Allometric scaling of pharmacokinetic parameters in drug discovery:: Can human CL, VSS and t1/2 be predicted from in-vivo rat data?
Allometric scaling of pharmacokinetic parameters in drug discovery:: Can human CL, VSS and t1/2 be predicted from in-vivo rat data?
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DOI：
10.1007/bf03190588
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发表时间：
2004-04-01
影响因子：
1.9
通讯作者：
Hageman, W
中科院分区：
文献类型：
--
作者：
Caldwell, GW;Masucci, JA;Hageman, W
在药物发现环境中，合理的进行/不进行人体体内药代动力学（PK）决策必须在最少数量的动物体内或体外数据的情况下及时做出。我们利用简单的异速缩放技术，研究了大鼠与人体内相关性预测总系统清除率（CL）、稳态分布体积（V,,）和半衰期（t(1/2)）的准确性。我们已经证明，使用大量不同的药物，固定指数异速缩放方法可以仅从大鼠体内PK数据中预测人类体内PK参数CL， V-ss和t(1/2)，并且具有可接受的准确性，可以在药物发现中做出决定。人体体内PK预测可以使用简单的异速缩放关系CLHuman近似为40 CL（大鼠）（L/hr）， V-ss（人）近似为200 V-ss Rat (L), t（1/2人）近似为4 t(1/2)（大鼠）（hr）获得。人类对N = 176种药物CL预测的平均折叠误差为2.25，其中79%的药物的折叠误差小于3。人类对N = 144种药物的V-ss预测的平均折叠误差为1.85，其中84%的药物的折叠误差小于3。对于N = 145种药物，人类t（1/2）预测的平均折叠误差为2.05,76%的药物的折叠误差小于3。利用这些简单的异速关系，从大鼠数据中也可以将候选药物分类为低/中/高/非常高的人类分类方案。由于大鼠和人类的CL、V-ss和t（1/2）之间的这些简单异速生长关系相当准确，易于记忆和计算，因此这些方程对于药物发现候选药物的早期进行/不进行活体人类PK决策很有用。
In a drug discovery environment, reasonable go/no-go human in-vivo pharmacokinetic (PK) decisions must be made in a timely manner with a minimum amount of animal in-vivo or in-vitro data. We have investigated the accuracy of the in-vivo correlation between rat and human for the prediction of the total systemic clearance (CL), the volume of distribution at steady state (V,,), and the half-life (t(1/2)) using simple allometric scaling techniques. We have shown, using a large diverse set of drugs, that a fixed exponent allometric scaling approach can be used to predict human in-vivo PK parameters CL, V-ss and t(1/2) solely from rat in-vivo PK data with acceptable accuracy for making go/no-go decisions in drug discovery. Human in-vivo PK predictions can be obtained using the simple allometric scaling relationships CLHuman approximate to 40 CL (Rat) (L/hr), V-ss (Human) approximate to 200 V-ss Rat (L), and t(1/2 Human) approximate to 4 t(1/2) (Rat) (hr). The average fold error for human CL predictions for N = 176 drugs was 2.25 with 79% of the drugs having a fold error less than 3. The average fold error for human V-ss predictions for N = 144 drugs was 1.85 with 84% of the drugs having a fold error less than 3. The average fold error for human t(1/2) predictions for N = 145 drugs was 2.05 with 76% of the drugs having a fold error less than 3. Using these simple allometric relationships, the sorting of drug candidates into a low/medium/high/very high human classification scheme was also possible from rat data. Since these simple allometric relationships between rat and human CL, V-ss, and t(1/2) are reasonably accurate, easy to remember and simple to calculate, these equations should be useful for making early go/no-go ill-vivo human PK decisions for drug discovery candidates.