BACKGROUND: Pharmacometabonomics is a new branch of science, first described in 2006 and defined as 'the prediction of the effects of a drug on the basis of a mathematical model of pre-dose metabolite profiles'. Pharmacometabonomics has been used to predict drug metabolism, pharmacokinetics (PK), drug safety and drug efficacy in both animals and humans and is complementary to both pharmacogenomics (PGx) and pharmacoproteomics. METHODS: A literature review using the search terms pharmacometabonomics, pharmacometabolomics, pharmaco-metabonomics, pharmaco-metabolomics and the singular form of all those terms was conducted in October 2012 using PubMed and Web of Science. The review was updated until mid April 2013. RESULTS: Since the original description of pharmacometabonomics in 2006, 21 original publications and eight reviews have emerged, covering a broad range of applications from the prediction of PK to the prediction of drug metabolism, efficacy and safety in humans and animals. CONCLUSIONS: Pharmacometabonomics promises to be an important new approach to the delivery of personalized medicine to improve both drug efficacy and safety for patients in the future. Pharmacometabonomics is particularly powerful as it is sensitive to both genetic and environmental factors such as diet, drug intake and most importantly, a person's microbiome. PGx is now over 50 years old and although it has not achieved as much as some hoped, it is starting to have important applications in personalized medicine. We predict that pharmacometabonomics will be equally important in the next few decades and will be both valuable in its own right and complementary to pharmacoproteomics and PGx.
BACKGROUND: Pharmacometabonomics is a new branch of science, first described in 2006 and defined as 'the prediction of the effects of a drug on the basis of a mathematical model of pre-dose metabolite profiles'. Pharmacometabonomics has been used to predict drug metabolism, pharmacokinetics (PK), drug safety and drug efficacy in both animals and humans and is complementary to both pharmacogenomics (PGx) and pharmacoproteomics. METHODS: A literature review using the search terms pharmacometabonomics, pharmacometabolomics, pharmaco-metabonomics, pharmaco-metabolomics and the singular form of all those terms was conducted in October 2012 using PubMed and Web of Science. The review was updated until mid April 2013. RESULTS: Since the original description of pharmacometabonomics in 2006, 21 original publications and eight reviews have emerged, covering a broad range of applications from the prediction of PK to the prediction of drug metabolism, efficacy and safety in humans and animals. CONCLUSIONS: Pharmacometabonomics promises to be an important new approach to the delivery of personalized medicine to improve both drug efficacy and safety for patients in the future. Pharmacometabonomics is particularly powerful as it is sensitive to both genetic and environmental factors such as diet, drug intake and most importantly, a person's microbiome. PGx is now over 50 years old and although it has not achieved as much as some hoped, it is starting to have important applications in personalized medicine. We predict that pharmacometabonomics will be equally important in the next few decades and will be both valuable in its own right and complementary to pharmacoproteomics and PGx.
Authors: Michael A Puskarich; Michael A Finkel; Alla Karnovsky; Alan E Jones; Julie Trexel; Brooke N Harris; Kathleen A Stringer Journal: Ann Am Thorac Soc Date: 2015-01
Authors: Richard D Beger; Sudeepa Bhattacharyya; Xi Yang; Pritmohinder S Gill; Laura K Schnackenberg; Jinchun Sun; Laura P James Journal: Arch Toxicol Date: 2015-05-17 Impact factor: 5.153
Authors: Anthony C Dona; Michael Kyriakides; Flora Scott; Elizabeth A Shephard; Dorsa Varshavi; Kirill Veselkov; Jeremy R Everett Journal: Comput Struct Biotechnol J Date: 2016-03-09 Impact factor: 7.271
Authors: Richard D Beger; Warwick Dunn; Michael A Schmidt; Steven S Gross; Jennifer A Kirwan; Marta Cascante; Lorraine Brennan; David S Wishart; Matej Oresic; Thomas Hankemeier; David I Broadhurst; Andrew N Lane; Karsten Suhre; Gabi Kastenmüller; Susan J Sumner; Ines Thiele; Oliver Fiehn; Rima Kaddurah-Daouk Journal: Metabolomics Date: 2016-09-02 Impact factor: 4.290