Pharmacokinetics-Based DNA-Encoded Library Screening

基于药代动力学的 DNA 编码文库筛选

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要该 K99/R00 提案旨在通过开发新颖的分析技术，将指导下一代、非 Lipinski 分子治疗和诊断，非类药物（即 Lipinski-）。等），对于解决不可成药的靶标，特别是蛋白质-蛋白质相互作用（PPI）具有巨大的前景。然而，较大的分子量和更复杂的二级结构导致不可预测且几乎细胞渗透性始终显着降低，削弱了它们作为候选药物甚至临床前工具的效用环肽很少会表现出异常高的渗透性，但数据不足。存在是为了揭示决定渗透的规则，因为细胞渗透的高通量测量在该提案中，与 DNA 编码兼容的高通量体外渗透测定根本不存在。将开发组合文库（DEL）筛选并将其小型化至微流体液滴规模。渗透测定将用于筛选 DEL，以确定有效渗透膜的物种可渗透的大环化合物命中将在基于细胞的渗透测定中得到进一步验证，该测定也将得到验证。该项目中开发的大型筛选数据集将揭示渗透率和渗透率之间的关系。结构，特别是超出 5 规则 (bRo5) 的分子，将使用机器进一步分析学习这些和其他大环肽药代动力学特性的经验模型。另一方面，我们最终可能能够将这种非利宾斯基分子从实验室大规模转移到临床。将显着促进 PI 的职业发展，并推动她实现成为一名拟议的项目提供切割方面的培训。边缘研究技能，包括基于细胞的测定开发和高内涵成像、DEL 设计、合成和筛选技术的开发，以及加州大学欧文分校的机器学习技术提供了理想的选择。学术培训环境，拥有世界知名的药物和计算化学专家，此外，UCI 还提供了一个知识环境：鼓励协作与合作，使候选人能够成长为科学界的一员事实上，PI 将参与旨在实现独立的活动，包括实验室培训。管理和资助、网络以及学术就业市场的准备。拟议的计划将使 PI 的科学和职业成长和独立性得以实现，进一步定位她以获得未来的 R01 资金。