Neurofibrillary tangles (NFTs) made up of aggregated tau protein have been identified as the pathologic hallmark of several neurodegenerative diseases including Alzheimer's disease. In vivo detection of NFTs using PET imaging represents a unique opportunity to develop a pharmacodynamic tool to accelerate the discovery of new disease modifying therapeutics targeting tau pathology. Herein, we present the discovery of 6-(fluoro-(18)F)-3-(1H-pyrrolo[2,3-c]pyridin-1-yl)isoquinolin-5-amine, 6 ([(18)F]-MK-6240), as a novel PET tracer for detecting NFTs. 6 exhibits high specificity and selectivity for binding to NFTs, with suitable physicochemical properties and in vivo pharmacokinetics.

Inhibition of O-GlcNAcase (OGA) has emerged as a promising therapeutic approach to treat tau pathology in neurodegenerative diseases such as Alzheimer’s disease and progressive supranuclear palsy. Beginning with carbohydrate-based lead molecules, we pursued an optimization strategy of reducing polar surface area to align the desired drug-like properties of potency, selectivity, high central nervous system (CNS) exposure, metabolic stability, favorable pharmacokinetics, and robust in vivo pharmacodynamic response. Herein, we describe the medicinal chemistry and pharmacological studies that led to the identification of (3aR,5S,6S,7R,7aR)-5-(difluoromethyl)-2-(ethylamino)-3a,6,7,7a-tetrahydro-5H-pyrano­[3,2-d]­thiazole-6,7-diol 42 (MK-8719), a highly potent and selective OGA inhibitor with excellent CNS penetration that has been advanced to first-in-human phase I clinical trials.

Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space–time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.