Saylor Kyle, Zhang Chenming
Department of Biological Systems Engineering, Virginia Tech, Seitz Hall, RM 210, 155 Ag Quad Lane, Blacksburg, VA 24061, USA.
Toxicol Appl Pharmacol. 2016 Sep 15;307:150-164. doi: 10.1016/j.taap.2016.07.017. Epub 2016 Jul 26.
Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down into different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies' ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence.
基于生理学的药代动力学(PBPK)模型被用于研究抗尼古丁抗体对大鼠和人类大脑中尼古丁处置的影响。通过将模型输出与已发表的血液和大脑中尼古丁浓度随时间变化的数据进行拟合,成功构建了大鼠和人类模型。使用人类模型选择了被认为对这些抗体阻止尼古丁进入大脑的能力影响最大的关键参数进行研究。这些参数包括抗体对尼古丁的亲和力、抗体与可替宁的交叉反应性以及抗体浓度,将它们分解为不同的、源自临床的计算机模拟处理水平,并输入到人类PBPK模型中。模型预测表明,除吸烟状态外,这三个参数对抗尼古丁抗体阻止尼古丁进入大脑的能力都有相当大的影响,并且目前人类疫苗引发的抗体没有足够的结合特性来降低大脑中的尼古丁浓度。然而,如果在动物研究中实现的抗体结合特性能够在人体研究中同样实现,那么预计尼古丁疫苗在降低大脑尼古丁浓度方面的疗效将达到减轻尼古丁依赖的阈值。