Combining In Vitro and In Silico Models to Investigate Antiretroviral Drug Transport Across the Blood Brain Barrier for the Treatment of HIV-1 Infection in the Brain

结合体外和计算机模型研究抗逆转录病毒药物跨血脑屏障转运以治疗大脑中的 HIV-1 感染

• 批准号: 10838759
• 负责人: Laurel Erin Hind
• 金额: $ 39.65万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2025-09-17
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10838759
• 关键词: 3-Dimensional; ABCB1 gene; Affinity; Anti-Retroviral Agents; Astrocytes; Back; Binding; Blood - brain barrier anatomy; Blood Vessels; Brain; Cell Membrane Permeability; Cells; Central Nervous System; Chemicals; Complex; Computer Models; Data; Development; Diffuse; Diffusion; Drug Controls; Drug Transport; Drug usage; Early treatment; Ensure; Equus caballus; Exhibits; Extracellular Matrix; FDA approved; Future; Goals; HIV; HIV Infections; HIV Protease Inhibitors; HIV antiretroviral; HIV-1; Immune; In Vitro; Individual; Infection; Intelligence; Knowledge; Lipid Bilayers; Lipids; Literature; Location; Measurement; Measures; Mediating; Membrane Lipids; Microfluidics; Modeling; Molecular; Molecular Weight; Nucleosides; Penetration; Pericytes; Permeability; Pharmaceutical Preparations; Physiological; Property; Protease Inhibitor; Proteins; PubMed; Reverse Transcriptase Inhibitors; Route; Structure; System; Testing; Therapeutic; Virus; Work; blood-brain barrier crossing; brain tissue; cerebral microvasculature; computerized tools; design; drug testing; experience; experimental study; improved; in silico; in vitro Model; in vivo; inhibitor; latent HIV reservoir; lipophilicity; machine learning model; migration; molecular drug target; molecular dynamics; molecular scale; monocyte; novel; simulation

SUMMARY HIV-1 establishes latent reservoirs throughout the body in the earliest stages of infection and can remain hidden and inactive inside long-lived immune cells for years, impeding our ability to cure HIV. Early treatment with antiretrovirals (ARVs) is considered the most effective means of reducing the total latent HIV reservoir size and can do so by up to 100-fold compared to untreated individuals after three years. One common location for HIV reservoirs to form is in the brain. Unfortunately, the ability of ARVs to penetrate and treat HIV infection in the brain is significantly limited by their poor ability to transport across the blood-brain barrier (BBB) and their tendency to be bound and cleared by BBB-embedded efflux proteins. Despite knowledge that such transport barriers exist, a detailed understanding of how ARVs interact with the BBB lipid membrane and BBB efflux proteins is still lacking, due in part to the oversimplification of past computational models that do not consider key interactions between ARVs and BBB lipids or BBB efflux proteins and the lack of relevant experimental transport data. The overarching goal of this proposal is to uncover the fundamental mechanisms and key features governing the interactions between ARVs and BBB components by testing the hypothesis that key physicochemical and/or structural properties of ARVs give rise to both their differential molecular-scale interactions with BBB efflux proteins and their differential abilities to diffuse across the BBB. To test our hypothesis, we will employ a physiologically relevant in vitro BBB model combined with atomistic simulations to identify properties of two classes of ARVs often used for treating HIV—protease inhibitors (PIs) and nucleoside reverse transcriptase inhibitors (NRTIs)—that mediate their transport across the BBB. We will determine the molecular mechanisms of ARV binding to the BBB lipid membrane (Aim 1) and efflux proteins (Aim 2) using molecular dynamics simulations. Drawing on the literature and our significant experience with vascular microfluidic models, we will optimize our in vitro BBB model to include a model brain microvasculature with efflux transporters, supported by pericytes and astrocytes, and will use it to quantify ARV transport across the BBB (Aim 1) and determine the effect of ARV/efflux protein interactions (Aim 2), validating our in silico results and generating new data for our machine learning model. Finally, we will develop machine learning models to identify properties and/or dynamical features of ARV/BBB interactions that govern transport and will use this model for forward-design and testing of novel ARVs. This collaborative, iterative approach will allow for the direct measurement of parameters needed to create accurate computational models and direct testing of predictions from the computational models, using a novel in vitro BBB system. In turn, this will increase the relevance and power of our findings and greatly facilitate our study of how ARVs transport across the BBB. Ultimately, this study will provide a refined understanding of how ARVs transport across the BBB and what ARV features can be leveraged to design new ARVs that can better penetrate and treat HIV infection in the brain.

总结 HIV-1在感染的最初阶段就在全身建立了潜伏的储存库，并且可以保持隐藏状态 并且在长寿的免疫细胞中多年不活跃，阻碍了我们治愈艾滋病病毒的能力。早期应用 抗逆转录病毒药物（ARV）被认为是减少总潜伏HIV库大小的最有效手段， 三年后，与未经治疗的个体相比，可以达到100倍。艾滋病病毒的一个常见部位 储存库是在大脑中形成的。不幸的是，抗逆转录病毒药物穿透和治疗艾滋病毒感染的能力， 脑的运输能力显著受限于其差的穿过血脑屏障（BBB）的能力和其 倾向于被BBB包埋的外排蛋白结合和清除。尽管知道这种运输 存在屏障，详细了解ARV如何与BBB脂质膜和BBB流出相互作用 蛋白质仍然缺乏，部分原因是过去的计算模型过于简单，没有考虑 抗逆转录病毒药物与血脑屏障脂质或血脑屏障外排蛋白之间的关键相互作用，以及缺乏相关的实验研究， 传输数据。该提案的总体目标是揭示基本机制和关键 控制ARV和BBB成分之间相互作用的特征， 抗逆转录病毒药物的物理化学和/或结构特性引起它们的差异分子尺度 与BBB外排蛋白的相互作用及其扩散穿过BBB的不同能力。来测试我们 假设，我们将采用生理相关的体外BBB模型结合原子模拟， 鉴定常用于治疗HIV的两类抗逆转录病毒药物--蛋白酶抑制剂（PI）和核苷类抗逆转录病毒药物的特性 逆转录酶抑制剂（NRTI）-介导其穿过BBB的运输。康贝特人将以 ARV与血脑屏障脂质膜（Aim 1）和外排蛋白（Aim 2）结合的分子机制， 分子动力学模拟根据文献和我们在血管方面的重要经验， 微流控模型，我们将优化我们的体外BBB模型，包括一个模型脑微血管与流出 转运蛋白，支持周细胞和星形胶质细胞，并将使用它来量化ARV运输通过血脑屏障 (Aim 1）并确定ARV/外排蛋白相互作用的影响（目的2），验证我们的计算机模拟结果， 为我们的机器学习模型生成新数据。最后，我们将开发机器学习模型来识别 ARV/BBB相互作用的特性和/或动力学特征，这些特性和/或动力学特征控制着运输，并将使用该模型 新型抗逆转录病毒药物的前瞻性设计和试验。这种协作、迭代的方法将允许直接 测量创建精确计算模型和直接测试预测所需的参数 从计算模型，使用一种新的体外血脑屏障系统。反过来，这将增加相关性， 我们的研究结果的力量，并大大促进了我们的研究如何通过血脑屏障的抗逆转录病毒药物运输。最终这 这项研究将进一步了解抗逆转录病毒药物如何通过血脑屏障转运，以及抗逆转录病毒药物的特点 可以用来设计新的抗逆转录病毒药物，可以更好地穿透和治疗大脑中的艾滋病毒感染。