Efficient identification of novel anti-glioma lead compounds by machine learning models

dc.creatorNeves, Bruno Junior
dc.creatorAgnes, Jonathan Paulo
dc.creatorGomes, Marcelo do Nascimento
dc.creatorDonza, Marcio Roberto Henriques
dc.creatorGonçalves, Rosângela Mayer
dc.creatorDelgobo, Marina
dc.creatorSouza Neto, Lauro Ribeiro de
dc.creatorSenger, Mario Roberto
dc.creatorSilva Junior, Floriano Paes
dc.creatorFerreira, Sabrina Baptista
dc.creatorZanotto Filho, Alfeu
dc.creatorAndrade, Carolina Horta
dc.date2020-07-28T18:08:25Z
dc.date2020-07-28T18:08:25Z
dc.date2020
dc.date.accessioned2023-09-27T00:06:12Z
dc.date.available2023-09-27T00:06:12Z
dc.identifierNEVES, Bruno Junior et al. Efficient identification of novel anti-glioma lead compounds by machine learning models. European Journal of Medicinal Chemistry, v. 189, 111981, 14p, 2020.
dc.identifier0223-5234/
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/42425
dc.identifier10.1016/j.ejmech.2019.111981
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8897457
dc.descriptionGlioblastoma multiforme (GBM) is the most devastating and widespread primary central nervous system tumor. Pharmacological treatment of this malignance is limited by the selective permeability of the blood-brain barrier (BBB) and relies on a single drug, temozolomide (TMZ), thus making the discovery of new compounds challenging and urgent. Therefore, aiming to discover new anti-glioma drugs, we developed robust machine learning models for predicting anti-glioma activity and BBB penetration ability of new compounds. Using these models, we prioritized 41 compounds from our in-house library of compounds, for further in vitro testing against three glioma cell lines and astrocytes. Subsequently, the most potent and selective compounds were resynthesized and tested in vivo using an orthotopic glioma model. This approach revealed two lead candidates, 4m and 4n, which efficiently decreased malignant glioma development in mice, probably by inhibiting thioredoxin reductase activity, as shown by our enzymological assays. Moreover, these two compounds did not promote body weight reduction, death of animals, or altered hematological and toxicological markers, making then good candidates for lead optimization as anti-glioma drug candidates.
dc.description2022-01-01
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier
dc.rightsrestricted access
dc.subjectCâncer
dc.subjectAprendizado de máquina
dc.subjectGlioblastoma
dc.subjectModelagem preditiva
dc.subjectModelo de glioma ortotópico
dc.subjectReductase de tiadoxiina
dc.subjectCancer
dc.subjectGlioblastoma
dc.subjectMachine learning
dc.subjectPredictive modeling
dc.subjectOrthotopic glioma model
dc.subjectThioredoxin reductase
dc.titleEfficient identification of novel anti-glioma lead compounds by machine learning models
dc.typeArticle


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