A novel high-throughput automated chip-based nanoelectrospray tandem mass spectrometric method for PAMPA sample analysis

Parallel artificial membrane permeability assay (PAMPA) has recently gained popularity as a novel, high-throughput assay capable of rapidly screening compounds for their permeability characteristics in early drug discovery. The analytical techniques typically used for PAMPA sample analysis are HPLC-UV, LC/MS or more recently UV-plate reader. The LC techniques, though sturdy and accurate, are often labor and time intensive and are not ideal for high-throughput. On the other hand, UV-plate reader technique is amenable to high-throughput but is not sensitive enough to detect the lower concentrations that are often encountered in early drug discovery work. This article investigates a novel analytical method, a chip-based automated nanoelectrospray mass spectrometric method for its ability to rapidly analyze PAMPA permeability samples. The utility and advantages of this novel analytical method is demonstrated by comparing PAMPA permeability values obtained from nanoelectrospray to those from conventional analytical methods. Ten marketed drugs having a broad range of structural space, physico-chemical properties and extent of intestinal absorption were selected as test compounds for this investigation. PAMPA permeability and recovery experiments were conducted with model compounds followed by analysis by UV-plate reader, UV-HPLC as well as the automated nanoelectrospray technique (nanoESI-MS/MS). There was a very good correlation (r2 > 0.9) between the results obtained using nanoelectrospray and the other analytical techniques tested. Moreover, the nanoelectrospray approach presented several advantages over the standard techniques such as higher sensitivity and ability to detect individual compounds in cassette studies, making it an attractive high-throughput analytical technique. Thus, it has been demonstrated that nanoelectrospray analysis provides a highly efficient and accurate analytical methodology to analyze PAMPA samples generated in early drug discovery.