Advanced Computational Methodologies Used in the Discovery of New Natural Anticancer Compounds.

Natural chemical compounds have been widely investigated for their programmed necrosis causing characteristics. One of the conventional methods for screening such compounds is the use of concentrated plant extracts without isolation of active moieties for understanding pharmacological activity. For the last two decades, modern medicine has relied mainly on the isolation and purification of one or two complicated active and isomeric compounds. The idea of multi-target drugs has advanced rapidly and impressively from an innovative model when first proposed in the early 2000s to one of the popular trends for drug development in 2021. Alternatively, fragment-based drug discovery is also explored in identifying target-based drug discovery for potent natural anticancer agents which is based on well-defined fragments opposite to use of naturally occurring mixtures. This review summarizes the current key advancements in natural anticancer compounds; computer-assisted/fragment-based structural elucidation and a multi-target approach for the exploration of natural compounds.

Pre-visual detection of iron and phosphorus deficiency by transformed reflectance spectra.

Reflectance spectroscopy and strategies for spectral analysis over the visible range from 380 to 780 nm were used to provide diagnostic information on iron (Fe) and phosphorus (P) status of Brassica chinensis L. var parachinensis (Bailey) grown under hydroponics conditions. Leaf reflectance (R) spectra were collected and normalized inner reflectance (NR(I)) spectra were calculated. The regression coefficients (B-matrix) and variable importance for projection (VIP) in partial least squares regression were used to determine important wavelengths that correlate with total chlorophyll (Chl) content. No single wavelength that showed good correlation with Chl content was found. Therefore, NR(I) was transformed into CIELAB color values, which simplified the whole visible spectrum into three values. Our results showed that upon Fe deprivation, plants entered into a deficiency state very rapidly, highlighting the importance of early diagnosis. The direct effect of Fe on leaf Chl content allowed CIELAB color values to be used for pre-visual detection of Fe deficiency 2 days before the appearance of visually distinguishable morphological changes. On the other hand, P-deprived plants showed a marked decline in cellular P levels but remained above critical threshold concentrations after 7 days. The Chl content was not affected by the leaf P content and CIELAB color values showed no difference with control plants.