2. Academic Validation
  3. Efficient identification of novel anti-glioma lead compounds by machine learning models

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

  • Eur J Med Chem. 2020 Mar 1:189:111981. doi: 10.1016/j.ejmech.2019.111981.
Bruno Junior Neves 1,  Jonathan Paulo Agnes 2,  Marcelo do Nascimento Gomes 3,  Marcio Roberto Henriques Donza 4,  Rosângela Mayer Gonçalves 2,  Marina Delgobo 2,  Lauro Ribeiro de Souza Neto 5,  Mario Roberto Senger 5,  Floriano Paes Silva-Junior 5,  Sabrina Baptista Ferreira 4,  Alfeu Zanotto-Filho 6,  Carolina Horta Andrade 7
Affiliations

Affiliations

  • 1 LabChem - Laboratory of Cheminformatics, Centro Universitário de Anápolis, UniEVANGÉLICA, Anápolis, GO, 75083-515, Brazil; LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, 74605-510, Brazil.
  • 2 LabCancer - Laboratório de Farmacologia e Bioquímica do Câncer, Departamento de Farmacologia, Centro de Ciências Biológicas, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil.
  • 3 LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, 74605-510, Brazil; InSiChem Drug Discovery - Universidade Estadual de Goiás, Anápolis, GO, 74643-090, Brazil.
  • 4 LSOPB - Laboratório de Síntese Orgânica e Prospecção Biológica, Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21949-900, Brazil.
  • 5 LaBECFar - Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-900, Brazil.
  • 6 LabCancer - Laboratório de Farmacologia e Bioquímica do Câncer, Departamento de Farmacologia, Centro de Ciências Biológicas, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil. Electronic address: alfeu.zanotto@ufsc.br.
  • 7 LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, 74605-510, Brazil. Electronic address: carolina@ufg.br.
PMID: 31978780 DOI: 10.1016/j.ejmech.2019.111981
Abstract

Glioblastoma 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.

Keywords

Cancer; Glioblastoma; Machine learning; Orthotopic glioma model; Predictive modeling; Thioredoxin reductase.

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