Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

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• 作者：Vasudev Kantae ; Elke H. J. Krekels ; Michiel J. Van Esdonk ; Peter Lindenburg…
• 关键词：Personalized medicine ; Pharmacology ; Pharmacokinetics ; Pharmacodynamics ; Pharmacometabolomics ; Metabolomics ; Biomarker
• 刊名：Metabolomics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：13
• 期：1
• 全文大小：1190KB
• 刊物主题：Biochemistry, general; Molecular Medicine; Cell Biology; Developmental Biology; Biomedicine, general;
• 出版者：Springer US
• ISSN：1573-3890
• 卷排序：13

文摘

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients’ (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.