measurement and prediction of hybridization-induced off-target effects of oligonucleotide drug...
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Talk from Oligonucleotide-based therapeutics conferenceTRANSCRIPT
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
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Does antisense oligonucleotides perturb the transcriptome more or less than small molecule drugs?
Measuring drug induced changes to the human transcriptome
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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 microarraysScience. 2006 Sep 29;313(5795):1929-35
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
L+P= anticancer and antiparasite drugs
Drug induced changes to transcript levels
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Paper from OSWG subcommitee on off-targets
Flow chart from Lindow et al 2012: OSWG off-target committee recommendations
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Focus on RNAseH recruiting single stranded oligonucleotides
Determinants for activity on (off-) target RNA
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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!
• 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
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
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
Aim of sequence search and selection
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affinity -DG
potency of (off-)target down-regulation perfect full target site
closest imperfect sites in non-targets
DDG
Oligonucleotide with too high-affinity!
more matches -> higher affinitymismatches, indels -> lower affinity
modifications affect affinityneighbouring bases affect affinity (stacking)
Prediction of affinity is possible with nearest neighbour models
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in vivo measurements
correspondence to in silico predictions
?
ApoB
Oligo2
Oligo1
Transcriptome wide experimental assessment of specificity
Two or more oligonucleotides that target the same mRNA in different places
Oligo1 against ApoB Oligo2 against Apob
Disentangle downstream pharmacological effects and class effectsfrom sequence specific off-target effects
Manuscript in preparation
• 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
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Summary
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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
• OSWG off-target committee• Peter Hagedorn, research bioinformatician• Danish Strategic Research Council
Acknowledgements
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