Large-Scale Structural Characterization of Drug and Drug-Like Compounds by High-Throughput Ion Mobility-Mass Spectrometry.
Anal Chem
; 89(17): 9023-9030, 2017 09 05.
Article
em En
| MEDLINE
| ID: mdl-28764324
ABSTRACT
Ion mobility-mass spectrometry (IM-MS) can provide orthogonal information, i.e., m/z and collision cross section (CCS), for the identification of drugs and drug metabolites. However, only a small number of CCS values are available for drugs, which limits the use of CCS as an identification parameter and the assessment of structure-function relationships of drugs using IM-MS. Here, we report the development of a rapid workflow for the measurement of CCS values of a large number of drug or drug-like molecules in nitrogen on the widely available traveling wave IM-MS (TWIM-MS) platform. Using a combination of small molecule and polypeptide CCS calibrants, we successfully determined the nitrogen CCS values of 1425 drug or drug-like molecules in the MicroSource Discovery Systems' Spectrum Collection using flow injection analysis of 384-well plates. Software was developed to streamline data extraction, processing, and calibration. We found that the overall drug collection covers a wide CCS range for the same mass, suggesting a large structural diversity of these drugs. However, individual drug classes appear to occupy a narrow and unique space in the CCS-mass 2D spectrum, suggesting a tight structure-function relationship for each class of drugs with a specific target. We observed bimodal distributions for several antibiotic species due to multiple protomers, including the known fluoroquinolone protomers and the new finding of cephalosporin protomers. Lastly, we demonstrated the utility of the high-throughput method and drug CCS database by quickly and confidently confirming the active component in a pharmaceutical product.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Espectrometria de Massas
/
Avaliação Pré-Clínica de Medicamentos
/
Espectrometria de Mobilidade Iônica
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article