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

详细信息    查看全文

• 作者：Robert Andersson^a ; ^{r.k.andersson@warwick.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; Mats Jirstrand^b ; Lambertus Peletier^c ; Michael J. Chappell^a ; Neil D. Evans^a ; Johan Gabrielsson^d
• 关键词：Biophase models ; Turnover ; Tolerance ; Feedback control ; Nicotinic acid (NiAc) ; Free fatty acids (FFA)
• 刊名：European Journal of Pharmaceutical Sciences
• 出版年：2016
• 出版时间：1 January 2016
• 年：2016
• 卷：81
• 期：Complete
• 页码：189-200
• 全文大小：2074 K

文摘

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 different 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 fitted 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 constant-rate infusion protocols of NiAc were used. The half-life of the adaptation process had a 90% prediction interval between 3.5–12 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 efficiently compute the gradients in the parameter optimization, were successfully used for the mixed-effects approach in the parameter estimation.