Synthesis and biological evaluation of 2-(5-methyl-4-phenyl-2-oxopyrrolidin-1-yl)-acetamide stereoisomers as novel positive allosteric modulators of sigma-1 receptor

Novel positive allosteric modulators of sigma-1 receptor represented by 2-(5-methyl-4-phenyl-2-oxopyrrolidin-1-yl)-acetamide enantiomers were synthesised using an asymmetric Michael addition of 2-nitroprop-1-enylbenzene to diethyl malonate. Following the chromatographic separation of the methyl erythro- and threo-4-nitro-3R- and 3S-phenylpentanoate diastereoisomers, target compounds were obtained by their reductive cyclisation into 5-methyl-4-phenylpyrrolidin-2-one enantiomers and the attachment of the acetamide group to the heterocyclic nitrogen. Experiments with electrically stimulated rat vas deference contractions induced by the PRE-084, an agonist of sigma-1 receptor, showed that (4R,5S)- and (4R,5R)-2-(5-methyl-4-phenyl-2-oxopyrrolidin-1-yl)-acetamides with an R-configuration at the C-4 chiral centre in the 2-pyrrolidone ring were more effective positive allosteric modulators of sigma-1 receptor than were their optical antipodes.     

Assessing uncertainties in land cover projections

Understanding uncertainties in land cover projections is critical to investigating land‐based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro‐economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.