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Envisagenics Validates AI/ML Approach for RNA Target Identification and SSO Therapeutic Development

Envisagenics

Results from evaluation of the company’s SpliceCore® AI/ML platform in Triple Negative Breast Cancer published in Molecular Systems Biology

Envisagenics, an AI-driven biotechnology company, announced the publication in the journal Molecular Systems Biology of study results evaluating the company’s SpliceCore AI/ML platform in Triple Negative Breast Cancer (TNBC). This study demonstrates the efficacy of artificial intelligence and machine learning (AI/ML) for target discovery in triple negative breast cancer (TNBC) and for identifying functional and verifiable splice-switching oligonucleotides (SSOs) crucial for the development of RNA therapeutics. The results also validate its potential to tackle a challenging disease such as TNBC, a particularly aggressive cancer affecting approximately 200,000 patients annually, with a dismal five-year survival rate of only 20%. Detailed results from the study, titled “Development and Validation of AI/ML Derived Splice-Switching Oligonucleotides,”.

SSOs are synthetic antisense oligonucleotide compounds that directly act on pre-mRNA to regulate the expression of alternatively spliced isoforms unique to cancer cells, which are key drivers of cancer progression and metastasis. While holding immense promise as a therapeutic approach for impeding cancer growth, the identification of functional SSOs using traditional methods is high cost and requires extensive time and labor.

“This study bridges the gap between computational predictions and experimental validation, positioning AI/ML as a critical force in validating RNA targets and advancing SSO therapeutic development,” said Martin Akerman, Ph.D., Envisagenics’ CTO and Co-Founder.

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In the study, Envisagenics achieved the following milestones:

“For patients with TNBC and other diseases that are difficult to treat, this study illustrates the usefulness of SpliceCore to discover novel therapeutic targets from patient’s RNA sequencing data,” said Dr. Akerman. “Our findings affirm the robustness and reliability of the platform and shed light on previously unrecognized avenues for therapeutic intervention.”

Source: PRNewsWire

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