Development of molecular-solvent interaction descriptors to improve accuracy of Quantitative Structure Retention Relationship models

开发分子-溶剂相互作用描述符以提高定量结构保留关系模型的准确性

• 批准号: 2609232
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2609232
• 关键词: Development; molecular; solvent; interaction; descriptors

State of the art predictions of chromatographic retention rely on commercially available molecular descriptors such as VolSurf+3D, Dragon, MOE. These descriptors can be used to evaluate molecular structure-activity or structure-property relationships, as well as for similarity analysis and high-throughput screening of molecule databases. The algorithms that are used to calculate these descriptors usually do not take the composition of chromatographic mobile phase into account. For example, chromatographic mobile phase often consists of buffer/organic solvent mixtures which affect the ionic state of analytes and enhance or suppress specific interactions with the stationary phase. Although hydrophobic interactions are the primary retention mechanism, it is often the secondary interactions which determine the elution order of structurally similar compounds. The objectives of this proposal include the development of novel molecular descriptors that outperform currently available options by virtue of: a) considering a medium more comparable with the chromatographic mobile phaseb) taking into consideration ion pairing with aqueous buffer componentsc) differentiating between isomeric compounds e.g. positional isomers or diastereomers Solvation descriptors will be developed using the integral equation theory of molecular liquids (IETML). IETML is a solvation model from statistical mechanics that provides a wealth of accurate solution-phase data, and is fast enough for high-throughput screening. The method implicitly differentiates between isomeric compounds, and is applicable to a wide-range of pure/mixed solvent systems, including those containing ionic species. It has been used successfully to compute solvation free energies, permeabilities, solubilities, partition coefficients, and binding free energies of druglike molecules [Chem. Rev., 2015, 115, 6312]. Solvation free energy density functions and thermodynamic parameters will be computed for each analyte for a matrix of different solvent compositions and temperatures. These solvation descriptors will be used to train novel Quantitative Structure Retention Relationship models (QSRR). We will follow a procedure similar to one used to predict Caco-2 permeability [Mol. Pharmaceutics, 2015, 12, 3420], but adapt it to chromatographic solvent systems and environmental conditions. All software packages that are developed by the University will be provided to Pfizer at the end of the project.

最先进的色谱保留预测依赖于市售的分子描述符，如VolSurf+3D， Dragon， MOE。这些描述符可用于评估分子结构-活性或结构-性质关系，以及相似性分析和分子数据库的高通量筛选。用于计算这些描述符的算法通常不考虑色谱流动相的组成。例如，色谱流动相通常由缓冲液/有机溶剂混合物组成，这些混合物会影响分析物的离子状态，增强或抑制与固定相的特定相互作用。虽然疏水相互作用是主要的保留机制，但通常是次级相互作用决定了结构相似化合物的洗脱顺序。本提案的目标包括开发优于当前可用选项的新型分子描述符，因为：a)考虑与色谱流动相更具可比性的介质；b)考虑与水溶液缓冲组分的离子配对；c)区分异构体化合物，例如位置异构体或非对映体；溶剂化描述符将使用分子液体的积分方程理论（IETML）开发。IETML是一种来自统计力学的溶剂化模型，它提供了大量准确的溶液相数据，并且足够快，可以进行高通量筛选。该方法隐式区分异构体化合物，适用于广泛的纯/混合溶剂体系，包括那些含有离子物种。它已被成功地用于计算类药物分子的溶剂化自由能、渗透性、溶解度、分配系数和结合自由能[化学]。[j].中国机械工程，2015,33(5):631 - 631。溶剂化自由能密度函数和热力学参数将计算每个分析物的不同溶剂组成和温度的矩阵。这些溶剂化描述符将用于训练新的定量结构保留关系模型（QSRR）。我们将遵循类似于预测Caco-2渗透率的程序[Mol. Pharmaceutics, 2015,12, 3420]，但将其调整为色谱溶剂系统和环境条件。该大学开发的所有软件包将在项目结束时提供给辉瑞公司。