The present study applies a systems biology approach for the in silico predictive modeling of drug toxicity on the basis of high-quality preclinical drug toxicity data with the aim of increasing the mechanistic understanding of toxic effects of compounds at different levels (pathway, cell, tissue, organ). The model development was carried out using 77 compounds for which gene expression data for treated primary human hepatocytes is available in the LINCS database and for which rodent in vivo hepatotoxicity information is available in the eTOX database. The data from LINCS were used to determine the type and number of pathways disturbed by each compound and to estimate the extent of disturbance (network perturbation elasticity), and were use...
2Abstract Drug-induced hepatotoxicity is a major issue for drug development, and toxicogenomics has ...
Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independent...
When evaluating compound similarity, addressing multiple sources of information to reach conclusions...
The present study applies a systems biology approach for the in silico predictive modeling of drug t...
Drug-induced liver toxicity is one of the leading causes of acute liver failure in the United States...
The liver and the kidney are the most common targets of chemical toxicity, due to their major metabo...
The liver and the kidney are the most common targets of chemical toxicity, due to their major metabo...
Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate...
In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico t...
New human pharmaceuticals are required by law to be tested in pre-clinical studies in order to predi...
Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a x...
Current testing models for predicting drug-induced liver injury are inadequate, as they basically un...
Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a x...
<div><p>Background</p><p>Several groups have employed genomic data from subchronic chemical toxicity...
Hepatotoxicity is one of the major causes of adverse drug reactions and withdrawal of drugs from the...
2Abstract Drug-induced hepatotoxicity is a major issue for drug development, and toxicogenomics has ...
Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independent...
When evaluating compound similarity, addressing multiple sources of information to reach conclusions...
The present study applies a systems biology approach for the in silico predictive modeling of drug t...
Drug-induced liver toxicity is one of the leading causes of acute liver failure in the United States...
The liver and the kidney are the most common targets of chemical toxicity, due to their major metabo...
The liver and the kidney are the most common targets of chemical toxicity, due to their major metabo...
Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate...
In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico t...
New human pharmaceuticals are required by law to be tested in pre-clinical studies in order to predi...
Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a x...
Current testing models for predicting drug-induced liver injury are inadequate, as they basically un...
Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a x...
<div><p>Background</p><p>Several groups have employed genomic data from subchronic chemical toxicity...
Hepatotoxicity is one of the major causes of adverse drug reactions and withdrawal of drugs from the...
2Abstract Drug-induced hepatotoxicity is a major issue for drug development, and toxicogenomics has ...
Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independent...
When evaluating compound similarity, addressing multiple sources of information to reach conclusions...