Network Dysregulation Analysis for De Novo Drug MoA Detection
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Solution Overview
Problem
Current methods for identifying the mechanism of action (MoA) of compounds are challenging, expensive, and often miss indirect or low-affinity targets, and are limited by their reliance on in vitro studies and prior knowledge, making them unsuitable for genome-wide analyses or de novo MoA elucidation.
Innovation Solution
A method called DeMAND, which uses network dysregulation analysis by processing gene expression data from tissue-specific regulatory networks to identify MoA by determining statistically significant dysregulated interactions and ranking candidate genes, leveraging Kullback-Leibler divergence and Brown's method for statistical significance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct binding assays are used to identify high-affinity binding targets, then binding target identification is improved, but indirect effectors and low-affinity binding targets are missed
Solution Approach 1:
The method segments the MoA identification process into two complementary approaches: direct binding assays for high-affinity targets and gene expression profiling for indirect effectors and low-affinity targets. This segmentation allows each method to focus on its strength while the results are integrated to provide comprehensive coverage of all MoA components.
Solution Approach 2:
The invention merges direct binding assay data with gene expression profiling data to create a unified MoA identification framework. By combining these two data sources, the method recovers both direct binding targets and indirect effectors that would be missed by either approach alone, thereby reducing information loss.
2Reliability
If in vitro studies are used for MoA analysis, then controlled experimental conditions are achieved, but complex in vivo effects are missed
Solution Approach 1:
The method adds a new dimension to MoA analysis by integrating gene expression data that reflects in vivo physiological responses. This dimensional expansion allows the detection of systemic effects including paracrine, endocrine, and contact signals that occur in living organisms but cannot be captured in isolated in vitro systems.
3Measurement precision
If chemo-informatics methods rely on detailed three-dimensional structures and prior knowledge, then structural and genomic information integration is improved, but de novo MoA elucidation is limited
Solution Approach 1:
The method performs preliminary action by building comprehensive gene expression profiles and regulatory networks beforehand. These pre-established resources enable rapid de novo MoA elucidation for new compounds without requiring prior structural knowledge or extensive three-dimensional modeling, thereby enhancing adaptability while maintaining precision.
4Reliability
If network-based methods require very large samples sizes, then statistical robustness is improved, but practical application to small compound libraries is made difficult
Solution Approach 1:
The method applies partial action by using a reduced but sufficient sample size tailored to the specific application. Rather than requiring very large sample sizes for all cases, the approach uses the minimum necessary samples to achieve adequate statistical robustness, thereby improving productivity for small compound libraries while maintaining reliability where needed.
Data Source
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AI summary
Techniques to identify a mechanism of action of a compound using network dysregulation are disclosed herein. An example method can include selecting at least a first interaction involving at least a first gene, determining a first n-dimensional probability density of gene expression levels for the first gene and one or more genes in a control state, determining a second n-dimensional probability density of gene expression levels for the first gene and one or more genes following treatment using at least one compound, estimating changes between the first probability density and the second probability density, and determining whether the estimated changes are statistically significant.