Modeling of dose-response-time data: four examples of estimating the turnover parameters and generating kinetic functions from response profiles.
The most common approach to in vivo pharmacokinetic and pharmacodynamic modeling involves sequential analysis of the plasma concentration versus time and then response versus time data, such that the plasma kinetic model provides an independent function, driving the dynamics. However, response versus time data, even in the absence of measured drug concentrations, inherently contain useful information about the turnover characteristics of response (turnover rate, half-life of response), the drug's biophase kinetics (F, half-life) as well as the pharmacodynamic characteristics (potency, intrinsic activity). Previous analyses have assumed linear kinetics, linear dynamics, no time lag between kinetics and dynamics (single-valued response), and time constant parameters. However, this report demonstrates that the drug effect can be indirect (antinociception, cortisol/adrenocorticotropin (ACTH), body temperature), display nonlinear kinetics, display feedback mechanisms (nonstationarity, cortiso/ACTH) and exhibit hysteresis with the drug levels in the biophase (antinociception, body temperature). It is also demonstrated that crucial determinants of the success of modeling dose-response-time data are the dose selection, multiple dosing, and to some extent different input rates and routes. This report exemplifies the possibility of assigning kinetic forcing functions in pharmacodynamic modeling in both preclinical and clinical studies for the purpose of characterizing (discrimination between turnover and drug-specific parameters) response data and optimizing subsequent clinical protocols, and for identification of inter-individual differences.
Gabrielsson
J
J
PK/PD Section, Preclinical Development, AstraZeneca R&D Södertälje, Sweden. johan.gabrielsson@astrazeneca.com
Jusko
W J
WJ
Alari
L
L
D000818:Animals; D001682:Biological Availability; D004305:Dose-Response Relationship, Drug; D006207:Half-Life; D006801:Humans; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D010599:Pharmacokinetics; D010600:Pharmacology; D013997:Time Factors