Open access peer-reviewed chapter

Pharmacometabolomics: A New Horizon in Personalized Medicine

Written By

Abdul-Hamid Emwas, Kacper Szczepski, Ryan T. McKay, Hiba Asfour, Chung-ke Chang, Joanna Lachowicz and Mariusz Jaremko

Submitted: 10 November 2020 Reviewed: 15 June 2021 Published: 09 August 2021

DOI: 10.5772/intechopen.98911

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Pharmacology is the predominant first-line treatment for most pathologies. However, various factors, such as genetics, gender, diet, and health status, significantly influence the efficacy of drugs in different patients, sometimes with fatal consequences. Personalized diagnosis substantially improves treatment efficacy but requires a more comprehensive process for health assessment. Pharmacometabolomics combines metabolomic, genomic, transcriptomic and proteomic approaches and therefore offers data that other analytical methods cannot provide. In this way, pharmacometabolomics more accurately guides medical professionals in predicting an individual’s response to selected drugs. In this chapter, we discuss the potentials and the advantages of metabolomics approaches for designing innovative and personalized drug treatments.


  • Personalized Medicine
  • Pharmacometabolomics
  • Metabolomics
  • NMR
  • metabolites

1. Introduction

Conventional drugs are developed as standard treatments for all patients diagnosed with particular diseases regardless of any differences between those patients. Consequently, this universal approach comes with a high degree of uncertainty regarding the treatment outcome. It is well-established that individuals can be differentially affected by the same disease due to factors such as general health status, genetics, gender, diet habits, smoking, alcoholic intake, etc. [1, 2]. The global COVID-19 pandemic has demonstrated clearly that a single disease can have different outcomes in different people, and the choice of therapeutic strategies needs to be calibrated to an individual rather than using a standard protocol for heterogenous populations. Indeed, the increasing incidence of treatment failure, especially with life threatening diseases such as cancer relapse, evidences a need for personalized drug regimens.

Each pathological state in humans affects multiple organs/systems and leads to the perturbation of metabolites and protein concentration levels. Thus, analysis of biomarkers (such as unique metabolites or proteins) is an effective way to monitor human health [3, 4]. Biomarkers can be used for disease prediction, diagnosis, and to screen the efficacy of treatment intervention. For example, the glucose level in blood is a biomarker of diabetes and can be used to monitor drug efficacy [5, 6, 7]. Table 1 summarizes the most prominent examples of protein biomarkers discovered recently.

Protein biomarkersUseful for:Ref
Apolipoprotein H, ApoCI, Complement C3a, Transthyretin, ApoAIPrediction of recurrence-free survival in women with estrogen receptor-negative tumors[8]
S100 calcium-binding protein B, Neuron-specific enolase, Glial fibrillary acidic protein, Ubiquitin
Carboxy-terminal hydrolase-L1, Tau, Neurofilament-light
Prediction of outcome and severity in traumatic brain injury patients[9]
S100A9, ThioredoxiN, Cadherin-related family member 2Diagnosis (presence) of cholangiocarcinoma[10]
TFF1, ADAM (male only), BARD (female only)Early diagnosis of gastric cancer[11]
Acidic nuclear phosphoprotein 32 family member B, Thrombospondin-4, Cardiac muscle troponin T, Glucocorticoid-induced TNFR-related protein, NAD-dependent deacetylase sirtuin-2Creating new utrophin modulation strategies that could help patients with Duchenne muscular dystrophy[12]
C-reactive protein, S100A8, S100A9, S100A12Prognosis of the severity of rheumatoid arthritis.[13]
S100A4, S100A8, S100A9, Carbonic anhydrase I, Annexin VDiagnosis of urinary bladder cancer and prognosis of patient outcome.[14]
Gelsolin, Fibronectin, Angiotensinogen, HaptoglobinDetection of lymph node metastasis of oral squamous cell carcinoma.[15]
Neurotrophic factor, Angiotensinogen, Insulin-like growth factor binding protein 2, Osteopontin, Cathepsin D, Serum amyloid P component, Complement C4, Prealbumin (transthyretin)Diagnosis of Alzheimer’s disease in Han Chinese.[16]
Alpha-2-macroglobulin, Chromogranin-A, Glutathione pertidase 3Obtaining qualitative and quantitative assessments of radiation exposure.[17]

Table 1.

Examples of biomarkers and their use in medicine.

Among all ‘-omics’ approaches, metabolomics is the most effective of qualifying and quantifying the perturbation of metabolite concentrations under external and internal factors. Thus, joining metabolomics with other ‘-omic’ sciences (e.g. genomics) is essential for a comprehensive understanding of disease onset and pathogenesis, and provides a better diagnosis and treatment.

The total number of endogenous metabolites (although it is not completely determined yet) in human bio-fluid and tissues are lower than the total number of expressed proteins, giving metabolomics an extra advantage in monitoring disease pathology. Moreover, the perturbation of metabolite levels in human bio-fluids is usually greater than that of protein concentrations, providing an easy and clearer bio-marker role [18, 19, 20]. For instance, cancer leads to changes in affected cells, which cause an up-regulation in metabolite concentration levels during carcinogenesis [21]. For example, increased lactate levels have long been associated with different types of cancer [22]. Recently, the development of computational methods, such as bioinformatics and human metabolome databases establishing large scale bio-banks and computer programs, have facilitated the employment of metabolomics in stratified medicine. Pharmacometabolomics is a new subset of the metabolomics field aiming to predict the response of an individual to a drug or to develop optimized treatment strategies based on previous knowledge of subject metabolomics information (individual’s metabolic profile). One should keep in mind that aerosolized treatment would never lead to the discovery of a novel drug for each individual subject. Indeed, the number of new drugs is almost constant in the last decades (Figure 1).

Figure 1.

Number of novel drugs approved annually by the FDA between 1993 and 2020 with graphical representation.

In this chapter, we briefly introduce metabolomics along with common metabolomics analytical platforms regarding the development of a personalized medicine approach and factors that will empower advances in personalized medicine.


2. FDA approved drugs since 1975

Over the past few decades, pharmaceutical product intervention has improved significantly resulting in more saved lives and enhanced public health. The annual number of newly approved drugs applicable for human use has varied greatly over the years (Figure 1). The Food and Drug Administration (FDA) is an agency within the United States. One of its primary responsibilities is the approval of human pharmaceutical products based on safety and efficacy. Regulating and managing the human pharmaceutical industry and the approval of new drugs is the responsibility of the Center for Drug Evaluation and Research (CDER) [23].

The FDA catalog contains most of the approved drug products since 1939. However, since 1998 a complete human drug database is available, known as the Orange Book, which includes patient information, drug labels, and drug reviews. The Orange Book is considered a comprehensive detailed list of all pharmaceutical products approved in the U.S. by the FDA. However, studying the number of pharmaceutical products approved annually is not straightforward. First, the number of approved human drugs was not accurate before 1981, as the Orange Book did not report pharmaceutical drug approval data until after 1981, including new molecular entities (NMWE), the pharmaceutically active ingredient, drug dosage form, combination, formulation, and indication [24]. In addition, the Orange Book excludes any withdrawn drug or ‘no-longer marketed’ pharmaceutical products due to either drug efficacy concerns or safety concerns. Below, the reader can find Figure 1 summarizing the number of FDA-approved novel human drugs per year from 1993 to 2020 [24, 25].

As is apparent from Figure 1, the year 2020 represents the second-highest number of FDA-approved novel human drugs over the past twenty years (53 drugs), while 2018 was the year when the highest number of drugs were approved by the FDA (57 drugs). In 2017, only 46 drugs were approved [26].

The average rate of new drug approvals by the FDA has increased over the years (Figure 2). Before 1950, the average annual drug approval was less than four, while the average annual drug approval in the 1960s and 1970s was 10. However, in the 1980s the average approval rate increased to more than twenty per year. It has continued to increase to reach more than twenty-five approvals per year from 2000 to 2010. Over the last several years there have been further increases, reaching an average of more than 39 approved compounds per year from 2010 till 2020 [26, 27]. The average novel drug approval by the FDA over the decades is listed in Figure 2.

Figure 2.

Average numbers of novel drug approvals by the FDA over the last five decades with graphical representation.


3. Metabolomics

Metabolomics is defined as “the measurement of metabolite concentrations and fluxes and secretion in cells and tissues in which there is a direct connection between the genetic activity, protein activity, and the metabolic activity itself” [28]. It is a relatively new field and is employed in a wide range of applications that monitor biological systems [3, 29, 30]. Integrating metabolomics with other ‘-omics’, including proteomics, transcriptomics, and genomics, provides an exhaustive description of the biological system under study. Metabolomics provides a snapshot of the metabolite dynamics, and is a powerful tool when investigating numerous perturbations in biological systems, including pathophysiological events, environmental stimuli, and genetic modifications [31, 32, 33, 34]. Moreover, metabolomics investigates every perturbation in metabolite compositions and/or concentrations, and it has already been applied in different fields such as biomedicine, environmental science, nutrition and diet studies, microbiology, and drug toxicology, as well as marine and plant sciences [35, 36, 37, 38, 39].

Metabolomics is usually classified into two main categories: (1) untargeted, and (2) targeted. Untargeted metabolomics is focused on the entire pool of “detectable” metabolites in a biological sample and makes no assumptions about metabolite(s) or class of metabolites, nor their concentrations. Untargeted metabolomics relies on fingerprinting approaches, where a group or different classes of samples (e.g., healthy control vs. pathological samples) are compared, and where absolute metabolite quantifications are not necessary. In contrast, targeted metabolomics focuses both on the identification and quantification of a specific number of metabolites. Targeted metabolomics approaches are relevant for drug development, where the drug mechanism (including drug absorption and drug distribution) needs to be precisely monitored.

The choice of proper analytical technique(s) in metabolomics is the crucial step, and particularly targeted metabolomics requires accurate metabolite quantification. Metabolomics applies different analytical techniques, including mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, Fourier transformed infrared (FT-IR) spectroscopy, and high-performance liquid chromatography (HPLC). Among them, NMR spectroscopy and MS spectrometry are the most common and powerful analytical tools [40, 41].

3.1 Analytical techniques in metabolomics

Similar to other ‘-omics’ disciplines, metabolomics uses different analytical platforms, separately or in combination (two or more techniques) [32, 42]. Although several analytical platforms are employed in metabolomics studies, including FT-IR spectroscopy [43, 44, 45], HPLC [46, 47], NMR spectroscopy [48, 49, 50, 51, 52, 53], and MS [54, 55, 56, 57] combined with gas or liquid chromatography [58, 59, 60, 61, 62], MS and NMR are the most common approaches [3, 50, 63, 64, 65]. There is no single optimum analytical technique that can elucidate all metabolites equally. Each analytical method has its advantages and limitations. For example, NMR is a non-destructive and highly reproducible technique where metabolic pathways or metabolic flux can be studied by using isotopic nuclei (such as 13C and 15N NMR), thus monitoring the flow of compounds through metabolic pathways [66, 67, 68, 69].

Nevertheless, it has two main drawbacks that must be kept in mind: inherently low sensitivity and potential signal overlap. Different technical approaches have been developed to overcome these two drawbacks, contributing to the development of new and more efficient NMR techniques. For example, dynamic nuclear polarization (DNP) can be used to increase the NMR signal enhancement [70, 71], and the use of cryoprobes and the introduction of ultra-high magnetic fields (e.g., 1GHz) helps to overcome the low sensitivity limitation [72, 73]. The peak overlap problem can be minimized by the use of the highest magnetic fields and multi-dimensional NMR methods such as HSQC, TOCSY, COSY, and HMBC techniques [66, 74, 75, 76, 77, 78].

As stated, no singular analytical technique can perform a complete quantification and identification of all the metabolites in one analysis. Therefore, in addition to one and two-dimensional NMR experiments, different complementary techniques are required, such as liquid chromatography-mass spectrometry (LC–MS) and gas chromatography–mass spectrometry (GC–MS), which help to maximize the number of identified and quantified metabolites [32, 65, 79, 80].

For instance, the human urine metabolome was analyzed by Wishart et al. with several different analytical tools (ICP-MS, NMR, GC–MS, DFI/LC–MS/MS, HPLC) to facilitate the detection of the highest possible number of human urine metabolites. Among all metabolites, 209 were identified by NMR, 179 by GC–MS, 127 by DFI/LC–MS/MS, 40 by ICP-MS, and 10 by HPLC [81].

