國家衛生研究院 NHRI:Item 3990099045/15615

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Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/15615

Title:	Multitask learning for predicting pulmonary absorption of chemicals
Authors:	Chiu, YW;Tung, CW;Wang, CC
Contributors:	Institute of Biotechnology and Pharmaceutical Research
Abstract:	Pulmonary absorption is an important route for drug delivery and chemical exposure. To streamline the chemical assessment process for the reduction of animal experiments, several animal-free models were developed for pulmonary absorption research. While Calu-3 and Caco-2 cells and their derived computational models were used in estimating pulmonary permeability, the ex vivo isolated perfused lung (IPL) models are considered more clinically relevant measurements. However, the IPL experiments are resource-consuming making it infeasible for the large-scale screening of potential inhaled toxicants and drugs. In silico models are desirable for estimating pulmonary absorption. This study presented a novel machine learning method that employed an extratrees-based multitask learning approach to predict the IPL absorption rate constant (ka(IPL)) of various chemicals. The shared permeability knowledge was extracted by simultaneously learning three relevant tasks of Caco-2 and Calu-3 cell permeability and IPL absorption rate. Seven informative physicochemical descriptors were identified. A rigorous evaluation of the developed prediction model showed good performance with a high correlation between predictions and observations (r = 0.84) in the independent test dataset. Two case studies of inhalation drugs and respiratory sensitizers revealed the potential application of this model, which may serve as a valuable tool for predicting pulmonary absorption of chemicals.
Date:	2024-03
Relation:	Food and Chemical Toxicology. 2024 Mar;185:Article number 114453.
Link to:	http://dx.doi.org/10.1016/j.fct.2024.114453
JIF/Ranking 2023:	http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0278-6915&DestApp=IC2JCR
Cited Times(WOS):	https://www.webofscience.com/wos/woscc/full-record/WOS:001171198700001
Cited Times(Scopus):	https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85183290968
Appears in Collections:	[Chun-Wei Tung] Periodical Articles

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