1-(2'-Bromobenzyl)-6,7-dihydroxy-N-methyl-tetrahydroisoquinoline and 1,2-Demethyl-nuciferine as Agonists in Human D2 Dopamine Receptors.

Certain D2-like dopamine receptor (DR) agonists are useful therapeutically as antiparkinsonian drugs, whereas D2-like DR antagonists or partial agonists are proven effective as antipsychotics. Two isoquinoline derivatives, 1-(2'-bromobenzyl)-6,7-dihydroxy-N-methyl-tetrahydroisoquinoline (Br-BTHIQ, 1) and 1,2-demethyl-nuciferine (aporphine, 2), were herein synthesized, and their dopaminergic affinity in cloned human D2R, D3R, and D4R subtypes and their behavior as agonists/antagonists were evaluated. They showed affinity values (Ki) for hD2, hD3, and hD4 DR within the nanomolar range. The trends in affinity were hD4R ≫ hD3R > hD2R for Br-BTHIQ (1) and hD2R > hD4R > hD3R for 1,2-demethyl-nuciferine (2). The functional assays of cyclic adenosine monophosphate signaling at human D2R showed a partial agonist effect for Br-BTHIQ (1) and full agonist behavior for aporphine (2), with half maximal effective concentration values of 2.95 and 10.2 µM, respectively. Therefore, both isoquinolines 1 and 2 have emerged as lead molecules for the synthesis of new therapeutic drugs that ultimately may be useful to prevent schizophrenia and Parkinson's disease, respectively.

Serotonin skews human macrophage polarization through HTR2B and HTR7.

Besides its role as a neurotransmitter, serotonin (5-hydroxytryptamine, 5HT) regulates inflammation and tissue repair via a set of receptors (5HT(1-7)) whose pattern of expression varies among cell lineages. Considering the importance of macrophage polarization plasticity for inflammatory responses and tissue repair, we evaluated whether 5HT modulates human macrophage polarization. 5HT inhibited the LPS-induced release of proinflammatory cytokines without affecting IL-10 production, upregulated the expression of M2 polarization-associated genes (SERPINB2, THBS1, STAB1, COL23A1), and reduced the expression of M1-associated genes (INHBA, CCR2, MMP12, SERPINE1, CD1B, ALDH1A2). Whereas only 5HT(7) mediated the inhibitory action of 5HT on the release of proinflammatory cytokines, both 5HT(2B) and 5HT(7) receptors mediated the pro-M2 skewing effect of 5HT. In fact, blockade of both receptors during in vitro monocyte-to-macrophage differentiation preferentially modulated the acquisition of M2 polarization markers. 5HT(2B) was found to be preferentially expressed by anti-inflammatory M2(M-CSF) macrophages and was detected in vivo in liver Kupffer cells and in tumor-associated macrophages. Therefore, 5HT modulates macrophage polarization and contributes to the maintenance of an anti-inflammatory state via 5HT(2B) and 5HT(7), whose identification as functionally relevant markers for anti-inflammatory/homeostatic human M2 macrophages suggests their potential therapeutic value in inflammatory pathologies.

Nose-to-brain delivery of enveloped RNA - cell permeating peptide nanocomplexes for the treatment of neurodegenerative diseases.

Direct nose-to-brain (N-to-B) delivery enables the rapid transport of drugs to the brain, while minimizing systemic exposure. The objective of this work was to engineer a nanocarrier intended to enhance N-to-B delivery of RNA and to explore its potential utility for the treatment of neurological disorders. Our approach involved the formation of electrostatically driven nanocomplexes between a hydrophobic derivative of octaarginine (r8), chemically conjugated with lauric acid (C12), and the RNA of interest. Subsequently, these cationic nanocomplexes were enveloped (enveloped nanocomplexes, ENCPs) with different protective polymers, i.e. polyethyleneglycol - polyglutamic acid (PEG-PGA) or hyaluronic acid (HA), intended to enhance their stability and mucodiffusion across the olfactory nasal mucosa. These rationally designed ENCPs were produced in bulk format and also using a microfluidics-based technique. This technique enabled the production of a scalable nanoformulation, exhibiting; (i) a unimodal size distribution with a tunable mean size, (ii) the capacity to highly associate (100%) and protect RNA from degradation, (iii) the ability to preserve its physicochemical properties in biorelevant media and prevent the premature RNA release. Moreover, in vitro cell culture studies showed the capacity of ENCPs to interact and be efficiently taken-up by CHO cells. Finally, in vivo experiments in a mouse model of Alzheimer's disease provided evidence of a statistically significant increase of a potentially therapeutic miRNA mimic in the hippocampus area and its further effect on two mRNA targets, following its intranasal administration. Overall, these findings stress the value of the rational design of nanocarriers towards overcoming the biological barriers associated to N-to-B RNA delivery and reveal their potential value as therapeutic strategies in Alzheimer's disease.

Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.

Scientific competency questions as the basis for semantically enriched open pharmacological space development.

Molecular information systems play an important part in modern data-driven drug discovery. They do not only support decision making but also enable new discoveries via association and inference. In this review, we outline the scientific requirements identified by the Innovative Medicines Initiative (IMI) Open PHACTS consortium for the design of an open pharmacological space (OPS) information system. The focus of this work is the integration of compound-target-pathway-disease/phenotype data for public and industrial drug discovery research. Typical scientific competency questions provided by the consortium members will be analyzed based on the underlying data concepts and associations needed to answer the questions. Publicly available data sources used to target these questions as well as the need for and potential of semantic web-based technology will be presented.