Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine

Abstract

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.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

“…The group-based study design of clinical metabolomics research (Table 1) is well-suited for biomarker discovery and selection of candidates for a diagnostic development pipeline. The starting point for translation of biomarker discovery candidates into targets measured in diagnostics assays, as elaborated by Kohler et al, 12 is to determine whether the end use of the diagnostic test is prognostic (static) or for longitudinal monitoring of disease progression or treatment response in a dynamic manner. With the end use clearly stated, it is recommended that the 100 s of metabolites from clinical metabolomics research be filtered to a short list of 1 to 10 biomarkers for subsequent targeted analytical assay development, validation, and algorithm development described by Xia et al 13 Clinical metabolomics testing, such as for IEM screening, is a nascent testing approach that is amenable to multiple sample types, [37][38][39] can substitute tens of smaller assays, and represents an important proof-of-principle to support future pre-clinical and clinical applications for metabolomics.…”

“…Metabolomics can encompass clinical research involving comparisons between groups, epidemiological or population‐based studies, and precision medicine profiling of individuals in comparison to a reference population for screening or diagnosing. The research uses of untargeted metabolomics have been employed for about two decades to identify biomarkers, drive fundamental discovery, stratify patient groups, and gain insight into mechanism of action . Important elements of a successful research metabolomics study include study design, proper control groups and conditions, statistical powering, and consistency of conditions (eg, uniform subject demographics, site of collection, collection method, sample storage, and handling; Table ).…”

“…The use of untargeted metabolomics for biomarker discovery followed by targeted diagnostic assay development, analytical validation, clinical validation in new patient cohorts, and launch of products under the Clinical Laboratory Improvements Amendments (CLIA) framework is an understood path, and articles about this path have been published recently. [11][12][13][14][15][16][17] This review is limited to uses of untargeted metabolomics, but since clinical metabolomics has been preceded by targeted assays using gas chromatography-mass spectrometry (GC-MS), 16,18 as well as liquid chromatography-mass spectrometry (LC-MS), 16,19 it is worth noting the important and rich legacy of targeted analyte measurement approaches that has benefited untargeted metabolomics.…”

Assessing the suitability of capillary electrophoresis‐mass spectrometry for biomarker discovery in plasma‐based metabolomics

“…Supervised and unsupervised chemometric approaches are often used to get visualization of the relations between the metabolic profiles and to define borders between groups of samples. Global profiling of (endogenous) metabolites in organisms has been vastly explored for its potential application in research areas, such as diagnosis of diseases, guidance for personalized medicine, and evaluation of therapeutic treatments . Despite the efforts dedicated to metabolomics for biomarker discovery, its impact on recent clinical practice is still rather limited due to various challenges encountered during the analytical process, including study design, sample handling, data acquisition and data analysis, which may potentially lead to contradictory results in reported biomarkers.…”

Blood‐Based Biomarkers of Quinpirole Pharmacology: Cluster‐Based PK/PD and Metabolomics to Unravel the Underlying Dynamics in Rat Plasma and Brain

“…Therefore, microdialysis is the method of choice to dynamically evaluate a metabolomics fingerprint in brain ECF simultaneously upon CNS drug treatment. Such dynamical evaluation would improve the quantitative insights into systemwide responses (i.e., changes in biomarker concentrations), thereby shifting CNS drug development from an empirical toward a mechanistic discipline …”