Québec City, Canada.
Clin Pharmacokinet. 2011 Oct;50(10):665-74. doi: 10.2165/11592610-000000000-00000.
Existing methods for the prediction of human clearance of therapeutic proteins involve the use of allometry approaches. In general, these approaches have concentrated on the role of body weight, with only occasional attention given to more specific physiological parameters. The objective of this study was to develop a mechanism-based model of hepatic clearance (CL(H)), which combines a single-species scaling approach with liver physiology, for predicting CL(H) of selected glycoprotein derivate therapeutics, and to compare the outcome of this novel method with those of two empirical methods obtained from the literature - namely, the single-exponent theory and multiple-species allometry. Thus, this study was designed as an explanatory study to verify if the addition of physiological information is of benefit for extrapolating clearance of selected therapeutic proteins from one species to another.
Five glycoprotein derivate therapeutics that are known to be principally eliminated by asialoglycoprotein receptors (ASGPRs) under in vivo conditions were selected. It was assumed that the interspecies differences in CL(H) reported for these compounds are reflected by the interspecies differences in the abundance of these receptors. Therefore, key scaling factors related to these differences were integrated into one model. Fourteen extrapolation (prediction) scenarios across species were used in this study while comparing the single-species model, based on physiology, with the single-exponent theory. In addition, the physiological model was compared with multiple-species allometry for three proteins.
In general, the novel physiological model is superior to the derived allometric methods. Overall, the physiological model produced a predicted CL(H) value with levels of accuracy of 100% within 3-fold, 100% within 2-fold and about 82% within 1.5-fold, compared with the observed values, whereas the levels of accuracy decreased to 93%, 77% and 53%, respectively, for allometry. The proposed physiological model is also superior to allometry on the basis of the root mean square error and absolute average fold error values.
It has been demonstrated that interspecies differences in the abundance of ASGPRs principally govern interspecies variations in CL(H) of compounds that are principally eliminated by ASGPRs. Overall, the proposed physiological model is an additional tool, which should facilitate investigation and prediction of human CL(H) of specific glycoproteins solely on the basis of clearance data determined in a single preclinical species.
现有的治疗性蛋白人体清除率预测方法涉及使用比例法。一般来说，这些方法主要集中在体重的作用上，偶尔也会关注更具体的生理参数。本研究的目的是建立一种基于机制的肝清除率（CL（H））模型，该模型将单一物种比例法与肝脏生理学相结合，用于预测选定糖蛋白衍生物治疗药物的 CL（H），并将这种新方法的结果与从文献中获得的两种经验方法进行比较-即单指数理论和多种物种比例法。因此，本研究旨在作为一个解释性研究，以验证是否可以通过添加生理信息来从一种物种推断出选定治疗性蛋白的清除率。
选择五种已知在体内条件下主要通过去唾液酸糖蛋白受体（ASGPR）消除的糖蛋白衍生物治疗药物。据假设，这些化合物的种间 CL（H）差异反映了这些受体的种间差异。因此，将与这些差异相关的关键比例因子整合到一个模型中。在本研究中，使用了 14 种跨物种的外推（预测）方案，将基于生理学的单一物种模型与单指数理论进行比较。此外，还将生理模型与三种蛋白质的多种物种比例法进行了比较。
一般来说，新型生理模型优于衍生的比例法。总体而言，与观察值相比，生理模型产生的预测 CL（H）值的准确性在 3 倍内为 100%，在 2 倍内为 100%，在 1.5 倍内约为 82%，而比例法的准确性分别降低至 93%、77%和 53%。生理模型在均方根误差和绝对平均折叠误差值方面也优于比例法。
研究表明，ASGPR 丰度的种间差异主要控制着主要通过 ASGPR 消除的化合物的种间 CL（H）差异。总体而言，所提出的生理模型是一种额外的工具，它应该有助于仅根据在单个临床前物种中确定的清除数据来研究和预测特定糖蛋白的人体 CL（H）。
