PDF(Pubmed)
Abstract：
Membrane permeability is an in vitro parameter that represents the apparent permeability (Papp) of a compound, and is a key absorption, distribution, metabolism, and excretion parameter in drug development. Although the Caco-2 cell lines are the most used cell lines to measure Papp, other cell lines, such as the Madin-Darby Canine Kidney (MDCK), LLC-Pig Kidney 1 (LLC-PK1), and Ralph Russ Canine Kidney (RRCK) cell lines, can also be used to estimate Papp. Therefore, constructing in silico models for Papp estimation using the MDCK, LLC-PK1, and RRCK cell lines requires collecting extensive amounts of in vitro Papp data. An open database offers extensive measurements of various compounds covering a vast chemical space; however, concerns were reported on the use of data published in open databases without the appropriate accuracy and quality checks. Ensuring the quality of datasets for training in silico models is critical because artificial intelligence (AI, including deep learning) was used to develop models to predict various pharmacokinetic properties, and data quality affects the performance of these models. Hence, careful curation of the collected data is imperative. Herein, we developed a new workflow that supports automatic curation of Papp data measured in the MDCK, LLC-PK1, and RRCK cell lines collected from ChEMBL using KNIME. The workflow consisted of four main phases. Data were extracted from ChEMBL and filtered to identify the target protocols. A total of 1661 high-quality entries were retained after checking 436 articles. The workflow is freely available, can be updated, and has high reusability. Our study provides a novel approach for data quality analysis and accelerates the development of helpful in silico models for effective drug discovery. Scientific Contribution: The cost of building highly accurate predictive models can be significantly reduced by automating the collection of reliable measurement data. Our tool reduces the time and effort required for data collection and will enable researchers to focus on constructing high-performance in silico models for other types of analysis. To the best of our knowledge, no such tool is available in the literature.
摘要：
膜通透性是代表化合物的表观通透性(Papp)的体外参数,是一个关键的吸收，分布，新陈代谢，药物开发中的排泄参数。尽管Caco-2细胞系是测量Papp最常用的细胞系，其他细胞系,例如Madin-Darby犬肾（MDCK），LLC-猪肾1（LLC-PK1），和RalphRuss犬肾（RRCK）细胞系，也可以用来估计Papp。因此,使用MDCK构建Papp估计的仿真模型，LLC-PK1和RRCK细胞系需要收集大量的体外Papp数据。一个开放的数据库提供了各种化合物的广泛测量，涵盖了广阔的化学空间;然而，在没有进行适当的准确性和质量检查的情况下，报告了对使用公开数据库中发布的数据的担忧。确保用于计算机模型训练的数据集的质量至关重要，因为人工智能(AI，包括深度学习)用于开发模型来预测各种药代动力学特性，和数据质量影响这些模型的性能。因此，必须对收集到的数据进行仔细的管理。在这里，我们开发了一个新的工作流程，支持MDCK中测量的Papp数据的自动管理，使用KNIME从ChEMBL收集的LLC-PK1和RRCK细胞系。工作流程由四个主要阶段组成。从ChEMBL中提取数据并过滤以鉴定目标方案。在检查436篇文章后，总共保留了1661个高质量条目。工作流程免费提供，可以更新，并具有较高的可重用性。我们的研究为数据质量分析提供了一种新颖的方法，并加速了有效药物发现的有用计算机模型的开发。科学贡献：通过自动收集可靠的测量数据，可以显着降低构建高度准确的预测模型的成本。我们的工具减少了数据收集所需的时间和精力，并使研究人员能够专注于为其他类型的分析构建高性能的计算机模型。据我们所知,文献中没有这样的工具。
