Identifying Suitable Target Regions and Analyzing Off-Target Effects of Therapeutic Oligonucleotides

  • Lykke PedersenEmail author
  • Peter H. Hagedorn
  • Troels Koch
Part of the Methods in Molecular Biology book series (MIMB, volume 2036)


Antisense oligonucleotides (AONs) that promote degradation of complementary RNA are being developed as therapeutics. Here, we describe a simple computational workflow for identification of the regions on an RNA that are suitable for targeting with such AONs. The workflow is based on the statistical programming language R, and the calculations and data processing can be carried out on a desktop computer. Our workflow integrates well-established data resources and RNA structure-prediction tools and can be modified easily and expanded as new resources become available.

Key words

Antisense oligonucleotides Target regions Off-target effects Computational prediction Ribonuclease H 



We thank Matteo Cassotti, Lukasz Kielpinski, and Sindri Traustason for helpful discussions during preparation of the manuscript.


  1. 1.
    Hughes JP, Rees S, Kalindjian SB, Philpott KL (2011) Principles of early drug discovery. Br J Pharmacol 162:1239–1249CrossRefGoogle Scholar
  2. 2.
    Muller PY, Milton MN (2012) The determination and interpretation of the therapeutic index in drug development. Nat Rev Drug Discov 11:751–761CrossRefGoogle Scholar
  3. 3.
    Huggins DJ, Sherman W, Tidor B (2012) Rational approaches to improving selectivity in drug design. J Med Chem 55:1424–1444CrossRefGoogle Scholar
  4. 4.
    Hagedorn PH, Hansen BR, Koch T, Lindow M (2017) Managing the sequence-specificity of antisense oligonucleotides in drug discovery. Nucleic Acids Res 45:2262–2282CrossRefGoogle Scholar
  5. 5.
    Hagedorn PH, Pontoppidan M, Bisgaard TS et al (2018) Identifying and avoiding off-target effects of RNase H-dependent antisense oligonucleotides in mice. Nucleic Acids Res 46:5366–5380CrossRefGoogle Scholar
  6. 6.
    Bennett CF, Swayze EE (2010) RNA targeting therapeutics: molecular mechanisms of antisense oligonucleotides as a therapeutic platform. Annu Rev Pharmacol Toxicol 50:259–293CrossRefGoogle Scholar
  7. 7.
    Wahlestedt C, Salmi P, Good L et al (2000) Potent and nontoxic antisense oligonucleotides containing locked nucleic acids. PNAS 97:5633–5638CrossRefGoogle Scholar
  8. 8.
    Lützelberger M, Kjems J (2006) Strategies to identify potential therapeutic target sites in RNA. In: RNA towards medicine. Springer, Berlin, Heidelberg, pp 243–259CrossRefGoogle Scholar
  9. 9.
    Watson JD, Crick FH (1953) Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid. Nature 171:737–738CrossRefGoogle Scholar
  10. 10.
    Hagedorn PH, Persson R, Funder ED et al (2018) Locked nucleic acid: modality, diversity, and drug discovery. Drug Discov Today 23:101–114CrossRefGoogle Scholar
  11. 11.
    Freier SM, Watt AT (2007) Basic principles of antisense drug discovery. In: Antisense drug technology principles. Springer, Berlin, Heidelberg, pp 117–141Google Scholar
  12. 12.
    Laxton C, Brady K, Moschos S et al (2011) Selection, optimization, and pharmacokinetic properties of a novel, potent antiviral locked nucleic acid-based antisense oligomer targeting hepatitis C virus internal ribosome entry site. Antimicrob Agents Chemother 55:3105–3114CrossRefGoogle Scholar
  13. 13.
    SantaLucia J (1998) A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. PNAS 95:1460–1465CrossRefGoogle Scholar
  14. 14.
    Pedersen L, Hagedorn PH, Lindholm MW, Lindow M (2014) A kinetic model explains why shorter and less affine enzyme-recruiting oligonucleotides can be more potent. Mol Ther Nucleic Acids 3:e149CrossRefGoogle Scholar
  15. 15.
    Tafer H, Ameres SL, Obernosterer G et al (2008) The impact of target site accessibility on the design of effective siRNAs. Nat Biotechnol 26:578–583CrossRefGoogle Scholar
  16. 16.
    Matveeva OV, Mathews DH, Tsodikov AD et al (2003) Thermodynamic criteria for high hit rate antisense oligonucleotide design. Nucleic Acids Res 31:4989–4994CrossRefGoogle Scholar
  17. 17.
    Lorenz R, Bernhart SH, Höner Zu Siederdissen C et al (2011) ViennaRNA Package 2.0. Algorithms Mol Biol 6:26CrossRefGoogle Scholar
  18. 18.
