Δ9-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. In the most general sense, the present brief review illuminates some intriguing findings about THC, along with the basic threats to the validity of the research that supports those findings. The intent is to highlight existing generic weaknesses in the existing randomized controlled trial literature and, most importantly, provide guidance for improved clinical research.

Systems biology reveals anatabine to be an NRF2 activator.

Anatabine, an alkaloid present in plants of the So lanaceae 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. The resulting dataset, consisting of measurements of various omics data types at different time points, was analyzed by using multiple computational techniques. To identify concentration-dependent activated pathways, we performed linear modeling followed by gene set enrichment. To predict the functional partners of anatabine and the involved pathways, we harnessed the LINCS L1000 dataset's wealth of information and implemented integer linear programming on directed graphs, respectively. Finally, we experimentally verified our key computational predictions. Using an appropriate luciferase reporter cell system, we were able to demonstrate that anatabine treatment results in NRF2 (nuclear factor-erythroid factor 2-related factor 2) translocation, and our systematic phosphoproteomic assays showed that anatabine treatment results in activation of MAPK signaling. While there are certain areas to be explored in deciphering the exact anti-inflammatory mechanisms of action of anatabine and other NRF2 activators, we believe that anatabine constitutes an interesting molecule for its therapeutic potential in NRF2-related diseases.

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 α4ß2 nAChRs can regulate anxiety, we first demonstrated the functional activities of alkaloids on these receptors in vitro. Their effects on anxiety-like behavior in zebrafish were then examined using the zebrafish novel tank test (NTT). The NTT is a relatively high-throughput behavioral paradigm that takes advantage of the natural tendency of fish to dive down when stressed or anxious. We report for the first time that cotinine, anatabine, and methylanatabine may suppress this anxiety-driven zebrafish behavior after a single 20-min treatment. Effective concentrations of these alkaloids were well above the concentrations naturally found in plants and the concentrations needed to induce anxiolytic-like effect by nicotine. These alkaloids showed good receptor interactions at the α4ß2 nAChR agonist site as demonstrated by in vitro binding and in silico docking model, although somewhat weaker than that for nicotine. Minimal or no significant effect of other compounds may have been due to low bioavailability of these compounds in the brain, which is supported by the in silico prediction of blood-brain barrier permeability. Taken together, our findings indicate that nicotine, although not risk-free, is the most potent anxiolytic-like alkaloid tested in this study, and other natural alkaloids may regulate anxiety as well.

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. Using this paradigm, we found that harmane, norharmane, and 1,2,3,4-tetrahydroisoquinoline (TIQ) induce anxiolytic-like effects in zebrafish, causing them to spend more time at the top of the test tank and less time at the bottom. 2,3,6-trimethyl-1,4-naphtoquinone (TMN) induced an interesting mix of both anxiolytic- and anxiogenic-like effects during the first and second halves of the test, respectively. TIQ was unique in having no observable effect on general movement. Similarly, a reference MAO inhibitor clorgyline-but not pargyline-increased the time spent at the top in a concentration-dependent manner. We also demonstrated that the brain bioavailability of these compounds are high based on the ex vivo bioavailability assay and in silico prediction models, which support the notion that the observed effects on anxiety-like behavior in zebrafish were most likely due to the direct effect of these compounds in the brain. This study is the first investigation to demonstrate the anxiolytic-like effects of MAO inhibitors on novel environment-induced anxiety in zebrafish.

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. Most notably, the variability of half-life values for individual compounds is large and only about one order of magnitude lower than the entire range of median half-life values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., Ar-OH methylation, or the extension of existing rules, e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of half-lives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitative-structure biotransformation relationships (QSBR), which is a novel opportunity offered by the simultaneous encoding of transformation reactions and corresponding half-lives in Eawag-Soil. Overall, Eawag-Soil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.

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. A QSAR-oriented design based on classification and regression models along with the estimation of physicochemical and biological properties have also been used to assist in the selection of compounds. Chosen compounds were synthesized using various synthetic procedures and evaluated against M. tuberculosis H37Rv strain. RESULTS: Selected compounds possess substituents at positions C5, C2 and N1 of the indole ring. The substituents involve p-halophenyl, pyridyl, benzyloxy and benzylamine groups. Four compounds were synthesised using suitable synthetic procedures to attain the desired substitution at the indole core. From these, three compounds are new and have been fully characterized, and tested in vitro against the H37Rv ATCC27294T Mtb strain, using isoniazid as a control. One of them, compound 2, with the pyridyl group at N1, has an experimental log (1/MIC) very close to 5 and can be considered as being (weakly) active. In fact, it is more active than 64% of all indole molecules in our data sets of experimental results from literature. The most active indole in this data sets has log (1/MIC)=5.93 with only 6 compounds with log (1/MIC) above 5.5. CONCLUSION: Despite the lower activity found for the tested compounds, when compared to other reported indole-derivatives, these structures, which rely on a hybrid design concept, may constitute interesting scaffolds to prepare a new family of TB inhibitors with improved activity.

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. Random forest models predicted an external test set of 9989 compounds achieving mean absolute error (MAE) up to 0.15 and 0.16 eV for the HOMO and LUMO orbitals, respectively. The impact of the quantum chemical calculation protocol was assessed with a subset of compounds. Inclusion of the orbital energy calculated by PM7 as an additional descriptor significantly improved the quality of estimations (reducing the MAE in >30%).

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. The present work enables the presentation of a new set of possible lead like bioactive compounds and corroborates the results of our previous investigations. By other side it is shown the usefulness of quantum-chemical descriptors in the discrimination of biologically active and inactive compounds. None of the compounds suggested by our approach have assigned non-antibiotic and non-antitumor activities in the AntiMarin database and almost all were lately reported as being active in the literature.

