Anticancer potential of aminomethylidene-diazinanes I. Synthesis of arylaminomethylidene of diazinetriones and its cytotoxic effects tested in glioblastoma cells

Prediction and Interpretation Microglia Cytotoxicity by Machine Learning

Ameliorating microglia-mediated neuroinflammation is a crucial strategy in developing new drugs for neurodegenerative diseases. Plant compounds are an important screening target for the discovery of drugs for the treatment of neurodegenerative diseases. However, due to the spatial complexity of phytochemicals, it becomes particularly important to evaluate the effectiveness of compounds while avoiding the mixing of cytotoxic substances in the early stages of compound screening. Traditional high-throughput screening methods suffer from high cost and low efficiency. A computational model based on machine learning provides a novel avenue for cytotoxicity determination. In this study, a microglia cytotoxicity classifier was developed using a machine learning approach. First, we proposed a data splitting strategy based on the molecule murcko generic scaffold, under this condition, three machine learning approaches were coupled with three kinds of molecular representation methods to construct microglia cytotoxicity classifier, which were then compared and assessed by the predictive accuracy, balanced accuracy, F1-score, and Matthews Correlation Coefficient. Then, the recursive feature elimination integrated with support vector machine (RFE-SVC) dimension reduction method was introduced to molecular fingerprints with high dimensions to further improve the model performance. Among all the microglial cytotoxicity classifiers, the SVM coupled with ECFP4 fingerprint after feature selection (ECFP4-RFE-SVM) obtained the most accurate classification for the test set (ACC of 0.99, BA of 0.99, F1-score of 0.99, MCC of 0.97). Finally, the Shapley additive explanations (SHAP) method was used in interpreting the microglia cytotoxicity classifier and key substructure smart identified as structural alerts. Experimental results show that ECFP4-RFE-SVM have reliable classification capability for microglia cytotoxicity, and SHAP can not only provide a rational explanation for microglia cytotoxicity predictions, but also offer a guideline for subsequent molecular cytotoxicity modifications.

Glioblastoma: Current Status, Emerging Targets, and Recent Advances

Glioblastoma (GBM) is a highly malignant brain tumor characterized by a heterogeneous population of genetically unstable and highly infiltrative cells that are resistant to chemotherapy. Although substantial efforts have been invested in the field of anti-GBM drug discovery in the past decade, success has primarily been confined to the preclinical level, and clinical studies have often been hampered due to efficacy-, selectivity-, or physicochemical property-related issues. Thus, expansion of the list of molecular targets coupled with a pragmatic design of new small-molecule inhibitors with central nervous system (CNS)-penetrating ability is required to steer the wheels of anti-GBM drug discovery endeavors. This Perspective presents various aspects of drug discovery (challenges in GBM drug discovery and delivery, therapeutic targets, and agents under clinical investigation). The comprehensively covered sections include the recent medicinal chemistry campaigns embarked upon to validate the potential of numerous enzymes/proteins/receptors as therapeutic targets in GBM.

Non-alkylator anti-glioblastoma agents induced cell cycle G2/M arrest and apoptosis: Design, in silico physicochemical and SAR studies of 2-aminoquinoline-3-carboxamides

Malignant gliomas are the most common brain tumors, with generally dismal prognosis, early clinical deterioration and high mortality. Recently, 2-aminoquinoline scaffold derivatives have shown pronounced activity in central nervous system disorders. We herein reported a series of 2-aminoquinoline-3-carboxamides as novel non-alkylator anti-glioblastoma agents. The synthesized compounds showed comparable activity to cisplatin against glioblastoma cell line U87 MG in vitro. Among them, we found that 6a displayed good inhibitory activity against A172 and U118 MG glioblastoma cell lines and induced cell cycle arrest in the G2/M phase and apoptosis in U87 MG by flow cytometry analysis. Additionally, 6a displayed low cytotoxicity to several normal human cell lines. In silico study showed 6a had promising physicochemical properties and was predicted to cross the blood-brain barrier. Moreover, preliminary structure-activity relationships are also investigated, shedding light on further modifications towards more potent agents on this series of compounds. Our results suggest this compound has a promising potential as an anti-glioblastoma agent with a differential effect between tumor and non-malignant cells.

Recent advances in the discovery of small molecules targeting glioblastoma

Glioblastoma (GBM) is one of the most common central nervous system cancers. It is characterized as a fast-growing tumor that arises from multiple cell types with neural stem-cell-like properties. Additionally, GBM tumors are highly invasive, which is attributed to the presence of glioblastoma stem cells that makes surgery ineffective in most cases. Currently, temozolomide is the unique chemotherapy option approved by the U.S. Food and Drug Administration for GBM treatment. This review analyzes the emergence and development of new synthetic small molecules discovered as promising anti-glioblastoma agents. A number of compounds were described herein and grouped according to the main chemical class used in the drug discovery process. Importantly, we focused only on synthetic compounds published in the last 10 years, thus excluding natural products. Furthermore, we included in this review only those most biologically active compounds with proven in vitro and/or in vivo efficacy.