Development of a Reverse Phase HPLC Retention Index Model for Nontargeted Metabolomics Using Synthetic Compounds

The MolFind application has been
developed as a nontargeted metabolomics
chemometric tool to facilitate structure identification when HPLC
biofluids analysis reveals a feature of interest. Here synthetic compounds
are selected and measured to form the basis of a new, more accurate,
HPLC retention index model for use with MolFind. We show that relatively
inexpensive synthetic screening compounds with simple structures can
be used to develop an artificial neural network model that is successful
in making quality predictions for human metabolites. A total of 1955
compounds were obtained and measured for the model. A separate set
of 202 human metabolites was used for independent validation. The
new ANN model showed improved accuracy over previous models. The model,
based on relatively simple compounds, was able to make quality predictions
for complex compounds not similar to training data. Independent validation
metabolites with feature combinations found in three or more training
compounds were predicted with 97% sensitivity while metabolites with
feature combinations found in less than three training compounds were
predicted with >90% sensitivity. The study describes the method
used
to select synthetic compounds and new descriptors developed to encode
the relationship between lipophilic molecular subgraphs and HPLC retention.
Finally, we introduce the QRI (qualitative range of interest) modification
of neural network backpropagation learning to generate models simultaneously
based on quantitative and qualitative data.