Distinguishing Cocrystals from Simple Eutectic Mixtures: Phenolic Acids as Potential Pharmaceutical Coformers,Crystal Growth & Design

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Distinguishing Cocrystals from Simple Eutectic Mixtures: Phenolic Acids as Potential Pharmaceutical Coformers
Crystal Growth & Design ( IF 3.2 ) Pub Date : 2018-04-23 00:00:00 , DOI: 10.1021/acs.cgd.8b00335
Maciej Przybyłek ₁ , Piotr Cysewski ₁

Affiliation

The multiparameter model comprising 1D and 2D QSPR/QSAR descriptors was proposed and validated for phenolic acid binary systems. This approach is based on the optimization of regression coefficients for maximization of the percentage of true positives in the pool of systems comprising either simple binary eutectics or cocrystals. The training set consisted of 58 eutectics and 168 cocrystals. The solid dispersions collection used for model generation comprised literature data enriched with our new experimental results. From all 1445 descriptors computable in PaDEL, only 13 orthogonal descriptors with the highest predicting power were taken into account. The analysis revealed the importance of the parameters characterizing atom types (naaN, SHsOH, SsssN, nHeteroRing, maxHBint6, C1SP2), autocorrelation functions (ATSC1i, AATSC1v, MATS8m, GATS1i), and also other molecule structure measures (WTPT-5, MLFER_A, MDEN-22). The proposed approach is very simple and requires only information about the structure encoded in canonical SMILES string. The inversion of the problem of cocrystal screening and focusing on the homogeneous group of coformers for cocrystallization with a variety of drugs rather than seeking coformers for a particular active pharmaceutical ingredient proved to be very efficient. This led to very valuable clues for selection of pairs for cocrystallization with a probability of about 80%.

中文翻译：

从简单的共晶混合物中区分共晶体：酚酸作为潜在的药物共形成者

提出了包含一维和二维QSPR / QSAR描述子的多参数模型，并针对酚酸二元系统进行了验证。此方法基于优化回归系数以最大程度地提高包含简单二元共晶或共晶体的系统池中真实正数的百分比。训练集包括58个共晶和168个共晶。用于模型生成的固体分散体集合包含丰富了我们新的实验结果的文献数据。在PaDEL中可计算的所有1445个描述符中，仅考虑了具有最高预测能力的13个正交描述符。分析揭示了表征原子类型（naaN，SHsOH，SsssN，nHeteroRing，maxHBint6，C1SP2），自相关函数（ATSC1i，AATSC1v，MATS8m，GATS1i）的参数的重要性，以及其他分子结构指标（WTPT-5，MLFER_A，MDEN-22）。所提出的方法非常简单，只需要有关规范SMILES字符串中编码的结构的信息。事实证明，共晶筛选问题的反转和关注于与多种药物共结晶的均相组均一的共形成剂，而不是寻找特定活性药物成分的共形成剂是非常有效的。这为选择共结晶对的可能性提供了非常有价值的线索，概率约为80％。事实证明，共晶筛选问题的反转和关注于与多种药物共结晶的均相组均一的共形成剂，而不是寻找特定活性药物成分的共形成剂是非常有效的。这为选择共结晶对的可能性提供了非常有价值的线索，概率约为80％。事实证明，共晶筛选问题的反转和关注于与多种药物共结晶的均相组均一的共形成剂，而不是寻找特定活性药物成分的共形成剂是非常有效的。这为选择共结晶对的可能性提供了非常有价值的线索，概率约为80％。

更新日期：2018-04-23

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