Finding underlying longitudinal patterns in pharmaco-metabolomics data
W. van den Brink, R. Hartman, M. de Bruin, B. Gonzalez, J. Elassaiss-Schaap, P.H. van der Graaf, T. Hankemeier, E.C.M. de Lange
Leiden Academic Centre for Drug Research, Leiden University, The Netherlands
Objectives: To develop a method that integrates multidimensional pharmaco-metabolomics data into a single PKPD systems pharmacology model in a data-driven manner, ultimately to reveal the underlying patterns in the pharmaco-metabolomics response.
Methods: Pharmacological experiments were performed in which 0, 0.7 or 3.5 mg/kg remoxipride was administered to rats. Plasma samples were obtained over a period of 4 hours and were subsequently analysed for biogenic amines using a metabolomics approach as described previously [1]. PKPD turnover models were developed for the pharmacodynamic response of each single metabolite using NONMEM V7.3.0 [2]. The PK parameters were fixed to those identified in an earlier study (not published). Principal component analysis was applied to the dataset with the model parameters as variables and the metabolites as individuals in order to reveal clusters of metabolites. Kmeans clustering was performed to identify the cluster means, which were subsequently used to inform on the structure towards a ‘whole system model’.
Results: 44 metabolites were analysed in plasma and most of these showed a dose dependent decrease after remoxipride administration. A type I turnover model (inhibiting effect on k_in) with an Emax drug effect model, was identified for each single metabolite. ‘Whole system’ models with 4, 5 and 6 clusters as identified by kmeans were compared, 5 clusters being the optimal number of clusters with a significant drop in OFV as compared to 4 clusters (-104 points), and no significant difference with 6 clusters (+2 points).
Conclusions: A methodology was developed for finding underlying patterns in pharmaco-metabolomics data. 5 different longitudinal patterns were identified in 44 metabolite profiles, all of which could be described by type I turnover models.
References:
[1] Noga MJ, Dane A, Shi S, Attali A, van Aken H, et al. (2012) Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis. Metabolomics 8(2): 253–63
[2] Beal SL, Sheiner LB, Boeckmann AJ & Bauer RJ (Eds.) NONMEM Users Guides. 1989-2013. Icon Development Solutions, Ellicott City, Maryland, USA.
