Oligonucleotide ML Screening for Pharmacology and Tissue Targeting
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Solution Overview
Problem
Traditional drug discovery processes for oligonucleotide-based medicines (OBMs) are inefficient, resource-intensive, and prone to late-stage failures due to the inability to predict pharmacology and limited tissue targeting, leading to high costs and low success rates in developing safe and effective OBMs.
Innovation Solution
A machine-learned model is used to design and test OBMs in silico, mapping oligonucleotide sequences to biophysical effects, enabling the generation of refined sets of OBMs that accurately predict pharmacology and target specific tissues or cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial-and-error screening is used to identify OBMs, then OBMs can be found with desirable pharmacology, but the process is resource-intensive and time-consuming
Solution Approach 1:
The machine learning model performs preliminary prediction of OBM pharmacology before experimental screening. The model is trained on existing OBM data to predict biophysical effects, allowing researchers to identify promising candidates in silico before wet lab testing, thereby reducing time and resources spent on ineffective screening.
Solution Approach 2:
The patent replaces the mechanical trial-and-error screening process with a computational machine learning system. The ML model substitutes for manual experimental screening by automatically predicting OBM pharmacology based on input sequences, enabling rapid virtual screening without physical laboratory intervention.
2Reliability
If traditional trial-and-error screening is used to identify OBMs, then OBMs can be found with desirable pharmacology, but the process is expensive and resource-intensive
Solution Approach 1:
The machine learning model creates a virtual copy of the OBM screening process. Instead of physically testing every possible OBM sequence in the laboratory, the model generates a computational representation that predicts pharmacology, allowing virtual screening of numerous candidates without consuming physical resources.
Solution Approach 2:
The model performs preliminary filtering of OBM candidates through computational prediction before actual experimental testing. This preliminary action identifies the most promising candidates, reducing the quantity of resources needed for subsequent experimental screening and development.
3Adaptability or versatility
If OBMs are designed without machine learning guidance, then design flexibility is maintained, but late-stage failures occur frequently
Solution Approach 1:
The machine learning model provides feedback on predicted pharmacology and biophysical effects during the OBM design process. Researchers can iterate on sequences based on model predictions, adjusting designs to improve predicted performance while maintaining flexibility in exploring different molecular configurations.
Solution Approach 2:
The model performs preliminary assessment of OBM candidates before final selection. By predicting pharmacology and potential issues in advance, the model enables early correction of design flaws, reducing late-stage failures while preserving the ability to explore diverse molecular designs.
4Adaptability or versatility
If OBMs are delivered systemically, then broad tissue coverage is achieved, but targeting precision for specific tissues is limited
Solution Approach 1:
The patent applies local quality by designing OBMs with tissue-specific characteristics. The machine learning model identifies sequence features associated with specific tissue targeting, allowing customization of OBM properties to preferentially accumulate in desired tissues while minimizing off-target effects, thus achieving both coverage and precision.
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
This approach significantly reduces the time and cost of developing OBMs by predicting their biophysical effects with high certainty, allowing for precise targeting and improved safety and efficacy across various tissues and cell types.
Implementation Method 1
Oligonucleotide-based medicine can be designed and tested in silico using a machine-learned model trained on data representative of OBM structure, pharmacology, and effectiveness
Implementation Method 2
OBMs are designed to engage with native DNA or RNA sequences in the cell by Watson Crick hybridization
Data Source
AI summary
A machine-learned model can be trained on and applied to oligonucleotide data. The machine-learned model can be, for example, a neural network, a random forest classifier, or a regression model, and can be trained in one or more stages. The machine-learned model can be applied in design settings, for instance by being configured to predict biophysical effects corresponding to oligonucleotides, by processing real-world experimental or laboratory data, and by retraining the machine-learned model in response to the processed data.