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. QSAR studies entailed RFs and ASNNs classification models, as well as MLR models. Strict validation procedures were used to guarantee the models' robustness and predictive ability. Lipophilicity was shown not to be relevant to explain the activity of these derivatives, whereas shorter N-N distances and lengthy substituents lead to more active compounds. Compounds 1, 2, 4, 5 and 6, showed measured activities against H37Rv higher than INH (i.e., MIC ≤ 0.28 µM), while compound 9 exhibited a six fold decrease in MIC against the katG (S315T) mutated strain, by comparison with INH (i.e., 6.9 vs. 43.8 µM). All compounds were ineffective against H37RvINH (ΔkatG), a strain with a full deletion of the katG gene, thus corroborating the importance of KatG in the activation of INH-based compounds. The most potent compounds were also shown not to be cytotoxic up to a concentration 500 times higher than MIC.

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. We encoded mixtures of chemical reactions with the difference between the (1)H NMR spectra of the products and the reactants. All the signals arising from all the reactants of the co-occurring reactions were taken together (a simulated spectrum of the mixture of reactants) and the same was done for products. The difference spectrum is taken as the representation of the mixture of chemical reactions. A data set of 181 chemical reactions was used, each reaction manually assigned to one of 6 types. From this dataset, we simulated mixtures where two reactions of different types would occur simultaneously. Automatic learning methods were trained to classify the reactions occurring in a mixture from the (1)H NMR-based descriptor of the mixture. Unsupervised learning methods (self-organizing maps) produced a reasonable clustering of the mixtures by reaction type, and allowed the correct classification of 80% and 63% of the mixtures in two independent test sets of different similarity to the training set. With random forests (RF), the percentage of correct classifications was increased to 99% and 80% for the same test sets. The RF probability associated to the predictions yielded a robust indication of their reliability. This study demonstrates the possibility of applying machine learning methods to automatically identify types of co-occurring chemical reactions from NMR data. Using no explicit structural information about the reactions participants, reaction elucidation is performed without structure elucidation of the molecules in the mixtures.

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. All compounds proposed by our approach are classified as non-antibiotic and non-antitumor compounds in the AntiMarin database. Recently several of the lead-like compounds proposed by us were reported as being active in the literature.

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. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models.

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. Pathways are represented from features of the metabolites involved in their reactions enabling to automatically perceive chemical similarities, and making no use of EC numbers. MOLMAP descriptors are based on atomic topological and physicochemical features of the bonds involved in reactions. The results show that self-organizing maps (SOM) can be trained with MOLMAPs of pathways to automatically recognize similarities between pathways of the same type of metabolism. The study also illustrates the possibility of applying the MOLMAP methodology at progressively higher levels of complexity, bridging chemical and biological information, and going all the way from atomic properties to the classification of organisms.

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. Models were built with multilinear regressions, k nearest neighbors, model trees, random forests, support vector machines (SVMs), associative neural networks, and counterpropagation neural networks. Quantum chemical descriptors were calculated with density functional theory (DFT) methods and incorporated in QSPR models. The best results were achieved with SVM using seven empirical and DFT descriptors; an R(2) of 0.92 was obtained for the test set (21 compounds). The final seven descriptors were the Parr electrophilicity index, ε(LUMO), hardness, and four CDK descriptors (FNSA-3, ATSc5, Kier2, and nAtomLAC). Screening of correlations between individual descriptors and Mayr electrophilicity revealed the highest absolute value of correlation for DFT ε(LUMO) (R = -0.82) and comparable correlations for some empirical descriptors, e.g., Dragon's folding degree index (R = -0.80), Kier flexibility index (R = -0.78), and Kier S2K index (R = -0.78). High correlations were observed in the training set between reactivity descriptors calculated by the PM6 semiempirical and DFT methods (R = 0.96 for ε(LUMO) and 0.94 for the electrophilicity index).

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., for genome-scale reconstruction (or comparison) of metabolic pathways, computer-aided validation of classification systems, or comparison of enzymatic mechanisms. This chapter presents different current approaches for the representation of chemical reactions enabling automatic reaction classification. Representations based on the encoding of the reaction center are illustrated, which use physicochemical features, Reaction Classification (RC) numbers, or Condensed Reaction Graphs (CRG). Representation of differences between the structures of products and reactants include reaction signatures, fingerprint differences, and the MOLMAP approach. The approaches are illustrated with applications to real datasets.

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. In this study, a genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors and was explored for the assignment of the official EC number from the reaction equation with Random Forests as the machine learning algorithm. EC numbers were correctly assigned in 95%, 90%, and 85% (for independent test sets) at the class, subclass, and subsubclass EC number level, respectively, with training sets including one reaction from each available full EC number. Increasing differences between training and test sets were explored, leading to decreased percentages of correct assignments. The classification of reactions only from the main reactants and products was obtained at the class, subclass, and subsubclass level with accuracies of 78%, 74%, and 63%, respectively.

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. Simultaneously, methods are required for the automatic assignment of EC numbers to reactions still not officially classified. RESULTS: We have proposed the MOLMAP reaction descriptors to numerically encode the structural transformations resulting from a chemical reaction. Here, such descriptors are applied to the mapping of a genome-scale database of almost 4000 metabolic reactions by Kohonen self-organizing maps (SOMs), and its screening for inconsistencies in EC numbers. This approach allowed for the SOMs to assign EC numbers at the class, subclass and sub-subclass levels for reactions of independent test sets with accuracies up to 92, 80 and 70%, respectively. Different levels of similarity between training and test sets were explored. The approach also led to the identification of a number of similar reactions bearing differences at the EC class level. AVAILABILITY: The programs to generate MOLMAP descriptors from atomic properties included in SDF files are available upon request for evaluation.