Fragment-to-lead and target validation

片段到先导和目标验证

• 批准号: 10513873
• 负责人: John Damon Chodera
• 金额: $ 847.72万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-16 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10513873
• 关键词: Lead; Structure; Validation

The discovery of an antiviral therapeutic requires a target that is robust to mutations, and suitable chemical matter that modulates the target. The Fragment-to-Lead and Target Validation project sits at the crucial interface between target validation and chemical lead generation. We aim to validate biological hypotheses behind target selection and produce lead molecules for downstream therapeutic development, producing leads against 9 antiviral targets. By tightly integrating the unique capabilities of extremely high-throughput X-ray crystallography at Diamond Light Source, this project leverages recent advances in artificial intelligence and machine learning (Al/ML) and exascale computing free energy calculations to rapidly generate novel potent lead compounds able to overcome resistance from initial X-ray fragment screens. This project builds on the successful COVID Moonshot initiative, which executed a rapid fragment-to-lead campaign against SARS-CoV-2 main protease - starting from fragment screen, a lead compound with IC50 = 140 nM was discovered in <6 months and <400 compounds made. In the first stage of a hit-to-lead campaign, we will use machine learning to learn pharmacophore features from high throughput fragment screen readout, and use these patterns to search for potent hits from virtual, synthetically accessible chemical space. The goal is to arrive at chemical matter which engages the viral protein with antiviral activity, which in turn enables experiments that validate the target. Working with Project 1 (Antiviral Targeting to Suppress Resistance), potent hits will be used to validate biological hypotheses of target engagement, and Deep Mutational Scanning and serial passaging will be used to evaluate the barrier to resistance and the mutations that give rise to resistance. These insights will be used in the iterative medicinal chemistry design process, in selecting chemical series with less resistance potential and focusing on expanding into vectors that target mutationally robust residues. In the second stage of the hit-to-lead campaign, we will build on the wealth of structural and bioactivity data generated in the first stage and use machine learning, alchemical free energy calculations, and high throughput nanomole chemistry to rapidly evaluate and synthesize analogues which expand into promising vectors. The goal of this phase is to arrive at: (i) potent inhibitor in biochemical assays with IC50<500 nM; (ii) inhibition in cellular antiviral assays with EC50<3μM and cytotoxicity CC50>50 μM; and (iii) developable Tier 1 ADME and physicochemical properties: clog P<4, kinetic solubility > 50 μM, rat and human microsomal stability Clint<50, MDCK-LE permeability Papp>1x108cm/s. These leads are inputs to Lead Optimization (Project 5), which will focus on further improvements in potency, ADMET and in vivo pharmacokinetics.

抗病毒疗法的发现需要一个对突变具有鲁棒性的靶标，以及合适的化学药物 片段到先导和目标验证项目处于关键地位。 我们的目标是验证生物学假设。 支持靶标选择并为下游治疗开发产生先导分子，产生先导化合物 通过紧密集成极高通量 X 射线的独特功能，针对 9 种抗病毒靶点。 钻石光源的晶体学，该项目利用了人工智能和 机器学习 (Al/ML) 和百亿亿次计算自由能计算，可快速生成新的有效能量 能够克服最初 X 射线碎片筛选的阻力的先导化合物。 成功的新冠登月计划，该计划针对 SARS-CoV-2 主要蛋白酶 - 从片段筛选开始，IC50 = 140 nM 的先导化合物 在 6 个月内发现并制造了 400 种化合物 在“先导先导”活动的第一阶段，我们将使用。 机器学习从高通量片段屏幕读数中学习药效团特征，并使用 这些模式从虚拟的、可合成的化学空间中寻找有效的化合物。 到达与具有抗病毒活性的病毒蛋白结合的化学物质，从而使 验证目标的实验（抗病毒靶向抑制耐药性）， 有效的命中将用于验证目标参与的生物学假设，以及深度突变扫描 连续传递将用于评估耐药性障碍和引起的突变 这些见解将用于迭代药物化学设计过程和选择。 耐药潜力较小的化学系列，专注于扩展到针对突变的载体 在“先发制人”活动的第二阶段，我们将在丰富的结构和成果的基础上再接再厉。 第一阶段生成的生物活性数据，并使用机器学习、炼金术自由能计算，以及 高通量纳摩尔化学可快速评估和合成类似物，这些类似物可扩展到 这一阶段的目标是实现：(i) 生化检测中 IC50<500 的有效抑制剂。 nM；(ii) 细胞抗病毒检测中的抑制作用，EC50<3μM，细胞毒性 CC50>50μM；以及 (iii) 可开发 Tier 1 ADME 和理化性质：clog P<4，动力学溶解度 > 50 μM，大鼠和人微粒体 稳定性 Clint<50，MDCK-LE 渗透性 Papp>1x108cm/s 这些导联是导联优化的输入。 （项目 5），将重点关注药效、ADMET 和体内药代动力学的进一步改进。