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.

摘要 HIV-1在感染的最早阶段就会在全身建立潜伏的蓄水池，并可能保持潜伏状态 在长期存活的免疫细胞内多年不活跃，阻碍了我们治愈艾滋病毒的能力。早期用药治疗 抗逆转录病毒药物(ARV)被认为是减少潜在艾滋病毒总储存库大小和 三年后，与未经治疗的人相比，这一比例可高达100倍。一个常见的艾滋病毒感染地点 蓄水池的形成是在大脑中。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 大脑严重受限于它们通过血脑屏障(BBB)的运输能力差，以及他们的 有被嵌入BBB的外排蛋白结合和清除的倾向。尽管知道这种运输方式 存在障碍，详细了解抗逆转录病毒药物如何与血脑屏障脂膜和血脑屏障外流相互作用 蛋白质仍然缺乏，部分原因是过去的计算模型过于简化，没有考虑到 抗逆转录病毒药物与血脑屏障脂类或血脑屏障外排蛋白之间的关键相互作用及相关实验的缺乏 传输数据。这项提议的总体目标是揭示基本机制和关键 通过检验以下假设来控制ARV和BBB组件之间的相互作用的特征 抗逆转录病毒药物的物理化学和/或结构特性导致其分子尺度的差异 与血脑屏障外流蛋白的相互作用及其在血脑屏障中扩散的差异能力。测试我们的 假设，我们将使用一个与生理相关的体外血脑屏障模型，并结合原子模拟来 鉴定两类常用于治疗HIV-蛋白酶抑制剂(PI)和核苷的ARV的性质 逆转录酶抑制物(NRTI)--调节它们在血脑屏障中的运输。我们将确定 ARV与血脑屏障脂膜(Aim 1)和外排蛋白(Aim 2)结合的分子机制 分子动力学模拟。借鉴文献和我们在血管方面的重要经验 微流体模型，我们将优化我们的体外血脑屏障模型，以包括一个有外流的脑微血管系统模型 由周细胞和星形胶质细胞支持的转运体，并将使用它来量化跨越血脑屏障的ARV转运 (目标1)和确定ARV/外排蛋白相互作用的影响(目标2)，验证我们的计算机结果和 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 管理运输的ARV/BBB相互作用的属性和/或动力学特征，并将使用此模型 新型抗逆转录病毒药物的正向设计和测试。这种协作、迭代的方法将允许直接 测量创建准确计算模型和直接测试预测所需的参数 从计算模型出发，采用了一种新颖的体外血脑屏障系统。反过来，这将增加相关性和 我们的发现证明了这一点，并极大地促进了我们对抗逆转录病毒药物如何通过血脑屏障运输的研究。归根结底，这 研究将提供对抗逆转录病毒药物如何通过血脑屏障运输以及抗逆转录病毒病毒特征的详细了解 可以被用来设计新的抗逆转录病毒药物，可以更好地渗透和治疗大脑中的艾滋病毒感染。