Drug Discovery Core

药物发现核心

• 批准号: 10845859
• 负责人: Heinz Ernst Moser
• 金额: $ 68.44万
• 依托单位: UNIVERSITY OF TEXAS MED BR GALVESTON
• 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/10845859
• 关键词: Academia; Address; Analytical Chemistry; Animal Model; Animals; Antiviral Agents; Archives; Artificial Intelligence; Automobile Driving; Behavior; Binding Proteins; Biological; Biological Availability; Biology; Biophysics; Cells; Chemicals; Clinic; Collaborations; Collection; Complement; Computers; Data; Development; Dimensions; Dose; Drug Design; Drug Kinetics; Evaluation; Funding; Goals; Human; In Vitro; Individual; Industry; Infrastructure; Lead; Libraries; Machine Learning; Measures; Metabolic; Modeling; Molecular Target; Natural Products; Oral; Permeability; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacodynamics; Phenotype; Plasma Proteins; Process; Property; Proteins; Resistance; Resistance development; Resources; Safety; Science; Solubility; Source; Structure; Structure-Activity Relationship; Testing; Therapeutic Index; Toxic effect; Translational Research; Triage; Validation; Viral; Work; analog; assay development; clinical candidate; computational chemistry; computational suite; computerized tools; design; drug candidate; drug discovery; experience; improved; in vivo; lead optimization; lipophilicity; pandemic disease; pandemic preparedness; pathogen; pre-clinical; preclinical development; pressure; response; scaffold; screening; skills; structural biology; success; tool; virology; virtual screening

DRUG DISCOVERY CORE – ABSTRACT The Drug Discovery Core will support all project teams to identify hits, convert them to tool compounds, leads and eventually development candidates for preclinical profiling. More specifically, we will rely on existing chemical matter (compound archive, commercial sources) and expertise at Novartis to optimize compound collections for screening purposes. Our extensive experience of using a suite of computational approaches, including machine learning and artificial intelligence, will allow us to improve hit rates and therefore obtaining hits more rapidly with fewer compounds being screened without compromising on chemical diversity. The identified hits, resulting from a collaborative effort with High-Throughput Biology Core, will be validated and further progressed towards tool and lead compounds by improving potency and selectivity and initiating a multi-parameter optimization at an early stage to achieve drug-like properties of lead and development compounds. Additional parameters of particular importance include in vitro and in vivo metabolic stability in multiple species, lipophilicity, solubility, plasma protein binding, permeability, and oral bioavailability. Projects with known targets will be supported by structure-based drug design (SBDD) and computational tools that cannot only add another dimension for compound design but will also allow the identification of structurally different chemical matter with similar properties (scaffold hopping) to add diversity and improve chances of success. As compounds enter the lead optimization stage and further advance towards development candidates, we will increasingly collaborate with the Translational Research Core to examine the in vivo pharmacokinetic behavior of advanced leads, better understand their potential for off-target activities, and examine their behavior in dose- range finding toxicity studies. At this stage, the Virology Core will examine compounds in relevant animal models for efficacy and pharmacodynamics and its main parameters driving it. This information is key to allow a decent human dose prediction that is important to assess a compound’s potential to become a successful drug. In summary, the Drug Discovery Core will – together with the other Cores – provide the infrastructure, resources, and expertise to identify hits, convert them to tool and lead compounds and optimize them to multiple, high quality development candidates with the goal to deliver 3 IND and 3 additional Development Candidates.

药物发现核心 - 摘要 药物发现核心将支持所有项目团队，以识别击球，将其转换为工具化合物， 领导并最终进行临床前分析的候选人。更具体地说，我们将依靠 现有的化学物质（复合档案，商业资源）和诺华的专业知识以优化 用于筛选目的的复合收集。我们使用一套计算套件的丰富经验 包括机器学习和人工智能在内的方法将使我们能够提高命中率，因此 获得较少的化合物而不会损害化学多样性，获得较少的化合物。 与高通量生物学核心的合作努力产生的确定的命中将得到验证 并通过提高效力和选择性并启动A，进一步发展了工具和铅化合物 在早期阶段的多参数优化，以实现铅和发育的类似药物样特性 化合物。特别重要的其他参数包括体外和体内代谢稳定性 多种物种，亲脂性，溶解度，血浆蛋白结合，渗透性和口服生物利用度。项目 有了已知目标，将由基于结构的药物设计（SBDD）和计算工具支持 仅为复合设计添加另一个维度，但还可以识别结构上的不同 具有相似特性的化学物质（脚手架跳跃）可以增加多样性并提高成功机会。作为 化合物进入铅优化阶段，并进一步迈向发展候选人，我们将 越来越多地与转化研究核心合作，以检查体内药代动力学行为 高级潜在客户，更好地了解其脱靶活动的潜力，并检查其在剂量中的行为 范围发现毒性研究。在此阶段，病毒学核心将检查相关动物模型中的化合物 为了效率和药效学及其主要参数驱动它。此信息是允许首次亮相的关键 人类剂量预测对于评估化合物成为成功药物的潜力很重要。 总结，药物发现核心将与其他核心一起提供基础设施， 资源和专业知识以识别命中，将其转换为工具并铅化合物并优化它们 到多个高质量的发展候选人，其目标是提供3个Ind和3个额外的3个 发展候选人。