title: Improved drug therapy: triangulating phenomics with genomics and metabolomics.
abstract: Embracing the complexity of biological systems has a greater likelihood to improve prediction of clinical drug response. Here we discuss limitations of a singular focus on genomics, epigenomics, proteomics, transcriptomics, metabolomics, or phenomics-highlighting the strengths and weaknesses of each individual technique. In contrast, 'systems biology' is proposed to allow clinicians and scientists to extract benefits from each technique, while limiting associated weaknesses by supplementing with other techniques when appropriate. Perfect predictive modeling is not possible, whereas modeling of intertwined phenomic responses using genomic stratification with metabolomic modifications may greatly improve predictive values for drug therapy. We thus propose a novel-integrated approach to personalized medicine that begins with phenomic data, is stratified by genomics, and ultimately refined by metabolomic pathway data. Whereas perfect prediction of efficacy and safety of drug therapy is not possible, improvements can be achieved by embracing the complexity of the biological system. Starting with phenomics, the combination of linking metabolomics to identify common biologic pathways and then stratifying by genomic architecture, might increase predictive values. This systems biology approach has the potential, in specific subsets of patients, to avoid drug therapy that will be either ineffective or unsafe.
authors.affiliation: University of Colorado Department of Emergency Medicine, Leprino Building, 7th Floor Campus Box B-215, 12401 E. 17th Avenue, Aurora, CO, 80045, USA. andrew.monte@ucdenver.edu.|Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, 80045, USA. andrew.monte@ucdenver.edu.|Rocky Mountain Poison & Drug Center, Denver, CO, 80204, USA. andrew.monte@ucdenver.edu.
authors.forename: Andrew A
authors.initials: AA
authors.lastname: Monte
authors.affiliation: Laboratory of Metabolism, Center for Cancer Research, National Institute of Cancer, Bethesda, MD, 20892, USA. chad.brocker@nih.gov.
authors.forename: Chad
authors.initials: C
authors.lastname: Brocker
authors.affiliation: Division of Human Genetics, Department of Pediatrics and Molecular Developmental Biology, University of Cincinnati Medical Center, Cincinnati, OH, 45220, USA. dan.nebert@uc.edu.|Department of Environmental Health and Center for Environmental Genetics, University of Cincinnati Medical Center, Cincinnati, OH, 45220, USA. dan.nebert@uc.edu.
authors.forename: Daniel W
authors.initials: DW
authors.lastname: Nebert
authors.affiliation: Laboratory of Metabolism, Center for Cancer Research, National Institute of Cancer, Bethesda, MD, 20892, USA. fjgonz@helix.nih.gov.
authors.forename: Frank J
authors.initials: FJ
authors.lastname: Gonzalez
authors.affiliation: Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, 80045, USA. David.thompson@ucdenver.edu.
authors.forename: David C
authors.initials: DC
authors.lastname: Thompson
authors.affiliation: Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, 80045, USA. Vasilis.vasiliou@ucdenver.edu.
authors.forename: Vasilis
authors.initials: V
authors.lastname: Vasiliou
mesh_terms: D004358:Drug Therapy / Q000379:methods*; D057890:Epigenomics / Q000379:methods; D056726:Genetic Association Studies / Q000379:methods; D023281:Genomics / Q000379:methods*; D006801:Humans; D055432:Metabolomics / Q000379:methods*; D057285:Precision Medicine / Q000379:methods*; D040901:Proteomics / Q000379:methods; D049490:Systems Biology / Q000379:methods; D059467:Transcriptome