Based on the ability to separate and detect a wide range of metabolites, LC–MS is one of the most widely used tools for carrying out metabolite profiling studies [82, 83, 84, 85, 86]. LC–MS combines HPLC and mass spectrometry, and provides a powerful analytical tool for the separation, identification, and quantification of metabolites in a studied sample [65, 87, 88, 89, 90]. HPLC separates molecules based on different physical and chemical properties such as charge, polarity, molecular size, and affinity towards column matrices [91, 92, 93, 94]. Thus, different successful chromatography methods have been developed, such as reversed-phase (RP) gradient chromatography [85, 86, 95, 96]. To obtain the best separation, and presumably the highest number of detected metabolites, each sample can be analyzed twice using RP and normal phase chromatography. Moreover, the column switching approach of 2-dimensional analysis in an “orthogonal” combination of hydrophilic interaction liquid chromatography (HILIC) and RP-L, in conjunction with utilizing different electro spray ionization (ESI) modes can also be used [85, 86, 97, 98, 99]. In addition to using different separation methods and/or ionization methods, LC–MS is inherently far more sensitive than NMR and enables researchers to detect secondary metabolites at lower concentrations [100, 101]. The drawback occurs with the consistency of the separation performance. For example, columns can degrade non-linearly over time, requiring constant monitoring, determination of effect(s), and compensation in the final analysis. Solvent purity, pump performance, and injector consistency can all come into play. The inclusion of quality control samples at the beginning, end, and randomly inserted into the experimental samples should allow the compensation and quality control of any introduced confounder(s), but adds material costs, extends batch run times, and introduces complexity to the analyses.

3.2 Development of ‘-omics’ in personalized medicine approach

Over the last decades, various fields of bioresearch (genetics, genomics, proteomics, and metabolomics) have quickly evolved and revealed mechanisms of diseases, and most importantly delivered new therapeutic outcomes. Although the current tenet regarding the uniformity of the drug response seems to be widely accepted, it does not take into account the individual differences. Individuals may not respond in the same way to the pharmacological treatments or present minor and serious side effects. For example, antidepressants [102], statins [103, 104], or antipsychotic drugs [105] have been shown to have reduced effects on some individuals, even to the extent that only a quarter of patients can achieve a functional remission of the disease [105]. Pharmaceutical treatments are ineffective for 30 to 60% of patients [106]. Moreover, a significant number of patients may develop adverse drug reactions (ADR) related to their treatment, with the incidence of fatal ADR being 0.32% [107]. In order to minimize the negative effects of pharmaceutical treatments, and at the same time optimize the drug therapy in terms of its efficiency, a more personalized approach has been proposed, which assesses various factors prior to the treatment through the application of the different ‘-omics’ [108].

This approach is not entirely new, as some characteristics (age, weight, co-morbidity, family history, and biochemical parameters) are already commonly considered. However, technological progress allows us to analyze individuals in more detail – from different genes, and single-nucleotide polymorphism (SNPs) genomics, to small, biologically active molecules (proteomics, metabolomics) and even the metabolic pathways of individuals (metabolomics, fluxomics) [109, 110]. In addition, personalized medicine not only takes into account the physiological status of a person’s body - it also considers the unique, psychosocial situation of the individual, which may have a direct effect when a given health condition manifests in that individual and how he/she will respond to treatment [111]. Although these aspects are taken into consideration for a more complete picture of a person’s medical status, separate approaches could also be used to focus on precise problems. For example, a fairly new field called pharmacogenomics tries to assess and validate the impact of human genetic variation on drug responses [112, 113]. Currently, we know that inherited variations in approximately 20 genes can affect around 80 medications and the way the body responds to them [114]. Another young field that has become a prominent branch of metabolomics is pharmacometabolomics, which is the subject of this review.

Personalized medicine has already shown its value in therapies to combat diabetes and cancer [115, 116, 117, 118, 119]. For example, the management of blood glucose in diabetes requires proper medication, for which the dosage and efficiency is suited to the individual patient. The efficacy of one of the drugs used in type 2 diabetes, metformin, has been associated with polymorphisms in several genes, specifically solute carrier family (SLC) 22 (an organic cation transporter) member 1 (SLC22A1), SLC22A2, SLC47A1, organic cation transporter 1 and 2 (OCT1 and OCT2), and the gene encoding for multidrug and toxin extrusion 1 protein [MATE1] [115, 120]. Sulfonylureas which are another class of drugs used to treat type 2 diabetes, have been shown to have a variable response effect depending on the genomic profile of the patient, e.g., the variant ‘TT’ at rs12255372 in the TCF7L2 gene results in a weaker response compared to the ‘GG’ version [116, 121]. Those genetic factors are usually not considered when therapy is administered, despite the fact that the information they provide can have direct and substantial effects on therapy optimization and the success of treatment.

Similar benefits from personalized medicine have been observed in the treatment of various types of cancer. One of the best examples that highlights recent progress is breast cancer. Based on the biomarkers present in tumors, such as the estrogen receptor, progesterone receptor, antigen Ki-67, human epidermal receptor 2 [122], and mutations in genes such as Breast cancer gene 1 and 2 (BRCA1, BRCA2), which are related to carcinogenesis [123], breast cancer can be divided into different subgroups [122]. Each of the cancer types has its own characteristics and requires a specific, more personalized approach to maximize treatment efficacy while minimizing the adverse effects [122, 124]. The decision regarding which therapy to choose becomes even more complicated when we also consider the genetic profile of an individual (the susceptibility to the treatment) [118, 122, 125]. For example, different variants of CYP2D6 (cytochrome P450, family 2, subfamily D, polypeptide 6), which interacts with tamoxifen (a standard drug used in steroid receptor positive breast cancer) have been shown to have direct impacts on the treatment. The impaired version of the protein could also be associated with the recurrence of breast cancer [118, 122]. On the other hand, a personalized approach could also be used in a preventive way. As an example, genetic testing with a focus on the identification of potential, carcinogenic mutations in the BRCA1 and BRCA2genes could be used to create a proactive strategy (MRI, chemoprevention, bilateral mastectomy), thus significantly decreasing the chances of developing a more severe disease [126].

3.3 Metabolomics databases

The demand for functional and inclusive metabolomics databases is driven by the need for fast data analysis including metabolite identification, quantification, and subsequent interpretation of complex metabolite data, and possibly from multiple instrument sources. As a result of collective efforts in this area, several different databases have been established, including the Human Metabolome Database (HMDB) ( [127, 128], Platform for RIKEN Metabolomics (PRIMe) [129], Biological Magnetic Resonance Data Bank (BMRB) [130], and the Madison Metabolomics Consortium Database (MMCD) [131]. The existing information on the human urine metabolome was published recently with detailed information on each reported metabolite, including concentration perturbation at normal and disease-related levels ( The human urine metabolome along with the human serum metabolome represent a significant development and resource for researchers, which may be critical when employing metabolomics approaches in clinical applications including stratified medicine. Furthermore, the human metabolome database serves as a cross-referencing and benchmarking tool for general metabolomics studies, including metabolite identification, quantification, and newly discovered disease biomarkers. The Madison-Qingdao Metabolomics Consortium Database ( contains information on more than 20,000 compounds, including NMR and MS data that are valuable in the identification and quantification of metabolites present in biological samples [131]. Among different freely available metabolomic databases, the HMDB ( [128, 132] (University of Alberta, Canada, David Wishart group) is becoming the de-facto standard reference for the metabolomics community. The HMDB contains information on 74,462 metabolite entries gathered and summarized from literature-derived data and also contains an extensive collection of experimental metabolite concentration information compiled from hundreds of MS and NMR metabolomics analyses performed on urine, blood, and cerebrospinal fluid samples. The data entries encompass a wide range of information, including structural, chemical, clinical, and biological information for many of the reported metabolites.

In 2012, the MetaboLights database ( [133] was established for the online storage of metabolomic experiments, associated raw data, and metadata, to interrogate databases of collected experimental information in publications. This database was first developed and maintained by the European Bioinformatics Institute [134], and later it has been endorsed and developed by the COSMOS consortium [135]. The continuous development of metabolomics databases alongside the uninterrupted advancements in software and supercomputer capabilities may lead to better clinical practices, including diagnosis, disease prognosis, and, ultimately, effective personalized treatments.

3.4 Biobanks and their impact on personalized medicine studies

Over the past decade, several high-capacity biobanks have been established to serve as baseline research and clinical studies tools in use by scientific institutions, clinics, private companies, and regulators at both national and international levels, encompassing a high number (i.e., millions) of samples necessary for medical research. Furthermore, the standardization of sample collection and storage conditions will help reduce sample collection bias and overcome the limitations afforded by variations between different studies, protocols, and practices. Biobanks usually also collect relevant data, such as whole-genome, genotype, geographic location, dietary preference(s), proteomic, and medical image information [136, 137, 138]. In addition to national registries, the incorporation of existing electronic health records (EHRs) is becoming more common, making large biobank datasets more applicable for a greater number of users [139, 140]. The availability of this additional information, combined with the collection of multiple samples over longer periods from the same individual, facilitates improved interpretation of experimental data and provides controls for possible confounders. Establishing large scale national and international biobanks therefore is an essential step and a valuable resource for clinical practitioners and in the development of public health policies, in addition to being crucial for the development of personalized treatments. These megabanks have the capacity to store samples from the same person over the course of many years, which in the future may be collected from childhood and followed up with the periodic collection of new samples throughout life [141].

As biobanks represent a major resource in large-scale global studies, we believe that the impact of metabolomics approaches will become ever more important in future medical research and public health efforts, including personalized health care and stratified medicine.

3.5 Pharmacometabolomics

As mentioned, pharmacometabolomics is a fairly new addition to the ‘-omics” family of studies. One of the pioneering works that helped create this novel field of science was carried out in 2006 by Clayton et al. on paracetamol [142]. Their main goal was to check if the metabolite profile of an animal, prior to the administration of a drug, would allow for the prediction of the metabolism of a drug as well as its toxic effects on an animal. For this purpose, the team collected urine samples from 65 rats, both before and after the administration of paracetamol. Later, samples were analyzed by 1H 1D NOESY NMR spectroscopy. After analyzing the spectra, researchers identified four paracetamol-related metabolites, specifically paracetamol sulphate, paracetamol glucuronide, mercapturic acid derived from paracetamol, and paracetamol. Compared to the histopathological results obtained from the same rats, a substantial model for predicting post-dose histology of the liver could not be established, but they discovered a relationship between the pre-dose metabolic profile of urine and the histological outcome. The main factors predicting that relationship were identified as taurine, trimethylamine-N-oxide (TMAO), and betaine, where higher pre-dose levels of taurine indicated less damage to the liver while higher levels of TMAO and betaine were associated with greater damage [142]. This pioneering work paved the way for the establishment of organizations focused on pharmacometabolomics, such as the Pharmacometabolomics Research Network (PMRN), where the main aim is to “integrate the rapidly evolving science of metabolomics with molecular pharmacology and pharmacogenomics” [143]. So far, PMRN has produced numerous publications, many of them pertaining to lifestyle disorders and diseases. One example concerns the lipidomic response to treatment with simvastatin [144]. The authors of this paper identified metabolites that could predict the outcome of treatment with simvastatin – phosphatidylcholine, including 18 carbon fatty acids with two double bonds at the n6 position, cholesterol esters with 18 carbon fatty acids with one double bond at the n7 position, and 18 carbon-free fatty acids with three double bonds at the n3 position [144]. Additionally, the authors discovered a group of metabolites that may help to predict the changes of C-reactive protein (CRP) after the treatment - five of them were plasmalogens (a specific group of glycerophospholipids containing a vinyl ether moiety at the sn-1-position of the glycerol backbone) [145], and the others were phosphatidylcholines and cholesterol esters [144]. Another interesting study worth mentioning is related to changes in lipids levels in schizophrenia and upon treatment with antipsychotics [146]. The authors measured the changes in the lipid profiles of patients before and after treatment with olanzapine, risperidone, and aripiprazole. They discovered that treatment with olanzapine and risperidone increased the levels of 50 lipids, raised the concentration of triacylglycerols and generally decreased free fatty acids. Moreover, the concentration of phosphatidylethanolamine that is suppressed in patients with schizophrenia was raised by all three drugs [146].

Presently, most of the pharmacometabolomics studies focus on identifying specific biomarkers related to administered medications. Those biomarkers can provide information ranging from predicting patient treatment response, to monitoring the changes during the treatment, or evaluating the end effects of treatment (i.e., if the patient responded positively or negatively to the therapy) (see Table 2 and Figure 3). Examples of pharmacometabolomic studies are shown in Table 3.

Type of BiomarkerDefinitionExample
Diagnostic BiomarkerBiomarker used to detect or confirm the presence of disease or to identify individuals with a subtype of the disease.Sweat chloride can be used to confirm cystic fibrosis [148].
Monitoring BiomarkerBiomarker measured constantly to assess the status of the disease or for evidence of exposure to (or effect of) a medical product or an environmental agent.HIV-RNA can be used as a monitoring biomarker to measure and guide treatment with antiretroviral therapy (ART) [149].
Pharmacodynamic/Response BiomarkerBiomarker used to show a biological response of an individual exposed to a medical product or an environmental agent.Serum LDL cholesterol can be used for evaluating response to a lipid- lowering agent in patients with hypercholesterolemia [150].
Predictive BiomarkerBiomarker used to identify individuals who will experience positive or negative outcome from exposure to a medical product or an environmental agent.Mutations in BRCA 1/2 genes can be used to identify women with platinum-sensitive ovarian cancer that will most likely respond to PARP inhibitors [151].
Prognostic BiomarkerBiomarker used to identify the likelihood of a clinical event such as disease recurrence or progression.Mutations in BRCA 1/2 genes can be used to evaluate the likelihood of a future second breast cancer in patients currently diagnosed with one [152].
Safety BiomarkerBiomarker used for indicating the likelihood or presence of a toxic effect, measured before or after the exposure to a medical product or an environmental agent.Hepatic aminotransferases and bilirubin can be used to evaluate potential hepatotoxicity [153]
Susceptibility/Risk BiomarkerBiomarker used for the estimation of a chance of disease or other medical condition in an individual who currently does not have clinically apparent disease or condition.Mutation in BRCA 1/2 genes can be used to identify individuals with a predisposition to develop breast cancer [154].

