Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Article discussing quantitative structure-property relationship studies on Ostwald solubility and partition coefficients of organic solutes in ionic liquids

Quantitative Structure-Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Article discussing quantitative structure-property relationship studies on Ostwald solubility and partition coefficients of organic solutes in ionic liquids

Genetic diversity and chemical variability of Lippia spp. (Verbenaceae)

Abstract Background The genus Lippia comprises 150 species, most of which have interesting medicinal properties. Lippia sidoides (syn. L. origanoides) exhibits strong antimicrobial activity and is included in the phytotherapy program implemented by the Brazilian Ministry of Health. Since species of Lippia are morphologically very similar, conventional taxonomic methods are sometimes insufficient for the unambiguous identification of plant material that is required for the production of certified phytomedicines. Therefore, genetic and chemical analysis with chemotype identification will contribute to a better characterization of Lippia species. Methods Amplified Length Polymorphism and Internal Transcribed Spacer molecular markers were applied to determine the plants’ genetic variability, and the chemical variability of Lippia spp. was determined by essential oil composition. Results Amplified Length Polymorphism markers were efficient in demonstrating the intra and inter-specific genetic variability of the genus and in separating the species L. alba, L. lupulina and L. origanoides into distinct groups. Phylogenetic analysis using Amplified Length Polymorphism and markers produced similar results and confirmed that L. alba and L. lupulina shared a common ancestor that differ from L. origanoides. Carvacrol, endo-fenchol and thymol were the most relevant chemical descriptors. Conclusion Based on the phylogenetic analysis it is proposed that L. grata should be grouped within L. origanoides due to its significant genetic similarity. Although Amplified Length Polymorphism and Internal Transcribed Spacer markers enabled the differentiation of individuals, the genotype selection for the production of certified phytomedicines must also consider the chemotype classification that reflects their real medicinal properties

CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.CONCLUSION: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points