Structure-Activity Relationships of Truncated 1'-Homologated Carbaadenosine Derivatives as New PPARγ/δ Ligands: A Study on Sugar Puckering Affecting Binding to PPARs.

Peroxisome proliferator-activated receptors (PPARs) are associated with the regulation of metabolic homeostasis. Based on a previous report that 1'-homologated 4'-thionucleoside acts as a dual PPARγ/δ modulator, carbocyclic nucleosides 2-5 with various sugar conformations were synthesized to determine whether sugar puckering affects binding to PPARs. (S)-conformer 2 was synthesized using Charette asymmetric cyclopropanation, whereas (N)-conformer 3 was synthesized using stereoselective Simmons-Smith cyclopropanation. All synthesized nucleosides did not exhibit binding affinity to PPARα but exhibited significant binding affinities to PPARγ/δ. The binding affinity of final nucleosides to PPARγ did not differ significantly based on their conformation, but their affinity to PPARδ depended greatly on their conformation, correlated with adiponectin production. (N)-conformer 3h was discovered to be the most potent PPARδ antagonist with good adiponectin production, which exhibited the most effective activity in inhibiting the mRNA levels of LPS-induced IL-1ß expression in RAW 264.7 macrophages, implicating its anti-inflammatory activity.

Discovery of (E)-3-(3-((2-Cyano-4'-dimethylaminobiphenyl-4-ylmethyl)cyclohexanecarbonylamino)-5-fluorophenyl)acrylic Acid Methyl Ester, an Intestine-Specific, FXR Partial Agonist for the Treatment of Nonalcoholic Steatohepatitis.

A series of fexaramine analogs were synthesized and evaluated to develop an intestine-selective/specific FXR partial agonist. Introduction of both a CN substituent at the C-2 in the biphenyl ring and a fluorine at the C-5 in the aniline ring in fexaramine markedly increased FXR agonistic activity. 27c showed 53 ± 3% maximum efficacy relative to GW4064 in an FXR agonist assay. A substantial amount of 27c was absorbed in the intestine after oral administration in rats, and then it was rapidly metabolized to inactive carboxylic acid 44 by serum esterases. In CDAHFD-fed mice, oral administration of 27c strongly induced multiple intestinal FXR target genes, FGF15, SHP, IBABP, and OST-α, but failed to activate SHP in the liver. 27c significantly reduced the liver fibrogenesis area, hepatic fibrosis markers, and serum level of AST. Rational optimization of fexaramine has led to the identification of an intestine-specific FXR partial agonist 27c.

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).

There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

2-(Halogenated Phenyl) acetamides and propanamides as potent TRPV1 antagonists.

A series consisting of 117 2-(halogenated phenyl) acetamide and propanamide analogs were investigated as TRPV1 antagonists. The structure-activity analysis targeting their three pharmacophoric regions indicated that halogenated phenyl A-region analogs exhibited a broad functional profile ranging from agonism to antagonism. Among the compounds, antagonists 28 and 92 exhibited potent antagonism toward capsaicin for hTRPV1 with Ki[CAP] = 2.6 and 6.9 nM, respectively. Further, antagonist 92 displayed promising analgesic activity in vivo in both phases of the formalin mouse pain model. A molecular modeling study of 92 indicated that the two fluoro groups in the A-region made hydrophobic interactions with the receptor.

Structural Characterization of Receptor-Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers.

G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein-protein interactions (PPI).