Marked Increase of Gamma-Glutamyltransferase as an Indicator of Drug-Induced Liver Injury in Patients without Conventional Diagnostic Criteria of Acute Liver Injury

2021 ◽  
pp. 1-6
Author(s):  
Sabine Weber ◽  
Julian Allgeier ◽  
Gerald Denk ◽  
Alexander L. Gerbes
Keyword(s):  
Liver Injury ◽  
Case Series ◽  
Assessment Method ◽  
Close Monitoring ◽  
In Vitro Test ◽  
Drug Induced ◽  
Gamma Glutamyltransferase ◽  
Drug Induced Liver Injury ◽  
Full Remission

<b><i>Introduction:</i></b> Clinically significant drug-induced liver injury (DILI) is defined by elevations of alanine aminotransferase (ALT) ≥5 times the upper limit of normal (ULN), alkaline phosphatase (ALP) ≥2 × ULN, or ALT ≥3 × ULN and total bilirubin TBIL &#x3e;2 × ULN. However, DILI might also occur in patients who do not reach those thresholds and still may benefit from discontinuation of medication. <b><i>Methods:</i></b> Fifteen patients recruited for our prospective study on potentially hepatotoxic drugs were included. DILI diagnosis was based on RUCAM (Roussel Uclaf Causality Assessment Method) score and expert opinion and was supported by an in vitro test using monocyte-derived hepatocyte-like (MH) cells. <b><i>Results:</i></b> Median RUCAM score was 6 (range 4–8), indicating that DILI was possible or probable in all cases. The predominant types of liver injury were mixed (60%) and cholestatic (40%). While no elevation above 2 × ULN of ALP and TBIL was observed, gamma-glutamyltransferase (GGT) above 2 × ULN was identified in 8 of the patients. Six of the 15 patients did not achieve full remission and showed persistent elevation of GGT, which was significantly associated with peak GGT elevation above 2 × ULN (<i>p</i> = 0.005). <b><i>Conclusion:</i></b> Here we present a case series of patients with liver enzyme elevation below the conventional thresholds who developed DILI with a predominant GGT elevation leading to drug withdrawal and/or chronic elevation of liver parameters, in particular of GGT. Thus, we propose that DILI should be considered in particular in cases with marked increase of GGT even if conventional DILI threshold levels are not reached, resulting in discontinuation of the causative drug and/or close monitoring of the patients.

scholarly journals A case report of a drug‐induced liver injury (DILI) caused by multiple antidepressants with causality established by the updated Roussel Uclaf causality assessment method (RUCAM) and in vitro testing

2020 ◽  
Vol 8 (12) ◽  
pp. 3105-3109
Author(s):  
Miguel González‐Muñoz ◽  
Jaime Monserrat Villatoro ◽  
Eva Marín‐Serrano ◽  
Stefan Stewart ◽  
Belén Bardón Rivera ◽  
...  
Keyword(s):  
Case Report ◽  
Liver Injury ◽  
Causality Assessment ◽  
Assessment Method ◽  
In Vitro Testing ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Development and Application of a Transcriptomic Signature of Bioactivation in an Advanced In Vitro Liver Model to Reduce Drug-induced Liver Injury Risk Early in the Pharmaceutical Pipeline

2020 ◽  
Vol 177 (1) ◽  
pp. 121-139 ◽  
Author(s):  
Wen Kang ◽  
Alexei A Podtelezhnikov ◽  
Keith Q Tanis ◽  
Stephen Pacchione ◽  
Ming Su ◽  
...  
Keyword(s):  
Liver Injury ◽  
Human Model ◽  
Reactive Metabolites ◽  
In Vitro Test ◽  
Drug Induced ◽  
Vitro Assay ◽  
Drug Induced Liver Injury ◽  
Drug Candidates ◽  
Transcriptomic Signature

Abstract Early risk assessment of drug-induced liver injury (DILI) potential for drug candidates remains a major challenge for pharmaceutical development. We have previously developed a set of rat liver transcriptional biomarkers in short-term toxicity studies to inform the potential of drug candidates to generate a high burden of chemically reactive metabolites that presents higher risk for human DILI. Here, we describe translation of those NRF1-/NRF2-mediated liver tissue biomarkers to an in vitro assay using an advanced micropatterned coculture system (HEPATOPAC) with primary hepatocytes from male Wistar Han rats. A 9-day, resource-sparing and higher throughput approach designed to identify new chemical entities with lower reactive metabolite-forming potential was qualified for internal decision making using 93 DILI-positive and -negative drugs. This assay provides 81% sensitivity and 90% specificity in detecting hepatotoxicants when a positive test outcome is defined as the bioactivation signature score of a test drug exceeding the threshold value at an in vitro test concentration that falls within 3-fold of the estimated maximum drug concentration at the human liver inlet following highest recommended clinical dose administrations. Using paired examples of compounds from distinct chemical series and close structural analogs, we demonstrate that this assay can differentiate drugs with lower DILI risk. The utility of this in vitro transcriptomic approach was also examined using human HEPATOPAC from a single donor, yielding 68% sensitivity and 86% specificity when the aforementioned criteria are applied to the same 93-drug test set. Routine use of the rat model has been adopted with deployment of the human model as warranted on a case-by-case basis. This in vitro transcriptomic signature-based strategy can be used early in drug discovery to derisk DILI potential from chemically reactive metabolites by guiding structure-activity relationship hypotheses and candidate selection.

