Modeling and prediction of polar organic chemical integrative
sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial
neural networks (ANNs) is presented for the first time. Two models were
constructed: the first was developed ab initio using a genetic algorithm
(GSD-model) to shortlist 24 descriptors covering constitutional, topological,
geometrical and physicochemical properties and the second model was
adapted for Rs prediction from a previous chromatographic retention model
(RTD-model). Mechanistic evaluation of descriptors showed that models did
not require comprehensive a priori information to predict Rs. Average
predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d−1
(RTD-model) and 0.03 ± 0.03 L d−1 (GSD-model) relative to
experimentally determined Rs. Prediction variability in replicated models
was the same or less than for measured Rs. Networks were externally
validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model
for these compounds (average absolute errors of 0.0145 ± 0.008 L d−1 and 0.0437 ± 0.02 L d−1, respectively). Improvements to
generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in
silico tools for Rs determination represents a more economical approach than laboratory calibrations.