OBJECTIVE：To optimize the synthesis process of dapoxetine hydrochloride. METHODS ：By chiral synthesis ， asymmetric reduction was carried out by using 3-chlorophenylacetone as raw material ，（1S，2R）-（-）-1-amino-2-indanol as catalyst，and borane- N，N-diethylaniline （DEANB） as reducing agent. Then ，it was reacted with α-naphthol etherification， sulfonation，dimethylamine substitution ，and HCl salt formation reaction to obtain the final products. The products were characterized by NMR and MS. The synthesis reaction of intermediate Ⅰ，intermediate Ⅱ，intermediate Ⅲ and the final product were optimized. RESULTS ：The final product was dapoxetine hydrochloride with purity of 99.8％ and yield of 58.9％. Compared with traditional splitting technology ，the chiral synthesis technology of this study did not need splitting ，and the yield of the technology was significantly higher than that of splitting technology reported in literature （31.9％）. The optimized technology reduced the generation of impurities and improved the product quality. CONCLUSIONS ：The improved technology has milder reaction conditions ，shorter synthesis route and higher yield.

Recent trends suggest that Chinese herbal medicine formulas(CHM formulas)are promising treatments for complex diseases.To characterize the precise syndromes,precise diseases and precise targets of the precise targets between complex diseases and CHM formulas,we developed an artificial intelligence-based quantitative predictive algorithm(DeepTCM).DeepTCM has gone through multilevel model cali-bration and validation against a comprehensive set of herb and disease data so that it accurately captures the complex cellular signaling,molecular and theoretical levels of traditional Chinese medicine(TCM).As an example,our model simulated the optimal CHM formulas for the treatment of coronary heart disease(CHD)with depression,and through model sensitivity analysis,we calculated the balanced scoring of the formulas.Furthermore,we constructed a biological knowledge graph representing interactions by associating herb-target and gene-disease interactions.Finally,we experimentally confirmed the thera-peutic effect and pharmacological mechanism of a novel model-predicted intervention in humans and mice.This novel multiscale model opened up a new avenue to combine"disease syndrome"and"macro micro"system modeling to facilitate translational research in CHM formulas.

Objective To explore the effective constituents from Sonchus arvensis L. and the potential mechanism in treating sepsis by network pharmacology. Methods The chemical ingredients reported in the literature were taken as research objects and Swiss Target Prediction database was used to collect the identify the potential targets of those ingredients. The GeneCards, OMIM and TTD databases were applied to screen the sepsis related molecular targets. The cross targets were obtained and used to construct the active ingredient-disease target network. In addition, the targets were also imported into STRING database to construct a PPI network. Finally, GO and KEGG enrichment analysis were performed on the target genes to predict the mechanism via DAVID database. Results 71 components from S. arvensis L. were obtained, which corresponded to 579 potential drug targets. There were 3437 related targets of sepsis. S. arvensis L. and sepsis shared 272 common targets. The results showed that 1366 terms were found by GO function enrichment, including 245 molecular functions (MF), 1002 biological processes (BP), and 119 cell composition (CC), The KEGG enrichment analysis suggested that 166 signaling pathways were involved. Conclusion The study revealed that TNF, AKT1, IL-6, IL-1β, TP53 and some other targets might be affected by potentially active ingredients of S arvensis L. such as linoleic acid, linolenic acid and oleic acid to regulate the expression of steroids, sphingolipids hormones as well as epidermal factors and chemokines in treating sepsis.