PMID- 38299552
OWN - NLM
STAT- Publisher
LR  - 20240201
IS  - 1744-7607 (Electronic)
IS  - 1742-5255 (Linking)
DP  - 2024 Feb 1
TI  - In-silico approaches to assessing multiple high-level drug-drug and drug-disease 
      adverse drug effects.
PG  - 1-14
LID - 10.1080/17425255.2023.2299337 [doi]
AB  - INTRODUCTION: Pharmacovigilance plays a pivotal role in monitoring adverse events 
      (AEs) related to chemical substances in human/animal populations. With increasing 
      spontaneous-reporting systems, researchers turned to in-silico approaches to 
      efficiently analyze drug safety profiles. Here, we review in-silico methods 
      employed for assessing multiple drug-drug/drug-disease AEs covered by comparative 
      analyses and visualization strategies. AREAS COVERED: Disproportionality, 
      involving multi-stage statistical methodologies and data processing, identifies 
      safety signals among drug-AE pairs. By stratifying data based on disease 
      indications/demographics, researchers address confounders and assess drug safety. 
      Comparative analyses, including clustering techniques and visualization 
      techniques, assess drug similarities, patterns, and trends, calculate 
      correlations, and identify distinct toxicities. Furthermore, we conducted a 
      thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications 
      spanning 2003 to 2023. EXPERT OPINION: Pharmacovigilance relies on diverse data 
      sources, presenting challenges in the integration of in-silico approaches and 
      requiring compliance with regulations and AI adoption. Systematic use of 
      statistical analyses enables identifications of potential risks with drugs. 
      Frequentist and Bayesian methods are used in disproportionalities, each with its 
      strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance 
      enables personalized medicine, with AI further enhancing patient engagement. This 
      multidisciplinary approach holds promise, improving drug efficacy and safety, and 
      should be a core mission of One-Health studies.
FAU - Xu, Xuan
AU  - Xu X
AD  - 1DATA Consortium, www.1DATA.life, Kansas State University Olathe, Olathe, KS, 
      USA.
AD  - Food Animal Residue Avoidance and Databank Program (FARAD), Kansas State 
      University Olathe, Olathe, KS, USA.
AD  - Department of Mathematics, Kansas State University, Manhattan, KS, USA.
FAU - Riviere, Jim E
AU  - Riviere JE
AD  - 1DATA Consortium, www.1DATA.life, Kansas State University Olathe, Olathe, KS, 
      USA.
AD  - Food Animal Residue Avoidance and Databank Program (FARAD), Kansas State 
      University Olathe, Olathe, KS, USA.
FAU - Raza, Shahzad
AU  - Raza S
AD  - Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA.
FAU - Millagaha Gedara, Nuwan Indika
AU  - Millagaha Gedara NI
AD  - 1DATA Consortium, www.1DATA.life, Kansas State University Olathe, Olathe, KS, 
      USA.
AD  - Department of Mathematics, Kansas State University, Manhattan, KS, USA.
FAU - Ampadi Ramachandran, Remya
AU  - Ampadi Ramachandran R
AD  - 1DATA Consortium, www.1DATA.life, Kansas State University Olathe, Olathe, KS, 
      USA.
AD  - Food Animal Residue Avoidance and Databank Program (FARAD), Kansas State 
      University Olathe, Olathe, KS, USA.
AD  - Department of Mathematics, Kansas State University, Manhattan, KS, USA.
FAU - Tell, Lisa A
AU  - Tell LA
AD  - FARAD, Department of Medicine and Epidemiology, School of Veterinary Medicine, 
      University of California-Davis, Davis, CA, USA.
FAU - Wyckoff, Gerald J
AU  - Wyckoff GJ
AD  - 1DATA Consortium, www.1DATA.life, Kansas State University Olathe, Olathe, KS, 
      USA.
AD  - School of Pharmacy, Division of Pharmacology and Pharmaceutical Sciences, 
      University of Missouri-Kansas, Kansas, USA.
FAU - Jaberi-Douraki, Majid
AU  - Jaberi-Douraki M
AUID- ORCID: 0000-0002-8505-6550
AD  - 1DATA Consortium, www.1DATA.life, Kansas State University Olathe, Olathe, KS, 
      USA.
AD  - Food Animal Residue Avoidance and Databank Program (FARAD), Kansas State 
      University Olathe, Olathe, KS, USA.
AD  - Department of Mathematics, Kansas State University, Manhattan, KS, USA.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20240201
PL  - England
TA  - Expert Opin Drug Metab Toxicol
JT  - Expert opinion on drug metabolism & toxicology
JID - 101228422
SB  - IM
OTO - NOTNLM
OT  - Pharmacovigilance
OT  - adverse effects
OT  - frequentist and bayesian methods
OT  - machine learning approach
OT  - one health
EDAT- 2024/02/01 12:44
MHDA- 2024/02/01 12:44
CRDT- 2024/02/01 08:39
PHST- 2024/02/01 12:44 [medline]
PHST- 2024/02/01 12:44 [pubmed]
PHST- 2024/02/01 08:39 [entrez]
AID - 10.1080/17425255.2023.2299337 [doi]
PST - aheadofprint
SO  - Expert Opin Drug Metab Toxicol. 2024 Feb 1:1-14. doi: 
      10.1080/17425255.2023.2299337.