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