Mahmood I, Balian J D
Office of Clinical Pharmacology and Biopharmaceutics, Food and Drug Administration, Rockville, Maryland, USA.
Clin Pharmacokinet. 1999 Jan;36(1):1-11. doi: 10.2165/00003088-199936010-00001.
Extrapolation of animal data to assess pharmacokinetic parameters in humans is an important tool in drug development. Allometric scaling has many proponents, and many different approaches and techniques have been proposed to optimise the prediction of pharmacokinetic parameters from animals to humans. The allometric approach is based on the power function Y = aWb, where the bodyweight of the species is plotted against the pharmacokinetic parameter of interest on a log-log scale. Clearance, volume of distribution and elimination half-life are the 3 most frequently extrapolated pharmacokinetic parameters. Clearance is not predicted very well (error between predicted and observed clearance > 30%) using the basic allometric equation in most cases. Thus, several other approaches have been proposed. An early approach was the concept of neoteny, where the clearance is predicted on the basis of species bodyweight and maximum life-span potential. A second approach uses a 2-term power equation based on brain and body weight to predict the intrinsic clearance of drugs that are primarily eliminated by phase I oxidative metabolism. Most recently, the use of the product of brain weight and clearance has been proposed. A literature review reveals different degrees of success of improved prediction with the different methods for various drugs. In a comparative study, the determining factor in selecting a method for prediction of clearance was found to be the value of the exponent. Integration of in vitro data into in vivo clearance to improve the predictive performance of clearance has also been suggested. Although there are proponents of using body surface area instead of bodyweight, no advantage has been noted in this approach. It has also been noted that the unbound clearance of a drug cannot be predicted any better than the total body clearance (CL). In general, there is a good correlation between bodyweight and volume of the central compartment (Vc); hence, Vc does not face the same complications as CL. The relationship between elimination half-life (t 1/2 beta) and bodyweight across species results in poor correlation, most probably because of the hybrid nature of this parameter. When a reasonable prediction of CL and Vc is made, t 1/2 beta may be predicted from the equation t 1/2 beta = 0.693 Vc/CL.
外推动物数据以评估人体药代动力学参数是药物研发中的一项重要工具。异速生长标度法有许多支持者，并且已经提出了许多不同的方法和技术来优化从动物到人体药代动力学参数的预测。异速生长法基于幂函数Y = aWb，其中在对数-对数尺度上绘制物种的体重与感兴趣的药代动力学参数。清除率、分布容积和消除半衰期是最常外推的3个药代动力学参数。在大多数情况下，使用基本的异速生长方程预测清除率的效果不太好（预测清除率与观察到的清除率之间的误差>30%）。因此，已经提出了其他几种方法。一种早期方法是幼态持续概念，即根据物种体重和最大寿命潜力预测清除率。第二种方法使用基于脑重和体重的二项幂方程来预测主要通过I相氧化代谢消除的药物的内在清除率。最近，有人提出使用脑重与清除率的乘积。文献综述揭示了不同方法对各种药物进行改进预测的不同程度的成功。在一项比较研究中，发现选择清除率预测方法的决定性因素是指数值。也有人建议将体外数据整合到体内清除率中以提高清除率的预测性能。尽管有人支持使用体表面积而非体重，但这种方法并未显示出优势。还注意到，药物的非结合清除率并不比总体清除率（CL）预测得更好。一般来说，体重与中央室容积（Vc）之间存在良好的相关性；因此，Vc不会面临与CL相同的复杂性。跨物种的消除半衰期（t 1/2 beta）与体重之间的关系相关性较差，很可能是因为该参数的混合性质。当对CL和Vc进行合理预测时，可以根据方程t 1/2 beta = 0.693 Vc/CL预测t 1/2 beta。
