Metabolites Profiling, In Vitro, In Vivo, Computational Pharmacokinetics and Biological Predictions of Aloe perryi Resins Methanolic Extract

Background: Aloe perryi is a traditional herb that has various biological and pharmacological properties such as anti-inflammatory, laxative, antiviral, antidiabetic, and antitumor effects, which have not been deliberated before. The current investigation aims to evaluate in vitro cytotoxicity against several cancer cell lines in addition to in vivo anti-inflammatory activities of Aloe perryi extract using a rat animal model. Moreover, the pharmacokinetic properties of bioactive constituents and possible biological targets were assessed and evaluated. The methanolic extract of Aloe perryi was prepared by maceration, to tentatively identify the biomolecules of the Aloe perryi extract, analytical LC–QTOF-MS method was employed for Aloe perryi methanolic extract. The cytotoxic activity was examined in six cancer cell lines using Titer-Glo assay and the IC_50s were calculated in addition to in silico target predictions and in vivo anti-inflammatory activity assessment. Subsequently, the pharmacokinetics of the identified active components of Aloe perryi were predicted using SwissADME, and target prediction using the Molinspiration webserver. The cytotoxic activity on HL60 and MDA-MB-231 was moderately affected by the Aloe perryi extract with IC₅₀ of 63.81, and 89.85 μg/mL, respectively, with no activity on other cells lines. Moreover, the Aloe perryi extract exhibited a significant increase in wound contraction, hair growth, and complete re-epithelization when compared with the negative control. The pharmacokinetic properties of the bioactive constituents suggested a good pharmaceutical profile for the active compounds and nuclear receptors and enzymes were the two main possible targets for these active compounds. Our results demonstrated the promising activity of Aloe perryi extract with cytotoxic and anti-inflammatory properties, indicating a potential therapeutic utility of this plant in various disease conditions.

Gene expression based leukemia sub-classification using committee neural networks

Analysis of gene expression data provides an objective and efficient technique for sub-classification of leukemia. The purpose of the present study was to design a committee neural networks based classification systems to subcategorize leukemia gene expression data. In the study, a binary classification system was considered to differentiate acute lymphoblastic leukemia from acute myeloid leukemia. A ternary classification system which classifies leukemia expression data into three subclasses including B-cell acute lymphoblastic leukemia, T-cell acute lymphoblastic leukemia and acute myeloid leukemia was also developed. In each classification system gene expression profiles of leukemia patients were first subjected to a sequence of simple preprocessing steps. This resulted in filtering out approximately 95 percent of the non-informative genes. The remaining 5 percent of the informative genes were used to train a set of artificial neural networks with different parameters and architectures. The networks that gave the best results during initial testing were recruited into a committee. The committee decision was by majority voting. The committee neural network system was later evaluated using data not used in training. The binary classification system classified microarray gene expression profiles into two categories with 100 percent accuracy and the ternary system correctly predicted the three subclasses of leukemia in over 97 percent of the cases.