A substructure-based screening approach to uncover N-nitrosamines in drug substances

Yu-Ting Kao ^a, Shu-Fen Wang ^b, Meng-Hsiu Wu ^b, Shwu-Huey Her ^b, Yi-Hsuan Yang ^b, Chung-Hsien Lee ^c, Hsiao-Feng Lee ^c, An-Rong Lee ^a,c, Li-Chien Chang ^a,c,**,1, Li-Heng Pao ^c,d,e,*,1

a School of Pharmacy, National Defense Medical Center, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City 11490, Taiwan,

Republic of China

b Pharmaceutical Plant of Controlled Drugs, Food and Drug Administration, No. 287, Datong Rd., Sanxia Dist., New Taipei City 23742,

Taiwan, Republic of China

c Taiwan Product Quality Research Institute, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City 11490, Taiwan, Republic of China

d Graduate Institute of Health Industry Technology, Research Center for Food and Cosmetic Safety, and Research Center for Chinese

Herbal Medicine, College of Human Ecology, Chang Gung University of Science and Technology, Taoyuan, Taiwan, Republic of China

e Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linko, Taoyuan, Taiwan, Republic of China

Drug substances are at risk of contamination with N-nitrosamines (NAs), well-known carcinogenic agents, during synthesis processes and/or long-term storage. Therefore, in this study, we developed an efficient data-based screening approach to systemically assess marketed products and investigated its scalability for benefiting both regulatory agencies and pharmaceutical industries. A substructure-based screening method employing DataWarrior, an open-source software, was established to evaluate the risks of NA impurities in drug substances. Eight NA substructures containing susceptible amino sources for N-nitrosation have been identified as screening targets: dimethylamine (DMA), diethylamine, isopropylethylamine, diisopropylamine, N-methyl-2-pyrrolidone, dibutylamine, methylphenylamine, and tetrazoles. Our method detected 192 drug substances with a theoretical possibility of NA impurity, 141 of which had not been reported previously. In addition, the DMA moiety was significantly dominant among the eight NA substructures. The results were validated using data from the literature, and a high detection sensitivity of 0.944 was demonstrated. Furthermore, our approach has the advantage of scalability, owing to which 31 additional drugs with suspected NA-contaminated substructures were identified using the substructures of 1-methyl-4-piperazine in rifampin and 1-cyclopentyl-4-piperazine in rifapentine. In conclusion, the reported substructure-based approach provides an effective and scalable method for the screening and investigation of NA impurities in various pharmaceuticals and might be used as an ancillary technique in the field of pharmaceutical quality control for risk assessments of potential NA impurities.

Keywords: Cheminformatics, Data mining, Nitrosamines, Risk assessment, Total quality management

https://doi.org/10.38212/2224-6614.3400