Development of a physiologically-based pharmacokinetic model of the rat central nervous system

Abstract

Central nervous system (CNS) drug disposition is dictated by a drug's physicochemical properties and its ability to permeate physiological barriers. The blood-brain barrier (BBB), blood-cerebrospinal fluid barrier and centrally located drug transporter proteins influence drug disposition within the central nervous system. Attainment of adequate brain-to-plasma and cerebrospinal fluid-to-plasma partitioning is important in determining the efficacy of centrally acting therapeutics. We have developed a physiologically-based pharmacokinetic model of the rat CNS which incorporates brain interstitial fluid (ISF), choroidal epithelial and total cerebrospinal fluid (CSF) compartments and accurately predicts CNS pharmacokinetics. The model yielded reasonable predictions of unbound brain-to-plasma partition ratio (Kpuu,brain) and CSF:plasma ratio (CSF:Plasmau) using a series of in vitro permeability and unbound fraction parameters. When using in vitro permeability data obtained from L-mdr1a cells to estimate rat in vivo permeability, the model successfully predicted, to within 4-fold, Kpuu,brain and CSF:Plasmau for 81.5% of compounds simulated. The model presented allows for simultaneous simulation and analysis of both brain biophase and CSF to accurately predict CNS pharmacokinetics from preclinical drug parameters routinely available during discovery and development pathways.

Figure 1

(A) Whole-body physiologically based pharmacokinetic (PBPK) model. CL: Clearance; CSF: Cerebrospinal fluid; and (B) Brain and CSF compartments. V: vascular compartment; EV: extra-vascular compartment; CL_passive: passive clearance; CL_active: active efflux clearance.

Figure 2

Model predicted norfloxacin plasma concentrations in rats. Small closed circles represent literature reported plasma concentrations determined in rats following an IV-bolus dose [88]. Large closed circles represent model predicted norfloxacin plasma concentrations in rats in the absence of efflux. Large open circles represent model predicted norfloxacin plasma concentrations in rats in the presence of efflux (efflux ratio = 3).

Figure 3

Model predicted norfloxacin brain concentrations in rats. Crosses represent literature reported brain concentration determined in rats following an IV-bolus dose [88]. Closed circles represent model predicted norfloxacin brain concentrations in rats in the absence of efflux. Open circles represent model predicted norfloxacin brain concentrations in rats in the presence of efflux (efflux ratio = 3).

Figure 4

Comparison of predicted and reported Kp_uu,brain in mice. (A) Solid bold mid-line represents the line of unity and solid outer-lines represent 4-fold prediction error; and (B) residuals plot.

Figure 5

Comparison of predicted and reported Kp_uu,brain in rat. (A) Solid bold mid-line represents the line of unity and solid outer-lines represent 4-fold prediction error; and (B) residuals plot.

Figure 6

Comparison of predicted and reported CSF_u:Plasma_u in rat. (A) Solid bold mid-line represents the line of unity and solid outer-lines represent 4-fold prediction error; and (B) residuals plot.

Figure 7

Sensitively analysis of the whole-body physiologically based pharmacokinetic (PBPK) model. The impact of variations in fu_brain, fu_plasma, CL_passive and efflux ratio on Kp_uu,brain and CSF_u:Plasma_u.

Figure 8

Sensitively analysis of the whole-body PBPK model. The impact of variations in fu_brain (A) low fu_brain and (B) high fu_brain, CL_passive and efflux ratio on Kp_uu,brain (see text for details).

Figure S1

Correlations and confidence interval plots of in situ brain perfusion and predictions of CL_passive.

Figure S2

LOESS regression and confidence interval plots.

Figure S3

Kp_uu,brain model predictions for loperamide and quinidine (highlighted in black) using an in vivo surrogate efflux ratio metric for highly effluxed transporter substrates.