Dose-Response Gene Knockdown Modeling for Off-Target Quantification

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Solution Overview

Problem

Existing methods for quantifying the on- and off-target dose response of synthetic antisense oligonucleotides and siRNAs in oligonucleotide-based medicines are under-powered and ambiguous, necessitating improved methods for designing precision gene-modulatory therapeutics with strong affinity to on-target genes and minimal off-target interactions.

Innovation Solution

A dose-responsive model is developed, incorporating a kinetic model, noise model, and Bayesian inference to quantify and characterize gene expression, using functional genomic analysis and machine learning to identify and validate oligonucleotide sequences with specific on- and off-target effects.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If in silico predictions of off-targets are used for putative off-target analysis, then the analysis can be performed computationally, but the quantification remains under-powered and ambiguous

Engineering Contradiction:

Improvecomputational analysis capabilityVSAvoidoff-target quantification accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent introduces a kinetic model as an intermediary framework that connects in silico predictions with experimental dose-response data. The model serves as a mediator that translates computational off-target predictions into quantifiable dose-response relationships, enabling precise measurement of both on-target and off-target effects through a unified mathematical framework

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent applies parameter changes by introducing kinetic parameters (kon, koff, kcat) and dose-response parameters (EC50, Hill coefficient) that transform static in silico predictions into dynamic, quantifiable measurements. By varying oligonucleotide concentration and measuring gene expression changes across multiple doses, the method converts ambiguous computational data into precise quantitative metrics

Inventive Principle:

Principle #35Parameter changes

2Quantity of substance

If next-generation sequencing is used to measure gene expression, then transcriptome-wide data can be obtained, but the quantification of on- and off-target dose response remains under-powered

Engineering Contradiction:

Improvetranscriptome-wide gene expression dataVSAvoiddose response quantification power

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The patent applies preliminary action by performing in silico off-target predictions and kinetic model setup before conducting the actual sequencing experiments. This preparatory phase includes identifying potential off-target genes, designing appropriate dose ranges, and establishing the kinetic framework in advance, which enables the subsequent sequencing data to be analyzed with predetermined precision and statistical power

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback through iterative refinement of the kinetic model using sequencing data. The model parameters are adjusted based on observed dose-response patterns, and the refined model is used to re-analyze the data, creating a feedback loop that continuously improves quantification precision. Machine learning algorithms provide automated feedback for model optimization and parameter estimation

Inventive Principle:

Principle #23Feedback

3Measurement precision

If a kinetic model with Bayesian inference is implemented, then dose-responsive gene features can be detected with higher accuracy, but the model complexity increases

Engineering Contradiction:

Improvegene feature detection accuracyVSAvoidmodel structure complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing the complex kinetic model into distinct modular components: (1) oligonucleotide-target binding kinetics (kon, koff), (2) enzymatic cleavage kinetics (kcat), and (3) gene expression output. Each module can be independently parameterized and validated, reducing overall model complexity while maintaining detection accuracy through systematic decomposition of the inference process

Inventive Principle:

Principle #1Segmentation

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

The model enhances the accuracy of off-target analysis, enabling the development of safer and more effective oligonucleotide-based medicines by precisely determining gene knockdown and minimizing off-target gene interactions.

Implementation Method 1

synthetic antisense oligonucleotides and siRNAs are a class of Oligonucleotide-Based Medicines (OBMs) that can hybridize with pre-mRNA and mRNA

Methodology Applied

Scientific EffectHybridization:

Data Source

PatentUS20250384958A1Dose-response model for accurate detection and quantification of transcriptome-wide gene knockdown for oligonucleotide-based medicines

Publication Date: 2025.12.18 CREYON BIO INC

AI summary

The methods of the present disclosure include a dose-response model (DoReSeq) and machine learned models for quantifying oligonucleotide mediated off-target gene or on-target gene knockdown, and/or characterizing the level of gene expression dependent upon concentration of an oligonucleotide.