Homology modeling and 3D-QSAR study of benzhydrylpiperazine δ opioid receptor agonists

Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method

Opioid receptors, a kind of G protein-coupled receptors (GPCRs), mainly mediate an analgesic response via allosterically transducing the signal of endogenous ligand binding in the extracellular domain to couple to effector proteins in the intracellular domain. The δ opioid receptor (DOP) is associated with emotional control besides pain control, which makes it an attractive therapeutic target. However, its allosteric mechanism and key residues responsible for the structural stability and signal communication are not completely clear. Here we utilize the Gaussian network model (GNM) and amino acid network (AAN) combined with perturbation methods to explore the issues. The constructed fcfGNM_MD, where the force constants are optimized with the inverse covariance estimation based on the correlated fluctuations from the available DOP molecular dynamics (MD) ensemble, shows a better performance than traditional GNM in reproducing residue fluctuations and cross-correlations and in capturing functionally low-frequency modes. Additionally, fcfGNM_MD can consider implicitly the environmental effects to some extent. The lowest mode can well divide DOP segments and identify the two sodium ion (important allosteric regulator) binding coordination shells, and from the fastest modes, the key residues important for structure stabilization are identified. Using fcfGNM_MD combined with a dynamic perturbation-response method, we explore the key residues related to the sodium ion binding. Interestingly, we identify not only the key residues in sodium ion binding shells but also the ones far away from the perturbation sites, which are involved in binding with DOP ligands, suggesting the possible long-range allosteric modulation of sodium binding for the ligand binding to DOP. Furthermore, utilizing the weighted AAN combined with attack perturbations, we identify the key residues for allosteric communication. This work helps strengthen the understanding of the allosteric communication mechanism in δ opioid receptor and can provide valuable information for drug design.

Penilloic acid is the chief culprit involved in non-IgE mediated, immediate penicillin-induced hypersensitivity reactions in mice

Metabolites/impurities (MIs) of penicillin are normally considered to be the main substances inducing immediate hypersensitivity reactions in penicillin treatment. Our previous research found that penicillin can cause non-allergic hypersensitivity reactions (NAHRs) by directly triggering vascular hyperpermeability and exudative inflammation. However, the chief culprits and underlying mechanisms involved in penicillin-induced NAHRs have not yet been fully elucidated. In this study, we used a combination of approaches including a mouse non-allergic hypersensitivity reaction model, UPLC-MS/MS analyses of arachidonic acid metabolites (AAMs), immunoblotting technique, and molecular docking, etc to investigate the culprits involved in penicillin-induced hypersensitivity reactions. We found penilloic acid, one of the main MIs of penicillin, could trigger NAHRs via inducing increased vascular permeability, while the other MIs did no exhibit similar effect. Penilloic acid-induced reactions were not IgE-dependent. Significantly increased arachidonic acids and cascade metabolites in lungs, and activation of RhoA/ROCK signaling pathway in the ears and lungs of mice were noticed after once administration of penilloic acid. This study revealed that penilloic acid was the chief culprit involved in penicillin-induced immediate NAHRs in mice, which mainly associated with direct stimulation of vascular hyperpermeability and exudative inflammation. The activations of AAMs and RhoA/ROCK signaling pathway played important roles in these reactions.

Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids

Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new candidates with low cost. In this study, we developed a novel data mining and computational modeling workflow proven to be applicable by screening new analgesic opioids. To this end, a large opioid data set was used as the probe to automatically obtain bioassay data from the PubChem portal. There were 114 PubChem bioassays selected to build quantitative structure-activity relationship (QSAR) models based on the testing results across the probe compounds. The compounds tested in each bioassay were used to develop 12 models using the combination of three machine learning approaches and four types of chemical descriptors. The model performance was evaluated by the coefficient of determination (R ²) obtained from 5-fold cross-validation. In total, 49 models developed for 14 bioassays were selected based on the criteria and were identified to be mainly associated with binding affinities to different opioid receptors. The models for these 14 bioassays were further used to fill data gaps in the probe opioids data set and to predict general drug compounds in the DrugBank data set. This study provides a universal modeling strategy that can take advantage of large public data sets for computer-aided drug design (CADD).

Forsythoside A and Forsythoside B Contribute to Shuanghuanglian Injection-Induced Pseudoallergic Reactions through the RhoA/ROCK Signaling Pathway

In recent years, hypersensitivity reactions to the Shuanghuanglian injection have attracted broad attention. However, the componential chief culprits inducing the reactions and the underlying mechanisms involved have not been completely defined. In this study, we used a combination of approaches based on the mouse model, human umbilical vein endothelial cell monolayer, real-time cellular monitoring, immunoblot analysis, pharmacological inhibition, and molecular docking. We demonstrated that forsythoside A and forsythoside B contributed to Shuanghuanglian injection-induced pseudoallergic reactions through activation of the RhoA/ROCK signaling pathway. Forsythoside A and forsythoside B could trigger dose-dependent vascular leakage in mice. Moreover, forsythoside A and forsythoside B slightly elicited mast cell degranulation. Correspondingly, treatment with forsythoside A and forsythoside B disrupted the endothelial barrier and augmented the expression of GTP-RhoA, p-MYPT1, and p-MLC2 in a concentration-dependent manner. Additionally, the ROCK inhibitor effectively alleviated forsythoside A/forsythoside B-induced hyperpermeability in both the endothelial cells and mice. Similar responses were not observed in the forsythoside E-treated animals and cells. These differences may be related to the potential of the tested compounds to react with RhoA-GTPγS and form stable interactions. This study innovatively revealed that some forsythosides may cause vascular leakage, and therefore, limiting their contents in injections should be considered.