School of Pharmacy, National Defense Medical Center, Taipei, Taiwan, PR China.
School of Pharmacy, National Defense Medical Center, Taipei, Taiwan, PR China; Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan, PR China.
Eur J Pharm Sci. 2022 Oct 1;177:106268. doi: 10.1016/j.ejps.2022.106268. Epub 2022 Jul 25.
In vitro to in vivo extrapolation (IVIVE), an approach for hepatic clearance (CL) prediction used worldwide, remains controversial due to systematic underprediction. Among the various probable factors, the original assumption of the hepatic mathematical model (i.e., the well-stirred model, WSM) may become problematic, leading to the underestimation of drug CL. Having a similar prerequisite that the well-stirred conditions are homogenous with perfectly mixed reactants, but using a different driving concentration, the modified well-stirred model (MWSM) stands apart from the WSM. However, we believe that both models should coexist so that the entire well-stirred scenario can be completely illustrated. Consequently, we collected published data from the literature and employed a logistic regression method to differentiate the optimal timing of use between WSM and MWSM in drug CL prediction. Generally, variances adopted in the regression, including partition coefficient (logP), fraction unbound (fu), volumes of distribution at steady-state (Vss), and mean residence time (MRT), corresponded to our assumption when protein-facilitated uptake was considered. Furthermore, a new empirical approach was introduced to allow practical use of the MWSM. The results showed that this model could provide a more precise prediction compared to previous empirical approaches. Therefore, these preliminary results not only delineated a more detailed structure and mechanism of MWSM but also highlighted its necessity and potential.
体外到体内外推法(IVIVE)是一种在全球范围内用于预测肝脏清除率(CL)的方法,但由于系统性低估,仍然存在争议。在各种可能的因素中,肝脏数学模型的原始假设(即均相搅拌模型,WSM)可能会出现问题,导致药物 CL 的低估。MWSM 与 WSM 具有相似的前提条件,即均相搅拌条件是同质的且反应物完全混合,但使用不同的驱动浓度,因此与 WSM 不同。然而,我们认为这两种模型应该共存,以便完整地说明整个均相搅拌情况。因此,我们从文献中收集了已发表的数据,并采用逻辑回归方法来区分 WSM 和 MWSM 在药物 CL 预测中的最佳使用时机。一般来说,回归中采用的方差,包括分配系数(logP)、未结合分数(fu)、稳态分布容积(Vss)和平均驻留时间(MRT),与我们考虑蛋白促进摄取时的假设相符。此外,还引入了一种新的经验方法来允许实际使用 MWSM。结果表明,与以前的经验方法相比,该模型可以提供更精确的预测。因此,这些初步结果不仅描绘了 MWSM 的更详细结构和机制,还强调了其必要性和潜力。