Investigation of BCRP-inhibitors using QSAR and machine learning methods
Investigation of BCRP-inhibitors using QSAR and machine learning methods
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DissertationDate of Exam
23.07.2019Date of Publication
05.08.2019Advisor
Wiese, MichaelCo-Referee
Bendas, GerdMetadata
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Abstract
BCRP is the second member of the subfamily G of the ABC transporters. BCRP is involved in several physiological functions, including protection of the human body from xenobiotics. The overexpression of this membrane protein in certain tumor cell lines leads to cross-resistance against various chemotherapeutic drugs. In this work, the inhibitory activity of several compounds against BCRP and P-gp were assayed using a Hoechst 33342 assay for BCRP and a Calcein AM assay for P-gp. Furthermore, the potency of the studied compounds has been rationalized using a classical QSAR approach. Finally, three machine learning algorithms (Self-Organizing Maps, Support Vector Machine and k-Nearest Neighbors) were used, in order to generate a global model useful to predict if small ligands could be (or not) BCRP-inhibitors.
Subjects
BCRP, QSAR, machine learning, drug design, SOM, SVM, k-NN, ABC-transporters
Classification (DDC)
615 Pharmakologie, TherapeutikMarighetti, Federico: Investigation of BCRP-inhibitors using QSAR and machine learning methods. - Bonn, 2019. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5n-55355
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5n-55355
@phdthesis{handle:20.500.11811/8055,
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5n-55355,
author = {{Federico Marighetti}},
title = {Investigation of BCRP-inhibitors using QSAR and machine learning methods},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2019,
month = aug,
note = {BCRP is the second member of the subfamily G of the ABC transporters. BCRP is involved in several physiological functions, including protection of the human body from xenobiotics. The overexpression of this membrane protein in certain tumor cell lines leads to cross-resistance against various chemotherapeutic drugs. In this work, the inhibitory activity of several compounds against BCRP and P-gp were assayed using a Hoechst 33342 assay for BCRP and a Calcein AM assay for P-gp. Furthermore, the potency of the studied compounds has been rationalized using a classical QSAR approach. Finally, three machine learning algorithms (Self-Organizing Maps, Support Vector Machine and k-Nearest Neighbors) were used, in order to generate a global model useful to predict if small ligands could be (or not) BCRP-inhibitors.},
url = {https://hdl.handle.net/20.500.11811/8055}
}
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5n-55355,
author = {{Federico Marighetti}},
title = {Investigation of BCRP-inhibitors using QSAR and machine learning methods},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2019,
month = aug,
note = {BCRP is the second member of the subfamily G of the ABC transporters. BCRP is involved in several physiological functions, including protection of the human body from xenobiotics. The overexpression of this membrane protein in certain tumor cell lines leads to cross-resistance against various chemotherapeutic drugs. In this work, the inhibitory activity of several compounds against BCRP and P-gp were assayed using a Hoechst 33342 assay for BCRP and a Calcein AM assay for P-gp. Furthermore, the potency of the studied compounds has been rationalized using a classical QSAR approach. Finally, three machine learning algorithms (Self-Organizing Maps, Support Vector Machine and k-Nearest Neighbors) were used, in order to generate a global model useful to predict if small ligands could be (or not) BCRP-inhibitors.},
url = {https://hdl.handle.net/20.500.11811/8055}
}
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