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首页> 外文期刊>Current drug metabolism >The impact of in vitro binding on in vitro-in vivo extrapolations, projections of metabolic clearance and clinical drug-drug interactions.
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The impact of in vitro binding on in vitro-in vivo extrapolations, projections of metabolic clearance and clinical drug-drug interactions.

机译:体外结合对体外-体内外推,代谢清除率预测和临床药物相互作用的影响。

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This review provides a vista of the current opportunities and remaining challenges in the area of in vitro-in vivo extrapolation, with particular emphasis on drug binding terms in predictive models, which has been the source of much controversy. Although the importance of fu(inc) (fraction unbound in in vitro incubations) has been acknowledged for decades, it is not always applied in practice. This is somewhat disappointing, since although it may be onerous to measure this term for large numbers of compounds, algorithms to estimate the term from logD(7.4) or logP have been detailed in the literature. These are sufficiently robust to negate routine measurement in early drug discovery. Several groups have recently established convincing relationships between unbound in vivo and in vitro metabolic intrinsic clearance (CL(int)). In the authors' laboratory, correlations of this type have been constructed for rat, dog and Man. The use and interpretation of these models within a drug discovery setting is discussed. The quantitative prediction of drug-drug interactions from in vitro cytochrome P450 (CYP) inhibition data remains a challenge. Although extensive literature databases are at last emerging, apparent ad hoc use of terms for in vivo inhibitor concentrations and only occasional consideration of fu(inc) may only have confused matters. The effect of accounting for drug binding on the accuracy of predictions is reviewed. Other themes including the impact of fu(inc) on relative activity factors (RAFs) and how in vitro data quality and inter-laboratory differences can confound quantitative human pharmacokinetic predictions are also developed.
机译:这篇综述提供了体外-体内外推领域当前的机遇和尚存的挑战,特别是在预测模型中着重研究了药物结合性术语,这已经引起了很多争议。尽管人们已经认识到fu(inc)(体外培养中未结合的馏分)的重要性,但在实践中并不总是如此。这有点令人失望,因为尽管为大量化合物测量该术语可能会很麻烦,但文献中已经详细描述了从logD(7.4)或logP估算术语的算法。这些足够强大,可以消除早期药物发现中的常规测量。几个小组最近建立了令人信服的体内和体外代谢固有清除率(CL(int))之间的令人信服的关系。在作者的实验室中,已经为大鼠,狗和人构建了这种类型的相关性。讨论了在药物发现环境中这些模型的使用和解释。从体外细胞色素P450(CYP)抑制数据定量预测药物-药物相互作用仍然是一个挑战。尽管最后涌现了大量文献数据库,但体内抑制剂浓度术语的明显临时使用以及仅偶尔考虑了fu(inc)才可能引起混淆。审查了对药物结合的影响对预测准确性的影响。还开发了其他主题,包括fu(inc)对相对活性因子(RAFs)的影响以及体外数据质量和实验室间差异如何混淆定量人类药代动力学预测。

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