A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria
Abstract Background Nearly half of the world’s population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current...
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ftdoajarticles:oai:doaj.org/article:1a2996ddff6044af88d621bd752446f5 2023-05-15T15:13:44+02:00 A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria Yasaman KalantarMotamedi Richard T. Eastman Rajarshi Guha Andreas Bender 2018-04-01T00:00:00Z https://doi.org/10.1186/s12936-018-2294-5 https://doaj.org/article/1a2996ddff6044af88d621bd752446f5 EN eng BMC http://link.springer.com/article/10.1186/s12936-018-2294-5 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-018-2294-5 1475-2875 https://doaj.org/article/1a2996ddff6044af88d621bd752446f5 Malaria Journal, Vol 17, Iss 1, Pp 1-15 (2018) Synergy prediction Malaria Machine learning Compound combination modelling Transcriptional drug repositioning Synergistic anti-malaria compound combinations Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2018 ftdoajarticles https://doi.org/10.1186/s12936-018-2294-5 2022-12-31T10:15:26Z Abstract Background Nearly half of the world’s population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed. Methods The integrated approach utilizes gene expression data from patient-derived samples, in combination with large-scale anti-malarial combination screening data, to predict synergistic compound combinations for three Plasmodium falciparum strains (3D7, DD2 and HB3). Both single compounds and combinations predicted to be active were prospectively tested in experiment. Results One of the predicted single agents, apicidin, was active with the AC50 values of 74.9, 84.1 and 74.9 nM in 3D7, DD2 and HB3 P. falciparum strains while its maximal safe plasma concentration in human is 547.6 ± 136.6 nM. Apicidin at the safe dose of 500 nM kills on average 97% of the parasite. The synergy prediction algorithm exhibited overall precision and recall of 83.5 and 65.1% for mild-to-strong, 48.8 and 75.5% for moderate-to-strong and 12.0 and 62.7% for strong synergies. Some of the prospectively predicted combinations, such as tacrolimus-hydroxyzine and raloxifene-thioridazine, exhibited significant synergy across the three P. falciparum strains included in the study. Conclusions Systematic approaches can play an important role in accelerating discovering novel combinational therapies for malaria as it enables selecting novel synergistic compound pairs in a more informed and cost-effective manner. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 17 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Synergy prediction Malaria Machine learning Compound combination modelling Transcriptional drug repositioning Synergistic anti-malaria compound combinations Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
spellingShingle |
Synergy prediction Malaria Machine learning Compound combination modelling Transcriptional drug repositioning Synergistic anti-malaria compound combinations Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Yasaman KalantarMotamedi Richard T. Eastman Rajarshi Guha Andreas Bender A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria |
topic_facet |
Synergy prediction Malaria Machine learning Compound combination modelling Transcriptional drug repositioning Synergistic anti-malaria compound combinations Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background Nearly half of the world’s population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed. Methods The integrated approach utilizes gene expression data from patient-derived samples, in combination with large-scale anti-malarial combination screening data, to predict synergistic compound combinations for three Plasmodium falciparum strains (3D7, DD2 and HB3). Both single compounds and combinations predicted to be active were prospectively tested in experiment. Results One of the predicted single agents, apicidin, was active with the AC50 values of 74.9, 84.1 and 74.9 nM in 3D7, DD2 and HB3 P. falciparum strains while its maximal safe plasma concentration in human is 547.6 ± 136.6 nM. Apicidin at the safe dose of 500 nM kills on average 97% of the parasite. The synergy prediction algorithm exhibited overall precision and recall of 83.5 and 65.1% for mild-to-strong, 48.8 and 75.5% for moderate-to-strong and 12.0 and 62.7% for strong synergies. Some of the prospectively predicted combinations, such as tacrolimus-hydroxyzine and raloxifene-thioridazine, exhibited significant synergy across the three P. falciparum strains included in the study. Conclusions Systematic approaches can play an important role in accelerating discovering novel combinational therapies for malaria as it enables selecting novel synergistic compound pairs in a more informed and cost-effective manner. |
format |
Article in Journal/Newspaper |
author |
Yasaman KalantarMotamedi Richard T. Eastman Rajarshi Guha Andreas Bender |
author_facet |
Yasaman KalantarMotamedi Richard T. Eastman Rajarshi Guha Andreas Bender |
author_sort |
Yasaman KalantarMotamedi |
title |
A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria |
title_short |
A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria |
title_full |
A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria |
title_fullStr |
A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria |
title_full_unstemmed |
A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria |
title_sort |
systematic and prospectively validated approach for identifying synergistic drug combinations against malaria |
publisher |
BMC |
publishDate |
2018 |
url |
https://doi.org/10.1186/s12936-018-2294-5 https://doaj.org/article/1a2996ddff6044af88d621bd752446f5 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 17, Iss 1, Pp 1-15 (2018) |
op_relation |
http://link.springer.com/article/10.1186/s12936-018-2294-5 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-018-2294-5 1475-2875 https://doaj.org/article/1a2996ddff6044af88d621bd752446f5 |
op_doi |
https://doi.org/10.1186/s12936-018-2294-5 |
container_title |
Malaria Journal |
container_volume |
17 |
container_issue |
1 |
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1766344265684746240 |