Therapeutic drug monitoring, liquid biopsies or pharmacogenomics for prediction of human drug metabolism and response
© 2024 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society..
Pharmacokinetics plays a central role in understanding the significant interindividual differences that exist in drug metabolism and response. Effectively addressing these differences requires a multi-faceted approach that encompasses a variety of tools and methods. In this review, we examine three key strategies to achieve this goal, namely pharmacogenomics, therapeutic drug monitoring (TDM) and liquid biopsy-based monitoring of hepatic ADME gene expression and highlight their advantages and limitations. We note that larger cohort studies are needed to validate the utility of liquid biopsy-based assessment of hepatic ADME gene expression, which includes prediction of drug metabolism in the clinical setting. Modern mass spectrometers have improved traditional TDM methods, offering versatility and sensitivity. In addition, the identification of endogenous or dietary markers for CYP metabolic traits offers simpler and more cost-effective alternatives to determine the phenotype. We believe that future pharmacogenomic applications in clinical practice should prioritize the identification of missing heritable factors, using larger, well-characterized patient studies and controlling for confounding factors such as diet, concomitant medication and physical health. The intricate regulation of ADME gene expression implies that large-scale studies combining long-read next-generation sequencing (NGS) of complete genomes with phenotyping of patients taking different medications are essential to identify these missing heritabilities. The continuous integration of such data into AI-driven analytical systems could provide a comprehensive and useful framework. This could lead to the development of highly effective algorithms to improve genetics-based precision treatment by predicting drug metabolism and response, significantly improving clinical outcomes.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
British journal of clinical pharmacology - (2024) vom: 24. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ingelman-Sundberg, Magnus [VerfasserIn] |
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| Links: |
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| Themen: |
ADME |
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| Anmerkungen: |
Date Revised 24.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1111/bcp.16048 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370126483 |
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| 520 | |a Pharmacokinetics plays a central role in understanding the significant interindividual differences that exist in drug metabolism and response. Effectively addressing these differences requires a multi-faceted approach that encompasses a variety of tools and methods. In this review, we examine three key strategies to achieve this goal, namely pharmacogenomics, therapeutic drug monitoring (TDM) and liquid biopsy-based monitoring of hepatic ADME gene expression and highlight their advantages and limitations. We note that larger cohort studies are needed to validate the utility of liquid biopsy-based assessment of hepatic ADME gene expression, which includes prediction of drug metabolism in the clinical setting. Modern mass spectrometers have improved traditional TDM methods, offering versatility and sensitivity. In addition, the identification of endogenous or dietary markers for CYP metabolic traits offers simpler and more cost-effective alternatives to determine the phenotype. We believe that future pharmacogenomic applications in clinical practice should prioritize the identification of missing heritable factors, using larger, well-characterized patient studies and controlling for confounding factors such as diet, concomitant medication and physical health. The intricate regulation of ADME gene expression implies that large-scale studies combining long-read next-generation sequencing (NGS) of complete genomes with phenotyping of patients taking different medications are essential to identify these missing heritabilities. The continuous integration of such data into AI-driven analytical systems could provide a comprehensive and useful framework. This could lead to the development of highly effective algorithms to improve genetics-based precision treatment by predicting drug metabolism and response, significantly improving clinical outcomes | ||
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