Kebotix, Cambridge, MA, USA.
National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA.
SLAS Technol. 2021 Dec;26(6):579-590. doi: 10.1177/24726303211053782.
Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the National Center for Advancing Translational Sciences (NCATS) ASPIRE (A Specialized Platform for Innovative Research Exploration) Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof of concept for biological assay development and system operationalization. Compared with a brute-force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing 21 assay conditions on average, with up to 20 conditions being tested simultaneously, as confirmed by repeated simulation. The algorithm could achieve a sevenfold reduction in costs for lab supplies and high-throughput experimentation runtime, all while being controlled from a remote site through a secure connection. Based on this proof of concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions.
目前，即使利用实验设计方法，高通量筛选检测的优化仍然是一个手动且耗时的过程。作为国家转化医学中心（NCATS）ASPIRE（创新研究探索专业化平台）计划的一部分，开发了一种跨平台、基于云的贝叶斯优化算法，以加速临床前药物发现。开发了一种无细胞木瓜蛋白酶酶活性检测方法，作为生物检测开发和系统运行的概念验证。与依次测试所有 294 个检测条件以找到全局最优的暴力方法相比，贝叶斯优化算法可以通过平均测试 21 个检测条件，最多同时测试 20 个条件来找到合适的条件，从而实现最佳检测性能，这一点通过重复模拟得到了证实。该算法可以将实验室用品和高通量实验运行时间的成本降低七倍，同时通过安全连接从远程站点进行控制。基于这一概念验证，该技术有望应用于更复杂的生物检测和 NCATS 的自动化化学反应筛选，并应可推广到其他机构。
