PMID- 37117211
OWN - NLM
STAT- MEDLINE
DCOM- 20230501
LR  - 20230510
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Apr 28
TI  - Predicting drug adverse effects using a new Gastro-Intestinal Pacemaker Activity 
      Drug Database (GIPADD).
PG  - 6935
LID - 10.1038/s41598-023-33655-5 [doi]
LID - 6935
AB  - Electrical data could be a new source of big-data for training artificial 
      intelligence (AI) for drug discovery. A Gastro-Intestinal Pacemaker Activity Drug 
      Database (GIPADD) was built using a standardized methodology to test drug effects 
      on electrical gastrointestinal (GI) pacemaker activity. The current report used 
      data obtained from 89 drugs with 4867 datasets to evaluate the potential use of 
      the GIPADD for predicting drug adverse effects (AEs) using a machine-learning 
      (ML) approach and to explore correlations between AEs and GI pacemaker activity. 
      Twenty-four "electrical" features (EFs) were extracted using an automated 
      analytical pipeline from the electrical signals recorded before and after acute 
      drug treatment at three concentrations (or more) on four-types of GI tissues 
      (stomach, duodenum, ileum and colon). Extracted features were normalized and 
      merged with an online side-effect resource (SIDER) database. Sixty-six common AEs 
      were selected. Different algorithms of classification ML models, including Naive 
      Bayes, discriminant analysis, classification tree, k-nearest neighbors, support 
      vector machine and an ensemble model were tested. Separated tissue models were 
      also tested. Averaging experimental repeats and dose adjustment were performed to 
      refine the prediction results. Random datasets were created for model validation. 
      After model validation, nine AEs classification ML model were constructed with 
      accuracy ranging from 67 to 80%. EF can be further grouped into 'excitatory' and 
      'inhibitory' types of AEs. This is the first time drugs are being clustered based 
      on EF. Drugs acting on similar receptors share similar EF profile, indicating 
      potential use of the database to predict drug targets too. GIPADD is a growing 
      database, where prediction accuracy is expected to improve. The current approach 
      provides novel insights on how EF may be used as new source of big-data in health 
      and disease.
CI  - (c) 2023. The Author(s).
FAU - Liu, Julia Yuen Hang
AU  - Liu JYH
AD  - School of Biomedical Sciences, Faculty of Medicine, The Chinese University of 
      Hong Kong, 704, Lo Kwee-Seong Integrated Biomedical Sciences Building, Shatin, 
      New Territories, Hong Kong, SAR, People's Republic of China. liuyh@cuhk.edu.hk.
FAU - Rudd, John A
AU  - Rudd JA
AD  - School of Biomedical Sciences, Faculty of Medicine, The Chinese University of 
      Hong Kong, 704, Lo Kwee-Seong Integrated Biomedical Sciences Building, Shatin, 
      New Territories, Hong Kong, SAR, People's Republic of China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230428
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Databases, Pharmaceutical
MH  - Bayes Theorem
MH  - Algorithms
MH  - Machine Learning
MH  - *Drug-Related Side Effects and Adverse Reactions
PMC - PMC10147650
COIS- The authors declare no competing interests.
EDAT- 2023/04/29 06:04
MHDA- 2023/05/01 06:42
PMCR- 2023/04/28
CRDT- 2023/04/28 23:16
PHST- 2023/01/11 00:00 [received]
PHST- 2023/04/17 00:00 [accepted]
PHST- 2023/05/01 06:42 [medline]
PHST- 2023/04/29 06:04 [pubmed]
PHST- 2023/04/28 23:16 [entrez]
PHST- 2023/04/28 00:00 [pmc-release]
AID - 10.1038/s41598-023-33655-5 [pii]
AID - 33655 [pii]
AID - 10.1038/s41598-023-33655-5 [doi]
PST - epublish
SO  - Sci Rep. 2023 Apr 28;13(1):6935. doi: 10.1038/s41598-023-33655-5.