MSACL 2016 EU Plenary Abstract

Metabolomics Enabling Personalized Medicine

Thomas Hankemeier
Leiden University/Netherlands Metabolomics Centre

Bio: Thomas Hankemeier is full professor at the Leiden Academic Centre for Drug Research, Leiden University and also leader of the Cluster Systems Pharmacology together. His research is aiming at innovative analytical tools for metabolomics-driven systems biology in personalized health strategies, and he is considered a pioneer in the field of metabolomics. He has discovered several explorative metabolic biomarkers such as for healthy aging, predicting treatment outcome, predicting clinical endpoints or to identify sub-groups of diseases. He is initiator of the Netherlands Metabolomics Centre. He is co-founder of Mimetas (www.mimetas.com), the first organ-on-a-chip company.

Authorship: T. Hankemeier
Leiden University, Netherlands Metabolomics Centre, Erasmus MC

Short Abstract Metabolism is at the core of physiology and therefore metabolomics is ideally suited to assess someone’s health state. In this presentation strategies are discussed how personalized medicine can be realized by using metabolomics and by integrating it with other omics data. Examples are shown how disease pathology can be studied using metabolomics in clinical studies, and how mechanistic insights can be obtained using advanced in-vitro models and translational metabolomics. Pharmacometabolomics can help to study how pharmacology modulates disease pathology, and to predict efficacy and adverse effects of pharmacological interventions . An outlook will be given how metabolomics will impact clinical research and ultimately clinical decision support.

Long Abstract

Introduction

Metabolism is at the core of physiology and therefore metabolomics is ideally suited to assess someone’s health state. Where genomics has proven to be successful to predict disease risk, metabolomics can predict the actual health state and monitor disease development and treatment response. Pharmacogenomics has been successfully used to predict the efficacy of pharmacological treatments. However, in an increasing number of cases pharmacometabolomics demonstrated the potential to predict treatment efficacy or adverse effects where pharmacogenomics is not successful. Therefore the combination of metabolomics and genomics are promising to realize personalized medicine by (i) diagnose disease development in time, (ii) support the choice of the proper pharmacological treatment in the clinic and (iii) identify novel treatment options in disease areas where now successful treatment options are available, or treatment often is inefficient.

Methods

Different LC-MS-based and CE-MS-based metabolomics and lipidomics platforms are used. Different clinical and in-vitro studies will be discussed, in which the following metabolomics platforms are used. Biogenic amines are profiled using LC-MS/MS after derivatization. Oxylipins are profiled using a LC-MS/MS platform. Lipids are profiled using LC-qTOFMS. Central carbon and energy metabolites are profiled using HILIC-MS and CE-MS using a sheathless CE-MS interface. And a novel oxidative stress platform is using LC-MS/MS.

Univariate and multivariate statistical methods are used for data analysis, as well as network analysis techniques. Longitudinal studies require special data analysis approaches that will be discussed.

In-vitro studies are conducted using a in house developed organ-on-a-chip platform. Blood vessels are created in a microfluidic platform using so-called phaseguides to allow patterning of gels and liquids. Next, neurological co-cultures are generated and studied in this microfluidic platform.

Results

The results of various clinical and in-vitro studies will be discussed.

In a first example, responders and non-responders to an anti-platelet therapy are compared, and metabolic biomarkers were identified to predict the different response with regards to drug response. Ex-vivo experiments were carried out to confirm clinical findings.

In a second example, the disease progression and disease severity of hepatitis B is studied using a range of metabolomics platforms. The data illustrate the strength of metabolomics to provide insight into the metabolic dysregulation experienced during chronic HBV. The immune tolerant phase is characterised with a speculated viral hijacking of the glycerol-3-phosphate-NADH shuttle explaining the reduced glycerophospholipids and increased plasmalogen species, indicating a strong link to HBV replication. The persisting impairment of the choline glycerophospholipids, even during the inactive carrier phase with minimal HBV activity, alludes to possible metabolic imprinting effects. Viral hijacking of glycerol-3-phosphate-NADH shuttle was identified, which is therefore actually a possible target for treatment.

Next, the use of microfluidic-based advanced in-vitro models with organotypic characteristics will be discussed. A microfluidic platform has been developed using phaseguides to allow the positioning of liquids and gels, which allows the development of complex co-cultures. For example, a vascular organ-on-a-chip platform has been realized, and validated for known factors causing vascular leakage. To study Alzheimer and Parkinson disease, a co-culture of astrocytes and neurons was realized in the organ-on-a-chip-platform. This requires miniaturized analytical approaches.

Finally, recent technological developments will be discussed to improve throughput of metabolomics.

Conclusions

The results discussed demonstrate that metabolomics can find biomarkers to predict treatment outcome, and that metabolomics is an important technology to realize personalized medicine. Especially the combination of metabolomics and genomics are promising to identify sub-types of diseases and to identify novel treatment options, that take better interindividual differences into account. An outlook will be given how metabolomics will impact clinical research and ultimately clinical decision support.

Financial Disclosure

IP Royalty: no

Planning to mention or discuss specific products or technology of the company(ies) listed above:

yes