Intuitive repositioning of an anti-depressant drug in combination with tivozanib: precision medicine for breast cancer therapy

Kumar, Naveen; Raza, Masoom; Sehrawat, Seema

doi:10.1007/s11010-021-04230-1

Mol Cell Biochem

2021

DOI: 10.1007/s11010-021-04230-1

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Intuitive repositioning of an anti-depressant drug in combination with tivozanib: precision medicine for breast cancer therapy

Naveen Kumar

Masoom Raza

Seema Sehrawat

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2024

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Cited by 2 publications

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Drug repurposing: a retrospective revolution in breast cancer medicine

Shivani,

Misari,

Anand

et al. 2024

Discov Med

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Breast cancer is a complex disease involving numerous pathways, making effective treatment quite challenging. Among the current scenario, drug repurposing serves as one of the best alternatives for drug discovery as a time-efficient and cost-effective strategy to determine new therapeutic indications for previously approved drug molecules. This is the reason behind the increased popularity of drug repurposing not only in the pharma industry but also in the civil sector and academics as a faster and less expensive way to increase the repertoire of licensed cancer therapies. In the current review, we have tried to perform an in-depth review of the current state of repurposed medications available for the treatment of breast cancer. Studies conducted on various noncancer medications showing possible anticancer effects were also included. Furthermore, the various repurposed drugs available for breast cancer treatment have been further segregated according to their indicative class and possible mechanism of action.

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Drug repurposing: a retrospective revolution in breast cancer medicine

Shivani,

Misari,

Anand

et al. 2024

Discov Med

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ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer

Karampuri,

Jakkula,

Perugu

2024

Sci Rep

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Breast cancer remains a leading cause of mortality among women worldwide, with drug resistance driven by transcription factors and mutations posing significant challenges. To address this, we present ResisenseNet, a predictive model for drug sensitivity and resistance. ResisenseNet integrates transcription factor expression, genomic markers, drugs, and molecular descriptors, employing a hybrid architecture of 1D-CNN + LSTM and DNN to effectively learn long-range and temporal patterns from amino acid sequences and transcription factor data. The model demonstrated exceptional predictive accuracy, achieving a validation accuracy of 0.9794 and a loss value of 0.042. Comprehensive validation included comparisons with state-of-the-art models and ablation studies, confirming the robustness of the developed architecture. ResisenseNet has been applied to repurpose existing anticancer drugs across 14 different cancers, with a focus on breast cancer. Among the malignancies studied, drugs targeting Low-grade Glioma (LGG) and Lung Adenocarcinoma (LUAD) showed increased sensitivity to breast cancer as per ResisenseNet’s assessment. Further evaluation of the predicted sensitive drugs revealed that 14 had no prior history of anticancer activity against breast cancer. These drugs target key signaling pathways involved in breast cancer, presenting novel therapeutic opportunities. ResisenseNet addresses drug resistance by filtering ineffective compounds and enhancing chemotherapy for breast cancer. In vitro studies on sensitive drugs provide valuable insights into breast cancer prognosis, contributing to improved treatment strategies.

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Intuitive repositioning of an anti-depressant drug in combination with tivozanib: precision medicine for breast cancer therapy

Cited by 2 publications

References 46 publications

Drug repurposing: a retrospective revolution in breast cancer medicine

Drug repurposing: a retrospective revolution in breast cancer medicine

ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer

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