1. Global Discovery Chemistry, 11 August 2017
Development of a Microscale Quantification Model Using Charged Aerosol Detection
D. Spencer, D. Dunstan, C. Springer
Global Discovery Chemistry, 11 August 2017
Context
Log-Log Plots of Area vs. Conc. For CAD and ELSD
There is a demand for a universal detection method in analytical chemistry to quantify small quantities of compounds, especially in the pharmaceutical industry. Quantification of microscale quantities usually requires expensive and complex equipment that can inhibit the fast output that is required in modern-day pharmaceutical labs. Ultra-high performance liquid chromatography (UPLC) coupled to charged aerosol detection (CAD) has been utilized as a cheaper and easier alternative. Despite these benefits, CAD is not a universal technique that applies to all compounds and is affected by acetonitrile composition of the inlet flow, chemical structure, and modifier. A computer model has been developed to work with UPLC-CAD results to allow for the rapid and high throughput and quantification of sub-milligram quantities, greatly improving the efficiency and turnover of drug discovery programs.
Validation of Computer Model
Figure 1: When plotted on a log-log scale, the signal peak area shows a linear dependence on the concentration of the analyte.[1] CAD proved more accurate and consistent in slope than ELSD. Calibration curves were generated for 30 small-molecule drugs over a range of MW, structures, and volatilities. Top: CAD, Bottom: ELSD
Effect of % ACN on CAD Area
Effect of Mobile Phase Acidic Modifier on CAD Signal
Figure 2: Test of small-molecule drugs of concentrations determined by NMR using a computer model developed with approximately 30 compounds to create a universal calibration curve. Numerical results appear in Table 1
Log([Analyte]) = 1.08*Log(CAD Area) + B
The computer model takes into account factors such as chemical structure, the number of heavy atoms, and the amount of ACN present in the mobile phase at the time of elution. The model calculates B for each compound but the slope was consistent across compounds at 1.08.
Figure 4: Effect of acidic modifier in the mobile phase on the CAD signal peak area was tested using two different acidic modifiers: formic acid (pKa = 3.77) and TFA (pKa = 0.23). The TFA yielded a sharper peak shape than formic acid giving an increased signal for amitriptyline and trimipramine, both with strong basic centers. Percent differences between peak areas using the two modifiers were found for the 16 small-molecule drugs and calculated by [(AFormic – ATFA) / ATFA] * 100%
Figure 3: The percentage of the mobile phase that is composed of organic solvent affects the CAD signal peak area exponentially for semi-volatile, lower MW compounds and according to the above trend for non-volatile compounds. Three different injection volumes were used to demonstrate linearity and expand dynamic range of detector
Conclusions
Compound
CAD Peak Area
NMR Conc. (mM)
Intercept from Model
Model Conc. (mM)
Error (%)
Sulfamethizole
8.29
13.38
-0.12
7.28
45.58
Sulfamethoxazole
10.04
12.99
-0.23
7.15
44.97
Quinizarin
12.44
9.35
-0.09
12.09
29.31
Flavone
7.52
11.67
-0.07
35.58
Sulfamethazine
6.83
9.34
-0.14
5.72
38.81
Ibuprofen*
0.72
8.06
0.53
93.46
Fenofibrate**
22.88
8.34
-0.41
11.35
36.09
Oxprenolol HCl
9.884
6.77
-0.17
8.02
18.40
Naproxen
12.14
10.17
10.67
4.90
Determined that CAD is more consistent and universal than ELSD or UV detection
Investigated effect of composition of mobile phase and chemical structure on CAD signal
Quantification model will be used in automated microscale purification workflow to enable rapid synthesis to DMSO stock of novel compounds
Future directions: more testing and validation of model to improve accuracy by retraining, optimizing conditions and accounting for effects of modifier, ACN content, volatility[1] etc.
Potential applications: rapid high-throughput screening of compounds, quantifying concentrations of metabolites in vivo, universal detection
Where do YOU see an application for this?
(1) J.P. Hutchinson, J. Li, W. Farrell, E. Groeber, R. Szucs, G. Dicinoski, P.R Haddad, Journal of Chromatography A. 2010, 1217, 7424.
Table 1: Summary of validation data for computer model using a variety of small molecule compounds of varying volatility and MW. Concentration determined by model compared against that determined by NMR. Average error = 31.71%. Model uses equation: 1.08*Log(CAD Area) + B, where B is the intercept determined by the model
*Low model concentration due to compound volatility
** Model corrects for high CAD peak area