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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2www.diahome.orgDIA

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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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