Dataset Information

A mechanistic model of drug-induced liver injury AIDS the interpretation of elevated liver transaminase levels in a phase I clinical trial.

ABSTRACT: Entolimod (CBLB502) is a Toll-like receptor 5 agonist in development as a single-dose countermeasure against total body irradiation. Efficacy can be assessed from animal studies, but the "Animal Rule" does not apply to safety assessment. Marked elevations of serum aminotransferases (exceeding 1,000 IU/l) were observed in some human subjects receiving Entolimod in a safety study, threatening its continued development. The percentage of total hepatocytes undergoing necrosis in these subjects was estimated using a mechanistic, multiscale, mathematical model (DILIsym). The simulations suggested that no subject in the safety study experienced more than a modest loss of hepatocytes (<5%), which was comparable to estimates from a study of healthy volunteers receiving treatment with heparins. The predicted hepatocyte loss with Entolimod was lower than that required to cause liver dysfunction or that is routinely excised from volunteers donating for autologous liver transplantation and did not likely represent a serious health risk.

SUBMITTER: Howell BA

PROVIDER: S-EPMC3944113 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A mechanistic model of drug-induced liver injury AIDS the interpretation of elevated liver transaminase levels in a phase I clinical trial.

Howell B A BA Siler S Q SQ Shoda L K M LK Yang Y Y Woodhead J L JL Watkins P B PB

CPT: pharmacometrics & systems pharmacology 20140205

Entolimod (CBLB502) is a Toll-like receptor 5 agonist in development as a single-dose countermeasure against total body irradiation. Efficacy can be assessed from animal studies, but the "Animal Rule" does not apply to safety assessment. Marked elevations of serum aminotransferases (exceeding 1,000 IU/l) were observed in some human subjects receiving Entolimod in a safety study, threatening its continued development. The percentage of total hepatocytes undergoing necrosis in these subjects was e ...[more]

PMID: 24500662

Similar Datasets

Project description:BackgroundDrug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models.ResultsAs part of the Critical Assessment of Massive Data Analysis (CAMDA) "CMap Drug Safety Challenge" 2019 ( http://camda2019.bioinf.jku.at ), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose.ConclusionUsing chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.

Project description:Previous animal studies have shown that Curcuma longa (turmeric) improves liver function. Turmeric may thus be a promising ingredient in functional foods aimed at improving liver function. The purpose of the study is to investigate the hepatoprotective effect of fermented turmeric powder (FTP) on liver function in subjects with elevated alanine transaminase (ALT) levels.A randomised, double-blind, placebo-controlled trial was conducted between November 2010 and April 2012 at the clinical trial center for functional foods of the Chonbuk National University Hospital. The trial included 60 subjects, 20 years old and above, who were diagnosed mild to moderate elevated ALT levels between 40 IU/L and 200 IU/L. Sixty subjects were randomised to receive FTP 3.0 g per day or placebo 3.0 g per day for 12 weeks. The treatment group received two capsules of FTP three times a day after meals, for 12 weeks. The primary efficacy endpoint was change in the ALT levels in the two groups. The secondary efficacy endpoints included its effect on aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), total bilirubin (TB), and lipid profiles. Safety was assessed throughout the study using ongoing laboratory tests. Adverse events (AEs) were also recorded.Sixty subjects were randomised in the study (30 into the FTP group, 30 into the placebo group), and among them, twelve subjects were excluded from the analysis for protocol violation, adverse events or consent withdrawal. The two groups did not differ in baseline characteristics. After 12 weeks of treatment, 48 subjects were evaluated. Of the 48 subjects, 26 randomly received FTP capsules and 22 received placebo. The FTP group showed a significant reduction in ALT levels after 12 weeks of treatment compared with the placebo group (p = 0.019). There was also observed that the serum AST levels were significantly reduce in the FTP group than placebo group (p = 0.02). The GGT levels showed a tendency to decrease, while the serum alkaline phosphatase (ALP), TB, and lipids levels were not modified. There were no reported severe AEs during this study, or abnormalities observed on blood glucose, total protein, albumin, blood urea nitrogen (BUN), and creatinine levels.The data of this trial indicate that FTP is effective and safe, generally well-tolerated without severe AEs, in the treatment of subjects with elevated ALT levels over a 12 weeks period.ClinicalTrials.gov: NCT01634256

Project description:BACKGROUND:Identification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples allow functional annotation of the genome, but do not provide ready answers regarding the effects of non-coding variants on phenotypes. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant's impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover transcription factors regulating phenotypic variation among individuals. RESULTS:We developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq, and Hi-C chromatin interaction data to answer this question. Using drug sensitivity of lymphoblastoid cell lines as the phenotype of interest, we tested if non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA binding strength of a TF and thus identified TFs regulating individual differences in the phenotype. Our approach relies on a new method for predicting variant impact on TF-DNA binding that uses a combination of biophysical modeling and machine learning. We report statistical and literature-based support for many of the TFs discovered here as regulators of drug response variation. We show that the use of mechanistically driven variant impact predictors can identify TF-drug associations that would otherwise be missed. We examined in depth one reported association-that of the transcription factor ELF1 with the drug doxorubicin-and identified several genes that may mediate this regulatory relationship. CONCLUSION:Our work represents initial steps in utilizing predictions of variant impact on TF binding sites for discovery of regulatory mechanisms underlying phenotypic variation. Future advances on this topic will be greatly beneficial to the reconstruction of phenotype-associated gene regulatory networks.

Dataset Information

A mechanistic model of drug-induced liver injury AIDS the interpretation of elevated liver transaminase levels in a phase I clinical trial.

Publications

A mechanistic model of drug-induced liver injury AIDS the interpretation of elevated liver transaminase levels in a phase I clinical trial.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets