Drug-induced liver injury in hospitalized patients with notably elevated alanine aminotransferase

AbstractInterpretations of elevated blood levels of alanine aminotransferase (ALT) for drug-induced liver injury often assume that the biomarker is released passively from dying cells. However, the mechanisms driving that release have not been explored experimentally. The usefulness of ALT and related biomarkers will improve by developing mechanism-based explanations of elevated levels that can be expanded and elaborated incrementally. We provide the means to challenge the ability of closely related concretized model mechanisms to generate patterns of simulated hepatic injury and ALT release that scale (or not) to be quantitatively similar to the wet-lab validation targets. The validation targets for this work are elevated measures of plasma ALT following acetaminophen (APAP) exposure in mice. We build on a published model mechanism that helps explain the generation of characteristic spatiotemporal features of APAP hepatotoxicity within hepatic lobules. Discrete event and agent-oriented software methods are most prominent. We instantiate and leverage a small constellation of concrete model mechanisms. Their details during execution help bring into focus ways in which particular sources of uncertainty become entangled within and across several levels with cause-effect details. Monte Carlo sampling and simulations comprise a virtual experiment. Falsification of one (or more) of the model mechanisms provides new knowledge and shrinks the model mechanism constellation incrementally. We challenge the sufficiency of four potentially explanatory theories for ALT release. The first model mechanism tested failed to achieve the initial validation target, but each of the three others succeeded. We scale ALT amounts in virtual mice directly to target plasma ALT measures in individual mice. Results for one of the three model mechanisms matched all target ALT measures quantitatively. We assert that the actual mechanisms responsible for ALT measures in individual mice and the virtual causal processes occurring during model execution are strongly analogous within and among real hepatic lobular levels.Author summaryInterpretations of elevated biomarkers for drug-induced liver injury assume passive release during hepatocyte death, yet indirect evidence indicates that plasma levels can increase absent injury. Limitations on measurements make it infeasible to resolve causal linkages between drug disposition and plasma levels of biomarkers. To improve explanatory knowledge, we instantiate within virtual mice, plausible mechanism-based causal linkages between acetaminophen disposition and alanine aminotransferase (ALT) behavior that enables simulation results to meet stringent quantitative validation prerequisites. We challenge the sufficiency of four model mechanisms by scaling ALT measurements in virtual mice to corresponding plasma values. Virtual experiment results in which ALT release is a combined consequence of lobular-location-dependent hepatocyte death and drug-induced cellular damage, matches all validation targets. We assert that the actual mechanisms responsible for plasma ALT measures in individual mice and the virtual causal processes occurring during model execution are strongly analogous within and among real hepatic lobular levels.

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Causes, clinical features and outcomes of drug-induced liver injury in hospitalized patients in a Chinese tertiary care hospital

SpringerPlus ◽

10.1186/s40064-015-1600-8 ◽

2015 ◽

Vol 4 (1) ◽

Cited By ~ 20

Author(s):

Pengcheng Ou ◽

Yi Chen ◽

Baozhen Li ◽

Min Zhang ◽

Xingyu Liu ◽

...

Keyword(s):

Liver Injury ◽

Clinical Features ◽

Tertiary Care Hospital ◽

Tertiary Care ◽

Hospitalized Patients ◽

Care Hospital ◽

Drug Induced ◽

Drug Induced Liver Injury

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Faculty Opinions recommendation of Keratin-18 and microRNA-122 complement alanine aminotransferase as novel safety biomarkers for drug-induced liver injury in two human cohorts.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718307643.793492207 ◽

2014 ◽

Author(s):

Bruno Stieger

Keyword(s):

Liver Injury ◽

Alanine Aminotransferase ◽

Drug Induced ◽

Drug Induced Liver Injury ◽

Keratin 18

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2409. Drug-Induced Liver Injury (DILI) in a National Cohort of Hospitalized Patients Treated With Aztreonam and Ceftazidime

Open Forum Infectious Diseases ◽

10.1093/ofid/ofy210.2062 ◽

2018 ◽

Vol 5 (suppl_1) ◽

pp. S720-S720

Author(s):

Elie Saade ◽

Brigid Wilson ◽

Nadim G El Chakhtoura ◽

Roberto Viau ◽

Federico Perez ◽

...

