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A streamlined method to predict hepatic clearance using human liver microsomes in the presence of human plasma.

机译:一种在人血浆存在下使用人肝微粒体预测肝脏清除率的简化方法。

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INTRODUCTION: Human liver microsomal incubations are often used to predict the metabolic lability of new chemical entities. The clearance values are scaled-up from in vitro data and mathematically corrected for plasma protein binding, or in some cases the free fraction ratio of plasma to microsomes, using well-established scaling methods such as the well-stirred model. This can be time consuming for multiple compounds since it requires separate experiments to determine in vitro lability, and free fraction. METHODS: We attempted to streamline clearance predictions by combining experiments into one. Firstly, we combined the free fraction experiments into one free fraction ratio by measuring the partitioning of compound between plasma and microsomes, and by applying this experimental ratio to clearance predictions found that it performed at least as well as free fractions determined separately. We also incubated compounds with plasma added to the incubation mixture and compared the predicted clearances tovalues determined using traditional mathematical protein binding corrections. RESULTS: Consistently, incubations with added plasma resulted in CL predictions closer to literature values than incubations only mathematically corrected for protein binding. For example, incorporating plasma into a ketamine incubation resulted in a CL value of 15.1 mL/min/kg, compared with a value of 10.2 using mathematical binding corrections. The literature value is 16.4 mL/min/kg. DISCUSSION: This work characterizes this new method and compares it to the traditional microsomal incubation method using several literature compounds, and suggests that streamlining the methods may generate quality data faster and with less resource investment.
机译:简介:人肝微粒体温育通常用于预测新化学实体的代谢不稳定性。使用成熟的定标方法(例如,搅拌良好的模型),可以从体外数据按比例放大清除值,并通过数学方法校正血浆蛋白结合率,或者在某些情况下,血浆与微粒体的自由级分比也得到校正。这对于多种化合物而言可能是耗时的,因为它需要单独的实验来确定体外不稳定性和游离分数。方法:我们尝试通过将实验合并为一个来简化间隙预测。首先,我们通过测量化合物在血浆和微粒体之间的分配,将游离级分实验组合为一个游离级分比率,并将该实验比率应用于清除率预测,发现其性能至少与单独测定的游离级分相同。我们还将化合物与血浆添加到孵育混合物中进行孵育,并将预测的清除率与使用传统数学蛋白质结合校正方法确定的值进行比较。结果:与仅对蛋白质结合进行数学校正的孵育一致,与添加血浆的孵育一致地导致CL预测更接近文献值。例如,将血浆掺入氯胺酮培养液中的CL值为15.1 mL / min / kg,而使用数学结合校正法的CL值为10.2。文献值是16.4 mL / min / kg。讨论:这项工作表征了这种新方法,并将其与使用几种文献化合物的传统微粒体温育方法进行了比较,并建议简化方法可以更快地产生高质量的数据,而资源投资却更少。

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