PMID- 35213968
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220301
IS  - 1999-4923 (Print)
IS  - 1999-4923 (Electronic)
IS  - 1999-4923 (Linking)
VI  - 14
IP  - 2
DP  - 2022 Jan 20
TI  - Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation 
      Data: A Machine Learning Approach.
LID - 10.3390/pharmaceutics14020234 [doi]
LID - 234
AB  - Identification of the correct targets is a key element for successful drug 
      development. However, there are limited approaches for predicting drug targets 
      for specific diseases using omics data, and few have leveraged expression 
      profiles from gene perturbations. We present a novel computational approach for 
      drug target discovery based on machine learning (ML) models. ML models are first 
      trained on drug-induced expression profiles with outcomes defined as whether the 
      drug treats the studied disease. The goal is to "learn" the expression patterns 
      associated with treatment. Then, the fitted ML models were applied to expression 
      profiles from gene perturbations (overexpression (OE)/knockdown (KD)). We 
      prioritized targets based on predicted probabilities from the ML model, which 
      reflects treatment potential. The methodology was applied to predict targets for 
      hypertension, diabetes mellitus (DM), rheumatoid arthritis (RA), and 
      schizophrenia (SCZ). We validated our approach by evaluating whether the 
      identified targets may 're-discover' known drug targets from an external database 
      (OpenTargets). Indeed, we found evidence of significant enrichment across all 
      diseases under study. A further literature search revealed that many candidates 
      were supported by previous studies. For example, we predicted PSMB8 inhibition to 
      be associated with the treatment of RA, which was supported by a study showing 
      that PSMB8 inhibitors (PR-957) ameliorated experimental RA in mice. In 
      conclusion, we propose a new ML approach to integrate the expression profiles 
      from drugs and gene perturbations and validated the framework. Our approach is 
      flexible and may provide an independent source of information when prioritizing 
      drug targets.
FAU - Zhao, Kai
AU  - Zhao K
AUID- ORCID: 0000-0002-2774-6326
AD  - School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong 
      Kong SAR, China.
FAU - Shi, Yujia
AU  - Shi Y
AD  - School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong 
      Kong SAR, China.
FAU - So, Hon-Cheong
AU  - So HC
AUID- ORCID: 0000-0002-7102-833X
AD  - School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong 
      Kong SAR, China.
AD  - KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common 
      Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 
      650201, China.
AD  - CUHK Shenzhen Research Institute, Shenzhen 518172, China.
AD  - Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong 
      SAR, China.
AD  - Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese 
      University of Hong Kong, Shatin, Hong Kong SAR, China.
AD  - Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong 
      SAR, China.
AD  - Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in 
      Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong 
      SAR, China.
LA  - eng
GR  - 81971706/National Natural Science Foundation of China/
GR  - NA/Lo Kwee Seong Biomedical Research Fund from The Chinese University of Hong 
      Kong/
GR  - NA/KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common 
      Diseases, Kunming Institute of Zoology ./
PT  - Journal Article
DEP - 20220120
PL  - Switzerland
TA  - Pharmaceutics
JT  - Pharmaceutics
JID - 101534003
PMC - PMC8878225
OTO - NOTNLM
OT  - drug repurposing
OT  - drug target
OT  - expression profiling
OT  - gene perturbation
OT  - machine learning
COIS- The authors declare no conflict of interest.
EDAT- 2022/02/27 06:00
MHDA- 2022/02/27 06:01
PMCR- 2022/01/20
CRDT- 2022/02/26 01:01
PHST- 2021/12/01 00:00 [received]
PHST- 2022/01/08 00:00 [revised]
PHST- 2022/01/14 00:00 [accepted]
PHST- 2022/02/26 01:01 [entrez]
PHST- 2022/02/27 06:00 [pubmed]
PHST- 2022/02/27 06:01 [medline]
PHST- 2022/01/20 00:00 [pmc-release]
AID - pharmaceutics14020234 [pii]
AID - pharmaceutics-14-00234 [pii]
AID - 10.3390/pharmaceutics14020234 [doi]
PST - epublish
SO  - Pharmaceutics. 2022 Jan 20;14(2):234. doi: 10.3390/pharmaceutics14020234.