Quantitative Structure-Cytotoxicity Relationship of Cinnamic Acid Phenetyl Esters

Bioguided isolation of a selective compound from Calea phyllolepis leaves against breast cancer cells

Plants of the Calea genus have been reported to contain lipophilic compounds, such as sesquiterpene lactones, with cytotoxic effect against different cancer cell lines. The aim of this manuscript was to investigate the chemical profile and cytotoxic activity of different fractions from Calea phylolepis leaves on different human cancer cell lines. The fractions were prepared using solvent extraction of increasing polarity, yielding hexane, ethyl acetate and methanolic fractions. All fractions were chemically analysed by thin layer chromatography (TLC), and their cytotoxic activity against HT-29 (colon adenocarcinoma), MCF-7 (breast cancer), U-251MG (malignant glioblastoma) and L929 (mouse fibroblast) cell lines was investigated. Among these, the hexane and ethyl acetate fractions showed higher cytotoxic effects, while the methanolic fraction did not show any cytotoxic effects. The major bioactive compound from the hexane fraction (12.15%) was isolated using chromatographic methods and was identified by nuclear magnetic resonance spectroscopy (NMR) and gas chromatography-mass spectrometry (GC-MS) analysis as 6-epi-β-verbesinol coumarate. This compound showed activity against breast cancer cells (IC₅₀  = 5.8 ± 1.0 μg/ml), similar to etoposide. Furthermore, 6-epi-β-verbesinol coumarate showed low cytotoxicity to normal fibroblast cells, suggesting a high selectivity index (SI = 7.39) against breast cancer cells.

Biologically Active Compounds of Plants: Structure-Related Antioxidant, Microbiological and Cytotoxic Activity of Selected Carboxylic Acids

Natural carboxylic acids are plant-derived compounds that are known to possess biological activity. The aim of this review was to compare the effect of structural differences of the selected carboxylic acids (benzoic acid (BA), cinnamic acid (CinA), p-coumaric acid (p-CA), caffeic acid (CFA), rosmarinic acid (RA), and chicoric acid (ChA)) on the antioxidant, antimicrobial, and cytotoxic activity. The studied compounds were arranged in a logic sequence of increasing number of hydroxyl groups and conjugated bonds in order to investigate the correlations between the structure and bioactivity. A review of the literature revealed that RA exhibited the highest antioxidant activity and this property decreased in the following order: RA > CFA ~ ChA > p-CA > CinA > BA. In the case of antimicrobial properties, structure-activity relationships were not easy to observe as they depended on the microbial strain and the experimental conditions. The highest antimicrobial activity was found for CFA and CinA, while the lowest for RA. Taking into account anti-cancer properties of studied NCA, it seems that the presence of hydroxyl groups had an influence on intermolecular interactions and the cytotoxic potential of the molecules, whereas the carboxyl group participated in the chelation of endogenous transition metal ions.

Development of Newly Synthesized Chromone Derivatives with High Tumor Specificity against Human Oral Squamous Cell Carcinoma

Since many anticancer drugs show severe adverse effects such as mucositis, peripheral neurotoxicity, and extravasation, it was crucial to explore new compounds with much reduced adverse effects. Comprehensive investigation with human malignant and nonmalignant cells demonstrated that derivatives of chromone, back-bone structure of flavonoid, showed much higher tumor specificity as compared with three major polyphenols in the natural kingdom, such as lignin-carbohydrate complex, tannin, and flavonoid. A total 291 newly synthesized compounds of 17 groups (consisting of 12 chromones, 2 esters, and 3 amides) gave a wide range of the intensity of tumor specificity, possibly reflecting the fitness for the optimal 3D structure and electric state. Among them, 7-methoxy-3-[(1E)-2-phenylethenyl]-4H-1-benzopyran-4-one (compound 22), which belongs to 3-styrylchromones, showed the highest tumor specificity. 22 induced subG1 and G2 + M cell population in human oral squamous cell carcinoma cell line, with much less keratinocyte toxicity as compared with doxorubicin and 5-FU. However, 12 active compounds selected did not necessarily induce apoptosis and mitotic arrest. This compound can be used as a lead compound to manufacture more active compound.

Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR

QSAR/QSPR (quantitative structure-activity/property relationship) modeling has been a prevalent approach in various, overlapping sub-fields of computational, medicinal and environmental chemistry for decades. The generation and selection of molecular descriptors is an essential part of this process. In typical QSAR workflows, the starting pool of molecular descriptors is rationalized based on filtering out descriptors which are (i) constant throughout the whole dataset, or (ii) very strongly correlated to another descriptor. While the former is fairly straightforward, the latter involves a level of subjectivity when deciding what exactly is considered to be a strong correlation. Despite that, most QSAR modeling studies do not report on this step. In this study, we examine in detail the effect of various possible descriptor intercorrelation limits on the resulting QSAR models. Statistical comparisons are carried out based on four case studies from contemporary QSAR literature, using a combined methodology based on sum of ranking differences (SRD) and analysis of variance (ANOVA).

QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer

Background: Anticancer drugs often have strong toxicity against tumours and normal cells. Some natural products demonstrate high tumour specificity. We have previously reported the cytotoxic activity and tumour specificity of various chemical compounds. In this study, we constructed a database of previously reported compound data and predictive models to screen a new anticancer drug. Methods: We collected compound data from our previous studies and built a database for analysis. Using this database, we constructed models that could predict cytotoxicity and tumour specificity using random forest method. The prediction performance was evaluated using an external validation set. Results: A total of 494 compounds were collected, and these activities and chemical structure data were merged as database for analysis. The structure-toxicity relationship prediction model showed higher prediction accuracy than the tumour selectivity prediction model. Descriptors with high contribution differed for tumour and normal cells. Conclusions: Further study is required to construct a tumour selective toxicity prediction model with higher predictive accuracy. Such a model is expected to contribute to the screening of candidate compounds for new anticancer drugs.