A High Volume, High Throughput LC MS Therapeutic Drug Monitoring System

Liquid chromatography-mass spectrometry (LC–MS) has been an important analytical tool in support of drug discovery and drug development for some time. LC–MS provides a combination of detection selectivity and sensitivity, speed of analysis, and robust performance that is well suited to the rapid structural characterization of candidate therapeutic substances and high-throughput screening assessment of pharmacological activity (1-3). More recently, LC–MS has been used for patient monitoring in clinical drug trials (4-6) and for the detection of drugs of abuse (7). As knowledge has grown about the relationship between genetic and environmental factors and individual responses to drugs, clinicians have sought new ways to monitor drug responses accurately to assess and tailor drug therapies more effectively. With its advantageous performance characteristics, LC–MS has proven to be a useful tool for collecting data characterizing these relationships. This, in turn, is generating broader interest in the application of LC–MS for the therapeutic monitoring of drugs and their metabolic products (8). Traditionally, therapeutic drug monitoring has been performed by immunoassay and LC–UV. Immunoassay is simple, rapid, and relatively inexpensive to perform. However, compared with LC–MS, it is limited in detection specificity, sensitivity, and accuracy. Moreover, only a handful of new assays have been introduced in the past decade despite the fact that many times that number of new drugs have been launched during the same period. Given the advantages and growing penetration of LC–MS, it is an open question whether any new immunoassays will be developed for future therapeutic drug monitoring applications. LC–UV determinations lack sufficient selectivity and sensitivity, making detection and quantitation problematic for the newer, more potent low-dose pharmaceuticals. In addition, the relatively broad UV absorption bands make analytical separation challenging, requiring long runtimes that compromise analytical throughput.

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