Table 2.

Types of biomarkers with examples of practical applications. Based on the BEST (Biomarkers, EndpointS, and other Tools) resource by the FDA-NIH Biomarker Working Group [147].

Figure 3.

A brief description of biomarkers of specific use in the drug development process. Based on “Context of use (COU) for a biomarker” by U.S. Food and Drug Administration [155].

Chemical CompoundGoal of the studyMain changes in metabolites post exposureConclusionsRef
AspirinTo investigate the mechanism of aspirin resistance.↑ Inosine, adenosine, guanosine
↓ Hypoxanthine, xanthine
  • Higher levels of adenosine and inosine were observed in the group categorized as “poor responders”.

  • A pharmacogenomics approach pinpointed an SNP in the adenosine kinase (ADK) intronic variant - rs16931294, where the G allele of this variant was associated with poor response to the treatment.

To define pathways implicated in variation of response to treatment with a focus on metabolites containing an amine functional group.↑ O-Phosphoethanolamine, serotonin
↓ Glycylglycine, L-aspartic acid, L-glutamic acid, L-leucine, L-phenylalanine, L-serine, ethanolamine, glycine, ornithine, taurine, L-asparagine, L-valine, beta-alanine, L-lysine, L-histidine, L-tyrosine, L-glutamine
  • The changes in metabolite profiles of healthy individuals treated with low dosage of aspirin cannot be directly attributed to COX-1 inhibition.

  • Increased levels of serotonin correlated with higher post-aspirin platelet reactivity.

To investigate:
  • The effects of low-dose aspirin therapy on the oxylipid metabolic pathways,

  • the sex differences in aspirin-induced oxylipid changes, and

  • potential association of oxylipid on aspirin-induced inhibition of platelet aggregation.

↑ 13,14-dihydroPGF2
↓ TXB2, 12-HHTrE, 11-HETE, 5-HETE, 12-HETE, 8-HETE, 15-HETE, 9-HODE, 13-HODE, 5-HETrE, 5-HEPE, 12-HEPE, 15-HEPE, 9-HOTrE, EpOMEs, DiHOMEs, DiHETrEs, 20-HETE.
  • Aspirin does not show any sex-specific effects on oxylipid levels.

  • Aspirin decreases almost all of the oxylipids measured in the samples.

  • Several LA-derived oxylipid (3-HODE, 9-HODE, 12,13-DiHOME, and 12,13-EpOME) metabolites might contribute to the variability of non-COX1-mediated response to aspirin.

To assess the metabolic pathways affected by aspirin administration that are potentially involved in cardiovascular and antitumoral protection.↑ 3-methylglutarylcarnitine
↓ L-histidine, hydantoin-5-propionate, 4-imidazolone-5-propanoate, N-formimino-L-glutamate, xanthosine, L-glutamine, 5-aminoimidazole-4-carboxamide-1-β-D ribofuranoside, butyryl-L-carnitine, tiglylcarnitine, isovalerylcarnitine, heptanoylcarnitine,
  • Aspirin decreases the levels of glutamine and metabolites involved in histidine and purine metabolism.

  • The ability of aspirin to increase the β-oxidation of fatty acids and decrease glutamine levels implicates reduced synthesis of acetyl-Co-A that could help explain aspirins potential anticancer effects.

Aspirin eugenol ester (AEE)To evaluate the protective effect of AEE on paraquat-induced acute liver injury (ALI) in rats.↑ L-histidine, D-asparagine, L-phenylalanine, pipecolic acid, acetylglycine, N-(2-methylpropyl)acetamide, inosine, xanthosine, melatonin radical, ophthalmic acid, glutamylarginine, S-(PGJ2)-glutathione, L-octanoylcarnitine, lysoPC(P−16:0), argininic acid, N-undecanoylglycine, chenodeoxyglycocholic acid,
↓ Glycerophosphocholine, hypoxanthine, nonyl isovalerate, glutamylleucine, pipecolic acid, deoxycholic acid glycine conjugate, dephospho-CoA, taurochenodesoxycholic acid, lysoPC(14:1), PA(22:2), cholic acid, 5,9,11-trihydroxyprosta-6E,14Z-dien-1-oate, lysoPE(18:2), lysoPE(20:4), lysoPE(16:0)
  • AEE shows protective effects against PQ-induced ALI.

  • The mechanisms in which aspirin eugenol ester protects against the effects on PQ-induced ALI are correlated with antioxidants that regulate amino acid, phospholipid, and energy metabolism metabolic pathway disorders and attenuate liver mitochondria apoptosis.

To identify the different proteins and small molecules in plasma to explore the mechanism of action of AEE against thrombosis.↑ Oleamide, palmitic amide, linoleic acid, L-acetylcarnitine, creatine, proline betaine, arachidonic acid
↓ L-carnitine, L-methionine, L-proline, L-pipecolic acid, allantoin, palmitic acid, citric acid, L-tryptophan*
  • Metabolomics results suggested that the therapeutic mechanism of action of AEE (as well as for aspirin and eugenol) could be involved with energy metabolism, amino acid metabolism, and fatty acid metabolism.

  • A total number of 38 (AEE), 41 (aspirin) and 54 (eugenol) proteins were differentially regulated in rats treated with those compounds.

BusulfanTo investigate biomarkers for predicting busulfan optimal dosage.↑ Deferoxamine-derived metabolites
↓ Carnitine C9:1, carnitine C12:1-OH, phenylacetylglutamine**
  • Busulfan metabolism is decreased in patients with high ferritin levels and reduced liver function.

GemcitabineTo investigate potential predictive biomarkers for the efficacy of gemcitabine-based chemotherapy while obtaining the most optimal therapeutic results in patients with pancreatic cancer.A total number of 38 and 26 different metabolites were identified between the gemcitabine resistant and gemcitabine sensitive pancreatic carcinomas from whom four of them: 3-hydroxyadipic acid, D-galactose, lysophosphatidylcholine (LysoPC) (P-16:0) and tetradecenoyl-L-carnitine, were significantly different between the carcinoma types.
  • 3-hydroxyadipic acid, D-galactose, lysophosphatidylcholine (LysoPC) (P-16:0) and tetradecenoyl-L-carnitine could be used as biomarkers for evaluating the efficacy of chemotherapy in pancreatic carcinoma.

Isoniazid (INH), Rifampicin (RIF), Pyrazinamide (PZA), Ethambutol (EMB) - DOTS treatment programTo identify metabolites that describe the changes related to tuberculosis therapy↑ Dodecyl acrylate, pyrazinamide, 1,6-hexylene glycol, ribitol, 1-decene, 2,4-dimethylbenzaldehyde, 2,6-dimethylnonane, 3,4-dihydroxybutyric
acid, 5-hydroxyindoleacetic
acid, alfa-isosaccharinic
1,4-lactone, beta-Isosaccharinic
1,4-lactone, decane, fumaric acid, hippuric acid, N-formylglycine, sebacic acid, threonic acid, undecane, urea, 3-ethyl-4-methyl-1Hpyrrole-
2,5-dione, D-lyxose, phosphoric acid,
↓ Pyrazinoic acid, ethylene glycol, oleic acid, 5-oxoproline, citric acid, ethyl ester, cumene, hemimellitene, hexadecane, indane, isocumene, o-ethyltoluene, oxalic acid, p-ethyltoluene, sorbose, vannilic acid, cyclobutanamine***
  • Metabolite markers that are associated with oxidative stress decline between weeks 2 and 4 of treatment – a sign of patient recovery.

  • During the tuberculosis therapy several enzymes (CYP2E1, CYP3A4, alcohol dehydrogenase, aminocarboxymuconate-semialdehyde decarboxylase) undergo inhibition in a time-dependent manner.

  • During treatment, the urea cycle is upregulated, and the production of insulin is altered.

PaclitaxelTo investigate the association between pretreatment metabolome, early treatment-induced metabolic changes, and the development of paclitaxel-induced peripheral neuropathy for breast cancer patients.↑ Pyruvate, alanine, threonine, phenylalanine, tyrosine, asparagine, lysine, o-acetylcarnitine, proline, lactate, glutamine, leucine
↓ 3-hydroxy-butyrate, 2-hydroxybutyrate****
  • Pre-treatment levels of histidine, phenylalanine, and threonine may predict severity of potential peripheral neuropathy.

To investigate metabolite signatures prior to the treatment, in order to explain the variability of paclitaxel-induced pharmacokinetics.↑ Creatinine, glucose, lysine, lactate
↓ Betaine
  • Pre-treatment levels of creatinine, glucose, lysine, lactate and betaine could be associated with variability of paclitaxel-induced pharmacokinetics

IrinotecanTo identify metabolite changes that could have potential implications on the mechanism of action of irinote and could serve as biomarkers for efficiency of a treatment.↑ N-α-acetyllysine, 2-aminoadipic acid, asymmetric dimethylarginine, cystathionine, propionylcarnitine,L-acetylcarnitine, malonylcarnitine, valerylcarnitine, thymine, uracil, xanthine
  • The increased levels of purine and pyrimidine nucleobase metabolites could be the result of purine/pyrimidine nucleotide degradation (break of double stranded DNA in cancer cells) as a response to the treatment with irinotecan.

  • The increased levels of acylcarnitines and amino acid metabolites could reflect dysfunction of mitochondria and oxidative stress in the liver.

Docetaxel (DTX)To evaluate the response of MCF7 tumor cells to high (5uM) and low (1 nM) doses of DTX.For high dosage (5uM):
↑ Phosphoethanolamine, cytidinediphosphocholine,
polyunsaturated fatty acid,
↓ Phosphatidylcholine, glycerophosphocholine, glycerophosphoethanolamine, total glutathione, glutamate, arginine, lysine, lactate, acetate,
For low dosage (1 nM):
↑ Phosphoethanolamine, cytidinediphosphocholine, homocysteine, aspartate,
↓ Phosphatidyl-choline, glycerophosphocholine, hypotaurine, taurine, total glutathione, arginine, alanine, threonine, lysine, acetate,
  • Both dosages result in inhibition of phosphatidylcholine biosynthesis and decreased levels of glutathione.

  • The mechanisms responsible for decreased glutathione levels are different. At high dosage, the extensive consumption and precursor starvation was the main reason, while for low dosage, it was the inhibition of trans-sulfuration that inhibited glutathione biosynthesis.

MetforminTo identify urinary markers of metformin responses in patients with type 2 diabetes mellitus.↑ Myoinositol, hypoxanthine
↓ Citric acid, pseudouridine, p-hydroxyphenylacetic acid, hippuric acid*****
  • Citric acid, myoinositol and hippuric acid have the potential to become biomarkers that could predict the response to metformin in patients with type 2 diabetes mellitus.

SimvastatinTo investigate the metabolic changes connected with the increased risk of developing hyperglycemia as an adverse response to simvastatin.↑ Glucose, glutamic acid, alanine,
↓ Lauric acid, myristic acid, linoleic acid, glycine¸ palmitoleic acid, 3-hydroxybutanoic acid¸ aminomalonate, oleic acid, N-methylalanine******
  • Patients showing a mild resistance to insulin tend to develop full insulin resistance after simvastatin treatment.

  • Branched-chain amino acids, and other metabolites such as ketoleucine, hydroxylamine and ethanolamine could predict type 2 diabetes mellitus risk following simvastatin therapy.

OlanzapinTo reveal the pharmacodynamics and mechanism of action of olanzapine.↑ Tyrosine, succinic acid semialdehyde, homovanillic acid, 3,4-dihydroxyphenylacetic acid, L-asparagine
↓ 5-hydroxytryptamine, −5- hydroxyindoleacetic acid, L-3,4-dihydroxyphenylalanine, γ-aminobutyric acid, kynurenine, kynurenine acid, tryptophan, glutamic acid, taurine, acetylcholine
  • Olanzapin alters glycerophospholipid metabolism, sphingolipid metabolism and the citrate cycle.

LosartanTo predict inter-individual variations in the metabolism of losartan.↑ Lipid CH3 (LDL/VLDL), lipid CH2 (LDL), lactate, citrate, creatine, α-glucose
↓ Lipid CH3 (HDL), creatinine, choline, glycine, phosphorylcholine
  • Identification of 11 potential biomarkers from whom lactic acid, creatinine, glucose, and choline showed a good score for prediction of metabolic processes of losartan.

