Novel Computational Approach to Predict Off-Target Interactions for Small Molecules
| Autor: | Scott W. Mittelstadt, Eric A. G. Blomme, Mufeng Hu, Srinivasa R. Mantena, Mohan S. Rao, Xin Huang, Terry R. Van Vleet, Rishi R Gupta, Michael J. Liguori |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Drug
Big Data Training set Drug discovery media_common.quotation_subject secondary pharmacology Computational biology Biology pocket search Small molecule Off-targets Off targets machine learning Artificial Intelligence In vivo Proteome Computer Science (miscellaneous) gene expression Repurposing Information Systems media_common Original Research toxicology |
| Zdroj: | Frontiers in Big Data |
| ISSN: | 2624-909X |
| Popis: | 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 polypharmacological 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 > 2,46,704 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 diverse 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 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 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% (2,025) 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. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW |
| Databáze: | OpenAIRE |
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