High-throughput LC-MS/MS method with 96-well plate precipitation for the determination of arotinolol and amlodipine in a small volume of rat plasma: Application to a pharmacokinetic interaction study.
Zheyuan QianJian LeXiujuan ChenShengni LiHongjie SongZhanying HongPublished in: Journal of separation science (2017)
A rapid, sensitive, and selective liquid chromatography with tandem mass spectrometry method was developed and fully validated for the simultaneous quantification of arotinolol and amlodipine in rat plasma. Two internal standards were introduced with metoprolol as the internal standard of arotinolol and (S)-amlodipine-d4 as the internal standard of amlodipine. The analytes were isolated from 50.0 μL plasma samples by a simple protein precipitation using acetonitrile. The chromatographic separation was achieved in 5 min on a C18 column. The mobile phase consisted of phase A 5% methanol and phase B 95% methanol (both containing 0.5% formic acid and 5 mM ammonium acetate) and was delivered in gradient elution at 0.300 mL/min. Quantification was performed in multiple reaction monitoring mode with the transition m/z 372.1 → 316.1 for arotinolol, m/z 268.2 → 116.2 for metoprolol, m/z 409.1 → 238.1 for amlodipine and m/z 413.1 → 238.1 for (S)-amlodipine-d4. Linearity was obtained over the range of 0.200-40.0 ng/mL for arotinolol (r2 = 0.9988) and 0.500-100 ng/mL for amlodipine (r2 = 0.9985) in rat plasma. The validated data have met the acceptance criteria in FDA guideline. This method was successfully applied to a pharmacokinetic interaction study in rats, and the results indicated that there was no significant drug-drug interaction between arotinolol and amlodipine.
Keyphrases
- hypertensive patients
- liquid chromatography
- tandem mass spectrometry
- blood pressure
- mass spectrometry
- solid phase extraction
- ultra high performance liquid chromatography
- simultaneous determination
- high throughput
- high resolution mass spectrometry
- high performance liquid chromatography
- gas chromatography
- small molecule
- drug induced
- ionic liquid
- tyrosine kinase
- deep learning
- data analysis