正确测定辛醇-水分配系数的隐藏症结。

IF 4.5 2区 医学 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Espen Fritschka,  and , Gabriele Sadowski*, 
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引用次数: 0

摘要

分子在水介质和有机介质之间的分配是药物开发和化学工业的主要兴趣。它描述物质对环境和人类的影响,例如它们在生物体中的积累。通常用这些物质的辛醇-水分配系数KOW来量化。虽然这是一个明确定义的热力学性质,但存在不同的实验方法来估计它。以活性药物成分(api)为例,我们证明了文献中报道的实验确定的分配系数的大分散。这对于弱碱或弱酸来说尤其严重,它们占所有原料药的95%左右。在某些情况下,同一物质的已知温度值甚至相差好几个数量级。这一点尤其令人担忧,因为该属性对于api的审批程序至关重要,也被用作各种估计方法(如机器学习算法)的输入。在这项工作中,我们讨论了报告的KOW值异常高的物理原因。利用物理化学定律表明,数据的大散射不是由分析不确定性引起的,而是由实验数据外推到溶质浓度为零引起的。在此基础上,我们提出了一种评价实验数据分割系数的新方法。这种方法涉及外推实验确定的分布系数相对于pH而不是浓度。我们将表明,这降低了实验获得的KOW值的不确定性,缩小了同一物质的最高和最低值之间的差异,目前约为2.4至约0.5个对数单位。该方法可与任何现有的浓度分析实验方法相结合。此外,得到的数据与明确考虑溶质电离的热力学模型得到的理论值非常吻合,从而验证了所提出的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Hidden Crux of Correctly Determining Octanol–Water Partition Coefficients

The Hidden Crux of Correctly Determining Octanol–Water Partition Coefficients

The Hidden Crux of Correctly Determining Octanol–Water Partition Coefficients

The Hidden Crux of Correctly Determining Octanol–Water Partition Coefficients

The partitioning of molecules between an aqueous and an organic medium is of major interest for pharmaceutical development and the chemical industry. It characterizes the impact of substances to the environment and to humans, e.g., their accumulation in living organisms. It is usually quantified in terms of the octanol–water partition coefficient KOW of these substances. Although this is a clearly defined thermodynamic property, different experimental approaches exist for its estimation. Using active pharmaceutical ingredients (APIs) as examples, we demonstrate the large scatter in experimentally determined partition coefficients reported in the literature. This is especially serious for weak bases or weak acids, which account for around 95% of all APIs. In some cases, reported KOW values for the same substance differ by even several orders of magnitude. This is particularly worrying because this property is crucial for approval procedures of APIs and is also used as input for a whole range of estimation methods, such as machine-learning algorithms. In this work, we discuss the physical reasons for the unusually high variety of reported KOW values. Using physicochemical laws, it is shown that the large scatter of the data is not caused by analytical uncertainties but by the extrapolation of the experimental data to a solute concentration of zero. Based on this, we propose a new approach for evaluating experimental data on partition coefficients. This approach involves extrapolating experimentally determined distribution coefficients with respect to pH rather than concentration. We will show that this reduces the uncertainty of the experimentally obtained KOW values, narrowing the difference between the highest and the lowest value for the same substance of currently about 2.4 to about 0.5 logarithmic units. The new approach can be combined with any existing experimental method for concentration analysis. Moreover, the obtained data agree very well with theoretical values obtained from thermodynamic modeling explicitly considering solute ionization, thus validating the proposed approach.

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来源期刊
Molecular Pharmaceutics
Molecular Pharmaceutics 医学-药学
CiteScore
8.00
自引率
6.10%
发文量
391
审稿时长
2 months
期刊介绍: Molecular Pharmaceutics publishes the results of original research that contributes significantly to the molecular mechanistic understanding of drug delivery and drug delivery systems. The journal encourages contributions describing research at the interface of drug discovery and drug development. Scientific areas within the scope of the journal include physical and pharmaceutical chemistry, biochemistry and biophysics, molecular and cellular biology, and polymer and materials science as they relate to drug and drug delivery system efficacy. Mechanistic Drug Delivery and Drug Targeting research on modulating activity and efficacy of a drug or drug product is within the scope of Molecular Pharmaceutics. Theoretical and experimental peer-reviewed research articles, communications, reviews, and perspectives are welcomed.
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