東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Determination of Xenobiotic and Endo...

Ross, Dylan H.

Linked to FindBook

Google Book

Amazon

博客來

Determination of Xenobiotic and Endogenous Metabolites Using Ion Mobility-Mass Spectrometry and Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Determination of Xenobiotic and Endogenous Metabolites Using Ion Mobility-Mass Spectrometry and Machine Learning./
Author:	Ross, Dylan H.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	178 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-11, Section: B.
Contained By:	Dissertations Abstracts International82-11B.
Subject:	Cellular biology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28412535
ISBN:	9798728230304

Determination of Xenobiotic and Endogenous Metabolites Using Ion Mobility-Mass Spectrometry and Machine Learning.
Ross, Dylan H.

Determination of Xenobiotic and Endogenous Metabolites Using Ion Mobility-Mass Spectrometry and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 178 p.

Source: Dissertations Abstracts International, Volume: 82-11, Section: B.

Thesis (Ph.D.)--University of Washington, 2021.

This item must not be sold to any third party vendors.

Confident identification of xenobiotic and endogenous metabolites is central to applications including metabolomics, lipidomics, and drug metabolism studies. The integration of ion mobility spectroscopy with mass spectrometry enhances the information gained from such studies without significantly impacting their analytical throughput and increases confidence in identification of unknown metabolites through the measurement of collision cross section (CCS). The diversity of small molecule chemical space necessitates the ability to predict CCS with high accuracy, rather than relying solely upon experimental CCS databases for annotation of unknowns. This dissertation aims to demonstrate the various applications of IM-MS in the large- scale determination of endogenous and xenobiotic metabolites, in addition to theory-based CCS calculation and machine learning-based CCS-prediction. First, I discuss the curation of a comprehensive and diverse database of experimental CCS values sourced from the literature and the development of a comprehensive CCS prediction model using this large experimental database, while providing insight into the structural characteristics of endogenous and xenobiotic metabolites that determine their CCS. Next, I examine the IM-MS characteristics of a panel of drugs and in vitro-generated metabolites using human liver microsomes and S9 fraction, with in-depth computational modeling and theoretical CCS calculation to rationalize experimental observations. I then present the results from scaling the in vitro drug metabolite generation and IM-MS analysis to a high-throughput format and its application to a diverse collection of over 2000 drug and drug-like compounds in order to build a drug- and drug metabolite-specific CCS database for use in building a ML-based CCS prediction model for drugs and drug metabolites. Next, I discuss the development of a bioinformatic tool for the analysis of lipidomics data, which includes specialized models for the prediction of CCS and HILIC retention time, demonstrating how specialized predictive models can be built for specific chemical classes that leverage class- specific structural trends to produce high-accuracy CCS predictions. Finally, I summarize the principal conclusions of this collective work and provide perspective on how research in this area may continue to expand.

ISBN: 9798728230304Subjects--Topical Terms:

3172791
Cellular biology.
Subjects--Index Terms:

Drug metabolism

Determination of Xenobiotic and Endogenous Metabolites Using Ion Mobility-Mass Spectrometry and Machine Learning.
LDR:03514nmm a2200361 4500 001 2282029
005 20210927083522.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798728230304
035 $a (MiAaPQ)AAI28412535
035 $a AAI28412535
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ross, Dylan H. $3 3560765
245 1 0 $a Determination of Xenobiotic and Endogenous Metabolites Using Ion Mobility-Mass Spectrometry and Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 178 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-11, Section: B.
500 $a Advisor: Xu, Libin.
502 $a Thesis (Ph.D.)--University of Washington, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Confident identification of xenobiotic and endogenous metabolites is central to applications including metabolomics, lipidomics, and drug metabolism studies. The integration of ion mobility spectroscopy with mass spectrometry enhances the information gained from such studies without significantly impacting their analytical throughput and increases confidence in identification of unknown metabolites through the measurement of collision cross section (CCS). The diversity of small molecule chemical space necessitates the ability to predict CCS with high accuracy, rather than relying solely upon experimental CCS databases for annotation of unknowns. This dissertation aims to demonstrate the various applications of IM-MS in the large- scale determination of endogenous and xenobiotic metabolites, in addition to theory-based CCS calculation and machine learning-based CCS-prediction. First, I discuss the curation of a comprehensive and diverse database of experimental CCS values sourced from the literature and the development of a comprehensive CCS prediction model using this large experimental database, while providing insight into the structural characteristics of endogenous and xenobiotic metabolites that determine their CCS. Next, I examine the IM-MS characteristics of a panel of drugs and in vitro-generated metabolites using human liver microsomes and S9 fraction, with in-depth computational modeling and theoretical CCS calculation to rationalize experimental observations. I then present the results from scaling the in vitro drug metabolite generation and IM-MS analysis to a high-throughput format and its application to a diverse collection of over 2000 drug and drug-like compounds in order to build a drug- and drug metabolite-specific CCS database for use in building a ML-based CCS prediction model for drugs and drug metabolites. Next, I discuss the development of a bioinformatic tool for the analysis of lipidomics data, which includes specialized models for the prediction of CCS and HILIC retention time, demonstrating how specialized predictive models can be built for specific chemical classes that leverage class- specific structural trends to produce high-accuracy CCS predictions. Finally, I summarize the principal conclusions of this collective work and provide perspective on how research in this area may continue to expand.
590 $a School code: 0250.
650 4 $a Cellular biology. $3 3172791
650 4 $a Pharmacology. $3 634543
650 4 $a Molecular biology. $3 517296
650 4 $a Analytical chemistry. $3 3168300
653 $a Drug metabolism
653 $a Ion mobility
653 $a Machine learning
690 $a 0379
690 $a 0486
690 $a 0419
690 $a 0307
710 2 $a University of Washington. $b Medicinal Chemistry. $3 3560766
773 0 $t Dissertations Abstracts International $g 82-11B.
790 $a 0250
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28412535