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

Abstract

This study presents a dose-response-time (DRT) analysis based on a large preclinical biomarker dataset on the interaction between nicotinic acid (NiAc) and free fatty acids (FFA). Data were collected from studies that examined dierent rates, routes, and modes of NiAc provocations on the FFA time course. All information regarding the exposure to NiAc was excluded in order to demonstrate the utility of a DRT model. Special emphasis was placed on the selection process of the biophase model. An inhibitory I max -model, driven by the biophase amount, acted on the turnover rate of FFA. A second generation NiAc/FFA model, which encompasses integral (slow buildup of tolerance -an extension of the previously used NiAc/FFA turnover models) and moderator (rapid and oscillatory) feedback control, was simultaneously tted to all time courses in normal rats. The integral feedback control managed to capture an observed 90% adaptation (i.e., almost a full return to baseline) when 10 days constantrate infusion protocols of NiAc were used. The half-life of the adaptation process had a 90% prediction interval between 3.5-12 h in the present population. The pharmacodynamic parameter estimates were highly consistent when compared to an exposure-driven analysis, partly validating the DRT modelling approach and suggesting the potential of DRT analysis in areas where exposure data are not attainable. Finally, new numerical algorithms, which rely on sensitivity equations to robustly and eciently compute the gradients in the parameter optimization, were successfully used for the mixed-eects approach in the parameter estimation.

Dose-Response-Time Data Analysis: An Underexploited Trinity

“…Pharmacological data of two intertwined biomarkers and of multiple doses, rates, routes, and schedules were simultaneously regressed. This attests to the view that the success of a DRT analysis is a matter of good experimental design Weiner, 2010, 2016;Andersson et al, 2016) rather than the number of observations, as was recently suggested (González-Sales et al, 2017).…”

“…These case studies were selected to demonstrate how to tackle baseline/no baseline, time delays, peak shifts, saturation, cell growth/kill data, functional adaptation, rebound, multiple sources of drug provocations and biomarkers, and rapid exposure-response data ( Table 2). Isaksson et al (2009) and Andersson et al (2016Andersson et al ( , 2017 successfully performed meta-analyses on preclinical data on NiAc (nonlinear absorption and elimination)-induced fatty acid lowering, with accurate and precise parameter estimates. The kinetic nonlinearities were well separated from the pharmacodynamic nonlinearities.…”

Modeling of free fatty acid dynamics: insulin and nicotinic acid resistance under acute and chronic treatments

“…The PK/PD model applied in this study was derived based on previous models [18, 20, 23], but we added, crucial mechanistic components in order to describe two different kinds of complete adaptation, one seen in lean rats and one in obese. In particular, insulin was included as an endogenous regulator of the turnover of FFA [9].…”

“…The interactions between the three models (NiAc, insulin, and FFA) are illustrated in Fig. 1b, and model interactions of previously published NiAc-FFA models [14, 19, 20, 23] are illustrated in Fig. 1a for comparison.…”

“…5) is founded on preceding approaches [14, 19, 20, 23]; however, insulin has been included as the main endogenous regulator of FFA as insulin provides a homeostatic force on the system—thereby keeping FFA levels in the vicinity of its baseline concentration. Furthermore, the NiAc efficacy is dynamic in that it is decreasing during long-term infusions, which allows for complete systemic adaptation - a feature apparent in the data [32, 33].…”