Kinase Mutation Prediction via Crystal Structure Library
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Solution Overview
Problem
Current cancer treatment methods lack effective means to predict patient response to therapeutics and develop resistance, leading to inefficiencies in drug development and high costs due to late determination of drug efficacy and high attrition rates.
Innovation Solution
A method using a proprietary crystal structure library and pattern matching algorithm to identify kinase mutations, predict the functionality of gene mutations, and streamline drug development by predicting the specificity of small molecule kinase inhibitors and designing new drug candidates based on mutation profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current small molecule drug discovery methodology is used, then drug development process is completed, but determination of drug efficacy occurs late in the development process after massive expenditures have already occurred
Solution Approach 1:
The patent applies preliminary action by using computational chemistry methods and molecular docking simulations to predict drug efficacy and identify potential resistance mutations before actual clinical trials begin. The system performs virtual screening and efficacy assessment in advance, allowing researchers to determine drug effectiveness early in the development process rather than late after massive expenditures have occurred.
Solution Approach 2:
The patent uses computational models and virtual simulations to create digital copies of biological systems and drug interactions. By simulating drug binding, efficacy, and resistance mechanisms in silico before physical experimentation, the system enables early determination of drug effectiveness without requiring extensive time-consuming in vitro or in vivo testing.
2Loss of information
If current cancer treatment methods are used, then treatment options are provided to patients, but there are no methods available to predict or monitor patients for the development of resistance to cancer treatments
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring patient tumor samples for resistance mutations and using this information to adjust treatment strategies. The system provides real-time feedback on treatment effectiveness and predicts future resistance development, enabling dynamic adaptation of therapy to maintain reliability of treatment outcomes.
Solution Approach 2:
The patent replaces traditional mechanical approaches to resistance monitoring (relying on clinical observation and post-hoc analysis) with computational methods including molecular dynamics simulations, machine learning algorithms, and predictive modeling. This substitution enables proactive prediction of resistance before it clinically manifests.
3Productivity
If traditional drug development process is followed, then new chemotherapeutic agents are developed, but the timeline is long and the attrition rate is high
Solution Approach 1:
The patent applies preliminary action by performing computational efficacy assessment and resistance prediction before initiating clinical trials. Virtual screening and molecular docking simulations are conducted in advance to identify promising drug candidates and predict their effectiveness, thereby accelerating the development timeline by eliminating ineffective candidates early.
Solution Approach 2:
The patent creates virtual models of drug-target interactions and uses computational simulations to replicate and predict drug behavior before physical experimentation. This copying approach allows rapid virtual testing of multiple drug candidates simultaneously, dramatically increasing productivity and reducing the time required for drug development.
4Reliability
If current drug discovery methodology is used, then drug candidates are identified, but the attrition rate is high because determination of the drug candidate's efficacy occurs late in the development process
Solution Approach 1:
The patent performs preliminary efficacy determination through computational methods before clinical development begins. By using molecular docking, virtual screening, and predictive algorithms to assess drug candidate effectiveness in advance, the system identifies and eliminates ineffective candidates early, reducing attrition rates and optimizing the development timeline.
Data Source
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.