AI-Predicted Cell Uptake Modulator Identification
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Solution Overview
Problem
Current methods for understanding and enhancing the intracellular transport of nucleic acids and nanocarriers are limited by the lack of effective mechanisms for identifying cell uptake modulators, often relying on biased or high-off-target-effect strategies such as pharmacological inhibitors or genetic knockdowns, which complicate the delivery and efficacy of therapeutic agents.
Innovation Solution
A method involving the use of gene-editing agents, such as CRISPR/Cas9 with sgRNAs, to identify cell uptake modulators by contacting cells with DNA polymer micelles and detecting their uptake, thereby isolating and profiling sgRNA fragments to determine the target genes involved in the uptake process, specifically identifying SLC18B1 as a key modulator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pharmacological inhibitors or genetic knockdowns are used to identify cell uptake modulators, then specific pathways can be studied, but off-target effects occur and outcomes are clouded
Solution Approach 1:
The patent uses an artificial intelligence model as an intermediary to predict cell uptake mechanisms. The AI model analyzes molecular characteristics and predicts uptake pathways without directly interfering with cellular processes, thus avoiding off-target effects while maintaining identification accuracy. This computational mediator replaces direct experimental manipulation with indirect prediction.
2Adaptability or versatility
If traditional methods are used to study intracellular transport, then previously studied pathways can be investigated, but the approach is biased and limited in scope
Solution Approach 1:
Instead of starting with known pathways and trying to fit new data into them, the patent inverts the approach by using AI to predict unknown uptake mechanisms directly from molecular characteristics. This bottom-up predictive approach eliminates researcher bias and expands the scope to include previously unstudied pathways.
3Measurement precision
If manual manipulation of individual effectors is performed, then specific pathways can be elucidated, but the process is time-consuming and biased toward investigator intuition
Solution Approach 1:
The AI model performs self-directed analysis of molecular characteristics and automatically predicts uptake mechanisms without requiring manual hypothesis generation or experimental design by researchers. The system serves itself by processing multiple molecules simultaneously through automated computational pipelines, dramatically increasing throughput while maintaining precision.
Data Source
AI summary
Provided herein are methods of identifying a cell uptake modulator of a molecule that include (a) contacting a plurality of cells of a cell-containing biological sample with a plurality of gene-editing agents, wherein a gene-editing agent from the plurality of gene-editing agents recognizes and alters a target gene of at least one cell of the plurality of cells; (b) contacting the plurality of cells with a plurality of molecules, wherein at least one molecule of the plurality of molecules is transported into at least one cell of the plurality of cells; and (c) detecting a presence of the at least one molecule in the plurality of cells, thereby identifying the cell uptake modulator of the molecule.