Midazolam, Ketoconazole, Rifampicin,To predict biomarkers related to midazolam sum of the clearance related to the induction and inhibition of CYP3A.↑ 6β-hydroxycortisol/cortisol, 6β-hydroxycortisone/cortisone, 16α-hydroxy-DHEA/DHEA, 16α-hydroxyandrostenedione/androstenedione, 4-hydroxyandrostenedione/androstenedione, 7β-hydroxy-DHEA/DHE,6β-hydroxyandrostenedione/androstenedione, 2-hydroxyestrone/estrone, 2-hydroxyestradiol/estradiol, 11β-hydroxyandrosterone/androsteron, 11β-hydroxyandrostenedione/androstenedione
↓16α-Hydroxytestosterone/testosterone, 11β-Hydroxytestosterone/testosterone*******
  • Urinary DHEA levels, 7β-hydroxy-DHEA:DHEA ratios, 6β-hydroxycortisone: cortisone ratios could be used to predict sum of the clearance for midazolam

DA-9701 (extract from Pharbitis nil seed and Corydalis yanhusuo tube)To monitor the changes of endogenous metabolites in order to understand better the mechanism of action.For 0–4 h after exposure:
↑ Uric acid, L-acetylcarnitine
↓ Azelaic acid, ophthalmic acid, suberic acid, ε-(γ-glutamyl)-lysine, pimelic acid
For 12–24 h after exposure********:
↑ Ophthalmic acid, pimelic acid, suberic acid, azelaic acid,
↓ Uric acid, ε-(γ-glutamyl)-lysine, L-acetylcarnitine
  • Application of DA-9701 affects purine metabolic pathway, lipid, fatty acid metabolism and lipid peroxidation. DA-9701 improves gastrointestinal motility.


Table 3.

Examples of pharmacometabolomic studies.

Rats treated with AEE versus model.

Patients from high busulfan concentration-time curve (high-AUC) compared with low-AUC group.

The differences between 2 weeks and 4 weeks of treatment.

Pre-treatment levels of metabolites compared to 24 hrs after the first infusion.

Differences between responders and non-responders.

Type of association between post-treatment metabolites levels and post-treatment insulin measures.

Fold change of mean urinary metabolite ratios in the induction phase.

When compared to mean fold-changes of 0–4 hours exposure.

The successful isolation of a metabolite that may become a biomarker depends on the type of sample and the approach. In addition to easily and commonly accessed samples like urine and blood serum, pharmacometabolomics studies can also utilize feces, saliva, human breast milk, and even breath [175, 176, 177]. Samples are usually collected before, during, and after the treatment, and can be further divided by type of response from an individual (e.g., mostly positive, mostly negative, or intermediate) [175, 178]. After obtaining data from a set of samples using various techniques adapted to the particular type [36, 175, 178], a database for each individual is created, with metabolites detected and identified before and after the treatment [178]. Lastly, a statistical analysis is applied to obtain information ranging from differences that can distinguish good and poor responders prior to the treatment, to changes in metabolites due to drug application that can be correlated with response phenotypes and assumptions of pathways connected to variants of response [178].

For example, Wikoff and colleagues [179] investigated atenolol-induced changes in Caucasians and African Americans. Atenolol is a beta-adrenergic receptor blocker used in a first line antihypertensive treatment. However, various patients responded quite differently. The main objective of this study was to obtain metabolic signatures of atenolol treatment that provided insight into racial differences in response to beta blockers. They found that atenolol has a strong impact on fatty acids in blood serum, but the results were different for different groups (e.g., effects of treatment were highly significant in Caucasians but minimal in African Americans). Furthermore, the authors examined associations between oleic acid and SNPs on the 16 genes encoding lipases. They discovered that a SNP in the LIPC (rs9652472) and PLA2G4C (rs7250148) genes were associated with the change in oleic acid concentration in Caucasians and African Americans, respectively [179]. Another example of utilizing a combined approach is the evaluation of aspirin response variability during antiplatelet therapy [180]. Lewis and colleagues identified that metabolites related to aspirin (salicylic acid and 2-hydroxyhippuric acid) were significantly increased, but exposure to aspirin also changed the levels of purines, fatty acids, glycerol metabolites, amino acids, and carbohydrate-related metabolites. Moreover, a substantial difference could be observed between good and poor responders in purine metabolites - higher levels of inosine and adenosine were observed in poor responders after aspirin intervention. Later, the authors identified 51 SNPs in the ADK gene region that had associations with platelet aggregation in response to aspirin exposure, the strongest of which was the rs16931294 variant. To confirm their findings, the authors compared their results to previously obtained metabolomic data and observed that rs16931294 was significantly associated with adenosine monophosphate, xanthine, and hypoxanthine levels before aspirin exposure. When compared with post-exposure results, this SNP was strongly associated with levels of inosine and guanosine [180]. The examples presented above [142, 144, 146, 179, 180] as well as other available literature [36, 175, 178] demonstrate the importance of pharmacometabolomics in drug design studies. Combined with other approaches, e.g., pharmacogenomic, pharmacometabolomics can greatly contribute to our understanding of individual differences in responses to drug treatment and thus directly aid us in the development of new generations of drugs. There is also potential for significantly extending our understanding of health sustenance and disease development, and thus reduce drug-dependent therapies. Perhaps not the most profitable news for the pharma industry, but good news for health workers and the general population who will be able to identify at risk individuals and indeed tailor health management strategies to prevent and/or reduce the impact of disease.


4. Future perspectives

An intense research on ‘-omics’ approaches, devoted to human health, led to the development of pharmacometabolomics, which is a new horizon in personalized medicine. Numerous research data on metabolomics, genomics, and transcriptomics can be combined and compared with health records around the world due to potent databases and biobanks collecting data and samples. Nowadays, software and informatics systems with sophisticated algorithms of artificial intelligence allow for deeper analyses of pharmacometabolomics data, and transform general medicine into a personalized approach.

The analytical techniques, databases, and biobanks presented here are the general trends, which need to be further developed. The sensitivity of the analytical platforms needs to be improved, and additional ameliorations related to time and overall costs must be done. Particular attention must be paid to the standardization of study protocols. The number of data and samples deposited in databases and biobank must be extended.

Up to now, major efforts in pharmacometabolomics have been concentrated on research aspects and method validation for medical applications. The results presented here show undoubtedly that pharmacometabolomics is key for personalized medicine and needs to be transferred ‘from bench to bedside’. Nevertheless, medical personnel can source from pharmacometabolomics only if the data are presented in a simple and comprehensive way. In the future, more effort is needed to increase the broad awareness of pharmacometabolomics among patients and healthcare system staff, and to introduce the benefits of pharmacometabolomics into clinical practice.


5. Conclusion

Human genetics and lifestyle variation directly influence pharmacological treatments, whose effect can be enhanced positively or negatively in some individuals over the statistical population used in clinical trials.

This chapter has described pharmacometabolomics as an innovative tool capable of assisting researchers and frontline medical personnel in establishing personalized therapeutic strategies. Pharmacometabolomics can be used to personalize treatment type, dosage, duration, and to monitor metabolites’ profiles during pharmacotherapy. The existing ‘-omics’ and health records databases, and biobanks of human fluid samples and tissues are a precious resource for pharmacometabolomics, which identify biomarkers of therapeutic effects over a disease course. The metabolomics databases are increasing their data pool every day, and are priceless for researchers combining ‘-omics’ knowledge for better and personalized pharmacotherapy.



We would like to thank King Abdullah University of Science and Technology for financial support. We would like to acknowledge Life Science Editors for editorial services. Special thanks to Dr. Kristin Strandenes, EddaTxT, Norway for her suggestions and feedback.


Conflict of interest

The authors declare no conflict of interest.



We would like to thank King Abdullah University of Science and Technology (KAUST) for financial support.



ADKAdenosine kinase
ADRAdverse drug reactions
AEEAspirin eugenol ester
ALIAcute liver injury
ARTAntiretroviral therapy
BMRBBiological Magnetic Resonance Data Bank
BRCA1Breast cancer gene 1
BRCA2Breast cancer gene 2
CDERCenter for Drug Evaluation and Research
CYP2D6Cytochrome P450 2D6
EHRsExisting electronic health records
ESIElectro spray ionization
FDAFood and Drug Administration
FT-IRFourier transformed infrared spectroscopy
HILICHydrophilic interaction liquid chromatography
HMDBHuman Metabolome Database
HPLCHigh-performance liquid chromatography
MATE1Multidrug and toxin extrusion 1
MMCDMadison Metabolomics Consortium Database
MSMass spectrometry
NMRNuclear magnetic resonance spectroscopy
OCT1Organic cation transporter 1
OCT2Organic cation transporter 2
PMRNPharmacometabolomics Research Network
PRIMePlatform for RIKEN Metabolomics
RPReversed-phase gradient chromatography
SLC22A1Solute carrier family 22 member 1
SLC22A2Solute carrier family 22 member 2
SLC47A1Solute carrier family 47, member 1
SNPSingle-nucleotide polymorphism


  1. 1. Heindel JJ, McAllister KA, Worth JL, Tyson FL. Environmental Epigenomics, Imprinting and Disease Susceptibility. Epigenetics. 2006;1(1):2-7. DOI: 10.4161/epi.1.1.2642.
  2. 2. Jirtle RL, Skinner MK. Environmental epigenomics and disease susceptibility. Nature Reviews Genetics. 2007;8(4):253-62. DOI: 10.1038/nrg2045.
  3. 3. Emwas AH, Roy R, McKay RT, Tenori L, Saccenti E, Gowda GAN, Raftery D, Alahmari F, Jaremko L, Jaremko M, Wishart DS. NMR Spectroscopy for Metabolomics Research. Metabolites. 2019;9(7). DOI: 10.3390/metabo9070123.
  4. 4. Eghbalnia HR, Romero PR, Westler WM, Baskaran K, Ulrich EL, Markley JL. Increasing rigor in NMR-based metabolomics through validated and open source tools. Current Opinion in Biotechnology. 2017;43:56-61. DOI: 10.1016/j.copbio.2016.08.005.
  5. 5. Bruen D, Delaney C, Florea L, Diamond D. Glucose Sensing for Diabetes Monitoring: Recent Developments. Sensors. 2017;17(8):1866. DOI: 10.3390/s17081866.
  6. 6. Association AD. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes. Diabetes Care. 2020;43(Supplement 1):S14-S31. DOI: 10.2337/dc20-S002.
  7. 7. Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK. Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomarker Insights. 2016;11:BMI.S38440. DOI: 10.4137/bmi.S38440.
  8. 8. Chung L, Moore K, Phillips L, Boyle FM, Marsh DJ, Baxter RC. Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer. Breast Cancer Research. 2014;16(3):R63. DOI: 10.1186/bcr3676.
  9. 9. Thelin E, Al Nimer F, Frostell A, Zetterberg H, Blennow K, Nyström H, Svensson M, Bellander B-M, Piehl F, Nelson DW. A Serum Protein Biomarker Panel Improves Outcome Prediction in Human Traumatic Brain Injury. Journal of Neurotrauma. 2019;36(20):2850-62. DOI: 10.1089/neu.2019.6375.
  10. 10. Duangkumpha K, Stoll T, Phetcharaburanin J, Yongvanit P, Thanan R, Techasen A, Namwat N, Khuntikeo N, Chamadol N, Roytrakul S, Mulvenna J, Mohamed A, Shah AK, Hill MM, Loilome W. Discovery and Qualification of Serum Protein Biomarker Candidates for Cholangiocarcinoma Diagnosis. Journal of Proteome Research. 2019;18(9):3305-16. DOI: 10.1021/acs.jproteome.9b00242.
  11. 11. Shimura T, Dayde D, Wang H, Okuda Y, Iwasaki H, Ebi M, Kitagawa M, Yamada T, Yamada T, Hanash SM, Taguchi A, Kataoka H. Novel urinary protein biomarker panel for early diagnosis of gastric cancer. British Journal of Cancer. 2020;123(11):1656-64. DOI: 10.1038/s41416-020-01063-5.
  12. 12. Guiraud S, Edwards B, Squire SE, Babbs A, Shah N, Berg A, Chen H, Davies KE. Identification of serum protein biomarkers for utrophin based DMD therapy. Scientific Reports. 2017;7(1):43697. DOI: 10.1038/srep43697.
  13. 13. Liao H, Wu J, Kuhn E, Chin W, Chang B, Jones MD, O'Neil S, Clauser KR, Karl J, Hasler F, Roubenoff R, Zolg W, Guild BC. Use of mass spectrometry to identify protein biomarkers of disease severity in the synovial fluid and serum of patients with rheumatoid arthritis. Arthritis & Rheumatism. 2004;50(12):3792-803. DOI: 10.1002/art.20720.
  14. 14. Bansal N, Gupta AK, Gupta A, Sankhwar SN, Mahdi AA. Serum-based protein biomarkers of bladder cancer: A pre- and post-operative evaluation. Journal of Pharmaceutical and Biomedical Analysis. 2016;124:22-5. DOI: 10.1016/j.jpba.2016.02.026.
  15. 15. Chai YD, Zhang L, Yang Y, Su T, Charugundla P, Ai J, Messadi D, Wong DT, Hu S. Discovery of potential serum protein biomarkers for lymph node metastasis in oral cancer. Head & Neck. 2016;38(1):118-25. DOI: 10.1002/hed.23870.
  16. 16. Cheng Z, Yin J, Yuan H, Jin C, Zhang F, Wang Z, Liu X, Wu Y, Wang T, Xiao S. Blood-Derived Plasma Protein Biomarkers for Alzheimer’s Disease in Han Chinese. Frontiers in Aging Neuroscience. 2018;10(414). DOI: 10.3389/fnagi.2018.00414.
  17. 17. Sun J-L, Li S, Lu X, Feng J-B, Cai T-J, Tian M, Liu Q-J. Identification of the differentially expressed protein biomarkers in rat blood plasma in response to gamma irradiation. International Journal of Radiation Biology. 2020;96(6):748-58. DOI: 10.1080/09553002.2020.1739775.
  18. 18. Kell DB. Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discovery Today. 2006;11(23):1085-92. DOI: 10.1016/j.drudis.2006.10.004.
  19. 19. Urbanczyk-Wochniak E, Luedemann A, Kopka J, Selbig J, Roessner-Tunali U, Willmitzer L, Fernie AR. Parallel analysis of transcript and metabolic profiles: a new approach in systems biology. EMBO reports. 2003;4(10):989-93. DOI: 10.1038/sj.embor.embor944.
  20. 20. Emwas A-HM, Al-Rifai N, Szczepski K, Alsuhaymi S, Rayyan S, Almahasheer H, Jaremko M, Brennan L, Lachowicz JI. You Are What You Eat: Application of Metabolomics Approaches to Advance Nutrition Research. Foods. 2021;10(6):1249.
  21. 21. Vizán P, Mazurek S, Cascante M. Robust metabolic adaptation underlying tumor progression. Metabolomics. 2008;4(1):1-12. DOI: 10.1007/s11306-007-0101-3.
  22. 22. Warburg O. On the Origin of Cancer Cells. Science. 1956;123(3191):309-14. DOI: 10.1126/science.123.3191.309
  23. 23. Center for Drug Evaluation and Research. New drug therapy APPROVALS 2019 [Available from:
  24. 24. FDA. Orange book: Approved drug products with therapeutic equivalence evaluations 2017 [Available from:
  25. 25. Knoben JE, Scott GR, Tonelli RJ. An overview of the FDA publication approved drug products with therapeutic equivalence evaluations. American journal of hospital pharmacy. 1990;47(12):2696-700. DOI: 10.1093/ajhp/47.12.2696.
  26. 26. Bawa R, Bawa S, Mehra R. The translational challenge in medicine at the nanoscale. Audette, GF, Reese, BE, asst eds Handbook of Clinical Nanomedicine: Law, Business, Regulation, Safety and Risk, Pan Stanford Publishing, Singapore. 2016:1291-346. DOI: 10.1201/b19910
  27. 27. Nair AK, Anand O, Chun N, Conner DP, Mehta MU, Nhu DT, Polli JE, Lawrence XY, Davit BM. Statistics on BCS classification of generic drug products approved between 2000 and 2011 in the USA. The AAPS journal. 2012;14(4):664-6. DOI: 10.1208/s12248-012-9384-z.
  28. 28. Nicholson JK, Wilson ID. Understanding 'Global' Systems Biology: Metabonomics and the Continuum of Metabolism. Nature Reviews Drug Discovery. 2003;2(8):668-76. DOI: 10.1038/nrd1157.
  29. 29. Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181-9. DOI: 10.1080/004982599238047.
  30. 30. Bedair M, Sumner LW. Current and emerging mass-spectrometry technologies for metabolomics. Trac-Trends in Analytical Chemistry. 2008;27(3):238-50. DOI: 10.1016/j.trac.2008.01.006.
  31. 31. Dudka I, Kossowska B, Senhadri H, Latajka R, Hajek J, Andrzejak R, Antonowicz-Juchniewicz J, Gancarz R. Metabonomic analysis of serum of workers occupationally exposed to arsenic, cadmium and lead for biomarker research: A preliminary study. Environment International. 2014;68:71-81. DOI: 10.1016/j.envint.2014.03.015.
  32. 32. Cui GX, Liew YJ, Li Y, Kharbatia N, Zahran NI, Emwas AH, Eguiluz VM, Aranda M. Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia. Plos Genetics. 2019;15(6). DOI: 10.1371/journal.pgen.1008189.
  33. 33. Guleria A, Pratap A, Dubey D, Rawat A, Chaurasia S, Sukesh E, Phatak S, Ajmani S, Kumar U, Khetrapal CL, Bacon P, Misra R, Kumar D. NMR based serum metabolomics reveals a distinctive signature in patients with Lupus Nephritis. Scientific Reports. 2016;6. DOI: 10.1038/srep35309.
  34. 34. Huang Y, Tian Y, Li G, Li Y, Yin X, Peng C, Xu F, Zhang Z. Discovery of safety biomarkers for realgar in rat urine using UFLC-IT-TOF/MS and H-1 NMR based metabolomics. Analytical and Bioanalytical Chemistry. 2013;405(14):4811-22. DOI: 10.1007/s00216-013-6842-0.
  35. 35. Stuart KA, Welsh K, Walker MC, Edrada-Ebel R. Metabolomic tools used in marine natural product drug discovery. Expert Opinion on Drug Discovery. 2020;15(4):499-522. DOI: 10.1080/17460441.2020.1722636.
  36. 36. Mussap M, Loddo C, Fanni C, Fanos V. Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics. Expert Review of Clinical Pharmacology. 2020;13(2):115-34. DOI: 10.1080/17512433.2020.1713750.
  37. 37. Zhang S, Gowda GAN, Asiago V, Shanaiah N, Barbas C, Raftery D. Correlative and quantitative H-1 NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats. Analytical Biochemistry. 2008;383(1):76-84. DOI: 10.1016/j.ab.2008.07.041.
  38. 38. Zhao Y, Fu L, Li R, Wang L-N, Yang Y, Liu N-N, Zhang C-M, Wang Y, Liu P, Tu B-B, Zhang X, Qiao J. Metabolic profiles characterizing different phenotypes of polycystic ovary syndrome: plasma metabolomics analysis. Bmc Medicine. 2012;10. DOI: 10.1186/1741-7015-10-153.
  39. 39. Zheng H, Lorenzen JK, Astrup A, Larsen LH, Yde CC, Clausen MR, Bertram HC. Metabolic Effects of a 24-Week Energy-Restricted Intervention Combined with Low or High Dairy Intake in Overweight Women: An NMR-Based Metabolomics Investigation. Nutrients. 2016;8(3). DOI: 10.3390/nu8030108.
  40. 40. Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chemical society reviews. 2011;40(1):387-426. DOI: 10.1039/B906712B.
  41. 41. Chandra K, Harthi S, Almulhim F, Emwas A-H, Jaremko L, Jaremko M. The robust NMR Toolbox for Metabolomics. Molecular Omics. 2021. DOI: 10.1039/D1MO00118C.
  42. 42. Beltran A, Suarez M, Rodriguez MA, Vinaixa M, Samino S, Arola L, Correig X, Yanes O. Assessment of Compatibility between Extraction Methods for NMR- and LC/MS-Based Metabolomics. Analytical Chemistry. 2012;84(14):5838-44. DOI: 10.1021/ac3005567.
  43. 43. Currie F, Broadhurst DI, Dunn WB, Sellick CA, Goodacre R. Metabolomics reveals the physiological response of Pseudomonas putida KT2440 (UWC1) after pharmaceutical exposure. Molecular Biosystems. 2016;12(4):1367-77. DOI: 10.1039/c5mb00889a.
  44. 44. Lee BJ, Zhou YY, Lee JS, Shine BK, Seo JA, Lee D, Kim YS, Choi HK. Discrimination and prediction of the origin of Chinese and Korean soybeans using Fourier transform infrared spectrometry (FT-IR) with multivariate statistical analysis. Plos One. 2018;13(4). DOI: 10.1371/journal.pone.0196315.
  45. 45. Qamar W, Ahamad SR, Ali R, Khan MR, Al-Ghadeer AR. Metabolomic analysis of lung epithelial secretions in rats: An investigation of bronchoalveolar lavage fluid by GC-MS and FT-IR. Experimental Lung Research. 2014;40(9):460-6. DOI: 10.3109/01902148.2014.947008.
  46. 46. Al-Talla ZA, Akrawi SH, Tolley LT, Sioud SH, Zaater MF, Emwas AH. Bioequivalence assessment of two formulations of ibuprofen. Drug Des Devel Ther. 2011;5:427-33. DOI: 10.2147/DDDT.S24504 dddt-5-427 [pii].
  47. 47. Liu M, Xie H, Ma Y, Li H, Li C, Chen L, Jiang B, Nian B, Guo T, Zhang Z, Jiao W, Liu Q, Ling T, Zhao M. High Performance Liquid Chromatography and Metabolomics Analysis of Tannase Metabolism of Gallic Acid and Gallates in Tea Leaves. Journal of Agricultural and Food Chemistry. 2020;68(17):4946-54. DOI: 10.1021/acs.jafc.0c00513.
  48. 48. Chandra K, Al-Harthi S, Sukumaran S, Almulhim F, Emwas A-H, Atreya HS, Jaremko Ł, Jaremko M. NMR-based metabolomics with enhanced sensitivity. RSC Advances. 2021;11(15):8694-700. DOI: 10.1039/D1RA01103K.
  49. 49. Emwas A-HM. The Strengths and Weaknesses of NMR Spectroscopy and Mass Spectrometry with Particular Focus on Metabolomics Research. In: Bjerrum JT, editor. Metabonomics: Methods and Protocols. New York, NY: Springer New York; 2015. p. 161-93 DOI: 10.1007/978-1-4939-2377-9_13.
