Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, logP, and retention factor ratio to identify optimal analyte training

- Chromatographic similarity-based localised QSRR modelling in ion chromatography is investigated.
- Retention times of unknown cations of pharmaceutical interest are predicted using QSRR models.
- Several filtering approaches to cluster only the ions similar to each target ion into the training set were compared.
- LogP-Dual filter-based clustering gave best results, producing accurate QSRR models for 50 eligible ions.

Retention prediction for unknown compounds based on Quantitative Structure-Retention Relationships (QSRR) can lead to rapid “scoping” method development in chromatography by simplifying the selection of chromatographic parameters. The use of retention factor ratio (or k-ratio) as a chromatographic similarity index can be a potent method to cluster similar compounds into a training set to generate an accurate predictive QSRR model provided that its limitation - that the method is impractical for retention prediction for unknown compounds - is successfully addressed. In this work, we propose a localised QSRR modelling approach with the aim of compensating the critical limitation in the otherwise successful k-ratio filter-based QSRR modelling. The approach is to combine a k-ratio filter with both Tanimoto similarity (TS) and a ΔlogP index (i.e., logP-Dual filter). QSRR models for two retention parameters (a and b) in the linear solvent strength (LSS) model in ion chromatography (IC), logk = a − blog[eluent], were generated for larger organic cations (molecular mass up to 506) on a Thermo Fisher Scientific CS17 column. The application of the developed logP-Dual filter resulted in the production of successful QSRR models for 50 organic cations out of 87 in the dataset. The predicted a- and b-values of the models were then applied to the LSS model to predict the corresponding retention times. External validation showed that QSRR models for a-, b- and tR- values with excellent accuracy and predictability (Qext(F2)2 of 0.96, 0.95, and 0.96, RMSEP of 0.06, 0.02, and 0.38 min) were created successfully, and these models can be employed to speed up the “scoping” phase of method development in IC.