This document discusses measuring off-target effects of oligonucleotide drug candidates. It finds that on average, antisense oligonucleotides (ASOs) impact the transcriptome to a similar extent as small molecule drugs by changing expression of a comparable number of genes. The best way to predict potential off-target effects is through a combination of in silico sequence analysis and in vitro validation of predicted off-targets. While current models only explain a small portion of measured off-targets, experimental transcriptomics allows refinement of prediction algorithms. Careful preclinical studies in relevant animal models and controlled human trials are ultimately needed to fully understand safety.

1. Measurement and Prediction of
Hybridization-induced Off-target Effects
of Oligonucleotide Drug Candidates
morten lindow, ph.d,
associate director, informatics
santaris pharma A/S
adjunct associate professor, bioinformatics
university of copenhagen
morten.lindow@gmail.com

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3. DIA www.diahome.org 4
Does antisense oligonucleotides
perturb the transcriptome more or
less than small molecule drugs?

4. Measuring drug induced changes to the human
transcriptome
DIA www.diahome.org 5
Connectivity map: Small
molecules
Antisense oligonucleotides
Database of 1309 small
molecules applied
systematically in 6100 cell
culture experiment
Mining of Gene Expression
Omnibus and Santaris internal
data
Stratifiable by drug type 24 different oligos (both
antimiRs and gapmers)
Cells subjected to
pharmacological dose
Cells subjected to
pharmacological dose (intended
target is knocked down)
Affymetrix microarrays Affymetrix microarrays
Science. 2006 Sep 29;313(5795):1929-35

5. Compare transcriptome changes induced by ASOs to
those induced by approved drugs
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Comparing across multiple expression
experiments is not straightforward
Took the path of minimal data
transformation:
• All compounds compared directly to
their designated vehicle control
• Compare number of genes that
change expression by more than 50%
(up or down)
• Tried a range of other
thresholds, conclusion is the same
Hagedorn et al., in preparation
Transcriptschangingmorethan50%
2
5
10
20
50
100
200
500
1000
2000
5000
ASO
SMC(rest)
SMC(L+P)
ns ***
Compounds
L+P=
anticancer and
antiparasite drugs

6. Drug induced changes to transcript levels
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drug
interaction with target
(protein or RNA)
interaction with
non-target proteins
Change of cell
state
expression
changes
Change of cell
state
expression
changes
Disease
improvement
Unwanted
pharmacology
Change of cell
state
expression
changes
interaction with
non-target RNA
Possible adverse
effects and toxicity
non-target RNA
relatively predictable
hybridization-dependentUnpredictable
hybridization-independent

7. Paper from OSWG subcommitee on off-targets
Candidate
oligonucleotide drug
In silico off-target
screen
Database of
transcripts
off-target present in
tox species?
Penultimate test: Preclinical toxicity studies in vivo
In vitro validation of
critical putative off-
targets.
Relative IC50 in
human cell line
Proceed to human
testing
Case by case evaluation of putative off-targets may include:
Technology
and
mechanism
based
algorithms
Comparison of tissue
expression of off-
target with tissue
accumulation of drug
candidate
Function of putative
off-target if known,
e.g. phenotype of
genetic knock-out
Flow chart from Lindow et al 2012: OSWG off-target committee recommendations

8. DIA www.diahome.org 11
Focus on RNAseH recruiting single
stranded oligonucleotides

9. Determinants for activity on (off-) target RNA
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10. For each possible oligonucleotide against
the intended target (~ 20 000 * modification variants)
Evaluate activity determinants against all
possible target sites in the transcriptome
(1.4E9 sites)
Ideal exhaustive in silico specificity evaluation
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NOT FEASIBLE!

11. • Sequence search to choose oligo-
sequences with minimal number of close
sequence matches to non-target RNAs
What is feasible?
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Late discovery phase:
a few candidates
transcriptome
sequences
~1E9 nt>5 yrs ago: search with
BLAST or FASTA
Design phase:
~tens of thousands
of possible oligo
sequences
faster computers, more RAM,
suffix arrays, BW-transforms,
hashing

12. in silico paradigms employed in practice
• Complete-with-mismatches
• Alignment score cutoff: plus for a match,
minus for a mismatch/indel
• Hybridization energy cutoff
character based
energy based

13. Number of off-targets
decrease with length
Number of off-targets
increase with length
Number of off-targets
increase with length
Complete with mismatches
Alignment score cut-off
Hybridization energy cut-off

14. Aim of sequence search and selection
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affinity
- G
potency of
(off-)target
down-regulation perfect full target site
closest imperfect sites
in non-targets
G
Oligonucleotide
with too high-
affinity!
more matches -> higher affinity
mismatches, indels -> lower affinity
modifications affect affinity
neighbouring bases affect affinity (stacking)
Prediction of affinity is
possible with nearest
neighbour models

15. Morten Lindow www.diahome.org 19
in vivo measurements
correspondence to in silico predictions
?

16. ApoB
Oligo2
Oligo1
Transcriptome wide experimental assessment of
specificity
Two or more oligonucleotides that target the same mRNA in different places

17. Oligo1 against ApoB Oligo2 against Apob
Disentangle downstream pharmacological effects and class effects
from sequence specific off-target effects
Manuscript in preparation

18. • Only small overlap between current in silico
predictions and measured off-targets
• Global transcriptomics measurements allows
data driven refinement of algorithms
– we use regression methods to combine
determinants
• our current best model includes two determinants
– predicted binding affinity between oligonucleotide and
(off-)target site
– predicted RNA structural accessibility of (off-)target site
Lessons from transcriptomics measurement of
specificity
Morten Lindow www.diahome.org 22

19. Summary
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Transcriptschangingmorethan50%
2
5
10
20
50
100
200
500
1000
2000
5000 ASO
SMC(rest)
SMC(L+P)
ns ***
Compounds
ASOs on par with small molecules:
• On average same size of impact on
transcriptome
• Penultimate test for toxicology is in relevant
animals models
• Understanding that the only way to truly test
for human responses is in carefully controlled
and monitored clinical trials
Sequence analysis for specificity allows:
• Risk minimization
• Guide exploratory toxicology
Experimental design to measure off-target
pertubation

20. • OSWG off-target committee
• Peter Hagedorn, research bioinformatician
• Danish Strategic Research Council
Acknowledgements
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