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预测药物发现和开发界面的 ARA/PPI 药物-药物相互作用。

Prediction of ARA/PPI Drug-Drug Interactions at the Drug Discovery and Development Interface.

机构信息

Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139.

Novartis Healthcare Pvt Ltd., Hyderabad 500078, India.

出版信息

J Pharm Sci. 2019 Jan;108(1):87-101. doi: 10.1016/j.xphs.2018.10.032. Epub 2018 Oct 29.

Abstract

Advances in understanding of human disease have prompted the U.S. Food and Drug Administration to classify certain molecules as "break-through therapies," providing an accelerated review that may potentially enhance the quality of patient lives. With this designation come compressed timelines to develop drug products, which are not only suitable for clinic trials but can also be approved and brought to the market rapidly. Early risk identification for decreased oral absorption due to drug-drug interactions with proton pump inhibitors (PPIs) or acid-reducing agents (ARAs) is paramount to an effective drug product development strategy. An early ARA/PPI drug-drug interaction (DDI) risk identification strategy has been developed using physiologically based absorption modeling that readily integrates ADMET predictor generated in silico estimates or measured in vitro solubility, permeability, and ionization constants. Observed or predicted pH-solubility profile data along with pKas and drug dosing parameters were used to calculate a fraction of drug absorbed ratio in absence and presence of ARAs/PPIs. An integrated physiologically based pharmacokinetic absorption model using GastroPlus™ with pKa values fitted to measured pH-solubility profile data along with measured permeability data correctly identified the observed ARA/PPI DDI for 78% (16/22) of the clinical studies. Formulation strategies for compounds with an anticipated pH-mediated DDI risk are presented.

摘要

对人类疾病的认识的进步促使美国食品和药物管理局将某些分子归类为“突破性疗法”,提供加速审查,可能会提高患者的生活质量。有了这个指定,就有了压缩的药品开发时间表,这些药品不仅适合临床试验,而且可以快速获得批准并推向市场。由于与质子泵抑制剂(PPIs)或酸减少剂(ARAs)的药物相互作用而导致的口服吸收减少的早期风险识别对于有效的药物产品开发策略至关重要。已经开发了一种使用基于生理学的吸收建模的早期 ARA/PPI 药物相互作用(DDI)风险识别策略,该策略易于整合在计算机上生成的或在体外测量的 ADMET 预测器的溶解度、渗透性和离解常数。使用观察到或预测的 pH-溶解度曲线数据以及 pKas 和药物剂量参数来计算在不存在和存在 ARAs/PPIs 的情况下吸收的药物比例。使用 GastroPlus 进行的基于生理学的药代动力学吸收模型,与测量的 pH-溶解度曲线数据以及测量的渗透性数据一起拟合 pKa 值,正确地识别了 78%(16/22)的临床研究中的观察到的 ARA/PPI DDI。提出了具有预期 pH 介导的 DDI 风险的化合物的制剂策略。

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