Merck KGaA, Darmstadt, Germany; Frankfurt Goethe University, Frankfurt, Germany.
Merck KGaA, Darmstadt, Germany.
Eur J Pharm Sci. 2019 Apr 30;132:142-156. doi: 10.1016/j.ejps.2019.03.006. Epub 2019 Mar 12.
Supersaturating formulations are widely used to improve the oral bioavailability of poorly soluble drugs. However, supersaturated solutions are thermodynamically unstable and such formulations often must include a precipitation inhibitor (PI) to sustain the increased concentrations to ensure that sufficient absorption will take place from the gastrointestinal tract. Recent advances in understanding the importance of drug-polymer interaction for successful precipitation inhibition have been encouraging. However, there still exists a gap in how this newfound understanding can be applied to improve the efficiency of PI screening and selection, which is still largely carried out with trial and error-based approaches. The aim of this study was to demonstrate how drug-polymer mixing enthalpy, calculated with the Conductor like Screening Model for Real Solvents (COSMO-RS), can be used as a parameter to select the most efficient precipitation inhibitors, and thus realize the most successful supersaturating formulations. This approach was tested for three different Biopharmaceutical Classification System (BCS) II compounds: dipyridamole, fenofibrate and glibenclamide, formulated with the supersaturating formulation, mesoporous silica. For all three compounds, precipitation was evident in mesoporous silica formulations without a precipitation inhibitor. Of the nine precipitation inhibitors studied, there was a strong positive correlation between the drug-polymer mixing enthalpy and the overall formulation performance, as measured by the area under the concentration-time curve in in vitro dissolution experiments. The data suggest that a rank-order based approach using calculated drug-polymer mixing enthalpy can be reliably used to select precipitation inhibitors for a more focused screening. Such an approach improves efficiency of precipitation inhibitor selection, whilst also improving the likelihood that the most optimal formulation will be realized.
超饱和制剂被广泛用于提高难溶性药物的口服生物利用度。然而，过饱和溶液在热力学上是不稳定的，因此这种制剂通常必须包含沉淀抑制剂（PI）来维持增加的浓度，以确保足够的吸收发生在胃肠道中。最近在理解药物-聚合物相互作用对成功抑制沉淀的重要性方面取得了进展，令人鼓舞。然而，如何将这一新的理解应用于提高 PI 筛选和选择的效率，仍然存在差距，目前这仍然主要是基于反复试验的方法。本研究的目的是展示如何使用具有真实溶剂的导体相似筛选模型（COSMO-RS）计算的药物-聚合物混合焓作为参数来选择最有效的沉淀抑制剂，从而实现最成功的超饱和制剂。该方法针对三种不同的生物药剂学分类系统（BCS）II 化合物进行了测试：双嘧达莫、非诺贝特和格列本脲，与超饱和制剂介孔硅一起进行制剂。对于所有三种化合物，在没有沉淀抑制剂的情况下，介孔硅制剂中都明显出现沉淀。在所研究的九种沉淀抑制剂中，药物-聚合物混合焓与整体制剂性能之间存在很强的正相关关系，这可以通过体外溶解实验中浓度-时间曲线下的面积来衡量。数据表明，使用计算药物-聚合物混合焓的基于排序的方法可以可靠地用于选择沉淀抑制剂，以进行更有针对性的筛选。这种方法提高了沉淀抑制剂选择的效率，同时也提高了实现最佳制剂的可能性。
