# Network pharmacology approach identifies novel anticancer botanicals: Experimental exploration of Falcaria vulgaris (Sickleweed) as a therapeutic candidate

## TL;DR

This paper introduces a network pharmacology method to find anticancer plants, validating Falcaria vulgaris as a promising new candidate through experiments.

## Contribution

A novel network pharmacology approach is introduced and experimentally validated for identifying anticancer botanicals.

## Key findings

- Falcaria vulgaris extract showed high peroxidase activity, flavonoid content, and total phenolics.
- GC-MS identified 22 bioactive compounds with antioxidant, anticancer, and anti-inflammatory properties.
- The extract caused significant cell death in breast cancer cell lines MCF-7 and 4T1.

## Abstract

Cancer remains a major global health challenge with limited therapeutic options. Medicinal plants have emerged as promising candidates for anticancer drug discovery. This study introduces an innovative network pharmacology approach to predict potential anticancer botanicals by analyzing their metabolite profiles. Through bipartite network analysis of 3,250 plants and 667 experimentally validated anticancer metabolites, we identified 61 top-ranked plants, among which 85.25% exhibited known anticancer properties, while 14.75% represented novel candidates. Further experimental validation of F. vulgaris extract revealed its predicted anticancer potential. Biochemical analyses demonstrated high peroxidase activity (0.063 AU/min per mg protein), substantial flavonoid content (178.33 ± 18.3 mg QE/g), and elevated total phenolics (643.3 ± 20.8 mg GAE/g). GC-MS analysis identified 22 bioactive compounds, predominantly featuring documented antioxidant, anticancer, and anti-inflammatory properties. The extract exhibited significant dose-dependent cytotoxicity against breast cancer cell lines, with MTT assays showing significant inhibition (p < 0.0001) in both MCF-7 and 4T1 cells. Flow cytometry analysis further confirmed remarkable cell death induction, with rates reaching 90.51% (MCF-7) and 97.4% (4T1) at 10 mg/mL concentration of the extract. These experimental results robustly validate our network pharmacology approach for efficiently identifying anticancer botanicals through their metabolite profiles. This integrative strategy connects computational prediction with experimental validation, providing a scalable framework for discovering plant-derived therapeutics. Our findings not only explore F. vulgaris as a promising candidate but also cover the way for systematic exploration of understudied botanicals in oncology.