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)