根据文献信息建立口服药物的内部 PBPK 建模工具。

Journal of sex education and therapy Pub Date : 2019-02-23 eCollection Date: 2019-01-01 DOI:10.5599/admet.638
Silvia Grandoni, Nicola Cesari, Giandomenico Brogin, Paola Puccini, Paolo Magni
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引用次数: 0

摘要

近年来,人们对使用基于生理学的药代动力学(PBPK)模型来支持药物开发决策过程的兴趣迅速增加。这类模型是 "自下而上 "建模策略的典范,在药物开发过程中,一旦获得不同的信息,就将其逐步整合到机理框架中。因此,从药物开发的早期阶段到临床阶段,PBPK 模型可用于不同的目的。如今有各种软件工具可供选择。它们可分为 "设计软件 "和 "开放软件"。第一类软件通常包括专门为实施 PBPK 模型而设计的商业平台,其中的模型结构是预先定义的,一般不明确声明假设,对用户而言方程是隐藏的。即使这些软件经过验证并在制药行业中得到了常规使用,有时也无法灵活地应对特定的应用/任务。因此,一些科学家倾向于在 "开放 "软件中定义和实施自己的 PBPK 工具。本文展示了如何根据文献中与物种相关的生理信息以及药物开发过程中通常提供的有限数量的药物特定参数来构建内部 PBPK 工具。本文还报告了模拟血浆浓度-时间曲线和相关药代动力学 (PK) 参数(即 AUC、Cmax 和 Tmax)与文献和内部数据的评估结果。这项评估涉及 25 种具有不同物理化学特性的药物,分别在三种不同物种(即大鼠、狗和人)中静脉注射或口服给药。比较结果表明,模型预测具有很高的准确性,因为所有考虑的 PK 参数的平均折叠误差都接近 1,只有少数情况下折叠误差大于 2。 总之,本文证明了在需要时通过创建性能令人满意的内部 PBPK 工具来实现特定目标是可行的,并提供了一些如何做到这一点的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Building in-house PBPK modelling tools for oral drug administration from literature information.

Building in-house PBPK modelling tools for oral drug administration from literature information.

Building in-house PBPK modelling tools for oral drug administration from literature information.

Building in-house PBPK modelling tools for oral drug administration from literature information.

The interest in using physiologically-based pharmacokinetic (PBPK) models as a support to the drug development decision making process has rapidly increased in the last years. These kind of models are examples of the "bottom up" modelling strategy, which progressively integrates into a mechanistic framework different information as soon as they become available along the drug development. For this reason PBPK models can be used with different aims, from the early stages of drug development up to the clinical phases. Different software tools are nowadays available. They can be categorized in "designed software" and "open software". The first ones typically include commercial platforms expressly designed to implement PBPK models, in which the model structure is pre-defined, assumptions are generally not explicitly declared and equations are hidden to the user. Even if the software is validated and routinely used in the pharmaceutical industry, sometimes they do not allow working with the flexibility needed to cope with specific applications/tasks. For this reason, some scientists prefer to define and implement their own PBPK tool in "open" software. This paper shows how to build an in-house PBPK tool from species-related physiological information available in the literature and a limited number of drug specific parameters generally made available by the drug development process. It also reports the results of an evaluation exercise that compares simulated plasma concentration-time profiles and related pharmacokinetic (PK) parameters (i.e., AUC, Cmax and Tmax) with literature and in-house data. This evaluation involved 25 drugs with different physico-chemical properties, intravenously or orally administrated in three different species (i.e., rat, dog and man). The comparison shows that model predictions have a good degree of accuracy, since the average fold error for all the considered PK parameters is close to 1 and only in few cases the fold error is greater than 2. In summary, the paper demonstrates that addressing specific aims when needed is possible by creation of in-house PBPK tools with satisfactory performances and it provides some suggestions how to do that.

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