Predicting Gentamicin Induced Nephrotoxicity Using Pharmacometabonomic Approach
Gentamicin has been one of the most widely used antibiotics. However, its nephrotoxicity is a major concern as it is filtered through the kidneys for excretion in the form of urine. However, early diagnosis is difficult and no reliable metabolites as biomarkers are currently available. Thus, in this...
Saved in:
Main Author: | |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | http://eprints.usm.my/48355/1/full%20version%2007032019%20%281%29.pdf%20cut.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my-usm-ep.48355 |
---|---|
record_format |
uketd_dc |
spelling |
my-usm-ep.483552021-02-18T08:01:33Z Predicting Gentamicin Induced Nephrotoxicity Using Pharmacometabonomic Approach 2019-03 Aziz, Fatimatuzzahra’ Abd. RS1-441 Pharmacy and materia medica Gentamicin has been one of the most widely used antibiotics. However, its nephrotoxicity is a major concern as it is filtered through the kidneys for excretion in the form of urine. However, early diagnosis is difficult and no reliable metabolites as biomarkers are currently available. Thus, in this study, a pharmacometabonomic approach using nuclear magnetic resonance (NMR) spectroscopy to investigate the altered metabolic pattern in serum and urine samples prior to administration of gentamicin has been employed to develop a model to predict nephrotoxicity induced by gentamicin and to identify the metabolic biomarkers associated. The aim of this study is therefore to determine the potential of urine and serum metabolites in predicting gentamicin induced nephrotoxicity. 2019-03 Thesis http://eprints.usm.my/48355/ http://eprints.usm.my/48355/1/full%20version%2007032019%20%281%29.pdf%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Farmasi (School of Pharmacy) |
institution |
Universiti Sains Malaysia |
collection |
USM Institutional Repository |
language |
English |
topic |
RS1-441 Pharmacy and materia medica |
spellingShingle |
RS1-441 Pharmacy and materia medica Aziz, Fatimatuzzahra’ Abd. Predicting Gentamicin Induced Nephrotoxicity Using Pharmacometabonomic Approach |
description |
Gentamicin has been one of the most widely used antibiotics. However, its nephrotoxicity is a major concern as it is filtered through the kidneys for excretion in the form of urine. However, early diagnosis is difficult and no reliable metabolites as biomarkers are currently available. Thus, in this study, a pharmacometabonomic approach using nuclear magnetic resonance (NMR) spectroscopy to investigate the altered metabolic pattern in serum and urine samples prior to administration of gentamicin has been employed to develop a model to predict nephrotoxicity induced by gentamicin and to identify the metabolic biomarkers associated. The aim of this study is therefore to determine the potential of urine and serum metabolites in predicting gentamicin induced nephrotoxicity. |
format |
Thesis |
qualification_name |
Doctor of Philosophy (PhD.) |
qualification_level |
Doctorate |
author |
Aziz, Fatimatuzzahra’ Abd. |
author_facet |
Aziz, Fatimatuzzahra’ Abd. |
author_sort |
Aziz, Fatimatuzzahra’ Abd. |
title |
Predicting Gentamicin Induced Nephrotoxicity Using
Pharmacometabonomic Approach |
title_short |
Predicting Gentamicin Induced Nephrotoxicity Using
Pharmacometabonomic Approach |
title_full |
Predicting Gentamicin Induced Nephrotoxicity Using
Pharmacometabonomic Approach |
title_fullStr |
Predicting Gentamicin Induced Nephrotoxicity Using
Pharmacometabonomic Approach |
title_full_unstemmed |
Predicting Gentamicin Induced Nephrotoxicity Using
Pharmacometabonomic Approach |
title_sort |
predicting gentamicin induced nephrotoxicity using
pharmacometabonomic approach |
granting_institution |
Universiti Sains Malaysia |
granting_department |
Pusat Pengajian Sains Farmasi (School of Pharmacy) |
publishDate |
2019 |
url |
http://eprints.usm.my/48355/1/full%20version%2007032019%20%281%29.pdf%20cut.pdf |
_version_ |
1747821923068280832 |