Multi-Property Design Selective PKC_epsilon Inhibitors

多性质设计选择性 PKC_epsilon 抑制剂

• 批准号: 6735748
• 负责人: Jay Jie Qiang Wu
• 金额: $ 9.82万
• 依托单位: VM DISCOVERY, INC.
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-01 至 2004-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6735748
• 关键词: alcoholism /alcohol abuse; alcoholism antagonist; analgesics; chemical registry /resource; chemical structure function; computational biology; drug design /synthesis /production; fluorescence polarization; isozymes; kinase inhibitor; mathematical model; neuropharmacology; protein kinase C; protein purification; tranquilizer

DESCRIPTION (provided by applicant): Protein kinase C inhibitors have been rendered as attractive targets for therapeutic agents. Recent studies have been shown that PKC epsilon isozyme is a valid new therapeutic target for treating alcoholism, anxiety and pain related to inflammation and alcoholic polyneuropathy. However, there is no selective inhibitor of PKC epsilon that can be administered systematically and cross the blood-brain barrier. Recently, we have been developing new algorithms and technology platform of computational modeling, optimization and virtual screen to parallel select novel small molecule drug leads with balanced potency and ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. We have performed preliminary studies both in modeling of known inhibitors of PKC isozymes as well as in experimental in-vitro screening. The key pharmacophoric and structural features and their differences, for example, between inhibitions of PKC epsilon and beta2 isozymes have been successfully identified. The convergence of in-vitro and in-silcio (computer-based) studies will allow us to not only better understand the structural and pharmacophoric requirements for discover novel, potent, selective PKC epsilon inhibitors, but also to optimize and select them with balanced ADMET properties in a shorter time and less resources. Our proposed research project will involve further studies to improve and fine-tune these technologies and in-vitro/in-silico iterative processes and explore the utility of them in discovering new, selective, oral- and brain-active PKC epsilon inhibitors for treatment of alcoholism, anxiety and pain.

描述（由申请人提供）：蛋白激酶C抑制剂已成为治疗剂的有吸引力的靶标。最近的研究表明，PKC epsilon 同工酶是治疗酒精中毒、与炎症和酒精性多发性神经病相关的焦虑和疼痛的有效新治疗靶点。然而，没有可以系统给药并穿过血脑屏障的选择性 PKC epsilon 抑制剂。最近，我们一直在开发计算建模、优化和虚拟筛选的新算法和技术平台，以并行选择具有平衡效力和ADMET（吸收、分布、代谢、排泄、毒性）特性的新型小分子药物先导物。我们在已知 PKC 同工酶抑制剂的建模以及实验体外筛选方面进行了初步研究。关键的药效和结构特征及其差异，例如 PKC epsilon 和 beta2 同工酶的抑制之间的差异已被成功鉴定。体外和计算机内（基于计算机）研究的融合将使我们不仅能够更好地了解发现新型、有效、选择性 PKC epsilon 抑制剂的结构和药效要求，而且能够在更短的时间和更少的资源内优化和选择具有平衡 ADMET 特性的它们。我们提出的研究项目将涉及进一步的研究，以改进和微调这些技术和体外/计算机迭代过程，并探索它们在发现新的、选择性的、口服和脑活性 PKC epsilon 抑制剂以治疗酗酒、焦虑和疼痛方面的效用。