PMID- 31882773
OWN - NLM
STAT- MEDLINE
DCOM- 20201207
LR  - 20210110
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 9
IP  - 1
DP  - 2019 Dec 27
TI  - Database of adverse events associated with drugs and drug combinations.
PG  - 20025
LID - 10.1038/s41598-019-56525-5 [doi]
LID - 20025
AB  - Due to the aging world population and increasing trend in clinical practice to 
      treat patients with multiple drugs, adverse events (AEs) are becoming a major 
      challenge in drug discovery and public health. In particular, identifying AEs 
      caused by drug combinations remains a challenging task. Clinical trials typically 
      focus on individual drugs rather than drug combinations and animal models are 
      unreliable. An added difficulty is the combinatorial explosion in the number of 
      possible combinations that can be made using the increasingly large set of FDA 
      approved chemicals. We present a statistical and computational technique for 
      identifying AEs caused by two-drug combinations. Taking advantage of the large 
      and increasing data deposited in FDA's postmarketing reports, we demonstrate that 
      the task of predicting AEs for 2-drug combinations is amenable to the Likelihood 
      Ratio Test (LRT). Our pAERS database constructed with LRT contains almost 77 
      thousand associations between pairs of drugs and corresponding AEs caused solely 
      by drug-drug interactions (DDIs). The DDIs stored in pAERS complement the 
      existing data sets. Due to our stringent statistical test, we expect many of the 
      associations in pAERS to be unrecorded or poorly documented in the literature.
FAU - Poleksic, Aleksandar
AU  - Poleksic A
AD  - Department of Computer Science, University of Northern Iowa, Cedar Falls, Iowa, 
      50614, USA. poleksic@cs.uni.edu.
FAU - Xie, Lei
AU  - Xie L
AUID- ORCID: 0000-0001-9051-2111
AD  - Department of Computer Science, Hunter College, The City University of New York, 
      New York, New York, 10065, USA. lxie@iscb.org.
AD  - Ph.D. Program in Computer Science, Biochemistry and Biology, The Graduate Center, 
      The City University of New York, New York, New York, 10065, USA. lxie@iscb.org.
LA  - eng
GR  - R01 AG057555/AG/NIA NIH HHS/United States
GR  - R01 GM122845/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20191227
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
RN  - 0 (Drug Combinations)
SB  - IM
MH  - *Adverse Drug Reaction Reporting Systems
MH  - *Databases, Factual
MH  - *Drug Combinations
MH  - Humans
MH  - United States
PMC - PMC6934730
COIS- The authors have no competing interests as defined by Nature Research, or other 
      interests that might be perceived to influence the results and/or discussion 
      reported in this paper.
EDAT- 2019/12/29 06:00
MHDA- 2020/12/15 06:00
PMCR- 2019/12/27
CRDT- 2019/12/29 06:00
PHST- 2019/04/09 00:00 [received]
PHST- 2019/12/13 00:00 [accepted]
PHST- 2019/12/29 06:00 [entrez]
PHST- 2019/12/29 06:00 [pubmed]
PHST- 2020/12/15 06:00 [medline]
PHST- 2019/12/27 00:00 [pmc-release]
AID - 10.1038/s41598-019-56525-5 [pii]
AID - 56525 [pii]
AID - 10.1038/s41598-019-56525-5 [doi]
PST - epublish
SO  - Sci Rep. 2019 Dec 27;9(1):20025. doi: 10.1038/s41598-019-56525-5.