Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery. Zhou, M., Zheng, C., & Xu, R. Bioinformatics (Oxford, England), 36(Suppl_1):i436–i444, July, 2020. ZSCC: 0000006
doi  abstract   bibtex   
MOTIVATION: Predicting drug-target interactions (DTIs) using human phenotypic data have the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration. RESULTS: We developed a network-based DTI prediction system (TargetPredict) by modeling 855 904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects, 1059 diseases and 17 860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-the-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients. The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-the-art phenome-driven DTI prediction system as measured by precision-recall curves [measured by average precision (MAP): 0.28 versus 0.23, P-value \textless 0.0001]. The EHR-based case-control studies identified that the prescriptions top-ranked repositioned drugs are significantly associated with lower odds of AD diagnosis. For example, we showed that the prescription of liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD diagnosis [adjusted odds ratios (AORs): 0.76; 95% confidence intervals (CI) (0.70, 0.82), P-value \textless 0.0001]. In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patients' EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases. AVAILABILITY AND IMPLEMENTATION: nlp.case.edu/public/data/TargetPredict.
@article{zhou_combining_2020,
	title = {Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery},
	volume = {36},
	issn = {1367-4811},
	doi = {10.1093/bioinformatics/btaa451},
	abstract = {MOTIVATION: Predicting drug-target interactions (DTIs) using human phenotypic data have the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration.
RESULTS: We developed a network-based DTI prediction system (TargetPredict) by modeling 855 904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects, 1059 diseases and 17 860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-the-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients. The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-the-art phenome-driven DTI prediction system as measured by precision-recall curves [measured by average precision (MAP): 0.28 versus 0.23, P-value {\textless} 0.0001]. The EHR-based case-control studies identified that the prescriptions top-ranked repositioned drugs are significantly associated with lower odds of AD diagnosis. For example, we showed that the prescription of liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD diagnosis [adjusted odds ratios (AORs): 0.76; 95\% confidence intervals (CI) (0.70, 0.82), P-value {\textless} 0.0001]. In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patients' EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases.
AVAILABILITY AND IMPLEMENTATION: nlp.case.edu/public/data/TargetPredict.},
	language = {eng},
	number = {Suppl\_1},
	journal = {Bioinformatics (Oxford, England)},
	author = {Zhou, Mengshi and Zheng, Chunlei and Xu, Rong},
	month = jul,
	year = {2020},
	pmid = {32657406},
	pmcid = {PMC7355254},
	note = {ZSCC: 0000006 },
	keywords = {Diabetes Mellitus, Type 2, Drug Development, Drug Discovery, Electronic Health Records, Humans, Pharmaceutical Preparations},
	pages = {i436--i444},
}
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