Eline M.T. van Maanen (1,2), Tamara van Steeg (2), Julie Stone (3), Justyna A. Dobrowolska (4), Maria S. Michener (3), Mary J. Savage (3), Matthew E. Kennedy (3), Randall J.Bateman (4), Huub Jan Kleijn (3), Meindert Danhof (1)
(1) Division of Pharmacology Leiden/Amsterdam Center for Drug Research, Leiden University, The Netherlands , (2) Leiden Experts on Advanced Pharmacokinetics & Pharmacodynamics, Leiden, The Netherlands, (3) Merck Research Laboratories, Whitehouse Station, NJ, (4) Department of Neurology, Washington University, St. Louis, MO
Objectives: Integrating biomarkers from different analytical tools to gain understanding of the underlying biological system in a comprehensive model can result in technical challenges. An example of such is discussed for the integration of data from tracer kinetic studies with absolute protein concentration measurements of the amyloid precursor protein (APP) pathway. Biomarkers of interest include fraction labeled total amyloid β (Aβ), fraction labeled secreted APP β (sAPPβ), fraction labeled sAPPα and absolute protein concentration measurements (Aβ40, Aβ42, sAPPβ, sAPPα). The objective was to establish a systems model characterizing APP metabolite responses to BACE inhibition, that could account for tracer dynamics throughout the APP pathway and therefore could aid the interpretation of the tracer kinetic data.
Methods: Dose-ranging, biomarker and pharmacokinetic (PK) data obtained from CSF in cisterna-magna-ported (CMP) rhesus monkeys receiving single doses of BACE inhibitor were available. Absolute protein concentrations were determined by enzyme linked immunosorbent assay (ELISA). Plasma enrichment labeled leucine (6C13) and fraction labeled proteins were determined by stable isotope labeling kinetics (SILK) [1]. Nonlinear mixed effects modeling (NONMEM) was used to analyze (A) the time course of the changes in APP metabolites on the basis of the underlying biological processes, using a comprehensive biomarker model; (B) the plasma tracer enrichment over time; (C) and the time course of changes of fraction labeled proteins following BACE inhibition by incorporating tracer dynamics in the model.
Results: (A) A comprehensive biomarker model quantified the response of all 4 biomarkers to BACE inhibition, using one drug effect term. (B) A two pool model related tracer infusion to the measured enrichment. (C) Tracer dynamics throughout the APP pathway was built-in the model, integrating information from the PK, plasma enrichment and the pharmacodynamics (ELISA and SILK) of the BACE inhibitor across time points, doses and endpoints. This yielded pertinent information on the dose response relationship, the dynamics of APP metabolite responses (sAPPβ, sAPPα, Aβ) and the similarities and differences in the responses as measured by ELISA and SILK.
Conclusion: The systems model integrating tracer kinetic data with absolute protein concentration measurements enabled a more informed interpretation of the tracer kinetic study and the APP pathway.
References:
[1] Bateman RJ, Munsell LY, Chen X, Holtzman DM, Yarasheski KE. Stable isotope labeling tandem mass spectrometry (SILT) to quantify protein production and clearance rates. J. Am. Soc. Mass Spectrom. 2007;18:6 997–1006.
Reference: PAGE 23 () Abstr 3155 [www.page-meeting.org/?abstract=3155]
Poster: Drug/Disease modeling - CNS