# Dose-response-time modelling - Second generation turnover model with integral feedback control

**Proceedings of the 24th Annual meeting of the Population Approach Group in Europe, PAGE2015**(2015)

[Konferensbidrag, poster]

Objectives: To demonstrate the utility of a dose-response-time (DRT) model using a large preclinical biomarker dataset of nicotinic acid (NiAc) induced changes on free fatty acids (FFA). Methods: Data were collected from studies where different rates, routes, and modes of NiAc provocations on the FFA time course had been tested [1]. All information of the exposure were excluded in order to use a DRT approach. Different models structures, describing the biophase kinetics, were assessed and quantitatively and qualitatively compared. The modeled biophase drug amount was assumed to act as the `driving force`of an inhibitory Imax-model which acted on the turnover of FFA. An integral feedback controller was used to model the slow adaptation process that forces FFA levels back to baseline values under long-term NiAc provocations. Finally, new numerical algorithms were applied, which rely on sensitivity equations to robustly and efficiently compute the gradients of the approximate population likelihood function in mixed-effects modelling [2]. Results: The DRT model successfully captured the behaviour of all FFA time courses. The model predicted 90% adaptation within four days of constant-rate infusions of NiAc, using rates that lead to therapeutic concentrations. High consistency of the pharmacodynamic parameters was shown when compared to an exposure-driven study by Tapani et al. [3]. Conclusions: The versatility of the DRT approach was shown by successfully fitting a DRT model to all FFA time courses. Different feedback mechanisms were described, using moderator compartments and integral feedback control. The consistency in the pharmacodynamic parameters, when comparing to an exposure-driven approach, demonstrates the utility of DRT analysis in a wider context. References: [1] Ahlström C. Modelling of tolerance and rebound in normal and diseased rats. Dissertation, University of Gothenburg. 2011. [2] Almquist J, Leander J, Jirstrand M. Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood. J Pharmacokin Pharmacodyn. 2015. [3] Tapani S, Almquist J, Leander J, Ahlström C, Peletier LA, Jirstrand M, Gabrielsson J. Joint feedback analysis modeling of nonesterified fatty acids in obese Zucker rats and normal Sprague-Dawley rats after different routes of administration of nicotinic acid. J Pharm Sci. 2014.

Denna post skapades 2016-01-18.

CPL Pubid: 230897