Abstract: FR-OR076
Quantitative Systems Pharmacology (QSP) Model of Metabolic Bone Disease in ESRD
Session Information
- Mineral Disease: FGF23 and Mineral Metabolism
November 03, 2017 | Location: Room 273, Morial Convention Center
Abstract Time: 06:06 PM - 06:18 PM
Category: Mineral Disease
- 1201 Mineral Disease: Ca/Mg/PO4
Authors
- Brier, Michael E., University of Louisville, Louisville, Kentucky, United States
- Graves, Matthew J, University of Louisville, Louisville, Kentucky, United States
- Lederer, Eleanor D., University of Louisville; Robley Rex VA Medical Center, Louisville, Kentucky, United States
- Gaweda, Adam E., University of Louisville, Louisville, Kentucky, United States
Background
Metabolic bone disorder (MBD), a universal complication of chronic kidney disease (CKD), is a recognized contributor to the accelerated mortality of CKD patients but achieving established goals for therapy is challenging. We tested the hypothesis that a QSP model of MBD-CKD could provide a precision medicine tool to guide pharmacologic interventions for MBD.
Methods
We modified the Peterson-Riggs (Bone 2010) model of phosphate (PO4), calcium (Ca), PTH, and Calcitriol (CTL) in CKD to apply to end-stage renal disease patients. The model consists of a set of nonlinear differential equations describing the regulation of serum PO4, Ca, PTH, and CTL, implemented in Matlab, and includes the effect of intermittent hemodialysis and the administration of phosphate binders, cinacalcet, and vitamin D analogs. We tested the model by simulating the administration of different agents and performed a Sensitivity Analysis to identify key model components. Data were obtained from in-center dialysis patients at the University of Louisville.
Results
The QSP model accurately predicts the concentration time profile of PTH following repeated administration of cinacalcet and CTL and the impact of intermittent hemodialysis on the serum concentrations of Ca and PO4. Sensitivity Analysis results for the top 5/100 parameters along with the contribution of each of the individual components are shown. The table entries represent the correlation between the model parameters and goodness of fit for an individual patient. The model is most sensitive to flux of PO4, kidney PO4 excretion, and Ca absorption and hydroxyapatite conversion.
Conclusion
We conclude that a QSP model of MBD-CKD accurately predicts the serum concentrations of PO4, Ca, PTH, and CTL and that these predictions are heavily influenced by PO4 flux, PO4 excretion sensitivity, and availability of Ca.
| Parameter | PTH | Ca | PO4 | CTL | Total |
| k85 | 0.01 | -0.06 | 0.11 | -0.39 | 0.57 |
| d59 | -0.04 | -0.01 | -0.08 | 0.42 | 0.55 |
| T13 | -0.34 | -0.08 | -0.06 | -0.06 | 0.54 |
| k58 | -0.05 | -0.02 | -0.08 | 0.38 | 0.54 |
| T15 | 0.33 | 0.03 | 0.08 | 0.05 | 0.49 |
k85 and k58 PO4 transfer rate between serum and intracellular compartment; d59 kidney phosphate excretion sensitivity; T13 normalized calcium absorption rate; and T15 hydroxyapatite conversion rate