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Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

ABSTRACT: Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-?B-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

SUBMITTER: Wink S

PROVIDER: S-EPMC5962642 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

Wink Steven S Hiemstra Steven W SW Huppelschoten Suzanne S Klip Janna E JE van de Water Bob B

Archives of toxicology 20180303 5

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We hav ...[more]

PMID: 29502165

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Project description:ObjectiveMachine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to assess risk and dynamically identify patient-specific modifiable factors critical to patient trajectory for multiple key outcomes after severe injury.MethodsSuperLearner, an ensemble machine-learning algorithm, was applied to prospective observational cohort data from 1494 critically-injured patients. Over 1000 agnostic predictors were used to generate prediction models from multiple candidate learners for outcomes of interest at serial time points post-injury. Model accuracy was estimated using cross-validation and area under the curve was compared to select among predictors. Clinical variables responsible for driving outcomes were estimated at each time point.ResultsSuperLearner fits demonstrated excellent cross-validated prediction of death (overall AUC 0.94-0.97), multi-organ failure (overall AUC 0.84-0.90), and transfusion (overall AUC 0.87-0.9) across multiple post-injury time points, and good prediction of Acute Respiratory Distress Syndrome (overall AUC 0.84-0.89) and venous thromboembolism (overall AUC 0.73-0.83). Outcomes with inferior data quality included coagulopathic trajectory (AUC 0.48-0.88). Key clinical predictors evolved over the post-injury timecourse and included both anticipated and unexpected variables. Non-random missingness of data was identified as a predictor of multiple outcomes over time.ConclusionsMachine learning algorithms can be used to generate dynamic prediction after injury while avoiding the risk of over- and under-fitting inherent in ad hoc statistical approaches. SuperLearner prediction after injury demonstrates promise as an adaptable means of helping clinicians integrate voluminous, evolving data on severely-injured patients into real-time, dynamic decision-making support.

Project description:Drug-induced liver failure is a significant indication for a liver transplant, and unexpected liver toxicity is a major reason that otherwise effective therapies are removed from the market. Various methods exist for monitoring liver injury but are often inadequate to predict liver failure. New diagnostic tools are needed. Methods: We evaluate in a preclinical model whether 18F-2-deoxy-2-fluoroarabinose (18F-DFA), a PET radiotracer that measures the ribose salvage pathway, can be used to monitor acetaminophen-induced liver injury and failure. Mice treated with vehicle, 100, 300, or 500 mg/kg acetaminophen for 7 or 21 h were imaged with 18F-FDG and 18F-DFA PET. Hepatic radiotracer accumulation was correlated to survival and percentage of nonnecrotic tissue in the liver. Mice treated with acetaminophen and vehicle or N-acetylcysteine were imaged with 18F-DFA PET. 18F-DFA accumulation was evaluated in human hepatocytes engrafted into the mouse liver. Results: We show that hepatic 18F-DFA accumulation is 49%-52% lower in mice treated with high-dose acetaminophen than in mice treated with low-dose acetaminophen or vehicle. Under these same conditions, hepatic 18F-FDG accumulation was unaffected. At 21 h after acetaminophen treatment, hepatic 18F-DFA accumulation can distinguish mice that will succumb to the liver injury from those that will survive it (6.2 vs. 9.7 signal to background, respectively). Hepatic 18F-DFA accumulation in this model provides a tomographic representation of hepatocyte density in the liver, with a R2 between hepatic 18F-DFA accumulation and percentage of nonnecrotic tissue of 0.70. PET imaging with 18F-DFA can be used to distinguish effective from ineffective resolution of acetaminophen-induced liver injury with N-acetylcysteine (15.6 vs. 6.2 signal to background, respectively). Human hepatocytes, in culture or engrafted into a mouse liver, have levels of ribose salvage activity similar to those of mouse hepatocytes. Conclusion: Our findings suggest that PET imaging with 18F-DFA can be used to visualize and quantify drug-induced acute liver injury and may provide information on the progression from liver injury to hepatic failure.

Dataset Information

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

Publications

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

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