Preclinical discovery of novel farnesyltransferase inhibitors for the treatment of Alzheimer's disease and related tauopathies

用于治疗阿尔茨海默病和相关 tau蛋白病的新型法尼基转移酶抑制剂的临床前发现

• 批准号: 10573238
• 负责人: Steven H Olson
• 金额: $ 89.46万
• 依托单位: SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10573238
• 关键词: Acceleration; Affinity; Alzheimer' s Disease; Alzheimer' s disease patient; American; Amyloid beta-Protein; Animal Model; Artificial Intelligence; Attenuated; Back; Behavioral; Binding; Biological Assay; Brain; Chemicals; Clinic; Clinical; Clinical Trials; Computer Assisted; Crystallization; Custom; Data; Dementia; Disease; Disease Progression; Dose; Drug Design; Drug Kinetics; Drug Targeting; Elements; Family; Farnesyl Transferase Inhibitor; Farnesylation Inhibition; Goals; Guanosine Triphosphate Phosphohydrolases; Human; Human Development; Impaired cognition; Knowledge; Laboratories; Lead; Legal patent; Libraries; Lonafarnib; Longevity; Longitudinal Studies; Lysosomes; Malignant Neoplasms; Mediating; Mus; Neurodegenerative Disorders; Neurofibrillary Tangles; Pathologic; Pathology; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacodynamics; Pharmacotherapy; Process; Property; Proteins; Public Health; Publishing; Pump; Reporting; Research; Research Design; Risk; Roentgen Rays; Senile Plaques; Small Interfering RNA; Structure; Tauopathies; Testing; Therapeutic; Translating; Validation; Work; aging population; analog; artificial intelligence algorithm; artificial intelligence method; cancer therapy; chemical property; design; drug candidate; drug discovery; effective therapy; efficacy study; efficacy testing; efflux pump; experience; extracellular; farnesylation; functional group; high throughput screening; in silico; in vivo; inhibitor; innovation; lead optimization; mouse model; neural network; neurofibrillary tangle formation; neuron loss; neuropathology; new therapeutic target; novel; pharmacodynamic model; pharmacologic; pre-clinical; preclinical evaluation; prevent; protein degradation; proteostasis; small molecule; small molecule libraries; tau Proteins; tau aggregation; tau mutation; virtual

PROJECT SUMMARY Alzheimer’s disease (AD) is a fatal disease that currently afflicts almost six million Americans. With an aging population, we risk a public health crisis by 2050, unless effective treatments are identified. Despite extensive research, there are currently no drugs that slow or alter the course of disease. AD is defined by the presence of β-amyloid (Aβ) plaques and intraneuronal tau inclusions called neurofibrillary tangles (NFTs) in the brain. Drug candidates that reduce Aβ plaques have not, yet, been shown to have clinical benefit, and growing data suggests that it may be more important to target NFTs over Aβ plaques to prevent cognitive decline. Recently, the macroautophagy-lysosomal pathway of protein degradation has emerged as a compelling target for reducing pathogenic tau in the brain. Our hypothesis is that increasing the rate of tau degradation will reduce tau levels and stop, or greatly slow, the rate of tau aggregation. We recently discovered a novel pathway to accomplish this objective. Inhibiting the farnesylation of Rhes, a GTPase protein in the Ras family, activates the lysosome and results in the selective degradation of pathological tau. Confirmation of the therapeutic hypothesis was achieved by administering a farnesyltransferase inhibitor (FTI) in a mouse model of tauopathy, which reduced tau pathology and attenuated behavioral abnormalities in the mice. Known FTIs are not suitable for human development as CNS drugs. Optimized for cancer indications, they are efficiently pumped out of the brain by efflux proteins. We propose a three-pronged approach to identify chemical matter that can reach pharmacologically significant and dose-proportional brain levels. For two of the known inhibitors, L-778,123 and lonafarnib, we will make strategic changes to the structures, eliminating functional groups that serve as recognition substrates for the efflux pumps. Concurrently, we will initiate a high- throughput screen of a chemical library with chemical properties consistent with known CNS drugs. As a third step, we will engage in a multi-million compound artificial intelligence-based virtual screen with AtomWise to identify novel FTIs. By generating x-ray co-crystal structures of the most promising hits and using computer- aided drug design, we plan to accelerate the process of hit validation, lead discovery, and optimization to identify small molecule drug candidates. We will advance inhibitors to an in vivo pharmacodynamic model and select compounds with linear pharmacokinetic/pharmacodynamic (PK/PD) relationships that can be advanced into the clinic. Three of the top compounds will be tested for efficacy in a tauopathy animal model using doses derived from the PK/PD relationship. Short-term studies will identify compounds that reduce all pathogenic tau species. The most efficacious compound will be moved into long-term dosing studies to evaluate life-span extension and reduction in NFT formation.

项目摘要 阿尔茨海默病（AD）是一种致命的疾病，目前困扰着近600万美国人。与 人口老龄化，除非找到有效的治疗方法，否则我们到2050年将面临公共卫生危机。尽管 经过广泛的研究，目前还没有药物可以减缓或改变疾病的进程。AD的定义是 β-淀粉样蛋白（Aβ）斑块和称为神经元缠结（NFT）的神经元内tau包涵体的存在， 大脑减少Aβ斑块的候选药物尚未显示出临床益处， 越来越多的数据表明，与Aβ斑块相比，靶向NFT可能更重要， 下降最近，蛋白质降解的大自噬-溶酶体途径已经成为一种新的途径。 减少大脑中致病性tau蛋白的引人注目的目标。我们的假设是增加tau蛋白的表达率 降解将降低tau水平并停止或大大减缓tau聚集的速率。我们最近 发现了一种新的途径来实现这一目标。抑制Rhes的法尼基化， Ras家族中的一种蛋白质，激活溶酶体并导致病理性细胞因子的选择性降解。 τ的通过给予法尼基转移酶抑制剂证实了治疗假设 (FTI)在tau蛋白病的小鼠模型中，其减少了tau蛋白病并减弱了行为异常， 老鼠 已知的FTI不适合作为CNS药物用于人类开发。针对癌症适应症进行优化， 它们通过外排蛋白被有效地泵出大脑。我们提出了一个三管齐下的方法来确定 化学物质，可以达到显著和剂量成比例的大脑水平。其中两 已知的抑制剂，L-778，123和洛那法尼，我们将对结构进行战略性改变，消除 作为外排泵识别底物的官能团。同时，我们将启动一个高- 通过筛选具有与已知CNS药物一致的化学性质的化学文库。作为第三 下一步，我们将使用AtomWise进行数百万个基于复合人工智能的虚拟屏幕， 识别新的FTI。通过生成最有希望的命中的X射线共晶结构，并使用计算机- 辅助药物设计，我们计划加快命中验证，铅发现和优化的过程， 鉴定小分子候选药物。我们将推进抑制剂的体内药效学模型 并选择具有线性药代动力学/药效学（PK/PD）关系的化合物， 走进诊所。将在tau蛋白病动物模型中测试三种顶级化合物的功效 使用源自PK/PD关系的剂量。短期研究将确定化合物， 致病性tau种类。最有效的化合物将进入长期给药研究， 评估寿命延长和NFT形成的减少。