Schurdak Mark, Vernetti Lawrence, Bergenthal Luke, Wolter Quinn K, Shun Tong Ying, Karcher Sandra, Taylor D Lansing, Gough Albert
Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Lab Chip. 2020 Apr 14;20(8):1472-1492. doi: 10.1039/c9lc01047e.
To accelerate the development and application of Microphysiological Systems (MPS) in biomedical research and drug discovery/development, a centralized resource is required to provide the detailed design, application, and performance data that enables industry and research scientists to select, optimize, and/or develop new MPS solutions, as well as to harness data from MPS models. We have previously implemented an open source Microphysiology Systems Database (MPS-Db), with a simple icon driven interface, as a resource for MPS researchers and drug discovery/development scientists (https://mps.csb.pitt.edu). The MPS-Db captures and aggregates data from MPS, ranging from static microplate models to integrated, multi-organ microfluidic models, and associates those data with reference data from chemical, biochemical, pre-clinical, clinical and post-marketing sources to support the design, development, validation, application and interpretation of the models. The MPS-Db enables users to manage their multifactor, multichip studies, then upload, analyze, review, computationally model and share data. Here we discuss how the sharing of MPS study data in the MS-Db is under user control and can be kept private to the individual user, shared with a select group of collaborators, or be made accessible to the general scientific community. We also present a test case using our liver acinus MPS model (LAMPS) as an example and discuss the use of the MPS-Db in managing, designing, and analyzing MPS study data, assessing the reproducibility of MPS models, and evaluating the concordance of MPS model results with clinical findings. We introduce the Disease Portal module with links to resources for the design of MPS disease models and studies and discuss the integration of computational models for the prediction of PK/PD and disease pathways using data generated from MPS models.
为加速微生理系统(MPS)在生物医学研究和药物发现/开发中的发展与应用,需要一个集中资源来提供详细的设计、应用和性能数据,使行业和科研人员能够选择、优化和/或开发新的MPS解决方案,并利用来自MPS模型的数据。我们之前实现了一个开源的微生理系统数据库(MPS-Db),它具有简单的图标驱动界面,作为MPS研究人员以及药物发现/开发科学家的资源(https://mps.csb.pitt.edu)。MPS-Db收集和汇总来自MPS的数据,范围从静态微孔板模型到集成的多器官微流控模型,并将这些数据与来自化学、生化、临床前、临床和上市后来源的参考数据相关联,以支持模型的设计、开发、验证、应用和解释。MPS-Db使用户能够管理他们的多因素、多芯片研究,然后上传、分析、审查、进行计算建模和共享数据。在这里,我们讨论在MPS-Db中MPS研究数据的共享如何处于用户控制之下,并且可以对单个用户保密、与选定的一组合作者共享,或者向广大科学界公开。我们还以我们的肝腺泡MPS模型(LAMPS)为例展示一个测试案例,并讨论MPS-Db在管理、设计和分析MPS研究数据、评估MPS模型的可重复性以及评估MPS模型结果与临床发现的一致性方面的应用。我们介绍疾病门户模块,其带有指向MPS疾病模型和研究设计资源的链接,并讨论使用MPS模型生成的数据进行PK/PD和疾病途径预测的计算模型的集成。