates in silico, the system identifies only the most promising candidates for actual clinical testing, dramatically reducing the number of drugs that proceed to expensive trial phases.
Solution Approach 2:
The patent replaces physical and biological testing mechanisms with computational methods. Instead of conducting wet-lab experiments and animal studies for every candidate, the system uses virtual docking and molecular dynamics simulations to predict drug efficacy, substituting expensive mechanical and biological processes with cheaper computational ones.
3Reliability
If patients receive cancer treatments, then therapeutic effects can be achieved, but patients frequently develop resistance due to mutations
Solution Approach 1:
The patent segments the analysis by examining specific mutations in kinase structures individually. By identifying and analyzing each mutation's impact on drug binding separately, the system can predict which drugs will remain effective against which specific mutants, enabling personalized treatment strategies that account for resistance mechanisms.
Solution Approach 2:
The system changes the parameter of drug selection based on the specific mutation present in the patient's tumor. By analyzing how mutations alter kinase structure and drug binding, the system adjusts drug recommendations to match the patient's specific genetic profile, preventing resistance by selecting drugs that remain effective despite mutations.
4Productivity
If crystal structure library and pattern matching algorithm are used, then drug candidates can be rapidly screened, but computational resources and algorithm complexity increase
Solution Approach 1:
The patent creates a universal crystal structure library that serves multiple functions: it stores wild-type kinase structures, mutant kinase structures, and pre-computed docking information. This single library infrastructure supports diverse queries and analysis types, reducing the need for separate systems and reducing overall complexity despite the sophisticated analysis performed.
Data Source
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AI summary
The present invention relates to the discovery of a method for identifying a treatment regimen for a patient diagnosed with cancer, predicting patient resistance to therapeutic agents and identifying new therapeutic agents. Specifically, the present invention relates to the use of an algorithm to identify a mutation in a kinase, determine if the mutation is an activation or resistance mutation and then to suggest an appropriate therapeutic regimen. The invention also relates to the use of a pattern matching algorithm and a crystal structure library to predict the functionality of a gene mutation, predict the specificity of small molecule kinase inhibitors and for the identification of new therapeutic agents.