Liquid chromatography (LC) based techniques in combination with mass spectrometry (MS) detection have had a large impact on the development of new pharmaceuticals in the past decades. Continuous improvements in mass spectrometry and interface technologies, combined with advanced liquid chromatographic techniques for high-throughput qualitative and quantitative analysis, have resulted in a wider scope of applications in the pharmaceutical field. LC-MS tools are increasingly used to analyze pharmaceuticals across a variety of stages in their discovery and development. These stages include drug discovery, product characterization, metabolism studies (in vitro and in vivo) and the identification of impurities and degradation products. The increase in LC-MS applications has been enormous, with retention times and molecular weights (and related fragmentation patterns) emerging as crucial analytical features in the drug development process. The goal of this review is to give an overview of the main developments in LC-MS based techniques for the analysis of small pharmaceutical molecules in the last decade and give a perspective on future trends in LC-MS in the pharmaceutical field.

Tuberculosis (TB) is the deadliest infectious disease, and yet accurate diagnostics for the disease are unavailable for many subpopulations. In this study, we investigate the possibility of using human breath for the diagnosis of active TB among TB suspect patients, considering also several risk factors for TB for smokers and those with human immunodeficiency virus (HIV). The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, non-invasive, and readily available diagnostic service that could positively change TB detection. A total of 50 individuals from a clinic in South Africa were included in this pilot study. Human breath has been investigated in the setting of active TB using the thermal desorption-comprehensive two-dimensional gas chromatography–time of flight mass spectrometry methodology and chemometric techniques. From the entire spectrum of volatile metabolites in breath, three machine learning algorithms (support vector machines, partial least squares discriminant analysis, and random forest) to select discriminatory volatile molecules that could potentially be useful for active TB diagnosis were employed. Random forest showed the best overall performance, with sensitivities of 0.82 and 1.00 and specificities of 0.92 and 0.60 in the training and test data respectively. Unsupervised analysis of the compounds implicated by these algorithms suggests that they provide important information to cluster active TB from other patients. These results suggest that developing a non-invasive diagnostic for active TB using patient breath is a potentially rich avenue of research, including among patients with HIV comorbidities.