Application of text-mining techniques for extraction and analysis of paracetamol and ibuprofen marketed products' qualitative composition

Abstract

Text mining (TM) applications in the field of biomedicine are gaining great interest. TM tools can facilitate formulation development by analyzing textual information from patent databases, scientific articles, summary of products characteristics, etc. The aim of this study was to utilize TM tools to perform qualitative analysis of paracetamol (PAR) and ibuprofen (IBU) formulations, in terms of identifying and evaluating the presence of excipients specific to the active pharmaceutical ingredient (API) and/or dosage form. A total of 152 products were analyzed. Web-scraping was used to retrieve the data, and Python-based open-source software Orange 3.31.1 was used for TM and statistical analysis (ANOVA) of the obtained results. The majority of marketed products for both APIs were tablets. The predominant excipients in all tablet formulations were povidone, starch, microcrystalline cellulose and hypromellose. Povidone, stearic acid, potassium sorbate, maize starch and pregelatinized starch occurred more frequently in PAR tablets. On the other hand, titanium dioxide, lactose, shellac, sucrose and ammonium hydroxide were specific to IBU tablets. PAR oral suspensions more frequently contained dispersible cellulose; liquid sorbitol; methyl and propyl parahydroxybenzoate, glycerol and acesulfame potassium. Specific excipients in other PAR dosage forms, such as effervescent tablets, hard capsules, oral powders, solutions and suspensions, as well as IBU gels and soft capsules, were also evaluated.