Computational Drug Therapy Matching via Pathway Behavior Signatures
Solution Overview
Problem
Current drug therapies often fail to effectively target pathogens, leading to inadequate treatment due to side effects, resistance, and high costs, with existing methods being time-consuming and resource-intensive for discovering new biological interactions or elements critical to cell function.
Innovation Solution
A method using a computer system to determine the effect of candidate drug therapies by obtaining a reference off-target treatment model for an off-target biological network, selecting candidate drug therapies, and generating corresponding treatment models to match a reference signature, thereby synthesizing new drug therapies that produce similar pathway behavior to existing therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If new drug therapies are discovered through traditional methods of identifying new biological elements and interactions, then the probability of significantly altering cell function is improved, but the time and resources required for discovery increase dramatically
Solution Approach 1:
The patent applies copying by creating a computational model that replicates the behavior of existing drug therapies. Instead of discovering entirely new biological elements through time-consuming traditional methods, the system copies the pathway behavior characteristics of known effective therapies and uses this replicated information to identify candidate drugs that can produce similar effects, thereby significantly reducing discovery time while maintaining reliability.
Solution Approach 2:
The patent implements preliminary action by pre-computing and storing pathway behavior characteristics of reference drug therapies in a database before actual drug discovery begins. This preliminary computational work creates a ready-to-use knowledge base that accelerates subsequent drug identification processes, eliminating the need to perform these complex analyses from scratch for each new drug candidate.
2Adaptability or versatility
If traditional drug discovery methods are used to identify new biological interactions, then new therapeutic targets are found, but the process becomes extremely resource-intensive and filled with false positives
Solution Approach 1:
The patent replaces the mechanical and resource-intensive wet lab experimentation with a computational system. Instead of physically testing numerous drug candidates and performing complex laboratory experiments to identify biological interactions, the system uses computational modeling and analysis to predict drug effects, thereby reducing resource intensity and false positives while maintaining the ability to discover new therapeutic targets.
Solution Approach 2:
The patent introduces a computational model as an intermediary between existing drug knowledge and new drug discovery. This intermediary system processes and analyzes pathway behavior data, translating complex biological information into actionable insights about candidate drugs, thereby simplifying the discovery process and reducing the need for resource-intensive direct experimentation.
3Ease of operation
If existing drug therapies are used to treat diseases, then treatment is provided, but side effects occur due to altered pathway behavior of targeted elements
Solution Approach 1:
The patent applies copying by replicating only the beneficial pathway behavior characteristics of reference drug therapies without copying their harmful side effects. The computational model identifies and copies the specific molecular pathway interactions that produce therapeutic effects, while deliberately excluding the pathway alterations that lead to adverse reactions, thereby enabling treatment availability with reduced side effects.
4Reliability
If drug therapies target specific pathways to treat diseases, then therapeutic effect is achieved, but pathogens evolve resistance over time
Solution Approach 1:
The patent applies segmentation by analyzing and targeting multiple distinct molecular pathways simultaneously rather than relying on a single pathway. The computational model identifies candidate drugs that can disrupt multiple pathogen pathways, thereby creating a more comprehensive treatment approach that makes it harder for pathogens to evolve resistance through single-mutation strategies, thus maintaining therapeutic efficacy over longer durations.
Data Source
AI summary
Systems and methods for determining effects of candidate therapies are provided. A reference model is obtained, for a biological network having nodes representing transitions between biological intermediates. The reference model includes a first subset of intervention nodes having reference intervention functions and constants for transitions responsive to reference therapy, and a first subset of control nodes having control functions for transitions absent reference therapy. A reference progression including a reference signature for an intermediate is produced using the reference model. For each candidate therapy, a candidate model for the network is determined, including a second subset of intervention nodes having candidate intervention functions and constants for transitions responsive to candidate therapy, and a second subset of control nodes having control functions for transitions absent candidate therapy. Candidate progressions including candidate signatures for the intermediate are generated using candidate models. Candidate therapies having signatures that match the reference signature are determined.