Jooybar Elaheh, Abdekhodaie Mohammad J, Farhadi Fatolla, Cheng Yu-Ling
Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.
Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran; Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada.
Math Biosci. 2014 Sep;255:11-20. doi: 10.1016/j.mbs.2014.06.008. Epub 2014 Jun 16.
A computational model was developed to simulate drug distribution in the posterior segment of the eye after intravitreal injection and ocular implantation. The effects of important factors in intravitreal injection such as injection time, needle gauge and needle angle on the ocular drug distribution were studied. Also, the influences of the position and the type of implant on the concentration profile in the posterior segment were investigated. Computational Fluid Dynamics (CFD) calculations were conducted to describe the 3D convective-diffusive transport. The geometrical model was constructed based on the human eye dimensions. To simulate intravitreal injection, unlike previous studies which considered the initial shape of the injected drug solution as a sphere or cylinder, the more accurate shape was obtained by level-set method in COMSOL. The results showed that in intravitreal injection the drug concentration profile and its maximum value depended on the injection time, needle gauge and penetration angle of the needle. Considering the actual shape of the injected solution was found necessary to obtain the real concentration profile. In implant insertion, the vitreous cavity received more drugs after intraocular implantation, but this method was more invasive compared to the periocular delivery. Locating the implant in posterior or anterior regions had a significant effect on local drug concentrations. Also, the shape of implant influenced on concentration profile inside the eye. The presented model is useful for optimizing the administration variables to ensure optimum therapeutic benefits. Predicting and quantifying different factors help to reduce the possibility of tissue toxicity and to improve the treatment efficiency.
开发了一种计算模型,用于模拟玻璃体内注射和眼内植入后药物在眼后段的分布。研究了玻璃体内注射中的重要因素,如注射时间、针径和针角度对眼内药物分布的影响。此外,还研究了植入物的位置和类型对后段浓度分布的影响。进行了计算流体动力学(CFD)计算以描述三维对流扩散传输。基于人眼尺寸构建几何模型。为了模拟玻璃体内注射,与以往将注射药物溶液的初始形状视为球体或圆柱体的研究不同,通过COMSOL中的水平集方法获得了更准确的形状。结果表明,在玻璃体内注射中,药物浓度分布及其最大值取决于注射时间、针径和针的刺入角度。发现考虑注射溶液的实际形状对于获得真实的浓度分布是必要的。在植入物植入过程中,眼内植入后玻璃体腔接受的药物更多,但与眼周给药相比,这种方法的侵入性更强。将植入物放置在后部或前部区域对局部药物浓度有显著影响。此外,植入物的形状会影响眼内的浓度分布。所提出的模型有助于优化给药变量,以确保获得最佳治疗效果。预测和量化不同因素有助于降低组织毒性的可能性并提高治疗效率。