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首页> 外文期刊>Journal of Chromatography, Biomedical Applications >Factors to consider in the development of generic bioanalytical high-performance liquid chromatographic-mass spectrometric methods to support drug discovery
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Factors to consider in the development of generic bioanalytical high-performance liquid chromatographic-mass spectrometric methods to support drug discovery

机译:在开发支持药物发现的通用生物分析高性能液相色谱-质谱法中要考虑的因素

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摘要

Mass spectrometric detection is gradually replacing ultraviolet (UV) as the method of choice in bioanalysis, especially in :he area of early drug discovery where high sensitivity and rapid sample throughput is required and where samples are frequently pooled or "cocktailed" prior to dosing or analysis. The change from UV to MS detection requires a significant ;hange in approach since the use of MS poses a number of unexpected problems and limitations which relate to instrument design and the ionisation process. Whilst electrospray ionisation (ESI) allows the analyst to focus on the analyte of interest it is non-selective and blind to background effects which can in certain instances alter the response of the compound of interest, leading to inaccurate data. In addition, when analysing compound mixtures, a number of precautions need to be taken since adduct formation in the MS source, the highly ESI responsive nature of formulating agents and the effect of the isotopic distribution in organic drug molecules can all lead to the production of cpmpromised data. Whilst many of these problems can be minimised or avoided this often results in a complex and inflexible analysis system. Ultimately the analyst has to assess the degre~ of risk involved and take actions which reflect the use the data.
机译:质谱检测正在逐步取代紫外线(UV)作为生物分析中的选择方法,尤其是在早期药物发现领域中,该领域要求高灵敏度和快速的样品通量,并且在给药前经常将样品合并或“混合”分析。从UV检测到MS检测的转变需要大量的方法,因为使用MS会带来许多与仪器设计和电离过程有关的意外问题和局限性。尽管电喷雾电离(ESI)可使分析人员专注于目标分析物,但它是非选择性的并且对背景效应无知,这在某些情况下可能会改变目标化合物的响应,从而导致数据不准确。此外,在分析化合物混合物时,需要采取许多预防措施,因为MS来源中会形成加合物,配制试剂的高度ESI响应性以及有机药物分子中同位素分布的影响都会导致产生cpmpromised数据。尽管可以最大程度地减少或避免这些问题,但这通常会导致复杂而僵化的分析系统。最终,分析人员必须评估涉及的风险程度,并采取反映数据使用情况的措施。

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