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Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge

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Abstract

The goal of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pKa and a range of bi-phasic logD values for a variety of pharmaceutical compounds were determined through a streamlined automated process to be utilized in the SAMPL8 physical property challenge. The goal of this paper is to provide an in-depth review of the experimental methods utilized to create a comprehensive data set for the blind prediction challenge. The significance of this work involves the use of high throughput experimentation equipment and instrumentation to produce acid dissociation constants for twenty-three drug molecules, as well as distribution coefficients for eleven of those molecules.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the GitHub repository, https://github.com/samplchallenges/SAMPL8. As of the time of this writing, only input data will be available, but at the close of the SAMPL8 challenge, measured values will also be released.

Abbreviations

OCTL:

Octanol

CYHL:

Cyclohexane

ETAC:

Ethyl acetate

HP:

Heptane

MEK:

Methyl ethyl ketone

TBME:

Tert butyl methyl ether

DMF:

Dimethylformamide

BR:

Britton Robinson

API:

Active pharmaceutical ingredient

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Acknowledgements

We appreciate the National Institutes of Health for its support of the SAMPL project via R01GM124270 to David L. Mobley (UC Irvine). The authors would also like to acknowledge Lisa McQueen and Alan Graves (formerly of GSK) for their contributions during the early stages of establishing the partnership, collecting the materials necessary for the study, and providing chemometrics insight. Lastly, we acknowledge the guidance and support from Kenneth Wells of GSK.

Author information

Author notes

  1. Matthew N. Bahr and Aakankschit Nandkeolyar have contributed equally to this work.

Authors and Affiliations

  1. Pharmaceutical Research and Development, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA, 19426, USA

    Matthew N. Bahr, Aakankschit Nandkeolyar, John K. Kenna & Neysa Nevins

  2. Pharmaceutical Research and Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, SG1 2NY, UK

    Luigi Da Vià

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

    Mehtap Işık & John D. Chodera

  4. Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA

    Aakankschit Nandkeolyar

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

    Aakankschit Nandkeolyar & David L. Mobley

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  1. Matthew N. Bahr
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This manuscript was written through the contributions from each of the authors MNB, AN. Each author has given approval to the final version of the manuscript.

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Correspondence to Matthew N. Bahr.

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Bahr, M.N., Nandkeolyar, A., Kenna, J.K. et al. Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge. J Comput Aided Mol Des 35, 1141–1155 (2021). https://doi.org/10.1007/s10822-021-00427-0

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