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Prediction of clinically relevant safety signals of nephrotoxicity through plasma metabolite profiling.

ABSTRACT: Addressing safety concerns such as drug-induced kidney injury (DIKI) early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin, cyclosporin A, doxorubicin, captopril, and lisinopril) was initiated by the Technology Evaluation Consortium under the auspices of the Drug Safety Executive Council (DSEC). The metabolite profiles for rats treated with these compounds matched established reference patterns in the MetaMap Tox metabolomics database indicative of each compound's well-described clinical toxicities. For example, the DIKI associated with cyclosporine A and doxorubicin was correctly predicted by metabolite profiling, while no evidence for DIKI was found for phenytoin, consistent with its clinical picture. In some cases the clinical toxicity (hepatotoxicity), not generally seen in animal studies, was detected with MetaMap Tox. Thus metabolite profiling coupled with the MetaMap Tox metabolomics database offers a unique and powerful approach for augmenting safety assessment and avoiding clinical adverse events such as DIKI.

SUBMITTER: Mattes WB

PROVIDER: S-EPMC3673329 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Prediction of clinically relevant safety signals of nephrotoxicity through plasma metabolite profiling.

Mattes W B WB Kamp H G HG Fabian E E Herold M M Krennrich G G Looser R R Mellert W W Prokoudine A A Strauss V V van Ravenzwaay B B Walk T T Naraoka H H Omura K K Schuppe-Koistinen I I Nadanaciva S S Bush E D ED Moeller N N Ruiz-Noppinger P P Piccoli S P SP

BioMed research international 20130521

Addressing safety concerns such as drug-induced kidney injury (DIKI) early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin ...[more]

PMID: 23762827

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Project description:ObjectivesPost-imaging mathematical prediction models (MPMs) provide guidance for the management of solid pulmonary nodules by providing a lung cancer risk score from demographic and radiologists-indicated imaging characteristics. We hypothesized calibrating the MPM risk score threshold to a local study cohort would result in improved performance over the original recommended MPM thresholds. We compared the pre- and post-calibration performance of four MPM models and determined if improvement in MPM prediction occurs as nodules are imaged longitudinally.Materials and methodsA common cohort of 317 individuals with computed tomography-detected, solid nodules (80 malignant, 237 benign) were used to evaluate the MPM performance. We created a web-based application for this study that allows others to easily calibrate thresholds and analyze the performance of MPMs on their local cohort. Thirty patients with repeated imaging were tested for improved performance longitudinally.ResultsUsing calibrated thresholds, Mayo Clinic and Brock University (BU) MPMs performed the best (AUC = 0.63, 0.61) compared to the Veteran's Affairs (0.51) and Peking University (0.55). Only BU had consensus with the original MPM threshold; the other calibrated thresholds improved MPM accuracy. No significant improvements in accuracy were found longitudinally between time points.ConclusionsCalibration to a common cohort can select the best-performing MPM for your institution. Without calibration, BU has the most stable performance in solid nodules ≥ 8 mm but has only moderate potential to refine subjects into appropriate workup. Application of MPM is recommended only at initial evaluation as no increase in accuracy was achieved over time.Key points• Post-imaging lung cancer risk mathematical predication models (MPMs) perform poorly on local populations without calibration. • An application is provided to facilitate calibration to new study cohorts: the Mayo Clinic model, the U.S. Department of Veteran's Affairs model, the Brock University model, and the Peking University model. • No significant improvement in risk prediction occurred in nodules with repeated imaging sessions, indicating the potential value of risk prediction application is limited to the initial evaluation.

Project description:The trend toward precision-based therapeutic approaches dictated by molecular alterations offers substantial promise for men with metastatic castration-resistant prostate cancer (mCRPC). However, current approaches for molecular characterization are primarily tissue based, necessitating serial biopsies to understand changes over time and are limited by the challenges inherent to extracting genomic material from predominantly bone metastases. Therefore, a circulating tumor cell (CTC)-based assay was developed to determine gene expression across a panel of clinically relevant and potentially actionable prostate cancer-related genes. CTCs were isolated from the whole blood of mCRPC patients (n = 41) and multiplex qPCR was performed to evaluate expression of prostate cancer-related target genes (n = 78). A large fraction of patients (27/41, 66%) had detectable CTCs. Increased androgen receptor (AR) expression (70% of samples) and evidence of Wnt signaling (67% of samples) were observed. The TMPRSS2:ERG fusion was expressed in 41% of samples, and the aggressive prostate cancer-associated long noncoding RNA SChLAP1 was upregulated in 70%. WNT5a [HR 3.62, 95% confidence interval (CI), 1.63-8.05, P = 0.002], AURKA (HR 5.56, 95% CI, 1.79-17.20, P = 0.003), and BMP7 (HR 3.86, 95% CI, 1.60-9.32, P = 0.003) were independently predictive of overall survival (FDR < 10%) after adjusting for a panel of previously established prognostic variables in mCRPC (Halabi nomogram). A model including Halabi, WNT5a, and AURKA expression, termed the miCTC score, outperformed the Halabi nomogram alone (AUC = 0.89 vs. AUC = 0.70). Understanding the molecular landscape of CTCs has utility in predicting clinical outcomes in patients with aggressive prostate cancer and provides an additional tool in the arsenal of precision-based therapeutic approaches in oncology.Implications: Analysis of CTC gene expression reveals a clinically prognostic "liquid biopsy" signature in patients with metastatic castrate-resistance prostate cancer. Mol Cancer Res; 16(4); 643-54. ©2018 AACR.

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Prediction of clinically relevant safety signals of nephrotoxicity through plasma metabolite profiling.

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Prediction of clinically relevant safety signals of nephrotoxicity through plasma metabolite profiling.

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