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Stratifying antimalarial compounds with similar mode of action using machine learning on chemo-transcriptomic profiles
Dissertation (MSc)--University of Pretoria, 2019.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2020
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| _version_ | 1867613569068040193 |
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| access_status_str | Open Access |
| author2 | Birkholtz, Lyn-Marie |
| author_browse | Birkholtz, Lyn-Marie |
| author_facet | Birkholtz, Lyn-Marie |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2019. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/73859 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:13.749Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/73859 Stratifying antimalarial compounds with similar mode of action using machine learning on chemo-transcriptomic profiles Birkholtz, Lyn-Marie u14020590@tuks.co.za Van Wyk, Rudi Van Heerden, Ashleigh UCTD Machine learning Plasmodium falciparum Dissertation (MSc)--University of Pretoria, 2019. Malaria is a terrible disease caused by a protozoan parasite within the Plasmodium genus, claiming the lives of hundreds of thousands of people yearly, the majority of whom are children under the age of five. Of the five species of Plasmodium causing malaria in humans, P. falciparum is responsible for most of the death toll. An increase in malaria cases was detected between the years 2016 to 2017 according to the World Malaria Report of 2017, despite control efforts. The rapid development of resistance within P. falciparum against antimalarials has led to the use of artemisinin combinational therapy as the current gold standard for malaria treatment. Yet decreased parasite clearance demonstrates that using combination therapy is insufficient in maintaining current antimalarials’ effectiveness against these resistant parasites. Hence, novel compounds with a mode of action (MoA) different than current antimalarials are required. Though phenotypic screening has delivered thousands of promising hit compounds, hit-to-lead optimisation is still one of the rate-limiting steps in pre-clinical antimalarial drug development. While knowing the exact target or MoA is not required to progress a compound in a medicinal chemistry program, identifying the MoA early can accelerate hit prioritization, hit-to-lead optimisation and preclinical combination studies in malaria research. In this study, we assessed machine learning (ML) approaches for their ability to stratify antimalarials based on transcriptional responses associated with the treatments. From our results, we conclude that it is possible to identify biomarkers from the transcriptional responses that define the MoA of compounds. Moreover, only a limited set of 50 genes was required to build a ML model that can stratify compounds with similar MoA with a classification accuracy of 76.6 ± 6.4%. These biomarkers will help stratify new compounds with similar MoA to those already defined with our strategy. Additionally, the biomarkers can also be used to monitor if the MoA of a compound has changed during hit-to-lead optimisation. This work will contribute to accelerating antimalarial drug discovery during the hit-to-lead optimisation phase and help the identification of compounds with novel MoA. Biochemistry MSc Unrestricted 2020-03-30T08:46:55Z 2020-03-30T08:46:55Z 2020 2019 Dissertation Van Heerden, A 2019, Stratifying antimalarial compounds with similar mode of action using machine learning on chemo-transcriptomic profiles, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/73859> S2020 http://hdl.handle.net/2263/73859 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Machine learning Plasmodium falciparum Stratifying antimalarial compounds with similar mode of action using machine learning on chemo-transcriptomic profiles |
| title | Stratifying antimalarial compounds with similar mode of action using machine learning on chemo-transcriptomic profiles |
| title_full | Stratifying antimalarial compounds with similar mode of action using machine learning on chemo-transcriptomic profiles |
| title_fullStr | Stratifying antimalarial compounds with similar mode of action using machine learning on chemo-transcriptomic profiles |
| title_full_unstemmed | Stratifying antimalarial compounds with similar mode of action using machine learning on chemo-transcriptomic profiles |
| title_short | Stratifying antimalarial compounds with similar mode of action using machine learning on chemo-transcriptomic profiles |
| title_sort | stratifying antimalarial compounds with similar mode of action using machine learning on chemo transcriptomic profiles |
| topic | UCTD Machine learning Plasmodium falciparum |
| url | http://hdl.handle.net/2263/73859 |