  50. 50. Wang JH, Byun J, Pennathur S. Analytical Approaches to Metabolomics and Applications to Systems Biology. Seminars in Nephrology. 2010;30(5):500-11. DOI: 10.1016/j.semnephrol.2010.07.007.
  51. 51. Valentino G, Graziani V, D’Abrosca B, Pacifico S, Fiorentino A, Scognamiglio M. NMR-Based Plant Metabolomics in Nutraceutical Research: An Overview. Molecules. 2020;25(6):1444. DOI: 10.3390/molecules25061444.
  52. 52. Zhang Y, Zhang H, Chang D, Guo F, Pan H, Yang Y. Metabolomics approach by 1H NMR spectroscopy of serum reveals progression axes for asymptomatic hyperuricemia and gout. Arthritis Research & Therapy. 2018;20(1):111. DOI: 10.1186/s13075-018-1600-5.
  53. 53. Emwas A-HM, Merzaban JS, Serrai H. Chapter 3 - Theory and Applications of NMR-Based Metabolomics in Human Disease Diagnosis. In: ur-Rahman A, Choudhary MI, editors. Applications of NMR Spectroscopy: Bentham Science Publishers; 2015. p. 93-130 DOI: 10.1016/B978-1-60805-963-8.50003-2.
  54. 54. Emwas A-HM, Al-Talla ZA, Kharbatia NM. Sample Collection and Preparation of Biofluids and Extracts for Gas Chromatography–Mass Spectrometry. In: Bjerrum JT, editor. Metabonomics: Methods and Protocols. New York, NY: Springer New York; 2015. p. 75-90 DOI: 10.1007/978-1-4939-2377-9_7.
  55. 55. Wang J, Li Y, Li S, Zhao W, Jiang R, Wang S. Application of mass spectrometry-based metabolomics in meat science: a review. Shipin Kexue / Food Science. 2020;41(23):293-302. DOI: 10.7506/spkx1002-6630-20200430-405.
  56. 56. Do KT, Wahl S, Raffler J, Molnos S, Laimighofer M, Adamski J, Suhre K, Strauch K, Peters A, Gieger C, Langenberg C, Stewart ID, Theis FJ, Grallert H, Kastenmüller G, Krumsiek J. Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies. Metabolomics : Official journal of the Metabolomic Society. 2018;14(10):128-. DOI: 10.1007/s11306-018-1420-2.
  57. 57. Zeng J, Wang Z, Huang X, Eckstein SS, Lin X, Piao H, Weigert C, Yin P, Lehmann R, Xu G. Comprehensive Profiling by Non-targeted Stable Isotope Tracing Capillary Electrophoresis-Mass Spectrometry: A New Tool Complementing Metabolomic Analyses of Polar Metabolites. Chemistry – A European Journal. 2019;25(21):5427-32. DOI: 10.1002/chem.201900539.
  58. 58. Emwas A-HM, Al-Talla ZA, Kharbatia NM. Sample collection and preparation of biofluids and extracts for gas chromatography-mass spectrometry. Methods in molecular biology (Clifton, NJ). 2015;1277:75-90. DOI: 10.1007/978-1-4939-2377-9_7.
  59. 59. Emwas A-HM, Al-Talla ZA, Yang Y, Kharbatia NM. Gas chromatography-mass spectrometry of biofluids and extracts. Methods in molecular biology (Clifton, NJ). 2015;1277:91-112. DOI: 10.1007/978-1-4939-2377-9_8.
  60. 60. Fayek NM, Farag MA, Saber FR. Metabolome classification via GC/MS and UHPLC/MS of olive fruit varieties grown in Egypt reveal pickling process impact on their composition. Food Chemistry. 2021;339. DOI: 10.1016/j.foodchem.2020.127861.
  61. 61. Gong YJ, Lyu WT, Shi XF, Zou XT, Lu LZ, Yang H, Xiao YP. A Serum Metabolic Profiling Analysis During the Formation of Fatty Liver in Landes Geese via GC-TOF/MS. Frontiers in Physiology. 2020;11. DOI: 10.3389/fphys.2020.581699.
  62. 62. Loyo RM, Zarate E, Barbosa CS, Simoes-Barbosa A. Gas chromatography-mass spectrometry (GC/MS) reveals urine metabolites associated to light and heavy infections by Schistosoma mansoni in mice. Parasitology International. 2021;80. DOI: 10.1016/j.parint.2020.102239.
  63. 63. Emwas AH, Luchinat C, Turano P, Tenori L, Roy R, Salek RM, Ryan D, Merzaban JS, Kaddurah-Daouk R, Zeri AC, Nagana Gowda GA, Raftery D, Wang Y, Brennan L, Wishart DS. Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review. Metabolomics. 2015;11(4):872-94. DOI: 10.1007/s11306-014-0746-7.
  64. 64. Bhinderwala F, Wase N, DiRusso C, Powers R. Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation. Journal of Proteome Research. 2018;17(11):4017-22. DOI: 10.1021/acs.jproteome.8b00567.
  65. 65. Nageeb A, Al-Tawashi A, Mohammad Emwas A-H, Abdel-Halim Al-Talla Z, Al-Rifai N. Comparison of Artemisia annua Bioactivities between Traditional Medicine and Chemical Extracts. Current bioactive compounds. 2013;9(4):324-32. DOI: 10.2174/157340720904140404151439.
  66. 66. Emwas AH, Szczepski K, Poulson BG, Chandra K, McKay RT, Dhahri M, Alahmari F, Jaremko L, Lachowicz JI, Jaremko M. NMR as a "Gold Standard" Method in Drug Design and Discovery. Molecules. 2020;25(20). DOI: 10.3390/molecules25204597.
  67. 67. Naqi HA, Woodman TJ, Husbands SM, Blagbrough IS. 19 F and 1 H quantitative-NMR spectroscopic analysis of fluorinated third-generation synthetic cannabinoids. Analytical Methods. 2019;11(24):3090-100. DOI: 10.1039/C9AY00814D
  68. 68. Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, Wishart DS. The future of NMR-based metabolomics. Current Opinion in Biotechnology. 2017;43:34-40. DOI: 10.1016/j.copbio.2016.08.001.
  69. 69. Emwas AH, Roy R, McKay RT, Ryan D, Brennan L, Tenori L, Luchinat C, Gao X, Zeri AC, Gowda GA, Raftery D, Steinbeck C, Salek RM, Wishart DS. Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis. J Proteome Res. 2016;15(2):360-73. DOI: 10.1021/acs.jproteome.5b00885.
  70. 70. Emwas AH, Saunders M, Ludwig C, Günther UL. Determinants for Optimal Enhancement in Ex Situ DNP Experiments. Applied Magnetic Resonance. 2008;34(3-4):483-94. DOI: 10.1007/s00723-008-0120-x.
  71. 71. Qiu XH, Redwine D, Beshah K, Livazovic S, Canlas CG, Guinov A, Emwas AHM. Amide versus amine ratio in the discrimination layer of reverse osmosis membrane by solid state N-15 NMR and DNP NMR. Journal of Membrane Science. 2019;581:243-51. DOI: 10.1016/j.memsci.2019.03.037.
  72. 72. Kovacs H, Moskau D, Spraul M. Cryogenically cooled probes—a leap in NMR technology. Progress in Nuclear Magnetic Resonance Spectroscopy. 2005;46(2-3):131-55. DOI: 10.1016/j.pnmrs.2005.03.001.
  73. 73. Webb AG. Advances in Probe Design for Protein NMR. In: Webb GA, editor. Annual Reports on NMR Spectroscopy. 58: Academic Press; 2006. p. 1-50 DOI: 10.1016/S0066-4103(05)58001-3.
  74. 74. Patra P, Bhanja SK, Sen IK, Nandi AK, Samanta S, Das D, Devi KSP, Maiti TK, Acharya K, Islam SS. Structural and immunological studies of hetero polysaccharide isolated from the alkaline extract of Tricholoma crassum (Berk.) Sacc. Carbohydrate Research. 2012;362:1-7. DOI: 10.1016/j.carres.2012.09.009.
  75. 75. Shiomi N, Abe T, Kikuchi H, Aritsuka T, Takata Y, Fukushi E, Fukushi Y, Kawabata J, Ueno K, Onodera S. Structural analysis of novel kestose isomers isolated from sugar beet molasses. Carbohydrate Research. 2016;424:1-7. DOI: 10.1016/j.carres.2016.02.002.
  76. 76. Blindauer CA, Emwas AH, Holý A, Dvořáková H, Sletten E, Sigel H. Complex Formation of the Antiviral 9-[2-(Phosphonomethoxy)Ethyl]Adenine (PMEA) and of Its N 1, N 3, and N 7 Deaza Derivatives with Copper(II) in Aqueous Solution. Chemistry – A European Journal. 1997;3(9):1526-36. DOI: 10.1002/chem.19970030922.
  77. 77. Susapto HH, Alhattab D, Abdelrahman S, Khan Z, Alshehri S, Kahin K, Ge R, Moretti M, Emwas A-H, Hauser CAE. Ultrashort Peptide Bioinks Support Automated Printing of Large-Scale Constructs Assuring Long-Term Survival of Printed Tissue Constructs. Nano Letters. 2021;21(7):2719-29. DOI: 10.1021/acs.nanolett.0c04426.
  78. 78. Emwas A-H, Alghrably M, Al-Harthi S, Poulson BG, Szczepski K, Chandra K, Jaremko M. New Advances in Fast Methods of 2D NMR Experiments. Nuclear Magnetic Resonance. 2019. DOI: 10.5772/intechopen.90263.
  79. 79. Abdul Jameel AG, Alquaity ABS, Campuzano F, Emwas A-H, Saxena S, Sarathy SM, Roberts WL. Surrogate formulation and molecular characterization of sulfur species in vacuum residues using APPI and ESI FT-ICR mass spectrometry. Fuel. 2021;293:120471. DOI:
  80. 80. Hajjar D, Kremb S, Sioud S, Emwas A-H, Voolstra CR, Ravasi T. Anti-cancer agents in Saudi Arabian herbals revealed by automated high-content imaging. PLoS ONE. 2017;12(6). DOI: 10.1371/journal.pone.0177316.
  81. 81. Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, Bjorndahl TC, Krishnamurthy R, Saleem F, Liu P, Dame ZT, Poelzer J, Huynh J, Yallou FS, Psychogios N, Dong E, Bogumil R, Roehring C, Wishart DS. The human urine metabolome2013. e73076-e p. DOI: 10.1371/journal.pone.0073076.
  82. 82. Zhou B, Xiao JF, Tuli L, Ressom HW. LC-MS-based metabolomics. Molecular bioSystems. 2012;8(2):470-81. DOI: 10.1039/c1mb05350g.
  83. 83. Li F, Gonzalez FJ, Ma X. LC–MS-based metabolomics in profiling of drug metabolism and bioactivation. Acta Pharmaceutica Sinica B. 2012;2(2):118-25. DOI: 10.1016/j.apsb.2012.02.010.
  84. 84. Liu J, Zhao M, Zhu Y, Wang X, Zheng L, Yin Y. LC–MS-Based Metabolomics and Lipidomics Study of High-Density-Lipoprotein-Modulated Glucose Metabolism with an apoA-I Knockout Mouse Model. Journal of Proteome Research. 2019;18(1):48-56. DOI: 10.1021/acs.jproteome.8b00290.
  85. 85. Theodoridis GA, Gika HG, Want EJ, Wilson ID. Liquid chromatography-mass spectrometry based global metabolite profiling: A review. Analytica Chimica Acta. 2012;711:7-16. DOI: 10.1016/j.aca.2011.09.042.
  86. 86. Theodoridis G, Gika HG, Wilson ID. LC-MS-based methodology for global metabolite profiling in metabonomics/metabolomics. Trac-Trends in Analytical Chemistry. 2008;27(3):251-60. DOI: 10.1016/j.trac.2008.01.008.
  87. 87. Wang X, Li L. Mass Spectrometry for Metabolome Analysis. Mass Spectrometry Letters. 2020;11(2):17-24. DOI: 10.5478/MSL.2020.11.2.17.
  88. 88. Yi L, Dong N, Yun Y, Deng B, Ren D, Liu S, Liang Y. Chemometric methods in data processing of mass spectrometry-based metabolomics: A review. Analytica Chimica Acta. 2016;914:17-34. DOI: 10.1016/j.aca.2016.02.001.
  89. 89. Mohammed SAA, Khan RA, El-Readi MZ, Emwas AH, Sioud S, Poulson BG, Jaremko M, Eldeeb HM, Al-Omar MS, Mohammed HA. Suaeda vermiculata Aqueous-Ethanolic Extract-Based Mitigation of CCl(4)-Induced Hepatotoxicity in Rats, and HepG-2 and HepG-2/ADR Cell-Lines-Based Cytotoxicity Evaluations. Plants (Basel). 2020;9(10). DOI: 10.3390/plants9101291.
  90. 90. Dhahri M, Sioud S, Dridi R, Hassine M, Boughattas NA, Almulhim F, Al Talla Z, Jaremko M, Emwas A-HM. Extraction, Characterization, and Anticoagulant Activity of a Sulfated Polysaccharide from Bursatella leachii Viscera. ACS Omega. 2020;5(24):14786-95. DOI: 10.1021/acsomega.0c01724.
  91. 91. Raji M, Amad M, Emwas AH. Dehydrodimerization of pterostilbene during electrospray ionization mass spectrometry. Rapid Commun Mass Spectrom. 2013;27(11):1260-6. DOI: 10.1002/rcm.6571.
  92. 92. Bird IM. High performance liquid chromatography: principles and clinical applications. BMJ (Clinical research ed). 1989;299(6702):783-7. DOI: 10.1136/bmj.299.6702.783.
  93. 93. Ingle KP, Deshmukh AG, Padole DA, Dudhare MS, Moharil MP, Khelurkar VC. Phytochemicals: Extraction methods, identification and detection of bioactive compounds from plant extracts. Journal of Pharmacognosy and Phytochemistry. 2017;6(1):32-6.
  94. 94. Donato P, Cacciola F, Tranchida PQ, Dugo P, Mondello L. Mass spectrometry detection in comprehensive liquid chromatography: Basic concepts, instrumental aspects, applications and trends. Mass Spectrometry Reviews. 2012;31(5):523-59. DOI: 10.1002/mas.20353.
