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Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

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Abstract

Introduction

Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug–target–effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug–target–effect paradigm, can be integrated to facilitate the inference of relationships between these entities.

Objective

This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature.

Materials and Methods

Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C–T–E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson’s paradigm to form C–T–E triangles. Missing C–E edges were then inferred as C–E relationships.

Results

Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C–E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale.

Conclusions

The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C–E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C–E inferences, this workflow may provide an effective computational method for the early detection of potential drug candidate ADEs that can be followed by targeted experimental investigations.

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Notes

  1. MedDRA® the Medical Dictionary for Regulatory Activities terminology is the international medical terminology developed under the auspices of the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH).

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Acknowledgements

The authors would like to thank Mr Alexander Gartland for fruitful discussions that helped to improve the quality of the manuscript.

The MedDRA® trademark is owned by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA) on behalf of the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH).

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Authors and Affiliations

  1. Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA

    Mary K. La

  2. Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA

    Alexander Sedykh, Eugene Muratov & Alexander Tropsha

  3. Sciome LLC, 2 Davis Drive, Research Triangle Park, NC, 27709, USA

    Alexander Sedykh

  4. Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA

    Denis Fourches

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  1. Mary K. La
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Correspondence to Alexander Tropsha.

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Funding

This study was supported in part by the National Institutes of Health (Grant 1U01CA207160).

Conflict of interest

Mary La, Alexander Sedykh, Denis Fourches, Eugene Muratov, and Alexander Tropsha declare no conflict of interests that are directly relevant to the content of this study.

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La, M.K., Sedykh, A., Fourches, D. et al. Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions. Drug Saf 41, 1059–1072 (2018). https://doi.org/10.1007/s40264-018-0688-5

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