Simultaneous Quantitative and Qualitative Measurements in a Single Workflow to Increase Productivity in Primary Drug Metabolism Investigations

The Application Notebook

The Application Notebook, The Application Notebook-09-01-2013, Volume 31, Issue 9

Bruker Daltonics

The ability to simultaneously collect quantitative and qualitative information from a DMPK analysis has the potential to significantly increase productivity in pharmaceutical drug discovery and development. We present a single workflow allowing P450 drug clearance values to be determined as well as metabolites identified, profiled, and their structures elucidated. To be able to do all of this on a high throughput UHPLC chromatographic timescale is essential for the high levels of productivity required for today's DMPK screening laboratories. Haloperidol provides a good example of what can be achieved.

Haloperidol

C21H23NO2FCl M+H+ = 376.1474

Workflow and Protocol

Microsomal incubations were carried out by Unilabs Bioanalytical Solutions at 1 μM drug concentration and a protein concentration of 0.5 mg/mL. Aliquots were taken and quenched with acetonitrile containing propranolol as an internal standard at eight time points over a period of 60 min.

Figure 1: In a single workflow, data dependent MS-MS spectra identify and elucidate metabolite structures and drug clearance is measured.

Chromatography

Column: Fortis, 1.7 μm, H2O, 2.10 mm × 30 mm

Column temperature: 30 °C

MPA: 0.1% formic acid in 95% H2O/CH3CN

MPB: 100% CH3CN

Gradient: 0.0 0.3 2.0 2.5 2.6 3.0 min

MP %: 95 95 5 5 95 95 %

Flow rate: 300 μL/min

Injection volume: 5 μL

The high surface area and lipophilic ligand combined with a hydrophilic end cap give this stationary phase a broad selectivity and resolving power for the target drug and the metabolites. The use of small particles allows UHPLC to compress the peak into a tighter and taller peak, therefore enhancing detection of very low level analytes.

Figure 2: Metabolite detection software compares the data file for the drug (in this case t60) with the corresponding control sample. A base peak chromatogram of the difference is created allowing the metabolites to be easily observed and their mass determined to 4 decimal places.

Metabolite Detection

Metabolite detect software compares the data file for the drug (in this case t60) with the corresponding control sample. A base peak chromatogram of the difference is created allowing metabolites m/z 354, 212, and even 392 to be easily observed.

Figure 3: Time profiles for the disappearance of haloperidol and the appearance of three metabolites.

Metabolite detection software is able to detect the m/z = 392 metabolite even though it co-elutes with the internal standard.

Figure 4: Linear calibration of 50 pg/mL to 50 ng/mL (3 decades) was achieved using the XIC for the measured m/z of each metabolite +/- 0.005 Da. R2 = 0.9974.

Drug and Metabolite Profiles

Integration is carried out on the XIC for the measured m/z of each metabolite +/– 0.005 Da. Plotting the ratio of metabolite to internal standard (M/IS) versus time produces the metabolite profiles. Half-life and clearance values are determined from the natural log (ln) of the drug profile versus time plot.

Figure 5: The structure of metabolite m/z = 354 is easily identified using Smartformula3D to understand the fragmentation pattern.

Linearity

MS–MS data was not available for m/z = 392 because of co-elution with the internal standard. The high quality data available, even for such a small peak, means SmartFormula is still able to predict the formula and deduce that it is a mono-oxidative metabolite.

m/z = 392.2422 Δm = 0.1 mDa (0.3 ppm)

C21H23NO3FCl Isotope fit = 23 ms

Comparison with 3Q

Both the AB Sciex API 5000 and Bruker impact QTOF yield equivalent results for the clearance values. This can be clearly seen by comparing the ln [Drug]/[IS] versus time plots.

Figure 6: The structure of metabolite m/z = 392 is easily identified using Smartformula3D to understand the fragmentation pattern.

The linearity and gradients of these plots are nearly identical and result in values for t1/2 of 45 and 47 min, respectively.

Figure 7: Clearance data from impact.

The difference in y intercept is a result of a difference in relative response of the internal standard and has no influence on the clearance results.

Figure 8: Clearance data from 3Q.

Conclusions

The quan–qual workflow is effective and robust using a rapid analytical method suitable for high throughput screening at 1 μM drug concentrations.

Metabolite detection software allows metabolites to be rapidly identified and profiled even when compounds co-elute.

Bruker Daltonics Inc.

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