Study of Chromatographic Retention of Natural Terpenoids by Chemoinformatic Tools

The
study of chromatographic retention of natural products can
be used to increase their identification speed in complex biological
matrices. In this work, six variables were used to study the retention
behavior in reversed phase liquid chromatography of 39 sesquiterpene
lactones (SL) from an in-house database using chemoinformatics tools.
To evaluate the retention of the SL, retention parameters on an ODS
C-18 column in two different solvent systems were experimentally
obtained, namely, MeOH–H2O 55:45 and MeCN–H2O 35:75. The chemoinformatics approach involved three descriptor
type sets (one 2D and two 3D) comprising three groups of each (four,
five, and six descriptors), two different training and test sets,
four algorithms for variable selection (best first, linear forward,
greedy stepwise, and genetic algorithm), and two modeling methods
(partial least-squares regression and back-propagation artificial
neural network). The influence of the six variables used in this study
was assessed in a holistic context, and influences on the best model
for each solvent system were analyzed. The best set for MeOH–H2O showed acceptable correlation statistics with training R2 = 0.91, cross-validation Q2 = 0.88, and external validation P2 = 0.80, and the best MeCN–H2O model showed
much higher correlation statistics with training R2 = 0.96, cross-validation Q2 = 0.92, and external validation P2 =
0.91. Consensus models were built for each chromatographic system,
and although all of them showed an improved statistical performance,
only one for the MeCN–H2O system was able to separate
isomers as well as to improve the performance. The approach described
herein can therefore be used to generate reproducible and robust models
for QSRR studies of natural products as well as an aid for dereplication
of complex biological matrices using plant metabolomics-based techniques.