Antibody Drug Conjugate (ADC) Workbench

抗体药物偶联物 (ADC) 工作台

基本信息

批准号：
10413117
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
项目类别：
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/10413117
关键词：
Address Antibodies Antibody-drug conjugates Architecture Area Benchmarking Biological Biological Assay Biological Products 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&apos 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

项目摘要

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，结合了选择性生物制剂的特异性（单克隆抗体）具有小分子有效载荷潜在的细胞毒性活性。尽管被证明可以在不同类型的癌症类型中产生临床益处（FDA已批准了5种ADC），许多分子失败了在晚期临床测试中。抗肿瘤活性与毒性的良好平衡最终源自ADC “设计空间”：目标，骨干（通常是单克隆抗体（MAB）），接头化学，细胞毒性的选择有效载荷和药物与抗体比（DAR）使许多可能无法完全探索的可能组合实验。因此非常有限且高度的临床前测定，以及从稀疏人类毒性中选择的临床给药时间表数据。基于机理的计算模型，可以合成不同的临床前机械数据以预测人类疗效和毒性，并预测硅中新型ADC的治疗指数（TI）对于指导很有价值早期开发期间的分子设计和临床决策。具体而言，如果目标选择和候选人可以通过计算进行筛选，将更好的ADC进行临床测试。同样，如果效果可以预先评估替代的给药时间表和患者人群，进入临床测试的分子成功的机会将有更高的机会，试验将得到加速，并将提高临床益处。我们建议开发基于定量系统药理学（QSP）的平台ADC模型 - 可以这样做-ADC 工作台。通过将可用于成功ADC的数据和生物学知识的不同体系集成到一个平台中模型，ADC工作台将基于模拟临床活动和毒性进行系统的候选评估（即Ti）。领先者的成功机会很差，将很早就淘汰，而那些前景更好的领导者会逐渐淘汰。 ADC Workbench将允许大量潜水员虚拟患者人群评估给药时间表，为临床试验设计提供了最大化Ti的合理方法。该平台将以模块化的方式构建，以便创新的新ADC分子（例如，具有新颖的mAb 可以在数据可用时合并骨干，链接器或有效负载）。 ADC Workbench工具将是预加载了几个参数集用于批准的ADC分子及其各个组件（mab，linker，有效载荷），以快速进行硅原型制作和潜在新候选者的基准测试。连续的随着更多的临床成功数据和失败变为，将对内置参数数据库进行改进可用的。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的基于云的仿真引擎的工具将允许例行和及时贡献ADC药物发现过程。 1 of 1