New Modalities and Parenteral Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield, UK.
Drug Product Technology, Amgen, Thousand Oaks, California, USA.
AAPS J. 2021 Jan 4;23(1):12. doi: 10.1208/s12248-020-00548-8.
Over the last 10 years, 40% of approved oral drugs exhibited a significant effect of food on their pharmacokinetics (PK) and currently the only method to characterize the effect of food on drug absorption, which is recognized by the authorities, is to conduct a clinical evaluation. Within the pharmaceutical industry, there is a significant effort to predict the mechanism and clinical relevance of a food effect. Physiologically based pharmacokinetic (PBPK) models combining both drug-specific and physiology-specific data have been used to predict the effect of food on absorption and to reveal the underlying mechanisms. This manuscript provides detailed descriptions of how a middle-out modeling approach, combining bottom-up in vitro-based predictions with limited top-down fitting of key model parameters for clinical data, can be successfully used to predict the magnitude and direction of food effect when it is predicted poorly by a bottom-up approach. For nefazodone, a mechanistic clearance for the gut and liver was added, for furosemide, an absorption window was introduced, and for aprepitant, the biorelevant solubility was refined using multiple solubility measurements. In all cases, these adjustments were supported by literature data and showcased a rational approach to assess the factors limiting absorption and exposure.
在过去的 10 年中,40%的批准的口服药物在药代动力学(PK)方面表现出明显的食物效应,目前唯一能够描述食物对药物吸收影响的方法是进行临床评估。在制药行业,人们正在努力预测食物对药物吸收的影响机制和临床相关性。结合药物特异性和生理学特异性数据的生理药代动力学(PBPK)模型已被用于预测食物对吸收的影响,并揭示潜在的机制。本文详细描述了如何使用中间向外建模方法,将基于底部向上的体外预测与对临床数据的关键模型参数进行有限的自上而下拟合相结合,成功地预测了当底部向上方法预测不准确时食物效应的大小和方向。对于奈法唑酮,增加了肠道和肝脏的机制清除率;对于呋塞米,引入了吸收窗;对于阿瑞匹坦,使用多个溶解度测量值细化了生物相关溶解度。在所有情况下,这些调整都得到了文献数据的支持,并展示了一种合理的方法来评估限制吸收和暴露的因素。