Lead optimization of agents targeting multiple kinetoplastid parasites

摘要/摘要 被忽视的热带疾病(NTDS)药物的开发在发现方面遇到了一个关键的缺口 过程中，在体外筛选的药物很少进展到临床前阶段。虽然学术研究 实验室经常在体外发现有效的新化合物，开发组织不愿执行 临床前研究直到先导化合物在适当的动物模型中显示出已证实的活性为止。 我们建议填补这一空白的三个NTD：皮肤利什曼病(CL)，内脏利什曼病(VL)， 和恰加斯病，通过向寻找候选人的管道贡献新的高质量化学物质。这 将通过生产在体内疾病模型中有效的化合物来实现 满足被忽视疾病药物倡议(DNDI)所述的目标产品概况。我们有 生产了数百种化合物，对克鲁兹毛虫(n=312)和大利什曼原虫(n=420)进行了测试， 并鉴定了83种抗查格化合物(EC_(50)≤5微米)，127种主要抑制剂(EC_(50)≤5微米)和53种 对这两种病原体都有很强活性的抑制剂。这些化合物的主要局限性在于它们的 代谢和物理性质：溶解度有限，清除度高，血浆蛋白结合率高。我们也 只有一种利什曼原虫(大利什曼原虫，引起CL)的体外数据。 为了解决这些限制，我们建议在该项目的R21阶段对化合物和 确定在R33阶段可以通过系统药物化学解决的关键弱点。这个 确定优先顺序需要完成杜氏乳杆菌的体外检测(与VL相关)，并确定优先顺序 用于小鼠药代动力学(PK)实验的化合物。一旦化合物具有可接受的效力和PK 从现有的类似物集合中确定属性，按优先顺序排列的化合物将被推进到评估 在所有三种疾病的小鼠感染模型中。那些在动物模型中显示出有效性的化合物 将在毒性分析(人类激酶、GPCR、离子通道活性、遗传毒性)中描述感染情况，完成 一个将驱动后来的药物化学优化程序的数据包。 为了过渡到R33相，我们将至少鉴定出两种化合物 满足至少一种已评估病原体的定义铅标准，并确定需要优化的关键问题 项目的R33阶段。该项目的这一阶段将侧重于引导临床前候选药物 化学优化，以达到进一步发展的目标性能。为了完成 为了实现这些目标，我们将采用学术、政府和行业合作伙伴的独特组合。由 在R33阶段结束后，我们将提供在查加斯动物模型中影响寄生虫治愈的化合物 疾病、VL和/或CL，使用DNDI所需的剂量范例和终点。使用NIAID治疗 开发服务，我们将为一个候选化合物和一个备份完成必要的数据包 为了做出继续推进临床试验的进行/不进行的决定，我们需要开发一种新型化合物。

SUMMARY/ABSTRACT The development of drugs for Neglected Tropical Diseases (NTDs) suffers from a critical gap in the discovery process, wherein drugs screened in vitro rarely progress to the preclinical phase. While academic research laboratories often discover new compounds effective in vitro, development organizations are reluctant to perform the pre-clinical research until lead compounds show proven activity in appropriate animal models. We propose to fill this gap for three NTDs: cutaneous leishmaniasis (CL), visceral leishmaniasis (VL), and Chagas disease by contributing new, high quality chemical matter to the candidate-seeking pipeline. This will be accomplished by producing compounds that are effective in the in vivo models of the diseases and that meet target-product profiles as described by the Drugs for Neglected Diseases initiative (DNDi). We have produced hundreds of compounds that have been tested against T. cruzi (n=312) and Leishmania major (n=420), and have identified 83 anti-chagasic compounds (EC50 ≤ 5 µM), 127 L. major inhibitors (EC50 ≤ 5 µM), and 53 inhibitors with potent activity against both pathogens. The key limitations of these compounds are in their metabolic and physical properties: limited solubility, high clearance, and high plasma protein binding. We also have in vitro data against only one species of Leishmania (L. major, which causes CL). To address these limitations we propose in the R21 phase of this project to prioritize the compounds and to identify key weaknesses that can be addressed by systematic medicinal chemistry in the R33 phase. The prioritization will require completion of in vitro L. donovani assays (of relevance to VL), and prioritization of compounds for mouse pharmacokinetic (PK) experiments. Once compounds with acceptable potency and PK properties are identified from the existing set of analogs, prioritized compounds will be advanced to assessment in mouse infection models of all three diseases. Those compounds that show efficacy in animal models of infection will be profiled in toxicity assays (human kinase, GPCR, ion channel activity, genotoxicity), completing a data package that will drive the later medicinal chemistry optimization program. In order to transition to the R33 phase, we will have minimally identified at least two compounds that meet the defined lead criteria for at least one pathogen assessed, with key issues identified for optimization in the R33 phase of the project. This phase of the project will be focused on lead-to-preclinical candidate medicinal chemistry optimization in order to achieve the targeted properties for further advancement. In order to accomplish these goals, we will employ a unique combination of academic, government, and industry collaborators. By the end of the R33 phase, we will deliver compounds that effect parasitological cure in animal models of Chagas disease, VL, and/or CL using the dosing paradigm and endpoints required by DNDi. With NIAID Therapeutic Development Services, we will round out the necessary data package for a candidate compound and one backup compound in order to either make a go/no-go decision for continued advancement towards clinical trials.