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9
Pharmacometabolomics: Applications and Challenges Theodora Katsila* and George P. Patrinos
Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece Abstract: Background: Individual drug response arises from the interplay between genes and the environment. Pharmacometabolomics, is an alternative, but complementary discipline to pharmacogenomics, aiming to predict or evaluate response to treatment. Methods: Data for this perspective article were identified by searches of PubMed and references from relevant articles using the search terms "pharmacogenomics", "pharmacometabolomics", and "personalized medicine". Only articles published in English between 2002 and 2015 were included.
Theodora Katsila
Results: This perspective article on pharmacometabolomics focuses on the current applications of this holistic approach and presents the challenges that still need to be met. Conclusion: The merge of pharmacogenomics with pharmacometabolomic data, rapidly and efficiently, remains a significant challenge. No doubt, a “pharmacometabolomics-informed pharmacogenomic” strategy is anticipated to enable our in-depth understanding of individual variations in drug response phenotypes and hence, the design of individualized therapeutic approaches via the prediction of metabotypes.
Keywords: Pharmacometabolomics, personalized medicine, metabotypes, Krebs’ cycle, biomarkers, microbiola. 1. INTRODUCTION Today, it is well established that not all individuals respond to drug treatment in the same way. Indeed, there is an interplay among genes and the so-called, environment (diet, lifestyle, toxins, gut microbiome) [1, 2]. Hence, individual variability in terms of response to a therapeutic approach is strongly influenced by the biochemical state of the individual at the time of treatment, as reflected by their metabolic phenotype (metabotype) [3, 4]. Pharmacogenomics focuses on genes with the aim of personalized treatment, enabling optimal efficacy and minimized toxicity. Although genetic variation is fundamental, there is increasing recognition of the limitations of pharmacogenomics on the basis that this approach does not consider environmental influences on drug absorption, distribution, metabolism and excretion. More recently, in an alternative, but yet complementary discipline, pharmacometabolomics aims to predict and/or evaluate drug on the basis of metabotypes [4, 5]. The latter are considered being the net result of genetic, physiological, chemical and environmental influences. 2. METABOTYPES Metabolic profiles refer to a huge list of both endogenous and exogenous chemical entities; peptides, amino acids, *Address correspondence to this author at the Department of Pharmacy, School of Health Sciences, University of Patras, University Campus, Rion, GR-26504, Patras, Greece; Tel: +30-2610962348; E-mail:
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nucleic acids, carbohydrates, fatty acids, organic acids, vitamins, hormones, drugs, food additives, phytochemicals, toxins as well as the chemicals synthesized or even, ingested by a cell or organism. Upon its completion, the first draft of the human metabolome consisted of a database of approximately 2,500 metabolites [6]. Surprisingly enough, despite the extremely high overall number of endogenous metabolites (~100,000), the number of major metabolites relevant for clinical diagnostics and drug development has been estimated at 1,400-3,000 molecules [7]. Taking into account that the majority of the endogenous metabolites are tied to specific biochemical pathways (glycolysis, Krebs' cycle, lipid or amino acid metabolism), signaling pathways (transmitters, hormones) and specific pathobiochemical processes (oxidative stress), alterations in specific metabolite patterns also reflect changes in pathways and biological processes [8]. Often, a combination of pre-dose metabolite profiling and chemometrics is employed towards the modeling and prediction of individual drug responses and hence, the identification of surrogate markers (candidate biomarkers) [9]. In terms of disease diagnosis and/or prognosis, O’Keefe et al. (2014) demonstrated that changes in the food content of fibre and fat had remarkable effects on the colonic microbiota and metabonome of individuals from high- and low-risk cancer populations (within 2 weeks) [10]. Notably, changes were associated with significant alterations in mucosal inflammation and proliferation. The determined metabotype can be also combined with information on the gut microbiota (metagenomic measurements) to provide enhanced knowledge on the complex interactions between the host and its gut microbiota [11]. © 2015 Bentham Science Publishers
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Katsila and Patrinos
It becomes evident that pharmacometabolomics is advantageous due to the availability and relative ease of biofluids (urine, blood plasma) analysis as well as its unbiased opportunity for finding nonpreselected, and hence unexpected, biomarkers and biomarker combinations, as multiple analytes are quantified simultaneously [12]. Thus, pharmacometabolomic modeling appears not to be limited by prior understanding or hypotheses. Instead, pharmacometabolomic modeling can be a powerful hypothesis-generating scenario. Nevertheless, an adequate monitoring of the analytical quality is always mandatory, prior to establishing the definitive clinical value of a cluster of metabolites featuring a welldefined physiopathological condition. Moreover, there is the need to standardize the physiological (age, sex, lifestyle, diet) and pre-analytical (sample collection, storage, etc.) variables of interest as well as the analytical methods employed (developing standard protocols and finally, data processing and presentation [13].
using a recent “pharmacometabolomics-informed pharmacogenomics” study that involved six genes encoding enzymes that catalyze glycine synthesis and degradation [14, 15]. In addition, the authors compared SNP imputation with their recent LD-based tag SNP genotype study.
