Precision Medicine


Precision Medicine in Acute Care

Seventy-four percent (74%) of all physician office visits involve drug therapy. These prescriptions accounted for $234.1 billion in patient costs in 2008. Forty-eight percent of people in the US take at least one prescription and more than 76% of people 60 years or older are on two or more. Despite this remarkable utilization of prescription drugs, many of these medications are ineffective. There has been an explosion on pharmacogenomic, pharmacokinetic, and clinical data associated with the efficacy of prescribed drugs. Unfortunately, this data has not resulted in widespread success of precision medicine; defined as the right drug, at the right dose, in the right patient. An approach that integrates and accounts for all of these factors together is more likely to predict the clinical drug effect. This integrated approach can be applied to numerous drugs increasing the efficacy and safety of prescriptions.

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  1. Monte AA, Heard KJ, Campbell J, Hamamura D, Weinshilboum RM, Vasiliou V. The effect of CYP2D6 drug-drug interactions on hydrocodone effectiveness. Acad Emerg Med. 2014 Aug;21(8):879-85. doi: 10.1111/acem.12431. Epub 2014 Aug 24. PubMed PMID: 25156930; PubMed Central PMCID: PMC4150819.

  2. Flaten HK, Kim HS, Campbell J, Hamilton L, Monte AA. CYP2C19 drug-drug and drug-gene interactions in ED patients. Am J Emerg Med. 2016 Feb;34(2):245-9. doi:10.1016/j.ajem.2015.10.055. Epub 2015 Nov 9. PubMed PMID: 26639454; PubMed Central PMCID: PMC4878122.

Prediction of Metoprolol Effectiveness


Hypertension (HTN) is the most common chronic medical diseases in the US and 64% of patients receiving antihypertensive treatment fail to achieve blood pressure (BP) control. Metoprolol is a drug used to treat hypertension and represents the first drug with enough pharmacogenomic, pharmacokinetic, and clinical data to build an integrated model. This study is a clinical trial that captures genomic, pharmacokinetic, metabolomic, and efficacy data to build and integrated model that predicts metoprolol efficacy for the treatment of hypertension. #: NCT02293096[AM1] .



  1. CYP2D6 and ADRB1 Genotyping Assay Development

  2. CYP2D6 Genotype Phenotype Evaluation in the Setting of Drug-Drug Interaction

  3. Metabolomic Profiling of Metoprolol Hypertension Treatment



  1. Monte AA, West K, McDaniel KT, Flaten HK, Saben J, Shelton S, Abdelmawla F, Bushman LR, Williamson K, Abbott D, Anderson PL. CYP2D6 Genotype Phenotype Discordance Due to Drug Drug Interaction. Clin Pharmacol Ther. 2018 Jun 8. doi:10.1002/cpt.1135. [Epub ahead of print] PubMed PMID: 29882961.

  2. Ben S, Cooper-DeHoff RM, Flaten HK, Evero O, Ferrara TM, Spritz RA, Monte AA. Multiplex SNaPshot-a new simple and efficient CYP2D6 and ADRB1 genotyping method. Hum Genomics. 2016 Apr 23;10:11. doi: 10.1186/s40246-016-0073-3. PubMed PMID: 27108086; PubMed Central PMCID: PMC4842286.



