In-silico Phenotypic Target Screening via Ontological Databanks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current target-based drug discovery approaches in the pharmaceutical industry are time-consuming and costly, often missing important phenotypic targets associated with disease pathology, highlighting the need for a more efficient method to screen phenotypic targets.
Innovation Solution
A system and method using in-silico techniques, coupled with a phenotype ontological databank, to identify and prioritize phenotypic targets by determining similarity scores, computing cumulative relevance scores, and performing Highly dysregulated pathway analysis (HDPA) to screen and compute mechanistic factors for disease-associated phenotypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-vitro approaches are used to study multiple phenotypes, then comprehensive phenotype analysis is achieved, but the process becomes highly time consuming and costly
Solution Approach 1:
The patent replaces in-vitro experimental approaches with in-silico computational methods. The system uses phenotype ontological databases, similarity scoring algorithms, and pathway analysis computations to identify phenotypic targets without requiring physical laboratory experiments, thereby eliminating time-consuming and costly wet lab procedures while maintaining comprehensive phenotype analysis capability
Solution Approach 2:
The patent creates a virtual phenotype ontological database that copies and organizes phenotype information from existing biological knowledge sources. This digital representation allows researchers to study multiple phenotypes in silico, replicating the comprehensive analysis capability of in-vitro approaches but in a computationally efficient manner that avoids the time and cost constraints of physical experiments
2Measurement precision
If target-based approaches are used to identify target proteins, then specific molecular targets are identified, but important phenotypic targets driving disease pathology are missed
Solution Approach 1:
The patent implements a multi-functional system that performs both traditional target identification and comprehensive phenotype-based target discovery. The system integrates phenotype ontological databases, similarity scoring, and pathway analysis to simultaneously identify molecular targets and phenotypic targets, ensuring that no important phenotypic information is lost while maintaining the ability to identify specific target proteins
Solution Approach 2:
The patent introduces phenotype ontological databases and computational analysis tools as intermediaries between disease phenotypes and target identification. This intermediary layer ensures that phenotypic information is systematically captured and analyzed, preventing loss of phenotypic target information while enabling accurate identification of targets that drive disease pathology
3Ease of operation
If existing target-based approaches are used, then a target protein can be switched on and off, but the role of phenotypes driving disease pathology is ignored
Solution Approach 1:
The patent adds a phenotypic dimension to traditional target-based approaches. By integrating phenotype ontological databases and performing phenotype similarity analysis, the system evaluates targets not only based on their molecular switchability but also based on their contribution to disease-driving phenotypes, thereby preserving target modulation simplicity while capturing phenotype contribution information
Data Source
AI summary
A system for screening phenotypic targets associated with a disease using in-silico techniques. The system communicably coupled to a phenotype ontological databank including a plurality of phenotypes and phenotypic targets associated with each of the plurality of phenotypes; wherein the system includes a processor communicably coupled to a memory. The processor configured to receive a first input of the disease, receive a second input relating to at least one phenotype associated with the disease, identify for each of the at least one phenotype a plurality of similar phenotypes relating to a particular phenotype of the at least one phenotype of the second input, determine a similarity score for each of the plurality of similar phenotypes in comparison with the particular phenotype of the at least one phenotype of the second input, extract, from the phenotype ontological databank, phenotypic targets associated with similar phenotypes having similarity score higher than a first predefined threshold, compute a cumulative score of the phenotypic targets based on a plurality of parameters, wherein the cumulative score of a given phenotypic target is indicative of relevance thereof with respect to the disease, screen out phenotypic targets with cumulative score lower than a second predefined threshold, compute relevant pathways for the phenotypic targets by performing Highly dysregulated pathway analysis (HDPA) for the screened phenotypic targets, compute mechanistic factors attributing to regulation of similar phenotypes and pathological information of the disease in association with the screened phenotypic targets.