Zebrafish Phenotypic Screening for Clinically Relevant Drug Discovery
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Solution Overview
Problem
Current drug discovery methods face challenges such as high failure rates, long timelines, and high costs due to inefficiencies in identifying drug efficacy and toxicity, particularly in clinical phases, and lack of disease-relevant phenotypic endpoints in traditional screening models like S. cerevisiae and C. elegans, limiting clinical applicability.
Innovation Solution
An in vivo, animal-based phenotypic screening platform using embryonic zebrafish to screen bioactive compounds, capturing heterologous effects and generating machine learning models for prioritizing compounds based on phenotypic outcomes, followed by validation across large chemical libraries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional screening models (S. cerevisiae, C. elegans) are used, then screening efficiency is improved, but disease-relevance and clinical applicability deteriorate
Solution Approach 1:
The patent uses embryonic zebrafish as an intermediary model organism that bridges the gap between simple invertebrate models and complex mammalian systems. Zebrafish provide vertebrate-relevant disease phenotypes while maintaining high screening throughput, thus mediating between screening efficiency and disease-relevance
Solution Approach 2:
The patent changes the biological complexity parameter by transitioning from simple organisms (yeast, worms) to vertebrate embryos (zebrafish). This parameter change enables capture of heterologous effects and human biology-relevant phenotypes while preserving high-throughput screening capabilities
2Measurement precision
If hypothesis-driven screens are used, then target-specific leads are identified, but failure rate in clinical phases increases
Solution Approach 1:
The patent performs preliminary phenotypic characterization in zebrafish embryos before advancing compounds to clinical development. This preliminary action in a vertebrate model identifies compounds with relevant disease phenotypes and reduces later-stage failures by filtering out compounds with off-target or non-translatable effects
Solution Approach 2:
The patent incorporates feedback from zebrafish phenotypic screens to guide compound selection and optimization. Phenotypic data from vertebrate models provides feedback on compound efficacy and safety relevant to human disease, improving clinical success rates by selecting compounds with validated vertebrate activity
3Adaptability or versatility
If phenotype-based screening is used, then multiple specificity and toxicity phenotypes are accessed, but systematic annotation impact is limited
Solution Approach 1:
The patent replaces manual phenotypic assessment with automated machine learning-based phenotyping systems. This substitution enables systematic annotation of large compound libraries across multiple phenotypes, transforming phenotype data into actionable drug development insights and increasing productivity
Data Source
AI summary
The present disclosure relates to the field of drug discovery and therapeutics, including systems and methods to predict drug function, to classify or prioritize drugs based on predicted function, and to test known and novel compounds for function in a vertebrate system. The present disclosure also relates to compounds identified using the disclosed systems and methods, the novel mechanisms of action of the compounds, and applications of the compounds as therapeutics, e.g., as cancer therapeutics.