Homoharringtonine demonstrates a cytotoxic effect against triple-negative breast cancer cell lines and acts synergistically with paclitaxel

Plett, Riley; Mellor, Paul; Kendall, Stephanie; Hammond, S. Austin; Boulet, Aren; Plaza, Kristine; Vizeacoumar, Frederick S.; Vizeacoumar, Franco J.; Anderson, Deborah H.

doi:10.1038/s41598-022-19621-7

Cited by 8 publications

(5 citation statements)

References 45 publications

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“…We also study the temperature-responsive release with an anticancer drug, palbociclib isethionate, which is one of the most promising drugs for the treatment of breast cancers; − although both palbociclib and palbociclib isethionate are effective anticancer drugs, we choose palbociclib isethionate due to relatively high solubility in water to increase payload. In common, hydrophilicity and solubility of drugs can be enhanced by adjusting pH to render functional groups of the drugs ionized, which potentially helps secure a high payload in the microcapsules.…”

Section: Resultsmentioning

confidence: 99%

Smart Microcapsules Designed for On-Demand Local Heating and Drug Release

Kim

Han

et al. 2023

Chem. Mater.

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Chemophotothermal therapy is promising for cancer treatment with its synergistic effect. Although various photothermal agents have been formulated as drug carriers, low payload, burst release, and severe dilution usually restrict their uses. To overcome the limitations, we design smart microcapsules as a pragmatic material for chemophotothermal therapy. With a microfluidic device, water-in-oil-in-water (W/O/W) double-emulsion templates are produced to have photothermal polydopamine nanoparticles and drugs in the water core and a mixture of photocurable monomers and phase-change material (PCM) in the oil shell. The droplets are significantly shrunken by the osmotic extraction of water from the core, which enables the size reduction for the ease of injection and enrichment of polydopamine nanoparticles and drugs for enhanced photothermal performance and high payload. The microcapsules, produced by photopolymerization of monomers, release drugs when PCM in continuous nanochannels of the polymer shell is molten while retaining drugs without leakage when frozen. As the concentrated polydopamine nanoparticles provide rapid and drastic heating by near-infrared irradiation, the drug release can be switched on and off in a programmed manner by manipulating the irradiation. The smart microcapsules secure high drug payload in a spacious core, negligible initial bursting, and dilution-free high photothermal performance, potentially serving as a pragmatic therapy platform.

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Section: Resultsmentioning

confidence: 99%

Smart Microcapsules Designed for On-Demand Local Heating and Drug Release

Kim

Han

et al. 2023

Chem. Mater.

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“…Although the majority of clinical trials utilize combination therapies, these drugs may play a role in a synergistic way. The other four drugs are still under in vitro or in vivo investigations of TNBC treatment (i.e., AZD7762, ouabain, homoharringtonine, and GSK461364) ( Giordano et al, 2019 ; Yakhni et al, 2019 ; Du et al, 2021 ; Zhu et al, 2021 ; Plett et al, 2022 ). Interestingly, leptomycin B (LMB) was considered unsafe in previous clinical trials due to its adequate adverse effect and thus not approved for use ( Wang and Liu, 2019 ), while it is the most effective drug identified by our model.…”

Section: Resultsmentioning

confidence: 99%

XMR: an explainable multimodal neural network for drug response prediction

et al. 2023

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Introduction: Existing large-scale preclinical cancer drug response databases provide us with a great opportunity to identify and predict potentially effective drugs to combat cancers. Deep learning models built on these databases have been developed and applied to tackle the cancer drug-response prediction task. Their prediction has been demonstrated to significantly outperform traditional machine learning methods. However, due to the “black box” characteristic, biologically faithful explanations are hardly derived from these deep learning models. Interpretable deep learning models that rely on visible neural networks (VNNs) have been proposed to provide biological justification for the predicted outcomes. However, their performance does not meet the expectation to be applied in clinical practice.Methods: In this paper, we develop an XMR model, an eXplainable Multimodal neural network for drug Response prediction. XMR is a new compact multimodal neural network consisting of two sub-networks: a visible neural network for learning genomic features and a graph neural network (GNN) for learning drugs’ structural features. Both sub-networks are integrated into a multimodal fusion layer to model the drug response for the given gene mutations and the drug’s molecular structures. Furthermore, a pruning approach is applied to provide better interpretations of the XMR model. We use five pathway hierarchies (cell cycle, DNA repair, diseases, signal transduction, and metabolism), which are obtained from the Reactome Pathway Database, as the architecture of VNN for our XMR model to predict drug responses of triple negative breast cancer.Results: We find that our model outperforms other state-of-the-art interpretable deep learning models in terms of predictive performance. In addition, our model can provide biological insights into explaining drug responses for triple-negative breast cancer.Discussion: Overall, combining both VNN and GNN in a multimodal fusion layer, XMR captures key genomic and molecular features and offers reasonable interpretability in biology, thereby better predicting drug responses in cancer patients. Our model would also benefit personalized cancer therapy in the future.

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“…However, most of the TNBCs showed downregulated CREB3L1 expression, its effectiveness and sensitivity to chemotherapy are limited. Combination treatment with homoharringtonine and paclitaxel exhibits synergistic cytotoxic effects on TNBC cells accompanied by decreased toxic side effects compared with separate curing 100 .…”

Section: The Pathological Functions Of Creb3l1mentioning

confidence: 99%

The Regulatory Network of CREB3L1 and Its Roles in Physiological and Pathological Conditions

Zhao,

Yu,

Song

et al. 2024

Int. J. Med. Sci.

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CREB3 subfamily belongs to the bZIP transcription factor family and comprises five members. Normally they are located on the endoplasmic reticulum (ER) membranes and proteolytically activated through RIP (regulated intramembrane proteolysis) on Golgi apparatus to liberate the N-terminus to serve as transcription factors. CREB3L1 acting as one of them transcriptionally regulates the expressions of target genes and exhibits distinct functions from the other members of CREB3 family in eukaryotes. Physiologically, CREB3L1 involves in the regulation of bone morphogenesis, neurogenesis, neuroendocrine, secretory cell differentiation, and angiogenesis. Pathologically, CREB3L1 implicates in the modulation of osteogenesis imperfecta, low grade fibro myxoid sarcoma (LGFMS), sclerosing epithelioid fibrosarcoma (SEF), glioma, breast cancer, thyroid cancer, and tissue fibrosis. This review summarizes the upstream and downstream regulatory network of CREB3L1 and thoroughly presents our current understanding of CREB3L1 research progress in both physiological and pathological conditions with special focus on the novel findings of CREB3L1 in cancers.

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Homoharringtonine demonstrates a cytotoxic effect against triple-negative breast cancer cell lines and acts synergistically with paclitaxel

Cited by 8 publications

References 45 publications

Smart Microcapsules Designed for On-Demand Local Heating and Drug Release

Smart Microcapsules Designed for On-Demand Local Heating and Drug Release

XMR: an explainable multimodal neural network for drug response prediction

The Regulatory Network of CREB3L1 and Its Roles in Physiological and Pathological Conditions

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