Discovery and development of Central Nervous System (CNS) drugs is hampered by high attrition rates. One of the reasons is the lack of blood-based biomarkers that represent the interaction between... Show moreDiscovery and development of Central Nervous System (CNS) drugs is hampered by high attrition rates. One of the reasons is the lack of blood-based biomarkers that represent the interaction between the drug and the neurological systems of interest. Here we present a systems-pharmacology approach that combines a multi-biomarker approach (e.g. metabolomics) with pharmacokinetic/pharmacodynamic (PK/PD) modeling to reveal quantitative pharmacological characteristics that are relevant to dopaminergic drug action. Moreover, we set out to identify biomarkers that can be obtained from the blood as non-invasive sampling site. In the first section of this thesis the methodology is introduced in the context of translational CNS drug development. Moreover, a systematic search is performed to available biomarkers of dopaminergic drug action. Then, in the second part, the multi-biomarker PK/PD approach is applied to biomarkers from the neuroendocrine system as connection between brain and blood. In the third section, the methodology is developed using the simultaneous, time-resolved metabolomics response in brain extracellular fluid and plasma. By applying multi-biomarker PK/PD modeling we revealed quantitative pharmacological characteristics of dopaminergic drugs with regard to multiple biological processes. Moreover, we identified potential blood-based biomarkers of dopaminergic effect in the brain. Show less
Brink, W.J. van den; Elassaiss, J.; Gonzalez Amoros, B.; Harms, A.C.; Graaf, P.H. van der; Hankemeier, T.; Lange, E.C.M. de 2017
IntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our... Show moreIntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.MethodsFasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.DiscussionMetabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery. Show less
