Antibody Drug Conjugate (ADC) Workbench

抗体药物偶联物 (ADC) 工作台

• 批准号: 10171603
• 负责人: Alison Mary Betts
• 金额: $ 54.7万
• 依托单位: APPLIED BIOMATH, LLC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10171603
• 关键词: Address; Antibodies; Antibody-drug conjugates; Architecture; Area; Benchmarking; Biological; Biological Assay; Biological Response Modifier Therapy; Bispecific Monoclonal Antibodies; Breast Cancer Patient; CD22 gene; Characteristics; Chemistry; Clinical; Clinical Data; Clinical Pharmacology; Clinical Trials; Clinical Trials Design; Complex; Computer Models; Cytotoxic agent; Data; Data Set; Databases; Development; Dose; Dose-Limiting; Drug Design; Drug Kinetics; ERBB2 gene; Equilibrium; Evaluation; Experimental Models; FDA approved; Failure; Gemtuzumab Ozogamicin; Hematology; High Performance Computing; Human; Individual; Knowledge; Literature; Macaca fascicularis; Malignant neoplasm of lung; Maximum Tolerated Dose; Measures; Medical; Modeling; Molecular; Monoclonal Antibodies; Mus; Nature; Neutropenia; Oncology; Patient Selection; Patients; Pharmaceutical Preparations; Pharmacodynamics; Pharmacology; Phase; Process; Progression-Free Survivals; Property; Publishing; Reaction; Regimen; Reporting; Risk; Schedule; Side; Specificity; System; Therapeutic; Therapeutic Index; Thrombocytopenia; Time; Tissues; Toxic effect; Translating; Trastuzumab; Variant; Vertebral column; Work; Xenograft procedure; anti-cancer therapeutic; base; cancer type; candidate selection; clinical development; clinical efficacy; cloud based; computational platform; cytotoxic; design; drug discovery; drug distribution; first-in-human; improved; in silico; in vitro activity; in vivo; innovation; large cell Diffuse non-Hodgkin' s lymphoma; lead candidate; malignant stomach neoplasm; model building; multiple data types; neoplastic cell; novel; outcome prediction; patient population; patient response; pre-clinical; programs; prototype; receptor; research clinical testing; response; screening; simulation; small molecule; success; tool; tumor; tumor growth; virtual patient

Project Summary/Abstract Antibody-Drug Conjugates (ADCs) are an exciting class of targeted anti-cancer therapeutics, combining the selectivity and specificity of biologics (monoclonal antibodies) with the potent cytotoxic activity of small molecule payloads. While proven to yield clinical benefit in different cancer types (5 ADCs have been approved by the FDA), many molecules fail in late stage clinical testing. The fine balance of anti-tumor activity vs. toxicity ultimately originates from the ADC ‘design space’: the choices of target, backbone (usually monoclonal antibodies (mAb)), linker chemistry, cytotoxic payload, and drug-to-antibody ratio (DAR) make for a vast number of possible combinations that cannot be fully explored experimentally. ADCs are thus currently designed empirically, often based on variations of existing ADCs, supported by very limited and highly-imperfect pre-clinical assays, and clinical dosing schedules selected from sparse human toxicity data. Mechanism-based computational models that could synthesize the different preclinical mechanistic data to predict human efficacy and toxicity, and anticipate the therapeutic index (TI) of novel ADCs in silico would be highly valuable to guide both molecule design during early development, and clinical decisions. Specifically, if target selection and candidate screening could be performed computationally, better ADCs would be taken into clinical testing. Similarly, if the effect of alternate dosing schedules and patient populations could be evaluated pre-emptively, molecules that enter clinical testing would have a higher chance of success, trials would be accelerated, and clinical benefit would be improved. We propose developing a Quantitative Systems Pharmacology (QSP)-based platform ADC model that could do so - the ADC Workbench. By integrating the disparate body of data and biological knowledge available for successful ADCs into one platform model, the ADC Workbench will enable systematic candidate evaluation based on simulated clinical activity and toxicity (i.e., the TI). Leads with a poor chance of success will be weeded out early, and those with better prospects taken forward. The ADC Workbench will allow dosing schedules to be evaluated in large numbers of diverse virtual patient populations, providing a rational approach to clinical trial designs that maximize TI. The platform will be constructed in a modular way so that innovative new ADC molecules (e.g. with novel mAB backbones, linkers or payloads) can be incorporated as data becomes available. The ADC Workbench tool will be preloaded with several parameter sets for approved ADC molecules and their individual components (mAB, linker, payload), to allow for rapid in silico prototyping and benchmarking of potential new candidates. Continuous improvements to the built-in parameter database will be made as more data of clinical success and failure becomes available. Combining the model- and parameter database with the powerful high performance computing (HPC) analysis tools of Applied BioMath’s cloud based simulation engine will allow for routine and timely contribution to the ADC drug discovery process. 1 of 1

项目总结/摘要 抗体-药物偶联物（ADC）是一类令人兴奋的靶向抗癌治疗剂，其结合了选择性和生物相容性。 和生物制剂（单克隆抗体）的特异性，具有小分子有效载荷的有效细胞毒活性。而 已被证明在不同的癌症类型中产生临床益处（5种ADC已被FDA批准），许多分子失败 在后期的临床试验中。抗肿瘤活性与毒性的良好平衡最终源于ADC “设计空间”：靶标、骨架（通常为单克隆抗体（mAb））、接头化学、细胞毒性 有效载荷和药物与抗体的比例（DAR）使得大量可能的组合无法完全探索 实验性的因此，ADC目前是根据经验设计的，通常基于现有ADC的变型，由 非常有限和高度不完善的临床前试验，以及从稀疏的人体毒性中选择的临床给药方案 数据 基于机制的计算模型，可以综合不同的临床前机制数据来预测人类 有效性和毒性，并预测计算机模拟的新型ADC的治疗指数（TI）将是非常有价值的指导 包括早期开发过程中的分子设计和临床决策。具体而言，如果目标选择和候选人 筛选可以通过计算进行，更好的ADC将被用于临床测试。同样，如果 替代给药方案和患者群体可以预先评估，进入临床测试的分子 将有更高的成功机会，试验将加速，临床效益将得到改善。我们提出 开发一个基于定量系统药理学（QSP）的平台ADC模型，可以做到这一点-ADC - 是的 通过将可用于成功ADC的不同数据和生物学知识整合到一个平台中， 模型，ADC药物代谢试验将能够根据模拟的临床活性和毒性进行系统的候选评价 (i.e.,的TI）。成功机会不佳的线索将被提前淘汰，而那些前景较好的线索将被提前淘汰。 ADC的研究将允许在大量不同的虚拟患者人群中评估给药方案， 为临床试验设计提供了一种合理的方法，使TI最大化。 该平台将以模块化的方式构建，使得创新的新ADC分子（例如，具有新的mAB）能够被用于构建ADC。 主链、连接体或有效载荷）可以在数据变得可用时并入。ADC故障诊断工具将 预加载了用于批准的ADC分子及其单个组分（mAB，接头， 有效载荷），以允许对潜在的新候选物进行快速的计算机原型制作和基准测试。连续 随着更多临床成功和失败的数据， available.将模型和参数数据库与强大的高性能计算（HPC）分析相结合 Applied BioMath基于云的模拟引擎工具将允许对ADC药物进行常规和及时的贡献 发现过程。 1 of 1