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Model evaluation for the prediction of solubility of active pharmaceutical ingredients (APIs) to guide solid-liquid separator design

机译:用于预测活性药物成分(API)溶解度的模型评价,引导固液分离器设计

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The assumptions and models for solubility modelling or prediction in systems using non-polar solvents, or water and complex triterpene and other active pharmaceutical ingredients as solutes aren't well studied. Furthermore, the assumptions concerning heat capacity effects (negligibility, experimental values or approximations) are explored, using non-polar solvents (benzene), or water as reference solvents, for systems with solute melting points in the range of 306-528 K and molecular weights in the range of 90-442 g/mol. New empirical estimation methods for the Delta C-fus(pi) of APIs are presented which correlate the solute molecular masses and van der Waals surface areas with Delta C-fus(pi). Separate empirical parameters were required for oxygenated and non-oxygenated solutes. Subsequently, the predictive capabilities of the various approaches to solubility modelling for complex pharmaceuticals, for which data is limited, are analysed. The solute selection is based on a principal component analysis, considering molecular weights, fusion temperatures, and solubilities in a non-polar solvent, alcohol, and water, where data was available. New NRTL-SAC parameters were determined for selected steroids, by regression. The original UNIFAC, modified UNIFAC (Dortmund), COSMO-RS (OL), and COSMO-SAC activity coefficient predictions are then conducted, based on the availability of group constants and sigma profiles. These are undertaken to assess the predictive capabilities of these models when each assumption concerning heat capacity is employed. The predictive qualities of the models are assessed, based on the mean square deviation and provide guidelines for model selection, and assumptions concerning phase equilibrium, when designing solid-liquid separators for the pharmaceutical industry on process simulation software. The most suitable assumption regarding Delta C-fus(pi) was found to be system specific, with modified UNIFAC (Dortmund) performing well in benzene as a solvent system, while original UNIFAC performs better in aqueous systems. Original UNIFAC outperforms other predictive models tested in the triterpene/steroidal systems, with no significant influence from the assumptions regarding Delta C-fus(pi). (C) 2018 Shenyang Pharmaceutical University. Production and hosting by Elsevier B.V.
机译:使用非极性溶剂的系统中的溶解度建模或预测的假设和模型,或者水和复合三萜和其他活性药物成分作为溶质,还没有得到很好地研究。此外,使用非极性溶剂(苯)或水作为参考溶剂,探索热容效应(贫穷,实验值或近似)的假设,用于306-528k和分子的溶质熔点的系统重量在90-442g / mol的范围内。介绍了API的δC-FU(PI)的新经验估计方法,其将溶质分子量和van der WaaS表面区域与Delta C-Fus(PI)相关联。氧化和非含氧溶质所需的单独经验参数。随后,分析了用于复合药物的各种方法的预测能力,用于复杂药物,其中数据被限制。溶质选择基于主要成分分析,考虑到非极性溶剂,醇和水中的分子量,熔化温度和溶解度,其中数据可用。通过回归测定选定类固醇的新NRT1-SAC参数。然后,基于组常量和SIGMA配置文件的可用性,对原始的UNIFAC,修改的Unifac(Dortifund),Cosmo-Rs(OL)和Cosmo-SAC活动系数预测。这些旨在评估这些模型的预测能力,当采用热容量的每个假设时。基于均方偏差,评估模型的预测性质,并提供模型选择的指导,以及在工艺模拟软件上设计用于制药工业的固液分离器时,可以提供模型选择的指导和相位平衡的假设。发现关于Delta C-Fus(PI)的最合适的假设是特定于系统的,改性的Unifac(Dortmund)在苯作为溶剂系统中表现良好,而原始unifac在水系统中表现更好。原始的Unifac优于Therspene /甾体系中测试的其他预测模型,没有关于Delta C-Fus(PI)的假设的显着影响。 (c)2018年沉阳药业大学。 Elsevier B.V的生产和托管

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