scholarly journals Circulatory Inflammatory Mediators in the Prediction of Anti-Tuberculous Drug-Induced Liver Injury Using RUCAM for Causality Assessment

Biomedicines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 891
Author(s):  
Cheng-Maw Ho ◽  
Chi-Ling Chen ◽  
Chia-Hao Chang ◽  
Meng-Rui Lee ◽  
Jann-Yuan Wang ◽  
...  
Keyword(s):  
Liver Injury ◽  
Inflammatory Mediators ◽  
Cox Regression ◽  
Causality Assessment ◽  
Area Under The Curve ◽  
Assessment Method ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Tb Treatment ◽  
Pre Treatment

Background: Anti-tuberculous (TB) medications are common causes of drug-induced liver injury (DILI). Limited data are available on systemic inflammatory mediators as biomarkers for predicting DILI before treatment. We aimed to select predictive markers among potential candidates and to formulate a predictive model of DILI for TB patients. Methods: Adult active TB patients from a prospective cohort were enrolled, and all participants received standard anti-tuberculous treatment. Development of DILI, defined as ≥5× ULN for alanine transaminase or ≥2.6× ULN of total bilirubin with causality assessment (RUCAM, Roussel Uclaf causality assessment method), was regularly monitored. Pre-treatment plasma was assayed for 15 candidates, and a set of risk prediction scores was established using Cox regression and receiver-operating characteristic analyses. Results: A total of 19 (7.9%) in 240 patients developed DILI (including six carriers of hepatitis B virus) following anti-TB treatment. Interleukin (IL)-22 binding protein (BP), interferon gamma-induced protein 1 (IP-10), soluble CD163 (sCD163), IL-6, and CD206 were significant univariable factors associated with DILI development, and the former three were backward selected as multivariable factors, with adjusted hazards of 0.20 (0.07–0.58), 3.71 (1.35–10.21), and 3.28 (1.07–10.06), respectively. A score set composed of IL-22BP, IP-10, and sCD163 had an improved area under the curve of 0.744 (p < 0.001). Conclusions: Pre-treatment IL-22BP was a protective biomarker against DILI development under anti-TB treatment, and a score set by additional risk factors of IP-10 and sCD163 employed an adequate DILI prediction.

Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

2014 ◽  
Vol 2 (4) ◽  
pp. 63-70 ◽  
Author(s):  
Danyel Jennen ◽  
Jan Polman ◽  
Mark Bessem ◽  
Maarten Coonen ◽  
Joost van Delft ◽  
...  
Keyword(s):  
Liver Injury ◽  
Clinical Chemistry ◽  
Classification Model ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Chemistry Data ◽  
Model Based ◽  
Injury Classification

scholarly journals Causality assessment in drug-induced liver injury using a structured expert opinion process: Comparison to the Roussel-Uclaf causality assessment method

Hepatology ◽  
2010 ◽  
Vol 51 (6) ◽  
pp. 2117-2126 ◽  
Author(s):  
Don C. Rockey ◽  
Leonard B. Seeff ◽  
James Rochon ◽  
James Freston ◽  
Naga Chalasani ◽  
...  
Keyword(s):  
Liver Injury ◽  
Expert Opinion ◽  
Causality Assessment ◽  
Assessment Method ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Process Comparison

scholarly journals Case series and review of Ayurvedic medication induced liver injury

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christopher M. Karousatos ◽  
Justin K. Lee ◽  
David R. Braxton ◽  
Tse-Ling Fong
Keyword(s):  
Liver Injury ◽  
Plant Extracts ◽  
Significant Proportion ◽  
Case Series ◽  
The United States ◽  
Tinospora Cordifolia ◽  
Individual Plant ◽  
Medicine Use ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Abstract Background Complementary and alternative medicine use among Americans is prevalent. Originating in India, Ayurvedic medicine use in the United States has grown 57% since 2002. CAM accounts for a significant proportion of drug induced liver injury in India and China, but there have been only three reports of drug induced liver injury from Ayurvedic medications in the U.S. We report three cases of suspected Ayurvedic medication associated liver injury seen at a Southern California community hospital and review literature of Ayurvedic medication induced liver injury. Case presentations Three patients presented with acute hepatocellular injury and jaundice after taking Ayurvedic supplements for 90–120 days. First patient took Giloy Kwath consisting solely of Tinospora cordifolia. Second patient took Manjishthadi Kwatham and Aragwadhi Kwatham, which contained 52 and 10 individual plant extracts, respectively. Third patient took Kanchnar Guggulu, containing 10 individual plant extracts. Aminotransferase activities decreased 50% in < 30 days and all 3 patients made a full recovery. Roussel Uclaf Causality Assessment Method (RUCAM) scores were 7–8, indicating probable causality. These products all contained ingredients in other Ayurvedic and traditional Chinese medicines with previously reported associations with drug induced liver injury. Conclusions These patients highlight the risk of drug induced liver injury from Ayurvedic medications and the complexity of determining causality. There is a need for a platform like LiverTox.gov to catalog Ayurvedic ingredients causing liver damage.

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting &ldquo;the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans&rdquo;, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Vol 21 (6) ◽  
pp. 2114
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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