Palmelund Henrik, Andersson Martin P, Asgreen Camilla J, Boyd Ben J, Rantanen Jukka, Löbmann Korbinian
University of Copenhagen, Department of Pharmacy, Universitetsparken 2, 2100 Copenhagen, Denmark.
Technical University of Denmark, Department of Chemical and Biochemical Engineering, CHEC Research Centre, Søltofts Plads 229, 2800 Kgs. Lyngby, Denmark.
Int J Pharm X. 2019 Oct 31;1:100034. doi: 10.1016/j.ijpx.2019.100034. eCollection 2019 Dec.
A deep eutectic solvent (DES) is a mixture of two or more chemicals that interact via hydrogen bonding and has a melting point far below that of the individual components. DESs have been proposed as alternative solvents for poorly soluble active pharmaceutical ingredients (API). In this study, the solvation capacities of six deep eutectic solvents were compared to water and three conventional pharmaceutical solvents (PEG 300, ethanol and glycerol) for 11 APIs. The experimentally determined solubilities were compared to computational solubilities predicted by the Conductor-like Screening Model for Real Solvents (COSMO-RS). While the conventional pharmaceutical solvents PEG 300 and ethanol were the best solvents for the majority of the studied APIs, API-DES combinations were identified, which exceeded the API solubility found in the conventional pharmaceutical solvents. Furthermore, it was also possible to obtain high solubilities in the DESs relative to water, suggesting DESs to be potential solvents for poorly water soluble APIs. In addition, the relative increase in solubility found in the experimental data could be well predicted ab initio using COSMO-RS. Hence, COSMO-RS may in the future be used to reduce the experimental screening of potential DESs for a given API.
深共熔溶剂(DES)是由两种或更多种通过氢键相互作用的化学物质组成的混合物,其熔点远低于各组分的熔点。DES已被提议作为难溶性活性药物成分(API)的替代溶剂。在本研究中,比较了六种深共熔溶剂与水以及三种传统药用溶剂(聚乙二醇300、乙醇和甘油)对11种API的溶剂化能力。将实验测定的溶解度与由真实溶剂类导体屏蔽模型(COSMO-RS)预测的计算溶解度进行了比较。虽然传统药用溶剂聚乙二醇300和乙醇对大多数所研究的API来说是最佳溶剂,但也确定了一些API-DES组合,其溶解度超过了在传统药用溶剂中所发现的API溶解度。此外,相对于水而言,在DES中也能够获得高溶解度,这表明DES是难溶性API的潜在溶剂。另外,使用COSMO-RS可以从头算很好地预测实验数据中溶解度的相对增加。因此,未来COSMO-RS可用于减少针对给定API对潜在DES进行的实验筛选。