Determination of dioxopiperazine metabolites of quinapril in biological fluids by gas chromatography-mass spectrometry

RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules

Background

Experimental approaches for determining the metabolic properties of the drug candidates are usually expensive, time-consuming and labor intensive. There is a great deal of interest in developing computational methods to accurately and efficiently predict the metabolic decomposition of drug-like molecules, which can provide decisive support and guidance for experimentalists.

Results

Here, we developed an integrated, low false positive and reaction types extensive metabolism prediction approach called RD-Metabolizer (Reaction Database-based Metabolizer). RD-Metabolizer firstly employed the detailed reaction SMARTS patterns to encode different metabolism reaction types with the aim of covering larger chemical reaction space. 2D fingerprint similarity calculation model was built to calculate the metabolic probability of each site in a molecule. RDKit was utilized to act on pre-written reaction SMARTS patterns to correct the metabolic ranking of each site in a molecule generated by the 2D fingerprint similarity calculation model as well as generate corresponding structures of metabolites, thus helping to reduce the false positive metabolites. Two test sets were adopted to evaluate the performance of RD-Metabolizer in predicting SOMs and structures of metabolites. The results indicated that RD-Metabolizer was better than or at least as good as several widely used SOMs prediction methods. Besides, the number of false positive metabolites was obviously reduced compared with MetaPrint2D-React.

Conclusions

The accuracy and efficiency of RD-Metabolizer was further illustrated by a metabolism prediction case of AZD9291, which is a mutant-selective EGFR inhibitor. RD-Metabolizer will serve as a useful toolkit for the early metabolic properties assessment of drug-like molecules at the preclinical stage of drug discovery.Graphical abstract

An overview of chromatographic methods coupled with mass spectrometric detection for determination of angiotensin-converting enzyme inhibitors in biological material

Gas and liquid chromatography-mass spectrometry (GC-MS, LC-MS) methods for the determination of angiotensin-converting enzyme inhibitors (ACEIs) and their metabolites in biological material have been reviewed. Since 1980s those hyphenated techniques have been applied to quantitate ACE inhibitors and the dynamic increase in the number of relevant publications can be observed in recent years. Although most of the methods available in the literature were analyses of plasma or serum, assays of blood and urine were also included. Additionally, sample pretreatment methods, separation conditions and ionization modes were overviewed. Some information on chemical structures, cis-trans izomerization and stability of compounds in question was also included. Most of the reported methods were successfully applied to the pharmacokinetic studies in humans.

Screening for the detection of angiotensin-converting enzyme inhibitors, their metabolites, and AT II receptor antagonists

A gas chromatography-mass spectrometry (GC-MS) screening procedure was developed for the detection of angiotensin-converting enzyme (ACE) inhibitors, their metabolites, and angiotensin (AT) II receptor antagonists in urine as part of a systematic toxicologic analysis procedure for acidic drugs and poisons after extractive methylation. The part of the phase-transfer catalyst remaining in the organic phase was removed by solid phase extraction on a diol phase. The compounds were separated by capillary GC and identified by computerized MS in the full scan mode. Using mass chromatography with the ions m/z 157, 160, 172, 192, 204, 220, 234, 248, 249, and 262, the possible presence of ACE inhibitors, their metabolites, and AT II antagonists could be indicated. The identity of positive signals in such mass chromatograms was confirmed by comparison of the peaks underlying full mass spectra with the reference spectra recorded during this study. This method allowed detection of therapeutic concentrations of ACE inhibitors (benazepril, enalapril, perindopril, quinapril, ramipril, trandolapril, their metabolites, or both) and therapeutic concentrations of the AT II antagonist, valsartan, in human urine samples. Human urine samples were not available for testing cilazapril, moexipril, and losartan; they were detected only in rat urine. The overall recoveries of ACE inhibitors ranged between 80% and 88%, with a coefficient of variation (CV) of less than 10% and the limit of detection of at least 10 ng/ml (signal to noise ratio 3) in the full-scan mode. The overall recovery of the valsartan was 68%, with a CV of less than 10%; the limit of detection was at least 10 ng/ml (S/N 3) in the full scan mode.