3. APPLICATIONS OF PHARMACOMETABOLOMICS
It should be noted that in neonatology and pediatrics, drug pharmacokinetic properties differ substantially from those in adults (in particular, during the neonatal age and over the first four years of life) [16]. Hence, it is rather difficult to obtain an effective and at the same time safe pediatric dose, as a linear reduction of the adult dose would not be considered a reliable approach. Even today, this is a great challenge as most drugs are not specifically approved for pediatric use and a dosage approximation becomes necessary. Pharmacometabolomics can improve the knowledge and understanding via a more accurate definition of drug response and the metabotype of a subject.
Pharmacogenomics and pharmacometabolomics appear to complement each other towards an era of individualized therapeutic approaches (Fig. 1). Pharmacometabolomics can guide genomic studies and data interpretation and vice versa. Indicatively, the two “omics” strategies could merge via the determination of the genetic variation within genes in pathways identified during metabolomic studies by genotype imputation rather than by traditional tag SNP genotyping. This pathway imputation is expected to both accelerate and broaden the scope of the analysis of pharmacogenomic candidate genes as well as pathways by making it possible to survey more widely and drastically reduce the need to genotype prior to replication. Abo et al. (2012), following the work by Ji et al. (2011) tested that hypothesis directly by
So far, pharmacometabolomics has been increasingly applied successfully in animal studies and humans and in several aspects of research, including physiology, functional genomics, nutrition, disease diagnosis, pharmacology and toxicology. A synopsis is depicted in Table 1. 3.1. Clinical Applications Metabolomics appears to be a promising tool in the clinical management, especially of newborns and infants. The metabolomic analysis of urine in the pediatric population is a great advantage of the approach due to the fact that urine may be collected by simple, non-invasive methods, providing meaningful diagnostic information.
The pioneering applications of pharmacometabolomics in the field of pediatric medicine include the gestational agerelated metabolic maturation, the intrauterine growth retardation, the perinatal asphyxia, the inborn errors of metabolism,
Fig. (1). The pharmacogenomics-pharmacometabolomics interplay. The metabolome corresponds to the total repertoire of molecules that reflect the interactions among gene/ protein expression, and the environment (in which the gut microbiome is of fundamental importance). In this interplay, SNPs serve as pharmacogenomics biomarkers. The metabotype is the resulting metabolic phenotype that will define the drug response phenotype (T.Katsila, G.P.Patrinos ©).
Pharmacometabolomics: Applications and Challenges
Table 1.
Current Pharmacogenomics and Personalized Medicine, 2015, Vol. 13, No. 1 11
A synopsis of pharmacometabolomics studies Topic
Year
Reference
The modulation of TMPT activity by SAM (a TPMT co-factor)
2014
[24]
The implication of serotonin in aspirin response variability
2014
[25]
Pharmacometabolomics of statin response
2013
[26]
Pharmacometabolomics of sertraline response in major depressive disorder. (methoxyindole pathway)
2013
[27]
The implication of purine pathway in aspirin resistance
2014
[28]
Variability in response to atenolol treatment
2013
[29]
A host-microbiome metabolic interaction that affects human drug metabolism
2009
[30]
Varying cellular responses at high and low doses of docetaxel in human MCF7 breast cancer cells
2009
[31]
Children with autism are differentiated from their unaffected siblings and age-matched controls by urinary metabolic phenotyping
2010
[32]
The metabolomic profiling of children's brains upon general anesthesia with sevoflurane and propofol.
