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Thermodynamic Proxies to Compensate for Biases in Drug Discovery Methods

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Abstract

Purpose

We propose a framework with simple proxies to dissect the relative energy contributions responsible for standard drug discovery binding activity.

Methods

We explore a rule of thumb using hydrogen-bond donors, hydrogen-bond acceptors and rotatable bonds as relative proxies for the thermodynamic terms. We apply this methodology to several datasets (e.g., multiple small molecules profiled against kinases, Mycobacterium tuberculosis (Mtb) high throughput screening (HTS) and structure based drug design (SBDD) derived compounds, and FDA approved drugs).

Results

We found that Mtb active compounds developed through SBDD methods had statistically significantly larger PEnthalpy values than HTS derived compounds, suggesting these compounds had relatively more hydrogen bond donor and hydrogen bond acceptors compared to rotatable bonds. In recent FDA approved medicines we found that compounds identified via target-based approaches had a more balanced enthalpic relationship between these descriptors compared to compounds identified via phenotypic screens

Conclusions

As it is common to experimentally optimize directly for total binding energy, these computational methods provide alternative calculations and approaches useful for compound optimization alongside other common metrics in available software and databases.

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Abbreviations

(FCFP):

Molecular function class fingerprints

ADME/Tox:

Absorption, distribution, metabolism, excretion and toxicity

AUC:

Area under the curve

CDD:

Collaborative Drug Discovery

FDA:

Food and Drug Administration

GSK:

GlaxoSmithKline

HAC:

heavy atom count

HBA:

Hydrogen bond acceptors

HBD:

Hydrogen bond donors

HBT:

Total number of HBA and HBD

HTS:

High-throughput screening

Mtb :

Mycobacterium tuberculosis

NCATS:

National Center for Advancing Translational Sciences

NIH:

National Institute of Health

PAINS:

Pan assay INterference compoundS

PKIS:

Published kinase inhibitor set

RB:

Rotatable bonds

ROC:

Receiver operator Characteristic

SBDD:

Structure based drug design

XV:

Cross validated

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ACKNOWLEDGMENTS AND DISCLOSURES

We gratefully acknowledge colleagues at CDD for the development of the CDD Vault. The CDD TB DB has been developed thanks to funding from the Bill and Melinda Gates Foundation (Grant#49852 “Collaborative drug discovery for TB through a novel database of SAR data optimized to promote data archiving and sharing”). We also thank Dr. Peter Kenny for suggesting various papers, Dr. Matthew Soellner for discussions and Dr. Daniel Erlanson and the reviewers for critical comments. BAB is an employee and SE is a consultant of CDD Inc. CAL and NKL are on the scientific advisory board of CDD Inc.

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Authors and Affiliations

  1. Collaborative Drug Discovery, Inc., 1633 Bayshore Highway Suite 342, Burlingame, California, 94010, USA

    Sean Ekins, Nadia K. Litterman & Barry A. Bunin

  2. Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, North Carolina, 27526, USA

    Sean Ekins

  3. 10 Connshire Drive, Waterford, Connecticut, 06385-4122, USA

    Christopher A. Lipinski

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  1. Sean Ekins
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  2. Nadia K. Litterman
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Correspondence to Sean Ekins.

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Ekins, S., Litterman, N.K., Lipinski, C.A. et al. Thermodynamic Proxies to Compensate for Biases in Drug Discovery Methods. Pharm Res 33, 194–205 (2016). https://doi.org/10.1007/s11095-015-1779-y

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  • DOI: https://doi.org/10.1007/s11095-015-1779-y

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