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

Abstract

Nicotinic acid (NiAc) is a potent inhibitor of adipose tissue lipolysis. Acute administration results in a rapid reduction of plasma free fatty acid (FFA) concentrations. Sustained NiAc exposure is associated with tolerance development (drug resistance) and complete adaptation (FFA returning to pretreatment levels). We conducted a meta-analysis on a rich pre-clinical data set of the NiAc–FFA interaction to establish the acute and chronic exposure-response relations from a macro perspective. The data were analyzed using a nonlinear mixed-effects framework. We also developed a new turnover model that describes the adaptation seen in plasma FFA concentrations in lean Sprague–Dawley and obese Zucker rats following acute and chronic NiAc exposure. The adaptive mechanisms within the system were described using integral control systems and dynamic efficacies in the traditional model. Insulin was incorporated in parallel with NiAc as the main endogenous co-variate of FFA dynamics. The model captured profound insulin resistance and complete drug resistance in obese rats. The efficacy of NiAc as an inhibitor of FFA release went from 1 to approximately 0 during sustained exposure in obese rats. The potency of NiAc as an inhibitor of insulin and of FFA release was estimated to be 0.338 and 0.436 , respectively, in obese rats. A range of dosing regimens was analyzed and predictions made for optimizing NiAc delivery to minimize FFA exposure. Given the exposure levels of the experiments, the importance of washout periods in-between NiAc infusions was illustrated. The washout periods should be 2 h longer than the infusions in order to optimize 24 h lowering of FFA in rats. However, the predicted concentration-response relationships suggests that higher AUC reductions might be attained at lower NiAc exposures.

Dose-Response-Time Data Analysis: An Underexploited Trinity

“…The standard pharmacological approach of activating a target around the clock often fails because of time-dependent loss of drug efficacy and postdosing rebound above predose baseline levels. This case study presents data that originally explored the idea that a more comprehensive understanding of the relationship between plasma exposure and a pharmacological response combined with knowledge of the physiologic regulation of metabolism (i.e., simultaneously analyzed considering the effect of key biomarkers) can be used to mitigate these barriers, allowing the invention of new pharmacostrategies (Andersson et al, 2017;Kroon et al, 2017). The metaanalysis aimed at investigating in what way our inference about the system is changed when all drug exposure data are removed.…”

“…A well defined dosing regimen, rate, extent, and timing of drug intervention were designed sequentially across several studies. This resulted in suppression of tolerance and rebound and, most importantly, in profound improvements of the metabolic profile of a preclinical disease model (Andersson et al, 2017;Kroon et al, 2017). In turn, this information may provide vital input to projects aimed at discovery and development of novel drugs for metabolic disorder.…”

“…The raw data for case studies 1-7 are available in a spreadsheet format (Gabrielsson and Weiner, 2010). The eighth case study is aimed at demonstrating how data from several studies in a drug discovery project can be merged and analyzed simultaneously (Andersson et al, 2017). This latter meta-analysis utilizes data from multiple dose provocations with nicotinic acid (NiAc; dose, route, rate, and mode), as well as two intertwined biomarkers (plasma fatty acids and insulin) in a large population of control and disease model rats.…”

“…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.…”

“…These studies cover a large range of therapeutic and experimental areas. Some of the preclinical studies use two or more modes of administration and are more experimental in nature (Schoenwald and Smolen, 1971;Smolen, 1971aSmolen, ,b, 1976aSmolen, ,b, 1978Smolen et al, 1972;Smolen and Weigand, 1973;Isaksson et al, 2009;Andersson et al, 2017). Other studies are performed in critically ill patients (Salem et al, 2016), in neonates (Trefz et al, 2015), under disease progression (Musuamba et al, 2015), for postoperative pain management (Abou Hammoud et al, 2009), in drug interaction studies (Gruwez et al, 2005(Gruwez et al, , 2007, and in metabolic systems (Fasanmade and Jusko, 1995;Hamberg et al, 2010;Ternant et al, 2014;Saffian et al, 2016), to mention just a few.…”