Cargando…
Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles
Toxicogenomics can measure the expression of thousands of genes to identify changes associated with drug induced toxicities. It is expected that toxicogenomics can be an alternative or complementary approach in preclinical drug safety evaluation to identify or predict drug induced toxicities. One of...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2007
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2194664/ https://www.ncbi.nlm.nih.gov/pubmed/17908307 http://dx.doi.org/10.1186/1479-5876-5-47 |
_version_ | 1782147668593606656 |
---|---|
author | Jiang, Ying Gerhold, David L Holder, Daniel J Figueroa, David J Bailey, Wendy J Guan, Ping Skopek, Thomas R Sistare, Frank D Sina, Joseph F |
author_facet | Jiang, Ying Gerhold, David L Holder, Daniel J Figueroa, David J Bailey, Wendy J Guan, Ping Skopek, Thomas R Sistare, Frank D Sina, Joseph F |
author_sort | Jiang, Ying |
collection | PubMed |
description | Toxicogenomics can measure the expression of thousands of genes to identify changes associated with drug induced toxicities. It is expected that toxicogenomics can be an alternative or complementary approach in preclinical drug safety evaluation to identify or predict drug induced toxicities. One of the major concerns in applying toxicogenomics to diagnose or predict drug induced organ toxicity, is how generalizable the statistical classification model is when derived from small datasets? Here we presented that a diagnosis of kidney proximal tubule toxicity, measured by pathology, can successfully be achieved even with a study design of limited number of training studies or samples. We selected a total of ten kidney toxicants, designed the in life study with multiple dose and multiple time points to cover samples at doses and time points with or without concurrent toxicity. We employed SVM (Support Vector Machine) as the classification algorithm for the toxicogenomic diagnosis of kidney proximal tubule toxicity. Instead of applying cross validation methods, we used an independent testing set by dividing the studies or samples into independent training and testing sets to evaluate the diagnostic performance. We achieved a Sn (sensitivity) = 88% and a Sp (specificity) = 91%. The diagnosis performance underscores the potential application of toxicogenomics in a preclinical lead optimization process of drugs entering into development. |
format | Text |
id | pubmed-2194664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-21946642008-01-12 Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles Jiang, Ying Gerhold, David L Holder, Daniel J Figueroa, David J Bailey, Wendy J Guan, Ping Skopek, Thomas R Sistare, Frank D Sina, Joseph F J Transl Med Research Toxicogenomics can measure the expression of thousands of genes to identify changes associated with drug induced toxicities. It is expected that toxicogenomics can be an alternative or complementary approach in preclinical drug safety evaluation to identify or predict drug induced toxicities. One of the major concerns in applying toxicogenomics to diagnose or predict drug induced organ toxicity, is how generalizable the statistical classification model is when derived from small datasets? Here we presented that a diagnosis of kidney proximal tubule toxicity, measured by pathology, can successfully be achieved even with a study design of limited number of training studies or samples. We selected a total of ten kidney toxicants, designed the in life study with multiple dose and multiple time points to cover samples at doses and time points with or without concurrent toxicity. We employed SVM (Support Vector Machine) as the classification algorithm for the toxicogenomic diagnosis of kidney proximal tubule toxicity. Instead of applying cross validation methods, we used an independent testing set by dividing the studies or samples into independent training and testing sets to evaluate the diagnostic performance. We achieved a Sn (sensitivity) = 88% and a Sp (specificity) = 91%. The diagnosis performance underscores the potential application of toxicogenomics in a preclinical lead optimization process of drugs entering into development. BioMed Central 2007-10-01 /pmc/articles/PMC2194664/ /pubmed/17908307 http://dx.doi.org/10.1186/1479-5876-5-47 Text en Copyright © 2007 Jiang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Jiang, Ying Gerhold, David L Holder, Daniel J Figueroa, David J Bailey, Wendy J Guan, Ping Skopek, Thomas R Sistare, Frank D Sina, Joseph F Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles |
title | Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles |
title_full | Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles |
title_fullStr | Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles |
title_full_unstemmed | Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles |
title_short | Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles |
title_sort | diagnosis of drug-induced renal tubular toxicity using global gene expression profiles |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2194664/ https://www.ncbi.nlm.nih.gov/pubmed/17908307 http://dx.doi.org/10.1186/1479-5876-5-47 |
work_keys_str_mv | AT jiangying diagnosisofdruginducedrenaltubulartoxicityusingglobalgeneexpressionprofiles AT gerholddavidl diagnosisofdruginducedrenaltubulartoxicityusingglobalgeneexpressionprofiles AT holderdanielj diagnosisofdruginducedrenaltubulartoxicityusingglobalgeneexpressionprofiles AT figueroadavidj diagnosisofdruginducedrenaltubulartoxicityusingglobalgeneexpressionprofiles AT baileywendyj diagnosisofdruginducedrenaltubulartoxicityusingglobalgeneexpressionprofiles AT guanping diagnosisofdruginducedrenaltubulartoxicityusingglobalgeneexpressionprofiles AT skopekthomasr diagnosisofdruginducedrenaltubulartoxicityusingglobalgeneexpressionprofiles AT sistarefrankd diagnosisofdruginducedrenaltubulartoxicityusingglobalgeneexpressionprofiles AT sinajosephf diagnosisofdruginducedrenaltubulartoxicityusingglobalgeneexpressionprofiles |