Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 d...

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Detalhes bibliográficos
Autores: Gerdes, Henry, Casado, Pedro, Dokal, Arran, Hijazi Vega, Maruan, Akhtar, Nosheen, Osuntola, Ruth, Rajeeve, Vinothini, Fitzgibbon, Jude, Travers, Jon, Britton, David, Khorsandi, Shirin, Cutillas, Pedro R
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/154843
Acesso em linha:http://hdl.handle.net/10366/154843
Access Level:acceso abierto
Palavra-chave:inteligencia artificial
cáncer
Terapia
Paciente oncológico
Medicamentos
Medicamentous Diagnosis
3209 Farmacología
6310.03 Enfermedad
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oai_identifier_str oai:gredos.usal.es:10366/154843
network_acronym_str ES
network_name_str España
repository_id_str
spelling Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugsGerdes, HenryCasado, PedroDokal, ArranHijazi Vega, MaruanAkhtar, NosheenOsuntola, RuthRajeeve, VinothiniFitzgibbon, JudeTravers, JonBritton, DavidKhorsandi, ShirinCutillas, Pedro Rinteligencia artificialcáncerTerapiaPaciente oncológicoMedicamentosMedicamentous Diagnosis3209 Farmacología6310.03 EnfermedadArtificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman’s rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.202420242021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/154843reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)Inglésinfo:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1548432026-06-07T06:28:51Z
dc.title.none.fl_str_mv Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
spellingShingle Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
Gerdes, Henry
inteligencia artificial
cáncer
Terapia
Paciente oncológico
Medicamentos
Medicamentous Diagnosis
3209 Farmacología
6310.03 Enfermedad
title_short Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title_full Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title_fullStr Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title_full_unstemmed Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title_sort Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
dc.creator.none.fl_str_mv Gerdes, Henry
Casado, Pedro
Dokal, Arran
Hijazi Vega, Maruan
Akhtar, Nosheen
Osuntola, Ruth
Rajeeve, Vinothini
Fitzgibbon, Jude
Travers, Jon
Britton, David
Khorsandi, Shirin
Cutillas, Pedro R
author Gerdes, Henry
author_facet Gerdes, Henry
Casado, Pedro
Dokal, Arran
Hijazi Vega, Maruan
Akhtar, Nosheen
Osuntola, Ruth
Rajeeve, Vinothini
Fitzgibbon, Jude
Travers, Jon
Britton, David
Khorsandi, Shirin
Cutillas, Pedro R
author_role author
author2 Casado, Pedro
Dokal, Arran
Hijazi Vega, Maruan
Akhtar, Nosheen
Osuntola, Ruth
Rajeeve, Vinothini
Fitzgibbon, Jude
Travers, Jon
Britton, David
Khorsandi, Shirin
Cutillas, Pedro R
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv inteligencia artificial
cáncer
Terapia
Paciente oncológico
Medicamentos
Medicamentous Diagnosis
3209 Farmacología
6310.03 Enfermedad
topic inteligencia artificial
cáncer
Terapia
Paciente oncológico
Medicamentos
Medicamentous Diagnosis
3209 Farmacología
6310.03 Enfermedad
description Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman’s rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/154843
url http://hdl.handle.net/10366/154843
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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