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人体药代动力学预测——肾脏代谢及排泄清除率

Prediction of human pharmacokinetics - renal metabolic and excretion clearance.

作者信息

Fagerholm Urban

机构信息

Clinical Pharmacology, AstraZeneca R&D Södertälje, S-151 85 Södertälje, Sweden.

出版信息

J Pharm Pharmacol. 2007 Nov;59(11):1463-71. doi: 10.1211/jpp.59.11.0002.

Abstract

The kidneys have the capability to both excrete and metabolise drugs. An understanding of mechanisms that determine these processes is required for the prediction of pharmacokinetics, exposures, doses and interactions of candidate drugs. This is particularly important for compounds predicted to have low or negligible non-renal clearance (CL). Clinically significant interactions in drug transport occur mostly in the kidneys. The main objective was to evaluate methods for prediction of excretion and metabolic renal CL (CL(R)) in humans. CL(R) is difficult to predict because of the involvement of bi-directional passive and active tubular transport, differences in uptake capacity, pH and residence time on luminal and blood sides of tubular cells, and limited knowledge about regional tubular residence time, permeability (P(e)) and metabolic capacity. Allometry provides poor predictions of excretion CL(R) because of species differences in unbound fraction, urine pH and active transport. The correlation between fraction excreted unchanged in urine (f(e)) in humans and animals is also poor, except for compounds with high passive P(e) (extensive/complete tubular reabsorption; zero/negligible f(e)) and/or high non-renal CL. Physiologically based in-vitro/in-vivo methods could potentially be useful for predicting CL(R). Filtration could easily be predicted. Prediction of tubular secretion CL requires an in-vitro transport model and establishment of an in-vitro/in-vivo relationship, and does not appear to have been attempted. The relationship between passive P(e) and tubular fraction reabsorbed (f(reabs)) for compounds with and without apparent secretion has recently been established and useful equations and limits for prediction were developed. The suggestion that reabsorption has a lipophilicity cut-off does not seem to hold. Instead, compounds with passive P(e) that is less than or equal to that of atenolol are expected to have negligible passive f(reabs). Compounds with passive P(e) that is equal to or higher than that of carbamazepine are expected to have complete f(reabs). For compounds with intermediate P(e) the relationship is irregular and f(reabs) is difficult to predict. Tubular cells are comparably impermeable (for passive diffusion), and show regional differences in enzymatic and transporter activities. This limits the usefulness of microsome data and makes microsome-based predictions of metabolic CL(R) questionable. Renal concentrations and activities of CYP450s are comparably low, suggesting that CYP450 substrates have negligible metabolic CL(R). The metabolic CL(R) of high-P(e) UDP-glucuronyltransferase substrates could contribute to the total CL.

摘要

肾脏具有排泄和代谢药物的能力。为预测候选药物的药代动力学、暴露量、剂量及相互作用,需要了解决定这些过程的机制。对于预计非肾清除率(CL)较低或可忽略不计的化合物而言,这一点尤为重要。药物转运中具有临床意义的相互作用大多发生在肾脏。主要目的是评估预测人体肾脏排泄和代谢性肾清除率(CL(R))的方法。由于存在双向被动和主动肾小管转运、摄取能力差异、肾小管细胞腔面和血面的pH值及停留时间不同,以及关于区域肾小管停留时间、通透性(P(e))和代谢能力的知识有限,CL(R)难以预测。由于未结合分数、尿液pH值和主动转运存在种属差异,异速生长法对排泄性CL(R)的预测效果不佳。除具有高被动P(e)(广泛/完全肾小管重吸收;零/可忽略不计的f(e))和/或高非肾CL的化合物外,人和动物尿液中未变化排泄分数(f(e))之间的相关性也很差。基于生理学的体外/体内方法可能有助于预测CL(R)。滤过很容易预测。肾小管分泌CL的预测需要体外转运模型并建立体外/体内关系,目前似乎尚未有人尝试。最近已建立了有或无明显分泌的化合物的被动P(e)与肾小管重吸收分数(f(reabs))之间的关系,并开发了有用的预测方程和限度。关于重吸收存在亲脂性临界值的观点似乎并不成立。相反,被动P(e)小于或等于阿替洛尔的化合物预计其被动f(reabs)可忽略不计。被动P(e)等于或高于卡马西平的化合物预计具有完全的f(reabs)。对于具有中等P(e)的化合物,其关系不规则,f(reabs)难以预测。肾小管细胞相对不可渗透(对于被动扩散而言),并且在酶和转运体活性方面存在区域差异。这限制了微粒体数据的实用性,并使得基于微粒体的代谢性CL(R)预测存在疑问。CYP450s的肾脏浓度和活性相对较低,这表明CYP450底物的代谢性CL(R)可忽略不计。高P(e)尿苷二磷酸葡萄糖醛酸转移酶底物的代谢性CL(R)可能对总CL有贡献。

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