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.