A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing
dc.contributor.author | Cabrera Andrade, Alejandro | |
dc.contributor.author | López Cortés, Andrés | |
dc.contributor.author | Jaramillo Koupermann, Gabriela | |
dc.contributor.author | González Díaz, Humberto | |
dc.contributor.author | Pazos, Alejandro | |
dc.contributor.author | Munteanu, Cristian R. | |
dc.contributor.author | Pérez Castillo, Yunierkis | |
dc.contributor.author | Tejera, Eduardo | |
dc.date.accessioned | 2020-12-11T09:10:49Z | |
dc.date.available | 2020-12-11T09:10:49Z | |
dc.date.issued | 2020-11-22 | |
dc.identifier.citation | Pharmaceuticals 13(11) : (2020) // Article ID 409 | es_ES |
dc.identifier.issn | 1424-8247 | |
dc.identifier.uri | http://hdl.handle.net/10810/48941 | |
dc.description.abstract | Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment. | es_ES |
dc.description.sponsorship | This research was funded by Universidad de Las Américas, Quito, Ecuador, grant number ENF.RCA.18.01, by Ministry of Competitiveness and Economy (CTQ2016-74881-P), Ministry of Science and Innovation (PID2019-104148GB-I00), and Basque Government (IT1045-16)-2016–2021. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/CTQ2016-74881-P | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIU/PID2019-104148GB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | osteosarcoma | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | multi-objective model | es_ES |
dc.subject | virtual screening | es_ES |
dc.subject | drug repositioning | es_ES |
dc.title | A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2020-11-26T14:10:03Z | |
dc.rights.holder | 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/1424-8247/13/11/409/htm | es_ES |
dc.identifier.doi | 10.3390/ph13110409 | |
dc.departamentoes | Química inorgánica | |
dc.departamentoeu | Kimika ez-organikoa |
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Except where otherwise noted, this item's license is described as 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).