  95. 95. Spagou K, Tsoukali H, Raikos N, Gika H, Wilson ID, Theodoridis G. Hydrophilic interaction chromatography coupled to MS for metabonomic/metabolomic studies. Journal of Separation Science. 2010;33(6-7):716-27. DOI: 10.1002/jssc.200900803.
  96. 96. Appiah-Amponsah E, Owusu-Sarfo K, Gowda GAN, Ye T, Raftery D. Combining Hydrophilic Interaction Chromatography (HILIC) and Isotope Tagging for Off-Line LC-NMR Applications in Metabolite Analysis. Metabolites. 2013;3(3):575-91. DOI: 10.3390/metabo3030575.
  97. 97. Fang Z-Z, Gonzalez FJ. LC-MS-based metabolomics: an update. Arch Toxicol. 2014;88(8):1491-502. DOI: 10.1007/s00204-014-1234-6.
  98. 98. Sana TR, Waddell K, Fischer SM. A sample extraction and chromatographic strategy for increasing LC/MS detection coverage of the erythrocyte metabolome. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2008;871(2):314-21. DOI: 10.1016/j.jchromb.2008.04.030.
  99. 99. Rodriguez-Morato J, Pozo OJ, Marcos J. Targeting human urinary metabolome by LC-MS/MS: a review. Bioanalysis. 2018;10(7):489-516. DOI: 10.4155/bio-2017-0285.
  100. 100. Shimizu T, Watanabe M, Fernie AR, Tohge T. Targeted LC-MS Analysis for Plant Secondary Metabolites. In: António C, editor. Plant Metabolomics: Methods and Protocols. New York, NY: Springer New York; 2018. p. 171-81 DOI: 10.1007/978-1-4939-7819-9_12.
  101. 101. Lu L, Wang J, Xu Y, Wang K, Hu Y, Tian R, Yang B, Lai Q, Li Y, Zhang W, Shao Z, Lam H, Qian P-Y. A High-Resolution LC-MS-Based Secondary Metabolite Fingerprint Database of Marine Bacteria. Scientific Reports. 2014;4(1):6537. DOI: 10.1038/srep06537.
  102. 102. Penn E, Tracy DK. The drugs don’t work? antidepressants and the current and future pharmacological management of depression. Therapeutic Advances in Psychopharmacology. 2012;2(5):179-88. DOI: 10.1177/2045125312445469.
  103. 103. Schork NJ. Personalized medicine: time for one-person trials. Nature News. 2015;520(7549):609. DOI: 10.1038/520609a.
  104. 104. Karumanchi SA, Thadhani R. Kidney complications: Why don't statins always work? Nature Medicine. 2010;16(1):38-40. DOI: 10.1038/nm0110-38.
  105. 105. Haro JM, Novick D, Bertsch J, Karagianis J, Dossenbach M, Jones PB. Cross-national clinical and functional remission rates: Worldwide Schizophrenia Outpatient Health Outcomes (W-SOHO) study. British Journal of Psychiatry. 2011;199(3):194-201. DOI: 10.1192/bjp.bp.110.082065.
  106. 106. Piquette-Miller M, Grant DM. The Art and Science of Personalized Medicine. Clinical Pharmacology & Therapeutics. 2007;81(3):311-5. DOI: 10.1038/sj.clpt.6100130.
  107. 107. Lazarou J, Pomeranz BH, Corey PN. Incidence of Adverse Drug Reactions in Hospitalized PatientsA Meta-analysis of Prospective Studies. JAMA. 1998;279(15):1200-5. DOI: 10.1001/jama.279.15.1200.
  108. 108. Pokorska-Bocci A, Stewart A, Sagoo GS, Hall A, Kroese M, Burton H. 'Personalized medicine': what’s in a name? Personalized Medicine. 2014;11(2):197-210. DOI: 10.2217/pme.13.107.
  109. 109. Balashova EE, Maslov DL, Lokhov PG. A Metabolomics Approach to Pharmacotherapy Personalization. J Pers Med. 2018;8(3). DOI: 10.3390/jpm8030028.
  110. 110. Li B, He X, Jia W, Li H. Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review. Molecules. 2017;22(7). DOI: 10.3390/molecules22071173.
  111. 111. Ziegelstein RC. Personomics: The Missing Link in the Evolution from Precision Medicine to Personalized Medicine. J Pers Med. 2017;7(4). DOI: 10.3390/jpm7040011.
  112. 112. Roden DM, Altman RB, Benowitz NL, Flockhart DA, Giacomini KM, Johnson JA, Krauss RM, McLeod HL, Ratain MJ, Relling MV, Ring HZ, Shuldiner AR, Weinshilboum RM, Weiss ST. Pharmacogenomics: Challenges and Opportunities. Annals of Internal Medicine. 2006;145(10):749-57. DOI: 10.7326/0003-4819-145-10-200611210-00007.
  113. 113. Eichelbaum M, Ingelman-Sundberg M, Evans WE. Pharmacogenomics and Individualized Drug Therapy. Annual Review of Medicine. 2006;57(1):119-37. DOI: 10.1146/
  114. 114. Relling MV, Evans WE. Pharmacogenomics in the clinic. Nature. 2015;526(7573):343-50. DOI: 10.1038/nature15817.
  115. 115. Malandrino N, Smith RJ. Personalized Medicine in Diabetes. Clinical Chemistry. 2011;57(2):231-40. DOI: 10.1373/clinchem.2010.156901.
  116. 116. Kleinberger JW, Pollin TI. Personalized medicine in diabetes mellitus: current opportunities and future prospects. Annals of the New York Academy of Sciences. 2015;1346(1):45-56. DOI: 10.1111/nyas.12757.
  117. 117. Pearson ER. Personalized medicine in diabetes: the role of ‘omics’ and biomarkers. Diabetic Medicine. 2016;33(6):712-7. DOI: 10.1111/dme.13075.
  118. 118. Verma M. Personalized Medicine and Cancer. Journal of Personalized Medicine. 2012;2(1):1-14. DOI: 10.3390/jpm2010001.
  119. 119. Gambardella V, Tarazona N, Cejalvo JM, Lombardi P, Huerta M, Roselló S, Fleitas T, Roda D, Cervantes A. Personalized Medicine: Recent Progress in Cancer Therapy. Cancers. 2020;12(4):1009. DOI: 10.3390/cancers12041009.
  120. 120. Congiu T, Alghrably M, Emwas A-H, Jaremko L, Lachowicz JI, Piludu M, Piras M, Faa G, Pichiri G, Jaremko M, Coni P. Undercover Toxic Ménage à Trois of Amylin, Copper (II) and Metformin in Human Embryonic Kidney Cells. Pharmaceutics. 2021;13(6):830.
  121. 121. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney AS, McCarthy MI, Hattersley AT, Morris AD, Palmer CN. Variation in TCF7L2 influences therapeutic response to sulfonylureas: a GoDARTs study. Diabetes. 2007;56(8):2178-82. DOI: 10.2337/db07-0440.
  122. 122. Chan CWH, Law BMH, So WKW, Chow KM, Waye MMY. Novel Strategies on Personalized Medicine for Breast Cancer Treatment: An Update. International Journal of Molecular Sciences. 2017;18(11):2423. DOI: 10.3390/ijms18112423
  123. 123. Amir E, Freedman OC, Seruga B, Evans DG. Assessing Women at High Risk of Breast Cancer: A Review of Risk Assessment Models. JNCI: Journal of the National Cancer Institute. 2010;102(10):680-91. DOI: 10.1093/jnci/djq088.
  124. 124. Harbeck N, Penault-Llorca F, Cortes J, Gnant M, Houssami N, Poortmans P, Ruddy K, Tsang J, Cardoso F. Breast cancer. Nature Reviews Disease Primers. 2019;5(1):66. DOI: 10.1038/s41572-019-0111-2.
  125. 125. Li Y, Steppi A, Zhou Y, Mao F, Miller PC, He MM, Zhao T, Sun Q, Zhang J. Tumoral expression of drug and xenobiotic metabolizing enzymes in breast cancer patients of different ethnicities with implications to personalized medicine. Scientific Reports. 2017;7(1):4747. DOI: 10.1038/s41598-017-04250-2.
  126. 126. Olopade OI, Grushko TA, Nanda R, Huo D. Advances in Breast Cancer: Pathways to Personalized Medicine. Clinical Cancer Research. 2008;14(24):7988-99. DOI: 10.1158/1078-0432.Ccr-08-1211.
  127. 127. Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD, Psychogios N, Dong E, Bouatra S, Mandal R, Sinelnikov I, Xia JG, Jia L, Cruz JA, Lim E, Sobsey CA, Shrivastava S, Huang P, Liu P, Fang L, Peng J, Fradette R, Cheng D, Tzur D, Clements M, Lewis A, De Souza A, Zuniga A, Dawe M, Xiong YP, Clive D, Greiner R, Nazyrova A, Shaykhutdinov R, Li L, Vogel HJ, Forsythe I. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Research. 2009;37:D603-D10. DOI: 10.1093/nar/gkn810.
  128. 128. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, Cheng D, Jewell K, Arndt D, Sawhney S, Fung C, Nikolai L, Lewis M, Coutouly M-A, Forsythe I, Tang P, Shrivastava S, Jeroncic K, Stothard P, Amegbey G, Block D, Hau DD, Wagner J, Miniaci J, Clements M, Gebremedhin M, Guo N, Zhang Y, Duggan GE, MacInnis GD, Weljie AM, Dowlatabadi R, Bamforth F, Clive D, Greiner R, Li L, Marrie T, Sykes BD, Vogel HJ, Querengesser L. HMDB: the human metabolome database. Nucleic Acids Research. 2007;35:D521-D6. DOI: 10.1093/nar/gkl923.
  129. 129. Sakurai T, Yamada Y, Sawada Y, Matsuda F, Akiyama K, Shinozaki K, Hirai MY, Saito K. PRIMe Update: Innovative Content for Plant Metabolomics and Integration of Gene Expression and Metabolite Accumulation. Plant and Cell Physiology. 2013;54(2):E5−+. DOI: 10.1093/pcp/pcs184.
  130. 130. Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, Livny M, Mading S, Maziuk D, Miller Z, Nakatani E, Schulte CF, Tolmie DE, Wenger RK, Yao HY, Markley JL. BioMagResBank. Nucleic Acids Research. 2008;36:D402-D8. DOI: 10.1093/nar/gkm957.
  131. 131. Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF, Westler WM, Eghbalnia HR, Sussman MR, Markley JL. Metabolite identification via the Madison Metabolomics Consortium Database. Nature Biotechnology. 2008;26(2):162-4. DOI: 10.1038/nbt0208-162.
  132. 132. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu YF, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia JG, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A. HMDB 3.0-The Human Metabolome Database in 2013. Nucleic Acids Research. 2013;41(D1):D801-D7. DOI: 10.1093/nar/gks1065.
  133. 133. Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M, Mahendraker T, Williams M, Neumann S, Rocca-Serra P, Maguire E, Gonzalez-Beltran A, Sansone S-A, Griffin JL, Steinbeck C. MetaboLights-an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Research. 2013;41(D1):D781-D6. DOI: 10.1093/nar/gks1004.
  134. 134. Salek, Haug K, Conesa P, Hastings J, Williams M, Mahendraker T, Maguire E, Gonzalez-Beltran AN, Rocca-Serra P, Sansone S-A, Steinbeck C. The MetaboLights repository: curation challenges in metabolomics. Database-the Journal of Biological Databases and Curation. 2013. DOI: 10.1093/database/bat029.
  135. 135. Salek RM, Neumann S, Schober D, Hummel J, Billiau K, Kopka J, Correa E, Reijmers T, Rosato A, Tenori L, Turano P, Marin S, Deborde C, Jacob D, Rolin D, Dartigues B, Conesa P, Haug K, Rocca-Serra P, O'Hagan S, Hao J, van Vliet M, Sysi-Aho M, Ludwig C, Bouwman J, Cascante M, Ebbels T, Griffin JL, Moing A, Nikolski M, Oresic M, Sansone SA, Viant MR, Goodacre R, Gunther UL, Hankemeier T, Luchinat C, Walther D, Steinbeck C. COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access. Metabolomics. 2015;11(6):1587-97. DOI: 10.1007/s11306-015-0810-y.
  136. 136. Allen NE, Sudlow C, Peakman T, Collins R. UK Biobank Data: Come and Get It. Science Translational Medicine. 2014;6(224). DOI: 10.1126/scitranslmed.3008601.
  137. 137. Fan CT, Lin JC, Lee C. Taiwan Biobank: a project aiming to aid Taiwan's transition into a biomedical island. Pharmacogenomics. 2008;9(2):235-46. DOI: 10.2217/14622416.9.2.235.
  138. 138. Kuriyama S, Yaegashi N, Nagami F, Arai T, Kawaguchi Y, Osumi N, Sakaida M, Suzuki Y, Nakayama K, Hashizume H, Tamiya G, Kawame H, Suzuki K, Hozawa A, Nakaya N, Kikuya M, Metoki H, Tsuji I, Fuse N, Kiyomoto H, Sugawara J, Tsuboi A, Egawa S, Ito K, Chida K, Ishii T, Tomita H, Taki Y, Minegishi N, Ishii N, Yasuda J, Igarashi K, Shimizu R, Nagasaki M, Koshiba S, Kinoshita K, Ogishima S, Takai-Igarashi T, Tominaga T, Tanabe O, Ohuchi N, Shimosegawa T, Kure S, Tanaka H, Ito S, Hitomi J, Tanno K, Nakamura M, Ogasawara K, Kobayashi S, Sakata K, Satoh M, Shimizu A, Sasaki M, Endo R, Sobue K, Yamamoto M, Tohoku Med Megabank Project S. The Tohoku Medical Megabank Project: Design and Mission. Journal of Epidemiology. 2016;26(9):493-511. DOI: 10.2188/jea.JE20150268.
