The in vivo antinociceptive and μ-opioid receptor activating effects of the combination of N-phenyl-2',4'-dimethyl-4,5'-bi-1,3-thiazol-2-amines and naloxone

Discovery of a mu-opioid receptor modulator that in combination with morphinan antagonists induces analgesia

Morphinan antagonists, which block opioid effects at mu-opioid receptors, have been studied for their analgesic potential. Previous studies have suggested that these antagonists elicit analgesia with fewer adverse effects in the presence of the mutant mu-opioid receptor (MOR; S196A). However, introducing a mutant receptor for medical applications represents significant challenges. We hypothesize that binding a chemical compound to the MOR may elicit a comparable effect to the S196A mutation. Through high-throughput screening and structure-activity relationship studies, we identified a modulator, 4-(2-(4-fluorophenyl)-4-oxothiazolidin-3-yl)-3-methylbenzoic acid (BPRMU191), which confers agonistic properties to small-molecule morphinan antagonists, which induce G protein-dependent MOR activation. Co-application of BPRMU191 and morphinan antagonists resulted in MOR-dependent analgesia with diminished side effects, including gastrointestinal dysfunction, antinociceptive tolerance, and physical and psychological dependence. Combining BPRMU191 and morphinan antagonists could serve as a potential therapeutic strategy for severe pain with reduced adverse effects and provide an avenue for studying G protein-coupled receptor modulation.

Structure-Based Drug Design and Synthesis of Novel N-Aryl-2,4-bithiazole-2-amine CYP1B1-Selective Inhibitors in Overcoming Taxol Resistance in A549 Cells

As a promising therapeutic target for cancer, CYP1B1 is overexpressed in Taxol-resistant A549 cells; however, its role in drug resistance still remains unclear. Bioinformatic analysis data indicated that CYP1B1 was closely correlated with AKT/ERK1/2 and focal adhesion pathways, thereby playing an important role in Taxol resistance and cancer migration/invasion. Along similar lines, the AhR agonist 7,12-dimethylbenz[a]anthracene (DMBA) enhanced Taxol resistance and promoted migration/invasion of A549 and H460 cells likely stemming from CYP1B1 upregulation. Moreover, 83 novel N-aryl-2,4-bithiazole-2-amine CYP1B1-selective inhibitors were designed and synthesized to verify the role of CYP1B1 in Taxol-resistant A549 cells. Impressively, the most potent and selective one, namely, 77, remarkably inhibited AKT/ERK1/2 and FAK/SRC pathways and thereby reversed Taxol resistance as well as inhibited both migration and invasion of A549/Taxol cells. Collectively, this study not only displayed the role of CYP1B1 in Taxol resistance and cancer migration/invasion but also helped unlock the CYP1B1-oriented anticancer discovery.

An Overview of the Synthesis and Antimicrobial, Antiprotozoal, and Antitumor Activity of Thiazole and Bisthiazole Derivatives

Thiazole, a five-membered heteroaromatic ring, is an important scaffold of a large number of synthetic compounds. Its diverse pharmacological activity is reflected in many clinically approved thiazole-containing molecules, with an extensive range of biological activities, such as antibacterial, antifungal, antiviral, antihelmintic, antitumor, and anti-inflammatory effects. Due to its significance in the field of medicinal chemistry, numerous biologically active thiazole and bisthiazole derivatives have been reported in the scientific literature. The current review provides an overview of different methods for the synthesis of thiazole and bisthiazole derivatives and describes various compounds bearing a thiazole and bisthiazole moiety possessing antibacterial, antifungal, antiprotozoal, and antitumor activity, encouraging further research on the discovery of thiazole-containing drugs.

Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery

Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision medicine and drug discovery. In this study, we performed comparative studies between deep neural networks (DNN) and other ligand-based virtual screening (LBVS) methods to demonstrate that DNN and random forest (RF) were superior in hit prediction efficiency. By using DNN, several triple-negative breast cancer (TNBC) inhibitors were identified as potent hits from a screening of an in-house database of 165,000 compounds. In broadening the application of this method, we harnessed the predictive properties of trained model in the discovery of G protein-coupled receptor (GPCR) agonist, by which computational structure-based design of molecules could be greatly hindered by lack of structural information. Notably, a potent (~ 500 nM) mu-opioid receptor (MOR) agonist was identified as a hit from a small-size training set of 63 compounds. Our results show that DNN could be an efficient module in hit prediction and provide experimental evidence that machine learning could identify potent hits in silico from a limited training set.