摘要
N-苄基-对甲苯磺酰胺(1)是骨骼肌肌球蛋白Ⅱ的抑制剂,可特异性抑制骨骼肌快速纤维的收缩。为开发1的连续流合成系统,并运用贝叶斯优化算法对主要的反应参数进行优化。经过10轮拉丁超立方采样和10轮贝叶斯优化迭代后,得到2组最优反应条件。①反应温度为3.8 ℃,停留时间为14.6 min,缚酸剂(N,N-二异丙基乙胺)用量为1.5当量,对甲苯磺酰氯(2)浓度为0.26 mol/L时,收率91.4%(以2计),纯度99.5%;②反应温度为7.5 ℃,停留时间为18.5 min,缚酸剂用量为1.6当量,2浓度为0.37 mol/L时,收率92.3%(以2计),纯度99.7%。贝叶斯优化能大幅提升搜寻最优条件的效率。优化后的工艺反应时间大幅降低,溶剂单一且经济易得,具有工业化应用价值。
N-Benzyl-p-toluenesulfonamide(1)is an inhibitor of skeletal muscle myosinⅡthat specifically inhibits contraction of skeletal muscle fast fibers.A continuous flow synthesis system was developed for 1 and the main reaction parameters were optimized with a Bayesian optimization algorithm.After 10 rounds of Latin hypercube sampling and 10 rounds of Bayesian optimization iterations,two sets of optimal reaction conditions were obtained.①The reaction temperature was 3.8℃,the residence time was 14.6 min,the dosage of acid binding agent(N,N-diisopropylethylamine)was 1.5 equiv.and the concentration of p-toluenesulfonyl chloride(2)was 0.26 mol/L,which corresponded to a product yield of 91.4%(based on 2)with a purity of 99.5%.②The reaction temperature was 7.5℃,the residence time was 18.5 min,the dosage of DIEA was 1.6 equiv.and the concentration of 2 was 0.37 mol/L,which corresponded to a product yield of 92.3%with a purity of 99.7%.The Bayesian optimization can greatly improve the efficiency of searching optimal conditions.The reaction time of the optimized process is greatly reduced,and the solvent is single and economical,which is valuable for industrial application.
作者
杨曦林
苏为科
罗贵华
苏安
YANG Xilin;SU Weike;LUO Guihua;SU An(Key Lab.of Pharmaceutical Engineering of Zhejiang Province,National Engineering Research Center for Process Development of Active Pharmaceutical Ingredients,Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals,Zhejiang University of Technology,Hangzhou 310014;Key Lab.of Green Chemistry-Synthesis Technology of Zhejiang Province,State Key Lab.Breeding Base of Green Chemistry-Synthesis Technology,College of Chemical Engineering,Zhejiang University of Technology,Hangzhou 310014)
出处
《中国医药工业杂志》
EI
CAS
CSCD
2024年第5期652-657,共6页
Chinese Journal of Pharmaceuticals
基金
浙江省自然科学基金华东医药企业创新发展联合基金资助项目(LHDMZ23B060001)
浙江省科技计划项目(2022C01179)。