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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), 2020-07-01
Topic: Risk, prevention and care in population studies
Plain-language summary
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 < 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 < 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.
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APA 7th
Zhou, M., Zheng, C., & Xu, R. (2020). Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery. Bioinformatics (Oxford, England). https://doi.org/10.1093/bioinformatics/btaa451
Vancouver
Zhou M, Zheng C, Xu R. Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery. Bioinformatics (Oxford, England). 2020. doi:10.1093/bioinformatics/btaa451. PMID: 32657406.
BibTeX
@article{zhou2020-32657406,
author = {Zhou, M. and Zheng, C. and Xu, R.},
title = {{Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery}},
journal = {Bioinformatics (Oxford, England)},
year = {2020},
doi = {10.1093/bioinformatics/btaa451},
pmid = {32657406},
url = {https://doi.org/10.1093/bioinformatics/btaa451}
}
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Key findings
- 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.
- 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 < 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 < 0.0001].
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Study details
- Study designcase-control study
- Evidence strengthModerate
How this study scored 55/100
Published in a peer-reviewed journal, and it is a case-control study. Scored zero for sample size, effect reported, studied in people and randomized / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multicenter collaboration, so those were left out: it earned 32 of the 58 points we could judge it on, which is 55 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Bioinformatics (Oxford, England)) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 6 / 12 | A case-control study |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32657406) |
| Free to read | 2 / 2 | Free to read |
| Points | 32 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 32 of the 58 points we could judge it on is 55 out of 100. |
| Total | 55 / 100 | — |
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