Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH-, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines

Abstract

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R² = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCS_H model (R² = 0.966) was ±4.05% with 95% confidence intervals. The CCS_H model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCS_Na, R² = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

Figure 1

Top: Comparison of experimental and predicted RT data using the RT model (A), CCS for protonated molecules using CCS_H model (B), CCS for deprotonated molecules using the CCS_H model (C), and CCS for sodium adducts using the CCS_Na model (D). (Red line indicates region where Experimental CCS = Predicted CCS) Bottom: Histogram distribution of deviations between experimental and predicted data for RT data using the RT model (A), CCS for protonated molecules using the CCS_H model (B), CCS for deprotonated molecules using the CCS_H model (C), and CCS for sodium adducts using the CCS_Na model (D). (Red vertical lines indicate 0% deviation).

Figure 2

95% prediction intervals (blue area) for the univariate MARS analysis on the square root of RT. Blue lines are placed at the predicted values ±2 min. Approximately, only 8% of observed retention times were more than 2 min away from their predicted value.

Figure 3

95% prediction intervals (blue area) for the univariate MARS analysis on (a) CCS_H and (b) CCS_Na models. blue lines are placed at 2% error bands and the purple at 5%. It is clear that the model still predicts well at higher values where there are less data but the prediction intervals are much larger to accommodate the uncertainty due to lack of data.

Figure 4

Online platform for the prediction of RT and CCS data using univariate models. (A) Selection of response to predict, that is, RT, CCS for (de)protonated molecules or CCS for sodium adducts; (B) introduction of molecular descriptors for the molecule of interest; (C) output of the predictor model together with the prediction intervals. Example illustrated by omeprazole.