Can Big Data Analyses Help Speed Up the Clinical Development of Mucoactive Drugs for Symptomatic RTIs?

Abstract

This paper highlights the need for validated models to demonstrate mucoactive drug efficacy in relieving respiratory tract infection (RTI) symptoms and suggests new concepts to further ongoing research. The review is based on the analyses of studies published on mucoactive drug in respiratory diseases, data supporting FDA's expectorant monograph, and related US consumer use and attitude surveys. The changes in the volume and consistency of respiratory mucus during RTIs may result in ciliary dysfunction, mucus accumulation, and symptoms like cough and chest congestion. Mucoactive drugs can provide relief, but limited choices exist in the US, due to the unavailability of validated clinical models and unequivocal efficacy results. Ongoing developments have not provided definitive solutions, and Big Data analysis techniques may help overcome current clinical research limitations by identifying differentiating disease and patient factors to speed up the development process to substantiate the effectiveness of expectorant/mucoactive drugs in relieving RTI symptoms.