Skip to main content

Advertisement

SpringerLink
Log in

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Shimadzu Cat. No. 228-45450-91.

  2. DMSO stocks from Genentech compound library.

  3. ACS grade \(\ge\)99 %, Sigma-Aldrich Cat. No 179191-2L, batch #00555ME.

  4. 136 mM NaCl, 2.6 mM KCl, 7.96 mM \(\hbox {Na}_2\hbox {HPO}_4\), 1.46 mM \(\hbox {KH}_2\hbox {PO}_4\), with pH adjusted to 7.4, prepared by the Genentech Media lab.

  5. Thermo Fisher Scientific, Titer Plate Shaker, model: 4625,Waltham, MA, USA.

  6. Agilent Technologies, Vial plate for holding 54 × 2 mL vials part no. G2255-68700.

  7. Eppendorf, Centrifuge 5804, Hamburg, Germany.

  8. 384-well glass coat plate:Thermo Scientific, Microplate, 384-Well; Webseal Plate; Glass-coated Polypropylene; Square well shape; U-Shape well bottom; 384 wells; 90 uL sample volume; Catalog Number: 3252187.

  9. ACROS Organics, 1-octanol 99 % pure, Catalog Number: AC150630010, Geel, Belgium.

  10. Waters Xbridge C18 2.130 mm with 2.5 m particles.

  11. Agilent Cat No 24214-001.

  12. All LC solvents were HPLC-grade and purchased from OmniSolv (Charlotte, NC, USA).

  13. This was done using a Shimadzu NexeraX2 consisting of an LC-30AD(pump), SIL-30AC (auto-injector), and SPD-20AC(UV/VIS detector) with Sciex API4000QTRP (MS).

  14. This was done using a Shimadzu NexeraX2 consisting of an LC-30AD(pump), SIL-30AC (auto-injector), and SPD-20AC(UV/VIS detector) with Sciex API4000 (MS).

  15. For the purpose of the D3R/SAMPL5 workshop, we originally erroneously reported the standard deviation \(\cdot \sqrt{3}\) instead of the standard error \(\cdot \sqrt{3}\). The factor of \(\sqrt{3}\) corrects the sample standard deviation across all MRM measurements for the correlation between the 3 replicate measurements belonging to a single independent experimental repeat.

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

References

  1. Guthrie JP (2009) J Phys Chem B 113:4501

    Article  CAS  Google Scholar 

  2. Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) J Comput Aided Mol Des 24:259

    Article  CAS  Google Scholar 

  3. Skillman AG (2012) J Comput Aided Mol Des 26:473

    Article  CAS  Google Scholar 

  4. Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) J Comput Aided Mol Des 28:305

    Article  CAS  Google Scholar 

  5. Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014) J Comput Aided Mol Des 28:135

    Article  CAS  Google Scholar 

  6. Czodrowski P, Sotriffer CA, Klebe G (2007) J Mol Biol 367:1347

    Article  CAS  Google Scholar 

  7. Steuber H, Czodrowski P, Sotriffer CA, Klebe G (2007) J Mol Biol 373:1305

    Article  CAS  Google Scholar 

  8. Martin YC (2009) J Comput Aid Mol Des 23:693

    Article  CAS  Google Scholar 

  9. Mannhold R, Poda GI, Ostermann C, Tetko IV (2009) J Pharm Sci 98:861

    Article  CAS  Google Scholar 

  10. Kollman PA (1996) Acc Chem Res 29:461

    Article  CAS  Google Scholar 

  11. Best SA, Merz KM, Reynolds CH (1999) J Phys Chem B 103:714

    Article  CAS  Google Scholar 

  12. Chen B, Siepmann JI (2006) J Phys Chem B 110:3555

    Article  CAS  Google Scholar 

  13. Lyubartsev AP, Jacobsson SP, Sundholm G, Laaksonen A (2001) J Phys Chem B 105:7775

    Article  CAS  Google Scholar 

  14. Bhatnagar N, Kamath G, Chelst I, Potoff JJ (2012) J Pharm Sci 137:014502

    Google Scholar 

  15. Margolis SA, Levenson M (2000) Fresenius’ J Anal Chem 367:1

    Article  CAS  Google Scholar 

  16. Stephenson R, Stuart J, Tabak M (1984) J Chem Eng Data 29:287

    Article  CAS  Google Scholar 

  17. Black C, Joris GG, Taylor HS (1948) J Chem Phys 16:537

    Article  CAS  Google Scholar 

  18. Yalkowsky SH, He Y, Jain P (2010) Handbook of aqueous solubility data. CRC Press, Boca Raton

    Book  Google Scholar 

  19. Harris JG, Stillinger FH (1991) J Chem Phys 95:5953

    Article  CAS  Google Scholar 

  20. Bannan CC, Burley KH, Chiu M, Shirts MR, Gilson MK, Mobley DL (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9954-8

