A multi-market comparison of composition, formulation, and label content of CBD e-liquids.

The prevalence of electronically vaporized cannabidiol (CBD) use is rising in many countries. However, few regulatory frameworks exist for inhaled CBD, and this lack of oversight may not protect consumers from adverse consequences. We generated a representative map of several global consumer vaporized CBD markets by collating data concerning cannabinoid levels, including CBD and Δ-tetrahydrocannabinol, from the scientific literature.

Δ-Tetrahydrocannabinol (THC): A Critical Overview of Recent Clinical Trials and Suggested Guidelines for Future Research.

In this overview, we seek to appraise recent experimental and observational studies investigating THC and its potential role as adjunctive therapy in various medical illnesses. Recent clinical trials are suggestive of the diverse pharmacologic potentials for THC but suffer from small sample sizes, short study duration, failure to address tolerance, little dose variation, ill-defined outcome measures, and failure to identify and/or evaluate confounds, all of which may constitute significant threats to the validity of most trials. However, the existing work underscores the potential therapeutic value of THC and, at the same time, calls attention to the critical need for better-designed protocols to fully explore and demonstrate safety and efficacy.

Systems biology reveals anatabine to be an NRF2 activator.

Anatabine, an alkaloid present in plants of the family (including tobacco and eggplant), has been shown to ameliorate chronic inflammatory conditions in mouse models, such as Alzheimer's disease, Hashimoto's thyroiditis, multiple sclerosis, and intestinal inflammation. However, the mechanisms of action of anatabine remain unclear. To understand the impact of anatabine on cellular systems and identify the molecular pathways that are perturbed, we designed a study to examine the concentration-dependent effects of anatabine on various cell types by using a systems pharmacology approach.

Effects of nicotinic acetylcholine receptor-activating alkaloids on anxiety-like behavior in zebrafish.

Alkaloids are a structurally complex group of natural products that have a diverse range of biological activities and significant therapeutic applications. In this study, we examined the acute, anxiolytic-like effects of nicotinic acetylcholine receptor (nAChR)-activating alkaloids with reported neuropharmacological effects but whose effects on anxiety are less well understood. Because α4β2 nAChRs can regulate anxiety, we first demonstrated the functional activities of alkaloids on these receptors in vitro.

Effects of Natural Monoamine Oxidase Inhibitors on Anxiety-Like Behavior in Zebrafish.

Monoamine oxidases (MAO) are a valuable class of mitochondrial enzymes with a critical role in neuromodulation. In this study, we investigated the effect of natural MAO inhibitors on novel environment-induced anxiety by using the zebrafish novel tank test (NTT). Because zebrafish spend more time at the bottom of the tank when they are anxious, anxiolytic compounds increase the time zebrafish spend at the top of the tank and vice versa.

Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data.

Developing models for the prediction of microbial biotransformation pathways and half-lives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the Eawag-Soil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation half-life values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development.

Synthesis and Biological Evaluation of Hybrid 1,5- and 2,5-Disubstituted Indoles as Potentially New Antitubercular Agents.

Background: Tuberculosis (TB) is the second leading cause of mortality worldwide being a highly contagious and insidious illness caused by Mycobacterium tuberculosis, Mtb. Additionally, the emergence of multidrug-resistant and extensively drug-resistant strains of Mtb, together with significant levels of co-infection with HIV and TB (HIV/TB) make the search for new antitubercular drugs urgent and challenging.

Methods: This work was based on the hypothesis that an active compound could be obtained if substituents present in some other active compounds were attached on a core of an important structure, in this case the indole scaffold, thus generating a hybrid compound.

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals.

Machine learning algorithms were explored for the fast estimation of HOMO and LUMO orbital energies calculated by DFT B3LYP, on the basis of molecular descriptors exclusively based on connectivity. The whole project involved the retrieval and generation of molecular structures, quantum chemical calculations for a database with >111 000 structures, development of new molecular descriptors, and training/validation of machine learning models. Several machine learning algorithms were screened, and an applicability domain was defined based on Euclidean distances to the training set.

QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery.

A Quantitative Structure-Activity Relationship (QSAR) approach for classification was used for the prediction of compounds as active/inactive relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1746 compounds from PubChem with empirical CDK descriptors and semi-empirical quantum-chemical descriptors. A data set of 183 active pharmaceutical ingredients was additionally used for the external validation of the best models. The best classification models for antibiotic and antitumor activities were used to screen a data set of marine and microbial natural products from the AntiMarin database-25 and four lead compounds for antibiotic and antitumor drug design were proposed, respectively.

Design, synthesis and biological evaluation of novel isoniazid derivatives with potent antitubercular activity.

The disturbing emergence of multidrug-resistant strains of Mycobacterium tuberculosis (Mtb) has been driving the scientific community to urgently search for new and efficient antitubercular drugs. Despite the various drugs currently under evaluation, isoniazid is still the key and most effective component in all multi-therapeutic regimens recommended by the WHO. This paper describes the QSAR-oriented design, synthesis and in vitro antitubercular activity of several potent isoniazid derivatives (isonicotinoyl hydrazones and isonicotinoyl hydrazides) against H37Rv and two resistant Mtb strains.

Automatic NMR-based identification of chemical reaction types in mixtures of co-occurring reactions.

The combination of chemoinformatics approaches with NMR techniques and the increasing availability of data allow the resolution of problems far beyond the original application of NMR in structure elucidation/verification. The diversity of applications can range from process monitoring, metabolic profiling, authentication of products, to quality control. An application related to the automatic analysis of complex mixtures concerns mixtures of chemical reactions.

A chemoinformatics approach to the discovery of lead-like molecules from marine and microbial sources en route to antitumor and antibiotic drugs.

The comprehensive information of small molecules and their biological activities in the PubChem database allows chemoinformatic researchers to access and make use of large-scale biological activity data to improve the precision of drug profiling. A Quantitative Structure-Activity Relationship approach, for classification, was used for the prediction of active/inactive compounds relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1804 compounds from PubChem. Using the best classification models for antibiotic and antitumor activities a data set of marine and microbial natural products from the AntiMarin database were screened-57 and 16 new lead compounds for antibiotic and antitumor drug design were proposed, respectively.

Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors.

Automatic Perception of Chemical Similarities Between Metabolic Pathways.

Metabolic pathways are at the crossroad between the chemical world of small molecules and the biological world of enzymes, genes and regulation. Methods for their processing are therefore required for a great variety of applications. The work presented here reports a new method to encode metabolic pathways and reactomes of organisms based on the MOLMAP approach.

Estimation of Mayr electrophilicity with a quantitative structure-property relationship approach using empirical and DFT descriptors.

Quantitative structure-property relationships (QSPRs) were investigated for the estimation of the Mayr electrophilicity parameter using a data set of 64 compounds, all currently available uncharged electrophiles in Mayr's Database of Reactivity Parameters. Three collections of empirical descriptors were employed, from Dragon, Adriana.Code, and CDK.

Classification of chemical reactions and chemoinformatic processing of enzymatic transformations.

The automatic perception of chemical similarities between chemical reactions is required for a variety of applications in chemistry and connected fields, namely with databases of metabolic reactions. Classification of enzymatic reactions is required, e.g.

Assignment of EC numbers to enzymatic reactions with MOLMAP reaction descriptors and random forests.

The MOLMAP descriptor relies on a Kohonen SOM that defines types of covalent bonds on the basis of their physicochemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants and numerically encodes the pattern of changes in bonds during a chemical reaction.

Genome-scale classification of metabolic reactions and assignment of EC numbers with self-organizing maps.

Motivation: The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer-aided validation of classification systems, to genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Comparison of metabolic reactions has been mostly based on Enzyme Commission (EC) numbers, which are extremely useful and widespread, but not always straightforward to apply, and often problematic when an enzyme catalyzes several reactions, when the same reaction is catalyzed by different enzymes, when official full EC numbers are unavailable or when reactions are not catalyzed by enzymes. Different methods should be available to compare metabolic reactions.