Toxicogenomic biomarkers for renal papillary injury in rats.

Renal papillary injury is a common side effect observed during nonclinical and clinical investigations in drug development. The present study aimed to identify genomic biomarkers for early and sensitive detection of renal papillary injury in rats. We hypothesized that previously identified genomic biomarkers for tubular injury might be applicable for the sensitive detection of papillary injury in rats. We selected 18 genes as candidate biomarkers for papillary injury based on previously published studies and analyzed their expression profiles by RT-PCR in each kidney region, namely the cortex, cortico-medullary junction, and papilla in various nephrotoxicity models. Comparative analysis of gene expression profiles revealed that some genes were commonly upregulated or downregulated in the renal papilla, reflecting papillary injuries induced by 2-bromoethylamine hydrobromide, phenylbutazone, or n-phenylanthranilic acid. By applying receiver operator characteristics analysis, six candidate biomarkers were identified and their usefulness was confirmed by using an independent data set. The three top-ranked genes, Timp1, Igf1, and Lamc2, exhibited the best prediction performance in an external data set with area under the curve (AUC) values of greater than 0.91. An optimized support vector machine model consisting of three genes achieved the highest AUC value of 0.99. In conclusion, even though definitive validation studies are required for the establishment of their usefulness and reliability, these identified genes may prove to be the most promising candidate genomic biomarkers of renal papillary injury in rats.

Toxicogenomic multigene biomarker for predicting the future onset of proximal tubular injury in rats.

Drug-induced renal tubular injury is a major concern in the preclinical safety evaluation of drug candidates. Toxicogenomics is now a generally accepted tool for identifying chemicals with potential safety problems. The specific aim of the present study was to develop a model for use in predicting the future onset of drug-induced proximal tubular injury following repeated dosing with various nephrotoxicants. In total, 41 nephrotoxic and nonnephrotoxic compounds were used for the present analysis. Male Sprague-Dawley rats were dosed orally or intravenously once daily. Animals were exposed to three different doses (low, middle, and high) of each compound, and kidney tissue was collected at 3, 6, 9, and 24 h after single dosing, and on days 4, 8, 15, and 29 after repeated dosing. Gene expression profiles were generated from kidney total RNA using Affymetrix DNA microarrays. Filter-type gene selection and linear classification algorithms were employed to discriminate future onset of proximal tubular injury. We identified genomic biomarkers for use in future onset prediction using the gene expression profiles determined on day 1, when most of the nephrotoxicants had yet to produce detectable histopathological changes. The model was evaluated using a five-fold cross validation, and achieved a sensitivity of 93% and selectivity of 90% with 19 probes. We also found that the prediction accuracy of the optimized model was substantially higher than that produced by any of the single genomic biomarkers or histopathology. The genes included in our model were primarily involved in DNA replication, cell cycle control, apoptosis, and responses to oxidative stress and chemical stimuli. In summary, our toxicogenomic model is particularly useful for predicting the future onset of proximal tubular injury.

Identification of a novel set of biomarkers for evaluating phospholipidosis-inducing potential of compounds using rat liver microarray data measured 24-h after single dose administration.

Phospholipid accumulation manifests as an adverse effect of cationic amphiphilic drugs in particular. Detection, however, by histopathology examination is time-consuming and may require repeated administration of compounds for several weeks. To eliminate compounds with potential for inducing phospholipidosis from the discovery pipeline, we have identified and validated a set of biomarkers for predicting the phospholipidosis-inducing potential utilizing a comprehensive rat transcriptome microarray database created by the Japanese Toxicogenomics and Toxicogenomics Informatics Projects (TGP/TGP2) together with in-house data. The set of biomarkers comprising 25 Affymetrix GeneChip probe sets was identified using genetic algorithm optimization on 24-h time-point microarray data from rats treated with single doses of hepatotoxic compounds including amiodarone, clomipramine, haloperidol, hydroxyzine, imipramine, and perhexiline. The set of novel biomarkers represents an early time-point gene-expression pattern characteristic for a condition eventually leading to phospholipidosis. This implies significant advantages in terms of time and resources over currently published biomarkers derived using repeated-dosing late time-point data. The biomarker set was validated by 11 independent compounds. Accuracy, sensitivity, and specificity values were 82%, 67%, and 100%, respectively and the area under the receiver operating characteristic curve was 0.97. These results show that the biomarker set possesses a high classification accuracy for novel compounds. Pathway analysis was carried out for the biomarkers and the detection of pathways related to lipid-metabolism was statistically significant. These pathways most probably reflect lipid metabolism changes associated with phospholipidosis supporting the validity of our novel biomarkers.

Prediction model of potential hepatocarcinogenicity of rat hepatocarcinogens using a large-scale toxicogenomics database.

