Pharmacometabolomics is an important direction for the future development of metabolomics, and Clatyon et al. successfully predicted acetaminophen-induced hepatotoxicity in rats with unknown genotype by metabolomic analysis of urine before drug administration. Based on this finding, the group first introduced the concept of "pharmacometabolomics " in Nature in 2006: to predict the disposition, exposure levels and effects of drugs or exogenous substances in vivo based on the differences and characteristics of the material basis of individual organisms. As research progresses, pharmacometabolomics is recognized as an effective method for evaluating individual differences and provides new ideas for individualized treatment.
Pharmacometabolomics is also based on metabolic profiling, which can depict the characteristic fingerprints of the functional state of the biochemical system of an organism under the combined effect of internal and external environmental factors. Pharmacometabolomics can provide a comprehensive picture of the biochemical status and functional reserve/stress potential of an individual/biological system and provide information on the organism's responsiveness to drugs and the drug disposition process from the underlying metabolic profile.
The development of pharmacometabolomics has advanced metabolomics from screening and identification of biomarkers targeting disease/pharmacodynamic/toxicity to prediction of organismal responsiveness to drugs. Through pharmacometabolomics studies, individual differences in metabolism and pharmacodynamic and toxic responses to drugs can be predicted, which is expected to enable targeted treatment of different phenotypes of patients' diseases and adjustment of drug doses and types according to the disease process to achieve truly individualized treatment.
Schematic of our pharmaco-metabolomic hypothesis (Clayton et al., 2016)
Pharmacometabolomics for drug toxicity prediction
Minimizing or avoiding the toxic side effects of drugs is one of the main goals of rational drug use. The different responsiveness of individual patients to drugs poses a great problem in the selection of clinical drug classes and dose determination. Drug metabolomics provides a good strategy to solve this problem. The phenotypes of metabolites in the body before or shortly after drug administration can reflect a variety of factors related to drug metabolism and drug effects, thus predicting individual responses. Drug metabolomics can also provide a better prognosis for other common adverse drug reactions such as nephrotoxicity, cardiotoxicity, and neurotoxicity.
Pharmacometabolomics for drug efficacy prediction
Krauss et al. explored the metabolic characteristics of statins using 3 complementary metabolomics platforms. The results confirmed that statins have multidirectional metabolic effects and produce cardiovascular markers of different effects. In addition, individual differences including metabolome, genome, in vivo flora, and living environment can combine to lead to differences in efficacy and even toxic side effects.
Pharmacometabolomics can be effectively complemented with pharmacogenomics. On the one hand, drug efficacy can be evaluated more comprehensively, and on the other hand, differences in drug efficacy can be predicted more reliably through the acquisition of genetic/metabolic phenotypes prior to drug administration. At the same time, the rich information obtained through pharmacometabolomics also provides direct clues and basis for the study of deeper mechanisms of individual differences in drug efficacy.
Pharmacometabolomics for pharmacokinetic property prediction
Many individual differences in drug response are caused by genetic factors. The genotype of the patient's drug metabolism influences the individual differences in drug response. Individual differences are usually reflected in differences in the in vivo processes of the drug, including bioavailability, tissue distribution, metabolism and excretion, and may further involve the effects of the drug (ineffectiveness or toxicity). There is a very close link between drug metabolomics and pharmacokinetics. The basal metabolic profile enables the prediction of pharmacokinetic behavior and parameters and even the activity and induced potential of drug metabolizing enzymes.
Clinical studies with drugs (Kaddurah et al., 2014)
Pharmacometabolomics for guiding oncology drug therapy
In the clinical use of anti-tumor drugs, due to the complexity of the lesion itself and the special characteristics of the drugs, the sensitivity of chemotherapy drugs and the toxic side effects produced by chemotherapy vary greatly among individuals. The restrictive and adverse effects of many chemotherapeutic drugs can directly affect the treatment and even survival of patients. Therefore, the rational selection and application of chemotherapeutic drugs is of great importance. Drug metabolomics can guide the rational use of antitumor drugs. In the field of oncology treatment, the main value of metabolomics is also reflected in the discovery of biomarkers and their use for diagnosis and prediction of therapeutic efficacy.
Pharmacometabolomics for individual differences studies
Individual differences are mainly manifested in genetics, age, gender, diet, environment, and physiopathology. Metabolomics has a mechanism-oriented role and can suggest information on metabolic molecular mechanisms. Combined with genomics and proteomics, it can provide a more systematic and comprehensive explanation of the causes of individual differences.
References
- Krauss, R. M., Zhu, H., & Kaddurah‐Daouk, R. (2013). Pharmacometabolomics of statin response. Clinical Pharmacology & Therapeutics, 94(5), 562-565.
- Clayton, T. A., Lindon, J. C., Cloarec, O., et al. (2006). Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature, 440(7087), 1073-1077.
- Kaddurah‐Daouk, R., Weinshilboum, R. M., & Pharmacometabolomics Research Network. (2014). Pharmacometabolomics: implications for clinical pharmacology and systems pharmacology. Clinical Pharmacology & Therapeutics, 95(2), 154-167.
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