A fundamental mistake in receptor theory has led to an enduring misunderstanding of how to estimate the affinity and efficacy of an agonist. These properties are inextricably linked and cannot be easily separated in any case where the binding of a ligand induces a conformation change in its receptor. Consequently, binding curves and concentration-response relationships for receptor agonists have no straightforward interpretation. This problem-the affinity-efficacy problem-remains overlooked and misunderstood despite it being recognized in 1987. To avoid the further propagation of this misunderstanding, we propose in this review that the affinity-efficacy problem should be included in the core curricula for pharmacology undergraduates proposed by the British Pharmacological Society and the International Union of Basic and Clinical Pharmacology (IUPHAR).

Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILES strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured Emax values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5 ± 3.9% and 91.8 ± 4.4%, respectively. To overcome the challenge of a small data set, a student-teacher learning method called tritraining with disagreement was tested using an unlabeled data set comprised of 15,816 ligands of human, mouse, and rat μOR, κOR, and δOR. We found that the tritraining scheme was able to increase the hold-out AUC of MPNN models to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

Drugs that act on α-adrenoceptors may contain morpholine and pyrimidinone heterocycles. The aim of this study was to synthesize a series of pyrimidinones (S6a-e and S8) and characterize their α-adrenoceptor activity. Cytotoxicity assays (MTT and LDH) were performed in A7r5 and HUVECs. Concentration-effect curves to phenylephrine (Phe) were performed in rat aortic rings in the presence of compounds S6a-e and S8 or vehicle. Nitric oxide (NO) production and NO stable metabolic products, nitrite and nitrate, expressed as total nitrogen oxides (NOx) were assessed in HUVECs by confocal microscopy with the DAF-2DA probe and by the Griess reaction, respectively. Molecular docking simulations were performed using the 6a compound and α2A-adrenoceptor. In the evaluated conditions, the percentage of viable cells and the release of LDH were similar between control cells and cells exposed to the tested pyrimidinones. S6d, S6e, S8, and the positive control prazosin (but not S6a, S6b, and S6c) decreased Phe-induced contractions in endothelium-denuded aortic rings. S6a, S6b, and S6c decreased Phe-induced contractions in endothelium-intact aortic rings. The effect of S6a was abolished by L-NAME. NO production and NOx levels were inhibited in the presence of the α2 receptor antagonist yohimbine and the NOS inhibitor L-NAME. The 6a docking simulation estimated that the mean binding free energy of the compound was lower than the estimated value for yohimbine. These data suggest that S6d, S6e, and S8 may be α1-adrenoceptor antagonists while S6a acts as an agonist of α2-adrenoceptors.

Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILE strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured E max values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5±3.9% and 91.8± 4.4%, respectively. To overcome the challenge of small dataset, a student-teacher learning method called tri-training with disagreement was tested using an unlabeled dataset comprised of 15,816 ligands of human, mouse, or rat μOR, κOR, or δOR. We found that the tri-training scheme was able to increase the hold-out AUC of MPNN to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

OBJECTIVE: The classical theory of receptor action has been used for decades as a powerful tool to estimate molecular determinants of ligand-induced receptor activation (i.e., affinity and efficacy) from experimentally observable biological responses. However, it is also a well-recognized fact that the receptor-binding and activation mechanisms, and the parameters thereof, described in the classical theory contradict with the modern view of receptor activation based on allosteric principles.
METHODS: We used mathematical analysis, along with some numerical simulations, to answer the key question as to what extent the classical theory is compatible-if at all-with the modern understanding of receptor activation.
RESULTS: Here, we showed conclusively that (1) receptor activation equations based on allosteric principles contain the logic of the classical theory in disguise, and therefore, (2) estimates of \"intrinsic efficacy\" (ε) obtained by means of classical techniques (i.e., null methods or fitting the operational model to concentration-response data) are equivalent to the allosteric coupling factors that represent the molecular efficacy of ligands.
CONCLUSIONS: Thus, we conclude that despite the justified criticisms it has received so far, the classical theory may continue to be useful in estimating ligand efficacy from experimental data, if used properly. Here, we also provide rigorous criteria for the proper use of the theory. These findings not only have implications for ligand classification but also resolve some long lasting discussions in the field of bias agonism in GPCR, which requires reasonable estimates of relative ligand efficacies at different signalling pathways.

