RNA Therapeutic Chemical Modification Selection for Off-Target Control
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Solution Overview
Problem
Existing RNA therapeutics face challenges in effectively modulating the activity of target mRNA while minimizing the side effect of modulating off-target mRNAs, and variations in therapeutic efficiency and in vivo stability due to different chemical modifications.
Innovation Solution
A method and system using an artificial neural network to determine an optimal chemical modification for an RNA therapeutic's nucleotide sequence by analyzing biological characteristics, selecting sequences with different modifications, and training the network to identify the best modification for maximizing therapeutic effect and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RNA therapeutics are designed to lower the activity of target mRNA, then therapeutic effect is improved, but off-target effects occur causing inhibition of normal protein synthesis
Solution Approach 1:
The patent applies local quality by designing RNA therapeutics with specific nucleotide sequences and chemical modifications tailored to target particular mRNA regions. The sequence modification module identifies specific positions in the nucleotide sequence where chemical modifications should be applied to enhance target specificity and reduce off-target effects, rather than using uniform modifications across the entire sequence.
Solution Approach 2:
The patent utilizes parameter changes by systematically varying chemical modification parameters (type of modification, position of modification, concentration of modification) to optimize the balance between therapeutic effect and off-target effects. The sequence modification module evaluates multiple chemical modification scenarios to determine the optimal parameters that maximize target mRNA inhibition while minimizing off-target impacts.
2Stability of the object's composition
If chemical modifications are applied to RNA therapeutic nucleotide sequence, then in vivo stability is improved, but therapeutic efficiency varies depending on the modification type
Solution Approach 1:
The patent applies parameter changes by systematically evaluating multiple chemical modification types and their positions in the nucleotide sequence. The sequence modification module analyzes how different chemical modifications (e.g., 2'-O-methyl, 5-methylcytidine, pseudouridine) at different positions affect both in vivo stability and therapeutic efficiency, determining the optimal modification strategy that achieves both goals simultaneously.
Solution Approach 2:
The patent uses composite materials by combining multiple chemical modifications within a single RNA therapeutic sequence. The sequence modification module identifies positions where different types of chemical modifications can be combined to create a composite modified RNA molecule that achieves enhanced in vivo stability while maintaining high therapeutic efficiency through synergistic effects of multiple modifications.
3Reliability
If multiple chemical modifications are tested to determine optimal modification, then therapeutic efficiency is optimized, but development time and complexity increase
Solution Approach 1:
The patent applies preliminary action by using in silico sequence modification modules that perform computational analysis and prediction before actual experimental testing. The system pre-evaluates multiple chemical modification scenarios through algorithms and models, ranking them by predicted therapeutic efficiency and stability, thereby reducing the number of experimental trials needed and accelerating the development process.
Solution Approach 2:
The patent uses copying by creating virtual models of RNA therapeutics with different chemical modifications through computational simulations. The sequence modification module generates digital representations of modified RNA sequences and predicts their behavior in vivo, allowing researchers to explore multiple modification options without physically synthesizing and testing each variant, thus saving time and resources.
Data Source
AI summary
In a method for determining an optimal chemical modification for a nucleotide sequence of an RNA therapeutic, a sequence modification module acquires, as learning data, biological characteristics corresponding to when multiple chemical modifications are applied to multiple nucleotide sequences, creates an optimal chemical modification prediction model by repeatedly performing a process of randomly selecting, among the learning data, at least two sequences in which different chemical modifications are applied to the same nucleotide sequence, sequentially inputting the selected sequences into an artificial neural network, and training the artificial neural network to compare output values for the input sequences and output a higher value as the biological characteristics for the input sequence are better, and uses the model to determine, as an optimal chemical modification, a chemical modification showing the best biological characteristics when applied to the nucleotide sequence of the RNA therapeutic, among first to wth chemical modifications.