NAAA Inhibitors for pain and inflammation

 描述（由申请人提供）：慢性疼痛管理的直接和间接成本约为1000亿美元。目前的治疗方法未能有效解决这一重大医疗问题：麻醉剂会导致成瘾和安全风险，而其他类型的镇痛剂则因疗效不足和副作用而受阻。慢性疼痛是一种进行性病症，其中由于慢性疾病（例如炎症）的存在而经常出现持续性异常性疼痛和痛觉过敏。因此，迫切需要开发新的、安全的疗法，以纠正慢性炎性疼痛中涉及的病理机制。我们建议与Piomelli博士（UC Irvine）合作开发N-酰基乙醇胺水解酸性酰胺酶（NAAA）的新型抑制剂，Piomelli博士是内源性脂质介质（包括NAAA信号传导）领域公认的世界领导者。NAAA是一种酶，作为关键的炎症检查点，并通过降解棕榈酰乙醇胺（PEA）来调节疼痛处理，棕榈酰乙醇胺是一种脂质，在广泛的啮齿动物模型中发挥深刻的镇痛和抗炎作用。此外，PEA在临床疼痛中具有镇痛作用，并已用于有效治疗患者的疼痛状况，但PEA代谢不稳定，药代动力学（PK）较差。更好的方法可能是抑制NAAA以提高炎性和伤害感受部位的PEA水平，从而产生抗炎和镇痛反应，但迄今为止仅描述了几种全身PK较差的NAAA抑制剂。由于缺乏与高通量筛选（HTS）相容的测定，适合于药物优化的新支架的发现受到阻碍。为了克服这一挑战，我们采取了两种方法：（i）我们开发了一种与HTS兼容的新NAAA检测方法，（ii）我们建立了一个NAAA的3D同源模型，可用于执行虚拟筛选并指导新型抑制剂的设计。使用该试验，我们对13 K不同的化合物进行了试验性筛选，并鉴定了27种新型抑制剂，这些抑制剂阻断NAAA但不抑制类似的水解酶。使用3D同源模型，我们已经表明，这些抑制剂显示适当的相互作用的催化口袋。此外，我们对200万种商业化合物进行了虚拟筛选，并检测到具有抑制活性的额外命中。本提案的前两个目标将侧重于（目标1） 完成我们的58 K小分子文库的HTS和（目标#2）所有虚拟筛选命中的表征。目的#3将确认对NAAA的活性、抑制机制、相对于其他类似水解酶的选择性以及在细胞测定中的活性。最后一个目的（目标4）将测试对其他受体和hERG的脱靶活性以及体外代谢稳定性。初始 将通过计算方法辅助探索优先支架的SAR。我们将根据药理学，化学和代谢特性优先考虑最佳支架。SBIR II期将重点关注电极导线优化，以改善电极导线系列的药理学和PK。

 DESCRIPTION (provided by applicant): Chronic pain management accounts for approximately US$100 billion in direct and indirect costs. Current treatments fail to address effectively this large medical problem: narcotics cause addiction and safety risks, while other types of analgesics are hampered by insufficient efficacy and side effects. Chronic pain is a progressive condition where persistent allodynia and hyperalgesia often emerge due to the presence of chronic disease (e.g. inflammation). Therefore, there is a critical need to develop novel, safe therapies that can rectify pathological mechanisms involved in chronic inflammatory pain. We propose to develop novel inhibitors of N-acylethanolamine-hydrolyzing acid amidase (NAAA) in collaboration with Dr. Piomelli (UC Irvine) who is a recognized world-leader in the field of endogenous lipid mediators including NAAA signaling. NAAA is an enzyme that serves as a key inflammatory checkpoint and regulates pain processing by degrading palmitoylethanolamine (PEA), a lipid that exerts profound analgesic and anti-inflammatory effects in a broad range of rodent models. Moreover, PEA is analgesic in clinical pain and has been used to treat effectively pain conditions in patients but PEA is metabolically labile and has poor pharmacokinetics (PK). A better approach may be to inhibit NAAA to elevate PEA levels at inflammatory and nociceptive sites, resulting in anti-inflammatory and analgesic responses but so far only a few NAAA inhibitors with poor systemic PK have been described. The discovery of new scaffolds suitable for drug optimization has been hindered by the lack of assays compatible with high-throughput screening (HTS). To overcome this challenge we are taking two approaches: (i) we developed a new NAAA assay that is compatible with HTS, and (ii) we built a 3D-homology model of NAAA that can be used to execute virtual screens and guide the design of novel inhibitors. Using this assay we ran a pilot screen of 13K diverse compounds and identified 27 novel inhibitors that block NAAA but do not inhibit similar hydrolases. Using the 3D-homology model we have shown that these inhibitors display proper interactions in the catalytic pocket. In addition, we ran a virtual screening of 2 million commercial compounds and detected additional hits with inhibitory activity. The first two aims of this proposal will focus on (Aim#1) completing the HTS of our library of 58K small molecules and (Aim#2) the characterization of all the virtual screening hits. Aim #3 will confirm activity on NAAA, mechanisms of inhibition, selectivity over other similar hydrolases, and activity in cellular assays. The last aim (Aim#4) wil test for off-target activity on other receptors and hERG, and metabolic stability in vitro. Initial SAR of prioritized scaffolds will be explored assisted by computational methods. We will prioritize the best scaffolds for lead optimization based on pharmacology, chemical, and metabolic properties. The SBIR phase II will focus on lead optimization to improve the pharmacology and PK of lead series.