Drug Repositioning via Multi-Omics Analysis for Renal Cell Carcinoma
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Solution Overview
Problem
Current treatments for clear cell renal cell carcinoma (ccRCC) have limited effectiveness, with response rates ranging from 6% to 50% and average duration of disease control being only 8-9 months for first-line and 5-6 months for second-line settings.
Innovation Solution
An integrated strategy involving disease-target prediction and drug-target prediction was applied, identifying four target genes (BUB1B, RRM2, ASF1B, and CCNB2) and repurposing drugs like TG-101209, which showed effectiveness in reducing cell viability in ccRCC cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional drug repositioning methods (drug-based or disease-based approaches) are used to identify potential drugs for ccRCC, then the pharmacology and safety of repositioned drugs are well-characterized, but the ability to discover new drug-disease pairs is limited and relies on prior knowledge
Solution Approach 1:
The patent replaces traditional knowledge-based drug repositioning methods with profile-based approaches using high-throughput multi-omics data analysis. Instead of relying on prior knowledge about drug mechanisms or disease pathways, the system uses computational algorithms to analyze transcriptomic profiles and identify novel drug-disease associations, thereby discovering new drug-disease pairs while maintaining safety characterization through systematic evaluation
Solution Approach 2:
The patent changes the approach from using traditional drug-target interaction parameters to using multi-omics data profiles (transcriptomic, proteomic, metabolomic) as the basis for drug repositioning. This parameter transformation enables the discovery of new drug-disease pairs by identifying drugs that reverse disease-specific gene expression signatures, expanding the search space beyond conventional knowledge bases
2Adaptability or versatility
If profile-based repositioning methods are used to identify drugs for ccRCC, then the ability to discover new drug-disease pairs is enhanced, but the identified drugs have multiple gene targets mixed by oncogenes and passenger genes, limiting identification and validation of key targets
Solution Approach 1:
The patent segments the complex drug effect into specific target genes by analyzing which genes are reversed by the drug treatment. Instead of viewing drug effects as a mixed bag of targets, the system identifies and isolates specific key targets (such as VEGF, PDGF, EGF family members) that are responsible for the therapeutic effect, allowing precise validation and mechanism study of individual targets
Solution Approach 2:
The patent introduces intermediate analysis steps including gene set enrichment analysis, pathway analysis, and network analysis to filter and prioritize potential targets. These intermediary computational layers help distinguish between oncogenic drivers and passenger genes, and identify the key targets that mediate the drug's therapeutic effect, thereby resolving the ambiguity of mixed targets
3Adaptability or versatility
If current treatment drugs (tyrosine kinase inhibitors, mTOR inhibitors, monoclonal antibodies) are used for ccRCC, then treatment options are available, but the response rates are limited (6% to 50%) and duration of disease control is short (8-9 months for first-line, 5-6 months for second-line)
Solution Approach 1:
The patent identifies drugs with multi-functional mechanisms that can target multiple pathways involved in ccRCC progression. By selecting drugs that reverse multiple disease-specific gene expression signatures simultaneously, the system achieves broader therapeutic coverage and potentially longer duration of control, as the drug addresses multiple oncogenic drivers rather than a single pathway
Solution Approach 2:
The patent employs composite computational approaches that integrate multiple data types (transcriptomics, proteomics, metabolomics) and multiple analysis methods (gene set enrichment, pathway analysis, network analysis, machine learning) to identify optimal drug candidates. This composite methodology enables more comprehensive evaluation of drug efficacy and identification of combination therapies that could achieve higher response rates and longer duration of control
Data Source
AI summary
There is provided a compound according to formula (I) or a pharmaceutically acceptable salt or prodrug thereof.