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基于机制的粒径依赖性溶解和吸收预测:西洛他唑在犬体内的药代动力学。

Mechanism-based prediction of particle size-dependent dissolution and absorption: cilostazol pharmacokinetics in dogs.

机构信息

Bayer Technology Services GmbH, Competence Center Systems Biology and Computational Solutions, Leverkusen, Germany.

出版信息

Eur J Pharm Biopharm. 2010 Sep;76(1):83-94. doi: 10.1016/j.ejpb.2010.06.003. Epub 2010 Jun 8.

Abstract

A previously developed physiologically based pharmacokinetic (PBPK) model for gastro-intestinal transit and absorption was combined with a mechanistic dissolution model of the Noyes-Whitney type for spherical particles with a predefined particle size distribution. To validate the combined model, the plasma concentration-time curves for cilostazol obtained in beagle dogs using three different types of suspensions with varying particle diameters were simulated. In vitro dissolution information was also available for different formulations, but this data could only predict the in vivo outcome qualitatively. The mechanistic PBPK model, on the other hand, could predict the influence of the particle size on the rate and extent of absorption under both fasted and fed conditions accurately, and the gap between the in vitro dissolution data and the in vivo outcome could successfully be explained. We conclude that by integrating the processes of particle dissolution, gastro-intestinal transit and permeation across the intestinal epithelium into a mechanistic model, oral drug absorption from suspensions can be predicted quantitatively. The model can be applied readily to typical formulation development data packages to better understand the relative importance of dissolution and permeability and pave the way for successful formulation of solid dosage forms.

摘要

先前开发的用于胃肠道传递和吸收的基于生理学的药代动力学(PBPK)模型与诺伊斯-惠特尼(Noyes-Whitney)型的用于具有预定粒径分布的球形颗粒的机械溶解模型相结合。为了验证组合模型,使用三种具有不同粒径的混悬剂在比格犬中获得的西洛他唑的血浆浓度-时间曲线进行了模拟。不同配方的体外溶解信息也可用,但这些数据只能定性预测体内结果。另一方面,机械 PBPK 模型可以准确预测在空腹和进食条件下粒径对吸收速率和程度的影响,并且可以成功解释体外溶解数据和体内结果之间的差距。我们得出结论,通过将颗粒溶解、胃肠道传递和跨肠上皮渗透的过程整合到一个机械模型中,可以定量预测混悬剂中的药物吸收。该模型可轻松应用于典型的制剂开发数据包,以更好地了解溶解和渗透的相对重要性,并为成功制备固体制剂铺平道路。

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