DeBoyace Kevin, Bookwala Mustafa, Zhou Deliang, Buckner Ira S, Wildfong Peter L D
School of Pharmacy and Graduate School of Pharmaceutical Sciences, Duquesne University, 600 Forbes Ave, Pittsburgh, Pennsylvania 15282, United States.
Pfizer Worldwide R&D, Eastern Point Road, Groton, Connecticut 06340, United States.
Mol Pharm. 2024 Feb 5;21(2):770-780. doi: 10.1021/acs.molpharmaceut.3c00909. Epub 2024 Jan 5.
The 3 molecular descriptor (R-GETAWAY third-order autocorrelation index weighted by the atomic mass) has previously been shown to encode molecular attributes that appear to be physically and chemically relevant to grouping diverse active pharmaceutical ingredients (API) according to their potential to form persistent amorphous solid dispersions (ASDs) with polyvinylpyrrolidone-vinyl acetate copolymer (PVPVA). The initial 3 dispersibility model was built by using a single three-dimensional (3D) conformation for each drug molecule. Since molecules in the amorphous state will adopt a distribution of conformations, molecular dynamics simulations were performed to sample conformations that are probable in the amorphous form, which resulted in a distribution of 3 values for each API. Although different conformations displayed 3 values that differed by as much as 0.4, the median of each 3 distribution and the value predicted from the single 3D conformation were very similar for most structures studied. The variability in 3 resulting from the distribution of conformations was incorporated into a logistic regression model for the prediction of ASD formation in PVPVA, which resulted in a refinement of the classification boundary relative to the model that only incorporated a single conformation of each API.
3分子描述符(由原子质量加权的R-GETAWAY三阶自相关指数)先前已被证明能够编码分子属性,这些属性在物理和化学上似乎与根据不同活性药物成分(API)与聚乙烯吡咯烷酮-醋酸乙烯酯共聚物(PVPVA)形成持久性无定形固体分散体(ASD)的潜力进行分组相关。最初的3分散性模型是通过为每个药物分子使用单一的三维(3D)构象构建的。由于处于无定形状态的分子会采用多种构象分布,因此进行了分子动力学模拟以采样无定形形式中可能的构象,这导致每个API有一个3值分布。尽管不同构象显示的3值差异高达0.4,但对于大多数研究的结构,每个3分布的中位数与从单一3D构象预测的值非常相似。构象分布导致的3的变异性被纳入一个逻辑回归模型,用于预测PVPVA中ASD的形成,这相对于仅纳入每个API单一构象的模型,导致分类边界得到了优化。