Keyword(s):

Liver Injury ◽

Hospitalized Patients ◽

Drug Induced ◽

Drug Induced Liver Injury ◽

National Cohort

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Enhancing the utility of alanine aminotransferase as a reference standard biomarker for drug-induced liver injury

Regulatory Toxicology and Pharmacology ◽

10.1016/j.yrtph.2009.11.001 ◽

2010 ◽

Vol 56 (3) ◽

pp. 237-246 ◽

Cited By ~ 50

Author(s):

Josef S. Ozer ◽

Raj Chetty ◽

Gerry Kenna ◽

Joe Palandra ◽

Yiqun Zhang ◽

...

Keyword(s):

Liver Injury ◽

Alanine Aminotransferase ◽

Reference Standard ◽

Drug Induced ◽

Drug Induced Liver Injury

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In silico modeling to optimize interpretation of liver safety biomarkers in clinical trials

Experimental Biology and Medicine ◽

10.1177/1535370217740853 ◽

2017 ◽

Vol 243 (3) ◽

pp. 300-307 ◽

Cited By ~ 13

Author(s):

Rachel J Church ◽

Paul B Watkins

Keyword(s):

Clinical Trials ◽

Liver Injury ◽

Alanine Aminotransferase ◽

In Silico ◽

Drug Induced ◽

Serum Alanine Aminotransferase ◽

Drug Induced Liver Injury ◽

Drug Candidates ◽

Liver Safety ◽

In Silico Modeling

Current strategies to delineate the risk of serious drug-induced liver injury associated with drugs rely on assessment of serum biomarkers that have been utilized for many decades. In particular, serum alanine aminotransferase and total bilirubin levels are typically used to assess hepatic integrity and function, respectively. Parallel measurement of these biomarkers is utilized to identify patients with drug-induced hepatocellular jaundice (“Hy’s Law” cases) which carries at least a 10% risk of death or liver transplant. However, current guidelines regarding use of these biomarkers in clinical trials can put study subjects at risk for life-threatening drug-induced liver injury, or result in over estimation of risk that may halt development of safe drugs. In addition, pharmaceutical companies are increasingly being required to conduct large and expensive clinical trials to “defend” the safety of their new drug when results from smaller trials are inconclusive. Innovative approaches and some novel biomarkers are now being employed to maximize the value of traditional biochemical tests. DILIsym®, a product of the DILIsim Initiative, utilizes serial serum alanine aminotransferase values, along with serum biomarkers of apoptosis vs necrosis, to estimate percent hepatocyte loss and total bilirubin elevations resulting from loss of global liver function. The results from analyses conducted with DILIsym have been reported to the FDA to support the safety of entolimod and cimaglermin alfa after elevations in serum alanine aminotransferase and/or bilirubin halted clinical development. DILIsym can also be utilized to determine whether rises in serum conjugated and unconjugated bilirubin are consistent with mechanisms unrelated to toxicity ( i.e. inhibition of bilirubin transport or metabolism). In silico modeling of traditional and novel drug-induced liver injury biomarker data obtained in clinical trials may be the most efficient and accurate way to define the liver safety profile of new drug candidates. Impact statement Blood tests used in clinical trials to detect and monitor drug-induced liver injury (DILI) have not changed in half a century. These tests have several shortcomings: their use has not completely prevented clinical trial participants from risk of life-threatening DILI, they can give false positive results that halt the development of safe drug candidates, and they can create liver safety “concerns” that require large additional clinical trials to accurately define DILI risk. This review highlights the use of in silico modeling to improve interpretation of the blood tests currently available to detect DILI risk in new drug candidates. This approach is increasingly being applied in clinical trials to more precisely assess the degree of hepatocellular injury and its functional impact. This new approach holds the promise of more accurately defining DILI risk in smaller clinical trials.

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