Improved drug therapy: triangulating phenomics with genomics and metabolomics

Human Genomics, Sep 2014

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.

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Improved drug therapy: triangulating phenomics with genomics and metabolomics

Human Genomics Improved drug therapy: triangulating phenomics with genomics and metabolomics Andrew A Monte 0 1 Chad Brocker Daniel W Nebert Frank J Gonzalez David C Thompson 0 Vasilis Vasiliou 0 0 Skaggs School of Pharmacy and Pharmaceutical Sciences , Aurora, CO 80045 , USA 1 University of Colorado Department of Emergency Medicine , Leprino Building, 7th Floor Campus Box B-215, 12401 E. 17th Avenue, Aurora, CO 80045 , USA 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. Individualized medicine; Genomics; Metabolomics; Omics; Personalized medicine; Phenomics; Systems biology; Transcriptomics - Introduction Recent advances in genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics have allowed identification of certain factors associated with variable drug responses. However, with few exceptions, high-fidelity prediction of drug efficacy and safety on a larger scale has proven elusive. We have experienced many failures with pharmacogenetic attempts to predict success of drug therapy; this is due to insufficient knowledge and oversimplification of experimental approaches, as well as failure to accept that even the simplest traits almost always reflect an intersection of multiple genetic, epigenetic, and environmental factors. Therefore, we make an argument for embracing complexity. Starting with a clinically relevant traitthe individual patient drug response, which we refer to as phenotypeand then working backward to integrate biological data, is more likely to identify common pathways and inter-individual variability between patient responses. We propose that this integrated-systems biology approach, focused on drug response and adverse drug reactions (ADRs), might be the best way to improve drug efficacy and safety. Limitations of single biological associations Personalized medicine has endured many failures. Clinical phenotype of complex diseases (defined as any biological, physiological, morphological, or behavioral trait) is difficult to predict because most common diseases represent multifactorial traits. Use of pharmacogenomics in predicting drug efficacy or toxicity is more advanced in oncology than perhaps in any other area of medicine. A high degree of efficacy and toxicity can be predicted for chemotherapeutic agents in cancer, based partially upon patient and tumor genotyping. For example, 5-fluorouracil toxicity is associated with dihydropyrimidine dehydrogenase polymorphisms [1,2], erlotinib [3] or cetuximab [4] responsiveness is linked to epidermal growth factor receptor polymorphisms, and there are other examples [5]. However, because tumor cells mutate or the patient develops associated co-morbidities (e.g. renal failure, pulmonary hypertension), then efficacy and toxicity remain difficult to predict. Focusing on a single biological association can sometimes efficiently predict drug response within a subset of a large cohort of patients, but rarely, if ever, can we expect to predict drug response in the individual patient [6]. Genotype-phenotype associations Virtually, all clinical traits are polygenic, raising the number of potential phenotypes factorially; even the simplest genetic predictors lead to a range of phenotypes. This was demonstrated in 1960 when the earliest pharmacogenomic association of polyneuropathy with slow acetylation of isoniazid by N-acetyltransferase-2 (NAT2) was reported [7]. Not all individuals with the slow-acetylator trait develop neuropathy, and there remains a range of serum drug concentrations within each group of NAT2 genotypes. Currently, at least 190 different NAT2 alleles have (...truncated)


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Andrew A Monte, Chad Brocker, Daniel W Nebert, Frank J Gonzalez, David C Thompson, Vasilis Vasiliou. Improved drug therapy: triangulating phenomics with genomics and metabolomics, Human Genomics, 2014, pp. 16, 8, DOI: 10.1186/s40246-014-0016-9