2012
[33]
Celiac disease autoimmunity in genetically at risk infants, gluten exposure and proof of concept of microbiome-metabolome analysis
2012
[34]
The study of urinary organic acids in respiratory chain deficiencies in children
2012
[35]
the respiratory distress syndrome; the patent ductus arteriosus, renal and respiratory diseases, drug treatments and even, the maternal milk [17]. 3.2. Apllications in Drug Research and Development The role of pharmacometabolomics in drug research and development is profound. The definition of metabolic profiles following the administration of a xenobiotic allows the identification of mechanisms of toxicity and inefficacy early in the process of drug discovery. In this context, both the cost and time associated with the development and marketing of new drug candidates are minimized. Focusing on the subject, pharmacometabolomics is a tool towards individualized therapeutic approaches again via the definition of metabolic phenotypes. 3.2.1. Metabotype-based Subtypes The pharmacokinetic properties of a drug are known to be one of the main factors affecting a subject’s response to treatment. Metabolic imbalances will result in detoxification or toxicity and hence, be responsible for a therapy being safe and effective or inducing an adverse drug reaction. No doubt, pharmacometabolomic analysis, through the use of a metabolomic approach, provides a comprehensive and detailed metabolic profile for a subject. Indeed, pharmacometabolomics aims to predict or assess individual drug response, allowing continued treatment with the right drug (or dosage based on the variations in drug metabolism) and ability to respond to treatment [18]. In such a case, the metabotype of an individual (defined as a “single-sample” data set) should be compared to an “asbig-as-possible” number of similar data sets to towards mo-
lecular molecular subtyping. Today, such efforts have been made with promising results in cancer research to define multi-gene classifiers that predict breast cancer recurrence (Mamma Print, PAM50 Signature) or molecular subtypes in colorectal cancer [19, 20]. Such (pharmaco) genomics-aided pharmacometabolomics may reveal metabotype-based subtypes that relate to disease or drug efficacy/toxicity and hence, help the decision making of physicians. Notwithstanding, a crosstalk between the intrinsic metabolome of an individual and that of its tumor cannot be excluded resulting in a rather complex cancer (pharmaco)metabolome. No doubt, the relative contribution of each metabolome may be difficult to differentiate. For this, pharmacogenomics data of both the host and the tumor would be of value. A cancer metabotype-based subtype should also account for the intratumoral heterogeneity of the genetically different populations of tumor cells and/or primary tumor and secondary metastases. 3.2.2. Metabotype-based Pharmacokinetics/Pharmacodynamics Pharmacometabolomics also serves the identification of alterations in metabolic pathways, following drug administration. For this, an individual’s metabolic profile is analyzed upon drug treatment. This analysis is often combined with a pre-treatment metabolic analysis, allowing the comparison of pre- and post-treatment metabolite levels. The latter will reveal the metabolic pathways that are being altered by the therapy, either intentionally or unintentionally. Today, there are a few studies that have employed the pharmacometabolomic approach in humans, focusing on drugs, such as the immunosuppressant drug tacrolimus and
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the oral anticoagulants acetaminophen and ximelagatran [17]. Notably, another study has shown that pre-treatment metabotypes may predict sertraline response (acute treatment) in patients with major depressive disorder [21]. Kaddurah-Daouk et al. (2007) investigated the lipid profiles of 50 patients with schizophrenia, before and after olanzapine-, risperidone- and aripiprazole-treatment. Comparing pre- and post-treatment profiles, baseline lipid alterations correlating with acute treatment response were identified [22]. Metabotype-based pharmacokinetics and/or pharmacodynamics data is envisaged to be a key component in biomarker research and development supporting patient stratification and precision medicine.
[4]
[5] [6]
[7]
[8]
[9]
CONCLUSION AND FUTURE PERSPECTIVES Today, pharmacometabolomics is little more than a promise. However, this holistic approach is anticipated to have a fundamental impact in the future, enabling our indepth understanding of individual variations in drug response phenotypes and hence, the design of individualized therapeutic approaches via the prediction of metabotypes. The merge of pharmacogenomics with pharmacometabolomic data, rapidly and efficiently, remains a significant challenge. No doubt, a “pharmacometabolomics-informed pharmacogenomic” strategy would be of great benefit [14]. Another challenging area, where pharmacometabolomics has not been applied yet is that of polypharmacy [12]. This is a common regime in elderly and when multifaceted dietary interventions occur (herbal mixtures, etc), resulting in combined effects of multiple xenobiotics, being co-administered, which cannot be easily predicted. Polypharmacy is also a rather challenging aspect in the context of combinatorial therapies against multifactorial and complex diseases, such as the metabolic syndrome or cancer. It has been shown that the activated form of epidermal growth factor correlates well with disease monitoring and drug response in KRAS-wt colorectal cancer patients upon cetuximab treatment [23]. Nevertheless, cetuximab is rarely administered as a monotherapy following clinical protocols’ update, while except for KRAS, also NRAS and BRAF genes play a crucial role in disease prognosis. In such a clinical scenario, pharmacometabolomics would be greatly beneficial.
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CONFLICT OF INTEREST The authors confirm that this article content has no conflict of interest.
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ACKNOWLEDGEMENTS Part of our own work has been supported by National (11_0415; eMoDiA) and European Commission (FP7305444; RD-Connect) grants to GPP.
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Received: April 8, 2015
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Revised: July 10, 2015
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Accepted: August 18, 2015
