Pharmacokinetics-Based DNA-Encoded Library Screening

基于药代动力学的 DNA 编码文库筛选

• 批准号: 10644211
• 负责人: Juan Hu
• 金额: $ 12.5万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644211
• 关键词: 3-Dimensional; A549; Academic Training; Address; Alkynes; Amino Acids; Area; Azides; Binding; Biological Assay; Biology; Carboxylic Acids; Career Mobility; Cell Line; Cell Membrane Permeability; Cells; Cellular Assay; Chemicals; Classification; Clinic; Collaborations; Communities; Complex; Copper; Coupled; Cyclic Peptides; Cysteine; DNA; Data; Data Set; Descriptor; Detection; Development; Diagnostic; Dimensions; Drug Kinetics; Encapsulated; Engineering; Environment; Exhibits; Funding; Future; Generations; Goals; Growth; Hand; Hela Cells; Human; Hydrogen Bonding; Image; In Vitro; Institution; Label; Libraries; Ligands; Lipids; Liposomes; Machine Learning; Measurement; Membrane; Microfluidics; Modality; Modeling; Molecular; Molecular Conformation; Penetration; Peptides; Periodicity; Permeability; Pharmaceutical Chemistry; Pharmaceutical Preparations; Phase; Phospholipids; Positioning Attribute; Proline; Property; Proteins; Proteome; Publishing; Radial; Reaction; Research; Research Personnel; S phase; Screening Result; Solid; Solvents; Sorting; Structure; Structure-Activity Relationship; Surface; Techniques; Technology; Testing; Thalidomide; Therapeutic; Training; Vesicle; Visualization; Water; academic preparation; assay development; career; career development; combinatorial; computational chemistry; conformer; cycloaddition; design; drug candidate; high throughput screening; high throughput technology; job market; machine learning model; member; miniaturize; molecular dynamics; molecular mass; next generation; novel; novel diagnostics; novel therapeutics; pre-clinical; propargylamine; protein protein interaction; screening; skills; small molecule; small molecule libraries; technology development; tool

PROJECT ABSTRACT This K99/R00 proposal seeks to expand the space of druggable protein targets through the development of novel analytical technology that will guide the design of next-generation, non- Lipinski molecular therapeutics and diagnostics. Cyclic peptides, which are not drug-like (i.e., Lipinski- like), hold great promise for addressing undruggable targets, especially protein-protein interactions (PPIs). However, the larger molecular mass and more complex secondary structures result in unpredictable and almost always low cell permeation, significantly blunting their utility as drug candidates and even as preclinical tool compounds in cells. Rarely, a cyclic peptide will exhibit anomalously high permeation. However, insufficient data exist to uncover the rules dictating permeation because high-throughput measurements of cell permeation simply do not exist. In this proposal, a high-throughput in vitro permeation assay compatible with DNA-encoded combinatorial library (DEL) screening will be developed and miniaturized to the microfluidic droplet scale. The permeation assay will be applied to screen DELs to identify the species that efficiently permeate membrane bilayers. The permeable macrocycle hits will be further validated in a cell-based permeation assay that will also be developed in the project. The large screening data sets will reveal relationships between permeability and structure, especially for molecules of beyond the Rule of 5 (bRo5), which will be further analyzed using machine learning. With these and other empirically derived models of macrocyclic peptide pharmacokinetic properties in hand, we may finally be able to move such non-Lipinski molecules from the lab to the clinic at scale. This proposal will significantly enhance the PI’s career development and advance her toward her career goal of becoming an independent investigator at a research-focused institution. The proposed project provides training in cutting- edge research skills, including cell-based assay development and high-content imaging, DEL design, synthesis and screening technology development, and machine learning techniques. UC Irvine provides an ideal environment for academic training, with world-renowned experts in medicinal and computational chemistry, chemical biology and microfluidics engineering. In addition, UCI provides an intellectual environment that encourages collaboration and cooperation, enabling the candidate’s growth as a member of the scientific community. Indeed, the PI will engage in activities designed to achieve independence, including training in lab management and grantsmanship, networking, and preparation for the academic job market. In summary, the proposed plan will enable the PI’s scientific and career-wise growth and independence, further positioning her to attain future R01 funding.

项目摘要 这项K99/R00提案寻求扩大可药物蛋白质靶标的空间，通过 开发新的分析技术，以指导下一代、非 利平斯基分子治疗学和诊断学。环肽，非类药物(即，利平斯基- 就像)，在解决不可药物的靶点方面有很大的希望，特别是蛋白质-蛋白质相互作用(PPI)。 然而，较大的分子质量和更复杂的二级结构导致了不可预测的和几乎 细胞渗透率总是很低，大大削弱了它们作为候选药物甚至作为临床前工具的效用 细胞中的化合物。很少有环肽会表现出异常的高渗透性。然而，数据不足 之所以存在是为了揭示决定渗透的规律，因为高通量测量细胞渗透 根本不存在。在这项建议中，一种与DNA编码的高通量体外渗透试验兼容 组合库(DEL)筛选将被开发并微型化到微流控液滴的规模。这个 渗透试验将应用于DELs的筛选，以确定有效渗透膜的物种 双层的。可渗透的大循环命中将在基于细胞的渗透测试中得到进一步验证，该测试还将 将在该项目中开发。大型筛选数据集将揭示渗透率和 结构，特别是超出规则5(BRo5)的分子，将使用机器进一步分析 学习。使用这些和其他经验性推导的大环肽药代动力学特性模型 另一方面，我们可能最终能够将这种非利平斯基分子从实验室大规模转移到临床。这项建议 将显著促进私家侦探的职业发展，并推动她朝着成为一名 一家专注于研究的机构的独立调查员。拟议的项目提供以下方面的培训： 前沿研究技能，包括基于细胞的分析开发和高含量成像、DEL设计、合成 和筛选技术的发展，以及机器学习技术。加州大学欧文分校提供了理想的 学术培训的环境，有世界知名的药物和计算化学专家， 化学生物学和微流体工程。此外，UCI还提供了一个智力环境， 鼓励协作和合作，使候选人能够成长为科学界的一员 社区。事实上，PI将参与旨在实现独立的活动，包括在实验室进行培训 管理和资质，网络，以及为学术就业市场做准备。总而言之， 拟议的计划将使PI在科学和职业方面的成长和独立，进一步定位她 以获得未来的R01资金。