Assessing the safety profile of antisense therapeutics through a novel computational and analytical framework
File(s)
Author(s)
Kamola, Piotr
Type
Thesis
Abstract
With many safety and technical limitations partly mitigated through chemical modifications,
antisense oligonucleotides (ASOs) are gaining recognition as therapeutic entities. The
increased potency realised by ‘third generation chemistries’ may, however, simultaneously
increase affinity to unintended targets with partial sequence complementarity.
Hybridisation-dependent off-target effects (OTEs), a risk historically regarded as low, are not
being adequately investigated.
My data shows an unexpectedly high OTEs confirmation rate for locked nucleic acid gapmer
ASOs, showing a wide range of mismatch (MM) and gap patterns. Critically, hybridisation
predicted against intronic regions of pre-mRNAs was tested and confirmed. This
dramatically increases the ASO-binding landscape which, together with the high potency of
such interactions, is a serious safety concern. OTEs were also found to translate in vivo in
the mouse and highly correlate with observed hepatotoxcity. With base pairing-driven
target recognition it is possible to predict the putative off-targets and a novel software
pipeline (‘RNArcher’) was developed to address several challenges faced during ASO drug
discovery. While the presence of chemical modifications limited the thermodynamics
analysis of ASOs, the concept was applied to siRNAs. A correlation was found between the
sequence composition of siRNA non-seed region (and its target sites) and seed-dependent
OTEs that can be used to increase specificity of this modality.
Overall, the hybridisation-mediated exonic and intronic OTEs are a significant safety concern
for ASOs with 3rd generation chemistry. The ASO and siRNA-based guidelines and
computational solutions developed throughout this project will aid the community in
designing more specific molecular biology tools and safer therapeutics.
antisense oligonucleotides (ASOs) are gaining recognition as therapeutic entities. The
increased potency realised by ‘third generation chemistries’ may, however, simultaneously
increase affinity to unintended targets with partial sequence complementarity.
Hybridisation-dependent off-target effects (OTEs), a risk historically regarded as low, are not
being adequately investigated.
My data shows an unexpectedly high OTEs confirmation rate for locked nucleic acid gapmer
ASOs, showing a wide range of mismatch (MM) and gap patterns. Critically, hybridisation
predicted against intronic regions of pre-mRNAs was tested and confirmed. This
dramatically increases the ASO-binding landscape which, together with the high potency of
such interactions, is a serious safety concern. OTEs were also found to translate in vivo in
the mouse and highly correlate with observed hepatotoxcity. With base pairing-driven
target recognition it is possible to predict the putative off-targets and a novel software
pipeline (‘RNArcher’) was developed to address several challenges faced during ASO drug
discovery. While the presence of chemical modifications limited the thermodynamics
analysis of ASOs, the concept was applied to siRNAs. A correlation was found between the
sequence composition of siRNA non-seed region (and its target sites) and seed-dependent
OTEs that can be used to increase specificity of this modality.
Overall, the hybridisation-mediated exonic and intronic OTEs are a significant safety concern
for ASOs with 3rd generation chemistry. The ASO and siRNA-based guidelines and
computational solutions developed throughout this project will aid the community in
designing more specific molecular biology tools and safer therapeutics.
Version
Open Access
Date Issued
2015-09
Date Awarded
2015-11
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Gooderham, Nigel
Sponsor
Engineering and Physical Sciences Research Council
GlaxoSmithKline
Grant Number
EP/J502017/1
Publisher Department
Department of Surgery & Cancer
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)