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Genome Sequence Variability Predicts Drug Precautions and Withdrawals from the Market.

Kye-Hwa LeeSu Youn BaikSoo Youn LeeChan Hee ParkPaul J ParkJu Han Kim
Published in: PloS one (2016)
Despite substantial premarket efforts, a significant portion of approved drugs has been withdrawn from the market for safety reasons. The deleterious impact of nonsynonymous substitutions predicted by the SIFT algorithm on structure and function of drug-related proteins was evaluated for 2504 personal genomes. Both withdrawn (n = 154) and precautionary (Beers criteria (n = 90), and US FDA pharmacogenomic biomarkers (n = 96)) drugs showed significantly lower genomic deleteriousness scores (P < 0.001) compared to others (n = 752). Furthermore, the rates of drug withdrawals and precautions correlated significantly with the deleteriousness scores of the drugs (P < 0.01); this trend was confirmed for all drugs included in the withdrawal and precaution lists by the United Nations, European Medicines Agency, DrugBank, Beers criteria, and US FDA. Our findings suggest that the person-to-person genome sequence variability is a strong independent predictor of drug withdrawals and precautions. We propose novel measures of drug safety based on personal genome sequence analysis.
Keyphrases
  • drug induced
  • adverse drug
  • genome wide
  • health insurance
  • machine learning
  • emergency department
  • gene expression
  • deep learning