Issue 16, 2016

Predicting skin permeation rate from nuclear magnetic resonance spectra

Abstract

In order to systematically approach the design of chemicals with minimal toxicity we are in need of predictive tools that can be applied seamlessly during chemical synthesis and characterization. Our approach is to develop models that utilize spectroscopic data, called Quantitative Spectrometric Data-Activity Relationships, to predict bioavailability and toxicity. Such models do not require knowledge of chemical structure and can be applied to chemical mixtures. Here we report a predictive QSDAR for skin permeation rate (log Kp) of organic chemicals from NMR spectroscopic data and molecular weight. The model is trained on a large data set consisting of structurally diverse chemicals and has been thoroughly externally validated – once with a withheld subset of the original data set, and once with a distinct set of complex biologically active compounds curated by Klimisch scoring (r2 = 0.838, qext12 = 0.837, qext22 = 0.419). The model performs equally or better than prevailing structure-based methods, and offers a number of advantages for facilitating rational design of safer chemicals.

Graphical abstract: Predicting skin permeation rate from nuclear magnetic resonance spectra

Supplementary files

Article information

Article type
Paper
Submitted
05 Apr 2016
Accepted
15 Jul 2016
First published
25 Jul 2016

Green Chem., 2016,18, 4468-4474

Predicting skin permeation rate from nuclear magnetic resonance spectra

N. An, J. Machado, Y. Tang, J. Kostal and A. Voutchkova-Kostal, Green Chem., 2016, 18, 4468 DOI: 10.1039/C6GC00945J

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