Nature is the best source of anti-inflammatory drugs: indexing natural products for their anti-inflammatory bioactivity.

OBJECTIVES: The aim was to index natural products for less expensive preventive or curative anti-inflammatory therapeutic drugs. MATERIALS: A set of 441 anti-inflammatory drugs representing the active domain and 2892 natural products representing the inactive domain was used to construct a predictive model for bioactivity-indexing purposes. METHOD: The model for indexing the natural products for potential anti-inflammatory activity was constructed using the iterative stochastic elimination algorithm (ISE). ISE is capable of differentiating between active and inactive anti-inflammatory molecules. RESULTS: By applying the prediction model to a mix set of (active/inactive) substances, we managed to capture 38% of the anti-inflammatory drugs in the top 1% of the screened set of chemicals, yielding enrichment factor of 38. Ten natural products that scored highly as potential anti-inflammatory drug candidates are disclosed. Searching the PubMed revealed that only three molecules (Moupinamide, Capsaicin, and Hypaphorine) out of the ten were tested and reported as anti-inflammatory. The other seven phytochemicals await evaluation for their anti-inflammatory activity in wet lab. CONCLUSION: The proposed anti-inflammatory model can be utilized for the virtual screening of large chemical databases and for indexing natural products for potential anti-inflammatory activity.

Measurement conditions for flow cytometry analyses of cell lines from urological carcinomas.

Prerequisites for successful flow cytometry investigations are specific antibodies labeled with appropriate fluorochromes and negligible autofluorescence of the untreated cells at the wavelength of interest. The aim of this study was (a) to characterize frequently used urological carcinoma cell lines with regard to their autofluorescence properties, (b) to demonstrate the autofluorescence as a serious interfering factor on FACS analysis of urological carcinoma cell lines and (c) to suggest an alternative to avoid interfering autofluorescence. Twenty-one cell lines originating from prostate carcinoma, renal cell carcinoma and bladder cancer were included in this study. The various cell lines were read on a flow cytometer in comparison to human erythrocytes as cells with low fluorescence intensity. Urological cell lines show a high autofluorescence when flow cytometry analyses are performed at the frequently used excitation wavelengths at 405 and 488 nm. At excitation wavelength of 633 nm, this problem was reduced and most of the cell lines (14/21) were without autofluorescence at the emission wavelength of 785 nm. In addition, with a spectrofluorometer three exemplary cell lysates were investigated. The above observations were confirmed. The dye APC-Cy7 is one suitable fluorochrome for successful investigation under these measurement conditions.

Indexing Natural Products for their Antifungal Activity by Filters-based Approach: Disclosure of Discriminative Properties.

BACKGROUND: A considerable worldwide increase in the rate of invasive fungal infections and resistance toward antifungal drugs was witnessed during the past few decades. Therefore, the need for newer antifungal candidates is paramount. Nature has been the core source of therapeutics for thousands of years, and an impressive number of modern drugs including antifungals were derived from natural sources. In order to facilitate the recognition of potential candidates that can be derived from natural sources, an iterative stochastic elimination optimization technique to index natural products for their antifungal activity was utilized. METHODS: A set of 240 FDA-approved antifungal drugs, which represent the active domain, and a set of 2,892 natural products, which represent the inactive domain, were used to construct predictive models and to index natural products for their antifungal bioactivity. The area under the curve for the produced predictive model was 0.89. When applying it to a database that is composed of active/inactive chemicals, we succeeded to detect 42% of the actives (antifungal drugs) in the top one percent of the screened chemicals, compared with one-percent when using a random model. RESULTS AND CONCLUSION: Eight natural products, which were highly scored as likely antifungal drugs, are disclosed. Searching PubMed showed only one molecule (Flindersine) out of the eight that have been tested was reported as an antifungal. The other seven phytochemicals await evaluation for their antifungal bioactivity in a wet laboratory.

Capturing antibacterial natural products with in silico techniques.

The aim of the present study was to index natural products in order to facilitate the discovery of less expensive antibacterial therapeutic drugs. Thus, for modeling purposes, the present study utilized a set of 628 antibacterial drugs, representing the active domain, and 2,892 natural products, representing the inactive domain. In addition, using the iterative stochastic elimination algorithm, 36 unique filters were identified, which were then used to construct a highly discriminative and robust model tailored to index natural products for their antibacterial bioactivity. The area attained under the curve was 0.957, indicating a highly discriminative and robust prediction model. Utilizing the proposed model to virtually screen a mixed set of active and inactive substances enabled the present study to capture 72% of the antibacterial drugs in the top 1% of the sample, yielding an enrichment factor of 72. In total, 10 natural products that scored highly as antibacterial drug candidates with the proposed indexing model were reported. PubMed searches revealed that 2 molecules out of the 10 (caffeine and ricinine) have been tested and identified as showing antibacterial activity. The other 8 phytochemicals await experimental evaluation. Due to the efficiency and rapidity of the proposed prediction model, it could be applied to the virtual screening of large chemical databases to facilitate the drug discovery and development processes for antibacterial drug candidates.