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