Discovering breast cancer drug candidates from biomedical literature

Breast cancer is one of the most common malignant tumors in women. It seriously threatens the safety of women worldwide. It is an important and urgent task to research and develop anti-breast cancer drugs and improve the therapeutic effect of breast cancer. Taking the actual sample data as the main starting point, firstly, the prediction model of pIC50 is established by ResNet residual network and neural network (NN) to judge the biological activity. Then the classification model of ADMET property is established by ResNet residual network and LightGBM, and the model fusion is realized by Choquet fuzzy integral. Finally, the NSGAII multi-objective optimization algorithm is used to determine the range of values that each molecular descriptor obtains in the range of good biological activity, and ultimately to optimize the modeling of anti-breast cancer drug candidates. The experimental results show that the algorithm improves the prediction accuracy of biological activity, realizes the efficient and accurate classification of ADMET properties, and accurately describes the impact of molecular descriptors on biological activity.

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Breast cancer drug gets helpmate

Nature ◽

10.1038/news040823-4 ◽

2004 ◽

Author(s):

Helen Pearson

Keyword(s):

Breast Cancer ◽

Cancer Drug

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Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy

10.21236/ada577102 ◽

2013 ◽

Author(s):

Susan F. Hudachek

Keyword(s):

Breast Cancer ◽

Combination Therapy ◽

Drug Combination ◽

Cancer Drug ◽

Drug Combination Therapy ◽

Adjuvant Breast Cancer

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From Nonsteroidal Aromatase Inhibitors to Multifunctional Drug Candidates: Classic and Innovative Strategies for the Treatment of Breast Cancer

Current Topics in Medicinal Chemistry ◽

10.2174/156802608784911590 ◽

2008 ◽

Vol 8 (10) ◽

pp. 869-887 ◽

Cited By ~ 11

Author(s):

Silvia Gobbi ◽

Andrea Cavalli ◽

Alessandra Bisi ◽

Maurizio Recanatini

Keyword(s):

Breast Cancer ◽

Aromatase Inhibitors ◽

Innovative Strategies ◽

Drug Candidates ◽

Nonsteroidal Aromatase Inhibitors

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Polymer Coated Iron Nanoparticles: Radiolabeling & In Vitro Studies

Current Radiopharmaceuticals ◽

10.2174/1874471013666200430094113 ◽

2020 ◽

Vol 13 ◽

Author(s):

Selin Yılmaz ◽

Çiğdem İçhedef ◽

Kadriye Buşra Karatay ◽

Serap Teksöz

Keyword(s):

Breast Cancer ◽

Drug Delivery ◽

Iron Oxide ◽

Cancer Cells ◽

Iron Oxide Nanoparticles ◽

Breast Cancer Cells ◽

Cancer Drug ◽

Oxide Nanoparticles ◽

Sem Images

Backgorund: Superparamagnetic iron oxide nanoparticles (SPIONs) have been extensively used for targeted drug delivery systems due to their unique magnetic properties. Objective: In this study, it’s aimed to develop a novel targeted 99mTc radiolabeled polymeric drug delivery system for Gemcitabine (GEM). Methods: Gemcitabine, an anticancer agent, was encapsulated into polymer nanoparticles (PLGA) together with iron oxide nanoparticles via double emulsion technique and then labeled with 99mTc. SPIONs were synthesized by reduction–coprecipitation method and encapsulated with oleic acid for surface modification. Size distribution and the morphology of the synthesized nanoparticles were caharacterized by dynamic light scattering(DLS)and scanning electron microscopy(SEM), respectively. Radiolabeling yield of SPION-PLGAGEM nanoparticles were determined via Thin Layer Radio Chromatography (TLRC). Cytotoxicity of GEM loaded SPION-PLGA were investigated on MDA-MB-231 and MCF7 breast cancer cells in vitro. Results: SEM images displayed that the average size of the drug-free nanoparticles was 40 nm and the size of the drug-loaded nanoparticles was 50 nm. The diameter of nanoparticles were determined as 366.6 nm by DLS, while zeta potential was found as-29 mV. SPION was successfully coated with PLGA, which was confirmed by FTIR. GEM encapsulation efficiency of SPION-PLGA was calculated as 4±0.16 % by means of HPLC. Radiolabeling yield of SPION-PLGA-GEM nanoparticles were determined as 97.8±1.75 % via TLRC. Cytotoxicity of GEM loaded SPION-PLGA were investigated on MDA-MB-231 and MCF7 breast cancer cells. SPION-PLGA-GEM showed high uptake on MCF-7, whilst incorporation rate was increased for both cell lines which external magnetic field application. Conclusion: 99mTc labeled SPION-PLGA nanoparticles loaded with GEM may overcome some of the obstacles in anti-cancer drug delivery because of their appropriate size, non-toxic, and supermagnetic characteristics.

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Clinical applications of circulating tumor DNA in monitoring breast cancer drug resistance

Future Oncology ◽

10.2217/fon-2019-0760 ◽

2020 ◽

Vol 16 (34) ◽

pp. 2863-2878

Author(s):

Yang Liu ◽

Qian Du ◽

Dan Sun ◽

Ruiying Han ◽

Mengmeng Teng ◽

...

Keyword(s):

Breast Cancer ◽

Drug Resistance ◽

Digital Pcr ◽

Circulating Tumor Dna ◽

Resistance Mechanisms ◽

Clinical Applications ◽

Current Status ◽

Cancer Drug ◽

Genomic Alteration ◽

Tumor Dna

Breast cancer is one of the leading causes of cancer-related deaths in women worldwide. Unfortunately, treatments often fail because of the development of drug resistance, the underlying mechanisms of which remain unclear. Circulating tumor DNA (ctDNA) is free DNA released into the blood by necrosis, apoptosis or direct secretion by tumor cells. In contrast to repeated, highly invasive tumor biopsies, ctDNA reflects all molecular alterations of tumors dynamically and captures both spatial and temporal tumor heterogeneity. Highly sensitive technologies, including personalized digital PCR and deep sequencing, make it possible to monitor response to therapies, predict drug resistance and tailor treatment regimens by identifying the genomic alteration profile of ctDNA, thereby achieving precision medicine. This review focuses on the current status of ctDNA biology, the technologies used to detect ctDNA and the potential clinical applications of identifying drug resistance mechanisms by detecting tumor-specific genomic alterations in breast cancer.

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