Comparison of molecular properties between herbal compounds and DrugBank drugs.

Comparison of molecular properties between herbal compounds and DrugBank drugs.</p

The information of breast cancer targets.

The information of breast cancer targets.</p

Distribution of the target proteins versus the drug node degree in the drug-target network.

<p>Distribution of the target proteins versus the drug node degree in the drug-target network.</p

A Methodology for Cancer Therapeutics by Systems Pharmacology-Based Analysis: A Case Study on Breast Cancer-Related Traditional Chinese Medicines

<div><p>Breast cancer is the most common carcinoma in women. Comprehensive therapy on breast cancer including surgical operation, chemotherapy, radiotherapy, endocrinotherapy, etc. could help, but still has serious side effect and resistance against anticancer drugs. Complementary and alternative medicine (CAM) may avoid these problems, in which traditional Chinese medicine (TCM) has been highlighted. In this section, to analyze the mechanism through which TCM act on breast cancer, we have built a virtual model consisting of the construction of database, oral bioavailability prediction, drug-likeness evaluation, target prediction, network construction. The 20 commonly employed herbs for the treatment of breast cancer were used as a database to carry out research. As a result, 150 ingredient compounds were screened out as active molecules for the herbs, with 33 target proteins predicted. Our analysis indicates that these herbs 1) takes a ‘Jun-Chen-Zuo-Shi” as rule of prescription, 2) which function mainly through perturbing three pathways involving the epidermal growth factor receptor, estrogen receptor, and inflammatory pathways, to 3) display the breast cancer-related anti-estrogen, anti-inflammatory, regulation of cell metabolism and proliferation activities. To sum it up, by providing a novel <i>in silico</i> strategy for investigation of the botanical drugs, this work may be of some help for understanding the action mechanisms of herbal medicines and for discovery of new drugs from plants.</p></div

Gene Ontology (GO) analysis of therapy target genes.

<p>The y-axis shows significantly enriched ‘Biological Process ‘ (BP) categories in GO relative to the target genes, and the x-axis depicts the enrichment scores of these terms (<i>p</i>-value≤0.05).</p

The global view of C-T network for the 20 breast cancer-related herbs.

<p>(A) 32 bioactive compounds (orange squares) from <i>Radix Salviae</i> applied as monarch herbal medicine play principal roles in therapeutic effect. 41 bioactive compounds (green squares) from 7 herbs represent those minister herbal medicine which increase the effects of <i>Radix Salviae</i>. 57 bioactive compounds (magenta and blue squares) from 12 herbs serve as assistant and messenger drugs, respectively. The yellow circles represent the target proteins of the active compounds. (B) The combination principle of Jun-Chen-Zuo-Shi.</p

The profile distributions of eight important molecular properties for DrugBank drugs and herbal compounds.

<p>The profile distributions of eight important molecular properties for DrugBank drugs and herbal compounds.</p

150 bioactive compounds of the 20 herbs with their predicted OB and DL values.

<p>150 bioactive compounds of the 20 herbs with their predicted OB and DL values.</p

The T-P network.

<p>A link is created between a target and a pathway if the pathway is lighted at the target, where blue, green and red nodes represent compounds, targets and pathways, respectively. The information of pathways is obtained by mapping the target proteins to the KEGG pathway database.</p

Statistics and association analysis between 20 herbs and breast cancer.

<p>Statistics and association analysis between 20 herbs and breast cancer.</p