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Front. Big Data | doi: 10.3389/fdata.2019.00025

Novel computational approach to predict off-target interactions for small molecules

 Mohan S. Rao1*, Rishi Gupta1, Michael J. Liguori1,  Mufeng Hu1, Xin Huang1, Srinivasa Mantena1, Scott W. Mittelstadt1,  Eric A. Blomme1 and  Terry R. Van Vleet1
  • 1AbbVie (United States), United States

Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental poly-pharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 246,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of > 1 million compounds (tracking > 20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7990 interactions for these 857 molecules. Of these, 3923 and 4067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for > 70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets.

Keywords: off-target, machine learning, Toxicology, secondary pharmacology, pocket search

Received: 05 Feb 2019; Accepted: 26 Jun 2019.

Edited by:

Yvonne Will, Pfizer (United States), United States

Reviewed by:

Matthew T. Martin, Pfizer (United States), United States
Minjun Chen, National Center for Toxicological Research (FDA), United States  

Copyright: © 2019 Rao, Gupta, Liguori, Hu, Huang, Mantena, Mittelstadt, Blomme and Van Vleet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Mohan S. Rao, AbbVie (United States), North Chicago, Illinois, United States, mohan.rao@abbvie.com