Aptamer-Functionalized DNA Origami for Targeted Codelivery of Antisense Oligonucleotides and Doxorubicin to Enhance Therapy in Drug-Resistant Cancer Cells

2019 ◽  
Vol 12 (1) ◽  
pp. 400-409 ◽  
Author(s):  
Qingshan Pan ◽  
Cunpeng Nie ◽  
Yanlei Hu ◽  
Jintao Yi ◽  
Chang Liu ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Antisense Oligonucleotides ◽  
Dna Origami ◽  
Drug Resistant

scholarly journals Instructed‐assembly of small peptides inhibits drug‐resistant prostate cancer cells

2019 ◽  
Vol 112 (1) ◽  
Author(s):  
Zhaoqianqi Feng ◽  
Huaimin Wang ◽  
Meihui Yi ◽  
Chieh‐Yun Lo ◽  
Ashanti Sallee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Cells ◽  
Drug Resistant ◽  
Prostate Cancer Cells ◽  
Small Peptides

Silencing PEX26 as an Unconventional Mode to Kill Drug-Resistant Cancer Cells and Forestall Drug Resistance

Autophagy ◽  
2021 ◽  
Author(s):  
Michael S. Dahabieh ◽  
Fan Huang ◽  
Christophe Goncalves ◽  
Raúl Ernesto Flores González ◽  
Sathyen Prabhu ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Cancer Cells ◽  
Drug Resistant

LDH-doped electrospun short fibers enable dual drug loading and multistage release for chemotherapy of drug-resistant cancer cells

2021 ◽  
Author(s):  
Yupei Ma ◽  
Du Li ◽  
Yunchao Xiao ◽  
Zhijun OuYang ◽  
Mingwu Shen ◽  
...  
Keyword(s):  
Multidrug Resistance ◽  
Cancer Cells ◽  
Cancer Chemotherapy ◽  
Side Effect ◽  
Drug Loading ◽  
Adverse Side Effect ◽  
Drug Resistant ◽  
Short Fibers ◽  
Dual Drug

Conventional cancer chemotherapy is facing difficulties in improving the bioavailability, overcoming the severe adverse side effect of chemotherapeutics and reversing the multidrug resistance of cancer cells. To address these challenges,...

scholarly journals Adaptation of cancer cells from different entities to the MDM2 inhibitor nutlin-3 results in the emergence of p53-mutated multi-drug-resistant cancer cells

2011 ◽  
Vol 2 (12) ◽  
pp. e243-e243 ◽  
Author(s):  
M Michaelis ◽  
F Rothweiler ◽  
S Barth ◽  
J Cinatl ◽  
M van Rikxoort ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Drug Resistant ◽  
Mdm2 Inhibitor

ABCB1/MDR1 contributes to the anticancer drug-resistant phenotype of IPH-926 human lobular breast cancer cells

2012 ◽  
Vol 315 (2) ◽  
pp. 153-160 ◽  
Author(s):  
Till Krech ◽  
Elisa Scheuerer ◽  
Robert Geffers ◽  
Hans Kreipe ◽  
Ulrich Lehmann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Anticancer Drug ◽  
Breast Cancer Cells ◽  
Drug Resistant ◽  
Lobular Breast Cancer ◽  
Resistant Phenotype ◽  
Drug Resistant Phenotype

scholarly journals Leptogorgins A–C, Humulane Sesquiterpenoids from the Vietnamese Gorgonian Leptogorgia sp.

Marine Drugs ◽  
2020 ◽  
Vol 18 (6) ◽  
pp. 310 ◽  
Author(s):  
Irina I. Kapustina ◽  
Tatyana N. Makarieva ◽  
Alla G. Guzii ◽  
Anatoly I. Kalinovsky ◽  
Roman S. Popov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
South China Sea ◽  
Cancer Cells ◽  
South China ◽  
Absolute Configuration ◽  
Marine Invertebrates ◽  
Drug Resistant ◽  
Prostate Cancer Cells ◽  
First Report ◽  
Nmr Data

Leptogorgins A–C (1–3), new humulane sesquiterpenoids, and leptogorgoid A (4), a new dihydroxyketosteroid, were isolated from the gorgonian Leptogorgia sp. collected from the South China Sea. The structures were established using MS and NMR data. The absolute configuration of 1 was confirmed by a modification of Mosher’s method. Configurations of double bonds followed from NMR data, including NOE correlations. This is the first report of humulane-type sesquiterpenoids from marine invertebrates. Sesquiterpenoids leptogorgins A (1) and B (2) exhibited a moderate cytotoxicity and some selectivity against human drug-resistant prostate cancer cells 22Rv1.

scholarly journals Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Hae Deok Jung ◽  
Yoo Jin Sung ◽  
Hyun Uk Kim
Keyword(s):  
Machine Learning ◽  
Computational Modeling ◽  
Cancer Treatment ◽  
Effective Treatment ◽  
Cancer Cells ◽  
Computational Models ◽  
Omics Data ◽  
Drug Resistant ◽  
Learning Models ◽  
Machine Learning Models

Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells.

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