Predictive Bioavailability of Oral Drugs Using Computational Fluid Dynamics

口服药物对全球医药市场贡献巨大。然而，在口服给药后正确估计人体中的药物浓度分布是具有挑战性的。这项合作计划旨在开发有用的计算方法和平台，可以准确预测人类胃肠道中草药的浓度。作为一种新的方法，我们将采用计算流体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特性。此外，几个中间交付物包括改进的计算工具，更准确的运输模型，因此更有用的预测药物浓度和药代动力学特性。这些结果将在短期内对有效药物递送和经济计算工具发现的研究和开发产生重大影响。从长远来看，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利益至关重要，通过提供筛选能力，并使用具有成本效益的计算方法有效地选择活性草药成分。

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.