Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754638
Title: Assessing the safety profile of antisense therapeutics through a novel computational and analytical framework
Author: Kamola, Piotr
ISNI:       0000 0004 7427 6579
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2015
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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.
Supervisor: Gooderham, Nigel Sponsor: Engineering and Physical Sciences Research Council ; GlaxoSmithKline
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.754638  DOI:
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