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Therapeutic strategies of glioblastoma (GBM): The current advances in the molecular targets and bioactive small molecule compounds.

ABSTRACT: Glioblastoma (GBM) is the most common aggressive malignant tumor in brain neuroepithelial tumors and remains incurable. A variety of treatment options are currently being explored to improve patient survival, including small molecule inhibitors, viral therapies, cancer vaccines, and monoclonal antibodies. Among them, the unique advantages of small molecule inhibitors have made them a focus of attention in the drug discovery of glioblastoma. Currently, the most used chemotherapeutic agents are small molecule inhibitors that target key dysregulated signaling pathways in glioblastoma, including receptor tyrosine kinase, PI3K/AKT/mTOR pathway, DNA damage response, TP53 and cell cycle inhibitors. This review analyzes the therapeutic benefit and clinical development of novel small molecule inhibitors discovered as promising anti-glioblastoma agents by the related targets of these major pathways. Meanwhile, the recent advances in temozolomide resistance and drug combination are also reviewed. In the last part, due to the constant clinical failure of targeted therapies, this paper reviewed the research progress of other therapeutic methods for glioblastoma, to provide patients and readers with a more comprehensive understanding of the treatment landscape of glioblastoma.

SUBMITTER: Liu H

PROVIDER: S-EPMC9279645 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Therapeutic strategies of glioblastoma (GBM): The current advances in the molecular targets and bioactive small molecule compounds.

Liu Hui H Qiu Weimin W Sun Tianyu T Wang Lei L Du Chenxi C Hu Yanyu Y Liu Wenyuan W Feng Feng F Chen Yao Y Sun Haopeng H

Acta pharmaceutica Sinica. B 20211231 4

Glioblastoma (GBM) is the most common aggressive malignant tumor in brain neuroepithelial tumors and remains incurable. A variety of treatment options are currently being explored to improve patient survival, including small molecule inhibitors, viral therapies, cancer vaccines, and monoclonal antibodies. Among them, the unique advantages of small molecule inhibitors have made them a focus of attention in the drug discovery of glioblastoma. Currently, the most used chemotherapeutic agents are sm ...[more]

PMID: 35847506

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Project description:Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Dataset Information

Therapeutic strategies of glioblastoma (GBM): The current advances in the molecular targets and bioactive small molecule compounds.

Publications

Therapeutic strategies of glioblastoma (GBM): The current advances in the molecular targets and bioactive small molecule compounds.

Similar Datasets

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