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 阿尔茨海默氏病（AD）是一种致命疾病，目前遭受了近600万美国人的困扰。与 人口老龄化，除非确定有效的治疗，否则我们可能会在2050年发生公共卫生危机。尽管 广泛的研究，目前尚无缓慢或改变疾病进程的药物。广告由 β-淀粉样蛋白（Aβ）斑块和神经元内tau包含在称为神经原纤维缠结（NFTS）中的存在 大脑。尚未证明减少Aβ斑块的候选药物尚未具有临床益处，并且 增长的数据表明，将NFT靶向Aβ斑块以防止认知可能更重要 衰退。最近，蛋白质降解的大自源性溶酶体途径已成为一种 降低大脑病原性TAU的诱人目标。我们的假设是提高tau的速率 降解将降低tau的水平，并停止或大大减慢tau聚合的速率。我们最近 发现了实现这一目标的新颖途径。抑制GTPase Rhes的Farnesylation RAS家族中的蛋白质，激活溶酶体并导致病理的选择性降解 tau。通过给予Farnesylsylansferase抑制剂来确认治疗假设 （FTI）在小鼠tauopathy模型中，该模型降低了tau病理的病理和减弱的行为异常 老鼠。 已知的FTI不适合作为中枢神经系统药物的人类发展。针对癌症的适应症进行了优化 它们通过外排蛋白有效地从大脑中抽出。我们提出了一种三管齐下的方法来识别 化学物质可以达到药物意义和剂量 - 偏好的大脑水平。对于两个 已知的抑制剂L-778,123和Lonafarnib，我们将对结构进行战略性更改，消除 用作排出泵的识别底物的官能团。同时，我们将启动一个高 具有与已知中枢神经系统药物一致的化学特性的化学特性的吞吐量屏幕。三分之一 步骤，我们将与Atomwise的数百万复合人工智能屏幕一起进行 识别新颖的FTI。通过生成最有前途的命中的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 鉴定小分子候选物。我们将使抑制剂推向体内药效学模型 并选择具有线性药代动力学/药效学（PK/PD）关系的化合物 进入诊所。三种顶部化合物将在陶氏动物模型中测试效率 使用从PK/PD关系得出的剂量。短期研究将确定减少所有的化合物 致病性tau物种。最有效的化合物将转移到长期的给药研究中 评估NFT形成的寿命延伸和减少。