Goteti Kosalaram, Brassil Patrick J, Good Steven S, Garner C Edwin
Department of Drug Metabolism and Pharmacokinetics, AstraZeneca R&D Boston, 35 Gatehouse Drive, Waltham, MA 02451; e-mail:
J Clin Pharmacol. 2008 Oct;48(10):1226-36. doi: 10.1177/0091270008320369. Epub 2008 Jun 16.
A multiexponential allometry (MA) method was developed to predict human drug clearance from preclinical data. Separate data sets containing clearances from human and preclinical species were chosen for the study. Human clearance was estimated using the MA technique according to the equation: CL = aBWb + cBWd, where CL is clearance in milliliters/minute, and a, b, c, and d are constants derived from preclinical pharmacokinetic data. Simple allometry (SA) gave the poorest prediction using any data set, and the percentage outliers remained larger than MA or monkey liver blood flow within 1.5-, 2-, and 3-fold error. Analysis of compounds common to both data sets suggested that MA could accurately predict human clearances within approximately 10% of 3-fold error. The analysis also showed that monkey is an important species for scaling, and MA is a better predictor of human clearance when the slope of SA is >0.7.
一种多指数异速生长（MA）方法被开发用于从临床前数据预测人体药物清除率。选择了包含人体和临床前物种清除率的单独数据集用于该研究。根据公式CL = aBWb + cBWd，使用MA技术估计人体清除率，其中CL是以毫升/分钟为单位的清除率，a、b、c和d是从临床前药代动力学数据得出的常数。使用任何数据集时，简单异速生长（SA）给出的预测最差，并且在1.5倍、2倍和3倍误差范围内，异常值百分比仍大于MA或猴肝血流量。对两个数据集共有的化合物进行分析表明，MA可以在约3倍误差的10%范围内准确预测人体清除率。分析还表明，猴是用于比例缩放的重要物种，当SA的斜率>0.7时，MA是人体清除率的更好预测指标。
