Drug Composition Matrix for Synergy Scoring
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Solution Overview
Problem
Current methods for developing new drugs are time-consuming and costly, and existing databases lack comprehensive information on drug efficacy, potency, and structural biology, making it difficult to efficiently generate potential drug compositions for disease targets.
Innovation Solution
A system and method using a computing device with a database arrangement and processor to identify parameters associated with a disease target, construct matrices to analyze direct and indirect synergies of drugs, calculate scores, rank drugs, and determine potential compositions, while validating them through biological evidence and differential expression analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional drug development methods are used, then comprehensive evaluation of drug compositions can be achieved, but the process takes a very long time and requires a lot of money
Solution Approach 1:
The patent creates a computational model that copies and simulates the complex biological interactions between drugs and disease targets. Instead of performing actual lengthy experiments, the system uses in-silico modeling to replicate molecular-level drug-disease associations, thereby achieving comprehensive evaluation without the time and resource costs of traditional wet-lab experiments
Solution Approach 2:
The system performs preliminary computational screening and evaluation of potential drug compositions before actual experimentation. By pre-identifying promising drug candidates through database mining and computational analysis, the system filters out unlikely candidates early, reducing the overall time required for drug development while maintaining evaluation accuracy
2Loss of information
If comprehensive data on drugs and trials is collected from public sources, then more information is available, but the sheer volume of data is overwhelming and cannot be accessed and correlated efficiently
Solution Approach 1:
The patent segments the overwhelming volume of drug data into structured categories including drug targets, pathways, molecular functions, biological processes, and adverse events. This segmentation is achieved through specialized database schemas that organize information from disparate public sources into manageable, queryable units, allowing efficient access and correlation without being overwhelmed by data volume
Solution Approach 2:
The system introduces an intermediary layer of computational processing and data normalization between raw public source data and the analysis engine. This intermediary layer standardizes data formats, resolves inconsistencies across different public sources, and pre-processes information into a unified structure that can be efficiently queried and correlated, thereby reducing the complexity of handling comprehensive drug information
3Loss of information
If data from disparate sources is collected, then a fuller picture can be derived, but it is extremely hard to piece together the data
Solution Approach 1:
The patent implements a universal data integration framework that can handle multiple types of data from disparate sources including clinical trials, scientific publications, and regulatory databases. The system uses standardized ontologies and controlled vocabularies that allow different data sources to be interconnected through common reference points, making it easier to piece together comprehensive drug information from heterogeneous sources
4Loss of information
If existing databases are used, then some clinical and experimental information is available, but they do not adequately cover specialized information pertaining to pharmaceutical drugs and structural biology
Solution Approach 1:
The patent merges multiple existing databases and information sources into a unified specialized database system that comprehensively covers pharmaceutical drugs and structural biology. By integrating data from diverse sources including drug targets, pathways, molecular functions, and clinical information into a single coordinated system, the patent achieves both broad information availability and high precision in drug efficacy data through cross-validation and specialized curation
Data Source
AI summary
A system for generating drug compositions for a disease target, the system comprises a database arrangement and a processor, wherein the processor is configured to receive information comprising one or more drugs associated with the disease target, identify a plurality of parameters associated with the disease target, using the database arrangement, construct a matrix to identify at least one of direct and indirect synergies of each of the drug with the plurality of parameters and assign weights thereby to each of the parameters with respect to each of the drug, based on the identified at least one of direct and indirect synergies, calculate a total score of each of the drug and rank the plurality of drugs based on the calculated total score and sort thereby the plurality of drugs. The processor then determines the one or more potential drug compositions on the basis of the sorted plurality of drugs.