Meta-analysis of pre-clinical studies on the effects of opioid receptor ligands on food intake, motivation, and choice.

The opioid receptors (OR) regulate food intake. Still, despite extensive pre-clinical research, the overall effects and individual contribution of the mu (MOR), kappa (KOR), and delta (DOR) OR subtypes to feeding behaviors and food intake remain unclear. To address this, we conducted a pre-registered systematic search and meta-analysis of rodent dose-response studies to evaluate the impact of central and peripheral administration of non-selective and selective OR ligands on intake, motivation, and choice of food. All studies had a high bias risk. Still, the meta-analysis confirmed the overall orexigenic and anorexigenic effects of OR agonists and antagonists, respectively. Our results support a larger orexigenic role for central MOR agonists among OR subtypes and that peripheral OR antagonists reduce motivation for and intake of preferred foods. In binary food choice studies, peripheral OR agonists selectively increase the intake of fat-preferred foods; in contrast, they did not increase the intake of sweet carbohydrate-preferred foods. Overall, these data support that OR regulation of intake, motivation, and choice is influenced by food macronutrient composition.

Molecular Basis of Inhibitory Mechanism of Naltrexone and Its Metabolites through Structural and Energetic Analyses.

Naltrexone is a potent opioid antagonist with good blood-brain barrier permeability, targeting different endogenous opioid receptors, particularly the mu-opioid receptor (MOR). Therefore, it represents a promising candidate for drug development against drug addiction. However, the details of the molecular interactions of naltrexone and its derivatives with MOR are not fully understood, hindering ligand-based drug discovery. In the present study, taking advantage of the high-resolution X-ray crystal structure of the murine MOR (mMOR), we constructed a homology model of the human MOR (hMOR). A solvated phospholipid bilayer was built around the hMOR and submitted to microsecond (µs) molecular dynamics (MD) simulations to obtain an optimized hMOR model. Naltrexone and its derivatives were docked into the optimized hMOR model and submitted to µs MD simulations in an aqueous membrane system. The MD simulation results were submitted to the molecular mechanics-generalized Born surface area (MMGBSA) binding free energy calculations and principal component analysis. Our results revealed that naltrexone and its derivatives showed differences in protein-ligand interactions; however, they shared contacts with residues at TM2, TM3, H6, and TM7. The binding free energy and principal component analysis revealed the structural and energetic effects responsible for the higher potency of naltrexone compared to its derivatives.

Encoding mu-opioid receptor biased agonism with interaction fingerprints.

Opioids are potent painkillers, however, their therapeutic use requires close medical monitoring to diminish the risk of severe adverse effects. The G-protein biased agonists of the µ-opioid receptor (MOR) have shown safer therapeutic profiles than non-biased ligands. In this work, we performed extensive all-atom molecular dynamics simulations of two markedly biased ligands and a balanced reference molecule. From those simulations, we identified a protein-ligand interaction fingerprint that characterizes biased ligands. Then, we built and virtually screened a database containing 68,740 ligands with proven or potential GPCR agonistic activity. Exemplary molecules that fulfill the interacting pattern for biased agonism are showcased, illustrating the usefulness of this work for the search of biased MOR ligands and how this contributes to the understanding of MOR biased signaling.

Allosteric modulation model of the mu opioid receptor by herkinorin, a potent not alkaloidal agonist.

Modulation of opioid receptors is the primary choice for pain management and structural information studies have gained new horizons with the recently available X-ray crystal structures. Herkinorin is one of the most remarkable salvinorin A derivative with high affinity for the mu opioid receptor, moderate selectivity and lack of nitrogen atoms on its structure. Surprisingly, binding models for herkinorin are lacking. In this work, we explore binding models of herkinorin using automated docking, molecular dynamics simulations, free energy calculations and available experimental information. Our herkinorin D-ICM-1 binding model predicted a binding free energy of -11.52 ± 1.14 kcal mol-1 by alchemical free energy estimations, which is close to the experimental values -10.91 ± 0.2 and -10.80 ± 0.05 kcal mol-1 and is in agreement with experimental structural information. Specifically, D-ICM-1 molecular dynamics simulations showed a water-mediated interaction between D-ICM-1 and the amino acid H2976.52, this interaction coincides with the co-crystallized ligands. Another relevant interaction, with N1272.63, allowed to rationalize herkinorin's selectivity to mu over delta opioid receptors. Our suggested binding model for herkinorin is in agreement with this and additional experimental data. The most remarkable observation derived from our D-ICM-1 model is that herkinorin reaches an allosteric sodium ion binding site near N1503.35. Key interactions in that region appear relevant for the lack of ß-arrestin recruitment by herkinorin. This interaction is key for downstream signaling pathways involved in the development of side effects, such as tolerance. Future SAR studies and medicinal chemistry efforts will benefit from the structural information presented in this work.

Genotyping the Mu-opioid receptor A118G polymorphism using the real-time amplification refractory mutation system: allele frequency distribution among Brazilians.

BACKGROUND: The mu-opioid receptor (OPRM1) A118G polymorphism has been associated with decreased analgesic effects of opioids and predisposition to addiction. However, its role in specific clinical scenarios and in different ethnicities must be better defined. No studies evaluating the A118G polymorphism in the Brazilian population have yet been published. METHODS: Genomic DNA was isolated from peripheral leukocytes of 200 surgical patients of the Center-West region of Brazil. Our genotyping protocol was developed based on the real-time amplification refractory mutation system and validated by comparison with cycle sequencing. Functional consequences of the A118G polymorphism were studied by comparing tobacco smoking prevalence and exposure between genotype groups. RESULTS: We observed perfect correlation between genotyping and sequencing results. Frequency of the G allele was 16% (IC 95% 12.7-19.9%) in our sample. Genotype distribution revealed 146 (73%) patients 118A homozygous, 44 (22%) heterozygous, and 10 (5%) homozygous for the G variant. After grouping patients according to the presence of the G allele, we did not observe differences in smoking prevalence; however, patients with one or two copies of the 118G allele reported higher tobacco exposure than patients 118A homozygous measured in pack-years (28.9 ± 12.5 vs. 21.5 ± 10.8, respectively, P = 0.02). CONCLUSIONS: We developed a fast and reliable genotyping method to identify the allele frequency distribution of the OPRM1 A118G polymorphism among patients from Center-West Brazil. Our preliminary results suggest functional consequences of the polymorphism on smoking behavior among Brazilians.