Drug Discovery Core

药物发现核心

基本信息

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
项目类别：
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

项目摘要

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，更具体地说，我们将依赖于临床前分析的线索和最终开发候选者。诺华现有的化学物质（化合物档案、商业来源）和专业知识可进行优化我们在使用一套计算方法方面拥有丰富的经验。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，从而在不影响化学多样性的情况下，用更少的筛选化合物快速获得更多的结果。与高通量生物学核心合作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早期多参数优化，实现先导药物的类药特性和开发特别重要的其他参数包括体外和体内代谢稳定性。多物种、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。已知靶点将得到基于结构的药物设计（SBDD）和计算工具的支持，而这些工具无法不仅为复合设计添加了另一个维度，而且还允许识别结构上不同的具有相似特性的化学物质（支架跳跃）可以增加多样性并提高成功机会。化合物进入先导优化阶段并进一步向开发候选者迈进，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先导化合物，更好地了解其脱靶活动的潜力，并检查其剂量行为在此阶段，病毒学核心将检查化合物相关的动物模型。功效和药效学及其主要参数驱动它是关键。人体剂量预测对于评估化合物成为成功药物的潜力非常重要。总之，药物发现核心将与其他核心一起提供基础设施，资源和专业知识来识别命中、将其转化为工具和先导化合物并对其进行优化向多个高质量的开发候选者提供，目标是提供 3 个 IND 和 3 个额外的发展候选人。