Abstract
The study of central nervous system (CNS) pharmacology is limited by a lack of drug effect biomarkers. Pharmacometabolomics is a promising new tool to identify multiple molecular responses upon drug treatment. However, the pharmacodynamics is typically not evaluated in metabolomics studies, although being important properties of biomarkers.In this study we integrated pharmacometabolomics with pharmacokinetic/pharmacodynamic (PKPD) modeling to identify and quantify the multiple endogenous metabolite dose-response relations for the dopamine D2 antagonist remoxipride.Remoxipride (vehicle, 0.7 or 3.5mg/kg) was administered to rats. Endogenous metabolites were analyzed in plasma using a biogenic amine platform and PKPD models were derived for each single metabolite. These models were clustered on basis of proximity between their PKPD parameter estimates, and PKPD models were subsequently fitted for the individual clusters. Finally, the metabolites were evaluated for being significantly affected by remoxipride.In total 44 metabolites were detected in plasma, many of them showing a dose dependent decrease from baseline. We identified 6 different clusters with different time and dose dependent responses and 18 metabolites were revealed as potential biomarker. The glycine, serine and threonine pathway was associated with remoxipride pharmacology, as well as the brain uptake of the dopamine and serotonin precursors.This is the first time that pharmacometabolomics and PKPD modeling were integrated. The resulting PKPD cluster model described diverse pharmacometabolomics responses and provided a further understanding of remoxipride pharmacodynamics. Future research should focus on the simultaneous pharmacometabolomics analysis in brain and plasma to increase the interpretability of these responses.
Highlights
Central nervous system (CNS) drug development is difficult and attrition rates are high (Kola and Landis, 2004)
While important progress has been made in the insight into human brain pharmacokinetics (PK) in response to plasma PK, insights into the relation to the time dependent CNS drug effects are limited
This study showed how the integration of pharmacometabolomics and PKPD modeling led to identification and significant description of 4 clusters of pharmacodynamic patterns
Summary
Central nervous system (CNS) drug development is difficult and attrition rates are high (Kola and Landis, 2004). It is essential to utilize biomarkers that provide proof of pharmacology and dosing guidance for early clinical drug development (Danhof et al, 2005; de Lange, 2013; de Lange and Hammarlund-Udenaes, 2015; Hurko, 2009; Hurko and Ryan, 2005; Morgan et al, 2012; Soares, 2010). These biomarkers are measured in the blood, since blood can be obtained from humans. A pharmacometabolomics approach has been successfully applied in CNS drug research for identification of serum biomarkers of antipsychotic drug efficacy (Xuan et al, 2011) or toxicity (Kaddurah-Daouk et al, 2007)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
