3D-QSAR comparative molecular field analysis on delta opioid receptor agonist SNC80 and its analogs

Determination of structural factors affecting binding to mu, kappa and delta opioid receptors

Addiction is a complex behavioral phenomenon in which naturally occurring or synthetic chemicals modulate the response of the reward system through their binding to a variety of neuroreceptors, resulting in compulsive substance-seeking and use despite harmful consequences to the individual. Among these, the opioid receptor (OR) family and more specifically, the mu-opioid receptor (MOR) subtype plays a critical role in the addiction to powerful prescription and illicit drugs such as hydrocodone, oxycodone, fentanyl, cocaine, and methamphetamine (Contet et al. in Curr Opin Neurobiol 14(3):370-378, 2004). Conversely, agonists binding to kappa (KOR) and antagonists binding to delta opioid receptors (DOR) have been reported to induce negative reinforcing effects. As more than 700 new psychoactive substances were illegally sold between 2009 and 2016 (DEA-DCT-DIR-032-18), most of them lacking basic toxicological and pharmacological profiles, molecular modeling approaches that could quickly and reliably fill the gaps in our knowledge would be highly desirable tools for determining the effects of these synthetics. Here, we report accurate 3D-spectrometric data-activity relationship classification models for large and diverse datasets of MOR, KOR and DOR binders with areas under the receiver operating characteristic curve for the "blind" prediction sets exceeding 0.88. Structural features associated with (selective) binding to MOR, KOR and/or DOR were identified. These models could assist regulatory agencies in evaluating the health risks associated with the use of unprofiled substances as well as to help the pharmaceutical industry in its search for new drugs to combat addiction.

Comprehensive 3D-QSAR Model Predicts Binding Affinity of Structurally Diverse Sigma 1 Receptor Ligands

The Sigma 1 Receptor (S1R) has attracted intense interest as a pharmaceutical target for various therapeutic indications, including the treatment of neuropathic pain and the potentiation of opioid analgesia. Efforts by drug developers to rationally design S1R antagonists have been spurred recently by the 2016 publication of the high-resolution X-ray crystal structure of the ligand-bound human S1R. Until now, however, the absence in the published literature of a single, large-scale, and comprehensive quantitative structure-activity relationship (QSAR) model that encompasses a structurally diverse collection of S1R ligands has impaired rapid progress. To our best knowledge, the present study represents the first report of a statistically robust and highly predictive 3D-QSAR model (R2 = 0.92, Q2 = 0.62, Rpred2 = 0.81) based on the X-ray crystal structure of human S1R and constructed from a pooled compilation of 180 S1R antagonists that encompass five structurally diverse chemical families investigated using identical experimental protocols. Best practices, as recommended by the Organization for Economic Cooperation and Development (OECD: http://www.oecd.org/ ), were adopted for pooling data from disparate sources and for QSAR model development and both internal and external model validation. The practical utility of the final 3D-QSAR model was tested by virtual screening of the DrugBank database of FDA approved drugs supplemented by eight reported S1R antagonists. Among the top-ranked 40 DrugBank hits, four approved drugs which were previously unknown as S1R antagonists were tested using in vitro radiolabeled human S1R binding assays. Of these, two drugs (diphenhydramine and phenyltoloxamine) exhibited potent S1R binding affinity with Ki = 58 nM and 160 nM, respectively. As diphenhydramine is approved as an antiallergic, and phenyltoloxamine as an analgesic and sedative, each of these compounds represents a viable starting point for a drug discovery campaign aimed at the development of novel S1R antagonists for a wide range of therapeutic indications.

Structural requirements for ligands of the δ-opioid receptor

The ?-opioid receptor is sensitive to ligand geometry. In order to assist the synthesis of new ?-selective opioid ligands, the structure elements of ?-selective opioid ligands necessary for their effective binding were investigated. The automated docking procedure with a flexible ligand was used to simulate the binding of 17 ?-selective ligands to the ?-receptor. It was found that voluminous N-alkyl groups reduce the binding potency of naltrindole derivatives by preventing the ligands from adopting the preferred conformation in the receptor. This was confirmed by enantiospecific binding of chiral compounds where only one enantiomer adopts the naltrindole-like preferred conformation in the binding pocket. Voluminous groups replacing the hydroxyl group in the 3-hydroxybenzyl fragment of naltrindole analogs reduce the binding potency due to unfavorable steric interactions with the receptor. The two diastereoisomers of the potent ?-opioid ligand SNC80 confirmed the preferred binding conformation and the major receptor-ligand interactions.

Docking studies suggest ligand-specific delta-opioid receptor conformations

An automated docking procedure was used to study binding of a series of delta-selective ligands to three models of the delta-opioid receptor. These models are thought to represent the three ligand-specific receptor conformations. Docking results are in agreement with point mutation studies and suggest that different ligands--agonists and antagonists--may bind to the same binding site under different receptor conformations. Docking to different receptor models (conformations) also suggests that by changing to a receptor-specific conformation, the receptor may open or close different binding sites to other ligands.

Quantitative conformationally sampled pharmacophore for delta opioid ligands: reevaluation of hydrophobic moieties essential for biological activity

Recent studies have indicated several therapeutic applications for delta opioid agonists and antagonists. To exploit the therapeutic potential of delta opioids developing a structural basis for the activity of ligands at the delta opioid receptor is essential. The conformationally sampled pharmacophore (CSP) method (Bernard et al. J. Am. Chem. Soc. 2003, 125, 3103-3107) is extended here to obtain quantitative models of delta opioid ligand efficacy and affinity. Quantification is performed via overlap integrals of the conformational space sampled by ligands with respect to a reference compound. Iterative refinement of the CSP model identified hydrophobic groups other than the traditional phenylalanine residues as important for efficacy and affinity in DSLET and ICI 174 864. The obtained models for a structurally diverse set of peptidic and nonpeptidic delta opioid ligands offer good predictions with R2 values>0.9, and the predicted efficacy for a set of test compounds was consistent with the experimental values.

Discovery of novel triazole-based opioid receptor antagonists

We report the computer-aided design, chemical synthesis, and biological evaluation of a novel family of delta opioid receptor (DOR) antagonists containing a 1,2,4-triazole core structure that are structurally distinct from other known opioid receptor active ligands. Among those delta antagonists sharing this core structure, 8 exhibited strong binding affinity (K(i) = 50 nM) for the DOR and appreciable selectivity for delta over mu and kappa opioid receptors (delta/mu = 80; delta/kappa > 200).