Measurement and Prediction of Hybridization-induced Off-target Effects of Oligonucleotide Drug Candidates


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  • First I will try to put the issue into perspective by asking:
  • In an attempt to look at this we mined the connectivity map, a database of more than thousand small molecules applied to cell cultures and subsequent transcriptome measurements. We compared this to in-house and public dataset of how oligonucleotides perturb transcriptomes of some of the same cell lines.
  • Such an analysis is not straight forward and has many caveat: different cell lines, different doses, different number of replicates, different times of treatment. It is therefore bound to be pretty rough and we could talk and discuss the methodollogy for hours. For now I just want to show the simplest analysis we could think about. Count the number of genes that change more than 50% up or down as a result of he treatment. If we look we notice that theres is quite a large spread in the number of transcripts that change. Also we found that oncology and antiparasitic drugs stuck out and changed more than the rest. Oligodrugs are not significantly diiferent from this rest-group of small molecules.
  • Bennett and collegues nicely summarized the obseved types of toxicities and whether they had been observed preclinically or clinically. What I will be focusing on is effects due to watson/crick hybridization to unintended target. When there is such unwanted activity is of significance it can be expected to change the expression level of the off-target RNA. Phrased in general terms it would change the transcriptome of the target cells
  • Let us review the various ways that drugs in general could lead to changes in transcript levels. It would (hopefully) interact with the intended target, which would lead to some desiredable change in cell stage and accompanying expression changes. This would lead to downstream effects of either disease improvement or unwanted pharmacology. The drug might also interact with non-target proteins that also leads to change of cell state. Interactions with non-target proteins are generally unpredictable. oOligonucleotides can also interact with non/target RNAs. The interactions, however, ought to relatively predicatable, because they are partially governed by basepairing interactions
  • In 2012 the OSWG published a position paper summarizing the consensus thinking about how these potential unintended effects should be assessed. That paper is summarized by this flow diagram
  • In the following I am going to elaborate on especially the in silico part of this flow chart. Focusing on a technology that we like to employ in Santaris, namely that of RNAseH recruiting single stranded oligonucleotides
  • Measurement and Prediction of Hybridization-induced Off-target Effects of Oligonucleotide Drug Candidates

    1. 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
    2. 2. The views and opinions expressed in the following PowerPoint slides are those of the individual presenter and should not be attributed to Drug Information Association, Inc. (“DIA”), its directors, officers, employees, volunteers, members, chapters, councils, Special Interest Area Communities or affiliates, or any organization with which the presenter is employed or affiliated. These PowerPoint slides are the intellectual property of the individual presenter and are protected under the copyright laws of the United States of America and other countries. Used by permission. All rights reserved. Drug Information Association, DIA and DIA logo are registered trademarks or trademarks of Drug Information Association Inc. All other trademarks are the property of their respective owners. 2www.diahome.orgDIA
    3. 3. DIA 4 Does antisense oligonucleotides perturb the transcriptome more or less than small molecule drugs?
    4. 4. Measuring drug induced changes to the human transcriptome DIA 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. 5. Compare transcriptome changes induced by ASOs to those induced by approved drugs DIA 6 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. 6. Drug induced changes to transcript levels DIA 9 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. 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. 8. DIA 11 Focus on RNAseH recruiting single stranded oligonucleotides
    9. 9. Determinants for activity on (off-) target RNA DIA 12
    10. 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 DIA 13 NOT FEASIBLE!
    11. 11. • Sequence search to choose oligo- sequences with minimal number of close sequence matches to non-target RNAs What is feasible? DIA 14 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. 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. 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. 14. Aim of sequence search and selection DIA 18 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. 15. Morten Lindow 19 in vivo measurements correspondence to in silico predictions ?
    16. 16. ApoB Oligo2 Oligo1 Transcriptome wide experimental assessment of specificity Two or more oligonucleotides that target the same mRNA in different places
    17. 17. Oligo1 against ApoB Oligo2 against Apob Disentangle downstream pharmacological effects and class effects from sequence specific off-target effects Manuscript in preparation
    18. 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 22
    19. 19. Summary DIA 23 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. 20. • OSWG off-target committee • Peter Hagedorn, research bioinformatician • Danish Strategic Research Council Acknowledgements DIA 24