    Google Scholar 

  21. Lin B, Pease JH (2013) Comb Chem High Throughput Screen 16:817

    Article  CAS  Google Scholar 

  22. Haynes WM (2014) CRC handbook of chemistry and physics. CRC Press, Boca Raton

    Google Scholar 

  23. Leo A, Hansch C, Elkins D (1971) Chem Rev 71:525

    Article  CAS  Google Scholar 

  24. Milletti F, Storchi L, Sforna G, Cruciani G (2007) J Chem Inf Model 47:2172

    Article  CAS  Google Scholar 

  25. Milletti F, Storchi L, Goracci L, Bendels S, Wagner B, Kansy M, Cruciani G (2010) Eur J Med Chem 45:4270

    Article  CAS  Google Scholar 

  26. Efron B (1979) Ann Stat 7:1

    Article  Google Scholar 

  27. Hanson SM, Ekins S, Chodera JD (2015) J Comput-Aided Mol Des 29:1073

    Article  CAS  Google Scholar 

  28. Efron B, Tibshirani RJ (1994) An introduction to the bootstrap. CRC Press, Boca Raton

    Google Scholar 

  29. Rainin Pipet-Lite Multi Pipette L8-200XLS+. https://www.shoprainin.com/Pipettes/Multichannel-Manual-Pipettes/Pipet-Lite-XLS%2B/Pipet-Lite-Multi-Pipette-L8-200XLS%2B/p/17013805. Accessed 06 June 2016

  30. Rainin Classic Pipette PR-10. https://www.shoprainin.com/Pipettes/Single-Channel-Manual-Pipettes/RAININ-Classic/Rainin-Classic-Pipette-PR-10/p/17008649. Accessed 06 June 2016

  31. Rosenblatt M (1956) Ann Math Stat 27:832

    Article  Google Scholar 

  32. Drewokane MWOB, Hobson P, Halchenko Y, Lukauskas S, Warmenhoven J, Cole JB, Hoyer S, Vanderplas J, Villalba S, Quintero E, Martin M, Miles A, Meyer K, Augspurger T, Yarkoni T, Bachant P, Evans C, Fitzgerald C, Nagy T, Ziegler E, Megies T, Wehner D, St-Jean S, Coelho LP, Hitz G, Lee A, Rocher L (2016) seaborn: v0.7.0 (January 2016)

  33. Nicholls A (2014) J Comput-Aided Mol Des 28:887

    Article  CAS  Google Scholar 

  34. J Chem Eng Data 12, 326 (1967)

  35. Speight JG et al (2005) Lange’s handbook of chemistry, vol 1. McGraw-Hill, New York

    Google Scholar 

  36. Klamt A, Eckert F, Reinisch J, Wichmann K (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9927-y

    Google Scholar 

  37. Hastings WK (1970) Biometrika 57:97

    Article  Google Scholar 

Download references

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).

Author information

Author notes

  1. Justin Dancer

    Present address: Theravance Biopharma, South San Francisco, CA, 94080, USA

Authors and Affiliations

  1. Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Medical College, New York, NY, 10065, USA

    Ariën S. Rustenburg

  2. Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Ariën S. Rustenburg & John D. Chodera

  3. Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA

    Justin Dancer, Baiwei Lin & Daniel F. Ortwine

  4. Denali Therapeutics, 151 Oyster Point Blvd, 2nd floor, South San Francisco, CA, 94080, USA

    Jianwen A. Feng

  5. Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA

    David L. Mobley

Authors
  1. Ariën S. Rustenburg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Justin Dancer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baiwei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianwen A. Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel F. Ortwine
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David L. Mobley
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. John D. Chodera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John D. Chodera.

Ethics declarations

Conflict of interest statement

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

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rustenburg, A.S., Dancer, J., Lin, B. et al. Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge. J Comput Aided Mol Des 30, 945–958 (2016). https://doi.org/10.1007/s10822-016-9971-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-016-9971-7

Keywords

Search

Navigation