期刊
REGULATORY TOXICOLOGY AND PHARMACOLOGY
卷 135, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.yrtph.2022.105265
关键词
Ensemble learning; Calu-3; Pulmonary permeability; Airway epithelial barrier
资金
- National Science and Technology Council [MOST-107-2221-E-400-004-MY3, MOST-110-2221-E-400-004-MY3, MOST-110-2313-B-002-051]
This study presents the development of a computational model for predicting in vitro pulmonary permeability of chemicals. By integrating multiple algorithms and applying applicability domain adjustment, the model achieved good performance in both cross-validation and independent testing.
Pulmonary is a potential route for drug delivery and exposure to toxic chemicals. The human bronchial epithelial cell line Calu-3 is generally considered to be a useful in vitro model of pulmonary permeability by calculating the apparent permeability coefficient (Papp) values. Since in vitro experiments are time-consuming and labor-intensive, computational models for pulmonary permeability are desirable for accelerating drug design and toxic chemical assessment. This study presents the first attempt for developing quantitative structure-activity relationship (QSAR) models for addressing this goal. A total of 57 chemicals with Papp values based on Calu-3 experiments was first curated from literature for model development and testing. Subsequently, eleven de-scriptors were identified by a sequential forward feature selection algorithm to maximize the cross-validation performance of a voting regression model integrating linear regression and nonlinear random forest algo-rithms. With applicability domain adjustment, the developed model achieved high performance with correlation coefficient values of 0.935 and 0.824 for cross-validation and independent test, respectively. The preliminary results showed that computational models could be helpful for predicting Calu-3-based in vitro pulmonary permeability of chemicals. Future works include the collection of more data for further validating and improving the model.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据