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Octanol–water partition coefficient measurements for the SAMPL6 blind prediction challenge

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Abstract

Partition coefficients describe the equilibrium partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol–water partition coefficients (\(K_{\rm ow}\)), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The partition coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II octanol–water partition coefficient prediction challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 \(\hbox {p}{K}_{{\rm a}}\) prediction challenge in a blind experimental benchmark. Following experimental data collection, the partition coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol–water log P dataset for this SAMPL6 Part II partition coefficient challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95–4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.

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Data availability statement

All SAMPL6 log P Challenge instructions, submissions, experimental data and analysis are available at https://github.com/samplchallenges/SAMPL6/tree/master/physical_properties/logP

Notes

  1. SAMPL6 was originally announced as featuring a log D prediction challenge, but there were difficulties in the collection of experimental data. The original plan was to measure log P0, log P−1, and log P+1 and calculate log D values at the experimental pH using these values. However, we were able to measure the partition coefficients of neutral species (log P0) much more reliably than ionic species with potentiometric log P method of Sirius T3, as elaborated further below.

Abbreviations

SAMPL:

Statistical Assessment of the Modeling of Proteins and Ligands

log P :

log\(_{10}\) of the organic solvent-water partition coefficient (\(K_{ow}\), refers to partition of neutral species unless stated otherwise)

log D :

log\(_{10}\) of organic solvent-water distribution coefficient (\(D_{ow}\))

log R :

log\(_{10}\) of the volumetric ratios of partition solvents (octanol to water)

\(\hbox {p}{K}_{{\rm a}}\) :

−log\(_{10}\) of the acid dissociation equilibrium constant

\(\hbox {p}_{{\rm o}}{K}_{{\rm a}}\) :

−log\(_{10}\) apparent acid dissociation equilibrium constant in octanol–water biphasic system

ISA:

Ionic-strength adjusted solution with 0.15 M KCl

SEM:

Standard error of the mean

LC-MS:

Liquid chromatography-mass spectrometry

NMR:

Nuclear magnetic resonance spectroscopy

HRMS:

High-resolution mass spectrometry

octanol:

1-Octanol, also known as n-octanol

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Acknowledgements

MI and JDC acknowledge support from the Sloan Kettering Institute. JDC acknowledges partial support from NIH Grant P30 CA008748. MI, JDC, ASR, and DLM gratefully acknowledge support from NIH Grant R01GM124270 supporting the SAMPL Blind Challenges. MI acknowledges support from a Doris J. Hutchinson Fellowship. DLM appreciates financial support from the National Institutes of Health (1R01GM108889-01), the National Science Foundation (CHE 1352608). The authors are extremely grateful for the assistance and support from the MRL Preformulations and NMR Structure Elucidation groups for materials, expertise, and instrument time, without which this SAMPL challenge would not have been possible. The authors would like to thank Ryan Cohen from the NMR Structure Elucidation group for the NMR and LC-MS analysis of SM13. MI and DL are grateful to Pion/Sirius Analytical for their technical support in the planning and execution of this study. We are especially thankful to Karl Box (Sirius Analytical) for the guidance on optimization and interpretation of log P measurements with the Sirius T3. We thank Brad Sherborne (MRL; ORCID: 0000-0002-0037-3427) for his valuable insights at the conception of the log P challenge and connecting us with TR and DL who were able to provide resources for experimental measurements. We acknowledge contributions from Caitlin Bannan who provided feedback on experimental data collection and structure of log P challenge from a computational chemist’s perspective. MI and JDC are grateful to OpenEye Scientific for providing a free academic software license for use in this work. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank anonymous reviewers for their input and constructive comments that improved this manuscript. Research reported in this publication was supported by National Institute for General Medical Sciences of the National Institutes of Health under Award Number R01GM124270 and R01GM108889, and from the National Cancer Institute of the National Institutes of Health under P30CA008748.

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Authors and Affiliations

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

    Mehtap Işık & John D. Chodera

  2. Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, 10065, USA

    Mehtap Işık

  3. Pharmaceutical Sciences, MRL, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, NJ, 07065, USA

    Dorothy Levorse & Timothy Rhodes

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

    David L. Mobley

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Contributions

Conceptualization, MI, JDC, TR, DLM ; Methodology, MI, DL; Software, MI ; Formal Analysis, MI ; Investigation, MI, DL; Resources, TR, DL; Data Curation, MI ; Writing-Original Draft, MI, DL; Writing - Review and Editing, MI, DL, JDC, TR, DLM; Visualization, MI ; Supervision, JDC, TR, DLM ; Project Administration, MI ; Funding Acquisition, DLM, JDC, TR, MI.

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Correspondence to Timothy Rhodes or John D. Chodera.

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Conflict of interest

JDC was a member of the Scientific Advisory Board for Schrödinger, LLC during part of this study. JDC and DLM are current members of the Scientific Advisory Board of OpenEye Scientific Software. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biosciences, XtalPi, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, The Einstein Foundation, and the Sloan Kettering Institute. A complete list of funding can be found at http://choderalab.org/funding.

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Işık, M., Levorse, D., Mobley, D.L. et al. Octanol–water partition coefficient measurements for the SAMPL6 blind prediction challenge. J Comput Aided Mol Des 34, 405–420 (2020). https://doi.org/10.1007/s10822-019-00271-3

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