  • CU-Department of Emergency Medicine Seed Grant

  • NIH-GMS: 1K23GM110516-01 September 2014 – August 2018

  • CCTSI/CTRC: MicroGrant April 2015 – April 2018

Pharmacogenomic and Metabolomic Predictors of Lisinopril Effectiveness


ACE inhibitors (ACEI) are the most commonly prescribed antihypertensive drugs and the third most commonly prescribed drug in the United States. Therefore, understanding the efficacy and safety of ACEI has widespread implications. The objective of this study is to evaluate genomic and metabolomic markers that predict individualized responses to ACE inhibitor (lisinopril) treatment. This study is a secondary analysis of subjects enrolled in a clinical trial examining the pharmacogenomic effectiveness of metoprolol succinate in HTN patients (NCT02293096). Hypertensive patients were started on an ACEI at the initial visit and had blood pressures reevaluated after 1 week. For this secondary analysis, we examined whole blood and plasma samples from patients at baseline and following 1 week of lisinopril treatment. Responders to lisinopril treatment were defined as individuals who had either a 10% decline in systolic blood pressure or a SBP of <140 at the week 1 visit. All others were considered non-responsive. Genomic (Ilumina MEGA CHIP) and metabolomic (unsupervised HPLC-MS/MS with electron ion spray) analyses were performed and clinical data was collected. A systematic approach integrating genomic, metabolomic and clinical factors will be used to identify predictors of lisinopril efficacy.



  1. Flaten HK, Monte AA. The Pharmacogenomic and Metabolomic Predictors of ACE Inhibitor and Angiotensin II Receptor Blocker Effectiveness and Safety. Cardiovasc Drugs Ther. 2017 Aug;31(4):471-482. doi: 10.1007/s10557-017-6733-2. Review. PubMed PMID: 28741243; PubMed Central PMCID: PMC5727913.



  • NIH-GMS: 1K23GM110516-01 September 2014 – August 2018

  • American Medical Association Seed Grant Research Program, 2017

PEGASUS (Personalizing EmerGency/Acute therapeuticS Utilizing Systems biology)


The global objective PEGASUS Research Program is to improve drug effectiveness and safety in common acute care conditions. Drug therapy for acute conditions in the emergency department is only effective in 25-60% of cases. There is tremendous opportunity to improve acute therapeutics by implementing precision medicine programs in acute care settings. We use phenotypic data from our electronic health record and link this data to pharmacogenomic and metabolomic data generated from samples collected through the Emergency Medicine Specimen Bank to discover new mechanisms of drug effectiveness and safety. The PEGASUS program is supported by the Colorado Center for Personalized Medicine and the CCPM Biobank. Through the NIH Maximizing Investigators’ Research Award for Early Stage Investigators (MIRA-ESI) funding, PEGASUS focuses on common acute care conditions such as nausea, pain, and cardiovascular disease with eventual expansion to uncommon drug safety conditions such as anaphylaxis and torsades de pointes. We utilize polymorphisms in drug metabolizing enzymes, such as CYP2D6, as the hub of our models and the metabolic perturbations present in acute illness to identify new pathways, mechanisms, and genetic variants associated with drug safety and effectiveness in these common conditions. Implementation of this systems biology approach to precision medicine will improve drug effectiveness and safety in acute conditions



  1. Limkakeng AT Jr, Monte AA, Kabrhel C, Puskarich M, Heitsch L, Tsalik EL, Shapiro NI. Systematic Molecular Phenotyping: A Path Toward Precision Emergency Medicine? Acad Emerg Med. 2016 Oct;23(10):1097-1106. doi: 10.1111/acem.13027. Epub 2016 Oct 3. Review. PubMed PMID: 27288269; PubMed Central PMCID: PMC5055430.

  2. Monte AA, Brocker C, Nebert DW, Gonzalez FJ, Thompson DC, Vasiliou V. Improved drug therapy: triangulating phenomics with genomics and metabolomics. Hum Genomics. 2014 Sep 1;8:16. doi: 10.1186/s40246-014-0016-9. Review. PubMed PMID:25181945; PubMed Central PMCID: PMC4445687.

  3. Monte AA, Heard KJ, Vasiliou V. Prediction of drug response and safety in clinical practice. J Med Toxicol. 2012 Mar;8(1):43-51. doi:10.1007/s13181-011-0198-7. PubMed PMID: 22160757; PubMed Central PMCID:PMC3550218.

  4. Monte AA, Vasiliou V, Heard KJ. Omics Screening for Pharmaceutical Efficacy and Safety in Clinical Practice. J Pharmacogenomics Pharmacoproteomics. 2012 Mar 16;S5. pii: 001. PubMed PMID: 23264882; PubMed Central PMCID: PMC3526192.



  • NIH-GMS: 1K23GM110516-01 September 2014 – August 2018

  • NIH-GMS: 1R35GM124939 September 2017 – July 31 2022