AbstractAnimals can form dominance relationships that vary from highly unequal, or despotic, to egalitarian, and this variation likely impacts the fitness of individuals. How and why these differences in relationships and fitness exist among groups, populations, and species has been the subject of much debate. Here, we investigated the influence of two major factors: (1) spatial resource distribution and (2) the presence or absence of winner-loser effects. To determine the effects of these factors, we built an agent-based model that represented 10 agents directly competing over food resources on a simple landscape. By varying the food distribution and using either asymmetry of strength or experience, we contrasted four scenarios from which we recorded attack decisions, fight outcomes, and individual energy intake to calculate dominance hierarchy steepness and energetic skew. Surprisingly, resource distribution and winner-loser effects did not have the predicted effects on hierarchy steepness. However, skew in energy intake arose when resources were distributed heterogeneously, despite hierarchy steepness frequently being higher in the homogeneous resource scenarios. Thus, this study confirms some decades-old predictions about feeding competition but also casts doubt on the ability to infer energetic consequences from observations of agonistic interactions.

The activation and mobilization of immune cells play a crucial role in immunotherapy. Existing therapeutic interventions, such as cytokines administration, aim to enhance immune cell activity. However, these approaches usually result in modest effectiveness and toxic side effects, thereby restricting their clinical application. Protease-activated receptors (PARs), a subfamily of G protein-coupled receptors, actively participate in the immune system by directly activating immune cells. The activation of PARs by proteases or synthetic ligands can modulate immune cell behavior, signaling, and responses to treat immune-related diseases, suggesting the significance of PARs agonism in immunotherapy. However, the agonism of PARs in therapeutical applications remains rarely discussed, since it has been traditionally considered that PARs activation facilitates disease progressions. This review aims to comprehensively summarize the activation, rather than inhibition, of PARs in immune-related physiological responses and diseases. Additionally, we will discuss the emerging immunotherapeutic potential of PARs agonism, providing a new strategic direction for PARs-mediated immunotherapy.

Pharmaceutical development of glucagon for use in acute hypoglycemia has proved challenging, due in large part to poor solubility, poor stability and aggregate formation. Herein, we describe highly soluble, low aggregating, glucagon conjugates generated through use of the commercially available vitamin B12 precursor dicyanocobinamide (\'corrination\'), which retain full stimulatory action at the human glucagon receptor. The modified glucagon analogs were tested in a chemical stability assay in 50 mM phosphate buffer and the percentage of original concentration retained was determined after two weeks of incubation at 37° C. Aggregate formation assays were also performed after 48 h of agitation at 37°C using a thioflavin (ThT) fluorescence-based assay. All corrinated compounds retained original concentration to a higher degree than glucagon controls and showed markedly decreased aggregation compared to their respective noncorrinated analogues. Based on the statistically significant increase in chemical stability coupled with the notably decreased tendency to form aggregates, analogues 2 and its corrinated conjugate 5 were used for a functional assay study performed after agitation at 37°C for 24-hr after which agonism was measured at the human glucagon receptor using a cAMP FRET assay. Corrinated 5 exhibited a 6.6-fold increased potency relative to glucagon, which was shown to have a 165-fold reduction in potency. The relative potency of 5 was also improved compared to that of 2 with EC50 values of 5.5 nM and 9.6 nM for 5 and 2, respectively. In conclusion, corrination of peptides mitigates aggregation, presenting a compound with prolonged stability and agonism as demonstrated for glucagon.

According to the ecological model of female social relationships (EMFSR), within-group competition and between-group competition in female-bonded species are shaped by food distribution. Strong between-group contests are expected over large, monopolizable resources and high population density, but not when low-quality food is distributed across large, undefended home ranges. Within-group contests are expected to be more frequent with increasing heterogeneity among feeding sites and with group size. We tested these predictions in female Asian elephants, which show traits associated with infrequent contests-graminivory, high fission-fusion and overlapping home ranges. We examined how food distribution and competitor density affected agonistic interactions within and between female elephant clans (social groupings) in the Kabini grassland, southern India. We found stronger between-clan contest in the grassland than that known from neighbouring forests, and more frequent agonism between females between clans than within clans. Such strong between-clan contest was attributable to the grassland being a food-rich habitat patch, thus supporting the EMFSR. Within-clan agonism was also frequent, but did not increase with food heterogeneity, contradicting the EMFSR. Contrary to recent claims, increasing within-clan agonism with group size suggested ecological constraints on large groups despite high fission-fusion. High population density may explain such frequent contests despite graminivory and fission-fusion.

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