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Hepatotoxic potential of therapeutic oligonucleotides can be predicted from their sequence and modification pattern

Peter H Hagedorn1, Victor Yakimov, Søren Ottosen

  • 1Department of Informatics, Santaris Pharma A/S, Hørsholm, Denmark.

Nucleic Acid Therapeutics
|August 20, 2013
PubMed

Insights

Researchers developed a machine learning model to predict the liver toxicity of therapeutic antisense oligonucleotides. This tool helps design safer drug candidates by analyzing sequence patterns, reducing potential hepatotoxicity.

Area of Science:

  • Pharmacology and Toxicology
  • Medicinal Chemistry
  • Bioinformatics

Background:

  • Antisense oligonucleotides (ASOs) are emerging therapeutics that degrade target messenger RNAs.
  • Hepatotoxicity, including hepatocyte necrosis and elevated alanine-aminotransferase, is a known side effect of some ASO treatments.
  • Understanding sequence-specific determinants of ASO hepatotoxicity is crucial for safe drug development.

Purpose of the Study:

  • To comprehensively analyze the hepatotoxic potential of locked nucleic acid-modified oligonucleotides in a preclinical mouse model.
  • To develop a predictive model for identifying ASO sequences with high or low hepatotoxic potential.
  • To guide the rational redesign of ASOs to mitigate liver toxicity.

Main Methods:

  • Development of a random forests machine learning classifier based on dinucleotide composition of oligonucleotides.
  • Training and validation of the classifier using a dataset of 206 locked nucleic acid-modified oligonucleotides with known hepatotoxic potential in mice.
  • Analysis of individual dinucleotide contributions and their complex interplay in determining hepatotoxicity.

Main Results:

  • The random forests classifier accurately distinguished between high and low hepatotoxic potential oligonucleotides with 80% accuracy (out-of-bag) and 74% accuracy on a validation set.
  • Specific dinucleotide units were identified as either increasing or decreasing hepatotoxic potential.
  • The model successfully guided the redesign of a high-hepatotoxicity oligonucleotide to significantly reduce its toxicity.

Conclusions:

  • Sequence and modification pattern analysis is a powerful tool for predicting and mitigating ASO-induced hepatotoxicity.
  • The developed classifier aids in the preclinical discovery and optimization of safer oligonucleotide-based therapeutics.
  • This approach facilitates the development of next-generation ASO medicines with improved safety profiles.

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