    Kielpinski LJ, Hagedorn PH, Lindow M, Vinther J (2017) RNase H sequence preferences influence antisense oligonucleotide efficiency. Nucleic Acids Res 45:12932–12944CrossRefGoogle Scholar
  19. 19.
    Watanabe DTA, Geary RS, Levin AA (2006) Plasma protein binding of an antisense oligonucleotide targeting human ICAM-1 (ISIS 2302). Oligonucleotides 16:169–180CrossRefGoogle Scholar
  20. 20.
    Crooke ST, Wang S, Vickers TA et al (2017) Cellular uptake and trafficking of antisense oligonucleotides. Nat Biotechnol 35:230–237CrossRefGoogle Scholar
  21. 21.
    Hung G, Xiao X, Peralta R et al (2013) Characterization of target mRNA reduction through in situ RNA hybridization in multiple organ systems following systemic antisense treatment in animals. Nucleic Acid Ther 23:369–378CrossRefGoogle Scholar
  22. 22.
    Liang X-H, Sun H, Shen W, Crooke ST (2015) Identification and characterization of intracellular proteins that bind oligonucleotides with phosphorothioate linkages. Nucleic Acids Res 43:2927–2945CrossRefGoogle Scholar
  23. 23.
    Denayer T, Stöhr T, Van Roy M (2014) Animal models in translational medicine: validation and prediction. Eur J Mol Clin Med 2:5CrossRefGoogle Scholar
  24. 24.
    Vickers TA, Freier SM, Bui H-H et al (2014) Targeting of repeated sequences unique to a gene results in significant increases in antisense oligonucleotide potency. PLoS One 9:e110615–e110612CrossRefGoogle Scholar
  25. 25.
    Zerbino DR, Achuthan P, Akanni W et al (2018) Ensembl 2018. Nucleic Acids Res 46:D754–D761CrossRefGoogle Scholar
  26. 26.
    Krieg AM (2006) Therapeutic potential of toll-like receptor 9 activation. Nat Rev Drug Discov 5:471–484CrossRefGoogle Scholar
  27. 27.
    Burdick AD, Sciabola S, Mantena SR et al (2014) Sequence motifs associated with hepatotoxicity of locked nucleic acid--modified antisense oligonucleotides. Nucleic Acids Res 42:4882–4891CrossRefGoogle Scholar
  28. 28.
    Hagedorn PH, Yakimov V, Ottosen S et al (2013) Hepatotoxic potential of therapeutic oligonucleotides can be predicted from their sequence and modification pattern. Nucleic Acid Ther 23:302–310CrossRefGoogle Scholar
  29. 29.
    Mitsuhashi M (1997) Strategy for designing specific antisense oligonucleotide sequences. J Gastroenterol 32:282–287CrossRefGoogle Scholar
  30. 30.
    Schiavone N, Donnini M, Nicolin A, Capaccioli S (2004) Antisense oligonucleotide drug design. Curr Pharm Des 10:769–784CrossRefGoogle Scholar
  31. 31.
    Chan JHP, Lim S, Wong WSF (2006) Antisense oligonucleotides: from design to therapeutic application. Clin Exp Pharmacol Physiol 33:533–540CrossRefGoogle Scholar
  32. 32.
    Chalk AM, Sonnhammer ELL (2002) Computational antisense oligo prediction with a neural network model. Bioinformatics 18:1567–1575CrossRefGoogle Scholar
  33. 33.
    Camps-Valls G, Chalk AM, Serrano-López AJ et al (2004) Profiled support vector machines for antisense oligonucleotide efficacy prediction. BMC Bioinformatics 5:135CrossRefGoogle Scholar
  34. 34.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874CrossRefGoogle Scholar
  35. 35.
    Stanton R, Sciabola S, Salatto C et al (2012) Chemical modification study of antisense gapmers. Nucleic Acid Ther 22:344–359CrossRefGoogle Scholar
  36. 36.
    R Core Team (2017) R: a language and environment for statistical computing, ViennaGoogle Scholar
  37. 37.
    Wickham H (2017) Tidyverse: easily install and load the “Tidyverse.” R packageGoogle Scholar
  38. 38.
    Maechler M, Rousseeuw P, Struyf A, et al (2017) Cluster: cluster analysis basics and extensions. R packageGoogle Scholar
  39. 39.
    Lawrence M, Huber W, Pagès H et al (2013) Software for computing and annotating genomic ranges. PLoS Comput Biol 9:e1003118CrossRefGoogle Scholar
  40. 40.
    Durinck S, Spellman PT, Birney E, Huber W (2009) Mapping identifiers for the integration of genomic datasets with the R/bioconductor package biomaRt. Nat Protoc 4:1184–1191CrossRefGoogle Scholar
  41. 41.
    Pagès H, Carlson M, Falcon S, Li N (2017) AnnotationDbi: annotation database interface. R packageGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Lykke Pedersen
    • 1
    Email author
  • Peter H. Hagedorn
    • 1
  • Troels Koch
    • 1
  1. 1.Therapeutic Modalities, Roche Pharma Research and Early DevelopmentRoche Innovation Center CopenhagenHørsholmDenmark

Personalised recommendations