Antibody Drug Conjugate (ADC) Workbench

抗体药物偶联物 (ADC) 工作台

• 批准号: 10009587
• 负责人: Alison Mary Betts
• 金额: $ 39万
• 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/10009587
• 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

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

项目摘要/摘要 抗体-药物结合物(ADC)是一类令人兴奋的靶向抗癌治疗药物，结合了选择性 以及具有强大的小分子有效载荷细胞毒活性的生物制品(单抗)的特异性。而当 已证实可在不同癌症类型中产生临床益处(FDA已批准了5种ADC)，许多分子失败 在后期临床试验中。抗肿瘤活性与毒性的微妙平衡最终源于adc。 “设计空间”：靶标、骨架(通常是单抗)、连接物化学、细胞毒性的选择 有效载荷和药物抗体比(DAR)形成了大量不能完全探索的可能组合 试验性的。因此，目前ADC的设计通常基于现有ADC的变体，由 非常有限和高度不完善的临床前测试，以及从稀疏的人体毒性中选择的临床给药计划 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 预期新型ADC在矽肺中的疗效和毒性，以及预期的治疗指数(TI)将具有很高的指导价值 无论是早期发展中的分子设计，还是临床决策。具体而言，如果目标选择和候选人 筛查可以通过计算机进行，更好的ADC将被用于临床测试。同样，如果 可以先发制人地评估替代剂量计划和患者群体，即进入临床测试的分子 将有更高的成功机会，试验将加快，临床效益将得到改善。我们建议 开发基于定量系统药理学(QSP)的平台ADC模型--ADC 工作台。 通过将可用于成功的ADC的不同数据和生物知识体系集成到一个平台中 模型，ADC工作台将能够基于模拟的临床活动和毒性进行系统的候选评估 (即TI)。成功机会不大的线索将被提早淘汰，而前景更好的线索将被推向前进。 ADC工作台将允许在大量不同的虚拟患者群体中评估剂量计划， 为临床试验设计提供了一种合理的方法，最大限度地提高了TI。 该平台将以模块化方式构建，以便创新的新ADC分子(例如，具有新的MAB 主干、链接器或有效负载)可以在数据变得可用时合并。ADC工作台工具将是 预加载了用于经批准的ADC分子及其单独组件(MAb，连接体， 有效载荷)，以允许快速进行计算机原型制作和潜在新候选对象的基准测试。连续式 随着临床成功和失败的数据越来越多，内置参数数据库将得到改进 可用。将模型和参数数据库与强大的高性能计算(HPC)分析相结合 应用生物数学公司基于云的模拟引擎的工具将允许对ADC药物进行常规和及时的贡献 发现过程。 1个中的1个