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

项目概要/摘要抗体药物偶联物 (ADC) 是一类令人兴奋的靶向抗癌疗法，结合了选择性以及具有小分子有效负载的有效细胞毒活性的生物制剂（单克隆抗体）的特异性。事实证明，在不同的癌症类型中可产生临床益处（FDA 已批准 5 种 ADC），但许多分子失败了在后期临床测试中，抗肿瘤活性与毒性的精细平衡最终源于 ADC。 “设计空间”：靶标、骨架（通常是单克隆抗体（mAb））、连接化学、细胞毒性的选择有效负载和药物抗体比 (DAR) 产生了大量无法充分探索的可能组合因此，目前 ADC 是根据经验设计的，通常基于现有 ADC 的变体，并得到了支持。非常有限且高度不完善的临床前测定，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械数据来预测人类功效和毒性，并在计算机中预测新型 ADC 的治疗指数 (TI) 对于指导非常有价值早期开发期间的分子设计和临床决策，具体来说，是靶点选择和候选者。筛选可以通过计算进行，更好的 ADC 将被纳入临床测试，类似地，如果效果良好。可以预先评估替代给药方案和患者群体，进入临床测试的分子我们建议，成功的机会会更高，试验会加速，临床效益也会提高。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 工作台。将成功的 ADC 可用的不同数据和生物知识集成到一个平台中模型中，ADC 工作台将能够根据模拟的临床活性和毒性对候选药物进行系统评估（即 TI）。成功机会较小的潜在客户将被尽早淘汰，而那些前景较好的潜在客户将被继续推进。 ADC 工作台将允许在大量不同的虚拟患者群体中评估给药方案，为最大化 TI 的临床试验设计提供合理的方法。该平台将以模块化方式构建，以便创新的新型 ADC 分子（例如新型 mAB）当数据可用时，可以将 ADC 工作台工具纳入其中。预加载了经批准的 ADC 分子及其各个组件（mAB、连接子、有效负载），以便对潜在的新候选者进行快速的计算机原型设计和基准测试。随着更多临床成功和失败的数据的出现，将对内置参数数据库进行改进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 中 1