Development of Dibenzothiazepine Derivatives as Multifunctional Compounds for Neuropathic Pain.

Neuropathic pain is a chronic and sometimes intractable condition caused by lesions or diseases of the somatosensory nervous system. Many drugs are available but unfortunately do not provide satisfactory effects in patients, producing limited analgesia and undesirable side effects. Thus, there is an urgent need to develop new pharmaceutical agents to treat neuropathic pain. To date, highly specific agents that modulate a single target, such as receptors or ion channels, never progress to the clinic, which may reflect the diverse etiologies of neuropathic pain seen in the human patient population. Therefore, the development of multifunctional compounds exhibiting two or more pharmacological activities is an attractive strategy for addressing unmet medical needs for the treatment of neuropathic pain. To develop novel multifunctional compounds, key pharmacophores of currently used clinical pain drugs, including pregabalin, fluoxetine and serotonin analogs, were hybridized to the side chain of tianeptine, which has been used as an antidepressant. The biological activities of the hybrid analogs were evaluated at the human transporters of neurotransmitters, including serotonin (hSERT), norepinephrine (hNET) and dopamine (hDAT), as well as mu (µ) and kappa (κ) opioid receptors. The most advanced hybrid of these multifunctional compounds, 17, exhibited multiple transporter inhibitory activities for the uptake of neurotransmitters with IC50 values of 70 nM, 154 nM and 2.01 µM at hSERT, hNET and hDAT, respectively. Additionally, compound 17 showed partial agonism (EC50 = 384 nM) at the µ-opioid receptor with no influence at the κ-opioid receptor. In in vivo pain animal experiments, the multifunctional compound 17 showed significantly reduced allodynia in a spinal nerve ligation (SNL) model by intrathecal administration, indicating that multitargeted strategies in single therapy could considerably benefit patients with multifactorial diseases, such as pain.

Synthesis and Structure-Activity Relationship Studies of Benzimidazole-4,7-dione-Based P2X3 Receptor Antagonists as Novel Anti-Nociceptive Agents.

P2X3 receptors (P2X3R) are ATP-gated ion channels predominantly expressed in C- and Aδ-fiber primary afferent neurons and have been introduced as a novel therapeutic target for neurological disorders, including neuropathic pain and chronic cough. Because of its localized distribution, antagonism of P2X3R has been thoroughly considered, and the avoidance of issues related to CNS side effects has been proven in clinical trials. In this article, benzimidazole-4,7-dione-based derivatives were introduced as a new chemical entity for the development of P2X3R antagonists. Starting from the discovery of a hit compound from the screening of 8364 random library compounds in the Korea Chemical Bank, which had an IC50 value of 1030 nM, studies of structure-activity and structure-property relationships enabled further optimization toward improving the antagonistic activities as well as the drug's physicochemical properties, including metabolic stability. As for the results, the final optimized compound 14h was developed with an IC50 value of 375 nM at P2X3R with more than 23-fold selectivity versus P2X2/3R, along with properties of metabolic stability and improved solubility. In neuropathic pain animal models evoked by either nerve ligation or chemotherapeutics in male Sprague-Dawley rats, compound 14h showed anti-nociceptive effects through an increase in the mechanical withdrawal threshold as measured by von Frey filament following intravenous administration.

Remaining Useful Life Prediction of Airplane Engine Based on PCA-BLSTM.

The accurate prediction of airplane engine failure can provide a reasonable decision basis for airplane engine maintenance, effectively reducing maintenance costs and reducing the incidence of failure. According to the characteristics of the monitoring data of airplane engine sensors, this work proposed a remaining useful life (RUL) prediction model based on principal component analysis and bidirectional long short-term memory. Principal component analysis is used for feature extraction to remove useless information and noise. After this, bidirectional long short-term memory is used to learn the relationship between the state monitoring data and remaining useful life. This work includes data preprocessing, the construction of a hybrid model, the use of the NASA's Commercial Aerodynamic System Simulation (C-MAPSS) data set for training and testing, and the comparison of results with those of support vector regression, long short-term memory and bidirectional long short-term memory models. The hybrid model shows better prediction accuracy and performance, which can provide a basis for formulating a reasonable airplane engine health management plan.