The present study was performed to develop a robust gene-based prediction model for early assessment of potential hepatocarcinogenicity of chemicals in rats by using our toxicogenomics database, TG-GATEs (Genomics-Assisted Toxicity Evaluation System developed by the Toxicogenomics Project in Japan). The positive training set consisted of high- or middle-dose groups that received 6 different non-genotoxic hepatocarcinogens during a 28-day period. The negative training set consisted of high- or middle-dose groups of 54 non-carcinogens. Support vector machine combined with wrapper-type gene selection algorithms was used for modeling. Consequently, our best classifier yielded prediction accuracies for hepatocarcinogenicity of 99% sensitivity and 97% specificity in the training data set, and false positive prediction was almost completely eliminated. Pathway analysis of feature genes revealed that the mitogen-activated protein kinase p38- and phosphatidylinositol-3-kinase-centered interactome and the v-myc myelocytomatosis viral oncogene homolog-centered interactome were the 2 most significant networks. The usefulness and robustness of our predictor were further confirmed in an independent validation data set obtained from the public database. Interestingly, similar positive predictions were obtained in several genotoxic hepatocarcinogens as well as non-genotoxic hepatocarcinogens. These results indicate that the expression profiles of our newly selected candidate biomarker genes might be common characteristics in the early stage of carcinogenesis for both genotoxic and non-genotoxic carcinogens in the rat liver. Our toxicogenomic model might be useful for the prospective screening of hepatocarcinogenicity of compounds and prioritization of compounds for carcinogenicity testing.

Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.

Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely, their chemical descriptors and toxicogenomics profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs ( http://toxico.nibio.go.jp/datalist.html ). The model end point was hepatotoxicity in the rat following 28 days of continuous exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (correct classification rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomics data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.

Analysis of multiple compound-protein interactions reveals novel bioactive molecules.

The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound-protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.

Identification of genomic biomarkers for concurrent diagnosis of drug-induced renal tubular injury using a large-scale toxicogenomics database.

Drug-induced renal tubular injury is one of the major concerns in preclinical safety evaluations. Toxicogenomics is becoming a generally accepted approach for identifying chemicals with potential safety problems. In the present study, we analyzed 33 nephrotoxicants and 8 non-nephrotoxic hepatotoxicants to elucidate time- and dose-dependent global gene expression changes associated with proximal tubular toxicity. The compounds were administered orally or intravenously once daily to male Sprague-Dawley rats. The animals were exposed to four different doses of the compounds, and kidney tissues were collected on days 4, 8, 15, and 29. Gene expression profiles were generated from kidney RNA by using Affymetrix GeneChips and analyzed in conjunction with the histopathological changes. We used the filter-type gene selection algorithm based on t-statistics conjugated with the SVM classifier, and achieved a sensitivity of 90% with a selectivity of 90%. Then, 92 genes were extracted as the genomic biomarker candidates that were used to construct the classifier. The gene list contains well-known biomarkers, such as Kidney injury molecule 1, Ceruloplasmin, Clusterin, Tissue inhibitor of metallopeptidase 1, and also novel biomarker candidates. Most of the genes involved in tissue remodeling, the immune/inflammatory response, cell adhesion/proliferation/migration, and metabolism were predominantly up-regulated. Down-regulated genes participated in cell adhesion/proliferation/migration, membrane transport, and signal transduction. Our classifier has better prediction accuracy than any of the well-known biomarkers. Therefore, the toxicogenomics approach would be useful for concurrent diagnosis of renal tubular injury.

GLIDA: GPCR--ligand database for chemical genomics drug discovery--database and tools update.

G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands. It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of Chemical Genomics research to easily retrieve such information from either biological or chemical starting points. GLIDA includes a variety of similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces. GLIDA is publicly available at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope that it will prove very useful for Chemical Genomics research and GPCR-related drug discovery.

Comprehensive analysis of distinctive polyketide and nonribosomal peptide structural motifs encoded in microbial genomes.

We developed a highly accurate method to predict polyketide (PK) and nonribosomal peptide (NRP) structures encoded in microbial genomes. PKs/NRPs are polymers of carbonyl/peptidyl chains synthesized by polyketide synthases (PKS) and nonribosomal peptide synthetases (NRPS). We analyzed domain sequences corresponding to specific substrates and physical interactions between PKSs/NRPSs in order to predict which substrates (carbonyl/peptidyl units) are selected and assembled into highly ordered chemical structures. The predicted PKs/NRPs were represented as the sequences of carbonyl/peptidyl units to extract the structural motifs efficiently. We applied our method to 4529 PKSs/NRPSs and found 619 PKs/NRPs. We also collected 1449 PKs/NRPs whose chemical structures have been determined experimentally. The structural sequences were compared using the Smith-Waterman algorithm, and clustered into 271 clusters. From the compound clusters, we extracted 33 structural motifs that are significantly related with their bioactivities. We used the structural motifs to infer functions of 13 novel PKs/NRPs clusters produced by Pseudomonas spp. and Burkholderia spp. and found a putative virulence factor. The integrative analysis of genomic and chemical information given here will provide a strategy to predict the chemical structures, the biosynthetic pathways, and the biological activities of PKs/NRPs, which is useful for the rational design of novel PKs/NRPs.