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Journal of Computer-Aided Molecular Design

, Volume 30, Issue 11, pp 945–958 | Cite as

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge

  • Ariën S. Rustenburg
  • Justin Dancer
  • Baiwei Lin
  • Jianwen A. Feng
  • Daniel F. Ortwine
  • David L. Mobley
  • John D. Chodera
Article

Abstract

Small molecule distribution coefficients between immiscible nonaqueuous and aqueous phases—such as cyclohexane and water—measure the degree to which small molecules prefer one phase over another at a given pH. As distribution coefficients capture both thermodynamic effects (the free energy of transfer between phases) and chemical effects (protonation state and tautomer effects in aqueous solution), they provide an exacting test of the thermodynamic and chemical accuracy of physical models without the long correlation times inherent to the prediction of more complex properties of relevance to drug discovery, such as protein-ligand binding affinities. For the SAMPL5 challenge, we carried out a blind prediction exercise in which participants were tasked with the prediction of distribution coefficients to assess its potential as a new route for the evaluation and systematic improvement of predictive physical models. These measurements are typically performed for octanol-water, but we opted to utilize cyclohexane for the nonpolar phase. Cyclohexane was suggested to avoid issues with the high water content and persistent heterogeneous structure of water-saturated octanol phases, since it has greatly reduced water content and a homogeneous liquid structure. Using a modified shake-flask LC-MS/MS protocol, we collected cyclohexane/water distribution coefficients for a set of 53 druglike compounds at pH 7.4. These measurements were used as the basis for the SAMPL5 Distribution Coefficient Challenge, where 18 research groups predicted these measurements before the experimental values reported here were released. In this work, we describe the experimental protocol we utilized for measurement of cyclohexane-water distribution coefficients, report the measured data, propose a new bootstrap-based data analysis procedure to incorporate multiple sources of experimental error, and provide insights to help guide future iterations of this valuable exercise in predictive modeling.

Keywords

Partition coefficients Distribution coefficients Blind challenge Predictive modeling SAMPL 

Abbreviations

SAMPL

Statistical assessment of the modeling of proteins and ligands

log P

\(\log _{10}\) partition coefficient

log D

\(\log _{10}\) distribution coefficient

LC-MS/MS

Liquid chromatography - tandem mass spectrometry

HPLC

High-pressure liquid chromatography

MRM

Multiple reaction monitoring

PTFE

Polytetrafluoroethylene

DMSO

Dimethyl sulfoxide

PBS

Phosphate buffered saline

RPM

Revolutions per minute

CV

Coefficient of variation

MAP

Maximum a posteriori

MCMC

Markov chain Monte Carlo

Notes

Acknowledgments

The authors acknowledge Christopher Bayly (OpenEye Scientific) and Robert Abel (Schrödinger) for their contributions to discussions on compound selection; Joseph Pease (Genentech) for discussions of the experimental approach and aid in compound selection; Delia Li (Genentech) for her assistance in performing experimental work; Alberto Gobbi (Genentech), Man-Ling Lee (Genentech), and Ignacio Aliagas (Genentech) for helpful feedback on experimental issues; Andreas Klamt (Cosmologic) and Jens Reinisch (Cosmologic) for invigorating discussions regarding experimental data; Patrick Grinaway (MSKCC) for helpful discussions on analysis procedures; and Anthony Nicholls (OpenEye) for originating and supporting earlier iterations of SAMPL challenges.

Funding

This work was performed as part of an internship by ASR sponsored by Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States. JDC acknowledges support from the Sloan Kettering Institute and NIH grant P30 CA008748. DLM appreciates financial support from National Science Foundation (CHE 1352608).

Compliance with ethical standard

Conflict of interest statement

D. L. M. and J. D. C. are members of the Scientific Advisory Board for Schrödinger, LLC.

Supplementary material

10822_2016_9971_MOESM1_ESM.pdf (115 kb)
Supplementary material 1 (PDF 115 kb)
10822_2016_9971_MOESM2_ESM.xlsx (10 kb)
Supplementary material 2 (XLSX 10 kb)
10822_2016_9971_MOESM3_ESM.xlsx (183 kb)
Supplementary material 3 (XLSX 182 kb)
10822_2016_9971_MOESM4_ESM.pdf (773 kb)
Supplementary material 4 (PDF 772 kb)
10822_2016_9971_MOESM5_ESM.csv (5 kb)
Supplementary material 5 (CSV 4 kb)
10822_2016_9971_MOESM6_ESM.xlsx (25 kb)
Supplementary material 6 (XLSX 25 kb)
10822_2016_9971_MOESM7_ESM.sdf (110 kb)
Supplementary material 7 (SDF 109 kb)

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Graduate Program in Physiology, Biophysics, and Systems BiologyWeill Cornell Medical CollegeNew YorkUSA
  2. 2.Computational Biology Program, Sloan Kettering InstituteMemorial Sloan Kettering Cancer CenterNew YorkUSA
  3. 3.Genentech, Inc.South San FranciscoUSA
  4. 4.Theravance BiopharmaSouth San FranciscoUSA
  5. 5.Denali TherapeuticsSouth San FranciscoUSA
  6. 6.Department of Pharmaceutical Sciences and Department of ChemistryUniversity of California, IrvineIrvineUSA

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