Efficient identification of novel anti-glioma lead compounds by machine learning models
| Autor: | Rosângela Mayer Gonçalves, Floriano P. Silva-Junior, Carolina Horta Andrade, Sabrina Baptista Ferreira, Lauro Ribeiro de Souza Neto, Bruno J. Neves, Marina Delgobo, Alfeu Zanotto-Filho, Marcio Roberto H. Donza, Jonathan Paulo Agnes, Marcelo N. Gomes, Mario Roberto Senger |
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| Rok vydání: | 2020 |
| Předmět: |
Male
Drug Nitrofurans media_common.quotation_subject Thioredoxin reductase Central nervous system Antineoplastic Agents Apoptosis Machine learning computer.software_genre Body weight 01 natural sciences Pharmacological treatment Machine Learning Mice 03 medical and health sciences Glioma Drug Discovery Tumor Cells Cultured medicine Animals Humans Cell Proliferation 030304 developmental biology media_common Pharmacology 0303 health sciences Models Statistical Temozolomide 010405 organic chemistry Chemistry business.industry Organic Chemistry General Medicine medicine.disease Xenograft Model Antitumor Assays 0104 chemical sciences Mice Inbred C57BL medicine.anatomical_structure Female Artificial intelligence business computer medicine.drug Glioblastoma |
| Zdroj: | European Journal of Medicinal Chemistry. 189:111981 |
| ISSN: | 0223-5234 |
| DOI: | 10.1016/j.ejmech.2019.111981 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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