Population pharmacokinetics and pharmacodynamics of a novel vascular adhesion protein-1 inhibitor using a multiple-target mediated drug disposition model

Abstract

ASP8232 is a novel inhibitor of vascular adhesion protein-1 that was under evaluation for reducing residual albuminuria in patients with diabetic kidney disease. To characterize the pharmacokinetics (PK) of ASP8232 and its effect on vascular adhesion protein 1 (VAP-1) plasma activity and VAP-1 concentrations (pharmacodynamics, PD) in an integrated and quantitative manner, a target mediated drug disposition model was developed based on pooled data from four completed clinical trials with ASP8232 in healthy volunteers, and in patients with diabetic kidney disease and diabetic macular edema, respectively. In this model, the binding of ASP8232 to its soluble and membrane-bound target in the central and peripheral compartments were included. The model was able to adequately describe the non-linear PK and PD of ASP8232. The observed difference in PK between healthy volunteers and renally impaired patients could be explained by an effect of baseline estimated glomerular filtration rate on ASP8232 clearance and relative bioavailability. The relationship between ASP8232 concentration and VAP-1 inhibition was successfully established and can be applied to simulate drug exposure and degree of VAP-1 inhibition for any given dose of ASP8232 across the spectrum of renal function.

Algorithmic multiscale analysis for the FcRn mediated regulation of antibody PK in human

“…( 1) and solving for the rate of the fast reaction R 3f : A reduced model can be constructed by using Eqs. (17)(18)(19)(20) or by using the algebraic relation Eq. (15) (which must be solved for the fast variable C IgG e ) and the differential Eqs.…”

“…(10) with time 52,53 , substituting from Eqs. (17)(18)(19)(20) and solving for the rate of the fast reaction R 4 : or where m = k deg /k 1 , and then substituting in Eqs. (17)(18)(19)(20):…”

“…(5-9), (10-14), (15)(16)(17)(18)(19)(20) and (21)(22)(23)(24)(25)(26) the investigator must identify (1) the proper number of QSSAs and (2) the variables and reactions that are considered fast. These are identifications, which are usually based on the experience of the investigator and must be subject to an a posteriori verification.…”

“…Several methodologies are available for model reduction, such as sensitivity analysis, singular perturbation (i.e., time-scale analysis), lumping, optimization etc; see Refs. 2,[7][8][9][10][11] for details.The model reduction methods employed most frequently are the quasi-steady-state approximation (QSSA) and partial-equilibrium approximation (PEA); e.g., 6,[12][13][14][15][16][17][18] . Both methodologies are based on the multiscale character of the full model and are limiting cases of singular perturbation analysis [19][20][21][22] .…”

“…The model reduction methods employed most frequently are the quasi-steady-state approximation (QSSA) and partial-equilibrium approximation (PEA); e.g., 6,[12][13][14][15][16][17][18] . Both methodologies are based on the multiscale character of the full model and are limiting cases of singular perturbation analysis [19][20][21][22] .…”