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Prediction of logP for Pt(II) and Pt(IV) complexes: Comparison of statistical and quantum-chemistry based approaches

Tetko, Igor V., Varbanov, Hristo P., Galanski, Markus, Talmaciu, Mona, Platts, James Alexis, Ravera, Mauro and Gabano, Elisabetta 2016. Prediction of logP for Pt(II) and Pt(IV) complexes: Comparison of statistical and quantum-chemistry based approaches. Journal of Inorganic Biochemistry 156 , pp. 1-13. 10.1016/j.jinorgbio.2015.12.006

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Abstract

The octanol/water partition coefficient, logP, is one of the most important physico-chemical parameters for the development of new metal-based anticancer drugs with improved pharmacokinetic properties. This study addresses an issue with the absence of publicly available models to predict logP of Pt(IV) complexes. Following data collection and subsequent development of models based on 187 complexes from literature, we validate new and previously published models on a new set of 11 Pt(II) and 35 Pt(IV) complexes, which were kept blind during the model development step. The error of the consensus model, 0.65 for Pt(IV) and 0.37 for Pt(II) complexes, indicates its good accuracy of predictions. The lower accuracy for Pt(IV) complexes was attributed to experimental difficulties with logP measurements for some poorly-soluble compounds. This model was developed using general-purpose descriptors such as extended functional groups, molecular fragments and E-state indices. Surprisingly, models based on quantum-chemistry calculations provided lower prediction accuracy. We also found that all the developed models strongly overestimate logP values for the three complexes measured in the presence of DMSO. Considering that DMSO is frequently used as a solvent to store chemicals, its effect should not be overlooked when logP measurements by means of the shake flask method are performed. The final models are freely available at http://ochem.eu/article/76903.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Chemistry
Subjects: Q Science > QD Chemistry
Publisher: Elsevier
ISSN: 0162-0134
Date of Acceptance: 9 December 2015
Last Modified: 16 May 2019 13:22
URI: http://orca-mwe.cf.ac.uk/id/eprint/87778

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