TY - JOUR

T1 - Optimal experimental design for precise estimation of the parameters of the axial dispersion model of hepatic elimination

AU - Chou, Chen Hsi

AU - Aarons, Leon

AU - Rowland, Malcolm

PY - 1998

Y1 - 1998

N2 - The axial dispersion model of hepatic drug elimination is characterized by two dimensionless parameters, the dispersion number, D(N), and the efficiency number, R(N), corresponding to the relative dispersion of material on transit through the organ and the relative efficiency of elimination of drug by the organ, respectively. Optimal design theory was applied to the estimation of these two parameters based on changes in availability (F) of drug at steady state for the closed boundary condition model, with particular attention to variations in the fraction of drug unbound in the perfusate (fu(B)). Sensitivity analysis indicates that precision in parameter estimation is greatest when F is low and that correlation between R(N) and D(N) is high, which is desirable for parameter estimation, when D(N) lies between 0.1 and 100. Optimal design points were obtained using D- optimization, taking into account the error variance model. If the error variance model is unknown, it is shown that choosing Poisson error model is reasonable. Furthermore, although not optimal, geometric spacing of fu(B) values is often reasonable and definitively superior to a uniform spacing strategy. In practice, the range of fu(B) available for selection may be limited by such practical considerations as assay sensitivity and acceptable concentration range of binding protein. Notwithstanding, optimal design theory provides a rational approach to precise parameter estimation.

AB - The axial dispersion model of hepatic drug elimination is characterized by two dimensionless parameters, the dispersion number, D(N), and the efficiency number, R(N), corresponding to the relative dispersion of material on transit through the organ and the relative efficiency of elimination of drug by the organ, respectively. Optimal design theory was applied to the estimation of these two parameters based on changes in availability (F) of drug at steady state for the closed boundary condition model, with particular attention to variations in the fraction of drug unbound in the perfusate (fu(B)). Sensitivity analysis indicates that precision in parameter estimation is greatest when F is low and that correlation between R(N) and D(N) is high, which is desirable for parameter estimation, when D(N) lies between 0.1 and 100. Optimal design points were obtained using D- optimization, taking into account the error variance model. If the error variance model is unknown, it is shown that choosing Poisson error model is reasonable. Furthermore, although not optimal, geometric spacing of fu(B) values is often reasonable and definitively superior to a uniform spacing strategy. In practice, the range of fu(B) available for selection may be limited by such practical considerations as assay sensitivity and acceptable concentration range of binding protein. Notwithstanding, optimal design theory provides a rational approach to precise parameter estimation.

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M3 - Article

C2 - 10205773

AN - SCOPUS:0032408463

SN - 0090-466X

VL - 26

SP - 595

EP - 615

JO - Journal of Pharmacokinetics and Biopharmaceutics

JF - Journal of Pharmacokinetics and Biopharmaceutics

IS - 5

ER -