Plant polysaccharides have gained interest in medical research for their ability to suppress various diseases, including cancer. However, information on some plant polysaccharides is yet to be uncovered. In this study, we screened crude polysaccharides extracted from six different plants for their antioxidative and antiproliferative activities. Crude polysaccharides were isolated from different parts of some plants using the established extraction protocol. The crude polysaccharides were evaluated for their chemical composition (protein, total sugar, and phenolics), free radical-scavenging activities, and antiproliferative activities against breast cancer MCF-7 cells as well as non-cancerous cells, i.e., human fibroblast MRC-5 cells and Cercopithecus aethiops kidney Vero cells, via an MTT assay and CM20 Incubation Monitoring System (Olympus) for MCF-7. The investigated crude polysaccharides showed significant variations in their chemical constituents and antioxidative properties. Only Moringa seed crude polysaccharide extracts showed significant antiproliferative activities at various concentrations, with an IC₅₀ value of 0.061 mg/mL, which was about 2.6 folds higher on MRC-5 and Vero cell lines. The antiproliferative activities toward cancer cell lines and lack of significant toxicity in the case of normal cells indicate that this extract may be promising as a valuable source for novel cancer therapy.

Describing the progress of reactions occurring along the polymer chain remains challenging due to the large number of dynamically evolving reaction sites present during post-polymerization modification (PPM) processes. Herein, we propose a kinetic Monte Carlo (kMC) model describing PPM reactions constrained by steric interactions between neighboring modified repeating units (r.u.), which dynamically modulate the reactivity of adjacent reaction sites. We demonstrate that the large number of potential reaction sites can be reduced to a limited set of kinetically distinguishable reaction centers, whose distribution and frequency of occurrence can be predicted using a stochastic Monte Carlo approach. The proposed model was validated using a series of aza-Michael reactions between acrylic esters and polyamines. Methyl, n-hexyl, and tert-butyl acrylates were reacted with polyethyleneimine (L-PEI) and polytrimethylenimine (L-PTMI) to investigate the influence of substituent size, spacing between reactive centers, and reaction temperature on the PPM process. Apart from describing reaction kinetics, the model also enables prediction of sequence distribution in partially modified polymer. We show that non-exhaustive PPM reactions do not lead to entirely random polymer structures, but instead generate predictable distributions of unreacted reaction centers and repeating units along the polymer chain.