TY - JOUR
T1 - Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine
AU - Kohler, Isabelle
AU - Hankemeier, Thomas
AU - van der Graaf, Piet H.
AU - Knibbe, Catherijne A.J.
AU - van Hasselt, J. G.Coen
PY - 2017/11/15
Y1 - 2017/11/15
N2 - Novel developments in biomarkers discovery are essential in modern health care, notably in treatment individualization and precision medicine. Clinical metabolomics, which aims to identify small molecule metabolites present in patient-derived samples, has attracted much attention to support discovery of novel biomarkers. However, the step from discriminatory features of disease states towards biomarkers that can truly individualize treatments is challenging. Biomarkers used for treatment individualization can either be dynamic or static prognostic biomarkers. Dynamic biomarkers are relevant for describing the clinical response, including dynamical disease progression and associated treatment response. Static (prognostic) biomarkers do not describe but rather predict a clinical response, and typically reflect aspects of the physiological state of a patient related to drug treatment response or disease progression dynamics. Pharmacokinetic-pharmacodynamic (PK-PD) modeling represents an established approach for drug treatment individualization based on drug exposure or treatment response biomarkers, as well as for the description of disease progression dynamics. Here, we discuss how novel treatment individualization biomarkers can be identified using a clinical metabolomics-based approach, and how concepts inspired from the field of PK-PD modeling can be integrated in this process in order to increase the clinical relevance of identified biomarkers and precision medicine.
AB - Novel developments in biomarkers discovery are essential in modern health care, notably in treatment individualization and precision medicine. Clinical metabolomics, which aims to identify small molecule metabolites present in patient-derived samples, has attracted much attention to support discovery of novel biomarkers. However, the step from discriminatory features of disease states towards biomarkers that can truly individualize treatments is challenging. Biomarkers used for treatment individualization can either be dynamic or static prognostic biomarkers. Dynamic biomarkers are relevant for describing the clinical response, including dynamical disease progression and associated treatment response. Static (prognostic) biomarkers do not describe but rather predict a clinical response, and typically reflect aspects of the physiological state of a patient related to drug treatment response or disease progression dynamics. Pharmacokinetic-pharmacodynamic (PK-PD) modeling represents an established approach for drug treatment individualization based on drug exposure or treatment response biomarkers, as well as for the description of disease progression dynamics. Here, we discuss how novel treatment individualization biomarkers can be identified using a clinical metabolomics-based approach, and how concepts inspired from the field of PK-PD modeling can be integrated in this process in order to increase the clinical relevance of identified biomarkers and precision medicine.
KW - Biomarkers
KW - Metabolomics
KW - Pharmacokinetic-pharmacodynamic modeling
KW - Pharmacology
KW - Precision medicine
UR - http://www.scopus.com/inward/record.url?scp=85019408321&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019408321&partnerID=8YFLogxK
U2 - 10.1016/j.ejps.2017.05.018
DO - 10.1016/j.ejps.2017.05.018
M3 - Review article
C2 - 28502671
AN - SCOPUS:85019408321
SN - 0928-0987
VL - 109
SP - S15-S21
JO - European Journal of Pharmaceutical Sciences
JF - European Journal of Pharmaceutical Sciences
ER -