Phenotypic Screening System for Polypharmacological Drug Discovery
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Solution Overview
Problem
Conventional high throughput screening (HTS) methodologies are inefficient as they require a substantial understanding of the disease etiology to design target-specific assays, making it difficult to identify candidate drugs acting through unknown molecular targets or with complex polypharmacological effects. Additionally, these methods struggle to differentiate between on-target and off-target effects, necessitating separate screens and increasing the cost and time of drug discovery.
Innovation Solution
The development of systems and methods for screening compound libraries in a target-agnostic fashion, utilizing automated biology and machine learning to measure high-dimensional phenotypes across multiple disease models. This approach allows for the identification of compounds that rescue cellular disease phenotypes with minimal off-target effects, facilitating the evaluation of on-target and off-target effects in a single assay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If target-specific assays are used in conventional HTS, then the screening can identify compounds with known mechanisms of action, but it requires substantial understanding of disease etiology and cannot capture polypharmacological effects or unknown targets
Solution Approach 1:
The patent implements a universal phenotypic screening platform that can evaluate compounds across multiple disease models and biological contexts simultaneously. Instead of developing separate target-specific assays for different diseases, the system uses high-dimensional phenotype measurements that capture cellular responses across diverse conditions, enabling one assay to serve multiple screening purposes and identify both known and unknown mechanisms of action.
Solution Approach 2:
The patent transitions from low-dimensional target-specific measurements to high-dimensional phenotypic measurements that capture multiple cellular parameters simultaneously. By measuring numerous phenotypic features across different disease models, the system creates a multidimensional characterization of compound effects, enabling detection of polypharmacological effects and unknown mechanisms that would be invisible in traditional single-parameter assays.
2Measurement precision
If separate screens are conducted for on-target and off-target effects, then each effect can be evaluated in detail, but the cost and time of drug discovery increases significantly
Solution Approach 1:
The patent merges the evaluation of on-target and off-target effects into a single integrated phenotypic screening assay. By simultaneously measuring multiple phenotypic parameters across different disease models in one experiment, the system captures both desired on-target effects and unwanted off-target effects without requiring separate screening campaigns, thereby reducing time and resource requirements while maintaining comprehensive evaluation capability.
3Reliability
If conventional HTS methods are used, then the screening process is well-established, but it is inefficient for diseases with poorly understood etiologies and requires substantial capital, labor, and time investment
Solution Approach 1:
The patent replaces the mechanical, step-by-step conventional HTS approach with an automated, high-throughput phenotypic screening system that leverages automated biology and machine learning. This substitution enables rapid evaluation of compounds across multiple disease models simultaneously, dramatically increasing productivity and reducing the capital, labor, and time investment required compared to traditional sequential screening methods.
Data Source
AI summary
Methods and systems for evaluating a query perturbation, in a cell based assay representing a test state, are provided. Control data points having dimensions representing measurements of different features across control cell aliquots are obtained. Test data points having dimensions representing measurements of different features across test cell aliquots are obtained. A composite test vector is computed between measures of central tendency across the control data points and measures of central tendency across the test data points. Query perturbation data points having dimensions representing measurements of different features across perturbation cell aliquots are obtained. A composite query perturbation vector is computed between measures of central tendency across the control data points and measures of central tendency across the plurality of query perturbation data points.


