Weekly vinorelbine and paclitaxel in older patients with advanced non-small cell lung cancer: A phase II Fred and Pamela Buffet Cancer Center Clinical Trials Network study

Huerter, Mary M.; Meza, Jane L.; Copur, Mehmet Sitki; Tolentino, Addison; Marr, Alissa S.; Ketcham, Marsha A.; DeSpiegelaere, Holly; Kruse, Susan; Kos, Mary E.; Swenson, Karin; Radniecki, Sarah; Kessinger, Anne; Ganti, Apar Kishor

doi:10.1016/j.jgo.2016.07.006

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…In-depth literature survey was performed and we find many cases are consistent with previous studies. For example, vinorelbine and paclitaxel interfere with mitotic spindle function through different mechanisms of action, and their combination have been proved to show a synergistic activity in non-small cell lung cancer (NSCLC) [ 21 , 22 ]. The prediction results of MGAE-DC for the combination in the three NSCLC cell lines including MSTO, NCIH23 and NCIH1650 are 33.49, 14.27, 10.34, respectively, indicating a synergistic potential in the treatment of NSCLC.…”

Section: Resultsmentioning

confidence: 99%

“…and their combination have been proved to show a synergistic activity in non-small cell lung cancer (NSCLC) [21,22]. The prediction results of MGAE-DC for the combination in the three NSCLC cell lines including MSTO, NCIH23 and NCIH1650 are 33.49, 14.27, 10.34, respectively, indicating a synergistic potential in the treatment of NSCLC.…”

Section: Plos Computational Biologymentioning

confidence: 99%

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MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders

Zhang

2023

PLoS Comput Biol

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Accurate prediction of synergistic effects of drug combinations can reduce the experimental costs for drug development and facilitate the discovery of novel efficacious combination therapies for clinical studies. The drug combinations with high synergy scores are regarded as synergistic ones, while those with moderate or low synergy scores are additive or antagonistic ones. The existing methods usually exploit the synergy data from the aspect of synergistic drug combinations, paying little attention to the additive or antagonistic ones. Also, they usually do not leverage the common patterns of drug combinations across different cell lines. In this paper, we propose a multi-channel graph autoencoder (MGAE)-based method for predicting the synergistic effects of drug combinations (DC), and shortly denote it as MGAE-DC. A MGAE model is built to learn the drug embeddings by considering not only synergistic combinations but also additive and antagonistic ones as three input channels. The later two channels guide the model to explicitly characterize the features of non-synergistic combinations through an encoder-decoder learning process, and thus the drug embeddings become more discriminative between synergistic and non-synergistic combinations. In addition, an attention mechanism is incorporated to fuse each cell-line’s drug embeddings across various cell lines, and a common drug embedding is extracted to capture the invariant patterns by developing a set of cell-line shared decoders. The generalization performance of our model is further improved with the invariant patterns. With the cell-line specific and common drug embeddings, our method is extended to predict the synergy scores of drug combinations by a neural network module. Experiments on four benchmark datasets demonstrate that MGAE-DC consistently outperforms the state-of-the-art methods. In-depth literature survey is conducted to find that many drug combinations predicted by MGAE-DC are supported by previous experimental studies. The source code and data are available at https://github.com/yushenshashen/MGAE-DC.

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Section: Resultsmentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders

Zhang

2023

PLoS Comput Biol

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“…Folate-targeted paclitaxel and vinorelbine encapsulating theranostic liposomes for non-small cell lung cancer can be constructed [195]. Paclitaxel (PCX) and vinorelbine (VNB) are both chemotherapeutic agents that have been approved for the treatment of NSCLC [196,197]. Folate in nanoparticles can be used for targeting folate receptors on the membranes of cancer cells [198], while the Tc-99m radioisotope in particles can be used for SPECT/CT imaging.…”

Section: Lung Cancermentioning

confidence: 99%

Liposomes for Cancer Theranostics

Fernandes

2023

Pharmaceutics

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Cancer is one of the most well-studied diseases and there have been significant advancements over the last few decades in understanding its molecular and cellular mechanisms. Although the current treatments (e.g., chemotherapy, radiotherapy, gene therapy and immunotherapy) have provided complete cancer remission for many patients, cancer still remains one of the most common causes of death in the world. The main reasons for the poor response rates for different cancers include the lack of drug specificity, drug resistance and toxic side effects (i.e., in healthy tissues). For addressing the limitations of conventional cancer treatments, nanotechnology has shown to be an important field for constructing different nanoparticles for destroying cancer cells. Due to their size (i.e., less than 1 μm), nanoparticles can deliver significant amounts of cancer drugs to tumors and are able to carry moieties (e.g., folate, peptides) for targeting specific types of cancer cells (i.e., through receptor-mediated endocytosis). Liposomes, composed of phospholipids and an interior aqueous core, can be used as specialized delivery vehicles as they can load different types of cancer therapy agents (e.g., drugs, photosensitizers, genetic material). In addition, the ability to load imaging agents (e.g., fluorophores, radioisotopes, MRI contrast media) enable these nanoparticles to be used for monitoring the progress of treatment. This review examines a wide variety of different liposomes for cancer theranostics, with the different available treatments (e.g., photothermal, photodynamic) and imaging modalities discussed for different cancers.

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“…These data point to the likelihood of combination vinorelbine and paclitaxel neurotoxicity. Paclitaxel treatment can cause latent neuronal damage, which is only clinically apparent after vinorelbine therapy [22].…”

Section: Additive Interactionsmentioning

confidence: 99%