Drug Discovery Core

药物发现核心

• 批准号: 10514150
• 负责人: Heinz Ernst Moser
• 金额: $ 1297.34万
• 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/10514150
• 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; base; 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识别命中，将其转化为工具化合物， 领导并最终开发用于临床前分析的候选药物。更具体地说，我们将依靠 诺华现有的化学物质（化合物档案，商业来源）和专业知识，以优化 用于筛选目的的复合集合。我们使用一套计算软件的丰富经验 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， 在不损害化学多样性的情况下，用更少的化合物筛选更快地获得命中。 将对与High-Put Biology Core合作产生的识别命中进行验证 并通过提高效力和选择性并引发一种新的药物来进一步发展工具和先导化合物。 在早期阶段进行多参数优化，以实现铅的药物样特性和开发 化合物.特别重要的其他参数包括在体外和体内代谢稳定性， 多物种、亲脂性、溶解性、血浆蛋白结合、渗透性和口服生物利用度。项目 基于结构的药物设计（SBDD）和计算工具将提供支持， 仅为化合物设计增加了另一个维度，但也将允许识别结构上不同的 具有相似性质的化学物质（支架跳跃），以增加多样性和提高成功的机会。作为 化合物进入先导优化阶段，并进一步向开发候选化合物迈进，我们将 越来越多地与转化研究中心合作，以检查体内药代动力学行为 先进的线索，更好地了解他们的潜在脱靶活动，并检查他们的行为剂量- 范围确定毒性研究。在这个阶段，病毒学核心将在相关动物模型中检查化合物 有效性和药效学及其驱动它的主要参数。这些信息是允许一个体面的 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。在 总之，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，将其转化为工具和铅化合物，并优化它们 多个高质量的发展候选人，目标是提供3个IND和3个额外的 发展候选人。