Greenness Assessment of HPLC Analytical Methods with Common Detectors for Assay of Paracetamol and Related Materials in Drug Products and Biological Fluids

Abstract

Paracetamol is one of the most widely consumed analgesic and antipyretic medications worldwide. It is frequently analyzed in many quality control (QC) laboratories in pharmaceutical companies, either in raw materials or drug products. It was reported that paracetamol self-toxicity often occurs, leading to the frequent analysis of paracetamol in toxicological centers in biological fluids. Green analytical chemistry (GAC) is growing to be a global philosophy; therefore, the high frequency of paracetamol analysis poses potential concerns. Chromatographic analytical methods used for the daily analysis of paracetamol could be a potential risk to the environment or the health of the analysts if not thoroughly considered. The presented study aims to establish greenness assessments of nine HPLC methods used to assay paracetamol in raw materials and drug products and twenty-one HPLC methods. The reason for selecting HPLC methods of analysis to be the core of the study is the known reproducibility, reliability and availability in most QC laboratories. The most commonly used metric systems for greenness evaluation are the Analytical GREEnness (AGREE), the eco-scale assessment (ESA) and the national environmental methods index (NEMI) which have been used in this comparative study. The greenest chromatographic method for the analysis of paracetamol in raw materials and drug products was introduced by Rao et al. (the obtained scores were ESA = 76 and AGREE = 0.62, while the greenest chromatographic method for the analysis of paracetamol in biological fluids was proposed by Modick et al.). The obtained scores were ESA = 85 and AGREE = 0.7. The NEMI tool proved to have limited performance compared to other metric systems, hence it could not be used alone. Accordingly, the collaboration of NEMI results with ESA and AGREE for greenness assessment is highly recommended to reach appropriate conclusions.