BTRX-246040 Acts Through the Ventrolateral Periaqueductal Gray to Exert Antidepressant-Relevant Actions in Mice.

BACKGROUND: BTRX-246040, a nociceptin/orphanin FQ peptide receptor antagonist, is being developed for the treatment of depressive patients. However, the underlying mechanism of this potential antidepressant is still largely unclear. Here, we studied the antidepressant-related actions of BTRX-246040 in the ventrolateral periaqueductal gray (vlPAG). METHODS: The tail suspension test, forced swim test, female urine sniffing test, sucrose preference test, and learned helplessness (LH) combined with pharmacological approaches were employed to examine the antidepressant-like effects and drug effects on LH-induced depressive-like behavior in C57BL/6J mice. Electrophysiological recordings in vlPAG neurons were used to study synaptic activity. RESULTS: Intraperitoneal administration of BTRX-246040 produced antidepressant-like behavioral effects in a dose-dependent manner. Systemic BTRX-246040 (10 mg/kg) resulted in an increased frequency and amplitude of miniature excitatory postsynaptic currents (EPSCs) in the vlPAG. Moreover, slice perfusion of BTRX-246040 directly elevated the frequency and amplitude of miniature EPSCs and enhanced the evoked EPSCs in the vlPAG, which were blocked by pretreatment with the nociceptin/orphanin FQ peptide receptor agonist Ro 64-6198. In addition, intra-vlPAG application of BTRX-246040 produced antidepressant-like behavioral effects in a dose-dependent manner. Moreover, intra-vlPAG pretreatment with 6-cyano-7-nitroquinoxaline-2,3-dione reversed both systemic and local BTRX-246040-mediated antidepressant-like behavioral effects. Furthermore, both systemic and local BTRX-246040 decreased the LH phenotype and reduced LH-induced depressive-like behavior. CONCLUSIONS: The results suggested that BTRX-246040 may act through the vlPAG to exert antidepressant-relevant actions. The present study provides new insight into a vlPAG-dependent mechanism underlying the antidepressant-like actions of BTRX-246040.

Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.

Histone deacetylase (HDAC) 1, a member of the histone deacetylases family, plays a pivotal role in various tumors. In this study, we collected 7313 human HDAC1 inhibitors with bioactivities to form a dataset. Then, the dataset was divided into a training set and a test set using two splitting methods: (1) Kohonen's self-organizing map and (2) random splitting. The molecular structures were represented by MACCS fingerprints, RDKit fingerprints, topological torsions fingerprints and ECFP4 fingerprints. A total of 80 classification models were built by using five machine learning methods, including decision tree (DT), random forest, support vector machine, eXtreme Gradient Boosting and deep neural network. Model 15A_2 built by the XGBoost algorithm based on ECFP4 fingerprints showed the best performance, with an accuracy of 88.08% and an MCC value of 0.76 on the test set. Finally, we clustered the 7313 HDAC1 inhibitors into 31 subsets, and the substructural features in each subset were investigated. Moreover, using DT algorithm we analyzed the structure-activity relationship of HDAC1 inhibitors. It may conclude that some substructures have a significant effect on high activity, such as N-(2-amino-phenyl)-benzamide, benzimidazole, AR-42 analogues, hydroxamic acid with a middle chain alkyl and 4-aryl imidazole with a midchain of alkyl whose α carbon is chiral.