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Updated: Jun 30, 2026

A New Straightforward Method for Lipophilicity (logP) Measurement using 19F NMR Spectroscopy
Published on: January 30, 2019
Development of liposome/water partition coefficients predictive models for neutral and ionogenic organic chemicals
Shiyu Lin1, Xianhai Yang1, Huihui Liu1
1Key Laboratory of New Membrane Materials, Ministry of Industry and Information Technology, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
Abstract:
Membrane/water partition coefficient (Km/w) is a vital parameter used to characterize the membrane permeability of compounds. Considering the Km/w value is difficult to observe experimentally for real biological membranes, liposome/water partition coefficient (Klip/w) is employed to approximate Km/w. Here, quantitative structure property relationship (QSPR) models for logKlip/w of the neutral organic chemicals and the neutral form of ionogenic organic chemicals (IOCs) (logKlip/w-neutral), ionic form of IOCs (logKlip/w-ionic), the speciation-corrected liposome-water distribution ratios at a pH = 7.40 (logDlip/w-(pH=7.40)) were developed. In the modeling, two modeling methods (multiple linear regressions (MLR) and k-nearest neighbor (kNN)) were used. The predictive variables employed here could be calculated from the molecular structure directly. For logKlip/w-neutral and logDlip/w-(pH=7.40), the logKOW and logDOW-based, non-logKOW and non-logDOW-based kNN-QSPR and MLR-QSPR models were developed, respectively. The evaluation results implied that the predictive performance of kNN-QSPR models is better than that of MLR-QSPR models. For logKlip/w-ionic, only one acceptable MLR-QSPR model was developed for cation and anion, respectively. The model quality of the derived models was evaluated following the OECD QSPR models validation guideline. The determination coefficient (R2), leave-one-out cross validation Q2 (Q2LOO) and bootstrapping coefficient (Q2BOOT), the external validation coefficient (Q2EXT) of all the models met the acceptable criteria (Q2 > 0.600, R2 > 0.700); while the root-mean-square error (RMSE) range from 0.351 to 0.857. All the results implied that the models had good goodness-of-fit, robustness and predictive ability. Therefore, the developed models could be used to fill the data gap for substances within the applicability domain on their missing logKlip/w-neutral, logKlip/w-ionic, logDlip/w-(pH=7.40) values.
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