Chromatography is one of the most popular methods for the separation of compounds in modern pharmaceutical industry and science. Despite the extensive use of the reversed phase chromatography in analytical and preparative applications, the normal phase adsorption chromatography has a special place in purifying post-reaction mixtures or the separation of natural extracts, especially in wet load mode, because of simplicity and high velocity of preparation. Complex mixtures, more difficult to separate, require gradient methods to obtain better results of separations. These methods can be developed by external software, but the automatic methods are often not very accurate and the negative impact of wet load application on separation quality is considerable in them. Therefore, we present the thin-layer chromatography (TLC) gradient optimization strategy for wet load separations to obtain repeatable results of separations for different compounds without worrying about negative impact of wet loading on separation quality. The strategy provides information about an elution model of desired compound, which is used to develop the gradient method. The strategy also allows to standardize the separation length, because gradient methods performed by the TLC gradient optimization strategy have a very similar duration time in column volumes. The method can also be simply scaled because of using the column volume as a base unit in calculations.

中文翻译：

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湿负载吸附快速色谱的薄层色谱梯度优化策略

色谱法是现代制药工业和科学中最流行的化合物分离方法之一。尽管反相色谱在分析和制备应用中广泛使用，但正相吸附色谱在纯化反应后混合物或分离天然提取物方面具有特殊的地位，尤其是在湿负载模式下，因为它的简单性和高速度准备。复杂的混合物，更难分离，需要梯度方法来获得更好的分离结果。这些方法可以通过外部软件开发，但自动方法往往不是很准确，而且湿负荷应用对分离质量的负面影响很大。所以，我们提出了用于湿负载分离的薄层色谱 (TLC) 梯度优化策略，以获得不同化合物的可重复分离结果，而无需担心湿负载对分离质量的负面影响。该策略提供了有关所需化合物洗脱模型的信息，用于开发梯度方法。该策略还允许标准化分离长度，因为由 TLC 梯度优化策略执行的梯度方法在柱体积中具有非常相似的持续时间。由于在计算中使用柱体积作为基本单位，该方法也可以简单地缩放。该策略提供了有关所需化合物洗脱模型的信息，用于开发梯度方法。该策略还允许标准化分离长度，因为由 TLC 梯度优化策略执行的梯度方法在柱体积中具有非常相似的持续时间。由于在计算中使用柱体积作为基本单位，该方法也可以简单地缩放。该策略提供了有关所需化合物洗脱模型的信息，用于开发梯度方法。该策略还允许标准化分离长度，因为由 TLC 梯度优化策略执行的梯度方法在柱体积中具有非常相似的持续时间。由于在计算中使用柱体积作为基本单位，该方法也可以简单地缩放。