  139. 139. Lin JC, Fan CT, Liao CC, Chen YS. Taiwan Biobank: making cross-database convergence possible in the Big Data era. Gigascience. 2017;7(1). DOI: 10.1093/gigascience/gix110.
  140. 140. Lin J-C, Fan C-T, Liao C-C, Chen Y-S. Taiwan Biobank: making cross-database convergence possible in the Big Data era. GigaScience. 2017;7(1):1-4. DOI: 10.1093/gigascience/gix110.
  141. 141. Watts G. UK Biobank opens its data vaults to researchers. BMJ : British Medical Journal. 2012;344. DOI: 10.1136/bmj.e2459.
  142. 142. Clayton AT, Lindon JC, Cloarec O, Antti H, Charuel C, Hanton G, Provost J-P, Le Net J-L, Baker D, Walley RJ, Everett JR, Nicholson JK. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature. 2006;440(7087):1073-7. DOI: 10.1038/nature04648.
  143. 143. Gowda GAN, Raftery D. Quantitating Metabolites in Protein Precipitated Serum Using NMR Spectroscopy. Analytical Chemistry. 2014;86(11):5433-40. DOI: 10.1021/ac5005103.
  144. 144. Kaddurah-Daouk R, Baillie RA, Zhu H, Zeng Z-B, Wiest MM, Nguyen UT, Watkins SM, Krauss RM. Lipidomic analysis of variation in response to simvastatin in the Cholesterol and Pharmacogenetics Study. Metabolomics. 2010;6(2):191-201. DOI: 10.1007/s11306-010-0207-x.
  145. 145. Leßig J, Fuchs B. Plasmalogens in Biological Systems: Their Role in Oxidative Processes in Biological Membranes, their Contribution to Pathological Processes and Aging and Plasmalogen Analysis. Current Medicinal Chemistry. 2009;16(16):2021-41. DOI: 10.2174/092986709788682164.
  146. 146. Kaddurah-Daouk R, McEvoy J, Baillie RA, Lee D, Yao JK, Doraiswamy PM, Krishnan KRR. Metabolomic mapping of atypical antipsychotic effects in schizophrenia. Molecular Psychiatry. 2007;12(10):934-45. DOI: 10.1038/
  147. 147. FDA-NIH Biomarker Working Group. Best (biomarkers, endpoints, and other tools): National Institutes of Health (US), Bethesda (MD); 2016.
  148. 148. Farrell PM, Rosenstein BJ, White TB, Accurso FJ, Castellani C, Cutting GR, Durie PR, LeGrys VA, Massie J, Parad RB, Rock MJ, Campbell PW. Guidelines for Diagnosis of Cystic Fibrosis in Newborns through Older Adults: Cystic Fibrosis Foundation Consensus Report. The Journal of Pediatrics. 2008;153(2):S4-S14. DOI: 10.1016/j.jpeds.2008.05.005.
  149. 149. Nagana Gowda GA, Raftery D. Quantitating metabolites in protein precipitated serum using NMR spectroscopy. Analytical chemistry. 2014;86(11):5433-40. DOI: 10.1021/ac5005103.
  150. 150. Stone NJ, Robinson JG, Lichtenstein AH, Merz CNB, Blum CB, Eckel RH, Goldberg AC, Gordon D, Levy D, Lloyd-Jones DM, McBride P, Schwartz JS, Shero ST, Smith SC, Watson K, Wilson PWF. 2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults. Journal of the American College of Cardiology. 2014;63(25_Part_B):2889-934. DOI: doi:10.1016/j.jacc.2013.11.002.
  151. 151. Ledermann J, Harter P, Matei D, Macpherson E, Watkins C, Carmichael J, Matulonis U, Gourley C, Friedlander M, Vergote I, Rustin G, Scott C, Meier W, Shapira-Frommer R, Safra T. Olaparib Maintenance Therapy in Platinum-Sensitive Relapsed Ovarian Cancer. The New England journal of medicine. 2012;366(15):1382-92. DOI: 10.1056/NEJMoa1105535
  152. 152. Basu NN, Ingham S, Hodson J, Lalloo F, Bulman M, Howell A, Evans DG. Risk of contralateral breast cancer in BRCA1 and BRCA2 mutation carriers: a 30-year semi-prospective analysis. Familial Cancer. 2015;14(4):531-8. DOI: 10.1007/s10689-015-9825-9.
  153. 153. Senior JR. Evolution of the Food and Drug Administration Approach to Liver Safety Assessment for New Drugs: Current Status and Challenges. Drug Safety. 2014;37(1):9-17. DOI: 10.1007/s40264-014-0182-7.
  154. 154. Struewing JP, Hartge P, Wacholder S, Baker SM, Berlin M, McAdams M, Timmerman MM, Brody LC, Tucker MA. The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. N Engl J Med. 1997;336(20):1401-8. DOI: 10.1056/nejm199705153362001.
  155. 155. What is a context of use (COU) for a biomarker? 2018 [Available from:
  156. 156. Yerges-Armstrong LM, Ellero-Simatos S, Georgiades A, Zhu H, Lewis JP, Horenstein RB, Beitelshees AL, Dane A, Reijmers T, Hankemeier T, Fiehn O, Shuldiner AR, Kaddurah-Daouk R. Purine pathway implicated in mechanism of resistance to aspirin therapy: pharmacometabolomics-informed pharmacogenomics. Clin Pharmacol Ther. 2013;94(4):525-32. DOI: 10.1038/clpt.2013.119.
  157. 157. Ellero-Simatos S, Lewis JP, Georgiades A, Yerges-Armstrong LM, Beitelshees AL, Horenstein RB, Dane A, Harms AC, Ramaker R, Vreeken RJ, Perry CG, Zhu H, Sanchez CL, Kuhn C, Ortel TL, Shuldiner AR, Hankemeier T, Kaddurah-Daouk R. Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability. CPT Pharmacometrics Syst Pharmacol. 2014;3:e125. DOI: 10.1038/psp.2014.22.
  158. 158. Ellero-Simatos S, Beitelshees AL, Lewis JP, Yerges-Armstrong LM, Georgiades A, Dane A, Harms AC, Strassburg K, Guled F, Hendriks MM, Horenstein RB, Shuldiner AR, Hankemeier T, Kaddurah-Daouk R, Pharmacometabolomics Research N. Oxylipid Profile of Low-Dose Aspirin Exposure: A Pharmacometabolomics Study. J Am Heart Assoc. 2015;4(10):e002203. DOI: 10.1161/JAHA.115.002203.
  159. 159. Di Minno A, Porro B, Turnu L, Manega CM, Eligini S, Barbieri S, Chiesa M, Poggio P, Squellerio I, Anesi A, Fiorelli S, Caruso D, Veglia F, Cavalca V, Tremoli E. Untargeted Metabolomics to Go beyond the Canonical Effect of Acetylsalicylic Acid. J Clin Med. 2019;9(1). DOI: 10.3390/jcm9010051.
  160. 160. Zhang ZD, Yang YJ, Liu XW, Qin Z, Li SH, Li JY. The Protective Effect of Aspirin Eugenol Ester on Paraquat-Induced Acute Liver Injury Rats. Front Med (Lausanne). 2020;7:589011. DOI: 10.3389/fmed.2020.589011.
  161. 161. Ma N, Yang Y, Liu X, Li S, Qin Z, Li J. Plasma metabonomics and proteomics studies on the anti-thrombosis mechanism of aspirin eugenol ester in rat tail thrombosis model. J Proteomics. 2020;215:103631. DOI: 10.1016/j.jprot.2019.103631.
  162. 162. Kim B, Lee JW, Hong KT, Yu KS, Jang IJ, Park KD, Shin HY, Ahn HS, Cho JY, Kang HJ. Pharmacometabolomics for predicting variable busulfan exposure in paediatric haematopoietic stem cell transplantation patients. Sci Rep. 2017;7(1):1711. DOI: 10.1038/s41598-017-01861-7.
  163. 163. Wu D, Li X, Zhang X, Han F, Lu X, Liu L, Zhang J, Dong M, Yang H, Li H. Pharmacometabolomics Identifies 3-Hydroxyadipic Acid, d-Galactose, Lysophosphatidylcholine (P-16:0), and Tetradecenoyl-l-Carnitine as Potential Predictive Indicators of Gemcitabine Efficacy in Pancreatic Cancer Patients. Frontiers in Oncology. 2020;9(1524). DOI: 10.3389/fonc.2019.01524.
  164. 164. Combrink M, du Preez I, Ronacher K, Walzl G, Loots DT. Time-Dependent Changes in Urinary Metabolome Before and After Intensive Phase Tuberculosis Therapy: A Pharmacometabolomics Study. OMICS. 2019;23(11):560-72. DOI: 10.1089/omi.2019.0140.
  165. 165. Sun Y, Kim JH, Vangipuram K, Hayes DF, Smith EML, Yeomans L, Henry NL, Stringer KA, Hertz DL. Pharmacometabolomics reveals a role for histidine, phenylalanine, and threonine in the development of paclitaxel-induced peripheral neuropathy. Breast Cancer Res Treat. 2018;171(3):657-66. DOI: 10.1007/s10549-018-4862-3.
  166. 166. Chen L, Chen C-S, Sun Y, Henry NL, Stringer KA, Hertz DL. Feasibility of pharmacometabolomics to identify potential predictors of paclitaxel pharmacokinetic variability. Cancer Chemotherapy and Pharmacology. 2021. DOI: 10.1007/s00280-021-04300-7.
  167. 167. Bao X, Wu J, Kim S, LoRusso P, Li J. Pharmacometabolomics Reveals Irinotecan Mechanism of Action in Cancer Patients. J Clin Pharmacol. 2019;59(1):20-34. DOI: 10.1002/jcph.1275.
  168. 168. Bayet-Robert M, Morvan D, Chollet P, Barthomeuf C. Pharmacometabolomics of docetaxel-treated human MCF7 breast cancer cells provides evidence of varying cellular responses at high and low doses. Breast Cancer Res Treat. 2010;120(3):613-26. DOI: 10.1007/s10549-009-0430-1.
  169. 169. Park JE, Jeong GH, Lee IK, Yoon YR, Liu KH, Gu N, Shin KH. A Pharmacometabolomic Approach to Predict Response to Metformin in Early-Phase Type 2 Diabetes Mellitus Patients. Molecules. 2018;23(7). DOI: 10.3390/molecules23071579.
  170. 170. Elbadawi-Sidhu M, Baillie RA, Zhu H, Chen YI, Goodarzi MO, Rotter JI, Krauss RM, Fiehn O, Kaddurah-Daouk R. Pharmacometabolomic signature links simvastatin therapy and insulin resistance. Metabolomics. 2017;13. DOI: 10.1007/s11306-016-1141-3.
  171. 171. Liu D, An Z, Li P, Chen Y, Zhang R, Liu L, He J, Abliz Z. A targeted neurotransmitter quantification and nontargeted metabolic profiling method for pharmacometabolomics analysis of olanzapine by using UPLC-HRMS. RSC Advances. 2020;10(31):18305-14. DOI: 10.1039/d0ra02406f.
  172. 172. He C, Liu Y, Wang Y, Tang J, Tan Z, Li X, Chen Y, Huang Y, Chen X, Ouyang D, Zhou H, Peng J. (1)H NMR based pharmacometabolomics analysis of metabolic phenotype on predicting metabolism characteristics of losartan in healthy volunteers. J Chromatogr B Analyt Technol Biomed Life Sci. 2018;1095:15-23. DOI: 10.1016/j.jchromb.2018.07.016.
  173. 173. Shin KH, Choi MH, Lim KS, Yu KS, Jang IJ, Cho JY. Evaluation of endogenous metabolic markers of hepatic CYP3A activity using metabolic profiling and midazolam clearance. Clin Pharmacol Ther. 2013;94(5):601-9. DOI: 10.1038/clpt.2013.128.
  174. 174. Jeong H-C, Park JE, Seo Y, Kim M-G, Shin K-H. Urinary Metabolomic Profiling after Administration of Corydalis Tuber and Pharbitis Seed Extract in Healthy Korean Volunteers. Pharmaceutics. 2021;13(4):522.
  175. 175. Beger RD, Schmidt MA, Kaddurah-Daouk R. Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine. Metabolites. 2020;10(4). DOI: 10.3390/metabo10040129.
  176. 176. Kim J. Human Milk: The Original Personalized Medicine February 2016 [Available from:
  177. 177. Verhasselt V. Breastfeeding, a personalized medicine with influence on short-and long-term immune health. Milk, Mucosal Immunity and the Microbiome: Impact on the Neonate. 2020;94:48-58. DOI: 10.1159/000505578.
  178. 178. Kaddurah-Daouk R, Weinshilboum RM, Network PR. Pharmacometabolomics: Implications for Clinical Pharmacology and Systems Pharmacology. Clinical Pharmacology & Therapeutics. 2014;95(2):154-67. DOI: 10.1038/clpt.2013.217.
  179. 179. Wikoff WR, Frye RF, Zhu H, Gong Y, Boyle S, Churchill E, Cooper-Dehoff RM, Beitelshees AL, Chapman AB, Fiehn O, Johnson JA, Kaddurah-Daouk R, Pharmacometabolomics Research N. Pharmacometabolomics Reveals Racial Differences in Response to Atenolol Treatment. PLOS ONE. 2013;8(3):e57639. DOI: 10.1371/journal.pone.0057639.
  180. 180. Lewis JP, Yerges-Armstrong LM, Ellero-Simatos S, Georgiades A, Kaddurah-Daouk R, Hankemeier T. Integration of Pharmacometabolomic and Pharmacogenomic Approaches Reveals Novel Insights Into Antiplatelet Therapy. Clinical Pharmacology & Therapeutics. 2013;94(5):570-3. DOI: 10.1038/clpt.2013.153.

Written By

Abdul-Hamid Emwas, Kacper Szczepski, Ryan T. McKay, Hiba Asfour, Chung-ke Chang, Joanna Lachowicz and Mariusz Jaremko

Submitted: 10 November 2020 Reviewed: 15 June 2021 Published: 09 August 2021