Palladium-Catalyzed Asymmetric Allylic Alkylation/α-Iminol Rearrangement:A Facile Access to 2-Spirocyclic-Indoline Derivatives

We report an unprecedented Pd-catalyzed asymmetric allylic alkylation of 1-(indol-2-yl)cyclobutanols followed by anα-iminol rearrangement.High yields with excellent chemo-,regio-,diastereo-,and enantioselectivities have been realized,affording a wide range of enantioenriched 2-spirocyclicindolines bearing two contiguous stereocenters.The facial selectivity of the ensuing rearrangement is controlled by the subtle difference of the substituents on the all-carbon quaternary stereogenic center formed in the allylic alkylation step.Nonactivated racemic terminal allylic alcohols are utilized as efficient electrophiles via kinetic resolution pathways for the first time in Pd-catalyzed asymmetric allylic alkylation.The role of Et3B additive is pivotal to activating allylic alcohols toward the formation of π-allylpalladium species and suppressing N/O allylic alkylation of indole with enhanced C3-nucleophilicity.Electrospray ionization high-resolution mass spectrometry(ESIHRMS)experiments provided strong evidence for the existence of the key nucleophilic boron anionic species,which fully accounts for the essential role of the Et_(3)B additive.The study of the mechanism indicates that the real catalytically active species is an electronic π-cinnamyl-palladium complex coordinated by two phosphoramidite ligands,which is consistent with the observed nonlinear effect and control experiments and is further confirmed by X-ray structure analysis.

Discovery of a highly specific ^(18)F-labeled PET ligand for phosphodiesterase 10A enabled by novel spirocyclic iodonium ylide radiofluorination

As a member of cyclic nucleotide phosphodiesterase(PDE)enzyme family,PDE10A is in charge of the degradation of cyclic adenosine(cAMP)and guanosine monophosphates(cGMP).While PDE10A is primarily expressed in the medium spiny neurons of the striatum,it has been implicated in a variety of neurological disorders.Indeed,inhibition of PDE10A has proven to be of potential use for the treatment of central nervous system(CNS)pathologies caused by dysfunction of the basal ganglia–of which the striatum constitutes the largest component.A PDE10A-targeted positron emission tomography(PET)radioligand would enable a better assessment of the pathophysiologic role of PDE10A,as well as confirm the relationship between target occupancy and administrated dose of a given drug candidate,thus accelerating the development of effective PDE10A inhibitors.In this study,we designed and synthesized a novel ^(18)F-aryl PDE10A PET radioligand,codenamed[^(18)F]P10A-1910([^(18)F]9),in high radiochemical yield and molar activity via spirocyclic iodonium ylide-mediated radiofluorination.[^(18)F]9 possessed good in vitro binding affinity(IC_(50)=2.1 nmol/L)and selectivity towards PDE10A.Further,[^(18)F]9 exhibited reasonable lipophilicity(logD=3.50)and brain permeability(P_(app)>10×10^(−6) cm/s in MDCK-MDR1 cells).PET imaging studies of[^(18)F]9 revealed high striatal uptake and excellent in vivo specificity with reversible tracer kinetics.Preclinical studies in rodents revealed an improved plasma and brain stability of[^(18)F]9 when compared to the current reference standard for PDE10A-targeted PET,[^(18)F]MNI659.Further,dose–response experiments with a series of escalating doses of PDE10A inhibitor 1 in rhesus monkey brains confirmed the utility of[^(18)F]9 for evaluating target occupancy in vivo in higher species.In conclusion,our results indicated that[^(18)F]9 is a promising PDE10A PET radioligand for clinical translation.

Supervised Deep Second-Order Covariance Hashing for Image Retrieval

Recently,deep hashing methods play a pivotal role in image retrieval tasks by combining advanced convolutional neural networks(CNNs)with efficient hashing.Meanwhile,second-order representations of deep convolutional activations have been established to effectively improve network performance in various computer vision applications.In this work,to obtain more compact hash codes,we propose a supervised deep second-order covariance hashing(SDSoCH)method by combining deep hashing with second-order statistic model.SDSoCH utilizes a powerful covariance pooling to model the second-order statistics of convolutional features,which is naturally integrated into the existing point-wise hashing network in an end-to-end manner.The embedded covariance pooling operation well captures the interaction of convolutional features and produces global feature representations with more discriminant capability,leading to the more informative hash codes.Extensive experiments conducted on two benchmarks demonstrate that the proposed SDSoCH outperforms its first-order counterparts and achieves superior retrieval performance.