Predictive Bioavailability of Oral Drugs Using Computational Fluid Dynamics

口服药物对全球医药市场的贡献很大。然而，口服给药后药物在人体内的浓度分布难以准确估计。该合作项目旨在开发有用的计算方法和平台，以准确预测人类胃肠道中草药的浓度。作为一种新颖的方法，我们将采用计算流体动力学来模拟浓度随时间和空间的变化。更具体地说，我们将开发强大的计算工具来准确地估计药物的吸收、分布、代谢和排泄，从而最终预测感兴趣的草药的生物利用度。预期的主要结果是开发一种有用的工具，可以准确地预测感兴趣的草药的药代动力学性质。此外，一些中间成果包括改进的计算工具，更准确的运输模型，因此更有用的药物浓度和药代动力学性质预测。这些结果将在短期内对经济计算工具的有效药物输送和发现的研究和开发产生重大影响。从长远来看，所开发的方法和计算工具将有助于提高天然草药的可负担性和效率。因此，该计划对加拿大的健康和经济效益至关重要，通过提供筛选和选择有效草药成分的能力，有效地使用具有成本效益的计算方法。

Oral drugs contribute significantly to the global pharmaceutical market. Yet, the drug concentration distribution in a human body is challenging to estimate correctly after an oral administration. This collaborative program aims to develop useful computational methods and platforms that can accurately predict the concentrations of herbal drugs in a human's gastrointestinal tract. As a novel approach, we will employ computational fluid dynamics to simulate the concentration changes varying in time and space. More specifically, we will develop powerful computational tools to estimate drug absorption, distribution, metabolism, and excretion accurately, and therefore eventually to predict the bioavailability of herbal medicines of interest. The primary outcome expected is to develop a useful tool that can accurately predict the pharmacokinetic properties of the herbal drug of interest. Furthermore, several intermediate deliverables include an improved computational tool, a more accurate transport model, and hence more useful predictions of drug concentrations and pharmacokinetic properties. These results would have a significant impact on the research and development of effective drug delivery and discovery with economic computational tools in the short term. In the long run, the methods and computational tools developed will help the affordability and efficiency of natural herbal medicines. This program is hence crucial to Canadian health and economic benefits, by offering the capabilities of the screen and select active herbal ingredients efficiently using cost-effective computational methods.